From 432aa03ab3788eebb92c6e9a96a6569275e5f68c Mon Sep 17 00:00:00 2001 From: Gilad Shaham Date: Thu, 15 Sep 2022 14:44:03 +0100 Subject: [PATCH 1/3] Remove data-ingestion-and-preparation notebooks and related data --- assets/images/nuclio-deploy.png | Bin 40772 -> 0 bytes assets/images/pipeline-diagram.png | Bin 476642 -> 0 bytes assets/images/workflow.png | Bin 135478 -> 0 bytes data-ingestion-and-preparation/README.ipynb | 636 ---- data-ingestion-and-preparation/README.md | 316 -- ...basic-data-ingestion-and-preparation.ipynb | 1063 ------- .../csv-to-hive.ipynb | 318 -- .../dask-cluster.ipynb | 690 ----- .../file-access.ipynb | 655 ----- data-ingestion-and-preparation/frames.ipynb | 1494 ---------- .../gpu-cudf-vs-pd.ipynb | 812 ------ .../grafana-grafwiz.ipynb | 479 --- .../mlrun-read-via-presto.ipynb | 475 --- .../nuclio-read-via-presto.ipynb | 333 --- .../parquet-read-write.ipynb | 1286 --------- .../parquet-to-hive.ipynb | 420 --- .../read-external-db.ipynb | 1824 ------------ 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b/data-ingestion-and-preparation/README.ipynb deleted file mode 100644 index b86690fc..00000000 --- a/data-ingestion-and-preparation/README.ipynb +++ /dev/null @@ -1,636 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Ingestion and Preparation Overview\n", - "\n", - "Learn about different methods for ingesting data into the Iguazio Data Science Platform, analyzing the data, and preparing it for the next step in your data pipeline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- [Overview](#data-ingest-overview)\n", - "- [Basic Flow](#data-ingest-basic-flow)\n", - "- [The Platform's Data Layer](#data-ingest-platform-data-layer)\n", - " - [Platform Data Containers](#platform-data-containers)\n", - " - [The Simple-Object Platform API](#data-ingest-platform-simple-object-api)\n", - " - [The NoSQL (Key-Value) Platform API](#data-ingest-platform-nosql-api)\n", - " - [The Streaming Platform API](#data-ingest-platform-streaming-api)\n", - "- [Reading from External Database](#data-ingest-external-dbs)\n", - " - [Using Spark over JDBC](#data-ingest-spark-over-jdbs)\n", - " - [Using SQLAlchemy](#data-ingest-sqlalchemy)\n", - "- [Working with Spark](#data-ingest-spark)\n", - " - [Using Spark SQL and DataFrames](#data-ingest-spark-sql-n-dfs)\n", - "- [Working with Streams](#data-ingest-streams)\n", - " - [Using Nuclio to Get Data from Common Streaming Engines](#data-ingest-streams-nuclio)\n", - " - [Using the Platform's Streaming Engine](#data-ingest-streams-platform)\n", - " - [Using Spark Streaming](#data-ingest-streams-spark)\n", - "- [Running SQL Queries on Platform Data](#data-ingest-sql)\n", - " - [Running Full ANSI Presto SQL Queries](#data-ingest-sql-presto)\n", - " - [Running Spark SQL Queries](#data-ingest-sql-spark)\n", - " - [Running SQL Queries from Nuclio Functions](#data-ingest-sql-nuclio)\n", - " - [Running SQL Queries from MLRun Jobs](#data-ingest-sql-mlrun)\n", - "- [Working with Parquet Files](#data-ingest-parquet)\n", - "- [Accessing Platform NoSQL and TSDB Data Using the Frames Library](#data-ingest-frames)\n", - "- [Getting Data from AWS S3 Using curl](data-ingest-s3-curl)\n", - "- [Running Distributed Python with Dask](#data-ingest-dask)\n", - "- [Running DataFrames on GPUs using NVIDIA cuDF](#data-ingest-gpu)\n", - "- [Visualizing Data with Grafana](#data-ingest-grafana)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Overview\n", - "\n", - "The Iguazio Data Science Platform (\"the platform\") allows storing data in any format.\n", - "The platform's multi-model data layer and related APIs provide enhanced support for working with NoSQL (\"key-value\"), time-series, and stream data.\n", - "Various steps of the data science life cycle (pipeline) might require different tools and frameworks for working with data, especially when it comes to the different mechanisms required during the research and development phase versus the operational production phase.\n", - "The platform features a wide set of methods for manipulating and managing data, of different formats, in each step of the data life cycle, using a variety of frameworks, tools, and APIs — such as Spark SQL and DataFrames, Spark Streaming, Presto SQL queries, pandas DataFrames, Dask, the V3IO Frames Python library, and web APIs.\n", - "\n", - "This tutorial provides an overview of various methods for collecting, storing, and manipulating data in the platform, and refers to sample tutorial notebooks that demonstrate how to use these methods.
\n", - "For an in-depth overview of the platform and how it can be used to implement a full data science workflow, see the [**platform-overview**](../platform-overview.ipynb) tutorial notebook.\n", - "For information about the available full end-to-end use-case application and how-to demos, see the [**welcome**](../welcome.ipynb#end-to-end-use-case-applications) notebook or the matching [**README.md**](../README.md#end-to-end-use-case-applications) file.\n", - "\n", - "
\"pipeline-diagram\"
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic Flow\n", - "\n", - "The [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparation.ipynb) tutorial walks you through basic scenarios of ingesting data from external sources into the platform's data store and manipulating the data using different data formats.\n", - "The tutorial includes an example of ingesting a CSV file from an AWS S3 bucket; converting it into a NoSQL table using Spark DataFrames; running SQL queries on the table; and converting the table into a Parquet file." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Platform's Data Layer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The platform features an extremely fast and secure data layer (a.k.a. \"data store\") that supports storing data in different formats — SQL, NoSQL, time-series databases, files (simple objects), and streaming.\n", - "The data is stored within data containers and can be accessed using a variety of APIs — including [simple-object](#data-ingest-platform-simple-object-api), [NoSQL (\"key-value\")](#data-ingest-platform-nosql-api), and [streaming](#data-ingest-platform-streaming-api) APIs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Platform Data Containers\n", - "\n", - "Data is stored within data containers in the platform's distributed file system (DFS), which makes up the platform's data layer.\n", - "All platform clusters have several predefined containers:\n", - "\n", - "- **\"users\"** — This container is designed to contain **<username>** directories that provide individual development environments for storing user-specific data.\n", - " The platform's Jupyter Notebook, Zeppelin, and web-based shell \"command-line services\" automatically create such a directory for the running user of the service and set it as the home directory of the service environment.\n", - " You can leverage the following environment variables, which are predefined in the platform's command-line services, to access this running-user directory from your code:\n", - "\n", - " - `V3IO_USERNAME` — set to the username of the running user of the Jupyter Notebook service.\n", - " - `V3IO_HOME` — set to the running-user directory in the \"users\" container — **users/<running user>**.\n", - " - `V3IO_HOME_URL` — set to the fully qualified `v3io` path to the running-user directory — `v3io://users/`.\n", - "- **\"projects\"** — This container is designed to store shared project artifacts.
\n", - " When creating a new project, the default artifacts path is **projects/<project name>/artifacts**.\n", - "- **\"bigdata\"** — This container has no special significance in the current release, and it will no longer be predefined in future releases.\n", - " However, you'll still be able to use your existing \"bigdata\" container and all its data, or create a custom container by this name if it doesn't already exist.\n", - "\n", - "The data containers and their contents are referenced differently depending on the programming interface.\n", - "For example:\n", - "\n", - "- In local file-system (FS) commands you use the predefined `v3io` root data mount — `/v3io/[/]`.\n", - " There's also a predefined local-FS `User` mount to the **users/<running user>** directory, and you can use the aforementioned environment variables when setting data paths.\n", - " For example, `/v3io/users/$V3IO_USERNAME`, `/v3io/$V3IO_HOME`, and `/User` are all valid ways of referencing the **users/<running user>** directory from a local FS command.\n", - "- In Hadoop FS or Spark DataFrame commands you use a fully qualified path of the format `v3io:///`.\n", - " You can also use environment variables with these interfaces.\n", - "\n", - "For detailed information and examples on how to set the data path for each interface, see [API Data Paths](https://www.iguazio.com/docs/v3.0/data-layer/apis/data-paths/) and the examples in the platform's tutorial Jupyter notebooks." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Simple-Object Platform API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The platform's Simple-Object API enables performing simple data-object and container operations that resemble the Amazon Web Services (AWS) Simple Storage Service (S3) API.\n", - "In addition to the S3-like capabilities, the Simple-Object Web API enables appending data to existing objects.\n", - "For more information and API usage examples, see the [**v3io-objects**](v3io-objects.ipynb) tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The NoSQL (Key-Value) Platform API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The platform's NoSQL (a.k.a. key-value/KV) API provides access to the platform's NoSQL data store (database service), which enables storing and consuming data in a tabular format.\n", - "For more information and API usage examples, see the [**v3io-kv**](v3io-kv.ipynb) tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The Streaming Platform API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The platform's Streaming API enables working with data in the platform as streams.\n", - "For more information and API usage examples, see the [**v3io-streams**](v3io-streams.ipynb) tutorial.\n", - "In addition, see the [Working with Streams](#data-ingest-streams) section in the current tutorial for general information about different methods for working with data streams in the platform." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Reading Data from External Databases\n", - "\n", - "You can use different methods to read data from external databases into the platform's data store, such Spark over JDBC or SQLAlchemy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using Spark over JDBC\n", - "\n", - "Spark SQL includes a data source that can read data from other databases using Java database connectivity (JDBC).\n", - "The results are returned as a Spark DataFrame that can easily be processed using Spark SQL, or joined with other data sources.\n", - "The [**spark-jdbc**](spark-jdbc.ipynb) tutorial includes several examples of using Spark JDBC to ingest data from various databases — such as MySQL, Oracle, and PostgreSQL." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using SQLAlchemy\n", - "\n", - "The [**read-external-db**](read-external-db.ipynb) tutorial outlines how to ingest data using [SQLAlchemy](https://www.sqlalchemy.org/) — a Python SQL toolkit and Object Relational Mapper, which gives application developers the full power and flexibility of SQL — and then use Python DataFrames to work on the ingested data set." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Working with Spark\n", - "\n", - "The platform has a default pre-deployed Spark service that enables ingesting, analyzing, and manipulating data using different [Spark](http://spark.apache.org) APIs:\n", - "\n", - "- Using Spark SQL and DataFrames\n", - "- Using the Spark Streaming API — see [Using Streaming Streaming](#data-ingest-streams-spark) under \"Working with Spark\"." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using Spark SQL and DataFrames\n", - "\n", - "Spark lets you write and query structured data inside Spark programs by using either SQL or a familiar DataFrame API.\n", - "DataFrames and SQL provide a common way to access a variety of data sources.\n", - "You can use the [Spark SQL and DataFrames](https://spark.apache.org/sql/) API to ingest data into the platform, for both batch and micro-batch processing, and analyze and manipulate large data sets, in a distributed manner.\n", - "\n", - "The platform's custom NoSQL Spark DataFrame implements the Spark data-source API to support a custom data source that enables reading and writing data in the platform's NoSQL store using Spark DataFrames, including enhanced features such as data pruning and filtering (predicate push down); queries are passed to the platform's data store, which returns only the relevant data.\n", - "This allows accelerated and high-speed access from Spark to data stored in the platform.\n", - "\n", - "The [**spark-sql-analytics**](spark-sql-analytics.ipynb) tutorial demonstrates how to use Spark SQL and DataFrames to access objects, tables, and unstructured data that persists in the platform's data store.\n", - "\n", - "For more information and examples of data ingestion with Spark DataFrames, see [Getting Started with Data Ingestion Using Spark](https://www.iguazio.com/docs/v3.0/data-layer/spark-data-ingestion-qs/).
\n", - "For more about running SQL queries with Spark, see [Running Spark SQL Queries](#data-ingest-sql-spark) under \"Running SQL Queries on Platform Data\"." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Working with Streams\n", - "\n", - "The platform supports various methods for working with data streams, including the following:\n", - "\n", - "- [Using Nuclio to Get Data from Common Streaming Engines](#data-ingest-streams-nuclio)\n", - "- [Using the Platform's Streaming Engine](#data-ingest-streams-platform)\n", - "- [Using Spark Streaming](#data-ingest-streams-spark)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using Nuclio to Get Data from Common Streaming Engines\n", - "\n", - "The platform has a default pre-deployed Nuclio service that uses Iguazio's [Nuclio](https://nuclio.io/) serverless-framework, which provides a mechanism for analyzing and processing real-time events from various streaming engines.\n", - "Nuclio currently supports the following streaming frameworks — Kafka, Kinesis, Azure Event Hubs, platform streams (a.k.a. V3IO streams), RabbitMQ, and MQTT.\n", - "\n", - "Using Nuclio functions to retrieve and analyze streaming data in real time is a very common practice when building a real-time data pipeline.\n", - "You can stream any type of data — such as telemetry (NetOps) metrics, financial transactions, web clicks, or sensors data — in any format, including images and videos. \n", - "You can also implement your own logic within the Nuclio function to manipulate or enrich the consumed stream data and prepare it for the next step in the pipeline.\n", - "\n", - "Nuclio serverless functions can sustain high workloads with very low latencies, thus making them very useful for building an event-driven applications with strict latency requirements.\n", - "\n", - "For more information about Nuclio, see [the platform's Nuclio service overview](https://www.iguazio.com/docs/v3.0/services/app-services/nuclio/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using the Platform's Streaming Engine \n", - "\n", - "The platform features a custom streaming engine and a related stream format — a platform stream (a.k.a. V3IO stream).\n", - "You can use the platform's streaming engine to write data into a queue in a real-time data pipeline, or as a standard streaming engine (similar to Kafka and Kinesis), so you don't need to use an external engine.\n", - "\n", - "The platform's streaming engine is currently available via the platform's [Streaming Web API](https://www.iguazio.com/docs/v3.0/data-layer/reference/web-apis/streaming-web-api/).
\n", - "In addition, the platform's Spark-Streaming Integration API enables using the Spark Streaming API to work with platform streams, as explained in the next section ([Using Spark Streaming](#data-ingest-streams-spark)).\n", - "\n", - "The [**v3io-streams**](v3io-streams.ipynb) tutorial demonstrates basic usage of the streaming API.\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using Spark Streaming\n", - "\n", - "You can use the [Spark Streaming](http://spark.apache.org/streaming/) API to ingest, consume, and analyze data using data streams.\n", - "The platform features a custom [Spark-Streaming Integration API](https://www.iguazio.com/docs/v3.0/data-layer/reference/spark-apis/spark-streaming-integration-api/) to allow using the Spark Streaming API with [platform streams](#data-ingest-streams-platform).\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running SQL Queries on Platform Data\n", - "\n", - "You can run SQL queries on NoSQL and Parquet data in the platform's data store, using any of the following methods:\n", - "\n", - "- [Running full ANSI Presto SQL queries](#data-ingest-sql-presto) using SQL magic\n", - "- [Running Spark SQL queries](#data-ingest-sql-spark)\n", - "- [Running SQL queries from Nuclio functions](#data-ingest-sql-nuclio)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running Full ANSI Presto SQL Queries\n", - "\n", - "The platform has a default pre-deployed Presto service that enables using the [Presto](https://trino.io/) open-source distributed SQL query engine to run interactive SQL queries and perform high-performance low-latency interactive analytics on data that's stored in the platform.\n", - "To run a Presto query from a Jupyter notebook, all you need is to use an SQL magic command — `%sql` followed by your Presto query.\n", - "Such queries are executed as distributed queries across the platform's application nodes.\n", - "The [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparationipynb) tutorial demonstrates how to run Presto queries using SQL magic.\n", - "\n", - "Note that for running queries on Parquet tables, you need to work with Hive tables.\n", - "The [**csv-to-hive**](csv-to-hive.ipynb) tutorial includes a script that converts a CSV file into a Hive table." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running Spark SQL Queries\n", - "\n", - "The [**spark-sql-analytics**](spark-sql-analytics.ipynb) tutorial demonstrates how to run Spark SQL queries on data in the platform's data store.\n", - "\n", - "For more information about the platform's Spark service, see [Working with Spark](#data-ingest-spark) in this tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running SQL Queries from Nuclio Functions \n", - "\n", - "In some cases, you might need to run SQL queries as part of an event-driven application.\n", - "The [**nuclio-read-via-presto**](nuclio-read-via-presto.ipynb) tutorial demonstrates how to run an SQL query from a serverless Nuclio function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running SQL Queries from MLRun Jobs\n", - "\n", - "In some cases, you might need to run SQL queries as part of an MLRun job.\n", - "The [**mlrun-read-via-presto**](mlrun-read-via-presto.ipynb) tutorial demonstrates how to run an SQL query from an MLRun job using Presto." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Working with Parquet Files\n", - "\n", - "Parquet is a columnar storage format that provides high-density high-performance file organization.
\n", - "The [**parquet-read-write**](parquet-read-write.ipynb) tutorial demonstrates how to create and write data to a Parquet table in the platform and read data from the table.\n", - "\n", - "After you ingest Parquet files into the platform, you might want to create related Hive tables and run SQL queries on these tables.
\n", - "The [**parquet-to-hive**](parquet-to-hive.ipynb) tutorial demonstrates how you can do this using Spark DataFrames." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing Platform NoSQL and TSDB Data Using the Frames Library\n", - "\n", - "[V3IO Frames](https://github.com/v3io/frames) (**\"Frames\"**) is a multi-model open-source data-access library, developed by Iguazio, which provides a unified high-performance DataFrame API for working with data in the platform's data store.\n", - "Frames currently supports the NoSQL (key-value) and time-series (TSDB) data models via its NoSQL (`nosql`|`kv`) and TSDB (`tsdb`) backends.\n", - "The [**frames**](frames.ipynb) tutorial provides an introduction to Frames and demonstrates how to use it to work with NoSQL and TSDB data in the platform." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting Data from AWS S3 Using curl\n", - "\n", - "A simple way to ingest data from the Amazon Simple Storage Service (S3) into the platform's data store is to run a curl command that sends an HTTP request to the relevant AWS S3 bucket, as demonstrated in the following code cell.\n", - "For more information and examples, see the [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparation.ipynb#ingest-from-amazon-s3-using-curl) tutorial.\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%sh\n", - "CSV_PATH=\"/User/examples/stocks.csv\"\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > ${CSV_PATH}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running Distributed Python Code with Dask\n", - "\n", - "[Dask](https://dask.org/) is a flexible library for parallel computation in Python, which is useful for computations that don't fit into a DataFrame.\n", - "Dask exposes low-level APIs that enable you to build custom systems for in-house applications.\n", - "This helps parallelize Python processes and dramatically accelerates their performance.\n", - "The [**dask-cluster**](dask-cluster.ipynb) tutorial demonstrates how to use Dask with platform data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running DataFrames on GPUs using NVIDIA cuDF\n", - "\n", - "The platform allows you to use NVIDIA's [RAPIDS](https://rapids.ai/) open-source libraries suite to execute end-to-end data science and analytics pipelines entirely on GPUs.\n", - "[cuDF](https://docs.rapids.ai/api/cudf/stable/) is a RAPIDS GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.\n", - "This library features a pandas-like API that will be familiar to data engineers and data scientists, who can use it to easily accelerate their workflows without going into the details of CUDA programming.\n", - "The [**gpu-cudf-vs-pd**](gpu-cudf-vs-pd.ipynb) tutorial demonstrates how to use the cuDF library and compares performance benchmarks with pandas and cuDF.\n", - "\n", - "> **Note:** To use the cuDF library, you need to create a RAPIDS Conda environment.\n", - "> For more information, see the [**virtual-env**](../virtual-env.ipynb) tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizing Data with Grafana\n", - "\n", - "The platform has a Grafana service with predefined dashboards that leverage the monitoring service to display monitoring data, such as performance statistics, for application services.\n", - "You can also define custom Grafana dashboards for monitoring, visualizing, and understanding data stored in the platform, such as time-series metrics and NoSQL data.\n", - "You can read and analyze data from the platform's data store and visualize it on Grafana dashboards in the desired formats, such as tables and graphs.\n", - "This can be done by using the custom `iguazio` data source, or by using a Prometheus data source for running Prometheus queries on platform TSDB tables.\n", - "You can also issue data alerts and create, explore, and share dashboards.\n", - "\n", - "You can use Iguazio's [grafwiz](https://github.com/v3io/grafwiz) Python library to create an deploy Grafana dashboards programmatically, as demonstrated in the [**grafana-grafwiz**](grafana-grafwiz.ipynb) tutorial." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data-ingestion-and-preparation/README.md b/data-ingestion-and-preparation/README.md deleted file mode 100644 index aa7872b5..00000000 --- a/data-ingestion-and-preparation/README.md +++ /dev/null @@ -1,316 +0,0 @@ -# Data Ingestion and Preparation Overview - -Learn about different methods for ingesting data into the Iguazio Data Science Platform, analyzing the data, and preparing it for the next step in your data pipeline. - -- [Overview](#data-ingest-overview) -- [Basic Flow](#data-ingest-basic-flow) -- [The Platform's Data Layer](#data-ingest-platform-data-layer) - - [Platform Data Containers](#platform-data-containers) - - [The Simple-Object Platform API](#data-ingest-platform-simple-object-api) - - [The NoSQL (Key-Value) Platform API](#data-ingest-platform-nosql-api) - - [The Streaming Platform API](#data-ingest-platform-streaming-api) -- [Reading from External Database](#data-ingest-external-dbs) - - [Using Spark over JDBC](#data-ingest-spark-over-jdbs) - - [Using SQLAlchemy](#data-ingest-sqlalchemy) -- [Working with Spark](#data-ingest-spark) - - [Using Spark SQL and DataFrames](#data-ingest-spark-sql-n-dfs) -- [Working with Streams](#data-ingest-streams) - - [Using Nuclio to Get Data from Common Streaming Engines](#data-ingest-streams-nuclio) - - [Using the Platform's Streaming Engine](#data-ingest-streams-platform) - - [Using Spark Streaming](#data-ingest-streams-spark) -- [Running SQL Queries on Platform Data](#data-ingest-sql) - - [Running Full ANSI Presto SQL Queries](#data-ingest-sql-presto) - - [Running Spark SQL Queries](#data-ingest-sql-spark) - - [Running SQL Queries from Nuclio Functions](#data-ingest-sql-nuclio) - - [Running SQL Queries from MLRun Jobs](#data-ingest-sql-mlrun) -- [Working with Parquet Files](#data-ingest-parquet) -- [Accessing Platform NoSQL and TSDB Data Using the Frames Library](#data-ingest-frames) -- [Getting Data from AWS S3 Using curl](data-ingest-s3-curl) -- [Running Distributed Python with Dask](#data-ingest-dask) -- [Running DataFrames on GPUs using NVIDIA cuDF](#data-ingest-gpu) -- [Visualizing Data with Grafana](#data-ingest-grafana) - - -## Overview - -The Iguazio Data Science Platform ("the platform") allows storing data in any format. -The platform's multi-model data layer and related APIs provide enhanced support for working with NoSQL ("key-value"), time-series, and stream data. -Various steps of the data science life cycle (pipeline) might require different tools and frameworks for working with data, especially when it comes to the different mechanisms required during the research and development phase versus the operational production phase. -The platform features a wide set of methods for manipulating and managing data, of different formats, in each step of the data life cycle, using a variety of frameworks, tools, and APIs — such as Spark SQL and DataFrames, Spark Streaming, Presto SQL queries, pandas DataFrames, Dask, the V3IO Frames Python library, and web APIs. - -This tutorial provides an overview of various methods for collecting, storing, and manipulating data in the platform, and refers to sample tutorial notebooks that demonstrate how to use these methods.
-For an in-depth overview of the platform and how it can be used to implement a full data science workflow, see the [**platform-overview**](../platform-overview.ipynb) tutorial notebook. -For information about the available full end-to-end use-case application and how-to demos, see the [**welcome**](../welcome.ipynb#end-to-end-use-case-applications) notebook or the matching [**README.md**](../README.md#end-to-end-use-case-applications) file. - -
pipeline-diagram
- - - -## Basic Flow - -The [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparation.ipynb) tutorial walks you through basic scenarios of ingesting data from external sources into the platform's data store and manipulating the data using different data formats. -The tutorial includes an example of ingesting a CSV file from an AWS S3 bucket; converting it into a NoSQL table using Spark DataFrames; running SQL queries on the table; and converting the table into a Parquet file. - - - -## The Platform's Data Layer - -The platform features an extremely fast and secure data layer (a.k.a. "data store") that supports storing data in different formats — SQL, NoSQL, time-series databases, files (simple objects), and streaming. -The data is stored within data containers and can be accessed using a variety of APIs — including [simple-object](#data-ingest-platform-simple-object-api), [NoSQL ("key-value")](#data-ingest-platform-nosql-api), and [streaming](#data-ingest-platform-streaming-api) APIs. - - - -### Platform Data Containers - -Data is stored within data containers in the platform's distributed file system (DFS), which makes up the platform's data layer. -All platform clusters have several predefined containers: - -- **"users"** — This container is designed to contain **<username>** directories that provide individual development environments for storing user-specific data. - The platform's Jupyter Notebook, Zeppelin, and web-based shell "command-line services" automatically create such a directory for the running user of the service and set it as the home directory of the service environment. - You can leverage the following environment variables, which are predefined in the platform's command-line services, to access this running-user directory from your code: - - - `V3IO_USERNAME` — set to the username of the running user of the Jupyter Notebook service. - - `V3IO_HOME` — set to the running-user directory in the "users" container — **users/<running user>**. - - `V3IO_HOME_URL` — set to the fully qualified `v3io` path to the running-user directory — `v3io://users/`. -- **"projects"** — This container is designed to store shared project artifacts.
- When creating a new project, the default artifacts path is **projects/<project name>/artifacts**. -- **"bigdata"** — This container has no special significance in the current release, and it will no longer be predefined in future releases. - However, you'll still be able to use your existing "bigdata" container and all its data, or create a custom container by this name if it doesn't already exist. - -The data containers and their contents are referenced differently depending on the programming interface. -For example: - -- In local file-system (FS) commands you use the predefined `v3io` root data mount — `/v3io/[/]`. - There's also a predefined local-FS `User` mount to the **users/<running user>** directory, and you can use the aforementioned environment variables when setting data paths. - For example, `/v3io/users/$V3IO_USERNAME`, `/v3io/$V3IO_HOME`, and `/User` are all valid ways of referencing the **users/<running user>** directory from a local FS command. -- In Hadoop FS or Spark DataFrame commands you use a fully qualified path of the format `v3io:///`. - You can also use environment variables with these interfaces. - -For detailed information and examples on how to set the data path for each interface, see [API Data Paths](https://www.iguazio.com/docs/v3.0/data-layer/apis/data-paths/) and the examples in the platform's tutorial Jupyter notebooks. - - - -### The Simple-Object Platform API - -The platform's Simple-Object API enables performing simple data-object and container operations that resemble the Amazon Web Services (AWS) Simple Storage Service (S3) API. -In addition to the S3-like capabilities, the Simple-Object Web API enables appending data to existing objects. -For more information and API usage examples, see the [**v3io-objects**](v3io-objects.ipynb) tutorial. - - - -### The NoSQL (Key-Value) Platform API - -The platform's NoSQL (a.k.a. key-value/KV) API provides access to the platform's NoSQL data store (database service), which enables storing and consuming data in a tabular format. -For more information and API usage examples, see the [**v3io-kv**](v3io-kv.ipynb) tutorial. - - - -### The Streaming Platform API - -The platform's Streaming API enables working with data in the platform as streams. -For more information and API usage examples, see the [**v3io-streams**](v3io-streams.ipynb) tutorial. -In addition, see the [Working with Streams](#data-ingest-streams) section in the current tutorial for general information about different methods for working with data streams in the platform. - - -## Reading Data from External Databases - -You can use different methods to read data from external databases into the platform's data store, such Spark over JDBC or SQLAlchemy. - - - -### Using Spark over JDBC - -Spark SQL includes a data source that can read data from other databases using Java database connectivity (JDBC). -The results are returned as a Spark DataFrame that can easily be processed using Spark SQL, or joined with other data sources. -The [**spark-jdbc**](spark-jdbc.ipynb) tutorial includes several examples of using Spark JDBC to ingest data from various databases — such as MySQL, Oracle, and PostgreSQL. - - - -### Using SQLAlchemy - -The [**read-external-db**](read-external-db.ipynb) tutorial outlines how to ingest data using [SQLAlchemy](https://www.sqlalchemy.org/) — a Python SQL toolkit and Object Relational Mapper, which gives application developers the full power and flexibility of SQL — and then use Python DataFrames to work on the ingested data set. - - - -## Working with Spark - -The platform has a default pre-deployed Spark service that enables ingesting, analyzing, and manipulating data using different [Spark](http://spark.apache.org) APIs: - -- Using Spark SQL and DataFrames -- Using the Spark Streaming API — see [Using Streaming Streaming](#data-ingest-streams-spark) under "Working with Spark". - - - -### Using Spark SQL and DataFrames - -Spark lets you write and query structured data inside Spark programs by using either SQL or a familiar DataFrame API. -DataFrames and SQL provide a common way to access a variety of data sources. -You can use the [Spark SQL and DataFrames](https://spark.apache.org/sql/) API to ingest data into the platform, for both batch and micro-batch processing, and analyze and manipulate large data sets, in a distributed manner. - -The platform's custom NoSQL Spark DataFrame implements the Spark data-source API to support a custom data source that enables reading and writing data in the platform's NoSQL store using Spark DataFrames, including enhanced features such as data pruning and filtering (predicate push down); queries are passed to the platform's data store, which returns only the relevant data. -This allows accelerated and high-speed access from Spark to data stored in the platform. - -The [**spark-sql-analytics**](spark-sql-analytics.ipynb) tutorial demonstrates how to use Spark SQL and DataFrames to access objects, tables, and unstructured data that persists in the platform's data store. - -For more information and examples of data ingestion with Spark DataFrames, see [Getting Started with Data Ingestion Using Spark](https://www.iguazio.com/docs/v3.0/data-layer/spark-data-ingestion-qs/).
-For more about running SQL queries with Spark, see [Running Spark SQL Queries](#data-ingest-sql-spark) under "Running SQL Queries on Platform Data". - - - -## Working with Streams - -The platform supports various methods for working with data streams, including the following: - -- [Using Nuclio to Get Data from Common Streaming Engines](#data-ingest-streams-nuclio) -- [Using the Platform's Streaming Engine](#data-ingest-streams-platform) -- [Using Spark Streaming](#data-ingest-streams-spark) - - - -### Using Nuclio to Get Data from Common Streaming Engines - -The platform has a default pre-deployed Nuclio service that uses Iguazio's [Nuclio](https://nuclio.io/) serverless-framework, which provides a mechanism for analyzing and processing real-time events from various streaming engines. -Nuclio currently supports the following streaming frameworks — Kafka, Kinesis, Azure Event Hubs, platform streams (a.k.a. V3IO streams), RabbitMQ, and MQTT. - -Using Nuclio functions to retrieve and analyze streaming data in real time is a very common practice when building a real-time data pipeline. -You can stream any type of data — such as telemetry (NetOps) metrics, financial transactions, web clicks, or sensors data — in any format, including images and videos. -You can also implement your own logic within the Nuclio function to manipulate or enrich the consumed stream data and prepare it for the next step in the pipeline. - -Nuclio serverless functions can sustain high workloads with very low latencies, thus making them very useful for building an event-driven applications with strict latency requirements. - -For more information about Nuclio, see [the platform's Nuclio service overview](https://www.iguazio.com/docs/v3.0/services/app-services/nuclio/). - - - -### Using the Platform's Streaming Engine - -The platform features a custom streaming engine and a related stream format — a platform stream (a.k.a. V3IO stream). -You can use the platform's streaming engine to write data into a queue in a real-time data pipeline, or as a standard streaming engine (similar to Kafka and Kinesis), so you don't need to use an external engine. - -The platform's streaming engine is currently available via the platform's [Streaming Web API](https://www.iguazio.com/docs/v3.0/data-layer/reference/web-apis/streaming-web-api/).
-In addition, the platform's Spark-Streaming Integration API enables using the Spark Streaming API to work with platform streams, as explained in the next section ([Using Spark Streaming](#data-ingest-streams-spark)). - -The [**v3io-streams**](v3io-streams.ipynb) tutorial demonstrates basic usage of the streaming API. - - - - - -### Using Spark Streaming - -You can use the [Spark Streaming](http://spark.apache.org/streaming/) API to ingest, consume, and analyze data using data streams. -The platform features a custom [Spark-Streaming Integration API](https://www.iguazio.com/docs/v3.0/data-layer/reference/spark-apis/spark-streaming-integration-api/) to allow using the Spark Streaming API with [platform streams](#data-ingest-streams-platform). - - - - - -## Running SQL Queries on Platform Data - -You can run SQL queries on NoSQL and Parquet data in the platform's data store, using any of the following methods: - -- [Running full ANSI Presto SQL queries](#data-ingest-sql-presto) using SQL magic -- [Running Spark SQL queries](#data-ingest-sql-spark) -- [Running SQL queries from Nuclio functions](#data-ingest-sql-nuclio) - - - -### Running Full ANSI Presto SQL Queries - -The platform has a default pre-deployed Presto service that enables using the [Presto](https://trino.io/) open-source distributed SQL query engine to run interactive SQL queries and perform high-performance low-latency interactive analytics on data that's stored in the platform. -To run a Presto query from a Jupyter notebook, all you need is to use an SQL magic command — `%sql` followed by your Presto query. -Such queries are executed as distributed queries across the platform's application nodes. -The [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparationipynb) tutorial demonstrates how to run Presto queries using SQL magic. - -Note that for running queries on Parquet tables, you need to work with Hive tables. -The [**csv-to-hive**](csv-to-hive.ipynb) tutorial includes a script that converts a CSV file into a Hive table. - - - -### Running Spark SQL Queries - -The [**spark-sql-analytics**](spark-sql-analytics.ipynb) tutorial demonstrates how to run Spark SQL queries on data in the platform's data store. - -For more information about the platform's Spark service, see [Working with Spark](#data-ingest-spark) in this tutorial. - - - -### Running SQL Queries from Nuclio Functions - -In some cases, you might need to run SQL queries as part of an event-driven application. -The [**nuclio-read-via-presto**](nuclio-read-via-presto.ipynb) tutorial demonstrates how to run an SQL query from a serverless Nuclio function. - - - -### Running SQL Queries from MLRun Jobs - -In some cases, you might need to run SQL queries as part of an MLRun job. -The [**mlrun-read-via-presto**](mlrun-read-via-presto.ipynb) tutorial demonstrates how to run an SQL query from an MLRun job using Presto. - - - -## Working with Parquet Files - -Parquet is a columnar storage format that provides high-density high-performance file organization.
-The [**parquet-read-write**](parquet-read-write.ipynb) tutorial demonstrates how to create and write data to a Parquet table in the platform and read data from the table. - -After you ingest Parquet files into the platform, you might want to create related Hive tables and run SQL queries on these tables.
-The [**parquet-to-hive**](parquet-to-hive.ipynb) tutorial demonstrates how you can do this using Spark DataFrames. - - - -## Accessing Platform NoSQL and TSDB Data Using the Frames Library - -[V3IO Frames](https://github.com/v3io/frames) (**"Frames"**) is a multi-model open-source data-access library, developed by Iguazio, which provides a unified high-performance DataFrame API for working with data in the platform's data store. -Frames currently supports the NoSQL (key-value) and time-series (TSDB) data models via its NoSQL (`nosql`|`kv`) and TSDB (`tsdb`) backends. -The [**frames**](frames.ipynb) tutorial provides an introduction to Frames and demonstrates how to use it to work with NoSQL and TSDB data in the platform. - - - -## Getting Data from AWS S3 Using curl - -A simple way to ingest data from the Amazon Simple Storage Service (S3) into the platform's data store is to run a curl command that sends an HTTP request to the relevant AWS S3 bucket, as demonstrated in the following code cell. -For more information and examples, see the [**basic-data-ingestion-and-preparation**](basic-data-ingestion-and-preparation.ipynb#ingest-from-amazon-s3-using-curl) tutorial. - - - -```sh -%%sh -CSV_PATH="/User/examples/stocks.csv" -curl -L "https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv" > ${CSV_PATH} -``` - - - -## Running Distributed Python Code with Dask - -[Dask](https://dask.org/) is a flexible library for parallel computation in Python, which is useful for computations that don't fit into a DataFrame. -Dask exposes low-level APIs that enable you to build custom systems for in-house applications. -This helps parallelize Python processes and dramatically accelerates their performance. -The [**dask-cluster**](dask-cluster.ipynb) tutorial demonstrates how to use Dask with platform data. - - - -## Running DataFrames on GPUs using NVIDIA cuDF - -The platform allows you to use NVIDIA's [RAPIDS](https://rapids.ai/) open-source libraries suite to execute end-to-end data science and analytics pipelines entirely on GPUs. -[cuDF](https://docs.rapids.ai/api/cudf/stable/) is a RAPIDS GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. -This library features a pandas-like API that will be familiar to data engineers and data scientists, who can use it to easily accelerate their workflows without going into the details of CUDA programming. -The [**gpu-cudf-vs-pd**](gpu-cudf-vs-pd.ipynb) tutorial demonstrates how to use the cuDF library and compares performance benchmarks with pandas and cuDF. - -> **Note:** To use the cuDF library, you need to create a RAPIDS Conda environment. -> For more information, see the [**virtual-env**](../virtual-env.ipynb) tutorial. - - - -## Visualizing Data with Grafana - -The platform has a Grafana service with predefined dashboards that leverage the monitoring service to display monitoring data, such as performance statistics, for application services. -You can also define custom Grafana dashboards for monitoring, visualizing, and understanding data stored in the platform, such as time-series metrics and NoSQL data. -You can read and analyze data from the platform's data store and visualize it on Grafana dashboards in the desired formats, such as tables and graphs. -This can be done by using the custom `iguazio` data source, or by using a Prometheus data source for running Prometheus queries on platform TSDB tables. -You can also issue data alerts and create, explore, and share dashboards. - -You can use Iguazio's [grafwiz](https://github.com/v3io/grafwiz) Python library to create an deploy Grafana dashboards programmatically, as demonstrated in the [**grafana-grafwiz**](grafana-grafwiz.ipynb) tutorial. diff --git a/data-ingestion-and-preparation/basic-data-ingestion-and-preparation.ipynb b/data-ingestion-and-preparation/basic-data-ingestion-and-preparation.ipynb deleted file mode 100644 index 249c09fb..00000000 --- a/data-ingestion-and-preparation/basic-data-ingestion-and-preparation.ipynb +++ /dev/null @@ -1,1063 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Getting Started with Data Ingestion and Preparation\n", - "\n", - "Learn how to quickly start using the Iguazio Data Science Platform to collect, ingest, and explore data.\n", - "\n", - "- [Overview](#gs-overview)\n", - "- [Collecting and Ingesting Data](#gs-data-collection-and-ingestion)\n", - " - [Ingesting Data From an External Database to a NoSQL Table Using V3IO Frames](#ingest-from-external-db-to-no-sql-using-frames)\n", - " - [Ingesting Files from Amazon S3](#ingest-from-amazon-s3)\n", - " - [Streaming Data From an External Streaming Engine Using Nuclio](#streaming-data-from-an-external-streaming-engine-using-nuclio)\n", - "- [Exploring and Processing Data](#gs-data-exploration-and-processing)\n", - " - [Exploring Data Using Spark DataFrames](#data-exploration-spark)\n", - " - [Exploring Data Using V3IO Frames and pandas DataFrames](#data-exploration-v3io-frames-n-pandas)\n", - " - [Exploring Data Using SQL](#data-exploration-sql)\n", - "- [Data Collection and Exploration Getting-Started Example](#getting-started-example)\n", - " - [Step 1: Ingest a Sample CSV File from Amazon S3](#getting-started-example-step-ingest-csv)\n", - " - [Step 2: Convert the Sample CSV File to a NoSQL Table](#getting-started-example-step-convert-csv-to-nosql-table)\n", - " - [Step 3: Run Interactive SQL Queries](#getting-started-example-step-run-sql-queries)\n", - " - [Step 4: Convert the Data to a Parquet Table](#getting-started-example-step-convert-data-to-parquet)\n", - " - [Step 5: Browse the Example Container Directory](#getting-started-example-step-browse-the-examples-dir)\n", - "- [Cleanup](#cleanup)\n", - " - [Delete Data](#delete-data)\n", - " - [Release Spark Resources](#release-spark-resources)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Overview\n", - "\n", - "This tutorial explains and demonstrates how to collect, ingest, and explore data with the Iguazio Data Science Platform (**\"the platform\"**).
\n", - "For an overview of working with data in the platform's data store and the various available methods for ingesting, storing, and manipulating data in the platform, see the data ingestion and preparation **README** ([notebook](README.ipynb) / [Markdown](README.md))." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Collecting and Ingesting Data\n", - "\n", - "The platform supports various alternative methods for collecting and ingesting data into its data containers (i.e., its data store).\n", - "For more information, see the [**platform-overview.ipynb**](../platform-overview.ipynb.ipynb#data-collection-and-ingestion) tutorial notebook\n", - "The data collection and ingestion can be done as a one-time operation, using different platform APIs — which can be run from your preferred programming interface, such as an interactive web-based Jupyter or Zeppelin notebook — or as an ongoing ingestion stream, using Nuclio serverless functions.\n", - "This section explains and demonstrates how to collect and ingest (import) data into the platform using code that's run from a Jupyter notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Ingesting Data From an External Database to a NoSQL Table Using V3IO Frames\n", - "\n", - "For an example of how to collect data from an external database — such as MySQL, Oracle, and PostgreSQL — and ingest (write) it into a NoSQL table in the platform, using the V3IO Frames API, see the [read-external-db](read-external-db.ipynb) tutorial." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Ingesting Files from Amazon S3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### Ingesting Files from Amazon S3 to the Platform File System Using curl\n", - "\n", - "You can use a simple [curl](https://curl.haxx.se/) command to ingest a file (object) from an external web data source, such as an Amazon S3 bucket, to the platform's distributed file system (i.e., into the platform's data store).\n", - "This is demonstrated in the following code example and in the [getting-started example](#getting-started-example) in this notebook.\n", - "The [spark-sql-analytics](spark-sql-analytics.ipynb) tutorial notebook demonstrates a similar ingestion using [Botocore](https://github.com/boto/botocore).\n", - "\n", - "The example in the following cells uses curl to read a CSV file from the Iguazio sample data-sets (`https://s3.wasabisys.com/iguazio/`) public S3 bucket and save it to an **examples/stocks** directory in the running-user directory of the predefined \"users\" data container (`/v3io/users/$V3IO_USERNAME` = `v3io/$V3IO_HOME` = `/User`)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir -p /User/examples/stocks # <=> /v3io/${V3IO_HOME}/examples/stocks or /v3io/users/${V3IO_USERNAME}/examples/stocks" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 861k 100 861k 0 0 3431k 0 --:--:-- --:--:-- --:--:-- 3431k\n" - ] - } - ], - "source": [ - "%%sh\n", - "CSV_PATH=\"/User/examples/stocks/stocks.csv\"\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > ${CSV_PATH}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### Ingesting Data from Amazon S3 to a NoSQL Table Using V3IO Frames and pandas\n", - "\n", - "For an example of how to import data from Amazon S3 and save it into a NoSQL table in the platform's data store by using V3IO Frames and pandas DataFrames, see the [frames](frames.ipynb) tutorial notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Streaming Data From an External Streaming Engine Using Nuclio\n", - "\n", - "To read data from an external streaming engine — such as Kafka, Kinesis, or RabbitMQ — create a Nuclio function that listens on the stream, and write the stream data to a NoSQL or time-series database (TSDB) table:\n", - "\n", - "1. In the dashboard's side navigation menu, select **Projects** to display the projects dashboard.\n", - "2. Create a new Nuclio project or select an existing project.\n", - "3. In the action toolbar, select **Create Function**.\n", - "4. Enter a function name, select an appropriate template, such as **kafka-to-tsdb**, configure the required template parameters, and apply your changes.\n", - "5. Select **Deploy** from the action toolbar to deploy your function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Exploring and Processing Data\n", - "\n", - "After you have ingested data into the platform's data containers, you can use various alternative methods and tools to explore and analyze the data.\n", - "Data scientists typically use Jupyter Notebook to run the exploration phase.\n", - "As outlined in the [**welcome**](../welcome.ipynb#data-exploration-and-processing) tutorial notebook, the platform's Jupyter Notebook service has a wide range of pre-deployed popular data science tools (such as Spark and Presto) and allows installation of additional tools and packages, enabling you to use different APIs to access the same data from a single Jupyter notebook.\n", - "This section explains and demonstrates how to explore data in the platform from a Jupyter notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Exploring Data using Spark DataFrames\n", - "\n", - "Spark is a distributed computation framework for data analytics.\n", - "You can easily run distributed Spark jobs on you platform cluster that use Spark DataFrames to access data files (objects), tables, or streams in the platform's data store.\n", - "For more information and examples, see the [spark-sql-analytics](spark-sql-analytics.ipynb) tutorial notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Exploring Data Using V3IO Frames and pandas DataFrames\n", - "\n", - "Iguazio's V3IO Frames open-source data-access library provides a unified high-performance DataFrames API for accessing NoSQL, stream, and time-series data in the platform's data store.\n", - "These DataFrames can also be used to analyze the data with pandas. \n", - "For details and examples, see the [frames](frames.ipynb) tutorial notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Exploring Data Using SQL\n", - "\n", - "You can run SQL statements (`SELECT` only) on top of NoSQL tables in the platform's data store.\n", - "To do this, you need to use the Jupyter `%sql` or `%%sql` IPython Jupyter magic followed by an SQL statement.\n", - "The platform supports standard ANSI SQL semantics.\n", - "Under the hood, the SQL statements are executed via [Presto](https://trino.io/), which is a distributed SQL engine designed from the ground up for fast analytics queries.\n", - "\n", - "In the example in the following cell, as a preparation for the SQL query, the **stocks.csv** file that was ingested to the **users/<running user>/examples/stocks** platform data-container directory in the previous [Ingesting Files from Amazon S3 to the Platform](#ingest-from-amazon-s3) example is written to a **stocks_example_tab** NoSQL table in the same directory.\n", - "Then, an SQL `SELECT` query is run on this table.\n", - "You can also find a similar example in the [getting-started example](#getting-started-example) in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Use V3IO Frames to convert the CSV file that was ingested in the AWS S3 data-collection example to a NoSQL table.\n", - "# NOTE: Make sure to first create a V3IO Frames service from the \"Services\" page of the platform dashboard, and run the\n", - "# \"Ingesting Files from Amazon S3 to the Platform File System Using curl\" example to create users/$V3IO_USERNAME/examples/stocks/stocks.csv.\n", - "import pandas as pd\n", - "import v3io_frames as v3f\n", - "import os\n", - "\n", - "# Create a V3IO Frames client for the \"users\" data container\n", - "client = v3f.Client(\"framesd:8081\", container=\"users\")\n", - "\n", - "# Full CSV file path\n", - "csv_path = os.path.join(\"/User\", \"examples\", \"stocks\", \"stocks.csv\")\n", - "# Relative NoSQL table path within the \"users\" container\n", - "rel_nosql_table_path = os.path.join(os.getenv('V3IO_USERNAME'), \"examples\", \"stocks\", \"stocks_example_tab\")\n", - "\n", - "# Read the CSV file into a Pandas DataFrame\n", - "df = pd.read_csv(csv_path, header=\"infer\")\n", - "\n", - "# Convert the CSV file to a NoSQL table\n", - "client.write(\"kv\", rel_nosql_table_path, df)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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HELLA GMBH+CO. KGAA O.N.Common stock08:04DE000A13SX2252.02018-03-2652.014HLEEUR250458055952.0238152.0
" - ], - "text/plain": [ - "[('ZALANDO SE', 'Common stock', '08:46', 'DE000ZAL1111', 45.23, '2018-03-26', 45.23, 3, 'ZAL', 'EUR', 2504863, 5669, 45.23, 152, 45.23),\n", - " ('PUBLITY AG NA O.N.', 'Common stock', '08:05', 'DE0006972508', 12.96, '2018-03-26', 13.1, 4, 'PBY', 'EUR', 2505068, 746, 13.1, 537, 12.96),\n", - " ('CELGENE CORP. DL-,01', 'Common stock', '08:47', 'US1510201049', 69.3, '2018-03-26', 69.3, 1, 'CG3', 'EUR', 2506454, 5852, 69.3, 20, 69.3),\n", - " ('DUERR AG O.N.', 'Common stock', '08:49', 'DE0005565204', 90.34, '2018-03-26', 90.34, 2, 'DUE', 'EUR', 2504956, 6036, 90.36, 30, 90.36),\n", - " ('HENKEL AG+CO.KGAA VZO', 'Common stock', '08:08', 'DE0006048432', 104.2, '2018-03-26', 104.2, 4, 'HEN3', 'EUR', 2505004, 1131, 104.2, 648, 104.2),\n", - " ('LPKF LASER+ELECTRON.', 'Common stock', '08:45', 'DE0006450000', 8.43, '2018-03-26', 8.43, 1, 'LPK', 'EUR', 2505039, 5578, 8.43, 225, 8.43),\n", - " ('BAYWA AG VINK.NA. O.N.', 'Common stock', '08:41', 'DE0005194062', 28.35, '2018-03-26', 28.35, 1, 'BYW6', 'EUR', 2504903, 5143, 28.35, 22, 28.35),\n", - " ('ISHS IV-HEALTHC.INNOV.ETF', 'ETF', '08:28', 'IE00BYZK4776', 5.444, '2018-03-26', 5.444, 1, '2B78', 'EUR', 2505553, 3598, 5.444, 2100, 5.444),\n", - " ('VONOVIA SE NA O.N.', 'Common stock', '08:14', 'DE000A1ML7J1', 39.18, '2018-03-26', 39.2, 9, 'VNA', 'EUR', 2504501, 1851, 39.21, 3202, 39.18),\n", - " ('HELLA GMBH+CO. KGAA O.N.', 'Common stock', '08:04', 'DE000A13SX22', 52.0, '2018-03-26', 52.0, 14, 'HLE', 'EUR', 2504580, 559, 52.0, 2381, 52.0)]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Use Presto to query the NoSQL table that was created in the previous step\n", - "presto_nosql_table_path = os.path.join('v3io.users.\"' + os.getenv('V3IO_USERNAME'), 'examples', \"stocks\", 'stocks_example_tab\"')\n", - "%sql select * from $presto_nosql_table_path limit 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Data Collection and Exploration Getting-Started Example\n", - "\n", - "This section demonstrates a data collection, ingestion, and exploration flow.\n", - "Follow the tutorial by running the code cells in order of appearance:\n", - "- [Step #1](#getting-started-example-step-ingest-csv) — a CSV file is read from an Amazon S3 bucket and saved into an examples data-container directory using curl.\n", - " The examples directory is first created by using a file-system command.\n", - "- [Step #2](#getting-started-example-step-convert-csv-to-nosql-table) — the ingested file is converted into a NoSQL table by using Spark DataFrames.\n", - "- [Step #3](#getting-started-example-step-run-sql-queries) — a Presto SQL query is run on the NoSQL table.\n", - "- [Step #4](#getting-started-example-step-convert-data-to-parquet) — the ingested CSV file is converted into a Parquet table by using Spark DataFrames.\n", - "- [Step #5](#getting-started-example-step-browse-the-examples-dir) — the examples container directory is browsed by using local and Hadoop file-system commands.\n", - "- At the end of the flow, you can optionally [delete](#getting-started-example-deleting-data) the examples directory using a file-system command.\n", - "\n", - "> **Tip:** You can also browse the files and directories that you write to the \"users\" container in this tutorial from the platform dashboard: in the side navigation menu, select **Data**, and then select the **users** container from the table. On the container data page, select the **Browse** tab, and then use the side directory-navigation tree to browse the directories. Selecting a file or directory in the browse table displays its metadata." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Step 1: Ingest a Sample CSV File from Amazon S3\n", - "\n", - "Use `curl` to download a sample stocks-data CSV file from the Iguazio sample data-sets (`https://s3.wasabisys.com/iguazio/`) public S3 bucket.\n", - "For additional public data sets, check out [Registry of Open Data on AWS](https://registry.opendata.aws/).\n", - "\n", - "> **NOTE:** All the platform tutorial notebook examples store the data in an **examples/stocks** directory in the running-user directory of the predefined \"users\" container — **users/<running user>/examples/stocks**.\n", - "> The running-user directory is automatically created by the Jupyter Notebook service.\n", - "> The `V3IO_HOME` environment variable is used to reference the **users/<running user>** directory.\n", - "> To save the data to a different root container directory or to a different container, you need to specify the data path in the local file-system commands as `/v3io//`, and in Spark DataFrames or Hadoop FS commands as a fully qualified path of the format `v3io:///`.\n", - "> For more information, see the [v3io-mount](#v3io-mount) and [running-user directory](#running-user-dir) information [Jupyter Notebook Basics](#jupyter-notebook-basics) section of this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 861k 100 861k 0 0 3794k 0 --:--:-- --:--:-- --:--:-- 3794k\n" - ] - } - ], - "source": [ - "%%sh\n", - "DIR_PATH=\"/User/examples/stocks/\" # <=> \"/v3io/${V3IO_HOME}/examples/stocks/\" or \"/v3io/users/${V3IO_USERNAME}/examples/stocks/\"\n", - "CSV_PATH=\"${DIR_PATH}stocks.csv\"\n", - "\n", - "# Create the examples directory\n", - "mkdir -p ${DIR_PATH}\n", - "\n", - "# Download a sample stocks CSV file from the Iguazio sample data-set Amazon S3 bucket to the examples/stocks directory\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > ${CSV_PATH}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Step 2: Convert the Sample CSV File to a NoSQL Table\n", - "\n", - "Read the sample **stocks.csv** file that you downloaded and ingested in the previous step into a Spark DataFrame, and write the data in NoSQL format to a new \"stocks_tab\" table in the same container directory (**users/<running user>/examples/stocks/stocks_tab**). \n", - "\n", - "> **Note**\n", - "> - To use the Iguazio Spark Connector, set the DataFrame's data-source format to `io.iguaz.v3io.spark.sql.kv`.\n", - "> - The data path in the Spark DataFrame is specified by using the `V3IO_HOME_URL` environment variable, which is set to `v3io://users/`.\n", - "> See the [running-user directory](#running-user-dir) information." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+------------+--------+--------------------+------------+--------+----------+-------------------+-----+----------+--------+--------+--------+------------+--------------+\n", - "| ISIN|Mnemonic| SecurityDesc|SecurityType|Currency|SecurityID| Date| Time|StartPrice|MaxPrice|MinPrice|EndPrice|TradedVolume|NumberOfTrades|\n", - "+------------+--------+--------------------+------------+--------+----------+-------------------+-----+----------+--------+--------+--------+------------+--------------+\n", - "|CH0038389992| BBZA|BB BIOTECH NAM. ...|Common stock| EUR| 2504244|2018-03-26 00:00:00|08:00| 56.4| 56.4| 56.4| 56.4| 320| 4|\n", - "|CH0038863350| NESR|NESTLE NAM. ...|Common stock| EUR| 2504245|2018-03-26 00:00:00|08:00| 63.04| 63.06| 63.0| 63.06| 314| 3|\n", - "|LU0378438732| C001|COMSTAGE-DAX UCIT...| ETF| EUR| 2504271|2018-03-26 00:00:00|08:00| 113.42| 113.42| 113.42| 113.42| 100| 1|\n", - "|LU0411075020| DBPD|XTR.SHORTDAX X2 D...| ETF| EUR| 2504272|2018-03-26 00:00:00|08:00| 4.1335| 4.1335| 4.1295| 4.13| 102993| 8|\n", - "|LU0838782315| XDDX| XTR.DAX INCOME 1D| ETF| EUR| 2504277|2018-03-26 00:00:00|08:00| 105.14| 105.2| 105.14| 105.2| 239| 3|\n", - "|DE000A0DJ6J9| S92|SMA SOLAR TECHNOL.AG|Common stock| EUR| 2504287|2018-03-26 00:00:00|08:00| 55.65| 55.65| 55.65| 55.65| 543| 3|\n", - "|DE000A0D6554| NDX1| NORDEX SE O.N.|Common stock| EUR| 2504290|2018-03-26 00:00:00|08:00| 8.182| 8.21| 8.174| 8.21| 10941| 8|\n", - "|DE000A0F5UE8| EXXU|IS.DJ CHINA OFFS....| ETF| EUR| 2504302|2018-03-26 00:00:00|08:00| 47.52| 47.52| 47.52| 47.52| 420| 1|\n", - "|DE000A0HN5C6| DWNI|DEUTSCHE WOHNEN S...|Common stock| EUR| 2504314|2018-03-26 00:00:00|08:00| 36.2| 36.24| 36.2| 36.24| 580| 5|\n", - "|DE000A0LD2U1| AOX|ALSTRIA OFFICE RE...|Common stock| EUR| 2504379|2018-03-26 00:00:00|08:00| 12.25| 12.25| 12.25| 12.25| 1728| 3|\n", - "|DE000A0LR936| ST5| STEICO SE|Common stock| EUR| 2504382|2018-03-26 00:00:00|08:00| 22.35| 22.35| 22.35| 22.35| 334| 1|\n", - "|DE000A0MZ4B0| DLX|DELIGNIT AG ...|Common stock| EUR| 2504390|2018-03-26 00:00:00|08:00| 10.3| 10.3| 10.3| 10.3| 850| 1|\n", - "|DE000A0Q8NC8| ETLX|ETFS DAXGL.G.MIN....| ETF| EUR| 2504397|2018-03-26 00:00:00|08:00| 17.844| 17.844| 17.838| 17.838| 3085| 5|\n", - "|DE000A0V9YU8| 4RT3|ETFS COM.SEC.DZ08...| ETC| EUR| 2504421|2018-03-26 00:00:00|08:00| 5.8895| 5.8895| 5.8895| 5.8895| 0| 1|\n", - "|DE000A0WMPJ6| AIXA| AIXTRON SE NA O.N.|Common stock| EUR| 2504428|2018-03-26 00:00:00|08:00| 16.8| 16.8| 16.75| 16.755| 3329| 8|\n", - "|DE000A0Z2XN6| RIB|RIB SOFTWARE SE ...|Common stock| EUR| 2504436|2018-03-26 00:00:00|08:00| 24.66| 24.66| 24.52| 24.52| 11741| 29|\n", - "|DE000A0Z2ZZ5| FNTN| FREENET AG NA O.N.|Common stock| EUR| 2504438|2018-03-26 00:00:00|08:00| 24.41| 24.42| 24.41| 24.42| 695| 6|\n", - "|DE000A1A6V48| KSC| KPS AG NA O.N.|Common stock| EUR| 2504441|2018-03-26 00:00:00|08:00| 9.15| 9.15| 9.15| 9.15| 73| 1|\n", - "|DE000A1DAHH0| BNR| BRENNTAG AG NA O.N.|Common stock| EUR| 2504453|2018-03-26 00:00:00|08:00| 48.14| 48.14| 48.14| 48.14| 185| 2|\n", - "|DE000A1EWWW0| ADS| ADIDAS AG NA O.N.|Common stock| EUR| 2504471|2018-03-26 00:00:00|08:00| 196.3| 196.35| 196.3| 196.35| 591| 12|\n", - "+------------+--------+--------------------+------------+--------+----------+-------------------+-----+----------+--------+--------+--------+------------+--------------+\n", - "only showing top 20 rows\n", - "\n", - "root\n", - " |-- ISIN: string (nullable = true)\n", - " |-- Mnemonic: string (nullable = true)\n", - " |-- SecurityDesc: string (nullable = true)\n", - " |-- SecurityType: string (nullable = true)\n", - " |-- Currency: string (nullable = true)\n", - " |-- SecurityID: integer (nullable = true)\n", - " |-- Date: timestamp (nullable = true)\n", - " |-- Time: string (nullable = true)\n", - " |-- StartPrice: double (nullable = true)\n", - " |-- MaxPrice: double (nullable = true)\n", - " |-- MinPrice: double (nullable = true)\n", - " |-- EndPrice: double (nullable = true)\n", - " |-- TradedVolume: integer (nullable = true)\n", - " |-- NumberOfTrades: integer (nullable = true)\n", - "\n" - ] - } - ], - "source": [ - "import os\n", - "from pyspark.sql import SparkSession\n", - "\n", - "# Example diretory path - a /examples/stocks directory in the \"users\" container\n", - "dir_path = os.path.join(os.getenv(\"V3IO_HOME_URL\"), \"examples\", \"stocks\")\n", - "# CSV file path\n", - "csv_path = os.path.join(dir_path, \"stocks.csv\")\n", - "# NoSQL table path\n", - "nosql_table_path = os.path.join(dir_path, \"stocks_tab\")\n", - "\n", - "# Create a new Spark session\n", - "spark = SparkSession.builder.appName(\"Iguazio data ingestion and preparation getting-started example\").getOrCreate()\n", - "\n", - "# Read the sample CSV file into a Spark DataFrame, and let Spark infer the schema of the data\n", - "df = spark.read.option(\"header\", \"true\").csv(csv_path, inferSchema=\"true\")\n", - "\n", - "# Show the DataFrame data\n", - "df.show()\n", - "\n", - "# Write the DataFrame data to a NoSQL table in a platform data container.\n", - "# Define the \"ISIN\" column (attribute) as the table's primary key.\n", - "df.write.format(\"io.iguaz.v3io.spark.sql.kv\").mode(\"append\") \\\n", - " .option(\"key\", \"ISIN\").option(\"allow-overwrite-schema\", \"true\") \\\n", - " .save(nosql_table_path)\n", - "\n", - "# Display the table schema:\n", - "df.printSchema()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Step 3: Run Interactive SQL Queries\n", - "\n", - "Use the `%sql` Jupyter magic to run an SQL queries on the \"stocks_tab\" table that was created in the previous step.\n", - "(The queries is processed using Presto.)\n", - "The example runs a `SELECT` query that reads the first ten table items." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "presto_nosql_table_path = os.path.join('v3io.users.\"' + os.getenv('V3IO_USERNAME'), 'examples', 'stocks', 'stocks_tab\"')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * presto://iguazio:***@presto-api-presto.default-tenant.app.aopaqdknqiot.iguazio-cd2.com:443/v3io?protocol=https\n", - "Done.\n" - ] - }, - { - "data": { - "text/html": [ - "
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securitydescsecuritytypetimeisinminpricedateendpricenumberoftradesmnemoniccurrencysecurityidmaxpricetradedvolumestartprice
FIELMANN AG O.N.Common stock08:00DE000577220665.72018-03-26 00:00:00.00065.71FIEEUR250496965.719965.7
BIOTEST AG VZ O.N.Common stock08:00DE000522723526.52018-03-26 00:00:00.00026.51BIO3EUR250491526.55026.5
ASMALLWORLD AG SF 1Common stock08:23CH040488012912.72018-03-26 00:00:00.00012.711Q7EUR308912212.740012.7
ISHSIV-MSCI CHINA A DL AETF08:33IE00BQT3WG133.56252018-03-26 00:00:00.0003.5625236BZEUR25054833.57228003.572
IS EURO DIVID.U.ETF EODETF08:16IE00B0M62S7221.442018-03-26 00:00:00.00021.441IQQAEUR250639521.4410521.44
ISHS ESTXX BNKS.30-15 UC.ETF08:03DE000628930912.0662018-03-26 00:00:00.00012.074EXX1EUR250502712.071627412.068
ISHSVII-MSCI EM AS.DL ACCETF08:22IE00B5L8K969128.682018-03-26 00:00:00.000128.681CEBLEUR2505690128.687128.68
ARCELORMITTAL S.A. NOUV.Common stock08:01LU159875768725.3952018-03-26 00:00:00.00025.3951ARRDEUR250619025.39517125.395
ETFS ISE CYBER SEC.GO DZETF08:15DE000A14ZT8510.732018-03-26 00:00:00.00010.731USPYEUR250459710.7350010.73
EINHELL GERMANY VZO O.N.Common stock08:05DE000565493390.02018-03-26 00:00:00.00090.01EIN3EUR250496190.02090.0
" - ], - "text/plain": [ - "[('FIELMANN AG O.N.', 'Common stock', '08:00', 'DE0005772206', 65.7, '2018-03-26 00:00:00.000', 65.7, 1, 'FIE', 'EUR', 2504969, 65.7, 199, 65.7),\n", - " ('BIOTEST AG VZ O.N.', 'Common stock', '08:00', 'DE0005227235', 26.5, '2018-03-26 00:00:00.000', 26.5, 1, 'BIO3', 'EUR', 2504915, 26.5, 50, 26.5),\n", - " ('ASMALLWORLD AG SF 1', 'Common stock', '08:23', 'CH0404880129', 12.7, '2018-03-26 00:00:00.000', 12.7, 1, '1Q7', 'EUR', 3089122, 12.7, 400, 12.7),\n", - " ('ISHSIV-MSCI CHINA A DL A', 'ETF', '08:33', 'IE00BQT3WG13', 3.5625, '2018-03-26 00:00:00.000', 3.5625, 2, '36BZ', 'EUR', 2505483, 3.572, 2800, 3.572),\n", - " ('IS EURO DIVID.U.ETF EOD', 'ETF', '08:16', 'IE00B0M62S72', 21.44, '2018-03-26 00:00:00.000', 21.44, 1, 'IQQA', 'EUR', 2506395, 21.44, 105, 21.44),\n", - " ('ISHS ESTXX BNKS.30-15 UC.', 'ETF', '08:03', 'DE0006289309', 12.066, '2018-03-26 00:00:00.000', 12.07, 4, 'EXX1', 'EUR', 2505027, 12.07, 16274, 12.068),\n", - " ('ISHSVII-MSCI EM AS.DL ACC', 'ETF', '08:22', 'IE00B5L8K969', 128.68, '2018-03-26 00:00:00.000', 128.68, 1, 'CEBL', 'EUR', 2505690, 128.68, 7, 128.68),\n", - " ('ARCELORMITTAL S.A. NOUV.', 'Common stock', '08:01', 'LU1598757687', 25.395, '2018-03-26 00:00:00.000', 25.395, 1, 'ARRD', 'EUR', 2506190, 25.395, 171, 25.395),\n", - " ('ETFS ISE CYBER SEC.GO DZ', 'ETF', '08:15', 'DE000A14ZT85', 10.73, '2018-03-26 00:00:00.000', 10.73, 1, 'USPY', 'EUR', 2504597, 10.73, 500, 10.73),\n", - " ('EINHELL GERMANY VZO O.N.', 'Common stock', '08:05', 'DE0005654933', 90.0, '2018-03-26 00:00:00.000', 90.0, 1, 'EIN3', 'EUR', 2504961, 90.0, 20, 90.0)]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%sql select * from $presto_nosql_table_path limit 10" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * presto://iguazio:***@presto-api-presto.default-tenant.app.aopaqdknqiot.iguazio-cd2.com:443/v3io?protocol=https\n", - "Done.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
_col0
737
" - ], - "text/plain": [ - "[(737,)]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%sql select count(*) from $presto_nosql_table_path " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Step 4: Convert the Data to a Parquet Table\n", - "\n", - "Use a Spark DataFrame `write` command to write the data in the Spark DaraFrame — which was created from the CSV file and used to create the NoSQL table in [Step 2](#getting-started-example-step-convert-csv-to-nosql-table) — to a new **users/<running user>/examples/stocks/stocks_prqt** Parquet table." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Write the DataFrame data that was read from the CSV file in Step 2 to a Parquet table in a platform data container\n", - "prqt_table_path = os.path.join(dir_path, \"stocks_prqt\")\n", - "df.write.mode('overwrite').parquet(prqt_table_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Step 5: Browse the Example Container Directory\n", - "\n", - "Use a file-system bash-shell command to list the contents of the **users/<running user>/examples/stocks** data-container directory to which all the ingested data in the previous steps were saved.\n", - "You should see in this directory the **stocks.csv** file, **stocks_tab** NoSQL table directory, and **stocks_prqt** Parquet table directory that you created in the previous steps.\n", - "The following cells demonstrate how to issue the same command using the local file system and using Hadoop FS." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 862\n", - "drwxrwxr-x 2 51 nogroup 0 Oct 18 09:18 stocks_example_tab\n", - "-rw-r--r-- 1 51 nogroup 882055 Oct 18 09:18 stocks.csv\n", - "drwxrwxrwx 2 51 nogroup 0 Oct 18 09:18 stocks_tab\n", - "drwxr-xr-x 2 51 nogroup 0 Oct 18 09:18 stocks_prqt\n" - ] - } - ], - "source": [ - "# List the contents of the users//examples/stocks directory using a local file-system command\n", - "!ls -lrt /User/examples/stocks\n", - "# The following are equivalent commands that demonstrate different ways to reference your user home directory:\n", - "#!ls -lrt /v3io/${V3IO_HOME}/examples/stocks\n", - "#!ls -lrt /v3io/users/${V3IO_USERNAME}/examples/stocks" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 4 items\n", - "-rw-r--r-- 1 51 nogroup 882055 2020-10-18 09:18 v3io://users/iguazio/examples/stocks/stocks.csv\n", - "drwxrwxr-x - 51 nogroup 0 2020-10-18 09:18 v3io://users/iguazio/examples/stocks/stocks_example_tab\n", - "drwxr-xr-x - 51 nogroup 0 2020-10-18 09:18 v3io://users/iguazio/examples/stocks/stocks_prqt\n", - "drwxrwxrwx - 51 nogroup 0 2020-10-18 09:18 v3io://users/iguazio/examples/stocks/stocks_tab\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "20/10/18 09:18:50 INFO slf_4j.Slf4jLogger: Slf4jLogger started\n" - ] - } - ], - "source": [ - "%%sh\n", - "# List the contents of the users//examples/stocks directory using an Hadoop FS command\n", - "hadoop fs -ls ${V3IO_HOME_URL}/examples/stocks\n", - "# The following are equivalent commands that demonstrate different ways to reference your user home directory:\n", - "#hadoop fs -ls v3io://${V3IO_HOME}/examples/stocks\n", - "#hadoop fs -ls v3io://users/${V3IO_USERNAME}/examples/stocks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Cleanup\n", - "\n", - "Prior to exiting, release disk space, computation, and memory resources consumed by the active session:\n", - "\n", - "- [Delete Data](#delete-data)\n", - "- [Release Spark Resources](#release-spark-resources)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Delete Data\n", - "\n", - "Optionally delete any of the directories or files that you created.\n", - "See the instructions in the [Creating and Deleting Container Directories](https://www.iguazio.com/docs/v3.0/data-layer/containers/working-with-containers/#create-delete-container-dirs) tutorial.\n", - "The following example uses a local file-system command to delete the entire contents of the **users/<running user>/examples/stocks** directory that was created in this example, but not the directory itself." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the contents of the stocks examples directory:\n", - "#!rm -rf /User/examples/stocks/*\n", - "# You can also delete the stocks examples directory iteself (and all its contents):\n", - "#!rm -rf /User/examples/stocks/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Release Spark Resources\n", - "\n", - "When you're done, run the following command to stop your Spark session and release its computation and memory resources:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "spark.stop()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data-ingestion-and-preparation/csv-to-hive.ipynb b/data-ingestion-and-preparation/csv-to-hive.ipynb deleted file mode 100644 index f9b8087c..00000000 --- a/data-ingestion-and-preparation/csv-to-hive.ipynb +++ /dev/null @@ -1,318 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This notebook is to help automatically import csv file to hive" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 861k 100 861k 0 0 380k 0 0:00:02 0:00:02 --:--:-- 380k\n", - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 861k 100 861k 0 0 356k 0 0:00:02 0:00:02 --:--:-- 356k\n", - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 861k 100 861k 0 0 306k 0 0:00:02 0:00:02 --:--:-- 306k\n" - ] - } - ], - "source": [ - "%%sh\n", - "mkdir -p /v3io/${V3IO_HOME}/examples\n", - "mkdir -p /v3io/${V3IO_HOME}/examples/csvc\n", - "\n", - "# Download a sample stocks file from Iguazio demo bucket in S3\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > /v3io/${V3IO_HOME}/examples/demo.csv\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > /v3io/${V3IO_HOME}/examples/csvs/example1.csv\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > /v3io/${V3IO_HOME}/examples/csvs/example2.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is import of all needed dependencies. And in this sell you should pass path where parquet files located. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "HOME = os.getenv('V3IO_HOME')\n", - "V3IO_HOME_URL=os.getenv('V3IO_HOME_URL')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is creating of spark context with hive support." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from pyspark.sql import SparkSession\n", - "spark = SparkSession.builder.appName(\"Import parquet schema to hive\").config(\"hive.metastore.uris\", \"thrift://trino-hive:9083\").enableHiveSupport().getOrCreate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define function below for getting sql script needed for creating table in hive using dataframe types as columns to table" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def getCreateTableScriptCSV(databaseName, tableName, path, df):\n", - " cols = df.dtypes\n", - " createScript = \"CREATE EXTERNAL TABLE IF NOT EXISTS \" + databaseName + \".\" + tableName + \"(\"\n", - " colArray = []\n", - " for colName, colType in cols:\n", - " colArray.append(colName.replace(\" \", \"_\") + \" \" + colType)\n", - " createColsScript = \", \".join(colArray )\n", - " \n", - " script = createScript + createColsScript + \") ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION '\" + path + \"' TBLPROPERTIES('skip.header.line.count'='1') \"\n", - " print (script)\n", - " return script" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#define main function for creating table where arqument 'path' is path to parquet files \n", - "def createTableCSV(databaseName, tableName, path): \n", - " df = spark.read.format(\"csv\").option(\"header\", \"true\").option(\"inferschema\",\"true\").load(path)\n", - " sqlScript = getCreateTableScriptCSV(databaseName, tableName, path, df)\n", - " spark.sql(sqlScript)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## One file example" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CREATE EXTERNAL TABLE IF NOT EXISTS default.csv_table(ISIN string, Mnemonic string, SecurityDesc string, SecurityType string, Currency string, SecurityID int, Date string, Time string, StartPrice double, MaxPrice double, MinPrice double, EndPrice double, TradedVolume int, NumberOfTrades int) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION 'v3io://users/dani/examples/demo.csv' TBLPROPERTIES('skip.header.line.count'='1') \n" - ] - } - ], - "source": [ - "# Set path where the csv file located.\n", - "my_csv_file_path = os.path.join(V3IO_HOME_URL, 'examples/demo.csv')\n", - "createTableCSV(\"default\",\"csv_table\",my_csv_file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## One folder example for spark output job" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CREATE EXTERNAL TABLE IF NOT EXISTS default.table_from_dir2(ISIN string, Mnemonic string, SecurityDesc string, SecurityType string, Currency string, SecurityID string, Date string, Time string, StartPrice string, MaxPrice string, MinPrice string, EndPrice string, TradedVolume string, NumberOfTrades int) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION 'v3io://users/dani/examples/csvs/' TBLPROPERTIES('skip.header.line.count'='1') \n" - ] - } - ], - "source": [ - "# Set path where parquet folder with csv files inside located.\n", - "folder_path = os.path.join(V3IO_HOME_URL, 'examples/csvs/')\n", - "createTableCSV(\"default\",\"table_from_dir2\",folder_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multiple files and folders example\n", - "\n", - "Write here name of database and path to folder where all csv's files (or folders with them) located. Database should be created.\n", - "In this cell code goes over all files and dirs in provided path and using them for creating table.\n", - "File should be ended with .csv format and should be \",\" seperated.\n", - "Directory (in which stored csv files) should be started with \".\"\n", - "Name of directory or file will be name of table." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CREATE EXTERNAL TABLE IF NOT EXISTS default.demo1(id int, street string, city string, zip int, state string, beds int, baths int, sq__ft int, type string, sale_date string, price int, latitude double, longitude double) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION 'v3io://users/dani/examples/csvs/demo1.csv' TBLPROPERTIES('skip.header.line.count'='1') \n", - "CREATE EXTERNAL TABLE IF NOT EXISTS default.demo2(id int, street string, city string, zip int, state string, beds int, baths int, sq__ft int, type string, sale_date string, price int, latitude double, longitude double) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION 'v3io://users/dani/examples/csvs/demo2.csv' TBLPROPERTIES('skip.header.line.count'='1') \n", - "CREATE EXTERNAL TABLE IF NOT EXISTS default.example1(ISIN string, Mnemonic string, SecurityDesc string, SecurityType string, Currency string, SecurityID int, Date string, Time string, StartPrice double, MaxPrice double, MinPrice double, EndPrice double, TradedVolume int, NumberOfTrades int) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION 'v3io://users/dani/examples/csvs/example1.csv' TBLPROPERTIES('skip.header.line.count'='1') \n", - "CREATE EXTERNAL TABLE IF NOT EXISTS default.example2(ISIN string, Mnemonic string, SecurityDesc string, SecurityType string, Currency string, SecurityID int, Date string, Time string, StartPrice double, MaxPrice double, MinPrice double, EndPrice double, TradedVolume int, NumberOfTrades int) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LOCATION 'v3io://users/dani/examples/csvs/example2.csv' TBLPROPERTIES('skip.header.line.count'='1') \n" - ] - } - ], - "source": [ - "databaseName = \"default\"\n", - "filepath = os.path.join('/v3io', HOME, 'examples/csvs')\n", - "\n", - "for fileOrDir in os.listdir(filepath):\n", - " if fileOrDir.endswith(\".csv\") :\n", - " createTableCSV(databaseName, fileOrDir.split(\".csv\")[0], filepath.replace(\"/v3io/\", \"v3io://\", 1) + \"/\" + fileOrDir)\n", - " elif not fileOrDir.startswith(\".\") :\n", - " createTableCSV(databaseName, fileOrDir, filepath.replace(\"/v3io/\", \"v3io://\", 1) + \"/\" + fileOrDir + \"/*\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test how it works" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+---------+\n", - "|namespace|\n", - "+---------+\n", - "| default|\n", - "+---------+\n", - "\n", - "+---------+---------------+-----------+\n", - "|namespace| tableName|isTemporary|\n", - "+---------+---------------+-----------+\n", - "| default| csv_table| false|\n", - "| default| demo1| false|\n", - "| default| demo2| false|\n", - "| default| example1| false|\n", - "| default| example2| false|\n", - "| default|table_from_dir2| false|\n", - "+---------+---------------+-----------+\n", - "\n" - ] - } - ], - "source": [ - "# test how the tables were saved\n", - "#spark.sql(\"drop database test CASCADE\")\n", - "spark.sql(\"show databases\").show()\n", - "spark.sql(\"show tables in \" + databaseName).show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DataFrame[ISIN: string, Mnemonic: string, SecurityDesc: string, SecurityType: string, Currency: string, SecurityID: int, Date: string, Time: string, StartPrice: double, MaxPrice: double, MinPrice: double, EndPrice: double, TradedVolume: int, NumberOfTrades: int]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# test how saving to table works\n", - "tableName = \"example1\"\n", - "spark.sql(\"select * from \" + databaseName + \".\" + tableName)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DataFrame[]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spark.sql(\"drop table \" + databaseName + \".example1\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data-ingestion-and-preparation/dask-cluster.ipynb b/data-ingestion-and-preparation/dask-cluster.ipynb deleted file mode 100644 index d35279d9..00000000 --- a/data-ingestion-and-preparation/dask-cluster.ipynb +++ /dev/null @@ -1,690 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Running Dask on the cluster with mlrun" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dask frameworks enables users to parallelize their python code and run it as a distributed process on Iguazio cluster and dramatically accelerate their performance.
\n", - "In this notebook you'll learn how to create a dask cluster and then an mlrun function running as a dask client.
\n", - "It also demonstrates how to run parallelize custom algorithm using Dask Delayed option\n", - "\n", - "For more information on dask over kubernetes: https://kubernetes.dask.org/en/latest/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Basic configuration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import mlrun and dask. nuclio is used just to convert the code into an mlrun function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load sample data" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir -p /User/examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "import mlrun\n", - "import requests\n", - "\n", - "source_url = mlrun.get_sample_path(\"/data/Taxi/yellow_tripdata_2019-01_subset.csv\")\n", - "response = requests.get(source_url, allow_redirects=True)\n", - "with open('/User/examples/ytrip.csv', 'wb') as csv_file:\n", - " csv_file.write(response.content)" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "# mlrun: start-code\n", - "from mlrun.execution import MLClientCtx\n", - "from mlrun.datastore import DataItem\n", - "\n", - "from dask.distributed import Client\n", - "from dask import delayed\n", - "from dask import dataframe as dd\n", - "\n", - "import warnings\n", - "import numpy as np\n", - "import os\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a python function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This simple function reads a csv file using dask dataframe and run group by and describe function on the dataset
\n", - "It also shows how to use the dask delayed function to run a python API that is not natively supported by Dask and leverage dask to run it as a distributed process .
\n", - "In this case we run numpy asmatrix which Interpret the input as a matrix. Using Dask Delayed it runs it in parallel" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "def test_dask(context,\n", - " dataset: DataItem,\n", - " dask_client: str=None) -> None:\n", - " \n", - " if dask_client:\n", - " client = Client(dask_client)\n", - " else:\n", - " client = Client()\n", - " \n", - " df = dataset.as_df(df_module=dd)\n", - " df_describe = df.describe().compute()\n", - " df_grpby = df.groupby(\"VendorID\").count().compute()\n", - " df_matrix = delayed(np.asmatrix)(df).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "# mlrun: end-code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up the enviroment" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-06-07 14:18:56,952 [info] loaded project default from MLRun DB\n" - ] - } - ], - "source": [ - "import mlrun\n", - "artifact_path = mlrun.set_environment(api_path = mlrun.mlconf.dbpath or 'http://mlrun-api:8080',\n", - " artifact_path = os.path.abspath('./'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert the code to MLRun function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use code_to_function to convert the code to MLRun and specify the configuration for the dask process (e.g. replicas, memory etc)
\n", - "Note that the resource configurations are per worker" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Init dask cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [], - "source": [ - "dsf = mlrun.new_function(\"dask_init\", kind='dask', image='mlrun/ml-models').apply(mlrun.mount_v3io())" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [], - "source": [ - "dsf.spec.remote = True\n", - "dsf.spec.replicas = 2\n", - "dsf.spec.max_replicas = 4\n", - "dsf.spec.service_type = \"NodePort\"\n", - "dsf.with_requests(mem='2G', cpu='2')" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-06-07 14:18:57,008 [info] trying dask client at: tcp://mlrun-dask-init-d0ef8acd-8.default-tenant:8786\n", - "> 2022-06-07 14:19:27,011 [warning] remote scheduler at tcp://mlrun-dask-init-d0ef8acd-8.default-tenant:8786 not ready, will try to restart Timed out trying to connect to tcp://mlrun-dask-init-d0ef8acd-8.default-tenant:8786 after 30 s\n", - "> 2022-06-07 14:19:37,599 [info] using remote dask scheduler (mlrun-dask-init-d1179375-8) at: tcp://mlrun-dask-init-d1179375-8.default-tenant:8786\n" - ] - }, - { - "data": { - "text/html": [ - "dashboard link: default-tenant.app.dev39.lab.iguazeng.com:32166" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "client = dsf.client\n", - "client" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Replace the Dask_Clinet with the client scheduler address (see above)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_URL = \"/User/examples/ytrip.csv\"\n", - "DASK_CLIENT = str(client.scheduler.address)\n", - "# e.g. DASK_CLIENT = 'tcp://mlrun-dask-init-9d8122b2-b.default-tenant:8786'" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tcp://mlrun-dask-init-d1179375-8.default-tenant:8786'" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "DASK_CLIENT" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "fn = mlrun.code_to_function(\"test_dask\", kind='job', handler=\"test_dask\", image='mlrun/ml-base').apply(mlrun.mount_v3io())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When running the function you would see a link as part of the result. click on this link takes you to the dask monitoring dashboard" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-06-07 14:21:19,759 [info] starting run dasking uid=38f5b7d118be4b5988ee8d767322e6bc DB=http://mlrun-api:8080\n", - "> 2022-06-07 14:21:19,935 [info] Job is running in the background, pod: dasking-4tmzj\n", - "> 2022-06-07 14:21:37,411 [info] run executed, status=completed\n", - "final state: completed\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
default0Jun 07 14:21:25completeddasking
v3io_user=avia
kind=job
owner=avia
mlrun/client_version=1.0.2
host=dasking-4tmzj
dataset
dask_client=tcp://mlrun-dask-init-d1179375-8.default-tenant:8786
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2022-06-07 14:21:39,307 [info] run executed, status=completed\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fn.run(name ='dasking',\n", - " handler = 'test_dask',\n", - " inputs={\"dataset\": DATA_URL},\n", - " params={\"dask_client\": DASK_CLIENT}\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Track the progress in the UI" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Users can view the progress and detailed information in the mlrun UI by clicking on the uid above.
\n", - "Also, to track the dask progress in the dask UI click on the \"dashboard link\" above the \"client\" section" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:root] *", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data-ingestion-and-preparation/file-access.ipynb b/data-ingestion-and-preparation/file-access.ipynb deleted file mode 100644 index b011f1bc..00000000 --- a/data-ingestion-and-preparation/file-access.ipynb +++ /dev/null @@ -1,655 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Ingesting and Reading and Writing Data Using Different Interfaces\n", - "\n", - "This tutorial demonstrates how to import a file from an external source into the file system of the Iguazio Data Science Platform (\"the platform\"), convert the data format, and read and write the data using different API interfaces." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- [Initialization](#file-access-init)\n", - "- [Ingesting a CSV File from AWS S3 into the Platform](#file-access-ingest-csv-from-s3)\n", - "- [Reading a CSV File into a pandas DataFrame](#file-access-read-csv-to-pandas-df)\n", - "- [Writing a pandas DataFrame to a NoSQL Table Using Frames](#file-access-frames-write-df-to-nosql-table)\n", - "- [Reading and Writing Data Using Spark DataFrames](#file-access-spark-df-rw)\n", - " - [Converting CSV Data to a NoSQL Table](#file-access-spark-df-convert-csv-to-nosql-table)\n", - " - [Converting NoSQL Data to a CSV File](#file-access-spark-df-convert-nosql-to-csv)\n", - "- [Viewing the Sample Files and Directories Using File-System Commands](#file-access-fs-view-sample-files)\n", - "- [Cleanup](#file-access-cleanup)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import required packages." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import v3io_frames as v3f\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a Frames client object." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Frames client\n", - "client = v3f.Client('framesd:8081', container='users')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initialize variables" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Relative sample-directory path\n", - "sample_dir = os.path.join('examples', 'stocks')\n", - "# Full sample-directory path in file-system format\n", - "sample_dir_path = os.path.join('/User', sample_dir)\n", - "# Full sample-directory path in URL format (used by Spark DataFrames)\n", - "# ($V3IO_HOME_URL <=> v3io://users/)\n", - "sample_dir_url_path = os.path.join(os.getenv('V3IO_HOME_URL'), sample_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the sample directory in your running-user home directory in the \"users\" container." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the sample directory\n", - "!mkdir -p /User/examples/stocks # /User <=> /v3io/${V3IO_HOME} <=> /v3io/users/${V3IO_USERNAME}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ingesting a CSV File from AWS S3 into the Platform\n", - "\n", - "Download a sample CSV file from Amazon Web Services (AWS) Simple Storage Service (S3) bucket into the platform's distributed file system (DFS).\n", - "The file is saved in a directory in a platform data container — the predefined \"users\" container, which is available via the `/User` file-system data mount — and is thus ingested into the platform's data layer." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 861k 100 861k 0 0 273k 0 0:00:03 0:00:03 --:--:-- 273k\n" - ] - } - ], - "source": [ - "%%sh\n", - "# Download a sample CSV file from an AWS S3 bucket to a platform data container\n", - "curl -L \"https://s3.wasabisys.com/iguazio/data/stocks/2018-03-26_BINS_XETR08.csv\" > /User/examples/stocks/stocks_example.csv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading a CSV File into a pandas DataFrame\n", - "\n", - "Read the ingested CSV file into a pandas DataFrame, set the DataFrame's index column (primary-key attribute), and print data from the DataFrame.\n", - "\n", - "> **Note:** It's also possible to download a file directly into a pandas DataFrame by passing the full HTTP file URL as a parameter to the `read` method (`pd.read_csv('http://path/to/file/')`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MnemonicSecurityDescSecurityTypeCurrencySecurityIDDateTimeStartPriceMaxPriceMinPriceEndPriceTradedVolumeNumberOfTrades
ISIN
CH0038389992BBZABB BIOTECH NAM. SF 0,20Common stockEUR25042442018-03-2608:0056.400056.400056.400056.403204
CH0038863350NESRNESTLE NAM. SF-,10Common stockEUR25042452018-03-2608:0063.040063.060063.000063.063143
LU0378438732C001COMSTAGE-DAX UCITS ETF IETFEUR25042712018-03-2608:00113.4200113.4200113.4200113.421001
LU0411075020DBPDXTR.SHORTDAX X2 DA.SW. 1CETFEUR25042722018-03-2608:004.13354.13354.12954.131029938
LU0838782315XDDXXTR.DAX INCOME 1DETFEUR25042772018-03-2608:00105.1400105.2000105.1400105.202393
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" - ], - "text/plain": [ - " Mnemonic SecurityDesc SecurityType Currency \\\n", - "ISIN \n", - "CH0038389992 BBZA BB BIOTECH NAM. SF 0,20 Common stock EUR \n", - "CH0038863350 NESR NESTLE NAM. SF-,10 Common stock EUR \n", - "LU0378438732 C001 COMSTAGE-DAX UCITS ETF I ETF EUR \n", - "LU0411075020 DBPD XTR.SHORTDAX X2 DA.SW. 1C ETF EUR \n", - "LU0838782315 XDDX XTR.DAX INCOME 1D ETF EUR \n", - "\n", - " SecurityID Date Time StartPrice MaxPrice MinPrice \\\n", - "ISIN \n", - "CH0038389992 2504244 2018-03-26 08:00 56.4000 56.4000 56.4000 \n", - "CH0038863350 2504245 2018-03-26 08:00 63.0400 63.0600 63.0000 \n", - "LU0378438732 2504271 2018-03-26 08:00 113.4200 113.4200 113.4200 \n", - "LU0411075020 2504272 2018-03-26 08:00 4.1335 4.1335 4.1295 \n", - "LU0838782315 2504277 2018-03-26 08:00 105.1400 105.2000 105.1400 \n", - "\n", - " EndPrice TradedVolume NumberOfTrades \n", - "ISIN \n", - "CH0038389992 56.40 320 4 \n", - "CH0038863350 63.06 314 3 \n", - "LU0378438732 113.42 100 1 \n", - "LU0411075020 4.13 102993 8 \n", - "LU0838782315 105.20 239 3 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read a CSV file from a platform data container into a pandas DataFrame\n", - "df = pd.read_csv(os.path.join(sample_dir_path, 'stocks_example.csv'))\n", - "\n", - "# Set the DataFrame's index column\n", - "df.set_index('ISIN', inplace=True)\n", - "\n", - "# Display the DataFrame's head\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Writing a pandas DataFrame to a NoSQL Table Using Frames\n", - "\n", - "Use the Frames client's NoSQL backend (`nosql`, a.k.a. `kv`) to write the data from the pandas DataFrame that was created in the previous step (containing the data from the ingested CSV file) into a NoSQL (\"key-value\") table in the platform's data store." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "nosql_table = os.path.join(sample_dir, 'stocks_example_tab')\n", - "client.write('nosql', nosql_table, df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading and Writing Data Using Spark DataFrames\n", - "\n", - "Use Spark DataFrames to read and write data in different formats:\n", - "\n", - "- [Converting CSV Data to a NoSQL Table](#file-access-spark-df-convert-csv-to-nosql-table)\n", - "- [Converting NoSQL Data to a CSV File](#file-access-spark-df-convert-nosql-to-csv)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting CSV Data to a NoSQL Table\n", - "\n", - "Read the CSV file that was written to the platform's data store in the previous step into a Spark DataFrame.\n", - "Then, write the data from the Spark DataFrame that contains CSV data to a new NoSQL table in the platform's data store (in the same sample directory in the \"users\" container) using the platform's custom NoSQL Spark data source — `io.iguaz.v3io.spark.sql.kv`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+------------+--------+--------------------+------------+--------+----------+----------+-----+----------+--------+--------+--------+------------+--------------+\n", - "| ISIN|Mnemonic| SecurityDesc|SecurityType|Currency|SecurityID| Date| Time|StartPrice|MaxPrice|MinPrice|EndPrice|TradedVolume|NumberOfTrades|\n", - "+------------+--------+--------------------+------------+--------+----------+----------+-----+----------+--------+--------+--------+------------+--------------+\n", - "|CH0038389992| BBZA|BB BIOTECH NAM. ...|Common stock| EUR| 2504244|2018-03-26|08:00| 56.4| 56.4| 56.4| 56.4| 320| 4|\n", - "|CH0038863350| NESR|NESTLE NAM. ...|Common stock| EUR| 2504245|2018-03-26|08:00| 63.04| 63.06| 63| 63.06| 314| 3|\n", - "|LU0378438732| C001|COMSTAGE-DAX UCIT...| ETF| EUR| 2504271|2018-03-26|08:00| 113.42| 113.42| 113.42| 113.42| 100| 1|\n", - "|LU0411075020| DBPD|XTR.SHORTDAX X2 D...| ETF| EUR| 2504272|2018-03-26|08:00| 4.1335| 4.1335| 4.1295| 4.13| 102993| 8|\n", - "|LU0838782315| XDDX| XTR.DAX INCOME 1D| ETF| EUR| 2504277|2018-03-26|08:00| 105.14| 105.2| 105.14| 105.2| 239| 3|\n", - "|DE000A0DJ6J9| S92|SMA SOLAR TECHNOL.AG|Common stock| EUR| 2504287|2018-03-26|08:00| 55.65| 55.65| 55.65| 55.65| 543| 3|\n", - "|DE000A0D6554| NDX1| NORDEX SE O.N.|Common stock| EUR| 2504290|2018-03-26|08:00| 8.182| 8.21| 8.174| 8.21| 10941| 8|\n", - "|DE000A0F5UE8| EXXU|IS.DJ CHINA OFFS....| ETF| EUR| 2504302|2018-03-26|08:00| 47.52| 47.52| 47.52| 47.52| 420| 1|\n", - "|DE000A0HN5C6| DWNI|DEUTSCHE WOHNEN S...|Common stock| EUR| 2504314|2018-03-26|08:00| 36.2| 36.24| 36.2| 36.24| 580| 5|\n", - "|DE000A0LD2U1| AOX|ALSTRIA OFFICE RE...|Common stock| EUR| 2504379|2018-03-26|08:00| 12.25| 12.25| 12.25| 12.25| 1728| 3|\n", - "|DE000A0LR936| ST5| STEICO SE|Common stock| EUR| 2504382|2018-03-26|08:00| 22.35| 22.35| 22.35| 22.35| 334| 1|\n", - "|DE000A0MZ4B0| DLX|DELIGNIT AG ...|Common stock| EUR| 2504390|2018-03-26|08:00| 10.3| 10.3| 10.3| 10.3| 850| 1|\n", - "|DE000A0Q8NC8| ETLX|ETFS DAXGL.G.MIN....| ETF| EUR| 2504397|2018-03-26|08:00| 17.844| 17.844| 17.838| 17.838| 3085| 5|\n", - "|DE000A0V9YU8| 4RT3|ETFS COM.SEC.DZ08...| ETC| EUR| 2504421|2018-03-26|08:00| 5.8895| 5.8895| 5.8895| 5.8895| 0| 1|\n", - "|DE000A0WMPJ6| AIXA| AIXTRON SE NA O.N.|Common stock| EUR| 2504428|2018-03-26|08:00| 16.8| 16.8| 16.75| 16.755| 3329| 8|\n", - "|DE000A0Z2XN6| RIB|RIB SOFTWARE SE ...|Common stock| EUR| 2504436|2018-03-26|08:00| 24.66| 24.66| 24.52| 24.52| 11741| 29|\n", - "|DE000A0Z2ZZ5| FNTN| FREENET AG NA O.N.|Common stock| EUR| 2504438|2018-03-26|08:00| 24.41| 24.42| 24.41| 24.42| 695| 6|\n", - "|DE000A1A6V48| KSC| KPS AG NA O.N.|Common stock| EUR| 2504441|2018-03-26|08:00| 9.15| 9.15| 9.15| 9.15| 73| 1|\n", - "|DE000A1DAHH0| BNR| BRENNTAG AG NA O.N.|Common stock| EUR| 2504453|2018-03-26|08:00| 48.14| 48.14| 48.14| 48.14| 185| 2|\n", - "|DE000A1EWWW0| ADS| ADIDAS AG NA O.N.|Common stock| EUR| 2504471|2018-03-26|08:00| 196.3| 196.35| 196.3| 196.35| 591| 12|\n", - "+------------+--------+--------------------+------------+--------+----------+----------+-----+----------+--------+--------+--------+------------+--------------+\n", - "only showing top 20 rows\n", - "\n" - ] - } - ], - "source": [ - "from pyspark.sql import SparkSession\n", - "spark = SparkSession.builder.appName(\"Iguazio Data Science Platform file-access tutorial\").getOrCreate()\n", - "\n", - "# Read the a CSV file from the platform's data store into a Spark DataFrame;\n", - "# let Spark infer the file's data schema\n", - "df = spark.read.option(\"header\", \"true\") \\\n", - " .csv(os.path.join(sample_dir_url_path, 'stocks_example.csv'))\n", - "\n", - "# Show the DataFrame data\n", - "df.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Write the DataFrame data to a NoSQL table in the platform's data store;\n", - "# set the \"ISIN\" column as the table's primary-key attribute\n", - "df.write.format(\"io.iguaz.v3io.spark.sql.kv\") \\\n", - " .mode(\"append\").option(\"key\", \"ISIN\") \\\n", - " .option(\"allow-overwrite-schema\", \"true\") \\\n", - " .save(os.path.join(sample_dir_url_path, 'stocks_tab_spark'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Converting NoSQL Data to a CSV File\n", - "\n", - "Read data from the NoSQL table that was created in the previous step (query the table) into a Spark DataFrame.\n", - "Then, write data from the DataFrame to a new CSV file in the platform's data store.\n", - "> **Note**: The `write` call uses `coalesce(1)` to save the output as a single file." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Read data from a NoSQL table into a Spark DataFrame\n", - "myDF2 = spark.read.format(\"io.iguaz.v3io.spark.sql.kv\") \\\n", - " .load(os.path.join(sample_dir_url_path, 'stocks_tab_spark')) \\\n", - " .where(\"TradedVolume>20000\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Write data from a NoSQL Spark DataFrame to a CSV file\n", - "myDF2.coalesce(1).write.mode('overwrite') \\\n", - " .csv(os.path.join(sample_dir_url_path, 'stocks_high_volume.csv'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Viewing the Sample Files and Directories Using File-System Commands \n", - "\n", - "You can use standard file-system commands, such as `ls`, to browse the contents of the sample directory.\n", - "\n", - "> **Note:** Platform NoSQL tables appear as directories in the platform's local file system." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 862\n", - "-rw-r--r-- 1 52 root 882055 Jan 12 01:05 stocks_example.csv\n", - "drwxr-xr-x 2 52 root 0 Jan 12 01:10 stocks_high_volume.csv\n", - "drwxrwxrwx 2 52 root 0 Jan 12 01:09 stocks_tab_spark\n" - ] - } - ], - "source": [ - "!ls -l /User/examples/stocks/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleanup\n", - "\n", - "Prior to exiting, release disk space, computation, and memory resources consumed by the active session:\n", - "\n", - "- [Delete Data](#file-access-delete-data)\n", - "- [Release Spark Resources](#file-access-release-spark-resources)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete Data\n", - "\n", - "You can optionally delete any of the directories or files that you created.\n", - "See the instructions in the [Creating and Deleting Container Directories](https://www.iguazio.com/docs/v3.0/data-layer/containers/working-with-containers/#create-delete-container-dirs) tutorial.\n", - "For example, the following code uses a local file-system command to delete a **<running user>/examples/stocks** directory in the \"users\" container.\n", - "Edit the path, as needed, then remove the comment mark (`#`) and run the code." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#!rm -rf /User/examples/stocks/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Release Spark Resources\n", - "\n", - "When you're done, run the following command to stop your Spark session and release its computation and memory resources:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "spark.stop()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/data-ingestion-and-preparation/frames.ipynb b/data-ingestion-and-preparation/frames.ipynb deleted file mode 100644 index 47181fd8..00000000 --- a/data-ingestion-and-preparation/frames.ipynb +++ /dev/null @@ -1,1494 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using the V3IO Frames Library for High-Performance Data Access \n", - "\n", - "- [Overview](#frames-overview)\n", - "- [Initialization](#frames-init)\n", - "- [Working with NoSQL Tables (kv Backend)](#frames-kv)\n", - "- [Working with Time-Series Databases (tsdb Backend)](#frames-tsdb)\n", - "- [Cleanup](#frames-cleanup)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Overview\n", - "\n", - "[V3IO Frames](https://github.com/v3io/frames) (**\"Frames\"**) is a multi-model open-source data-access library, developed by Iguazio, which provides a unified high-performance DataFrame API for working with data in the data store of the Iguazio Data Science Platform (**\"the platform\"**).\n", - "Frames currently supports the NoSQL (key-value) and time-series (TSDB) data models via its `nosql`|`kv` and `tsdb` backends.\n", - "> **Note:** You can replace any reference to the `kv` backend in this tutorial with the `nosql` alias.\n", - "\n", - "To use Frames, you first need to import the **v3io_frames** library and create and initialize a client object — an instance of the`Client` class.
\n", - "The `Client` class features the following object methods for supporting basic data operations; the type of data is derived from the backend type (`kv` — NoSQL table / `tsdb` — TSDB table):\n", - "\n", - "- `create` — creates a new TSDB table (\"backend data\").\n", - "- `delete` — deletes a table.\n", - "- `read` — reads data from a table into pandas DataFrames.\n", - "- `write` — writes data from pandas DataFrames to a table.\n", - "- `execute` — executes a command on a table.\n", - " Each backend may support multiple commands.\n", - "\n", - "For a detailed description of the Frames API, see the [Frames API reference](https://www.iguazio.com/docs/v3.0/data-layer/reference/frames/).
\n", - "For more help and usage details, use the internal API help — `.?` in Jupyter Notebook or `print(..__doc__)`.
\n", - "For example, the following command returns information about the read operation for a client object named `client`:\n", - "```\n", - "client.read?\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Initialization\n", - "\n", - "To use V3IO Frames, first ensure that your platform tenant has a shared tenant-wide instance of the V3IO Frames service.\n", - "This can be done by a platform service administrator from the **Services** dashboard page.
\n", - "Then, import the required libraries and create a Frames client object (an instance of the `Client` class), as demonstrated in the following code, which creates a client object named `client`.\n", - "\n", - "> **Note:**\n", - "> - The client constructor's `container` parameter is set to `\"users\"` for accessing data in the platform's \"users\" data container.\n", - "> - Because no authentication credentials are passed to the constructor, Frames will use the access key that's assigned to the `V3IO_ACCESS_KEY` environment variable.\n", - "> The platform's Jupyter Notebook service defines this variable automatically and initializes it to a valid access key for the running user of the service.\n", - "> You can pass different credentials by using the constructor's `token` parameter (platform access key) or `user` and `password` parameters (platform username and password)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import v3io_frames as v3f\n", - "import os\n", - "\n", - "# Create a Frames client\n", - "client = v3f.Client(\"framesd:8081\", container=\"users\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Working with NoSQL Tables (kv Backend)\n", - "\n", - "This section demonstrates how to use the `kv` Frames backend to write and read NoSQL data in the platform.\n", - "\n", - "- [Initialization](#frames-kv-init)\n", - "- [Write to a NoSQL Table](#frames-kv-write)\n", - "- [Read from the Table Using an SQL Query](#frames-kv-read-sql-query)\n", - "- [Read from the Table Using the Frames API](#frames-kv-read-frames-api)\n", - " - [Read Using a Single DataFrame](#frames-kv-read-frames-api-single-df)\n", - " - [Read Using a DataFrames Iterator (Streaming)](#frames-kv-read-frames-api-df-iterator)\n", - "- [Delete the NoSQL Table](#frames-kv-delete)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Initialization\n", - "\n", - "Start out by defining table-path variables that will be used in the tutorial's code examples.
\n", - "The table path (`table`) is relative to the configured parent data container; see [Write to a NoSQL Table](#frames-kv-write)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Relative path to the NoSQL table within the parent platform data container\n", - "table = os.path.join(os.getenv(\"V3IO_USERNAME\"), \"examples\", \"bank\")\n", - "\n", - "# Full path to the NoSQL table for SQL queries (platform Presto data-path syntax);\n", - "# use the same data container as used for the Frames client (\"users\")\n", - "sql_table_path = 'v3io.users.\"' + table + '\"'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Write to a NoSQL Table\n", - "\n", - "Read a file from a Simple Storage (S3) bucket into a Frames pandas DataFrame, and use the `write` method of the Frames client with the `kv` backend to write the data to a NoSQL table.
\n", - "The mandatory `table` parameter specifies the relative table path within the data container that was configured for the Frames client (see the [main initialization](#frames-init) step).\n", - "In the following example, the relative table path is set by using the `table` variable that was defined in the [kv backend initialization](#frames-kv-init) step.
\n", - "The `dfs` parameter can be set either to a single DataFrame (as done in the following example) or to multiple DataFrames — either as a DataFrames iterator or as a list of DataFrames." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare the ingestion data by reading an AWS S3 file into a DataFrame\n", - "df = pd.read_csv(\"https://s3.wasabisys.com/iguazio/data/bank/bank.csv\", sep=\";\")\n", - "# Display DataFrame info & head (optional - for testing)\n", - "# display(df.info(), df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Write data from a DataFrame to a NoSQL table\n", - "client.write(\"kv\", table=table, dfs=df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Read from the Table Using an SQL Query\n", - "\n", - "You can run SQL queries on your NoSQL table (using Presto) to offload data filtering, grouping, joins, etc. to a scale-out high-speed database engine.\n", - "\n", - "> **Note:** To query a table in a platform data container, the table path in the `from` section of the SQL query should be of the format `v3io..\"/path/to/table\"`.\n", - "> See [Presto Data Paths](https://www.iguazio.com/docs/v3.0/data-layer/apis/data-paths/#data-paths-presto) in the platform documentation.\n", - "> In the following example, the path is set by using the `sql_table_path` variable that was defined in the [kv backend initialization](#frames-kv-init) step.\n", - "> Unless you changed the code, this variable translates to `v3io.users.\"/examples/bank\"`; for example, `v3io.users.\"iguazio/examples/bank\"` for user \"iguazio\"." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "The `read` method can return a DataFrame or a DataFrames iterator (a stream), as demonstrated in the following examples.\n", - "\n", - "- [Read Using a Single DataFrame](#frames-kv-read-frames-api-single-df)\n", - "- [Read Using a DataFrames Iterator (Streaming)](#frames-kv-read-frames-api-df-iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### Read Using a Single DataFrame\n", - "\n", - "The following example uses a single command to read data from the NoSQL table into a DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " age balance campaign contact day default duration education \\\n", - "idx \n", - "3700 60 71188 1 cellular 6 no 205 primary \n", - "3332 31 22546 6 cellular 14 no 8 tertiary \n", - "2624 53 22370 1 unknown 15 no 106 tertiary \n", - "2776 37 22856 1 cellular 2 no 154 primary \n", - "1483 43 27733 7 unknown 3 no 164 tertiary \n", - "650 33 23663 2 cellular 16 no 199 tertiary \n", - "4334 37 20453 1 telephone 4 no 115 secondary \n", - "1031 49 25824 1 unknown 17 no 94 primary \n", - "\n", - " housing job loan marital month pdays poutcome previous y \n", - "idx \n", - "3700 no retired no married oct -1 unknown 0 no \n", - "3332 yes management no married may 267 failure 4 no \n", - "2624 yes entrepreneur no married may -1 unknown 0 no \n", - "2776 no management no married jul 388 failure 1 no \n", - "1483 yes technician no single jun -1 unknown 0 no \n", - "650 yes housemaid no single apr 146 failure 2 no \n", - "4334 yes entrepreneur no single may -1 unknown 0 no \n", - "1031 no retired no single jun -1 unknown 0 no " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = client.read(backend=\"kv\", table=table, filter=\"balance > 20000\")\n", - "df.head(8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "#### Read Using a DataFrames Iterator (Streaming)\n", - "\n", - "The following example uses a DataFrames iterator to stream data from the NoSQL table into multiple DataFrames and allow concurrent data movement and processing.
\n", - "The example sets the `iterator` parameter to `True` to receive a DataFrames iterator (instead of the default single DataFrame), and then iterates the DataFrames in the returned iterator; you can also use `concat` instead of iterating the DataFrames.\n", - "\n", - "> **Note:** Iterators work with all Frames backends and can be used as input to write functions that support this, such as the `write` method of the Frames client." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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agebalancecampaigncontactdaydefaultdurationeducationhousingjobloanmaritalmonthpdayspoutcomepreviousy
idx
370060711881cellular6no205primarynoretirednomarriedoct-1unknown0no
333231225466cellular14no8tertiaryyesmanagementnomarriedmay267failure4no
148343277337unknown3no164tertiaryyestechniciannosinglejun-1unknown0no
65033236632cellular16no199tertiaryyeshousemaidnosingleapr146failure2no
433437204531telephone4no115secondaryyesentrepreneurnosinglemay-1unknown0no
\n", - "
" - ], - "text/plain": [ - " age balance campaign contact day default duration education \\\n", - "idx \n", - "3700 60 71188 1 cellular 6 no 205 primary \n", - "3332 31 22546 6 cellular 14 no 8 tertiary \n", - "1483 43 27733 7 unknown 3 no 164 tertiary \n", - "650 33 23663 2 cellular 16 no 199 tertiary \n", - "4334 37 20453 1 telephone 4 no 115 secondary \n", - "\n", - " housing job loan marital month pdays poutcome previous y \n", - "idx \n", - "3700 no retired no married oct -1 unknown 0 no \n", - "3332 yes management no married may 267 failure 4 no \n", - "1483 yes technician no single jun -1 unknown 0 no \n", - "650 yes housemaid no single apr 146 failure 2 no \n", - "4334 yes entrepreneur no single may -1 unknown 0 no " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dfs = client.read(backend=\"kv\", table=table, filter=\"balance > 20000\",\n", - " iterator=True)\n", - "for df in dfs:\n", - " display(df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Delete the NoSQL Table\n", - "\n", - "Use the `delete` method of the Frames client with the `kv` backend to delete the NoSQL table that was used in the previous steps." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Delete the `table` NoSQL table\n", - "client.delete(\"kv\", table)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Working with Time-Series Databases (tsdb Backend)\n", - "\n", - "This section demonstrates how to use the `tsdb` Frames backend to create a time-series database (TSDB) table in the platform, ingest data into the table, and read from the table (i.e., submit TSDB queries).\n", - "\n", - "- [Initialization](#frames-tsdb-init)\n", - "- [Create a TSDB Table](#frames-tsdb-create)\n", - "- [Write to the TSDB Table](#frames-tsdb-write)\n", - "- [Read from the TSDB Table](#frames-tsdb-read)\n", - " - [Conditional Read](#frames-tsdb-read-conditional)\n", - "- [Delete the TSDB Table](#frames-tsdb-delete)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Initialization\n", - "\n", - "Start out by defining a TSDB table-path variable that will be used in the tutorial's code examples.
\n", - "The table path (`tsdb_table`) is relative to the configured parent data container; see [Create a TSDB Table](#frames-tsdb-create)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Relative path to the TSDB table within the parent platform data container\n", - "tsdb_table = os.path.join(os.getenv(\"V3IO_USERNAME\"), \"examples\", \"tsdb_tab\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Create a TSDB Table\n", - "\n", - "Use the `create` method of the Frames client with the `tsdb` backend to create a new TSDB table.
\n", - "The mandatory `table` parameter specifies the relative table path within the data container that was configured for the Frames client (see the [main initialization](#frames-init) step).\n", - "In the following example, the relative table path is set by using the `tsdb_table` variable that was defined in the [tsdb backend initialization](#frames-tsdb-init) step.
\n", - "You must set the `rate` argument to the ingestion rate of the TSDB metric-samples, as `\"[0-9]+/[smh]\"` (where '`s`' = seconds, '`m`' = minutes, and '`h`' = hours); for example, `1/s` (one sample per minute).\n", - "It's recommended that you set the rate to the average expected ingestion rate, and that the ingestion rates for a given TSDB table don't vary significantly; when there's a big difference in the ingestion rates (for example, x10), use separate TSDB tables.\n", - "You can also set additional optional arguments, such as `aggregates` or `aggregation_granularity`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a new TSDB table; ingestion rate = one sample per hour (\"1/h\")\n", - "client.create(backend=\"tsdb\", table=tsdb_table, rate=\"1/h\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Write to the TSDB Table\n", - "\n", - "Use the `write` method of the Frames client with the `tsdb` backend to ingest data from a pandas DataFrame into your TSDB table.
\n", - "The primary-key attribute of platform TSDB tables (i.e., the DataFrame index column) must hold the sample time of the data (displayed as `time` in read outputs).
\n", - "In addition, TSDB table items (rows) can optionally have sub-index columns (attributes) that are called labels.\n", - "You can add labels to TSDB table items in one of two ways; you can also combine these methods:\n", - "\n", - "- Use the `labels` dictionary parameter of the `write` method to add labels to all the written metric-sample table items (DataFrame rows) — `{

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