large-scale stream processing in the hadoop ecosystem · large-scale stream processing in the...

Large-Scale Stream Processing in the Hadoop Ecosystem

Gyula Fó[email protected]

Márton [email protected]

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

Stream processing by example

2015-‐‑09-‐‑28 Apache: Big Data Europe 3

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


Input

Input

InputInput

Process/Enrich

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


Open source stream processors


Apache Streaming landscape

72015-‐‑09-‐‑28 Apache: Big Data Europe

Apache Storm

§ Started in 2010, development driven by BackType, then Twitter

§ Pioneer in large scale stream processing§Distributed dataflow abstraction (spouts &

bolts)


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


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


Apache Samza

§Developed at LinkedIn, open sourced in 2013§ Builds heavily on Kafka’s log based philosophy§ Pluggable messaging system and execution

backend


System comparison

12

Streamingmodel 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

Statefuloperators

Statefuloperators

Windowing Not built-in Time based Not built-in Policy based

Latency Very-Low Medium Low LowThroughput Medium High High High


Runtime and programming model


Native Streaming


Distributed dataflow runtime

§ Storm, Samza and Flink§General properties

• Long standing operators• Pipelined execution• Usually possible to create

cyclic flows


Pros• Full expressivity• Low-latency execution• Stateful operators

Cons• Fault-tolerance is hard• Throughput may suffer• Load balancing is an

issue

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


Micro-batching


Micro-batch runtime§ Implemented by Apache Spark§General properties• Computation broken down

to time intervals• Load aware scheduling• Easy interaction with batch


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

Programming model


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

Programming model


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

Counting words…


WordCount


storm budapest flinkapache storm sparkstreaming samza stormflink apache flinkbigdata stormflink streaming

(storm, 4)(budapest, 1)(flink, 4)(apache, 2)(spark, 1)(streaming, 2)(samza, 1)(bigdata, 1)

StormAssembling the topology


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

Samza


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

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


SparkWindow word count

Rolling word count (kind of)


Fault tolerance and stateful processing


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


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


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)


Flink checkpointing

§ Based on consistent global snapshots§Algorithm designed for stateful dataflows

(minimal runtime overhead) § Exactly-once semantics


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 failedRDDs

§ Exactly-once semantics


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


§ States available only in Trident API§Dedicated operators for state updates and

queries§ State access methods• stateQuery(…)• partitionPersist(…)• persistentAggregate(…)

§ It’s very difficult toimplement transactionalstates

Exactly-‐‑once guarantee


§ 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[𝒊𝒏𝒌], 𝒔𝒕𝒂𝒕𝒆𝒌 ⟶ 𝒔𝒕𝒂𝒕𝒆.𝒌§ 𝒇 is scoped to a specific key

§ Exactly-once semantics


§ Stateful dataflow operators(Any task can hold state)

§ State changes are storedas a log by Kafka

§Custom storage engines canbe plugged in to the log

§ 𝒇 is scoped to a specific task§At-least-once processing

semantics


§ 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


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


Closing


Comparison revisited

40

Streamingmodel 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

Statefuloperators

Statefuloperators

Windowing Not built-in Time based Not built-in Policy based

Latency Very-Low Medium Low LowThroughput Medium High High High


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


Thank you!

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://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/checkpointing.svg


§ https://storm.apache.org/documentation/images/spout-vs-state.png

§ http://samza.apache.org/img/0.9/learn/documentation/container/stateful_job.png


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