Spark summit2014 techtalk - testing spark
- 1. Testing Spark: Best Practices Anupama Shetty Senior SDET, Analytics, Ooyala Inc Neil Marshall SDET, Analytics, Ooyala Inc Spark Summit 2014
- 2. Agenda - Anu 1. Application Overview ● Batch mode ● Streaming mode with kafka 2. Test Overview ● Test environment setup ● Unit testing spark applications ● Integration testing spark applications 3. Best Practices ● Code coverage support with scoverage and scct ● Auto build trigger using jenkins hook via github
- 3. Agenda - Neil 4. Performance testing of Spark ● Architecture & technology overview ● Performance testing setup & run ● Result analysis ● Best practices
- 4. Company Overview ● Founded in 2007 ● 300+ employees worldwide ● Global footprint of 200M unique users in 130 countries ● Ooyala works with the most successful broadcasts and media companies in the world ● Reach, measure, monetize video business ● Cross-device video analytics and monetization products and services
- 5. Application Overview ● Analytics ETL pipeline service ● Receives 5B+ player generated events such as plays, displays on a daily basis. ● Computed metrics include player conversion rate, video conversion rate and engagement metrics. ● Third party services used are ○ Spark 1.0 used to process player generated big data. ○ Kafka 0.9.0 with Zookeeper as our message queue ○ CDH5 HDFS as our intermediate storage file system
- 6. Spark based Log Processor details ● Supports two input data formats ○ Json ○ Thrift ● Batch Mode Support ○ Uses Spark Context ○ Consumes input data via a text file ● Streaming Mode Support ○ Uses Spark streaming context ○ Consumes data via kafka stream
- 7. Test pipeline setup ● Player simulation done using Watir (ruby gem based on Selenium). ● Kafka(with zookeeper) setup as local virtual machine using vagrant. VMs can be monitored using VirtualBox. ● Spark cluster run in local mode.
- 8. Unit test setup - Spark in Batch mode ● Spark cluster setup for testing ○ Build your spark application jar using `sbt “assembly”` ○ Create config with spark.jar set to application jar and spark.master to “local” ■ var config = ConfigFactory parseString """spark.jar = "target/scala-2.10 /SparkLogProcessor.jar",spark.master = "local" """ ○ Store local spark directory path for spark context creation ■ val sparkDir = <path to local spark directory> + “spark-0.9.0- incubating-bin-hadoop2/assembly/target/scala-2.10/spark-assembly_2. 10-0.9.0-incubating-hadoop2.2.0.jar").mkString ● Creating spark context ○ var sc: SparkContext = new SparkContext("local", getClass.getSimpleName, sparkDir, List(config.getString("spark.jar")))
- 9. Test Setup for batch mode using Spark Context Before block After block Scala test framework “FunSpec” is used with “ShouldMatchers” (for assertions) and “BeforeAndAfter” (for setup/teardown).
- 10. Kafka setup for spark streaming ● Bring up Kafka virtual machine using Vagrantfile with following command `vagrant up kafkavm` ● Configure Kafka ○ Create topic ■ `bin/kafka-create-topic.sh --zookeeper "localhost:2181" --topic "thrift_pings"` ○ Consume messages using ■ `bin/kafka-console-consumer.sh --zookeeper "localhost:2181" --topic "thrift_pings" --group "testThrift" &>/tmp/thrift-consumer-msgs.log &`
- 11. Testing streaming mode with Spark Streaming Context
- 12. Test ‘After’ block and assertion block for spark streaming mode After Block Test Assertion
- 13. Testing best practices - Code Coverage ● Tracking code coverage with Scoverage and/or Scct ● Enable fork = true to avoid spark exceptions caused by spark context conflicts. ● SCCT configurations ○ ScctPlugin.instrumentSettings ○ parallelExecution in ScctTest := false ○ fork in ScctTest := true ○ Command to run it - `sbt “scct:test”` ● Scoverage configurations ○ ScoverageSbtPlugin.instrumentSettings ○ ScoverageSbtPlugin.ScoverageKeys.excludedPackages in ScoverageSbtPlugin.scoverage := ".*benchmark.*;.*util.*” ○ parallelExecution in ScoverageSbtPlugin.scoverageTest := false ○ fork in ScoverageSbtPlugin.scoverageTest := true ○ Command to run it - `sbt “scoverage:test”`
- 14. Testing best practices - Jenkins auto test build trigger ● Requires enabling 'github-webhook' on github repo settings page. Requires admin access for the repo. ● Jenkins job should be configured with corresponding github repo via “GitHub Project” field. ● Test jenkins hook by triggering a test run from github repo. ● "Github pull request builder" can be used while configuring jenkins job to auto publish test results on github pull requests after every test run. This also lets you rerun failed tests via github pull request.
- 15. What is a performance testing? ● A practice striving to build performance into the implementation, design and architecture of a system. ● Determine how a system performs in terms of responsiveness and stability under a particular workload. ● Can serve to investigate, measure, validate or verify other quality attributes of a system, such as scalability, reliability and resource usage.
- 16. What is a Gatling? ● Stress test tool
- 17. Why is Gatling selected over other Perf Test tools as JMeter? ● Powerful scripting using Scala ● Akka + Netty ● Run multiple scenarios in one simulation ● Scenarios = code + DSL ● Graphical reports with clear & concise graphs
- 18. How does Gatling work with Spark ● Access Web applications / services
- 19. Develop & setup a simple perf test example A perf test will run against spark-jobserver for word counts.
- 20. What is a spark jobserver? ● Provides a RESTful interface for submitting and managing Apache Spark jobs, jars and job contexts ● Scala 2.10 + CDH5/Hadoop 2.2 + Spark 0.9.0 ● For more depths on jobserver, see Evan Chan & Kelvin Chu’s Spark Query Service presentation.
- 21. Steps to set up & run Spark-jobserver ● Clone spark-jobserver from git-hub ● Install SBT and type “sbt” in the spark- jobserver repo ● From SBT shell, simply type “re-start” $ git clone https://github.com/ooyala/spark-jobserver > re-start $ sbt
- 22. Steps to package & upload a jar to the jobserver ● Package the test jar of the word count example ● Upload the jar to the jobserver $ curl --data-binary @job-server-tests/target/job-server-tests-0.3.1.jar localhost:8090/jars/test $ sbt job-server-tests/package
- 23. Run a request against the jobserver $ curl -d "input.string = a b c a b see" 'http://localhost:8090/jobs? appName=test&classPath=spark.jobserver.WordCountExample&sync=true' { "status": "OK", "result": { "a": 2, "b": 2, "c": 1, "see": 1 } }⏎
- 24. Source code of Word Count Example
- 25. Script Gatling for the Word Count Example Scenario defines steps that Gatling does during a runtime:
- 26. Script Gatling for the Word Count Example Setup puts users and scenarios as workflows plus assertions together in a performance test simulation ● Inject 10 users in 10 seconds into scenarios in 2 cycles ● Ensure successful requests greater than 80%
- 27. Test Results in Terminal Window
- 28. Gatling Graph - Indicator
- 29. Gatling Graph - Active Sessions
- 30. Best Practices on Performance Tests ● Run performance tests on Jenkins ● Set up baselines for any of performance tests with different scenarios & users
- 31. Any Questions?
- 32. References Contact Info: Anupama Shetty: anupama@ooyala.com Neil Marshall: nmarshall@ooyala.com References: http://www.slideshare.net/AnuShetty/spark-summit2014-techtalk-testing-spark