Building Big Data Streaming Architectures
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In this talk we depict how to build streaming architectures connecting to Kafka with different technologies. Includes links to Github code samples.
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Building Big Data Streaming Architectures
- 1. Building streaming architectures David Martinez Rego BigD@ta Coruña 25-26 April 2016
- 2. Index • Why? • When? • What? • How?
- 3. Why?
- 4. The world does not wait • Big data applications are build with the sole purpose of managing a business case of gathering an understanding about the word that would give an advantage. • The necessity of building streaming applications arises from the fact that in many applications, the value of the information gathered drops dramatically with time.
- 9. Batch/streaming duality • Streaming applications can bring value by giving an approximate answer just on time. If timing is not an issue (daily), batch pipelines can provide a good solution. time value streaming batch
- 10. When?
- 11. Start big, grow small • Despite the advertisement of vendors, jump in to a streaming application is not always advisable • It is harder to get it right and you encounter limitations: probabilistic data structures, guarantees, … • The value of the data you are about to gather is not clear in a discovery phase. • Some new libraries provide the same set of primitives both for batch and streaming. It is possible to develop the core of the idea and just translate that to a streaming pipeline later.
- 12. Not always practical • As a developer, you can face any of the following situations • It is mandatory • It is doubtful • It will never be necessary
- 17. What?
- 18. Gathering Brokering SinkProcessing/Analysis Execution engine Coordination Persistent Persistent ~Persistent External systems
- 20. Lambda architecture • Batch layer (ex. Spark, HDFS): process the master dataset (append only) to precompute batch views (a view the front end will query • Speed layer (streaming): calculate ephemeral views only based on recent data! • Motto: take into account reprocessing and recovery
- 21. Lambda architecture • Problems:! • Maintaing two code bases in sync (often different because speed layer cannot reproduce the same) • Synchronisation of the two layers in the query layer is an additional problem
- 22. Gathering Brokering Reservoir of Master Data Production Production Catching up… Production pipeline New pipeline
- 23. Gathering Brokering Reservoir of Master Data Production Done! Old pipeline Production pipeline
- 24. Kappa approach • Only maintain one code base and reduce accidental complexity by using too many technologies. • Can reverse back if something goes wrong • Not a silver bullet and not prescription of technologies, just a framework.
- 25. Gathering Brokering SinkProcessing/Analysis Execution engine Coordination Persistent Persistent ~Persistent External systems
- 26. How?
- 27. Concepts are basic • There are multiple frameworks available nowadays who change terminology trying to differentiate. • It makes starting on streaming a bit confusing…
- 28. Concepts are basic • It makes starting on streaming a bit confusing… • Actually there are many concepts which are shared between them and they are quite logical.
- 29. Step 1: data structure • The basic data structure is made of 4 elements • Sink: where is this thing going? • Partition key: to which shard? • Sequence id: when was this produced? • Data: anything that can be serialised (JSON, Avro, photo, …) Partition key Sequence idSink Data ( , , ),
- 30. Step 2: hashing • The holy grail trick of big data to split the work, and also major block of streaming • We use hashing in the reverse of classical, force the clashing of the things that are if my interest Partition key Sequence idSink Data ( , , ), h(k) mod N
- 31. Step 3: fault tolerance “Distributed computing is parallel computing when you cannot trust anything or anyone”
- 32. Step 3: fault tolerance • At any point any node producing the data in the source can stop working • Non persistent: data is lost • Persistent: data is replicated so it can always be recovered from other node
- 33. Step 3: fault tolerance • At any point any node computing our pipeline can go down • at most once: we let data be lost, once delivered do not reprocess. • at least once, we ensure delivery, can be reprocessed. • exactly once, we ensure delivery and no reprocessing
- 34. Step 3: fault tolerance • At any point any node computing our pipeline can go down • checkpointing: If we have been running the pipeline for hours and something goes wrong, do I have to start from the beginning? • Streaming systems put in place mechanisms to checkpoint progress so the new worker knows previous state and where to start from. • Usually involves other systems to save checkpoints and synchronise.
- 35. Step 4: delivery • One at a time: we process each message individually. Increases response time per message. • Micro-batch: we always process data in batches gathered at specified time intervals or size. Makes it impossible to reduce message processing below a limit.
- 36. Gathering Brokering SinkProcessing/Analysis Execution engine Coordination Persistent Persistent ~Persistent External systems
- 37. Gathering
- 38. Partition keyTopic Data , , )( … Partition keyTopic Data , , )( Partition keyTopic Data , , )( … Partition keyTopic Data , , )(
- 39. Partition keyTopic Data , , )( … Partition keyTopic Data , , )( Partition keyTopic Data , , )( … Partition keyTopic Data , , )( h(k) mod N h(k) mod N h(k) mod N h(k) mod N
- 41. … Consumer 2 Consumer 1 Zookeeper … … Consumer 2 … Consumer 1 …
- 42. Produce to Kafka, consume from Kafka
- 43. Gathering Brokering SinkProcessing/Analysis Execution engine Coordination Persistent Persistent ~Persistent External systems
- 44. one! at-a-time mini! batch exactly! once Deploy Windowing Functional Catch Yes Yes * Yes * Custom YARN Yes * ~ DRPC No Yes Yes YARN Mesos Yes Yes MLlib, ecosystem Yes Yes Yes YARN Yes Yes Flexible windowing Yes ~ No YARN ~ No DB update log plugin Yes Yes Yes Google Yes ~ Google ecosystem Yes you No AWS you No AWS ecosystem * with Trident
- 45. Flink basic concepts • Stream: source of data that feeds computations (a batch dataset is a bounded stream) • Transformations: operation that takes one or more streams as input and computes an output stream. They can be stateless of stateful (exactly once). • Sink: endpoint that received the output stream of a transformation • Dataflow: DAG of streams, transformations and sinks.
- 49. Samza basic concepts • Streams: persistent set of immutable messages of similar type and category with transactional nature. • Jobs: code that performs logical transformations on a set of input streams to append to a set of output streams. • Partitions: Each stream breaks into partitions, set of totally ordered sequence of examples. • Tasks: Each task consumes data from one partition. • Dataflow: composition of jobs that connects a set of streams. • Containers: physical unit of parallelism.
- 52. Storm basic concepts • Spout: source of data from any external system. • Bolts: transformations of one or more streams into another set of output streams. • Stream grouping: shuffling of streaming data between bolts. • Topology: set of spouts and bolts that process a stream of data. • Tasks and Workers: unit of work deployable into one container. Workers can process one or more tasks. Task deploy to one worker.
- 53. Storm basic concepts Trident Topology Compile
- 55. Spark basic concepts • DStream: continuous stream of data represented by a series of RDDs. Each RDD contains data for a specific time interval. • Input DStream and Receiver: source of data that feeds a DStream. • Transformations: operations that transform one DStream in another DStream (stateless and stateful with exactly once semantics). • Output operations: operations that periodically push data of a DStream to a specific output system.
- 58. Conclusions… • Think on streaming when there is a hard constraint on time-to-information • Use a queue system as your place of orchestration • Select the processing system that best suits to your use case • Samza: early stage, more to come in the close future. • Spark: good option if mini batch will always work for you. • Storm: good option if you can setup the infrastructure. DRPC provides an interesting pattern for some use cases. • Flink: reduced ecosystem because it has a shorter history. Its design learnt from all past frameworks and is the most flexible. • Datastream: original inspiration for Flink. Good and flexible model if you want to go the managed route and make use of Google toolbox (Bigtable, etc) • Kinesis: Only if you have some legacy. Probably better off using Spark connector in AWS EMR.
- 59. Where to go… • All code examples are available in Github • Kafka https://github.com/torito1984/kafka- playground.git, https://github.com/torito1984/kafka- doyle-generator.git • Spark https://github.com/torito1984/spark-doyle.git! • Storm https://github.com/torito1984/trident-doyle.git! • Flink https://github.com/torito1984/flink-sherlock.git! • Samza https://github.com/torito1984/samza-locations.git
- 60. Building streaming architectures David Martinez Rego BigD@ta Coruña 25-26 April 2016