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.

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"
}
}

23. 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"
}
}

24. 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

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?

26. Amaterasu + demos
https://github.com/shintoio/
Slack
http://shintoio.slack.com
Getting started

27. Thank you!