Apache Kafka: Powering Real-Time Data Streaming

What is Kafka?

Apache Kafka is a distributed event streaming platform designed to handle high-throughput, fault-tolerant, and scalable data pipelines in real-time.

Benefits of Kafka:

• High throughput for both publishing and subscribing
• Low latency (as low as 10 milliseconds)
• Fault-tolerant and durability
• Scalability (can handle trillions of events a day)
• Real-time processing

Why use Kafka for data streaming and integration?

1. Decoupling of data streams and systems: Kafka acts as a central data backbone.
2. Scalability: Can handle growing amounts of data effortlessly.
3. Durability and reliability: Data is persisted on disk and replicated within the cluster.
4. High performance: High-throughput message publishing and subscribing.
5. Ecosystem: Wide range of tools and frameworks that integrate with Kafka.

Key Concepts

Topics

• A particular stream of data
• Similar to a table in a database (without constraints)
• Identified by name
• Can have multiple topics in a cluster
• The topic schema can be anything (e.g., JSON, Avro, etc.)

Partitions

• Topics are split into partitions
• Each partition is ordered
• Each message within a partition gets an incremental id called offset
• Each message has a unique identifier that determines its position and the partition it belongs to

Kafka Architecture

ZooKeeper

• Manages and coordinates Kafka brokers
• Notifies producers and consumers about new brokers or broker failures
• Manages topic configuration
• Stores metadata about the Kafka cluster

Brokers

• A Kafka cluster is composed of multiple brokers (servers)
• Each broker contains certain topic partitions
• Each broker only manages one partition at a time
• After connecting to any broker (bootstrap server), you're connected to the entire cluster

Leaders

• For a particular partition, one broker will be the leader
• Only the leader can receive and serve data for a partition
• Other brokers will synchronize the data (replication)

Replication

• Each partition has multiple replicas
• Replicas are just copies of the partition's Data
• Replicas are never Leaders
• Replicas ensure fault tolerance
• If a broker goes down, another replica can take over as the Leader

Producers

• Write data to topics (which is made of partitions)
• Automatically know which broker and partition to write to
• Can choose to receive acknowledgment of data writes

Consumers

• Read data from a topic (identified by name)
• Know which broker to read from
• Can read from one or many partitions
• Data is read in order within each partition

Coordinators

Coordinators play a crucial role in managing consumer groups and ensuring efficient message consumption. There are two types of coordinators in Kafka:

Group Coordinator

• Responsible for managing the overall state of a consumer group
• Handles consumer group joins, leaves, and failures
• Assigns partitions to consumers within a group (rebalancing)
• Typically runs on one of the Kafka brokers

Key responsibilities:

1. Group Membership: Keeps track of all consumers in a group
2. Partition Assignment: Decides which consumer should read from which partitions
3. Rebalancing: Triggers and manages the rebalancing process when consumers join or leave the group

Consumer Coordinator

• A component within each Kafka consumer
• Communicates with the Group Coordinator to manage the consumer's participation in its group

Key responsibilities:

1. Heartbeats: Sends periodic heartbeats to the Group Coordinator to signal that the consumer is alive
2. Partition Assignment: Receives partition assignments from the Group Coordinator and applies them
3. Offset Management: Keeps track of the consumer's progress by managing offsets

The interaction between Group Coordinators and Consumer Coordinators ensures that:

• Consumers are evenly distributed across partitions
• The system can quickly detect and respond to consumer failures
• Consumption can be scaled up or down by adding or removing consumers from a group

Understanding coordinators is crucial for optimizing Kafka's performance and ensuring smooth operation of consumer groups, especially in large-scale, distributed systems.

Consumer Groups

Consumer groups allow you to parallelize the consumption of messages from Kafka topics.

How they work:

1. Each consumer in a group reads from exclusive partitions
2. If you have more consumers than partitions, some consumers will be inactive

Importance:

• Enables scalable message consumption
• Provides fault tolerance for consumers
• Allows for easy scaling up and down of consumers

The infrastructure backbone

Examples of Kafka Use Cases with EMP and CRM Systems