From a096fca24f0351fc4bf477be6320006bf41d65fd Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Thu, 24 Oct 2024 15:25:28 -0700 Subject: [PATCH 01/52] Create observability.md --- .../observability.md | 696 ++++++++++++++++++ 1 file changed, 696 insertions(+) create mode 100644 content/integrate/prometheus-with-redis-enterprise/observability.md diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md new file mode 100644 index 000000000..d707b8cb9 --- /dev/null +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -0,0 +1,696 @@ +--- +Title: Redis Enterprise observability and monitoring guidance +alwaysopen: false +categories: +- docs +- integrate +- rs +description: Using monitoring and observability with Redis Enterprise +group: observability +linkTitle: Observability and monitoring +summary: Observe Redis Enterprise resources and database perfomance indicators. +type: integration +weight: 45 +--- + +## Introduction + +This document provides monitoring guidance for developers running applications +that connect to Redis Enterprise. In particular, this guide focuses on the systems +and resources that are most likely to impact the performance of your application. + +.Dashboard showing relevant statistics for a Node +![Image](images/node_summary.png) + +To effectively monitor a Redis Enterprise cluster you need to observe +core cluster resources and key database performance indicators. + +Core cluster resources include: + +* [Memory utilization](#Memory) +* [CPU utilization](#CPU) +* [Database connections](#Connections) +* [Network traffic](#Network) +* [Synchronization](#Synchronization) + +Key database performance indicators include: + +* Latency +* Cache hit rate +* Key eviction rate +* Proxy Performance + +.Dashboard showing an overview of cluster metrics +![Image](images/cluster_overview.png) + +In addition to manually monitoring these resources and indicators), it is best practice to set up alerts. + +## Core cluster resource monitoring + +### Memory + +Every Redis Enterprise database has a maximum configured memory limit to ensure isolation +in a multi-database cluster. + +Memory usage percentage metric - Percentage of used memory relative to the configured memory limit for a given database + +.Dashboard displaying high-level cluster metrics - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/cluster_dashboard_v9-11.json[Cluster Dashboard] +![Image](images/playbook_used-memory.png) + +#### Thresholds + +The appropriate memory threshold depends on how the application is using Redis. + +* Caching workloads, which permit Redis to evict keys, can safely use 100% of available memory. +* Non-caching workloadsdo not permit key eviction and should be closely monitored as soon as memory usage reaches 80%. + +#### Caching workloads + +For applications using Redis solely as a cache, you can safely let the memory usage +reach 100% as long as you have an (https://redis.io/blog/cache-eviction-strategies/)[eviction policy] in place. This will ensure +that Redis can evict keys while continuing to accept new writes. + +**NB** Eviction will increase write command latency as Redis has to cleanup the memory/objects before accepting a new write to prevent OOM when memory usage is at 100% + +While your Redis database is using 100% of available memory in a caching context, +it's still important to monitor performance. The key performance indicators include: + +* Latency +* Cache hit ratio +* Evicted keys + +#### Read latency + +**Latency** has two important definitions, depending on context: + +** In context Redis itself, latency is **the time it takes for Redis +to respond to a request**. See [latency is **the time it takes for the application +to process a request*. This will include the time it takes to execute both reads and writes +to Redis, as well as calls to other databases and services. Note that its possible for +Redis to report low latency while the application is experiencing high latency. +This may indicate a low cache hit ratio, ultimately caused by insufficient memory. + +You need to monitor both application-level and Redis-level latency to diagnose +caching performance issues in production. + +#### Cache hit ratio and eviction + +**Cache hit ratio** is the percentage of read requests that Redis serves successfully. +**Eviction rate** is the rate at which Redis evicts keys from the cache. These metrics +are often inversely correlated: a high eviction rate may cause a low cache hit ratio. + +If the Redis server is empty, the hit ratio will be 0%. As the application runs and the fills the cache, +the hit ratio will increase. + +**When the entire cached working set fits in memory**, then the cache hit ratio will reach close to 100% +while the percent of used memory will remain below 100%. + +**When the working set cannot fit in memory**, the eviction policy will start to evict keys. +The greater the rate of key eviction, the lower the cache hit ratio. + +In both cases, keys will may be manually invalidated by the application or evicted through +the uses of TTLs (time-to-live) and an eviction policy. + +The ideal cache hit ratio depends on the application, but generally, the ratio should be greater than 50%. +Low hit ratios coupled with high numbers of object evictions may indicate that your cache is too small. +This can cause thrashing on the application side, a scenario where the cache is constantly being invalidated. + +The upshot here is that when your Redis database is using 100% of available memory, you need +to measure the rate of +https://redis.io/docs/latest/operate/rs/references/metrics/database-operations/#evicted-objectssec[key evictions]. + +An acceptable rate of key evictions depends on the total number of keys in the database +and the measure of application-level latency. If application latency is high, +check to see that key evictions have not increased. + +#### Eviction Policies + +| Name | Description | +| ------ | :------ | +|noeviction | New values aren’t saved when memory limit is reached. When a database uses replication, this applies to the primary database | +|allkeys-lru | Keeps most recently used keys; removes least recently used (LRU) keys | +|allkeys-lfu | Keeps frequently used keys; removes least frequently used (LFU) keys | +|volatile-lru | Removes least recently used keys with the expire field set to true. | +|volatile-lfu | Removes least frequently used keys with the expire field set to true. | +|allkeys-random | Randomly removes keys to make space for the new data added. | +|volatile-random | Randomly removes keys with expire field set to true. | +|volatile-ttl | Removes keys with expire field set to true and the shortest remaining time-to-live (TTL) value. | + + +#### Eviction policy guidelines + +* Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests. That is, you expect a subset of elements will be accessed far more often than the rest. This is a good pick if you are unsure. + +* Use the allkeys-random if you have a cyclic access where all the keys are scanned continuously, or when you expect the distribution to be uniform. + +* Use the volatile-ttl if you want to be able to provide hints to Redis about what are good candidate for expiration by using different TTL values when you create your cache objects. + +The volatile-lru and volatile-random policies are mainly useful when you want to use a single instance for both caching and to have a set of persistent keys. However it is usually a better idea to run two Redis instances to solve such a problem. + +**NB** Setting an expire value to a key costs memory, so using a policy like allkeys-lru is more memory efficient since there is no need for an expire configuration for the key to be evicted under memory pressure. + +#### Non-caching workloads + +If no eviction policy is enabled, then Redis will stop accepting writes once memory reaches 100%. +Therefore, for non-caching workloads, we recommend that you configure an alert at 80% memory usage. +Once your database reaches this 80% threshold, you should closely review the rate of memory usage growth. + +#### Troubleshooting + +|Issue | Possible causes | Remediation | +| ------ | ------ | :------ | +|Redis memory usage has reached 100% |This may indicate an insufficient Redis memory limit for your application's workload | For non-caching workloads (where eviction is unacceptable), immediately increase the memory limit for the database. You can accomplish this through the Redis Enterprise console or its API. Alternatively, you can contact Redis support to assist. For caching workloads, you need to monitor performance closely. Confirm that you have an (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy] in place. If your application's performance starts to degrade, you may need to increase the memory limit, as described above. | +|Redis has stopped accepting writes | Memory is at 100% and no eviction policy is in place | Increase the database's total amount of memory. If this is for a caching workload, consider enabling an (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy]. In addition, you may want to determine whether the application can set a reasonable TTL (time-to-live) on some or all of the data being written to Redis. | +|Cache hit ratio is steadily decreasing | The application's working set size may be steadily increasing. Alternatively, the application may be misconfigured (e.g., generating more than one unique cache key per cached item.) | If the working set size is increasing, consider increasing the memory limit for the database. If the application is misconfigured, review the application's cache key generation logic. | + + + +### CPU + +Redis Enterprise provides several CPU metrics: + +| **Metric name** | **Definition** | **Unit** +| Shard CPU - CPU time portion spent by database shards as a percentage, up to 100% per shard +| Proxy CPU - CPU time portion spent by the cluster's proxy(s) as a percentage, 100% per proxy thread +| Node CPU (User and System) - CPU time portion spent by all user-space and kernel-level processesas a Percentage, 100% per node CPU + + +To understand CPU metrics, it's worth recalling how a Redis Enterprise cluster is organized. +A cluster consists of one or more nodes. Each node is a VM (or cloud compute instance) or +a bare-metal server. + +A database is a set of processes, known as shards, deployed across the nodes of a cluster. + +In the dashboard, shard CPU is the CPU utilization of the processes that make up the database. +When diagnosing performance issues, start by looking at shard CPU. + +.Dashboard displaying CPU usage - (https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json)[Database Dashboard] +![Image](images/playbook_database-cpu-shard.png) + +#### Thresholds + +In general, we define high CPU as any CPU utilization above 80% of total capacity. + +Shard CPU should remain below 80%. Shards are single-threaded, so a shard CPU of 100% means that the shard is fully utilized. + +.Display showing Proxy CPU usage - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/proxy_dashboard_v9-11.json[Proxy Dashboard] +![Image](images/playbook_proxy-cpu-usage.png) + +Proxy CPU should remain below 80% of total capacity. +The proxy is a multi-threaded process that handles client connections and forwards requests to the appropriate shard. +Because the total number of proxy threads is configurable, the proxy CPU may exceed 100%. +A proxy configured with 6 threads can reach 600% CPU utilization, so in this case, +keeping utilization below 80% means keeping the total proxy CPU usage below 480%. + +.Dashboard displaying an ensemble of Node CPU usage data - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/node_dashboard_v9-11.json[Node Dashboard] +![Image](images/node_cpu.png) + +Node CPU should also remain below 80% of total capacity. As with the proxy, the node CPU is variable depending +on the CPU capacity of the node. You will need to calibrate your alerting based on the number of cores in your nodes. + +#### Troubleshooting + +High CPU utilization has multiple possible causes. Common causes include an under-provisioned cluster, +excess inefficient Redis operations, and hot master shards. + + +|**Issue**|**Possible causes**|**Remediation** +|High CPU utilization across all shards of a database +|This usually indicates that the database is under-provisioned in terms of number of shards. +A secondary cause may be that the application is running too many inefficient Redis operations. +You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. +|First, rule out inefficient Redis operations as the cause of the high CPU utilization. +See <> for a broader discussion of this metric. +* In the context of your application) for details on this. +If inefficient Redis operations are not the cause, then increase the number of shards in the database. +|High CPU utilization on a single shard, with the remaining shards having low CPU utilization +|This usually indicates a master shard with at least one hot key. +Hot keys are keys that are accessed extremely frequently (e.g., more than 1000 times per second). +|Hot key issues generally cannot be resolved by increasing the number of shards. +To resole this issue, see [review the behavior of connections to the database. +Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. +This is especially true when you see more than 100 connections per second per thread. +Such behavior is almost always a sign of a misbehaving application. +Second, review the total number of operations per second against the cluster. +If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. +|In the case of high connection cycling, review the application's connection behavior. +In the case of high operations per second, https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy[increase the number of proxy threads]. +|High Node CPU +|You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. +Use the remediation steps above to address high shard and proxy CPU utilization. +In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. +|Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. +This is a complex operation and should be done with the help of Redis support. +|High System CPU +|Most of the issues above will reflect user-space CPU utilization. +However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. +|Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. +You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. +For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. + +### Connections + +The Redis Enterprise database dashboard indicates to the total number of connections to the database. + +This connection count metric should be monitored with both a minimum and maximum number of connections in mind. +Based on the number of application instances connecting to Redis (and whether your application uses connection pooling), +you should have a rough idea of the minimum and maximum number of connections you expect to see for any given database. +This number should remain relatively constant over time. + +#### Troubleshooting + +|**Issue**|**Possible causes**|**Remediation** +|Fewer connections to Redis than expected +|The application may not be connecting to the correct Redis database. +There may be a network partition between the application and the Redis database. +|Confirm that the application can successfully connect to Redis. +This may require consulting the application logs or the application's connection configuration. +|Connection count continues to grow over time +|Your application may not be releasing connections. +The most common of such a connection leak is a manually implemented +connection pool or a connection pool that is not properly configured. +|Review the application's connection configuration +|Erratic connection counts (e.g, spikes and drops) +|Application misbehavior (thundering herds, connection cycling, ) or networking issues +|Review the application logs and network traffic to determine the cause of the erratic connection counts. + + +.Dashboard displaying connections - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] +![Image](images/playbook_database-used-connections.png) + +#### Network ingress / egress + +The network ingress / egress panel show the amount of data being sent to and received from the database. +Large spikes in network traffic can indicate that the cluster is under-provisioned or that +the application is reading and/or writing unusually large keys. A correlation between high network traffic +and high CPU utilization may indicate a large key scenario. + +##### Unbalanced database endpoint + +One possible cause is that the database endpoint is not located on the same node as master shards. In addition to added network latency, if data plane internode encryption is enabled, CPU consumption can increase as well. + +One solution is to used the optimal shard placement and proxy policy to ensure endpoints are collocated on nodes hosting master shards. If you need to restore balance (for example, after node failure) you can manually failover shard(s) with the `rladmin` cli tool. + +Extreme network traffic utilization may approach the limits of the underlying network infrastructure. +In this case, the only remediation is to add additional nodes to the cluster and scale the database's shards across them. + +### Synchronization + +In Redis Enterprise, geographically-distributed synchronization is based on CRDT technology. +The Redis Enterprise implementation of CRDT is called an Active-Active database (formerly known as CRDB). +With Active-Active databases, applications can read and write to the same data set from different geographical locations seamlessly and with low latency, without changing the way the application connects to the database. + +An Active-Active architecture is a data resiliency architecture that distributes the database information over multiple data centers via independent and geographically distributed clusters and nodes. +It is a network of separate processing nodes, each having access to a common replicated database such that all nodes can participate in a common application ensuring local low latency with each region being able to run in isolation. + +To achieve consistency between participating clusters, Redis Active-Active synchronization uses a process called the syncer. + +The syncer keeps a replication backlog, which stores changes to the dataset that the syncer sends to other participating clusters. +The syncer uses partial syncs to keep replicas up to date with changes, or a full sync in the event a replica or primary is lost. + +.Dashboard displaying connection metrics between zones - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/synchronization_dashboard_v9-11.json[Synchronization Dashboard] +![Image](images/playbook_network-connectivity.png) + +CRDT provides three fundamental benefits over other geo-distributed solutions: + +* It offers local latency on read and write operations, regardless of the number of geo-replicated regions and their distance from each other. +* It enables seamless conflict resolution (“conflict-free”) for simple and complex data types like those of Redis core. +* Even if most of the geo-replicated regions in a CRDT database (for example, 3 out of 5) are down, the remaining geo-replicated regions are uninterrupted and can continue to handle read and write operations, ensuring business continuity. + +## Database performance indicators + +There several key performance indicators that report your database's performance against your application's workload: + +* Latency +* Cache hit rate +* Key eviction rate + +### Latency + +Latency is **the time it takes for Redis to respond to a request**. +Redis Enterprise measures latency from the first byte received by the proxy to the last byte sent in the command's response. + +An adequately provisioned Redis database running efficient Redis operations will report an average latency below 1 millisecond. In fact, it's common to measure +latency in terms is microseconds. Customers regularly achieve, and sometime require, average latencies of 400-600 +microseconds. + +.Dashboard display of latency metrics - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] +![Image](images/playbook_database-cluster-latency.png) + +The metrics distinguish between read and write latency. Understanding whether high latency is due +to read or writes can help you to isolate the underlying issue. + +Note that these latency metrics do not include network round trip time or application-level serialization, +which is why it's essential to measure request latency at the application, as well. + +.Display showing a noticeable spike in latency +![Image](images/latency_spike.png) + +#### Troubleshooting + +Here are some possible causes of high database latency. Note that high database latency is just one possible +cause of high application latency. Application latency can be caused by a variety of factors, including +a low cache hit rate. + +High Proxy CPU - There are several possible causes of high proxy CPU. +First, a high rate of evictions, or a +networking issue. + + +|**Issue**|**Possible causes**|**Remediation** +|Slow database operations +|Confirm that there are no excessive slow operations in the [Redis slow log](#Slow operations). +|If possible, reduce the number of slow operations being sent to the database. +If this not possible, consider increasing the number of shards in the database. +|Increased traffic to the database +|Review the [network traffic](#Network ingress / egress) and the database operations per second chart +to determine if increased traffic is causing the latency. +|If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. +|Insufficient CPU +|Check to see if the [CPU utilization](#CPU) is increasing. +|Confirm that [slow operations](#Slow operations) are not causing the high CPU utilization. +If the high CPU utilization is due to increased load, consider adding shards to the database. + +### Cache hit rate + +**Cache hit rate** is the percentage of all read operations that return a response.footnote:[Cache hit rate is a composite statistic that is computed by dividing the number of read hits by the total number of read operations.] +When an application tries to read a key that exists, this is known as a **cache hit**. +Alternatively, when an application tries to read a key that does not exist, this is knows as a **cache miss**. + +For [caching workloads](#Caching workloads), the cache hit rate should generally be above 50%, although +the exact ideal cache hit rate can vary greatly depending on the application and depending on whether the cache +is already populated. + +.Dashboard showing the cache hit ratio along with read/write misses - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] +![Image](images/playbook_cache-hit.png) + +Note: Redis Enterprise actually reports four different cache hit / miss metrics. +These are defined as follows: + +bdb_read_hits - The number of successful read operations +bdb_read_misses - The number of read operations returning null +bdb_write_hits - The number of write operations against existing keys +bdb_write_misses - The number of write operations that create new keys + +#### Troubleshooting + +Cache hit rate is usually only relevant for caching workloads. Eeviction will begin after the database approaches its max memory capacity. + +A high or increasing rate of evictions will negatively affect database latency, especially +if the rate of necessary key evictions exceeds the rate of new key insertions. + +See Cache hit ratio and eviction +for tips on troubleshooting cache hit rate. + +### Key eviction rate + +They **key eviction rate** is rate at which objects are being evicted from the database. +See (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy] for a discussion if key eviction and its relationship with memory usage. + +.Dashboard displaying object evictions - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] +![Image](images/playbook_eviction-expiration.png) + +## Proxy Performance + +Redis Enterprise Software (RS) provides high-performance data access through a proxy process that manages and optimizes access to shards within the RS cluster. Each node contains a single proxy process. Each proxy can be active and take incoming traffic or it can be passive and wait for failovers. + +### Proxy Policies + + +|**Policy**|**Description** +|Single|There is only a single proxy that is bound to the database. This is the default database configuration and preferable in most use cases. +|All Master Shards|There are multiple proxies that are bound to the database, one on each node that hosts a database master shard. This mode fits most use cases that require multiple proxies. +|All Nodes|There are multiple proxies that are bound to the database, one on each node in the cluster, regardless of whether or not there is a shard from this database on the node. This mode should be used only in special cases, such as using a load balancer. + +.Dashboard displaying proxy thread activity - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/cloud/basic/redis-cloud-proxy-dashboard_v9-11.json[Proxy Thread Dashboard] +![Image](images/proxy-thread-dashboard.png) + +When needed, we can tune the number of proxy threads using the "rladmin tune proxy" command in order to be able to make the proxy use more CPU cores. +Nevertheless, cores used by the proxy won't be available for Redis, therefore we need to take into account the number of Redis nodes on the host and the total number of available cores. + +How to set a new number of proxy cores using the command: + +* `id|all` - you can either tune a specific proxy by its id, or all proxies. + +* `mode` - determines whether or not the proxy can automatically adjust the number of threads depending on load. + +* `threads` and `max_threads` - determine the initial number of threads created on startup, and the maximum number of threads allowed. + +* `scale_threshold` - determines the CPU utilization threshold that triggers spawning new threads. This CPU utilization level needs to be maintained for at least scale_duration seconds before automatic scaling is performed. + +The following table indicates ideal proxy thread counts for the specified environments. + + +|**Total Cores**|**Redis (ROR)**|**Redis on Flash (ROF)** +|1|1|1 +|4|3|3 +|8|5|3 +|12|8|4 +|16|10|5 +|32|24|10 +|64/96|32|20 +|128|32|32 + + +## Data access anti-patterns + +There are three data access patterns that can limit the performance of your Redis database: + +* Slow operations +* Hot keys +* Large keys + +This section defines each of these patterns and describes how to diagnose and mitigate them. + +### Slow operations + +**Slow operations** are operations that take longer than a few milliseconds to complete. + +Not all Redis operations are equally efficient. +The most efficient Redis operations are O(1) operations; that is, they have a constant time complexity. +Example of such operations include https://redis.io/docs/latest/commands/get/[GET], +https://redis.io/docs/latest/commands/set/[SET], https://redis.io/docs/latest/commands/sadd/[SADD], +and https://redis.io/docs/latest/commands/hset/[HSET]. + +These constant time operations are unlikely to cause high CPU utilization.footnote:[Even so, +it's still possible for a high rate of constant time operations to overwhelm an underprovisioned database.] + +Other Redis operations exhibit greater levels of time complexity. +O(n) (linear time) operations are more likely to cause high CPU utilization. +Examples include (https://redis.io/docs/latest/commands/keys/)[HGETALL], (https://redis.io/docs/latest/commands/smembers/)[SMEMBERS], +and (https://redis.io/docs/latest/commands/lrem/)[LREM]. +These operations are not necessarily problematic, but they can be if executed against data structures holding +a large number of elements (for example, a list with 1 million elements). + +That said, the (https://redis.io/docs/latest/commands/keys/)[KEYS] command should almost never be run against a +production system, since returning a list of all keys in a large Redis database can cause significant slowdowns +and block other operations. If you need to scan the keyspace, especially in a production cluster, always use the +(https://redis.io/docs/latest/commands/scan)[SCAN] command instead. + +==== Troubleshooting + +The best way to discover slow operations is to view the slow log. +The slow low is available in the Redis Enterprise and Redis Cloud consoles: +* (https://redis.io/docs/latest/operate/rs/clusters/logging/redis-slow-log/)[Redis Enterprise slow log docs] +* (https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info)[Redis cloud slow log docs] + +.Redis Cloud dashboard showing slow database operations +![Image](images/slow_log.png) + +|**Issue**|**Remediation** +|The KEYS command shows up in the slow log +|Find the application that issuing the KEYS command and replace it with a SCAN command. +In an emergency situation, you can https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/[alter the ACLs for the database user] +so that Redis will reject the KEYS command altogether. +|The slow log shows a significant number of slow, O(n) operations +|If these operations are being issued against large data structures, +then the application may need to be refactored to use more efficient Redis commands. +|The slow logs contains only O(1) commands, and these commands are taking several milliseconds +or more to complete +|This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. + + +### Hot keys + +A **hot key** is a key that is accessed extremely frequently (e.g., thousands of time a second or more). + +Each key in Redis belongs to one, and only one, shard. +For this reason, a hot key can cause high CPU utilization on that one shard, +which can increase latency for all other operations. + +#### Troubleshooting + +You may suspect that you have a hot key if you see high CPU utilization on a single shard. +There are two main way to identify hot keys: using the Redis CLI and sampling the operations against Redis. + +To use the Redis CLI to identify hot keys: + +1. First confirm that you have enough available memory to enable an eviction policy. +2. Next, enable the LFU (least-frequently used) eviction policy on the database. +3. Finally, run `+redis-cli --hotkeys+` + +You may also identify hot keys by sampling the operations against Redis. +You can use do this by running the https://redis.io/docs/latest/commands/monitor/[MONITOR] command +against the high CPU shard. Since this a potentially high-impact operation, you should only +use this technique as a secondary restort. For mission-critical databases, consider +contact Redis support for assistance. + +#### Remediation + +Once you discover a hot key, you need to find a way to reduce the number of operations against it. +This means getting an understanding of the application's access pattern and the reasons for such frequently access. + +If the hot key operations are read-only, then consider implementing an application-local cache so +that fewer read request are sent to Redis. For example, even a local cache that expires every 5 seconds +can entirely eliminate a hot key issue. + +### Large keys + +**Large keys** are keys that are hundreds of kilobytes or larger. +High network traffic and high CPU utilization can be caused by large keys. + +#### Troubleshooting + +To identify large keys, you can sample the keyspace using the Redis CLI. + +Run `+redis-cli --memkeys+` against your database to sample the keyspace in real time +and potentially identify the largest keys in your database. + +#### Remediation + +Addressing a large key issues requires understanding why the application is creating large keys in the first place. +As such, it's difficult to provide general advice to solving this issue. Resolution often requires a change +to the application's data model or the way it interacts with Redis. + +## Alerting + +The Redis Enterprise observability package includes +https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana#alerts[a suite of alerts and their associated tests for use with Prometheus].footnote:[Not all the alerts are appropriate for all environments; for example, installations that do not use persistence have no need for storage alerts.] + +The alerts are packaged with https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/tests[a series of test] +that validate the individual triggers. You can use these test to validate your modification to these alerts for specific environments and use cases. + +To use these alerts, install https://prometheus.io/docs/alerting/latest/configuration/[Prometheus Alertmanager]. +For a comprehensive guide to alerting with Prometheus and Grafana, +see the https://grafana.com/blog/2020/02/25/step-by-step-guide-to-setting-up-prometheus-alertmanager-with-slack-pagerduty-and-gmail/[Grafana blog post on the subject]. + +### Configuring Prometheus + +To configure Prometheus for alerting, open the `prometheus.yml` configuration file. + +Uncomment the `Alertmanager` section of the file. +The following configuration starts Alertmanager and instructs it to listen on its default port of 9093. + +[subs="+quotes"] +---- +# Alertmanager configuration +alerting: + alertmanagers: + - static_configs: + - targets: + - alertmanager:9093 +---- + +The Rule file section of the config file instructs Alertmanager to read specific rules files. +If you pasted the 'alerts.yml' file into '/etc/prometheus' then the following configuration would be required. + +[subs="+quotes"] +---- +# Load rules once and periodically evaluate them according to the global 'evaluation_interval'. +rule_files: + - "error_rules.yml" + - "alerts.yml" +---- + +After this is done, restart Prometheus. + +The built-in configuration, `error_rules.yml`, has a single alert: Critical Connection Exception. +If you open the Prometheus console, by default located at port 9090, and select the Alert tab, +you will see this alert, as well as the alerts in any other file you have included as a rules file. + +![Image](images/playbook_prometheus-alerts.png) + +The following is a list of alerts contained in the `alerts.yml` file. There are several points consider: + +- Not all Redis Enterprise deployments export all metrics +- Most metrics only alert if the specified trigger persists for a given duration + +### List of alerts + +|**Description**|**Trigger** +|Average latency has reached a warning level +|round(bdb_avg_latency * 1000) > 1 +|Average latency has reached a critical level indicating system degradation +|round(bdb_avg_latency * 1000) > 4 +|Absence of any connection indicates improper configuration or firewall issue +|bdb_conns < 1 +|A flood of connections has occurred that will impact normal operations +|bdb_conns > 64000 +|Absence of any requests indicates improperly configured clients +|bdb_total_req < 1 +|Excessive number of client requests indicates configuration and/or programmatic issues +|bdb_total_req > 1000000 +|The database in question will soon be unable to accept new data +|round((bdb_used_memory/bdb_memory_limit) * 100) > 98 +|The database in question will be unable to accept new data in two hours +|round((bdb_used_memory/bdb_memory_limit) ** 100) < 98 and (predict_linear(bdb_used_memory[15m], 2 ** 3600) / bdb_memory_limit) > 0.3 and round(predict_linear(bdb_used_memory[15m], 2 * 3600)/bdb_memory_limit) > 0.98 +|Database read operations are failing to find entries more than 50% of the time +|(100 * bdb_read_hits)/(bdb_read_hits + bdb_read_misses) < 50 +|In situations where TTL values are not set this indicates a problem +|bdb_evicted_objects > 1 +|Replication between nodes is not in a satisfactory state +|bdb_replicaof_syncer_status > 0 +|Record synchronization between nodes is not in a satisfactory state +|bdb_crdt_syncer_status > 0 +|The amount by which replication lags behind events is worrisome +|bdb_replicaof_syncer_local_ingress_lag_time > 500 +|The amount by which object replication lags behind events is worrisome +|bdb_crdt_syncer_local_ingress_lag_time > 500 +|The expected number of active nodes is less than expected +|count(node_up) != 3 +|Persistent storage will soon be exhausted +|round((node_persistent_storage_free/node_persistent_storage_avail) * 100) <= 5 +|Ephemeral storage will soon be exhausted +|round((node_ephemeral_storage_free/node_ephemeral_storage_avail) * 100) <= 5 +|The node in question is close to running out of memory +|round((node_available_memory/node_free_memory) * 100) <= 15 +|The node in question has exceeded expected levels of CPU usage +|round((1 - node_cpu_idle) * 100) >= 80 +|The shard in question is not reachable +|redis_up == 0 +|The master shard is not reachable +|floor(redis_master_link_status{role="slave"}) < 1 +|The shard in question has exceeded expected levels of CPU usage +|redis_process_cpu_usage_percent >= 80 +|The master shard has exceeded expected levels of CPU usage +|redis_process_cpu_usage_percent{role="master"} > 0.75 and redis_process_cpu_usage_percent{role="master"} > on (bdb) group_left() (avg by (bdb)(redis_process_cpu_usage_percent{role="master"}) + on(bdb) 1.2 * stddev by (bdb) (redis_process_cpu_usage_percent{role="master"})) +|The shard in question has an unhealthily high level of connections +|redis_connected_clients > 500 + +## Appendix A: Grafana Dashboards + +Grafana dashboards are available for Redis Enterprise Software and Redis Cloud deployments. + +These dashboards come in three styles, which may be used in concert with one another to provide +a holistic picture of your deployment. + +1. Classic dashboards provide detailed information about the cluster, nodes, and individual databases. +2. Basic dashboards provide a high-level overviews of the various cluster components. +3. Extended dashboards which requires a third-party library to perform ReST calls. + +There are two additional sets of dashboards for Redis Enterprise software that provide drill-down functionality: the workflow dashboards. + +### Software +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/basic[Basic] +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/extended[Extended] +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/classic[Classic] + +### Workflow +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/databases[Database] +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/nodes[Node] + +### Cloud +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/basic[Basic] +- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/extended[Extended] + +**NB** - The 'workflow' dashboards are intended to be used as a package. Therefore they should all be installed, as they contain links to the other dashboards in the group permitting rapid navigation between the overview and the drill-down views. From 96add4aeb151a3bbe16520db5f5d60e1cdee3662 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Wed, 30 Oct 2024 14:19:35 -0700 Subject: [PATCH 02/52] Add files via upload --- static/images/cluster_overview.png | Bin 0 -> 190366 bytes static/images/latency_spike.png | Bin 0 -> 99910 bytes static/images/node_cpu.png | Bin 0 -> 142285 bytes static/images/node_summary.png | Bin 0 -> 259067 bytes static/images/playbook_cache-hit.png | Bin 0 -> 45972 bytes .../playbook_database-cluster-latency.png | Bin 0 -> 58502 bytes static/images/playbook_database-cpu-shard.png | Bin 0 -> 95810 bytes .../playbook_database-used-connections.png | Bin 0 -> 29967 bytes static/images/playbook_eviction-expiration.png | Bin 0 -> 33591 bytes .../images/playbook_network-connectivity.png | Bin 0 -> 198727 bytes static/images/playbook_prometheus-alerts.png | Bin 0 -> 38323 bytes static/images/playbook_proxy-cpu-usage.png | Bin 0 -> 36079 bytes 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z%C_M|>5AKA`Qu8W=Jxgj)Z56#FX+!gI^g@X5(8%he`S46gqyO9U@=FUcr8;gb;OwC zQA|_Oxt{2Bx7!|~m2(gAJYU6MNwKl*VH)XbTi;3cu~{VH*bJM5)c;he{*TIlCWL?mhXu!}A-aP6$7`~)Sq zk^G-~)*yH~`@x60mcLkP33u5+vCDp4!b1TGI{HLL`{SDVe|7CY>yRznht)~>>&rN#;N0UYC&QfgYDfu6 z{KDQGm(UqLB9g&+JI8kZApiPf#VYbs!Pf^oI=h z|Jv~PCx540O4$w*ys65IvJTX)=7Nj|k$%cR|3icQ Date: Wed, 30 Oct 2024 16:51:03 -0700 Subject: [PATCH 03/52] Update observability.md --- .../observability.md | 118 +++++++++--------- 1 file changed, 59 insertions(+), 59 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index d707b8cb9..cc26692e4 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -19,8 +19,8 @@ This document provides monitoring guidance for developers running applications that connect to Redis Enterprise. In particular, this guide focuses on the systems and resources that are most likely to impact the performance of your application. -.Dashboard showing relevant statistics for a Node -![Image](images/node_summary.png) +Dashboard showing relevant statistics for a Node +{{< image filename="/images/node_summary.png" alt="Dashboard showing relevant statistics for a Node" >}} To effectively monitor a Redis Enterprise cluster you need to observe core cluster resources and key database performance indicators. @@ -40,8 +40,8 @@ Key database performance indicators include: * Key eviction rate * Proxy Performance -.Dashboard showing an overview of cluster metrics -![Image](images/cluster_overview.png) +Dashboard showing an overview of cluster metrics +{{< image filename="/images/cluster_overview.png" alt="Dashboard showing an overview of cluster metrics" >}} In addition to manually monitoring these resources and indicators), it is best practice to set up alerts. @@ -54,8 +54,8 @@ in a multi-database cluster. Memory usage percentage metric - Percentage of used memory relative to the configured memory limit for a given database -.Dashboard displaying high-level cluster metrics - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/cluster_dashboard_v9-11.json[Cluster Dashboard] -![Image](images/playbook_used-memory.png) +Dashboard displaying high-level cluster metrics - [Cluster Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/cluster_dashboard_v9-11.json) +{{< image filename="/images/playbook_used-memory.png" alt="Dashboard displaying high-level cluster metrics" >}} #### Thresholds @@ -67,7 +67,7 @@ The appropriate memory threshold depends on how the application is using Redis. #### Caching workloads For applications using Redis solely as a cache, you can safely let the memory usage -reach 100% as long as you have an (https://redis.io/blog/cache-eviction-strategies/)[eviction policy] in place. This will ensure +reach 100% as long as you have an [eviction policy](https://redis.io/blog/cache-eviction-strategies/)in place. This will ensure that Redis can evict keys while continuing to accept new writes. **NB** Eviction will increase write command latency as Redis has to cleanup the memory/objects before accepting a new write to prevent OOM when memory usage is at 100% @@ -117,7 +117,7 @@ This can cause thrashing on the application side, a scenario where the cache is The upshot here is that when your Redis database is using 100% of available memory, you need to measure the rate of -https://redis.io/docs/latest/operate/rs/references/metrics/database-operations/#evicted-objectssec[key evictions]. +[key evictions](https://redis.io/docs/latest/operate/rs/references/metrics/database-operations/#evicted-objectssec). An acceptable rate of key evictions depends on the total number of keys in the database and the measure of application-level latency. If application latency is high, @@ -159,9 +159,9 @@ Once your database reaches this 80% threshold, you should closely review the rat |Issue | Possible causes | Remediation | | ------ | ------ | :------ | -|Redis memory usage has reached 100% |This may indicate an insufficient Redis memory limit for your application's workload | For non-caching workloads (where eviction is unacceptable), immediately increase the memory limit for the database. You can accomplish this through the Redis Enterprise console or its API. Alternatively, you can contact Redis support to assist. For caching workloads, you need to monitor performance closely. Confirm that you have an (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy] in place. If your application's performance starts to degrade, you may need to increase the memory limit, as described above. | -|Redis has stopped accepting writes | Memory is at 100% and no eviction policy is in place | Increase the database's total amount of memory. If this is for a caching workload, consider enabling an (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy]. In addition, you may want to determine whether the application can set a reasonable TTL (time-to-live) on some or all of the data being written to Redis. | -|Cache hit ratio is steadily decreasing | The application's working set size may be steadily increasing. Alternatively, the application may be misconfigured (e.g., generating more than one unique cache key per cached item.) | If the working set size is increasing, consider increasing the memory limit for the database. If the application is misconfigured, review the application's cache key generation logic. | +|Redis memory usage has reached 100% |This may indicate an insufficient Redis memory limit for your application's workload | For non-caching workloads (where eviction is unacceptable), immediately increase the memory limit for the database. You can accomplish this through the Redis Enterprise console or its API. Alternatively, you can contact Redis support to assist. For caching workloads, you need to monitor performance closely. Confirm that you have an [eviction policy](https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/) in place. If your application's performance starts to degrade, you may need to increase the memory limit, as described above. | +|Redis has stopped accepting writes | Memory is at 100% and no eviction policy is in place | Increase the database's total amount of memory. If this is for a caching workload, consider enabling an [eviction policy](https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/). In addition, you may want to determine whether the application can set a reasonable TTL (time-to-live) on some or all of the data being written to Redis. | +|Cache hit ratio is steadily decreasing | The application's working set size may be steadily increasing. Alternatively, the application may be misconfigured (for example, generating more than one unique cache key per cached item.) | If the working set size is increasing, consider increasing the memory limit for the database. If the application is misconfigured, review the application's cache key generation logic. | @@ -184,8 +184,8 @@ A database is a set of processes, known as shards, deployed across the nodes of In the dashboard, shard CPU is the CPU utilization of the processes that make up the database. When diagnosing performance issues, start by looking at shard CPU. -.Dashboard displaying CPU usage - (https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json)[Database Dashboard] -![Image](images/playbook_database-cpu-shard.png) +Dashboard displaying CPU usage - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) +{{< image filename="/images/playbook_database-cpu-shard.png" alt="Dashboard displaying CPU usage" >}} #### Thresholds @@ -193,8 +193,8 @@ In general, we define high CPU as any CPU utilization above 80% of total capacit Shard CPU should remain below 80%. Shards are single-threaded, so a shard CPU of 100% means that the shard is fully utilized. -.Display showing Proxy CPU usage - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/proxy_dashboard_v9-11.json[Proxy Dashboard] -![Image](images/playbook_proxy-cpu-usage.png) +Display showing Proxy CPU usage - [Proxy Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/proxy_dashboard_v9-11.json) +{{< image filename="/images/playbook_proxy-cpu-usage.png" alt="Display showing Proxy CPU usage" >}} Proxy CPU should remain below 80% of total capacity. The proxy is a multi-threaded process that handles client connections and forwards requests to the appropriate shard. @@ -202,8 +202,8 @@ Because the total number of proxy threads is configurable, the proxy CPU may exc A proxy configured with 6 threads can reach 600% CPU utilization, so in this case, keeping utilization below 80% means keeping the total proxy CPU usage below 480%. -.Dashboard displaying an ensemble of Node CPU usage data - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/node_dashboard_v9-11.json[Node Dashboard] -![Image](images/node_cpu.png) +Dashboard displaying an ensemble of Node CPU usage data - [Node Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/node_dashboard_v9-11.json) +{{< image filename="/images/node_cpu.png" alt="Dashboard displaying an ensemble of Node CPU usage data" >}} Node CPU should also remain below 80% of total capacity. As with the proxy, the node CPU is variable depending on the CPU capacity of the node. You will need to calibrate your alerting based on the number of cores in your nodes. @@ -234,7 +234,7 @@ Such behavior is almost always a sign of a misbehaving application. Second, review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. |In the case of high connection cycling, review the application's connection behavior. -In the case of high operations per second, https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy[increase the number of proxy threads]. +In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). |High Node CPU |You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. @@ -270,13 +270,13 @@ This may require consulting the application logs or the application's connection The most common of such a connection leak is a manually implemented connection pool or a connection pool that is not properly configured. |Review the application's connection configuration -|Erratic connection counts (e.g, spikes and drops) -|Application misbehavior (thundering herds, connection cycling, ) or networking issues +|Erratic connection counts (for example, spikes and drops) +|Application misbehavior (thundering herds, connection cycling, or networking issues) |Review the application logs and network traffic to determine the cause of the erratic connection counts. -.Dashboard displaying connections - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] -![Image](images/playbook_database-used-connections.png) +Dashboard displaying connections - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] +{{< image filename="/images/playbook_database-used-connections.png" alt="Dashboard displaying connections" >}} #### Network ingress / egress @@ -308,8 +308,8 @@ To achieve consistency between participating clusters, Redis Active-Active synch The syncer keeps a replication backlog, which stores changes to the dataset that the syncer sends to other participating clusters. The syncer uses partial syncs to keep replicas up to date with changes, or a full sync in the event a replica or primary is lost. -.Dashboard displaying connection metrics between zones - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/synchronization_dashboard_v9-11.json[Synchronization Dashboard] -![Image](images/playbook_network-connectivity.png) +Dashboard displaying connection metrics between zones - [Synchronization Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/synchronization_dashboard_v9-11.json) +{{< image filename="/images/playbook_network-connectivity.png" alt="Dashboard displaying connection metrics between zones" >}} CRDT provides three fundamental benefits over other geo-distributed solutions: @@ -334,8 +334,8 @@ An adequately provisioned Redis database running efficient Redis operations will latency in terms is microseconds. Customers regularly achieve, and sometime require, average latencies of 400-600 microseconds. -.Dashboard display of latency metrics - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] -![Image](images/playbook_database-cluster-latency.png) +Dashboard display of latency metrics - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) +{{< image filename="/images/playbook_database-cluster-latency.png" alt="Dashboard display of latency metrics" >}} The metrics distinguish between read and write latency. Understanding whether high latency is due to read or writes can help you to isolate the underlying issue. @@ -343,8 +343,8 @@ to read or writes can help you to isolate the underlying issue. Note that these latency metrics do not include network round trip time or application-level serialization, which is why it's essential to measure request latency at the application, as well. -.Display showing a noticeable spike in latency -![Image](images/latency_spike.png) +Display showing a noticeable spike in latency +{{< image filename="/images/latency_spike.png)" alt="Display showing a noticeable spike in latency" >}} #### Troubleshooting @@ -381,8 +381,8 @@ For [caching workloads](#Caching workloads), the cache hit rate should generally the exact ideal cache hit rate can vary greatly depending on the application and depending on whether the cache is already populated. -.Dashboard showing the cache hit ratio along with read/write misses - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] -![Image](images/playbook_cache-hit.png) +Dashboard showing the cache hit ratio along with read/write misses - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) +{{< image filename="/images/playbook_cache-hit.png)" alt="Dashboard showing the cache hit ratio along with read/write misses" >}} Note: Redis Enterprise actually reports four different cache hit / miss metrics. These are defined as follows: @@ -407,8 +407,8 @@ for tips on troubleshooting cache hit rate. They **key eviction rate** is rate at which objects are being evicted from the database. See (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy] for a discussion if key eviction and its relationship with memory usage. -.Dashboard displaying object evictions - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] -![Image](images/playbook_eviction-expiration.png) +Dashboard displaying object evictions - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) +{{< image filename="/images/playbook_eviction-expiration.png" alt="Dashboard displaying object evictions">}} ## Proxy Performance @@ -422,8 +422,8 @@ Redis Enterprise Software (RS) provides high-performance data access through a p |All Master Shards|There are multiple proxies that are bound to the database, one on each node that hosts a database master shard. This mode fits most use cases that require multiple proxies. |All Nodes|There are multiple proxies that are bound to the database, one on each node in the cluster, regardless of whether or not there is a shard from this database on the node. This mode should be used only in special cases, such as using a load balancer. -.Dashboard displaying proxy thread activity - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/cloud/basic/redis-cloud-proxy-dashboard_v9-11.json[Proxy Thread Dashboard] -![Image](images/proxy-thread-dashboard.png) +Dashboard displaying proxy thread activity - [Proxy Thread Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/cloud/basic/redis-cloud-proxy-dashboard_v9-11.json) +{{< image filename="/images/proxy-thread-dashboard.png" alt="Dashboard displaying proxy thread activity" >}} When needed, we can tune the number of proxy threads using the "rladmin tune proxy" command in order to be able to make the proxy use more CPU cores. Nevertheless, cores used by the proxy won't be available for Redis, therefore we need to take into account the number of Redis nodes on the host and the total number of available cores. @@ -468,39 +468,39 @@ This section defines each of these patterns and describes how to diagnose and mi Not all Redis operations are equally efficient. The most efficient Redis operations are O(1) operations; that is, they have a constant time complexity. -Example of such operations include https://redis.io/docs/latest/commands/get/[GET], -https://redis.io/docs/latest/commands/set/[SET], https://redis.io/docs/latest/commands/sadd/[SADD], -and https://redis.io/docs/latest/commands/hset/[HSET]. +Example of such operations include [GET](https://redis.io/docs/latest/commands/get/), +[SET](https://redis.io/docs/latest/commands/set/)[SET], [SADD](https://redis.io/docs/latest/commands/sadd/), +and [HSET](https://redis.io/docs/latest/commands/hset/). These constant time operations are unlikely to cause high CPU utilization.footnote:[Even so, it's still possible for a high rate of constant time operations to overwhelm an underprovisioned database.] Other Redis operations exhibit greater levels of time complexity. O(n) (linear time) operations are more likely to cause high CPU utilization. -Examples include (https://redis.io/docs/latest/commands/keys/)[HGETALL], (https://redis.io/docs/latest/commands/smembers/)[SMEMBERS], -and (https://redis.io/docs/latest/commands/lrem/)[LREM]. +Examples include [HGETALL](https://redis.io/docs/latest/commands/keys/), [SMEMBERS](https://redis.io/docs/latest/commands/smembers/), +and [LREM](https://redis.io/docs/latest/commands/lrem/). These operations are not necessarily problematic, but they can be if executed against data structures holding a large number of elements (for example, a list with 1 million elements). -That said, the (https://redis.io/docs/latest/commands/keys/)[KEYS] command should almost never be run against a +That said, the [KEYS](https://redis.io/docs/latest/commands/keys/) command should almost never be run against a production system, since returning a list of all keys in a large Redis database can cause significant slowdowns and block other operations. If you need to scan the keyspace, especially in a production cluster, always use the -(https://redis.io/docs/latest/commands/scan)[SCAN] command instead. +[SCAN](https://redis.io/docs/latest/commands/scan) command instead. ==== Troubleshooting The best way to discover slow operations is to view the slow log. The slow low is available in the Redis Enterprise and Redis Cloud consoles: -* (https://redis.io/docs/latest/operate/rs/clusters/logging/redis-slow-log/)[Redis Enterprise slow log docs] -* (https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info)[Redis cloud slow log docs] +* [Redis Enterprise slow log docs](https://redis.io/docs/latest/operate/rs/clusters/logging/redis-slow-log/) +* [Redis cloud slow log docs](https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info) .Redis Cloud dashboard showing slow database operations -![Image](images/slow_log.png) +{{< image filename="/images/slow_log.png)" >}} |**Issue**|**Remediation** |The KEYS command shows up in the slow log |Find the application that issuing the KEYS command and replace it with a SCAN command. -In an emergency situation, you can https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/[alter the ACLs for the database user] +In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) so that Redis will reject the KEYS command altogether. |The slow log shows a significant number of slow, O(n) operations |If these operations are being issued against large data structures, @@ -512,7 +512,7 @@ or more to complete ### Hot keys -A **hot key** is a key that is accessed extremely frequently (e.g., thousands of time a second or more). +A **hot key** is a key that is accessed extremely frequently (for example, thousands of times a second or more). Each key in Redis belongs to one, and only one, shard. For this reason, a hot key can cause high CPU utilization on that one shard, @@ -530,7 +530,7 @@ To use the Redis CLI to identify hot keys: 3. Finally, run `+redis-cli --hotkeys+` You may also identify hot keys by sampling the operations against Redis. -You can use do this by running the https://redis.io/docs/latest/commands/monitor/[MONITOR] command +You can use do this by running the [MONITOR](https://redis.io/docs/latest/commands/monitor/) command against the high CPU shard. Since this a potentially high-impact operation, you should only use this technique as a secondary restort. For mission-critical databases, consider contact Redis support for assistance. @@ -565,14 +565,14 @@ to the application's data model or the way it interacts with Redis. ## Alerting The Redis Enterprise observability package includes -https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana#alerts[a suite of alerts and their associated tests for use with Prometheus].footnote:[Not all the alerts are appropriate for all environments; for example, installations that do not use persistence have no need for storage alerts.] +[a suite of alerts and their associated tests for use with Prometheus](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana#alerts). Footnote:[Not all the alerts are appropriate for all environments; for example, installations that do not use persistence have no need for storage alerts.] -The alerts are packaged with https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/tests[a series of test] +The alerts are packaged with [a series of test](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/tests) that validate the individual triggers. You can use these test to validate your modification to these alerts for specific environments and use cases. -To use these alerts, install https://prometheus.io/docs/alerting/latest/configuration/[Prometheus Alertmanager]. +To use these alerts, install [Prometheus Alertmanager](https://prometheus.io/docs/alerting/latest/configuration/). For a comprehensive guide to alerting with Prometheus and Grafana, -see the https://grafana.com/blog/2020/02/25/step-by-step-guide-to-setting-up-prometheus-alertmanager-with-slack-pagerduty-and-gmail/[Grafana blog post on the subject]. +see the [Grafana blog post on the subject](https://grafana.com/blog/2020/02/25/step-by-step-guide-to-setting-up-prometheus-alertmanager-with-slack-pagerduty-and-gmail/). ### Configuring Prometheus @@ -608,7 +608,7 @@ The built-in configuration, `error_rules.yml`, has a single alert: Critical Conn If you open the Prometheus console, by default located at port 9090, and select the Alert tab, you will see this alert, as well as the alerts in any other file you have included as a rules file. -![Image](images/playbook_prometheus-alerts.png) +{{< image filename="/images/playbook_prometheus-alerts.png" alt="prometheus alerts image" >}} The following is a list of alerts contained in the `alerts.yml` file. There are several points consider: @@ -681,16 +681,16 @@ a holistic picture of your deployment. There are two additional sets of dashboards for Redis Enterprise software that provide drill-down functionality: the workflow dashboards. ### Software -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/basic[Basic] -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/extended[Extended] -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/classic[Classic] +- [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/basic) +- [Extended](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/extended) +- [Classic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/classic) ### Workflow -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/databases[Database] -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/nodes[Node] +- [Database](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/databases) +- [Node](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/nodes) ### Cloud -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/basic[Basic] -- https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/extended[Extended] +- [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/basic) +- [Extended](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/extended) **NB** - The 'workflow' dashboards are intended to be used as a package. Therefore they should all be installed, as they contain links to the other dashboards in the group permitting rapid navigation between the overview and the drill-down views. From 42c8c2f073bcc97929ff3a2d729eb4117870f545 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Fri, 1 Nov 2024 11:57:01 -0700 Subject: [PATCH 04/52] Update observability.md --- .../observability.md | 210 +++++++----------- 1 file changed, 82 insertions(+), 128 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index cc26692e4..962700ce0 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -15,7 +15,7 @@ weight: 45 ## Introduction -This document provides monitoring guidance for developers running applications +This document provides observability and monitoring guidance for developers running applications that connect to Redis Enterprise. In particular, this guide focuses on the systems and resources that are most likely to impact the performance of your application. @@ -169,10 +169,11 @@ Once your database reaches this 80% threshold, you should closely review the rat Redis Enterprise provides several CPU metrics: -| **Metric name** | **Definition** | **Unit** -| Shard CPU - CPU time portion spent by database shards as a percentage, up to 100% per shard -| Proxy CPU - CPU time portion spent by the cluster's proxy(s) as a percentage, 100% per proxy thread -| Node CPU (User and System) - CPU time portion spent by all user-space and kernel-level processesas a Percentage, 100% per node CPU +| Metric name | Definition | Unit | +| ------ | ------ | :------ | +| Shard CPU | CPU time portion spent by database shards as a percentage | up to 100% per shard | +| Proxy CPU | CPU time portion spent by the cluster's proxy(s) as a percentage | 100% per proxy thread | +| Node CPU (User and System) | CPU time portion spent by all user-space and kernel-level processesas a Percentage | 100% per node CPU | To understand CPU metrics, it's worth recalling how a Redis Enterprise cluster is organized. @@ -214,39 +215,12 @@ High CPU utilization has multiple possible causes. Common causes include an unde excess inefficient Redis operations, and hot master shards. -|**Issue**|**Possible causes**|**Remediation** -|High CPU utilization across all shards of a database -|This usually indicates that the database is under-provisioned in terms of number of shards. -A secondary cause may be that the application is running too many inefficient Redis operations. -You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. -|First, rule out inefficient Redis operations as the cause of the high CPU utilization. -See <> for a broader discussion of this metric. -* In the context of your application) for details on this. -If inefficient Redis operations are not the cause, then increase the number of shards in the database. -|High CPU utilization on a single shard, with the remaining shards having low CPU utilization -|This usually indicates a master shard with at least one hot key. -Hot keys are keys that are accessed extremely frequently (e.g., more than 1000 times per second). -|Hot key issues generally cannot be resolved by increasing the number of shards. -To resole this issue, see [review the behavior of connections to the database. -Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. -This is especially true when you see more than 100 connections per second per thread. -Such behavior is almost always a sign of a misbehaving application. -Second, review the total number of operations per second against the cluster. -If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. -|In the case of high connection cycling, review the application's connection behavior. -In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). -|High Node CPU -|You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. -Use the remediation steps above to address high shard and proxy CPU utilization. -In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. -|Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. -This is a complex operation and should be done with the help of Redis support. -|High System CPU -|Most of the issues above will reflect user-space CPU utilization. -However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. -|Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. -You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. -For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. +| Issue | Possible causes | Remediation +| ------ | ------ | :------ | +|High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. See <> for a broader discussion of this metric in the context of your application) for details on this. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | +|High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see [review the behavior of connections to the database]. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. | Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | +|High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. | Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | +|High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | ### Connections @@ -259,20 +233,20 @@ This number should remain relatively constant over time. #### Troubleshooting -|**Issue**|**Possible causes**|**Remediation** -|Fewer connections to Redis than expected -|The application may not be connecting to the correct Redis database. -There may be a network partition between the application and the Redis database. +| Issue | Possible causes | Remediation | +| ------ | ------ | :------ | +|Fewer connections to Redis than expected |The application may not be connecting to the correct Redis database. +There may be a network partition between the application and the Redis database. | |Confirm that the application can successfully connect to Redis. -This may require consulting the application logs or the application's connection configuration. -|Connection count continues to grow over time +This may require consulting the application logs or the application's connection configuration. | +|Connection count continues to grow over time | |Your application may not be releasing connections. The most common of such a connection leak is a manually implemented -connection pool or a connection pool that is not properly configured. -|Review the application's connection configuration -|Erratic connection counts (for example, spikes and drops) -|Application misbehavior (thundering herds, connection cycling, or networking issues) -|Review the application logs and network traffic to determine the cause of the erratic connection counts. +connection pool or a connection pool that is not properly configured. | +|Review the application's connection configuration | +|Erratic connection counts (for example, spikes and drops) | +|Application misbehavior (thundering herds, connection cycling, or networking issues) | +|Review the application logs and network traffic to determine the cause of the erratic connection counts. | Dashboard displaying connections - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] @@ -357,19 +331,19 @@ First, a high rate of evictions, or a networking issue. -|**Issue**|**Possible causes**|**Remediation** -|Slow database operations -|Confirm that there are no excessive slow operations in the [Redis slow log](#Slow operations). -|If possible, reduce the number of slow operations being sent to the database. -If this not possible, consider increasing the number of shards in the database. -|Increased traffic to the database +| Issue | Possible causes | Remediation | +|Slow database operations | +|Confirm that there are no excessive slow operations in the [Redis slow log](#Slow operations). | +|If possible, reduce the number of slow operations being sent to the database. +If this not possible, consider increasing the number of shards in the database. | +|Increased traffic to the database | |Review the [network traffic](#Network ingress / egress) and the database operations per second chart -to determine if increased traffic is causing the latency. -|If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. -|Insufficient CPU -|Check to see if the [CPU utilization](#CPU) is increasing. +to determine if increased traffic is causing the latency. | +|If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | +|Insufficient CPU | +|Check to see if the [CPU utilization](#CPU) is increasing. | |Confirm that [slow operations](#Slow operations) are not causing the high CPU utilization. -If the high CPU utilization is due to increased load, consider adding shards to the database. +If the high CPU utilization is due to increased load, consider adding shards to the database. | ### Cache hit rate @@ -417,10 +391,11 @@ Redis Enterprise Software (RS) provides high-performance data access through a p ### Proxy Policies -|**Policy**|**Description** -|Single|There is only a single proxy that is bound to the database. This is the default database configuration and preferable in most use cases. -|All Master Shards|There are multiple proxies that are bound to the database, one on each node that hosts a database master shard. This mode fits most use cases that require multiple proxies. -|All Nodes|There are multiple proxies that are bound to the database, one on each node in the cluster, regardless of whether or not there is a shard from this database on the node. This mode should be used only in special cases, such as using a load balancer. +| Policy | Description | +| ------ | :------ | +|Single | There is only a single proxy that is bound to the database. This is the default database configuration and preferable in most use cases. | +|All Master Shards | There are multiple proxies that are bound to the database, one on each node that hosts a database master shard. This mode fits most use cases that require multiple proxies. | +|All Nodes | There are multiple proxies that are bound to the database, one on each node in the cluster, regardless of whether or not there is a shard from this database on the node. This mode should be used only in special cases, such as using a load balancer. | Dashboard displaying proxy thread activity - [Proxy Thread Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/cloud/basic/redis-cloud-proxy-dashboard_v9-11.json) {{< image filename="/images/proxy-thread-dashboard.png" alt="Dashboard displaying proxy thread activity" >}} @@ -441,15 +416,16 @@ How to set a new number of proxy cores using the command: The following table indicates ideal proxy thread counts for the specified environments. -|**Total Cores**|**Redis (ROR)**|**Redis on Flash (ROF)** -|1|1|1 -|4|3|3 -|8|5|3 -|12|8|4 -|16|10|5 -|32|24|10 -|64/96|32|20 -|128|32|32 +| Total Cores | Redis (ROR) | Redis on Flash (ROF) | +| ------ | ------ | :------ | +|1|1|1 | +|4|3|3 | +|8|5|3 | +|12|8|4 | +|16|10|5 | +|32|24|10 | +|64/96|32|20 | +|128|32|32 | ## Data access anti-patterns @@ -497,17 +473,18 @@ The slow low is available in the Redis Enterprise and Redis Cloud consoles: .Redis Cloud dashboard showing slow database operations {{< image filename="/images/slow_log.png)" >}} -|**Issue**|**Remediation** +| Issue | Remediation | +| ------ | :------ | |The KEYS command shows up in the slow log |Find the application that issuing the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) -so that Redis will reject the KEYS command altogether. -|The slow log shows a significant number of slow, O(n) operations +so that Redis will reject the KEYS command altogether. | +|The slow log shows a significant number of slow, O(n) operations | |If these operations are being issued against large data structures, -then the application may need to be refactored to use more efficient Redis commands. -|The slow logs contains only O(1) commands, and these commands are taking several milliseconds +then the application may need to be refactored to use more efficient Redis commands. | +|The slow logs contains only O(1) commands, and these commands are taking several milliseconds | or more to complete -|This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. +|This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | ### Hot keys @@ -617,55 +594,32 @@ The following is a list of alerts contained in the `alerts.yml` file. There are ### List of alerts -|**Description**|**Trigger** -|Average latency has reached a warning level -|round(bdb_avg_latency * 1000) > 1 -|Average latency has reached a critical level indicating system degradation -|round(bdb_avg_latency * 1000) > 4 -|Absence of any connection indicates improper configuration or firewall issue -|bdb_conns < 1 -|A flood of connections has occurred that will impact normal operations -|bdb_conns > 64000 -|Absence of any requests indicates improperly configured clients -|bdb_total_req < 1 -|Excessive number of client requests indicates configuration and/or programmatic issues -|bdb_total_req > 1000000 -|The database in question will soon be unable to accept new data -|round((bdb_used_memory/bdb_memory_limit) * 100) > 98 -|The database in question will be unable to accept new data in two hours -|round((bdb_used_memory/bdb_memory_limit) ** 100) < 98 and (predict_linear(bdb_used_memory[15m], 2 ** 3600) / bdb_memory_limit) > 0.3 and round(predict_linear(bdb_used_memory[15m], 2 * 3600)/bdb_memory_limit) > 0.98 -|Database read operations are failing to find entries more than 50% of the time -|(100 * bdb_read_hits)/(bdb_read_hits + bdb_read_misses) < 50 -|In situations where TTL values are not set this indicates a problem -|bdb_evicted_objects > 1 -|Replication between nodes is not in a satisfactory state -|bdb_replicaof_syncer_status > 0 -|Record synchronization between nodes is not in a satisfactory state -|bdb_crdt_syncer_status > 0 -|The amount by which replication lags behind events is worrisome -|bdb_replicaof_syncer_local_ingress_lag_time > 500 -|The amount by which object replication lags behind events is worrisome -|bdb_crdt_syncer_local_ingress_lag_time > 500 -|The expected number of active nodes is less than expected -|count(node_up) != 3 -|Persistent storage will soon be exhausted -|round((node_persistent_storage_free/node_persistent_storage_avail) * 100) <= 5 -|Ephemeral storage will soon be exhausted -|round((node_ephemeral_storage_free/node_ephemeral_storage_avail) * 100) <= 5 -|The node in question is close to running out of memory -|round((node_available_memory/node_free_memory) * 100) <= 15 -|The node in question has exceeded expected levels of CPU usage -|round((1 - node_cpu_idle) * 100) >= 80 -|The shard in question is not reachable -|redis_up == 0 -|The master shard is not reachable -|floor(redis_master_link_status{role="slave"}) < 1 -|The shard in question has exceeded expected levels of CPU usage -|redis_process_cpu_usage_percent >= 80 -|The master shard has exceeded expected levels of CPU usage -|redis_process_cpu_usage_percent{role="master"} > 0.75 and redis_process_cpu_usage_percent{role="master"} > on (bdb) group_left() (avg by (bdb)(redis_process_cpu_usage_percent{role="master"}) + on(bdb) 1.2 * stddev by (bdb) (redis_process_cpu_usage_percent{role="master"})) -|The shard in question has an unhealthily high level of connections -|redis_connected_clients > 500 +| Description | Trigger | +| ------ | :------ | +|Average latency has reached a warning level | round(bdb_avg_latency * 1000) > 1 | +|Average latency has reached a critical level indicating system degradation | round(bdb_avg_latency * 1000) > 4 | +|Absence of any connection indicates improper configuration or firewall issue | bdb_conns < 1 | +|A flood of connections has occurred that will impact normal operations | bdb_conns > 64000 | +|Absence of any requests indicates improperly configured clients | bdb_total_req < 1 | +|Excessive number of client requests indicates configuration and/or programmatic issues | bdb_total_req > 1000000 | +|The database in question will soon be unable to accept new data | round((bdb_used_memory/bdb_memory_limit) * 100) > 98 | +|The database in question will be unable to accept new data in two hours | round((bdb_used_memory/bdb_memory_limit) ** 100) < 98 and (predict_linear(bdb_used_memory[15m], 2 ** 3600) / bdb_memory_limit) > 0.3 and round(predict_linear(bdb_used_memory[15m], 2 * 3600)/bdb_memory_limit) > 0.98 | +|Database read operations are failing to find entries more than 50% of the time | (100 * bdb_read_hits)/(bdb_read_hits + bdb_read_misses) < 50 | +|In situations where TTL values are not set this indicates a problem | bdb_evicted_objects > 1 | +|Replication between nodes is not in a satisfactory state | bdb_replicaof_syncer_status > 0 | +|Record synchronization between nodes is not in a satisfactory state | bdb_crdt_syncer_status > 0 | +|The amount by which replication lags behind events is worrisome | bdb_replicaof_syncer_local_ingress_lag_time > 500 | +|The amount by which object replication lags behind events is worrisome | bdb_crdt_syncer_local_ingress_lag_time > 500 | +|The expected number of active nodes is less than expected | count(node_up) != 3 | +|Persistent storage will soon be exhausted | round((node_persistent_storage_free/node_persistent_storage_avail) * 100) <= 5 | +|Ephemeral storage will soon be exhausted | round((node_ephemeral_storage_free/node_ephemeral_storage_avail) * 100) <= 5 | +|The node in question is close to running out of memory | round((node_available_memory/node_free_memory) * 100) <= 15 | +|The node in question has exceeded expected levels of CPU usage | round((1 - node_cpu_idle) * 100) >= 80 | +|The shard in question is not reachable | redis_up == 0 | +|The master shard is not reachable | floor(redis_master_link_status{role="slave"}) < 1 | +|The shard in question has exceeded expected levels of CPU usage | redis_process_cpu_usage_percent >= 80 | +|The master shard has exceeded expected levels of CPU usage | redis_process_cpu_usage_percent{role="master"} > 0.75 and redis_process_cpu_usage_percent{role="master"} > on (bdb) group_left() (avg by (bdb)(redis_process_cpu_usage_percent{role="master"}) + on(bdb) 1.2 * stddev by (bdb) (redis_process_cpu_usage_percent{role="master"})) | +|The shard in question has an unhealthily high level of connections | redis_connected_clients > 500 | ## Appendix A: Grafana Dashboards From 9ff50d6f757ae19a636cc1801d001183ce3e9970 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Fri, 1 Nov 2024 13:16:27 -0700 Subject: [PATCH 05/52] Update observability.md --- .../observability.md | 66 +++++++++---------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 962700ce0..5486fb630 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -47,7 +47,7 @@ In addition to manually monitoring these resources and indicators), it is best p ## Core cluster resource monitoring -### Memory +## Memory Every Redis Enterprise database has a maximum configured memory limit to ensure isolation in a multi-database cluster. @@ -57,14 +57,14 @@ Memory usage percentage metric - Percentage of used memory relative to the confi Dashboard displaying high-level cluster metrics - [Cluster Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/cluster_dashboard_v9-11.json) {{< image filename="/images/playbook_used-memory.png" alt="Dashboard displaying high-level cluster metrics" >}} -#### Thresholds +### Thresholds The appropriate memory threshold depends on how the application is using Redis. * Caching workloads, which permit Redis to evict keys, can safely use 100% of available memory. * Non-caching workloadsdo not permit key eviction and should be closely monitored as soon as memory usage reaches 80%. -#### Caching workloads +### Caching workloads For applications using Redis solely as a cache, you can safely let the memory usage reach 100% as long as you have an [eviction policy](https://redis.io/blog/cache-eviction-strategies/)in place. This will ensure @@ -79,7 +79,7 @@ it's still important to monitor performance. The key performance indicators incl * Cache hit ratio * Evicted keys -#### Read latency +### Read latency **Latency** has two important definitions, depending on context: @@ -93,7 +93,7 @@ This may indicate a low cache hit ratio, ultimately caused by insufficient memor You need to monitor both application-level and Redis-level latency to diagnose caching performance issues in production. -#### Cache hit ratio and eviction +### Cache hit ratio and eviction **Cache hit ratio** is the percentage of read requests that Redis serves successfully. **Eviction rate** is the rate at which Redis evicts keys from the cache. These metrics @@ -123,7 +123,7 @@ An acceptable rate of key evictions depends on the total number of keys in the d and the measure of application-level latency. If application latency is high, check to see that key evictions have not increased. -#### Eviction Policies +### Eviction Policies | Name | Description | | ------ | :------ | @@ -137,7 +137,7 @@ check to see that key evictions have not increased. |volatile-ttl | Removes keys with expire field set to true and the shortest remaining time-to-live (TTL) value. | -#### Eviction policy guidelines +### Eviction policy guidelines * Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests. That is, you expect a subset of elements will be accessed far more often than the rest. This is a good pick if you are unsure. @@ -149,13 +149,13 @@ The volatile-lru and volatile-random policies are mainly useful when you want to **NB** Setting an expire value to a key costs memory, so using a policy like allkeys-lru is more memory efficient since there is no need for an expire configuration for the key to be evicted under memory pressure. -#### Non-caching workloads +### Non-caching workloads If no eviction policy is enabled, then Redis will stop accepting writes once memory reaches 100%. Therefore, for non-caching workloads, we recommend that you configure an alert at 80% memory usage. Once your database reaches this 80% threshold, you should closely review the rate of memory usage growth. -#### Troubleshooting +### Troubleshooting |Issue | Possible causes | Remediation | | ------ | ------ | :------ | @@ -165,7 +165,7 @@ Once your database reaches this 80% threshold, you should closely review the rat -### CPU +## CPU Redis Enterprise provides several CPU metrics: @@ -188,7 +188,7 @@ When diagnosing performance issues, start by looking at shard CPU. Dashboard displaying CPU usage - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) {{< image filename="/images/playbook_database-cpu-shard.png" alt="Dashboard displaying CPU usage" >}} -#### Thresholds +### Thresholds In general, we define high CPU as any CPU utilization above 80% of total capacity. @@ -209,7 +209,7 @@ Dashboard displaying an ensemble of Node CPU usage data - [Node Dashboard](https Node CPU should also remain below 80% of total capacity. As with the proxy, the node CPU is variable depending on the CPU capacity of the node. You will need to calibrate your alerting based on the number of cores in your nodes. -#### Troubleshooting +### Troubleshooting High CPU utilization has multiple possible causes. Common causes include an under-provisioned cluster, excess inefficient Redis operations, and hot master shards. @@ -222,7 +222,7 @@ excess inefficient Redis operations, and hot master shards. |High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. | Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | |High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | -### Connections +## Connections The Redis Enterprise database dashboard indicates to the total number of connections to the database. @@ -231,7 +231,7 @@ Based on the number of application instances connecting to Redis (and whether yo you should have a rough idea of the minimum and maximum number of connections you expect to see for any given database. This number should remain relatively constant over time. -#### Troubleshooting +### Troubleshooting | Issue | Possible causes | Remediation | | ------ | ------ | :------ | @@ -252,14 +252,14 @@ connection pool or a connection pool that is not properly configured. | Dashboard displaying connections - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] {{< image filename="/images/playbook_database-used-connections.png" alt="Dashboard displaying connections" >}} -#### Network ingress / egress +### Network ingress / egress The network ingress / egress panel show the amount of data being sent to and received from the database. Large spikes in network traffic can indicate that the cluster is under-provisioned or that the application is reading and/or writing unusually large keys. A correlation between high network traffic and high CPU utilization may indicate a large key scenario. -##### Unbalanced database endpoint +#### Unbalanced database endpoint One possible cause is that the database endpoint is not located on the same node as master shards. In addition to added network latency, if data plane internode encryption is enabled, CPU consumption can increase as well. @@ -268,7 +268,7 @@ One solution is to used the optimal shard placement and proxy policy to ensure e Extreme network traffic utilization may approach the limits of the underlying network infrastructure. In this case, the only remediation is to add additional nodes to the cluster and scale the database's shards across them. -### Synchronization +## Synchronization In Redis Enterprise, geographically-distributed synchronization is based on CRDT technology. The Redis Enterprise implementation of CRDT is called an Active-Active database (formerly known as CRDB). @@ -320,7 +320,7 @@ which is why it's essential to measure request latency at the application, as we Display showing a noticeable spike in latency {{< image filename="/images/latency_spike.png)" alt="Display showing a noticeable spike in latency" >}} -#### Troubleshooting +### Troubleshooting Here are some possible causes of high database latency. Note that high database latency is just one possible cause of high application latency. Application latency can be caused by a variety of factors, including @@ -345,7 +345,7 @@ to determine if increased traffic is causing the latency. | |Confirm that [slow operations](#Slow operations) are not causing the high CPU utilization. If the high CPU utilization is due to increased load, consider adding shards to the database. | -### Cache hit rate +## Cache hit rate **Cache hit rate** is the percentage of all read operations that return a response.footnote:[Cache hit rate is a composite statistic that is computed by dividing the number of read hits by the total number of read operations.] When an application tries to read a key that exists, this is known as a **cache hit**. @@ -366,7 +366,7 @@ bdb_read_misses - The number of read operations returning null bdb_write_hits - The number of write operations against existing keys bdb_write_misses - The number of write operations that create new keys -#### Troubleshooting +### Troubleshooting Cache hit rate is usually only relevant for caching workloads. Eeviction will begin after the database approaches its max memory capacity. @@ -376,7 +376,7 @@ if the rate of necessary key evictions exceeds the rate of new key insertions. See Cache hit ratio and eviction for tips on troubleshooting cache hit rate. -### Key eviction rate +## Key eviction rate They **key eviction rate** is rate at which objects are being evicted from the database. See (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy] for a discussion if key eviction and its relationship with memory usage. @@ -438,7 +438,7 @@ There are three data access patterns that can limit the performance of your Redi This section defines each of these patterns and describes how to diagnose and mitigate them. -### Slow operations +## Slow operations **Slow operations** are operations that take longer than a few milliseconds to complete. @@ -487,7 +487,7 @@ or more to complete |This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | -### Hot keys +## Hot keys A **hot key** is a key that is accessed extremely frequently (for example, thousands of times a second or more). @@ -495,7 +495,7 @@ Each key in Redis belongs to one, and only one, shard. For this reason, a hot key can cause high CPU utilization on that one shard, which can increase latency for all other operations. -#### Troubleshooting +### Troubleshooting You may suspect that you have a hot key if you see high CPU utilization on a single shard. There are two main way to identify hot keys: using the Redis CLI and sampling the operations against Redis. @@ -512,7 +512,7 @@ against the high CPU shard. Since this a potentially high-impact operation, you use this technique as a secondary restort. For mission-critical databases, consider contact Redis support for assistance. -#### Remediation +### Remediation Once you discover a hot key, you need to find a way to reduce the number of operations against it. This means getting an understanding of the application's access pattern and the reasons for such frequently access. @@ -521,19 +521,19 @@ If the hot key operations are read-only, then consider implementing an applicati that fewer read request are sent to Redis. For example, even a local cache that expires every 5 seconds can entirely eliminate a hot key issue. -### Large keys +## Large keys **Large keys** are keys that are hundreds of kilobytes or larger. High network traffic and high CPU utilization can be caused by large keys. -#### Troubleshooting +### Troubleshooting To identify large keys, you can sample the keyspace using the Redis CLI. Run `+redis-cli --memkeys+` against your database to sample the keyspace in real time and potentially identify the largest keys in your database. -#### Remediation +### Remediation Addressing a large key issues requires understanding why the application is creating large keys in the first place. As such, it's difficult to provide general advice to solving this issue. Resolution often requires a change @@ -551,7 +551,7 @@ To use these alerts, install [Prometheus Alertmanager](https://prometheus.io/doc For a comprehensive guide to alerting with Prometheus and Grafana, see the [Grafana blog post on the subject](https://grafana.com/blog/2020/02/25/step-by-step-guide-to-setting-up-prometheus-alertmanager-with-slack-pagerduty-and-gmail/). -### Configuring Prometheus +## Configuring Prometheus To configure Prometheus for alerting, open the `prometheus.yml` configuration file. @@ -592,7 +592,7 @@ The following is a list of alerts contained in the `alerts.yml` file. There are - Not all Redis Enterprise deployments export all metrics - Most metrics only alert if the specified trigger persists for a given duration -### List of alerts +## List of alerts | Description | Trigger | | ------ | :------ | @@ -634,16 +634,16 @@ a holistic picture of your deployment. There are two additional sets of dashboards for Redis Enterprise software that provide drill-down functionality: the workflow dashboards. -### Software +## Software - [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/basic) - [Extended](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/extended) - [Classic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/classic) -### Workflow +## Workflow - [Database](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/databases) - [Node](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/nodes) -### Cloud +## Cloud - [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/basic) - [Extended](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/extended) From 76584bfa5dcdb6c6923b2a153e559cd501c86c0c Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Fri, 1 Nov 2024 14:21:55 -0700 Subject: [PATCH 06/52] Update observability.md --- .../observability.md | 189 ++++++++---------- 1 file changed, 85 insertions(+), 104 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 5486fb630..4f88c889c 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -19,7 +19,7 @@ This document provides observability and monitoring guidance for developers runn that connect to Redis Enterprise. In particular, this guide focuses on the systems and resources that are most likely to impact the performance of your application. -Dashboard showing relevant statistics for a Node +Dashboard showing relevant statistics for a node {{< image filename="/images/node_summary.png" alt="Dashboard showing relevant statistics for a Node" >}} To effectively monitor a Redis Enterprise cluster you need to observe @@ -43,16 +43,18 @@ Key database performance indicators include: Dashboard showing an overview of cluster metrics {{< image filename="/images/cluster_overview.png" alt="Dashboard showing an overview of cluster metrics" >}} -In addition to manually monitoring these resources and indicators), it is best practice to set up alerts. +In addition to manually monitoring these resources and indicators, it is best practice to set up alerts. ## Core cluster resource monitoring -## Memory +### Memory Every Redis Enterprise database has a maximum configured memory limit to ensure isolation in a multi-database cluster. -Memory usage percentage metric - Percentage of used memory relative to the configured memory limit for a given database +| Metric name | Definition | Unit | +| ------ | ------ | :------ | +| Memory usage percentage metric | Percentage of used memory relative to the configured memory limit for a given database | Percentage | Dashboard displaying high-level cluster metrics - [Cluster Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/cluster_dashboard_v9-11.json) {{< image filename="/images/playbook_used-memory.png" alt="Dashboard displaying high-level cluster metrics" >}} @@ -62,15 +64,15 @@ Dashboard displaying high-level cluster metrics - [Cluster Dashboard](https://gi The appropriate memory threshold depends on how the application is using Redis. * Caching workloads, which permit Redis to evict keys, can safely use 100% of available memory. -* Non-caching workloadsdo not permit key eviction and should be closely monitored as soon as memory usage reaches 80%. +* Non-caching workloads do not permit key eviction and should be closely monitored as soon as memory usage reaches 80%. ### Caching workloads For applications using Redis solely as a cache, you can safely let the memory usage -reach 100% as long as you have an [eviction policy](https://redis.io/blog/cache-eviction-strategies/)in place. This will ensure +reach 100% as long as you have an [eviction policy](https://redis.io/blog/cache-eviction-strategies/) in place. This will ensure that Redis can evict keys while continuing to accept new writes. -**NB** Eviction will increase write command latency as Redis has to cleanup the memory/objects before accepting a new write to prevent OOM when memory usage is at 100% +**Note:** Eviction will increase write command latency as Redis has to cleanup the memory/objects before accepting a new write to prevent OOM when memory usage is at 100%. While your Redis database is using 100% of available memory in a caching context, it's still important to monitor performance. The key performance indicators include: @@ -83,9 +85,11 @@ it's still important to monitor performance. The key performance indicators incl **Latency** has two important definitions, depending on context: -** In context Redis itself, latency is **the time it takes for Redis -to respond to a request**. See [latency is **the time it takes for the application -to process a request*. This will include the time it takes to execute both reads and writes +* In context Redis itself, latency is **the time it takes for Redis +to respond to a request**. See latency is + +* In the context of your application, Latency is **the time it takes for the application +to process a request**. This will include the time it takes to execute both reads and writes to Redis, as well as calls to other databases and services. Note that its possible for Redis to report low latency while the application is experiencing high latency. This may indicate a low cache hit ratio, ultimately caused by insufficient memory. @@ -115,7 +119,7 @@ The ideal cache hit ratio depends on the application, but generally, the ratio s Low hit ratios coupled with high numbers of object evictions may indicate that your cache is too small. This can cause thrashing on the application side, a scenario where the cache is constantly being invalidated. -The upshot here is that when your Redis database is using 100% of available memory, you need +This means that when your Redis database is using 100% of available memory, you need to measure the rate of [key evictions](https://redis.io/docs/latest/operate/rs/references/metrics/database-operations/#evicted-objectssec). @@ -139,21 +143,21 @@ check to see that key evictions have not increased. ### Eviction policy guidelines -* Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests. That is, you expect a subset of elements will be accessed far more often than the rest. This is a good pick if you are unsure. +* Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests. That is, you expect a subset of elements will be accessed far more often than the rest. This is a good policy to choose if you are unsure. * Use the allkeys-random if you have a cyclic access where all the keys are scanned continuously, or when you expect the distribution to be uniform. -* Use the volatile-ttl if you want to be able to provide hints to Redis about what are good candidate for expiration by using different TTL values when you create your cache objects. +* Use the volatile-ttl if you want to be able to provide hints to Redis about what are good candidates for expiration by using different TTL values when you create your cache objects. The volatile-lru and volatile-random policies are mainly useful when you want to use a single instance for both caching and to have a set of persistent keys. However it is usually a better idea to run two Redis instances to solve such a problem. -**NB** Setting an expire value to a key costs memory, so using a policy like allkeys-lru is more memory efficient since there is no need for an expire configuration for the key to be evicted under memory pressure. +**Note:** Setting an expire value to a key costs memory, so using a policy like allkeys-lru is more memory efficient because there is no need for an expire configuration for the key to be evicted under memory pressure. ### Non-caching workloads If no eviction policy is enabled, then Redis will stop accepting writes once memory reaches 100%. -Therefore, for non-caching workloads, we recommend that you configure an alert at 80% memory usage. -Once your database reaches this 80% threshold, you should closely review the rate of memory usage growth. +Therefore, for non-caching workloads, it is best practice to configure an alert at 80% memory usage. +After your database reaches this 80% threshold, you should closely review the rate of memory usage growth. ### Troubleshooting @@ -218,8 +222,9 @@ excess inefficient Redis operations, and hot master shards. | Issue | Possible causes | Remediation | ------ | ------ | :------ | |High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. See <> for a broader discussion of this metric in the context of your application) for details on this. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | -|High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see [review the behavior of connections to the database]. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. | Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | -|High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. | Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | +|High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see [Hot keys]. | +| High Proxy CPU | There are several possible causes of high proxy CPU. Firs, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | +|High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. | Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | |High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | ## Connections @@ -235,21 +240,12 @@ This number should remain relatively constant over time. | Issue | Possible causes | Remediation | | ------ | ------ | :------ | -|Fewer connections to Redis than expected |The application may not be connecting to the correct Redis database. -There may be a network partition between the application and the Redis database. | -|Confirm that the application can successfully connect to Redis. -This may require consulting the application logs or the application's connection configuration. | -|Connection count continues to grow over time | -|Your application may not be releasing connections. -The most common of such a connection leak is a manually implemented -connection pool or a connection pool that is not properly configured. | -|Review the application's connection configuration | -|Erratic connection counts (for example, spikes and drops) | -|Application misbehavior (thundering herds, connection cycling, or networking issues) | -|Review the application logs and network traffic to determine the cause of the erratic connection counts. | - - -Dashboard displaying connections - https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json[Database Dashboard] +|Fewer connections to Redis than expected |The application may not be connecting to the correct Redis database. There may be a network partition between the application and the Redis database. | Confirm that the application can successfully connect to Redis. This may require consulting the application logs or the application's connection configuration. | +|Connection count continues to grow over time | Your application may not be releasing connections. The most common of such a connection leak is a manually implemented connection pool or a connection pool that is not properly configured. | Review the application's connection configuration | +|Erratic connection counts (for example, spikes and drops) | Application misbehavior (thundering herds, connection cycling, or networking issues) | Review the application logs and network traffic to determine the cause of the erratic connection counts. | + + +Dashboard displaying connections - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) {{< image filename="/images/playbook_database-used-connections.png" alt="Dashboard displaying connections" >}} ### Network ingress / egress @@ -263,7 +259,7 @@ and high CPU utilization may indicate a large key scenario. One possible cause is that the database endpoint is not located on the same node as master shards. In addition to added network latency, if data plane internode encryption is enabled, CPU consumption can increase as well. -One solution is to used the optimal shard placement and proxy policy to ensure endpoints are collocated on nodes hosting master shards. If you need to restore balance (for example, after node failure) you can manually failover shard(s) with the `rladmin` cli tool. +One solution is to use the optimal shard placement and proxy policy to ensure endpoints are collocated on nodes hosting master shards. If you need to restore balance (for example, after node failure) you can manually failover shard(s) with the `rladmin` cli tool. Extreme network traffic utilization may approach the limits of the underlying network infrastructure. In this case, the only remediation is to add additional nodes to the cluster and scale the database's shards across them. @@ -274,7 +270,7 @@ In Redis Enterprise, geographically-distributed synchronization is based on CRDT The Redis Enterprise implementation of CRDT is called an Active-Active database (formerly known as CRDB). With Active-Active databases, applications can read and write to the same data set from different geographical locations seamlessly and with low latency, without changing the way the application connects to the database. -An Active-Active architecture is a data resiliency architecture that distributes the database information over multiple data centers via independent and geographically distributed clusters and nodes. +An Active-Active architecture is a data resiliency architecture that distributes the database information over multiple data centers using independent and geographically distributed clusters and nodes. It is a network of separate processing nodes, each having access to a common replicated database such that all nodes can participate in a common application ensuring local low latency with each region being able to run in isolation. To achieve consistency between participating clusters, Redis Active-Active synchronization uses a process called the syncer. @@ -305,7 +301,7 @@ Latency is **the time it takes for Redis to respond to a request**. Redis Enterprise measures latency from the first byte received by the proxy to the last byte sent in the command's response. An adequately provisioned Redis database running efficient Redis operations will report an average latency below 1 millisecond. In fact, it's common to measure -latency in terms is microseconds. Customers regularly achieve, and sometime require, average latencies of 400-600 +latency in terms is microseconds. Businesses regularly achieve, and sometimes require, average latencies of 400-600 microseconds. Dashboard display of latency metrics - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) @@ -318,7 +314,7 @@ Note that these latency metrics do not include network round trip time or applic which is why it's essential to measure request latency at the application, as well. Display showing a noticeable spike in latency -{{< image filename="/images/latency_spike.png)" alt="Display showing a noticeable spike in latency" >}} +{{< image filename="/images/latency_spike.png" alt="Display showing a noticeable spike in latency" >}} ### Troubleshooting @@ -330,56 +326,51 @@ High Proxy CPU - There are several possible causes of high proxy CPU. First, a high rate of evictions, or a networking issue. - | Issue | Possible causes | Remediation | -|Slow database operations | -|Confirm that there are no excessive slow operations in the [Redis slow log](#Slow operations). | -|If possible, reduce the number of slow operations being sent to the database. +| ------ | ------ | :------ | +|Slow database operations | Confirm that there are no excessive slow operations in the [Redis slow log](#Slow operations). | If possible, reduce the number of slow operations being sent to the database. If this not possible, consider increasing the number of shards in the database. | -|Increased traffic to the database | -|Review the [network traffic](#Network ingress / egress) and the database operations per second chart -to determine if increased traffic is causing the latency. | -|If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | -|Insufficient CPU | -|Check to see if the [CPU utilization](#CPU) is increasing. | -|Confirm that [slow operations](#Slow operations) are not causing the high CPU utilization. +|Increased traffic to the database | Review the [network traffic](#Network ingress / egress) and the database operations per second chart +to determine if increased traffic is causing the latency. | If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | +|Insufficient CPU | Check to see if the [CPU utilization](#CPU) is increasing. | Confirm that [slow operations](#Slow operations) are not causing the high CPU utilization. If the high CPU utilization is due to increased load, consider adding shards to the database. | ## Cache hit rate -**Cache hit rate** is the percentage of all read operations that return a response.footnote:[Cache hit rate is a composite statistic that is computed by dividing the number of read hits by the total number of read operations.] +**Cache hit rate** is the percentage of all read operations that return a response. **Note:** Cache hit rate is a composite statistic that is computed by dividing the number of read hits by the total number of read operations. When an application tries to read a key that exists, this is known as a **cache hit**. Alternatively, when an application tries to read a key that does not exist, this is knows as a **cache miss**. -For [caching workloads](#Caching workloads), the cache hit rate should generally be above 50%, although +For caching workloads, the cache hit rate should generally be above 50%, although the exact ideal cache hit rate can vary greatly depending on the application and depending on whether the cache is already populated. Dashboard showing the cache hit ratio along with read/write misses - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) -{{< image filename="/images/playbook_cache-hit.png)" alt="Dashboard showing the cache hit ratio along with read/write misses" >}} +{{< image filename="/images/playbook_cache-hit.png" alt="Dashboard showing the cache hit ratio along with read/write misses" >}} -Note: Redis Enterprise actually reports four different cache hit / miss metrics. +**Note:** Redis Enterprise actually reports four different cache hit / miss metrics. These are defined as follows: -bdb_read_hits - The number of successful read operations -bdb_read_misses - The number of read operations returning null -bdb_write_hits - The number of write operations against existing keys -bdb_write_misses - The number of write operations that create new keys +| Metric name | Definition | +| ------ | :------ | +| bdb_read_hits | The number of successful read operations | +| bdb_read_misses | The number of read operations returning null | +| bdb_write_hits | The number of write operations against existing keys | +| bdb_write_misses | The number of write operations that create new keys | ### Troubleshooting -Cache hit rate is usually only relevant for caching workloads. Eeviction will begin after the database approaches its max memory capacity. +Cache hit rate is usually only relevant for caching workloads. Eviction will begin after the database approaches its max memory capacity. A high or increasing rate of evictions will negatively affect database latency, especially if the rate of necessary key evictions exceeds the rate of new key insertions. -See Cache hit ratio and eviction -for tips on troubleshooting cache hit rate. +See (Cache hit ratio and eviction) for tips on troubleshooting cache hit rate. ## Key eviction rate They **key eviction rate** is rate at which objects are being evicted from the database. -See (https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/)[eviction policy] for a discussion if key eviction and its relationship with memory usage. +See [eviction policy](https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/) for a discussion of key eviction and its relationship with memory usage. Dashboard displaying object evictions - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) {{< image filename="/images/playbook_eviction-expiration.png" alt="Dashboard displaying object evictions">}} @@ -400,8 +391,8 @@ Redis Enterprise Software (RS) provides high-performance data access through a p Dashboard displaying proxy thread activity - [Proxy Thread Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/cloud/basic/redis-cloud-proxy-dashboard_v9-11.json) {{< image filename="/images/proxy-thread-dashboard.png" alt="Dashboard displaying proxy thread activity" >}} -When needed, we can tune the number of proxy threads using the "rladmin tune proxy" command in order to be able to make the proxy use more CPU cores. -Nevertheless, cores used by the proxy won't be available for Redis, therefore we need to take into account the number of Redis nodes on the host and the total number of available cores. +When needed, you can tune the number of proxy threads using the `rladmin tune proxy` command to make the proxy use more CPU cores. +Cores used by the proxy won't be available for Redis, therefore we need to take into account the number of Redis nodes on the host and the total number of available cores. How to set a new number of proxy cores using the command: @@ -445,11 +436,11 @@ This section defines each of these patterns and describes how to diagnose and mi Not all Redis operations are equally efficient. The most efficient Redis operations are O(1) operations; that is, they have a constant time complexity. Example of such operations include [GET](https://redis.io/docs/latest/commands/get/), -[SET](https://redis.io/docs/latest/commands/set/)[SET], [SADD](https://redis.io/docs/latest/commands/sadd/), +[SET](https://redis.io/docs/latest/commands/set/), [SADD](https://redis.io/docs/latest/commands/sadd/), and [HSET](https://redis.io/docs/latest/commands/hset/). -These constant time operations are unlikely to cause high CPU utilization.footnote:[Even so, -it's still possible for a high rate of constant time operations to overwhelm an underprovisioned database.] +These constant time operations are unlikely to cause high CPU utilization. **Note:** Even so, +it's still possible for a high rate of constant time operations to overwhelm an underprovisioned database. Other Redis operations exhibit greater levels of time complexity. O(n) (linear time) operations are more likely to cause high CPU utilization. @@ -458,33 +449,26 @@ and [LREM](https://redis.io/docs/latest/commands/lrem/). These operations are not necessarily problematic, but they can be if executed against data structures holding a large number of elements (for example, a list with 1 million elements). -That said, the [KEYS](https://redis.io/docs/latest/commands/keys/) command should almost never be run against a +However, the [KEYS](https://redis.io/docs/latest/commands/keys/) command should almost never be run against a production system, since returning a list of all keys in a large Redis database can cause significant slowdowns and block other operations. If you need to scan the keyspace, especially in a production cluster, always use the [SCAN](https://redis.io/docs/latest/commands/scan) command instead. -==== Troubleshooting +### Troubleshooting The best way to discover slow operations is to view the slow log. -The slow low is available in the Redis Enterprise and Redis Cloud consoles: +The slow log is available in the Redis Enterprise and Redis Cloud consoles: * [Redis Enterprise slow log docs](https://redis.io/docs/latest/operate/rs/clusters/logging/redis-slow-log/) * [Redis cloud slow log docs](https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info) -.Redis Cloud dashboard showing slow database operations -{{< image filename="/images/slow_log.png)" >}} +Redis Cloud dashboard showing slow database operations +{{< image filename="/images/slow_log.png" alt="Redis Cloud dashboard showing slow database operations" >}} | Issue | Remediation | | ------ | :------ | -|The KEYS command shows up in the slow log -|Find the application that issuing the KEYS command and replace it with a SCAN command. -In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) -so that Redis will reject the KEYS command altogether. | -|The slow log shows a significant number of slow, O(n) operations | -|If these operations are being issued against large data structures, -then the application may need to be refactored to use more efficient Redis commands. | -|The slow logs contains only O(1) commands, and these commands are taking several milliseconds | -or more to complete -|This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | +|The KEYS command shows up in the slow log |Find the application that issuing the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) so that Redis will reject the KEYS command altogether. | +|The slow log shows a significant number of slow, O(n) operations | If these operations are being issued against large data structures, then the application may need to be refactored to use more efficient Redis commands. | +|The slow logs contains only O(1) commands, and these commands are taking several milliseconds or more to completen |This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | ## Hot keys @@ -504,21 +488,21 @@ To use the Redis CLI to identify hot keys: 1. First confirm that you have enough available memory to enable an eviction policy. 2. Next, enable the LFU (least-frequently used) eviction policy on the database. -3. Finally, run `+redis-cli --hotkeys+` +3. Finally, run `redis-cli --hotkeys` You may also identify hot keys by sampling the operations against Redis. You can use do this by running the [MONITOR](https://redis.io/docs/latest/commands/monitor/) command -against the high CPU shard. Since this a potentially high-impact operation, you should only -use this technique as a secondary restort. For mission-critical databases, consider -contact Redis support for assistance. +against the high CPU shard. Because this is a potentially high-impact operation, you should only +use this technique as a secondary option. For mission-critical databases, consider +contacting Redis support for assistance. ### Remediation -Once you discover a hot key, you need to find a way to reduce the number of operations against it. -This means getting an understanding of the application's access pattern and the reasons for such frequently access. +After you discover a hot key, you need to find a way to reduce the number of operations against it. +This means getting an understanding of the application's access pattern and the reasons for such frequent access. -If the hot key operations are read-only, then consider implementing an application-local cache so -that fewer read request are sent to Redis. For example, even a local cache that expires every 5 seconds +If the hot key operations are read-only, consider implementing an application-local cache so +that fewer read requests are sent to Redis. For example, even a local cache that expires every 5 seconds can entirely eliminate a hot key issue. ## Large keys @@ -530,22 +514,21 @@ High network traffic and high CPU utilization can be caused by large keys. To identify large keys, you can sample the keyspace using the Redis CLI. -Run `+redis-cli --memkeys+` against your database to sample the keyspace in real time +Run `redis-cli --memkeys` against your database to sample the keyspace in real time and potentially identify the largest keys in your database. ### Remediation -Addressing a large key issues requires understanding why the application is creating large keys in the first place. +Addressing a large key issue requires understanding why the application is creating large keys in the first place. As such, it's difficult to provide general advice to solving this issue. Resolution often requires a change to the application's data model or the way it interacts with Redis. ## Alerting -The Redis Enterprise observability package includes -[a suite of alerts and their associated tests for use with Prometheus](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana#alerts). Footnote:[Not all the alerts are appropriate for all environments; for example, installations that do not use persistence have no need for storage alerts.] +The Redis Enterprise observability package includes [a suite of alerts and their associated tests for use with Prometheus](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana#alerts). **Note:** Not all the alerts are appropriate for all environments; for example, installations that do not use persistence have no need for storage alerts. -The alerts are packaged with [a series of test](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/tests) -that validate the individual triggers. You can use these test to validate your modification to these alerts for specific environments and use cases. +The alerts are packaged with [a series of tests](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/tests) +that validate the individual triggers. You can use these tests to validate your modifications to these alerts for specific environments and use cases. To use these alerts, install [Prometheus Alertmanager](https://prometheus.io/docs/alerting/latest/configuration/). For a comprehensive guide to alerting with Prometheus and Grafana, @@ -558,26 +541,24 @@ To configure Prometheus for alerting, open the `prometheus.yml` configuration fi Uncomment the `Alertmanager` section of the file. The following configuration starts Alertmanager and instructs it to listen on its default port of 9093. -[subs="+quotes"] ----- +``` # Alertmanager configuration alerting: alertmanagers: - static_configs: - targets: - alertmanager:9093 ----- +``` The Rule file section of the config file instructs Alertmanager to read specific rules files. If you pasted the 'alerts.yml' file into '/etc/prometheus' then the following configuration would be required. -[subs="+quotes"] ----- +``` # Load rules once and periodically evaluate them according to the global 'evaluation_interval'. rule_files: - "error_rules.yml" - "alerts.yml" ----- +``` After this is done, restart Prometheus. @@ -634,17 +615,17 @@ a holistic picture of your deployment. There are two additional sets of dashboards for Redis Enterprise software that provide drill-down functionality: the workflow dashboards. -## Software +### Software - [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/basic) - [Extended](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/extended) - [Classic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/classic) -## Workflow +### Workflow - [Database](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/databases) - [Node](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/workflow/nodes) -## Cloud +### Cloud - [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/basic) - [Extended](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/cloud/extended) -**NB** - The 'workflow' dashboards are intended to be used as a package. Therefore they should all be installed, as they contain links to the other dashboards in the group permitting rapid navigation between the overview and the drill-down views. +**Note:** - The 'workflow' dashboards are intended to be used as a package. Therefore they should all be installed, as they contain links to the other dashboards in the group permitting rapid navigation between the overview and the drill-down views. From b11af49842397a20f703e28f4b9f7470ef273136 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Fri, 1 Nov 2024 15:07:01 -0700 Subject: [PATCH 07/52] Update observability.md --- .../prometheus-with-redis-enterprise/observability.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 4f88c889c..3301bc900 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -19,7 +19,7 @@ This document provides observability and monitoring guidance for developers runn that connect to Redis Enterprise. In particular, this guide focuses on the systems and resources that are most likely to impact the performance of your application. -Dashboard showing relevant statistics for a node +Dashboard showing relevant statistics for a node: {{< image filename="/images/node_summary.png" alt="Dashboard showing relevant statistics for a Node" >}} To effectively monitor a Redis Enterprise cluster you need to observe @@ -40,7 +40,7 @@ Key database performance indicators include: * Key eviction rate * Proxy Performance -Dashboard showing an overview of cluster metrics +Dashboard showing an overview of cluster metrics: {{< image filename="/images/cluster_overview.png" alt="Dashboard showing an overview of cluster metrics" >}} In addition to manually monitoring these resources and indicators, it is best practice to set up alerts. From e542a805b820a1e40b212446de022daf7ae889c6 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Fri, 22 Nov 2024 11:44:54 -0800 Subject: [PATCH 08/52] Update observability.md updates now that 7.8.2 is out --- .../observability.md | 44 +++++++++++-------- 1 file changed, 26 insertions(+), 18 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 3301bc900..361979cf9 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -1,11 +1,11 @@ --- -Title: Redis Enterprise observability and monitoring guidance +Title: Redis Enterprise Software observability and monitoring guidance alwaysopen: false categories: - docs - integrate - rs -description: Using monitoring and observability with Redis Enterprise +description: Using monitoring and observability with Redis Enterprise group: observability linkTitle: Observability and monitoring summary: Observe Redis Enterprise resources and database perfomance indicators. @@ -23,15 +23,15 @@ Dashboard showing relevant statistics for a node: {{< image filename="/images/node_summary.png" alt="Dashboard showing relevant statistics for a Node" >}} To effectively monitor a Redis Enterprise cluster you need to observe -core cluster resources and key database performance indicators. +core cluster resources and key database performance indicators as described in the following sections for this guide. Core cluster resources include: -* [Memory utilization](#Memory) -* [CPU utilization](#CPU) -* [Database connections](#Connections) -* [Network traffic](#Network) -* [Synchronization](#Synchronization) +* Memory utilization +* CPU utilization +* Database connections +* Network traffic +* Synchronization Key database performance indicators include: @@ -47,6 +47,12 @@ In addition to manually monitoring these resources and indicators, it is best pr ## Core cluster resource monitoring +Redis Enterprise version 7.8.2 introduces a preview of the new metrics stream engine that exposes the v2 Prometheus scraping endpoint at https://:8070/v2. This new engine exports all time-series metrics to external monitoring tools such as Grafana, DataDog, NewRelic, and Dynatrace using Prometheus. + +The new engine enables real-time monitoring, including full monitoring during maintenance operations, providing full visibility into performance during events such as shards' failovers and scaling operations. See [Monitoring with metrics and alerts]({{}}) for more details. + +If you are already using the existing scraping endpoint for integration, follow [this guide]({{}}) to transition and try the new engine. It is possible to scrape both existing and new endpoints simultaneously, allowing advanced dashboard preparation and a smooth transition. + ### Memory Every Redis Enterprise database has a maximum configured memory limit to ensure isolation @@ -85,8 +91,10 @@ it's still important to monitor performance. The key performance indicators incl **Latency** has two important definitions, depending on context: -* In context Redis itself, latency is **the time it takes for Redis -to respond to a request**. See latency is +* In the context Redis itself, latency is **the time it takes for Redis +to respond to a request**. The following section of Latency provides a broader discussion of this metric. + + * In the context of your application, Latency is **the time it takes for the application to process a request**. This will include the time it takes to execute both reads and writes @@ -106,7 +114,7 @@ are often inversely correlated: a high eviction rate may cause a low cache hit r If the Redis server is empty, the hit ratio will be 0%. As the application runs and the fills the cache, the hit ratio will increase. -**When the entire cached working set fits in memory**, then the cache hit ratio will reach close to 100% +**When the entire cached working set fits in memory**, the cache hit ratio will reach close to 100% while the percent of used memory will remain below 100%. **When the working set cannot fit in memory**, the eviction policy will start to evict keys. @@ -221,8 +229,8 @@ excess inefficient Redis operations, and hot master shards. | Issue | Possible causes | Remediation | ------ | ------ | :------ | -|High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. See <> for a broader discussion of this metric in the context of your application) for details on this. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | -|High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see [Hot keys]. | +|High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. The Latency section below includes a broader discussion of this metric in the context of your application. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | +|High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see the section on Hot keys below. | | High Proxy CPU | There are several possible causes of high proxy CPU. Firs, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | |High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. | Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | |High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | @@ -328,11 +336,11 @@ networking issue. | Issue | Possible causes | Remediation | | ------ | ------ | :------ | -|Slow database operations | Confirm that there are no excessive slow operations in the [Redis slow log](#Slow operations). | If possible, reduce the number of slow operations being sent to the database. +|Slow database operations | Confirm that there are no excessive slow operations in the Redis slow log. | If possible, reduce the number of slow operations being sent to the database. If this not possible, consider increasing the number of shards in the database. | -|Increased traffic to the database | Review the [network traffic](#Network ingress / egress) and the database operations per second chart +|Increased traffic to the database | Review the network traffic and the database operations per second chart to determine if increased traffic is causing the latency. | If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | -|Insufficient CPU | Check to see if the [CPU utilization](#CPU) is increasing. | Confirm that [slow operations](#Slow operations) are not causing the high CPU utilization. +|Insufficient CPU | Check to see if the CPU utilization is increasing. | Confirm that slow operations are not causing the high CPU utilization. If the high CPU utilization is due to increased load, consider adding shards to the database. | ## Cache hit rate @@ -365,7 +373,7 @@ Cache hit rate is usually only relevant for caching workloads. Eviction will beg A high or increasing rate of evictions will negatively affect database latency, especially if the rate of necessary key evictions exceeds the rate of new key insertions. -See (Cache hit ratio and eviction) for tips on troubleshooting cache hit rate. +See the Cache hit ratio and eviction section for tips on troubleshooting cache hit rate. ## Key eviction rate @@ -459,7 +467,7 @@ and block other operations. If you need to scan the keyspace, especially in a pr The best way to discover slow operations is to view the slow log. The slow log is available in the Redis Enterprise and Redis Cloud consoles: * [Redis Enterprise slow log docs](https://redis.io/docs/latest/operate/rs/clusters/logging/redis-slow-log/) -* [Redis cloud slow log docs](https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info) +* [Redis Cloud slow log docs](https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info) Redis Cloud dashboard showing slow database operations {{< image filename="/images/slow_log.png" alt="Redis Cloud dashboard showing slow database operations" >}} From 0cf4750d128447c50a6e022d49c5a0d1c527f515 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:39:16 -0800 Subject: [PATCH 09/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 361979cf9..1e8c9c997 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -19,7 +19,7 @@ This document provides observability and monitoring guidance for developers runn that connect to Redis Enterprise. In particular, this guide focuses on the systems and resources that are most likely to impact the performance of your application. -Dashboard showing relevant statistics for a node: +The screenshot below shows a dashboard with relevant statistics for a node: {{< image filename="/images/node_summary.png" alt="Dashboard showing relevant statistics for a Node" >}} To effectively monitor a Redis Enterprise cluster you need to observe From 99c6f5b53771be76b1c0c28886b6b120dd71ae02 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:39:52 -0800 Subject: [PATCH 10/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 1e8c9c997..1dc94f1d2 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -51,7 +51,7 @@ Redis Enterprise version 7.8.2 introduces a preview of the new metrics stream en The new engine enables real-time monitoring, including full monitoring during maintenance operations, providing full visibility into performance during events such as shards' failovers and scaling operations. See [Monitoring with metrics and alerts]({{}}) for more details. -If you are already using the existing scraping endpoint for integration, follow [this guide]({{}}) to transition and try the new engine. It is possible to scrape both existing and new endpoints simultaneously, allowing advanced dashboard preparation and a smooth transition. +If you are already using the existing scraping endpoint for integration, follow [this guide]({{}}) to transition and try the new engine. You can scrape both existing and new endpoints simultaneously, which lets you create advanced dashboards and transition smoothly. ### Memory From bc42be4b9b7ca469b229f80c7e73579d8d4c36b0 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:40:11 -0800 Subject: [PATCH 11/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 1dc94f1d2..ec596a08d 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -92,7 +92,7 @@ it's still important to monitor performance. The key performance indicators incl **Latency** has two important definitions, depending on context: * In the context Redis itself, latency is **the time it takes for Redis -to respond to a request**. The following section of Latency provides a broader discussion of this metric. +to respond to a request**. The [Latency](#latency) section below provides a broader discussion of this metric. From e98d1c3c291febe43f6d32e9f78ccc8986a16a07 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:40:39 -0800 Subject: [PATCH 12/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index ec596a08d..2c14464d3 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -91,7 +91,7 @@ it's still important to monitor performance. The key performance indicators incl **Latency** has two important definitions, depending on context: -* In the context Redis itself, latency is **the time it takes for Redis +* In the context of Redis itself, latency is **the time it takes for Redis to respond to a request**. The [Latency](#latency) section below provides a broader discussion of this metric. From 8bf5ab4ca29f285ad7d20be06e007cf34fbe5a11 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:40:57 -0800 Subject: [PATCH 13/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 2c14464d3..88528581b 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -109,7 +109,7 @@ caching performance issues in production. **Cache hit ratio** is the percentage of read requests that Redis serves successfully. **Eviction rate** is the rate at which Redis evicts keys from the cache. These metrics -are often inversely correlated: a high eviction rate may cause a low cache hit ratio. +are sometimes inversely correlated: a high eviction rate may cause a low cache hit ratio if too many frequently-used keys are being evicted. If the Redis server is empty, the hit ratio will be 0%. As the application runs and the fills the cache, the hit ratio will increase. From 45b045d960b9239db58a5a3384dc2e5f485789e3 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:41:16 -0800 Subject: [PATCH 14/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 88528581b..65adb32f1 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -576,7 +576,7 @@ you will see this alert, as well as the alerts in any other file you have includ {{< image filename="/images/playbook_prometheus-alerts.png" alt="prometheus alerts image" >}} -The following is a list of alerts contained in the `alerts.yml` file. There are several points consider: +The following is a list of alerts contained in the `alerts.yml` file. There are several points to consider: - Not all Redis Enterprise deployments export all metrics - Most metrics only alert if the specified trigger persists for a given duration From 2ff1de317ffbd2263c2e82ada0362d49ab7ded78 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:42:16 -0800 Subject: [PATCH 15/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 65adb32f1..5f5059919 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -118,7 +118,7 @@ the hit ratio will increase. while the percent of used memory will remain below 100%. **When the working set cannot fit in memory**, the eviction policy will start to evict keys. -The greater the rate of key eviction, the lower the cache hit ratio. +It is important to choose a policy that generally evicts rarely-used keys to keep the cache hit ratio as high as possible. In both cases, keys will may be manually invalidated by the application or evicted through the uses of TTLs (time-to-live) and an eviction policy. From a189287304156191645694f4abdaf9651645572e Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:42:33 -0800 Subject: [PATCH 16/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 5f5059919..ffb83c665 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -599,7 +599,7 @@ The following is a list of alerts contained in the `alerts.yml` file. There are |Record synchronization between nodes is not in a satisfactory state | bdb_crdt_syncer_status > 0 | |The amount by which replication lags behind events is worrisome | bdb_replicaof_syncer_local_ingress_lag_time > 500 | |The amount by which object replication lags behind events is worrisome | bdb_crdt_syncer_local_ingress_lag_time > 500 | -|The expected number of active nodes is less than expected | count(node_up) != 3 | +|The number of active nodes is less than expected | count(node_up) != 3 | |Persistent storage will soon be exhausted | round((node_persistent_storage_free/node_persistent_storage_avail) * 100) <= 5 | |Ephemeral storage will soon be exhausted | round((node_ephemeral_storage_free/node_ephemeral_storage_avail) * 100) <= 5 | |The node in question is close to running out of memory | round((node_available_memory/node_free_memory) * 100) <= 15 | From 63ecd013c624e43b28877c599c8eaf9f3073622f Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:42:49 -0800 Subject: [PATCH 17/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index ffb83c665..6d15a77af 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -614,8 +614,8 @@ The following is a list of alerts contained in the `alerts.yml` file. There are Grafana dashboards are available for Redis Enterprise Software and Redis Cloud deployments. -These dashboards come in three styles, which may be used in concert with one another to provide -a holistic picture of your deployment. +These dashboards come in three styles, which may be used together to provide +a full picture of your deployment. 1. Classic dashboards provide detailed information about the cluster, nodes, and individual databases. 2. Basic dashboards provide a high-level overviews of the various cluster components. From c94d14a0a094c37f9b34b3f0134b64be23182335 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:43:05 -0800 Subject: [PATCH 18/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 6d15a77af..61f124405 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -619,7 +619,7 @@ a full picture of your deployment. 1. Classic dashboards provide detailed information about the cluster, nodes, and individual databases. 2. Basic dashboards provide a high-level overviews of the various cluster components. -3. Extended dashboards which requires a third-party library to perform ReST calls. +3. Extended dashboards. These require a third-party library to perform ReST calls. There are two additional sets of dashboards for Redis Enterprise software that provide drill-down functionality: the workflow dashboards. From 80bafce057edc76755edf240f55fa81e83fc9589 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:43:24 -0800 Subject: [PATCH 19/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 61f124405..2a9a8284a 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -621,7 +621,7 @@ a full picture of your deployment. 2. Basic dashboards provide a high-level overviews of the various cluster components. 3. Extended dashboards. These require a third-party library to perform ReST calls. -There are two additional sets of dashboards for Redis Enterprise software that provide drill-down functionality: the workflow dashboards. +There are also two workflow dashboards for Redis Enterprise software that provide drill-down functionality. ### Software - [Basic](https://github.com/redis-field-engineering/redis-enterprise-observability/tree/main/grafana/dashboards/grafana_v9-11/software/basic) From 74597864533d560435e89f7964a95e919b85d8a5 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:43:45 -0800 Subject: [PATCH 20/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 2a9a8284a..9bb7cb439 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -163,7 +163,7 @@ The volatile-lru and volatile-random policies are mainly useful when you want to ### Non-caching workloads -If no eviction policy is enabled, then Redis will stop accepting writes once memory reaches 100%. +If no eviction policy is enabled, then Redis will stop accepting writes when memory usage reaches 100%. Therefore, for non-caching workloads, it is best practice to configure an alert at 80% memory usage. After your database reaches this 80% threshold, you should closely review the rate of memory usage growth. From 296b5849ab5027239f5aa09b05c04237807181b1 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:44:11 -0800 Subject: [PATCH 21/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 9bb7cb439..47a20a6c0 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -231,7 +231,7 @@ excess inefficient Redis operations, and hot master shards. | ------ | ------ | :------ | |High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. The Latency section below includes a broader discussion of this metric in the context of your application. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | |High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see the section on Hot keys below. | -| High Proxy CPU | There are several possible causes of high proxy CPU. Firs, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | +| High Proxy CPU | There are several possible causes of high proxy CPU. First, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | |High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. | Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | |High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | From 7e4f1fe3f3bdc94a4b58b49c391c8cb15e7a1838 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:44:31 -0800 Subject: [PATCH 22/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 47a20a6c0..bd4d12adb 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -232,7 +232,7 @@ excess inefficient Redis operations, and hot master shards. |High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. The Latency section below includes a broader discussion of this metric in the context of your application. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | |High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see the section on Hot keys below. | | High Proxy CPU | There are several possible causes of high proxy CPU. First, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | -|High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. | Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and should be done with the help of Redis support. | +|High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. | Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and you should do it with the help of Redis support. | |High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | ## Connections From d20f5d323beb8488eb062a3bb842b970889172a2 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:44:50 -0800 Subject: [PATCH 23/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index bd4d12adb..780877d39 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -237,7 +237,7 @@ excess inefficient Redis operations, and hot master shards. ## Connections -The Redis Enterprise database dashboard indicates to the total number of connections to the database. +The Redis Enterprise database dashboard indicates the total number of connections to the database. This connection count metric should be monitored with both a minimum and maximum number of connections in mind. Based on the number of application instances connecting to Redis (and whether your application uses connection pooling), From 5c4c1a377f9dbc4be3bbcf4822d2f47da9ac00ef Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:45:06 -0800 Subject: [PATCH 24/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 780877d39..066741cef 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -239,7 +239,7 @@ excess inefficient Redis operations, and hot master shards. The Redis Enterprise database dashboard indicates the total number of connections to the database. -This connection count metric should be monitored with both a minimum and maximum number of connections in mind. +You should monitor this connection count metric with both a minimum and maximum number of connections in mind. Based on the number of application instances connecting to Redis (and whether your application uses connection pooling), you should have a rough idea of the minimum and maximum number of connections you expect to see for any given database. This number should remain relatively constant over time. From c7d446383717a3391e4bf25fc89add91988dcba6 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:45:42 -0800 Subject: [PATCH 25/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 066741cef..063c13da6 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -240,7 +240,7 @@ excess inefficient Redis operations, and hot master shards. The Redis Enterprise database dashboard indicates the total number of connections to the database. You should monitor this connection count metric with both a minimum and maximum number of connections in mind. -Based on the number of application instances connecting to Redis (and whether your application uses connection pooling), +Based on the number of application instances connecting to Redis (and whether your application uses [connection pooling]({{< relref "/develop/clients/pools-and-muxing" >}})), you should have a rough idea of the minimum and maximum number of connections you expect to see for any given database. This number should remain relatively constant over time. From 08be715fd0b6840ae946d043d443ed2f9e10eb29 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:46:02 -0800 Subject: [PATCH 26/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 063c13da6..70c81f2aa 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -250,7 +250,7 @@ This number should remain relatively constant over time. | ------ | ------ | :------ | |Fewer connections to Redis than expected |The application may not be connecting to the correct Redis database. There may be a network partition between the application and the Redis database. | Confirm that the application can successfully connect to Redis. This may require consulting the application logs or the application's connection configuration. | |Connection count continues to grow over time | Your application may not be releasing connections. The most common of such a connection leak is a manually implemented connection pool or a connection pool that is not properly configured. | Review the application's connection configuration | -|Erratic connection counts (for example, spikes and drops) | Application misbehavior (thundering herds, connection cycling, or networking issues) | Review the application logs and network traffic to determine the cause of the erratic connection counts. | +|Erratic connection counts (for example, spikes and drops) | Application misbehavior ([thundering herds](https://en.wikipedia.org/wiki/Thundering_herd_problem), connection cycling, or networking issues) | Review the application logs and network traffic to determine the cause of the erratic connection counts. | Dashboard displaying connections - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) From 1a3f6af98d48d5ff8c869760e653540c32d6a43f Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:46:26 -0800 Subject: [PATCH 27/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 70c81f2aa..23ed25eb9 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -256,9 +256,9 @@ This number should remain relatively constant over time. Dashboard displaying connections - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) {{< image filename="/images/playbook_database-used-connections.png" alt="Dashboard displaying connections" >}} -### Network ingress / egress +### Network ingress/egress -The network ingress / egress panel show the amount of data being sent to and received from the database. +The network ingress/egress panel shows the amount of data being sent to and received from the database. Large spikes in network traffic can indicate that the cluster is under-provisioned or that the application is reading and/or writing unusually large keys. A correlation between high network traffic and high CPU utilization may indicate a large key scenario. From f31a17a7861ab3b6c8419e145ea5a6612c02cc57 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:46:52 -0800 Subject: [PATCH 28/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 23ed25eb9..816a2ab93 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -260,7 +260,7 @@ Dashboard displaying connections - [Database Dashboard](https://github.com/redis The network ingress/egress panel shows the amount of data being sent to and received from the database. Large spikes in network traffic can indicate that the cluster is under-provisioned or that -the application is reading and/or writing unusually large keys. A correlation between high network traffic +the application is reading and/or writing unusually [large keys](#large-keys). A correlation between high network traffic and high CPU utilization may indicate a large key scenario. #### Unbalanced database endpoint From 941c40e2b77d305ca91496b8398b3995dd11bda8 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:47:29 -0800 Subject: [PATCH 29/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 816a2ab93..c5a3a85fb 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -265,7 +265,7 @@ and high CPU utilization may indicate a large key scenario. #### Unbalanced database endpoint -One possible cause is that the database endpoint is not located on the same node as master shards. In addition to added network latency, if data plane internode encryption is enabled, CPU consumption can increase as well. +One possible cause of network traffic spikes is that the database endpoint is not located on the same node as the master shards. In addition to added network latency, if data plane internode encryption is enabled, CPU consumption can increase as well. One solution is to use the optimal shard placement and proxy policy to ensure endpoints are collocated on nodes hosting master shards. If you need to restore balance (for example, after node failure) you can manually failover shard(s) with the `rladmin` cli tool. From 1e024739aa375cc5c7bfe5e6cb5693b8305b17b0 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:47:46 -0800 Subject: [PATCH 30/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index c5a3a85fb..8e2980154 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -270,7 +270,7 @@ One possible cause of network traffic spikes is that the database endpoint is no One solution is to use the optimal shard placement and proxy policy to ensure endpoints are collocated on nodes hosting master shards. If you need to restore balance (for example, after node failure) you can manually failover shard(s) with the `rladmin` cli tool. Extreme network traffic utilization may approach the limits of the underlying network infrastructure. -In this case, the only remediation is to add additional nodes to the cluster and scale the database's shards across them. +In this case, the only remediation is to add more nodes to the cluster and scale the database's shards across them. ## Synchronization From 493d53749b6acfb8550b4c0018246f52d11f64ea Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:48:10 -0800 Subject: [PATCH 31/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 8e2980154..fbfb1ebce 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -309,7 +309,7 @@ Latency is **the time it takes for Redis to respond to a request**. Redis Enterprise measures latency from the first byte received by the proxy to the last byte sent in the command's response. An adequately provisioned Redis database running efficient Redis operations will report an average latency below 1 millisecond. In fact, it's common to measure -latency in terms is microseconds. Businesses regularly achieve, and sometimes require, average latencies of 400-600 +latency in terms of microseconds. Businesses regularly achieve, and sometimes require, average latencies of 400-600 microseconds. Dashboard display of latency metrics - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) From 13589ef86cffd3a1e00e372f3960334b8ce75f5e Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:48:31 -0800 Subject: [PATCH 32/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index fbfb1ebce..380493829 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -274,7 +274,7 @@ In this case, the only remediation is to add more nodes to the cluster and scale ## Synchronization -In Redis Enterprise, geographically-distributed synchronization is based on CRDT technology. +In Redis Enterprise, geographically-distributed synchronization is based on Conflict-free replicated data types (CRDT) technology. The Redis Enterprise implementation of CRDT is called an Active-Active database (formerly known as CRDB). With Active-Active databases, applications can read and write to the same data set from different geographical locations seamlessly and with low latency, without changing the way the application connects to the database. From f57426d885aa5f1eb0b51cd06cfbd0bacb200013 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:48:51 -0800 Subject: [PATCH 33/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 380493829..328e0e080 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -297,7 +297,7 @@ CRDT provides three fundamental benefits over other geo-distributed solutions: ## Database performance indicators -There several key performance indicators that report your database's performance against your application's workload: +There are several key performance indicators that report your database's performance against your application's workload: * Latency * Cache hit rate From d4c3776be5b4ce759102fe2a0baef4044d79b340 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:49:35 -0800 Subject: [PATCH 34/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 328e0e080..47cd72c7f 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -326,8 +326,8 @@ Display showing a noticeable spike in latency ### Troubleshooting -Here are some possible causes of high database latency. Note that high database latency is just one possible -cause of high application latency. Application latency can be caused by a variety of factors, including +Here are some possible causes of high database latency. Note that high database latency is just one of the reasons +why application latency might be high. Application latency can be caused by a variety of factors, including a low cache hit rate. High Proxy CPU - There are several possible causes of high proxy CPU. From 994cbeb6c45088bf31c4571d303ba40cf639166e Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:51:24 -0800 Subject: [PATCH 35/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 47cd72c7f..d398fd0d3 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -336,8 +336,7 @@ networking issue. | Issue | Possible causes | Remediation | | ------ | ------ | :------ | -|Slow database operations | Confirm that there are no excessive slow operations in the Redis slow log. | If possible, reduce the number of slow operations being sent to the database. -If this not possible, consider increasing the number of shards in the database. | +|Slow database operations | Confirm that there are no excessive slow operations in the Redis slow log. | If possible, reduce the number of slow operations being sent to the database.
If this not possible, consider increasing the number of shards in the database. | |Increased traffic to the database | Review the network traffic and the database operations per second chart to determine if increased traffic is causing the latency. | If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | |Insufficient CPU | Check to see if the CPU utilization is increasing. | Confirm that slow operations are not causing the high CPU utilization. From 7b317e3902b871979217699620d96b416884b254 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:51:52 -0800 Subject: [PATCH 36/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index d398fd0d3..40812bc08 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -337,8 +337,7 @@ networking issue. | Issue | Possible causes | Remediation | | ------ | ------ | :------ | |Slow database operations | Confirm that there are no excessive slow operations in the Redis slow log. | If possible, reduce the number of slow operations being sent to the database.
If this not possible, consider increasing the number of shards in the database. | -|Increased traffic to the database | Review the network traffic and the database operations per second chart -to determine if increased traffic is causing the latency. | If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | +|Increased traffic to the database | Review the network traffic and the database operations per second chart to determine if increased traffic is causing the latency. | If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | |Insufficient CPU | Check to see if the CPU utilization is increasing. | Confirm that slow operations are not causing the high CPU utilization. If the high CPU utilization is due to increased load, consider adding shards to the database. | From 6b239436c9e87ef3110a32ef78aa48266b826a15 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:52:12 -0800 Subject: [PATCH 37/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 40812bc08..ba2a479b8 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -338,8 +338,7 @@ networking issue. | ------ | ------ | :------ | |Slow database operations | Confirm that there are no excessive slow operations in the Redis slow log. | If possible, reduce the number of slow operations being sent to the database.
If this not possible, consider increasing the number of shards in the database. | |Increased traffic to the database | Review the network traffic and the database operations per second chart to determine if increased traffic is causing the latency. | If the database is underprovisioned due to increased traffic, consider increasing the number of shards in the database. | -|Insufficient CPU | Check to see if the CPU utilization is increasing. | Confirm that slow operations are not causing the high CPU utilization. -If the high CPU utilization is due to increased load, consider adding shards to the database. | +|Insufficient CPU | Check to see if the CPU utilization is increasing. | Confirm that slow operations are not causing the high CPU utilization. If the high CPU utilization is due to increased load, consider adding shards to the database. | ## Cache hit rate From 1c839d903e7e86e80174e4531c013af4b384958d Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:52:35 -0800 Subject: [PATCH 38/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index ba2a479b8..9ced79a1a 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -365,7 +365,7 @@ These are defined as follows: ### Troubleshooting -Cache hit rate is usually only relevant for caching workloads. Eviction will begin after the database approaches its max memory capacity. +Cache hit rate is usually only relevant for caching workloads. Eviction will begin after the database approaches its maximum memory capacity. A high or increasing rate of evictions will negatively affect database latency, especially if the rate of necessary key evictions exceeds the rate of new key insertions. From 9fa9d531241c863c2112363be774ea3b04dbdac2 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:53:00 -0800 Subject: [PATCH 39/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 9ced79a1a..219f16550 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -370,7 +370,7 @@ Cache hit rate is usually only relevant for caching workloads. Eviction will beg A high or increasing rate of evictions will negatively affect database latency, especially if the rate of necessary key evictions exceeds the rate of new key insertions. -See the Cache hit ratio and eviction section for tips on troubleshooting cache hit rate. +See the [Cache hit ratio and eviction](#cache-hit-ratio-and-eviction) section for tips on troubleshooting cache hit rate. ## Key eviction rate From 84085c963f68bf86fc3ab1c70ce1b1395305a39e Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:53:27 -0800 Subject: [PATCH 40/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 219f16550..18ac5779d 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -396,7 +396,7 @@ Redis Enterprise Software (RS) provides high-performance data access through a p Dashboard displaying proxy thread activity - [Proxy Thread Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/cloud/basic/redis-cloud-proxy-dashboard_v9-11.json) {{< image filename="/images/proxy-thread-dashboard.png" alt="Dashboard displaying proxy thread activity" >}} -When needed, you can tune the number of proxy threads using the `rladmin tune proxy` command to make the proxy use more CPU cores. +If you need to, you can tune the number of proxy threads using the [`rladmin tune proxy`]({{< relref "/operate/rs/references/cli-utilities/rladmin/tune#tune-proxy" >}}) command to make the proxy use more CPU cores. Cores used by the proxy won't be available for Redis, therefore we need to take into account the number of Redis nodes on the host and the total number of available cores. How to set a new number of proxy cores using the command: From 2078c978838a55fe271f9a7c9c7487ee2617677a Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:54:00 -0800 Subject: [PATCH 41/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 18ac5779d..da4a170f8 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -399,7 +399,7 @@ Dashboard displaying proxy thread activity - [Proxy Thread Dashboard](https://gi If you need to, you can tune the number of proxy threads using the [`rladmin tune proxy`]({{< relref "/operate/rs/references/cli-utilities/rladmin/tune#tune-proxy" >}}) command to make the proxy use more CPU cores. Cores used by the proxy won't be available for Redis, therefore we need to take into account the number of Redis nodes on the host and the total number of available cores. -How to set a new number of proxy cores using the command: +The command has a few parameters you can use to set a new number of proxy cores: * `id|all` - you can either tune a specific proxy by its id, or all proxies. From db1fd06d85733c097760c2f38d1a6ae9129d6287 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:54:42 -0800 Subject: [PATCH 42/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index da4a170f8..6cee16739 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -440,9 +440,9 @@ This section defines each of these patterns and describes how to diagnose and mi Not all Redis operations are equally efficient. The most efficient Redis operations are O(1) operations; that is, they have a constant time complexity. -Example of such operations include [GET](https://redis.io/docs/latest/commands/get/), -[SET](https://redis.io/docs/latest/commands/set/), [SADD](https://redis.io/docs/latest/commands/sadd/), -and [HSET](https://redis.io/docs/latest/commands/hset/). +Example of such operations include [GET]({{< relref "/commands/get" >}}), +[SET]({{< relref "/commands/set" >}}), [SADD]({{< relref "/commands/sadd" >}}), +and [HSET]({{< relref "/commands/hset" >}}). These constant time operations are unlikely to cause high CPU utilization. **Note:** Even so, it's still possible for a high rate of constant time operations to overwhelm an underprovisioned database. From 965beac54a39e8727f261fba3317bfbe302a95ed Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:55:10 -0800 Subject: [PATCH 43/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../prometheus-with-redis-enterprise/observability.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 6cee16739..cf5d038d2 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -449,8 +449,8 @@ it's still possible for a high rate of constant time operations to overwhelm an Other Redis operations exhibit greater levels of time complexity. O(n) (linear time) operations are more likely to cause high CPU utilization. -Examples include [HGETALL](https://redis.io/docs/latest/commands/keys/), [SMEMBERS](https://redis.io/docs/latest/commands/smembers/), -and [LREM](https://redis.io/docs/latest/commands/lrem/). +Examples include [HGETALL]({{< relref "/commands/hgetall" >}}), [SMEMBERS]({{< relref "/commands/smembers" >}}), +and [LREM]({{< relref "/commands/lrem" >}}). These operations are not necessarily problematic, but they can be if executed against data structures holding a large number of elements (for example, a list with 1 million elements). From ec15e7d5a4d7dc0ba07fa28406b3437e31660e30 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:55:30 -0800 Subject: [PATCH 44/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index cf5d038d2..c5dcf1452 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -454,7 +454,7 @@ and [LREM]({{< relref "/commands/lrem" >}}). These operations are not necessarily problematic, but they can be if executed against data structures holding a large number of elements (for example, a list with 1 million elements). -However, the [KEYS](https://redis.io/docs/latest/commands/keys/) command should almost never be run against a +However, the [KEYS]({{< relref "/commands/keys" >}}) command should almost never be run against a production system, since returning a list of all keys in a large Redis database can cause significant slowdowns and block other operations. If you need to scan the keyspace, especially in a production cluster, always use the [SCAN](https://redis.io/docs/latest/commands/scan) command instead. From f1ec9140037c80d8eb7805d3f45b2d140269c2eb Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:55:52 -0800 Subject: [PATCH 45/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index c5dcf1452..30846a715 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -457,7 +457,7 @@ a large number of elements (for example, a list with 1 million elements). However, the [KEYS]({{< relref "/commands/keys" >}}) command should almost never be run against a production system, since returning a list of all keys in a large Redis database can cause significant slowdowns and block other operations. If you need to scan the keyspace, especially in a production cluster, always use the -[SCAN](https://redis.io/docs/latest/commands/scan) command instead. +[SCAN]({{< relref "/commands/scan" >}}) command instead. ### Troubleshooting From 38d96fcd5922dcb5e9d9bb909015813d714505ea Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:56:26 -0800 Subject: [PATCH 46/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 30846a715..39df17e22 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -471,7 +471,7 @@ Redis Cloud dashboard showing slow database operations | Issue | Remediation | | ------ | :------ | -|The KEYS command shows up in the slow log |Find the application that issuing the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) so that Redis will reject the KEYS command altogether. | +|The KEYS command shows up in the slow log |Find the application that issues the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) so that Redis will reject the KEYS command altogether. | |The slow log shows a significant number of slow, O(n) operations | If these operations are being issued against large data structures, then the application may need to be refactored to use more efficient Redis commands. | |The slow logs contains only O(1) commands, and these commands are taking several milliseconds or more to completen |This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | From 1d7f152d4d572a637de2a9f8d5621bf299e8b971 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:57:13 -0800 Subject: [PATCH 47/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 39df17e22..a2d5f6c1e 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -556,7 +556,7 @@ alerting: ``` The Rule file section of the config file instructs Alertmanager to read specific rules files. -If you pasted the 'alerts.yml' file into '/etc/prometheus' then the following configuration would be required. +If you pasted the `alerts.yml` file into `/etc/prometheus` then the following configuration would be required. ``` # Load rules once and periodically evaluate them according to the global 'evaluation_interval'. From 59e5338081a3e63029ebe764f75de01401364d92 Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:57:46 -0800 Subject: [PATCH 48/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index a2d5f6c1e..a0a4ef717 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -565,7 +565,7 @@ rule_files: - "alerts.yml" ``` -After this is done, restart Prometheus. +After you've done this, restart Prometheus. The built-in configuration, `error_rules.yml`, has a single alert: Critical Connection Exception. If you open the Prometheus console, by default located at port 9090, and select the Alert tab, From 70f965896f61ad9b266299d0a6fe00675324ae51 Mon Sep 17 00:00:00 2001 From: Andy Stark Date: Mon, 9 Dec 2024 11:02:48 +0000 Subject: [PATCH 49/52] Replaced absolute links with relrefs --- .../observability.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index a0a4ef717..6a94d6350 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -129,7 +129,7 @@ This can cause thrashing on the application side, a scenario where the cache is This means that when your Redis database is using 100% of available memory, you need to measure the rate of -[key evictions](https://redis.io/docs/latest/operate/rs/references/metrics/database-operations/#evicted-objectssec). +[key evictions]({{< relref "/operate/rs/references/metrics/database-operations#evicted-objectssec" >}}). An acceptable rate of key evictions depends on the total number of keys in the database and the measure of application-level latency. If application latency is high, @@ -171,8 +171,8 @@ After your database reaches this 80% threshold, you should closely review the ra |Issue | Possible causes | Remediation | | ------ | ------ | :------ | -|Redis memory usage has reached 100% |This may indicate an insufficient Redis memory limit for your application's workload | For non-caching workloads (where eviction is unacceptable), immediately increase the memory limit for the database. You can accomplish this through the Redis Enterprise console or its API. Alternatively, you can contact Redis support to assist. For caching workloads, you need to monitor performance closely. Confirm that you have an [eviction policy](https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/) in place. If your application's performance starts to degrade, you may need to increase the memory limit, as described above. | -|Redis has stopped accepting writes | Memory is at 100% and no eviction policy is in place | Increase the database's total amount of memory. If this is for a caching workload, consider enabling an [eviction policy](https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/). In addition, you may want to determine whether the application can set a reasonable TTL (time-to-live) on some or all of the data being written to Redis. | +|Redis memory usage has reached 100% |This may indicate an insufficient Redis memory limit for your application's workload | For non-caching workloads (where eviction is unacceptable), immediately increase the memory limit for the database. You can accomplish this through the Redis Enterprise console or its API. Alternatively, you can contact Redis support to assist. For caching workloads, you need to monitor performance closely. Confirm that you have an [eviction policy]({{< relref "/operate/rs/databases/memory-performance/eviction-policy" >}}) in place. If your application's performance starts to degrade, you may need to increase the memory limit, as described above. | +|Redis has stopped accepting writes | Memory is at 100% and no eviction policy is in place | Increase the database's total amount of memory. If this is for a caching workload, consider enabling an [eviction policy]({{< relref "/operate/rs/databases/memory-performance/eviction-policy" >}}). In addition, you may want to determine whether the application can set a reasonable TTL (time-to-live) on some or all of the data being written to Redis. | |Cache hit ratio is steadily decreasing | The application's working set size may be steadily increasing. Alternatively, the application may be misconfigured (for example, generating more than one unique cache key per cached item.) | If the working set size is increasing, consider increasing the memory limit for the database. If the application is misconfigured, review the application's cache key generation logic. | @@ -231,7 +231,7 @@ excess inefficient Redis operations, and hot master shards. | ------ | ------ | :------ | |High CPU utilization across all shards of a database | This usually indicates that the database is under-provisioned in terms of number of shards. A secondary cause may be that the application is running too many inefficient Redis operations. | You can detect slow Redis operations by enabling the slow log in the Redis Enterprise UI. First, rule out inefficient Redis operations as the cause of the high CPU utilization. The Latency section below includes a broader discussion of this metric in the context of your application. If inefficient Redis operations are not the cause, then increase the number of shards in the database. | |High CPU utilization on a single shard, with the remaining shards having low CPU utilization | This usually indicates a master shard with at least one hot key. Hot keys are keys that are accessed extremely frequently (for example, more than 1000 times per second). | Hot key issues generally cannot be resolved by increasing the number of shards. To resolve this issue, see the section on Hot keys below. | -| High Proxy CPU | There are several possible causes of high proxy CPU. First, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads](https://redis.io/docs/latest/operate/rs/references/cli-utilities/rladmin/tune/#tune-proxy). | +| High Proxy CPU | There are several possible causes of high proxy CPU. First, review the behavior of connections to the database. Frequent cycling of connections, especially with TLS is enabled, can cause high proxy CPU utilization. This is especially true when you see more than 100 connections per second per thread. Such behavior is almost always a sign of a misbehaving application. Review the total number of operations per second against the cluster. If you see more than 50k operations per second per thread, you may need to increase the number of proxy threads. | In the case of high connection cycling, review the application's connection behavior. In the case of high operations per second, [increase the number of proxy threads]({{< relref "/operate/rs/references/cli-utilities/rladmin/tune#tune-proxy" >}}). | |High Node CPU | You will typically detect high shard or proxy CPU utilization before you detect high node CPU utilization. Use the remediation steps above to address high shard and proxy CPU utilization. In spite of this, if you see high node CPU utilization, you may need to increase the number of nodes in the cluster. | Consider increasing the number of nodes in the cluster and the rebalancing the shards across the new nodes. This is a complex operation and you should do it with the help of Redis support. | |High System CPU | Most of the issues above will reflect user-space CPU utilization. However, if you see high system CPU utilization, this may indicate a problem at the network or storage level. | Review network bytes in and network bytes out to rule out any unexpected spikes in network traffic. You may need perform some deeper network diagnostics to identify the cause of the high system CPU utilization. For example, with high rates of packet loss, you may need to review network configurations or even the network hardware. | @@ -375,7 +375,7 @@ See the [Cache hit ratio and eviction](#cache-hit-ratio-and-eviction) section fo ## Key eviction rate They **key eviction rate** is rate at which objects are being evicted from the database. -See [eviction policy](https://redis.io/docs/latest/operate/rs/databases/memory-performance/eviction-policy/) for a discussion of key eviction and its relationship with memory usage. +See [eviction policy]({{< relref "/operate/rs/databases/memory-performance/eviction-policy" >}}) for a discussion of key eviction and its relationship with memory usage. Dashboard displaying object evictions - [Database Dashboard](https://github.com/redis-field-engineering/redis-enterprise-observability/blob/main/grafana/dashboards/grafana_v9-11/software/classic/database_dashboard_v9-11.json) {{< image filename="/images/playbook_eviction-expiration.png" alt="Dashboard displaying object evictions">}} @@ -463,8 +463,8 @@ and block other operations. If you need to scan the keyspace, especially in a pr The best way to discover slow operations is to view the slow log. The slow log is available in the Redis Enterprise and Redis Cloud consoles: -* [Redis Enterprise slow log docs](https://redis.io/docs/latest/operate/rs/clusters/logging/redis-slow-log/) -* [Redis Cloud slow log docs](https://redis.io/docs/latest/operate/rc/databases/view-edit-database/#other-actions-and-info) +* [Redis Enterprise slow log docs]({{< relref "/operate/rs/clusters/logging/redis-slow-log" >}}) +* [Redis Cloud slow log docs]({{< relref "/operate/rc/databases/view-edit-database#other-actions-and-info" >}}) Redis Cloud dashboard showing slow database operations {{< image filename="/images/slow_log.png" alt="Redis Cloud dashboard showing slow database operations" >}} @@ -496,7 +496,7 @@ To use the Redis CLI to identify hot keys: 3. Finally, run `redis-cli --hotkeys` You may also identify hot keys by sampling the operations against Redis. -You can use do this by running the [MONITOR](https://redis.io/docs/latest/commands/monitor/) command +You can use do this by running the [MONITOR]({{< relref "/commands/monitor" >}}) command against the high CPU shard. Because this is a potentially high-impact operation, you should only use this technique as a secondary option. For mission-critical databases, consider contacting Redis support for assistance. From b8293f0c359955e2fdb4be8b657f3e1d21b9d71c Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Wed, 11 Dec 2024 11:35:58 -0800 Subject: [PATCH 50/52] Update content/integrate/prometheus-with-redis-enterprise/observability.md Co-authored-by: andy-stark-redis <164213578+andy-stark-redis@users.noreply.github.com> --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index 6a94d6350..cc915ac5e 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -473,7 +473,7 @@ Redis Cloud dashboard showing slow database operations | ------ | :------ | |The KEYS command shows up in the slow log |Find the application that issues the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) so that Redis will reject the KEYS command altogether. | |The slow log shows a significant number of slow, O(n) operations | If these operations are being issued against large data structures, then the application may need to be refactored to use more efficient Redis commands. | -|The slow logs contains only O(1) commands, and these commands are taking several milliseconds or more to completen |This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | +|The slow logs contains only O(1) commands, and these commands are taking several milliseconds or more to complete |This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. | ## Hot keys From 2296996f9dba065df880cddfe6fb0810d637673b Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Wed, 11 Dec 2024 11:49:52 -0800 Subject: [PATCH 51/52] Update observability.md --- .../prometheus-with-redis-enterprise/observability.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index cc915ac5e..fc2249955 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -330,10 +330,6 @@ Here are some possible causes of high database latency. Note that high database why application latency might be high. Application latency can be caused by a variety of factors, including a low cache hit rate. -High Proxy CPU - There are several possible causes of high proxy CPU. -First, a high rate of evictions, or a -networking issue. - | Issue | Possible causes | Remediation | | ------ | ------ | :------ | |Slow database operations | Confirm that there are no excessive slow operations in the Redis slow log. | If possible, reduce the number of slow operations being sent to the database.
If this not possible, consider increasing the number of shards in the database. | From 394c7cb4ba5e706f0e15fcf5e750b588bd90563a Mon Sep 17 00:00:00 2001 From: mich-elle-luna <153109578+mich-elle-luna@users.noreply.github.com> Date: Wed, 11 Dec 2024 11:55:00 -0800 Subject: [PATCH 52/52] Update observability.md --- .../integrate/prometheus-with-redis-enterprise/observability.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/integrate/prometheus-with-redis-enterprise/observability.md b/content/integrate/prometheus-with-redis-enterprise/observability.md index fc2249955..28329d919 100644 --- a/content/integrate/prometheus-with-redis-enterprise/observability.md +++ b/content/integrate/prometheus-with-redis-enterprise/observability.md @@ -467,7 +467,7 @@ Redis Cloud dashboard showing slow database operations | Issue | Remediation | | ------ | :------ | -|The KEYS command shows up in the slow log |Find the application that issues the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user](https://redis.io/docs/latest/operate/rs/security/access-control/rbac/configure-acl/) so that Redis will reject the KEYS command altogether. | +|The KEYS command shows up in the slow log |Find the application that issues the KEYS command and replace it with a SCAN command. In an emergency situation, you can [alter the ACLs for the database user]({{< relref "/operate/rs/security/access-control/redis-acl-overview/" >}}) so that Redis will reject the KEYS command altogether. | |The slow log shows a significant number of slow, O(n) operations | If these operations are being issued against large data structures, then the application may need to be refactored to use more efficient Redis commands. | |The slow logs contains only O(1) commands, and these commands are taking several milliseconds or more to complete |This likely indicates that the database is underprovisioned. Consider increasing the number of shards and/or nodes. |