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Apache storm vs. Spark Streaming

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Apache storm vs. Spark Streaming

  1. 1. Apache Storm and Spark Streaming Compared P. Taylor Goetz, Hortonworks @ptgoetz
  2. 2. Honestly... • I know a lot more about Apache Storm than I do Apache Spark Streaming. • I've been involved with Apache Storm, in one way or another, since it was open-sourced. • I'm admittedly biased.
  3. 3. But... • A number of articles/papers comparing Apache Storm and Spark Streaming are inaccurate in terms of Storm’s features and performance characteristics. • Code and configuration for those studies is not available, so independent verification is impossible. • Claims don't match real-world observations.
  4. 4. But... • There is an inherent “Home Team Advantage” in any benchmark comparison. • Without open source code, any benchmark claims are essentially marketing fluff, and should be taken with a grain or two of NaCl. • Any benchmark claim should be independently verifiable.
  5. 5. Spark Streaming Paper • Compares Spark Streaming (Micro-Batch) to Core Storm (One-at-a-Time) • A more appropriate comparison would have been with Storm’s Trident (Micro-Batch) API • Trident mentioned only in passing (on pages 3 and 12) http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf
  6. 6. Spark Streaming Paper • Benchmark code/configuration not publicly available • Performance claims not independently verifiable http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf
  7. 7. Spark Streaming Paper • Granted, the Spark Streaming paper is almost 2 years old and written at a time when Trident was relatively new. • However, that paper is often cited when comparing Apache Storm and Spark Streaming, particularly in terms of performance. • A lot can change in 2 years. http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf
  8. 8. Streaming and batch processing are fundamentally different.
  9. 9. Batch vs. Streaming • Storm is a stream processing framework that also does micro-batching (Trident).
 • Spark is a batch processing framework that also does micro-batching (Spark Streaming).
  10. 10. Batch vs. Streaming Batch Streaming
  11. 11. Batch vs. Streaming Batch Streaming Micro-Batch
  12. 12. Apache Storm: Two Streaming APIs Core Storm (Spouts and Bolts)! • One at a Time • Lower Latency • Operates on Tuple Streams Trident (Streams and Operations)! • Micro-Batch • Higher Throughput • Operates on Streams of Tuple Batches and Partitions
  13. 13. Language Options Core Storm Storm Trident Spark Streaming • Java • Clojure • Scala • Python • Ruby • others* • Java • Clojure • Scala • Java • Scala • Python *Storm’s Multi-Lang feature allows the use of virtually any programming language.
  14. 14. Reliability Models Core Storm Storm Trident Spark Streaming At Most Once Yes Yes No At Least Once Yes Yes No* Exactly Once No Yes Yes* *In some node failure scenarios, Spark Streaming falls back to at-least-once processing or data loss.
  15. 15. Programing Model Core Storm Storm Trident Spark Streaming Stream Primitive Tuple Tuple, Tuple Batch, Partition DStream Stream Source Spouts Spouts, Trident Spouts HDFS, Network Computation/ Transformation Bolts Filters, Functions, Aggregations, Joins Transformation, Window Operations Stateful Operations No (roll your own) Yes Yes Output/ Persistence Bolts State, MapState foreachRDD
  16. 16. Production Deployments Apache Storm Spark Streaming • Too many to list
 http:// storm.incubator.apache.org/ documentation/Powered- By.html • Sharethrough
 http:// engineering.sharethrough.com/blog/ 2014/06/27/sharethrough-at-spark- summit-2014-spark-streaming-for- realtime-auctions/
  17. 17. Support Apache Storm Spark Spark Streaming Hadoop Distro Hortonworks, MapR Cloudera, MapR, Hortonworks (preview) Hortonworks, Cloudera, MapR Resource Management YARN, Mesos YARN, Mesos YARN*, Mesos Provisioning/ Monitoring Apache Ambari Cloudera Manager ? *With issues: http://spark-summit.org/wp-content/uploads/2014/07/ Productionizing-a-247-Spark-Streaming-Service-on-YARN-Ooyala.pdf
  18. 18. Failure Scenarios
  19. 19. Worker Failure: Spark Streaming "So if a worker node fails, then the system can recompute the lost from the the left over copy of the input data. However, if the worker node where a network receiver was running fails, then a tiny bit of data may be lost, that is, the data received by the system but not yet replicated to other node(s)." Only HDFS-backed data sources are fully fault tolerant. https://spark.apache.org/docs/latest/streaming-programming- guide.html#fault-tolerance-properties
  20. 20. Worker Failure: Spark Streaming https://databricks.com/blog/2015/01/15/improved-driver-fault-tolerance-and- zero-data-loss-in-spark-streaming.html Solution?: Write Ahead Logs (SPARK-3129) • Enabling WAL requires DFS (HDFS, S3) — no such requirement with Storm • Incurs a performance penalty that adds to overall latency • Full fault tolerance still requires a data source that can replay data (e.g. Kafka)! • Architectural band aid?
  21. 21. Worker Failure: Apache Storm • If a supervisor node fails, Nimbus will reassign that node's tasks to other nodes in the cluster. • Any tuples sent to a failed node will time out and be replayed (In Trident, any batches will be replayed). • Delivery guarantees dependent on a reliable data source.
  22. 22. Data Source Reliability • A data source is considered unreliable if there is no means to replay a previously-received message. • A data source is considered reliable if it can somehow replay a message if processing fails at any point. • A data source is considered durable if it can replay any message or set of messages given the necessary selection criteria. ! (These are my terms.)
  23. 23. Reliability Limitations: Apache Storm • Exactly once processing requires a durable data source. • At least once processing requires a reliable data source. • An unreliable data source can be wrapped to provide additional guarantees. • With durable and reliable sources, Storm will not drop data. • Common pattern: Back unreliable data sources with Apache Kafka (minor latency hit traded for 100% durability).
  24. 24. Apache Storm Spouts Durable! Kafka
 Reliable! JMS RabbitMQ / AMQP Kestrel Amazon SQS Amazon Kinesis Unreliable! Twitter Scribe MongoDB
  25. 25. Apache Storm Output (Bolts, Trident State) • Cassandra • HBase • HDFS • Kafka • Redis • Memcached • R • JMS • MongoDB • RDBMS
  26. 26. Apache Storm + Kafka Apache Kafka is an ideal source for Storm topologies. It provides everything necessary for: • At most once processing • At least once processing • Exactly once processing Apache Storm includes Kafka spout implementations for all levels of reliability. Kafka Supports a wide variety of languages and integration points for both producers and consumers.
  27. 27. Reliability Limitations: Spark Streaming • Fault tolerance and reliability guarantees require HDFS-backed data source. • Moving data to HDFS prior to stream processing introduces additional latency. • Network data sources (Kafka, etc.) are vulnerable to data loss in the event of a worker node failure. https://spark.apache.org/docs/latest/streaming-programming- guide.html#fault-tolerance-properties
  28. 28. Performance “The main reason cited by Tathagata for Spark's performance gain over Storm is the aggregation of small records that occurs through the mechanics of RDDs.” http://www.cs.duke.edu/~kmoses/cps516/dstream.html In other words: Micro-Batching
  29. 29. Performance http://www.cs.duke.edu/~kmoses/cps516/dstream.html Storm capped at 10k msgs/sec/node? Spark Streaming 40x faster than Storm? Others may disagree…
  30. 30. https://twitter.com/ nathanmarz/status/ 207989068519317505 http://www.slideshare.net/ JamesSirota/cisco-opensoc
  31. 31. Netty Transport • Introduced in Apache Storm 0.9.0 • Faster, pure Java alternative for 0MQ • Yahoo! Engineering announcement:
 http://yahooeng.tumblr.com/post/ 64758709722/making-storm-fly- with-netty • Performance Test Code:
 https://github.com/yahoo/storm- perf-test Netty 0mq
  32. 32. STORM-297 • Introduced in Apache Storm 0.9.2-incubating • Big performance boost, especially for small messages • JIRA Discussion:
 https://issues.apache.org/jira/ browse/STORM-297 • Performance Test Code:
 https://github.com/yahoo/storm- perf-test
  33. 33. Benchmarking Storm • 5 nodes on AWS (m1.large - not very powerful) • 1 ZooKeeper, 1 Nimbus, 3 Supervisors • Storm Core API and Trident API benchmarks • Is Trident API slower than Core API? https://github.com/ptgoetz/storm-benchmark
  34. 34. Is Trident API slower than Core API? • On low-power hardware with 3 supervisor nodes… • Core API: ~150k msg./sec. with ~80 ms. latency • Trident API: ~300k msg./sec. with ~250 ms. latency • Higher throughput possible with increased latency • Better performance with bigger hardware
  35. 35. Is Spark + Spark Streaming a "Lambda Architecture in a Box?" • No! • Lambda is a lot more than batch + streaming. • Lambda is powerful when applied correctly, but is not right for every use case. • Spark and Spark Streaming have overlapping programming models for batch and micro-batch. • The rest is up to you (as it is with Storm).
  36. 36. Final Thoughts In general (not specific to Spark Streaming):! • Beware any claim that A is X times faster than B. • Performance is a matter of proper tuning for the use case at hand. • Any system can be hobbled to look bad in a benchmark.
  37. 37. Recommendation • It is up to you, and your specific use case. • Consider fault tolerance. Is data loss acceptable? • Consider all facets and make informed decisions. • Rely on your own benchmarks
  38. 38. Questions?
  39. 39. Thank you!