Headaches and Breakthroughs in Building Continuous Applications

At SpotX, we have built and maintained a portfolio of Spark Streaming applications — all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We’ll detail what we’ve learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.
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1.WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2.Spark Streaming Headaches and Breakthroughs in Building Continuous Applications Landon Robinson & Jack Chapa #UnifiedAnalytics #SparkAISummit

3.Who We Are Landon Robinson Jack Chapa Data Engineer Data Engineer Big Data Team @ SpotX #UnifiedAnalytics #SparkAISummit 3

4.But first… why are we here? Because Spark Streaming... • is very powerful • can supercharge your infrastructure • … and can be very complex! Lots of headaches and breakthroughs! #UnifiedAnalytics #SparkAISummit 4

5.Takeaways Leave with a few actionable items that we wish we knew when we started with Spark Streaming. Focus Areas ● Streaming Basics ● Helpful Configurations ● Testing ● Backpressure ● Monitoring & Alerts ● Data Enrichment & ● Batch Intervals & Resources Transformations

6.Our Company The Trusted Platform For Premium Publishers and Broadcasters #UnifiedAnalytics #SparkAISummit 6

7.We Process a Lot of Data Data: - 220 MM+ Total Files/Blocks - 8 PB+ HDFS Space - 20 TB+ new data daily - 100MM+ records/minute - 300+ Data Nodes Apps: - Thousands of daily Spark apps - Hundreds of daily user queries - Multiple 24/7 Streaming apps

8. Spark Streaming is Key for Us Our uses include: - Rapid ingestion of data into warehouse for querying - Machine learning on near-live data streams - Ability to react to and impact live situations - Accelerated processing / updating of metadata - Real-time visualization of data streams and processing

9.Spark Streaming Basics a brief overview

10. Spark Streaming Basics Spark Streaming is an extension of Spark that enables scalable, high-throughput, fault-tolerant processing of live data streams. • Stream == live data stream – Topic == Kafka’s name for a stream • DStream == sequence of RDDs formed from reading a data stream • Batch == a self-contained job within your Streaming app that processes a segment of the stream.

11. Testing Rapid development and testing of Spark apps

12.Use Spark in Local Mode You can start building Spark Streaming apps in minutes, using Spark locally! On your local machine • No cluster needed! • Great for rough testing We Recommend: IntelliJ Community Edition • with SBT: For dependency management

13.Use Spark in Local Mode The Scala Build Tool is your friend! Simply: In your build.sbt: • Import Spark libraries • src/test/scala => “provided” • Invoke a Context and/or Session • src/main/scala => “compiled” • Set master to local[*] or local[n]

14. Example Unit Test using just a SparkContext Invoke a local session: • In your unit test classes Add to your deployment pipeline • Test logic on small datasets for a nice pre-release gut check!

15. Unit Testing Spark Streaming Apps can easily be unit tested - Using .queueStream() - Using a spark testing library Libraries Use Cases - spark-testing-base - DStream actions - sscheck - Business Logic - spark-tests - Integration

16. Example Library: spark-testing-base - Easy to Use - Helpful wrappers - Integrates w/ scalatest - Minimal code required - Clock management - Runs alongside other tests GitHub: https://github.com/holdenk/spark-testing-base

17.Monitoring Tracking and visualizing performance of your app

18. Monitoring is Awesome It can reveal: • How your app is performing • Problems + Bugs! And provide opportunities to: • See and address issues • Observe behavior visually But monitoring can be tough to implement!

19.Monitoring (a less than ideal approach) You could do it all in the app... Example: Looping over RDDs to: • Count records • Track Kafka offsets • Processing time / delays But it’s less than ideal... • Calculating performance significantly impacts performance… not great. • All these metrics are calculated by Spark!

20.Monitoring and Visualization (using Listeners) Use Spark Listeners to access metrics in the background! Let Spark do the hard work: • Batch duration, delays • Record throughput • Stream position recovery Come to our talk: Spark Listeners: A Crash Course in Fast, Easy Monitoring! • Room 3016 | Today @ 5:30 PM

21.Kafka Offset Recovery Saving your place elsewhere

22. Writing Offsets to MySQL Inside the Spark Listener class, after a batch completes... You can access an object generated by Spark containing your offsets processed. Take those offsets and back them up to a DB...

23.Reading Offsets from MySQL Your offsets are now stored in a DB after each batch completes. Whenever your app restarts, it reads those offsets from the DB... And starts processing where it last left off!

24.Getting Offsets from the Database

25.Example: Reading Offsets from MySQL

26.Example: Reading Offsets from MySQL

27. Timing Logging (around actions) - Record timing info for fast troubleshooting - Escalate alarms to the appropriate team - Quickly resolve while app continues running

28.React How do I react to this monitoring? ● Heartbeats ● Scheduled Monitor Jobs ○ Version Updates ○ Ensure Running ○ Act on failure/problem ● Monitoring Alarms ● Look at them!

29.Batch Intervals Optimizing for speed and resource efficiency