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E2E Data Pipeline - Apache Spark/Airflow/Livy
- 1. Confidential End to End Pipelines Using Apache Spark/Livy/Airflow An integrated solution to batch data processing Rikin Tanna and Karunasri Maram Capital One Auto Finance February 12, 2020
- 2. 2 Agenda 1. Problem Statement 2. Integrated Solution, Brief Overview 3. Explanation of Components • Apache Spark • Apache Livy • Apache Airflow 4. Integrated Solution, Fully Explained 5. Demo
- 3. 3 Problem StatementNeed: Batch data processing on schedule
- 4. 4 Solution Requirements ? Scalable Handle jobs with growing data sets End to End Data Pipeline Parallel Execution Ability to run multiple jobs in parallel Open-Source Support Active contributions to components used to stay efficient Dynamic Generation of pipeline on demand to support varying characteristics Dependency Enabled Support ordering of tasks based on dependency
- 5. 5 A fully integrated big data pipeline…. with just 3 components! • Apache Spark • Unified data analytics engine for large-scale data processing • Served on EMR cluster • Apache Livy • REST Interface to enable easy interaction with Apache Spark • Served on master node of EMR cluster • Apache Airflow • WMS to schedule, trigger, and monitor workflows on a single compute resource • Served on single compute instance, with metadata DB on separate RDS instance Solution: Brief 5
- 6. Confidential
- 7. 7 What is Airflow? An open source platform to programatically author, schedule, and monitor workflows Dynamic: Airflow pipelines are configured as code, allowing for dynamic pipeline generation as DAGs Extensible: Easily extend the library and usability by creating your own operators and executors General-Purpose: Airflow is written in Python, and all pipelines are configured in Python Accessible: Rich UI allows for non-technical users to monitor workflows
- 8. 8 Airflow Luigi Oozie Azkaban Dynamic Pipelines Rich, Interactable UI General Purpose Usability Scalability Dependency Management Maturity/Support Why Airflow? Comparison of common open source workflow management systems Use this box for citations, sources, statements, notes, and legal disclaimers that are required. Use this box for citations, sources, statements, notes, and legal disclaimers that are required.
- 9. 9 Apache Airflow Architecture Source: https://medium.com/@dustinstansbury/understanding-apache-airflows-key-concepts-a96efed52b1a ● Metadata Database ○ stores information necessary for scheduler/executor and webserver ○ task state, DAG definitions, log locations, etc ● Scheduler/Executor ○ process that uses DAG definitions and task states to push tasks onto queue for execution ● Workers ○ process(es) that execute the logic of the tasks ● Webserver ○ process that renders web UI, interacting with metadata database to allow user monitoring and interaction with workflows
- 10. 10 How to Get Started with Apache Airflow 1. Install Python 2. “pip install apache-airflow” a. install from pypi using pip b. AIRFLOW_HOME = ~/airflow (default) 3. “airflow initdb” a. initialize database 4. “airflow webserver -p 8080” a. start web server, default port is 8080 5. “airflow scheduler” a. start scheduler (also starts executor processes) 6. visit localhost:8080 in browser and enable example dags Deeper Understanding 1. Connect to database (using Datagrip or DBeaver) and view tables. See how the data is altered as workflows execute and changes are made 2. Dig into source code (https://github.com/apache/airflow) and view actions triggered by scheduler CLI command
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- 12. 12 Spark Flink Storm Streaming Batch/Interactive/Iterative Processing General Purpose Usability Scalability Product Maturity Community Support Why Spark? Comparison of common open source big data processing systems. Use this box for citations, sources, statements, notes, and legal disclaimers that are required. Use this box for citations, sources, statements, notes, and legal disclaimers that are required.
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- 16. Confidential What is that? Why do we need?
- 17. 17 Livy spark-jobserver Mist Apache Toree Streaming jobs Batch Jobs General Purpose Usability High Availability Supports major languages (Scala/Java/Python) Dependency (No Code changes required) Why Livy? Comparison of common open source Spark Interfaces. Use this box for citations, sources, statements, notes, and legal disclaimers that are required. Use this box for citations, sources, statements, notes, and legal disclaimers that are required.
- 18. 18 ○ Apache Livy is a service that enables easy interaction with a Spark cluster over a REST interface. ○ It enables easy submission of Spark jobs or snippets of Spark code, synchronous or asynchronous result retrieval, as well as Spark Context management, all via a simple REST interface or an RPC client library. A Rest Service for Spark Jobs 18Confidential
- 20. 20 • Workflow Management • Schedules, monitors, and triggers workflows • Characteristics • Dynamic • Dependency Enabled • Open-Source Apache Airflow • REST Interface to Interact with Apache Spark Apache Livy • Big-Data Processing • platform to execute large scale data processing • Characteristics • Parallel jobs • Scalable • Open-Source Apache Spark Summary of Components
- 22. 22 Failure Resiliency ● Current weakness ○ Current solution lacks resiliency in Airflow (single EC2 instance) ○ solution: ■ containerize Airflow, deploy on pod with separate worker pod, distribute tasks using external queue ● Livy ○ It supports session recovery using Zookeeper and reconnects to the existing session even if its fails while executing the job. ● Spark ○ Failed tasks can be re-launched in parallel on all the other nodes in the cluster and distribute the recomputations across many nodes,and recovering from the failures very fast.