Large-Scale Stream Processing in the Hadoop Ecosystem
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The document discusses large-scale stream processing within the Hadoop ecosystem, focusing on various open-source stream processors such as Apache Storm, Flink, Spark, and Samza. It highlights the differences in runtime architecture, programming models, fault tolerance, stateful processing, and applications of each system. Ultimately, it emphasizes the diversity of streaming applications and the need for choosing the right framework based on specific requirements.
![Flink
val lines: DataStream[String] = env.fromSocketStream(...)
lines.flatMap {line => line.split(" ")
.map(word => Word(word,1))}
.groupBy("word").sum("frequency")
.print()
case class Word (word: String, frequency: Int)
val lines: DataStream[String] = env.fromSocketStream(...)
lines.flatMap {line => line.split(" ")
.map(word => Word(word,1))}
.window(Time.of(5,SECONDS)).every(Time.of(1,SECONDS))
.groupBy("word").sum("frequency")
.print()
Rolling word count
Window word count
252015-‐‑09-‐‑28 Apache: Big Data Europe](https://image.slidesharecdn.com/opensourcestreaming-150928093329-lva1-app6891/85/Large-Scale-Stream-Processing-in-the-Hadoop-Ecosystem-25-320.jpg)
![§ Stateless runtime by design
• No continuous operators
• UDFs are assumed to be stateless
§ State can be generated as a separate
stream of RDDs: updateStateByKey(…)
𝒇:
𝑺𝒆𝒒[𝒊𝒏 𝒌], 𝒔𝒕𝒂𝒕𝒆 𝒌 ⟶ 𝒔𝒕𝒂𝒕𝒆.
𝒌
§ 𝒇 is scoped to a specific key
§ Exactly-once semantics
35Apache: Big Data Europe2015-‐‑09-‐‑28](https://image.slidesharecdn.com/opensourcestreaming-150928093329-lva1-app6891/85/Large-Scale-Stream-Processing-in-the-Hadoop-Ecosystem-35-320.jpg)
Large-Scale Stream Processing in the Hadoop Ecosystem
- 1. Large-Scale Stream Processing in the Hadoop Ecosystem Gyula Fóra [email protected] Márton Balassi [email protected]
- 2. This talk § Stream processing by example § Open source stream processors § Runtime architecture and programming model § Counting words… § Fault tolerance and stateful processing § Closing 2Apache: Big Data Europe2015-‐‑09-‐‑28
- 3. Stream processing by example 2015-‐‑09-‐‑28 Apache: Big Data Europe 3
- 4. Streaming applications ETL style operations • Filter incoming data, Log analysis • High throughput, connectors, at-least-once processing Window aggregations • Trending tweets, User sessions, Stream joins • Window abstractions 2015-‐‑09-‐‑28 Apache: Big Data Europe 4 Inpu t Inpu t Inpu tInput Process/Enrich
- 5. Streaming applications Machine learning • Fitting trends to the evolving stream, Stream clustering • Model state, cyclic flows Pattern recognition • Fraud detection, Triggering signals based on activity • Exactly-once processing 5Apache: Big Data Europe2015-‐‑09-‐‑28
- 6. Open source stream processors 2015-‐‑09-‐‑28 Apache: Big Data Europe 6
- 7. Apache Streaming landscape 72015-‐‑09-‐‑28 Apache: Big Data Europe
- 8. Apache Storm § Started in 2010, development driven by BackType, then Twitter § Pioneer in large scale stream processing § Distributed dataflow abstraction (spouts & bolts) 82015-‐‑09-‐‑28 Apache: Big Data Europe
- 9. Apache Flink § Started in 2008 as a research project (Stratosphere) at European universities § Unique combination of low latency streaming and high throughput batch analysis § Flexible operator states and windowing 9 Batch data Kafka, RabbitMQ, ... HDFS, JDBC, ... Stream Data 2015-‐‑09-‐‑28 Apache: Big Data Europe
- 10. Apache Spark § Started in 2009 at UC Berkley, Apache since 2013 § Very strong community, wide adoption § Unified batch and stream processing over a batch runtime § Good integration with batch programs 102015-‐‑09-‐‑28 Apache: Big Data Europe
- 11. Apache Samza § Developed at LinkedIn, open sourced in 2013 § Builds heavily on Kafka’s log based philosophy § Pluggable messaging system and execution backend 112015-‐‑09-‐‑28 Apache: Big Data Europe
- 12. System comparison 12 Streaming model Native Micro-batching Native Native API Compositional Declarative Compositional Declarative Fault tolerance Record ACKs RDD-based Log-based Checkpoints Guarantee At-least-once Exactly-once At-least-once Exactly-once State Only in Trident State as DStream Stateful operators Stateful operators Windowing Not built-in Time based Not built-in Policy based Latency Very-Low Medium Low Low Throughput Medium High High High 2015-‐‑09-‐‑28 Apache: Big Data Europe
- 13. Runtime and programming model 2015-‐‑09-‐‑28 Apache: Big Data Europe 13
- 14. Native Streaming 2015-‐‑09-‐‑28 Apache: Big Data Europe 14
- 15. Distributed dataflow runtime § Storm, Samza and Flink § General properties • Long standing operators • Pipelined execution • Usually possible to create cyclic flows 2015-‐‑09-‐‑28 Apache: Big Data Europe 15 Pros • Full expressivity • Low-latency execution • Stateful operators Cons • Fault-tolerance is hard • Throughput may suffer • Load balancing is an issue
- 16. Distributed dataflow runtime § Storm • Dynamic typing + Kryo • Dynamic topology rebalancing § Samza • Almost every component pluggable • Full task isolation, no backpressure (buffering handled by the messaging layer) § Flink • Strongly typed streams + custom serializers • Flow control mechanism • Memory management 2015-‐‑09-‐‑28 Apache: Big Data Europe 16
- 17. Micro-batching 2015-‐‑09-‐‑28 Apache: Big Data Europe 17
- 18. Micro-batch runtime § Implemented by Apache Spark § General properties • Computation broken down to time intervals • Load aware scheduling • Easy interaction with batch 2015-‐‑09-‐‑28 Apache: Big Data Europe 18 Pros • Easy to reason about • High-throughput • FT comes for “free” • Dynamic load balancing Cons • Latency depends on batch size • Limited expressivity • Stateless by nature
- 19. Programming model 2015-‐‑09-‐‑28 Apache: Big Data Europe 19 Declarative § Expose a high-level API § Operators are higher order functions on abstract data stream types § Advanced behavior such as windowing is supported § Query optimization Compositional § Offer basic building blocks for composing custom operators and topologies § Advanced behavior such as windowing is often missing § Topology needs to be hand- optimized
- 20. Programming model 2015-‐‑09-‐‑28 Apache: Big Data Europe 20 DStream, DataStream § Transformations abstract operator details § Suitable for engineers and data analysts Spout, Consumer, Bolt, Task, Topology § Direct access to the execution graph / topology • Suitable for engineers
- 21. Counting words… 2015-‐‑09-‐‑28 Apache: Big Data Europe 21
- 22. WordCount 2015-‐‑09-‐‑28 Apache: Big Data Europe 22 storm budapest flink apache storm spark streaming samza storm flink apache flink bigdata storm flink streaming (storm, 4) (budapest, 1) (flink, 4) (apache, 2) (spark, 1) (streaming, 2) (samza, 1) (bigdata, 1)
- 23. Storm Assembling the topology 232015-‐‑09-‐‑28 Apache: Big Data Europe TopologyBuilder builder = new TopologyBuilder(); builder.setSpout("spout", new SentenceSpout(), 5); builder.setBolt("split", new Splitter(), 8).shuffleGrouping("spout"); builder.setBolt("count", new Counter(), 12) .fieldsGrouping("split", new Fields("word")); public class Counter extends BaseBasicBolt { Map<String, Integer> counts = new HashMap<String, Integer>(); public void execute(Tuple tuple, BasicOutputCollector collector) { String word = tuple.getString(0); Integer count = counts.containsKey(word) ? counts.get(word) + 1 : 1; counts.put(word, count); collector.emit(new Values(word, count)); } } Rolling word count bolt
- 24. Samza 2015-‐‑09-‐‑28 Apache: Big Data Europe 24 public class WordCountTask implements StreamTask { private KeyValueStore<String, Integer> store; public void process( IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) { String word = envelope.getMessage(); Integer count = store.get(word); if(count == null){count = 0;} store.put(word, count + 1); collector.send(new OutgoingMessageEnvelope(new SystemStream("kafka", ”wc"), Tuple2.of(word, count))); } } Rolling word count task
- 25. Flink val lines: DataStream[String] = env.fromSocketStream(...) lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .groupBy("word").sum("frequency") .print() case class Word (word: String, frequency: Int) val lines: DataStream[String] = env.fromSocketStream(...) lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .window(Time.of(5,SECONDS)).every(Time.of(1,SECONDS)) .groupBy("word").sum("frequency") .print() Rolling word count Window word count 252015-‐‑09-‐‑28 Apache: Big Data Europe
- 26. Spark Window word count Rolling word count (kind of) 262015-‐‑09-‐‑28 Apache: Big Data Europe
- 27. Fault tolerance and stateful processing 2015-‐‑09-‐‑28 Apache: Big Data Europe 27
- 28. Fault tolerance intro § Fault-tolerance in streaming systems is inherently harder than in batch • Can’t just restart computation • State is a problem • Fast recovery is crucial • Streaming topologies run 24/7 for a long period § Fault-tolerance is a complex issue • No single point of failure is allowed • Guaranteeing input processing • Consistent operator state • Fast recovery • At-least-once vs Exactly-once semantics 2015-‐‑09-‐‑28 Apache: Big Data Europe 28
- 29. Storm record acknowledgements § Track the lineage of tuples as they are processed (anchors and acks) § Special “acker” bolts track each lineage DAG (efficient xor based algorithm) § Replay the root of failed (or timed out) tuples 2015-‐‑09-‐‑28 Apache: Big Data Europe 29
- 30. Samza offset tracking § Exploits the properties of a durable, offset based messaging layer § Each task maintains its current offset, which moves forward as it processes elements § The offset is checkpointed and restored on failure (some messages might be repeated) 2015-‐‑09-‐‑28 Apache: Big Data Europe 30
- 31. Flink checkpointing § Based on consistent global snapshots § Algorithm designed for stateful dataflows (minimal runtime overhead) § Exactly-once semantics 31Apache: Big Data Europe2015-‐‑09-‐‑28
- 32. Spark RDD recomputation § Immutable data model with repeatable computation § Failed RDDs are recomputed using their lineage § Checkpoint RDDs to reduce lineage length § Parallel recovery of failed RDDs § Exactly-once semantics 2015-‐‑09-‐‑28 Apache: Big Data Europe 32
- 33. State in streaming programs § Almost all non-trivial streaming programs are stateful § Stateful operators (in essence): 𝒇: 𝒊𝒏, 𝒔𝒕𝒂𝒕𝒆 ⟶ 𝒐𝒖𝒕, 𝒔𝒕𝒂𝒕𝒆. § State hangs around and can be read and modified as the stream evolves § Goal: Get as close as possible while maintaining scalability and fault-tolerance 33Apache: Big Data Europe2015-‐‑09-‐‑28
- 34. § States available only in Trident API § Dedicated operators for state updates and queries § State access methods • stateQuery(…) • partitionPersist(…) • persistentAggregate(…) § It’s very difficult to implement transactional states Exactly-‐‑once guarantee 34Apache: Big Data Europe2015-‐‑09-‐‑28
- 35. § Stateless runtime by design • No continuous operators • UDFs are assumed to be stateless § State can be generated as a separate stream of RDDs: updateStateByKey(…) 𝒇: 𝑺𝒆𝒒[𝒊𝒏 𝒌], 𝒔𝒕𝒂𝒕𝒆 𝒌 ⟶ 𝒔𝒕𝒂𝒕𝒆. 𝒌 § 𝒇 is scoped to a specific key § Exactly-once semantics 35Apache: Big Data Europe2015-‐‑09-‐‑28
- 36. § Stateful dataflow operators (Any task can hold state) § State changes are stored as a log by Kafka § Custom storage engines can be plugged in to the log § 𝒇 is scoped to a specific task § At-least-once processing semantics 36Apache: Big Data Europe2015-‐‑09-‐‑28
- 37. § Stateful dataflow operators (conceptually similar to Samza) § Two state access patterns • Local (Task) state • Partitioned (Key) state § Proper API integration • Java: OperatorState interface • Scala: mapWithState, flatMapWithState… § Exactly-once semantics by checkpointing 37Apache: Big Data Europe2015-‐‑09-‐‑28
- 38. Performance § Throughput/Latency • A cost of a network hop is 25+ msecs • 1 million records/sec/core is nice § Size of Network Buffers/Batching § Buffer Timeout § Cost of Fault Tolerance § Operator chaining/Stages § Serialization/Types 2015-‐‑09-‐‑28 Apache: Big Data Europe 38
- 39. Closing 2015-‐‑09-‐‑28 Apache: Big Data Europe 39
- 40. Comparison revisited 40 Streaming model Native Micro-batching Native Native API Compositional Declarative Compositional Declarative Fault tolerance Record ACKs RDD-based Log-based Checkpoints Guarantee At-least-once Exactly-once At-least-once Exactly-once State Only in Trident State as DStream Stateful operators Stateful operators Windowing Not built-in Time based Not built-in Policy based Latency Very-Low Medium Low Low Throughput Medium High High High 2015-‐‑09-‐‑28 Apache: Big Data Europe
- 41. Summary § Streaming applications and stream processors are very diverse § 2 main runtime designs • Dataflow based (Storm, Samza, Flink) • Micro-batch based (Spark) § The best framework varies based on application specific needs § But high-level APIs are nice J 2015-‐‑09-‐‑28 Apache: Big Data Europe 41
- 42. Thank you!
- 43. List of Figures (in order of usage) § https://upload.wikimedia.org/wikipedia/commons/thumb/2/2a/CPT-FSM- abcd.svg/326px-CPT-FSM-abcd.svg.png § https://storm.apache.org/images/topology.png § https://databricks.com/wp-content/uploads/2015/07/image11-1024x655.png § https://databricks.com/wp-content/uploads/2015/07/image21-1024x734.png § https://people.csail.mit.edu/matei/papers/2012/hotcloud_spark_streaming.pdf, page 2. § http://www.slideshare.net/ptgoetz/storm-hadoop-summit2014, page 69-71. § http://samza.apache.org/img/0.9/learn/documentation/container/checkpointi ng.svg § https://databricks.com/wp-content/uploads/2015/07/image41-1024x602.png § https://storm.apache.org/documentation/images/spout-vs-state.png § http://samza.apache.org/img/0.9/learn/documentation/container/stateful_job. png 2015-‐‑09-‐‑28 Apache: Big Data Europe 43