DataOps with Project Amaterasu
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This document discusses Project Amaterasu, a tool for simplifying the deployment of big data applications. Amaterasu uses Mesos to deploy Spark jobs and other frameworks across clusters. It defines workflows, actions, and environments in YAML and JSON files. Workflows contain a series of actions like Spark jobs. Actions are written in Scala and interface with Amaterasu's context. Environments configure settings for different clusters. Amaterasu aims to improve collaboration and testing for big data teams through continuous integration and deployment of data pipelines.
![import io.shinto.amaterasu.runtime._
val oddRdd = AmaContext.getRDD[Int]("start", "rdd")
.filter(x=>x%2 == 0)
oddRdd.take(5).foreach(println)
val highNoDf = AmaContext.getDataFrame("start", “odd")
.where("_1 > 3")
highNoDf.write.json("file:///tmp/test1")
Actions DSL (in action)
import io.shinto.amaterasu.runtime._
val data = Array(1, 2, 3, 4, 5)
val x = data.tail
val rdd = AmaContext.sc.parallelize(data)
val odd = rdd.filter(n => n%2 != 0)
Action 1 (“start”) Action 2](https://image.slidesharecdn.com/m15wed1500karelsagueyanivrodenskifinal-160906142038/85/DataOps-with-Project-Amaterasu-20-320.jpg)
![dev.json
{
"name":"test",
"sparkMasterUrl":"local[*]",
"inputRootPath":"file:///tmp/input",
"outputRootPath":"file:///tmp/output",
"workingDir":"file:///tmp/work",
"configuration":{
"spark.cassandra.connection.host":"127.0.0.1",
"sourceTable":"documents"
}
}](https://image.slidesharecdn.com/m15wed1500karelsagueyanivrodenskifinal-160906142038/85/DataOps-with-Project-Amaterasu-23-320.jpg)
![import io.shinto.amaterasu.runtime._
val oddRdd = AmaContext.getRDD[Int]("start", "rdd").filter(x=>x/2 == 0)
oddRdd.take(5).foreach(println)
val highNoDf = AmaContext.getDataFrame("start", “x").where("_1 > 3")
highNoDf.write.json(Env.outputPath)
Environments in the Actions DSL](https://image.slidesharecdn.com/m15wed1500karelsagueyanivrodenskifinal-160906142038/85/DataOps-with-Project-Amaterasu-24-320.jpg)
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DataOps with Project Amaterasu
- 1. DataOps with Project Amaterasu Yaniv Rodenski Karel Alfonso
- 2. What Data Pipelines are Made Off • Big Data applications: • Ingestion • Storage • Processing • Serving • Workflows • Machine learning • Data Sources and Destinations • Tests? • Schemas??
- 3. Archetypes of Data Pipelines Builders • Exploratory workloads • Data centric • Simple Deployment Data People (Data Scientist/ Analysts/BI Devs) Software Developers • Code centric • Heavy on methodologies • Heavy tooling • Very complex deployment
- 4. Making Big Data Teams Scale • Scaling teams is hard • Scaling Big Data teams is harder • Different mentality between data professionals/ engineers • Mixture of technologies • Data as integration point • Often schema-less • Lack of tools
- 5. Continuous Delivery • Keep software in a production ready state • Test all the changes: unit, integration • Exercise deployments • Faster feedback cycle
- 6. No silos Autonomous teams Feedback Automation Build quality in Shared responsibility DevOps & Collaboration
- 7. The case for CI/CD/DevOps in Big Data Projects • Coordination: data engineers, analysts, business, ops • Integrate and test critical jobs • Complex infrastructure: multiple distributed systems • Need to decouple cluster operation via APIs/DSLs • DevOps team to manage cluster operations: scaling, monitoring, deployment. • Include CI/CD practices are part of the delivery process.
- 9. How are these techniques applicable to Big Data applications?
- 10. What Do We Need for Deploying our apps? • Source control system: Git, Hg, etc • CI process to run tests and package app • A repository to store packaged app • A repository to store configuration • An API/DSL to deploy to the cluster • Mechanism to monitor the behaviour and performance of the app
- 11. Who are we? Software developers with years of Big Data experience What do we want? Simple and robust way to deploy Big Data applications How will we get it? Write thousands of lines of code on top of Mesos
- 12. Amaterasu - Simple Continually Deployed Data Apps • Amaterasu is the Shinto goddess of sun • In the Japanese manga series Naruto Amaterasu is a super-natural power in the shape of a black flame that can only be taken out by its Sender • Started as a framework to reliably execute Spark driver programs
- 13. Amaterasu - Simple Continually Deployed Data Apps • Big Data apps in Multiple Frameworks (Currently Only Spark is Supported) • Multiple Languages (soon) • Workflow as YAML • Simple to Write, easy to deploy • Reliable execution (via Mesos) • Multiple Environments
- 14. Big Data Pipeline Ops Requirements • Support managing multiple distributed technologies: Apache Spark, HDFS, Kafka, Cassandra, etc. • Treat data center as the OS while providing resource isolation, scalability and fault tolerance. • Ability to run multiple tasks per machine to maximize utilization
- 15. Why Mesos? • General purpose, battle tested cluster resource scheduler. • Can run major modern Big Data systems: Hadoop, Spark, Kafka, Cassandra • Can deploys spark as part of the execution • Supports scheduled and long running apps. • Improves resource management and efficiency • Great APIs • DC/OS provides an even reacher environment
- 16. Amaterasu Repositories • Jobs are defined in repositories • Current implementation - git repositories • Local directories support is planned for future release • Repos structure • maki.yml - The workflow definition • src - a folder containing the actions (spark scripts, etc.) to be executed • env - a folder containing configuration per environment • Benefits of using git: • Branching • Tooling
- 17. Workflow DSL - maki.yml --- job-name: amaterasu-test flow: - name: start type: spark-scala file: file.scala - name: step2 type: spark-scala file: file2.scala error: file2.scala name: handle-error type: spark-scala file: cleanup.scala ... Actions Error handling actions
- 18. Amaterasu is not a workflow engine, it’s a deployment tool that understands that Big Data applications are rarely deployed independently of other Big Data applications
- 19. Actions DSL • Your Scala/Future languages Spark code • Few changes: • Don’t create a new sc/sqlContext, use the one in scope or access via AmaContext.sc and AmaContext.sqlContext • AmaContext.getDataFrame and AmaContext.getRDD are used to access data from previously executed actions
- 20. import io.shinto.amaterasu.runtime._ val oddRdd = AmaContext.getRDD[Int]("start", "rdd") .filter(x=>x%2 == 0) oddRdd.take(5).foreach(println) val highNoDf = AmaContext.getDataFrame("start", “odd") .where("_1 > 3") highNoDf.write.json("file:///tmp/test1") Actions DSL (in action) import io.shinto.amaterasu.runtime._ val data = Array(1, 2, 3, 4, 5) val x = data.tail val rdd = AmaContext.sc.parallelize(data) val odd = rdd.filter(n => n%2 != 0) Action 1 (“start”) Action 2
- 21. Environments • Configuration is stored per environment • Stored as JSON • Contains: • Spark master URI • Input/output path • Work dir • User defined key-values
- 22. production.json { "name":"production", "sparkMasterUrl":"mesos://server1:5050", "inputPath":"hdfs://hdfsprd:9000/user/amaterasu/input", "outputPath":"hdfs://hdfsprd:9000/user/amaterasu/output", "workingDir":"alluxio://server3:19998/", "configuration":{ "spark.cassandra.connection.host":"cassie-prod", "sourceTable":"documents" } }
- 25. Future Development • Continuous integration and test automation • R, shell and Python support (R is already in progress) • Extend environments to support: • Full spark configuration (spark-defaults.conf, etc.) • Extendable configuration model • Better tooling • DC/OS universe package • Other frameworks: Flink, vowpal wabbit • YARN?
- 27. Thank you!