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Learning spark ch10 - Spark Streaming

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Learning spark ch10 - Spark Streaming

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Learning spark ch10 - Spark Streaming

  1. 1. C H A P T E R 1 0 : S P A R K S T R E A M I N G Learning Spark by Holden Karau et. al.
  2. 2. Overview: Spark Streaming  A Simple Example  Architecture and Abstraction  Transformations  Stateless  Stateful  Output Operations  Input Sources  Core Sources  Additional Sources  Multiple Sources and Cluster Sizing  24/7 Operation  Checkpointing  Driver Fault Tolerance  Worker Fault Tolerance  Receiver Fault Tolerance  Processing Guarantees  Streaming UI  Performance Considerations  Batch and Window Sizes  Level of Parallelism  Garbage Collection and Memory Usage  Conclusion
  3. 3. 10.1 A Simple Example  Before we dive into the details of Spark Streaming, let’s consider a simple example. We will receive a stream of newline-delimited lines of text from a server running at port 7777, filter only the lines that contain the word error, and print them.  Spark Streaming programs are best run as standalone applications built using Maven or sbt. Spark Streaming, while part of Spark, ships as a separate Maven artifact and has some additional imports you will want to add to your project.
  4. 4. 10.2 Architecture and Abstraction
  5. 5. Edx and Coursera Courses  Introduction to Big Data with Apache Spark  Spark Fundamentals I  Functional Programming Principles in Scala
  6. 6. 10.2 Architecture and Abstraction (cont.)
  7. 7. 10.3 Transformations  Stateless  the processing of each batch does not depend on the data of its previous batches  include the common RDD transformations like map(), filter(), and reduceByKey()  Stateful  use data or intermediate results from previous batches to compute the results of the current batch  include transformations based on:  sliding windows  tracking state across time
  8. 8. 10.3.1 Stateless Transformations
  9. 9. 10.3.2 Stateless Transformations  Windowed Transformation  compute results across a longer time period than the StreamingContext’s batch interval, by combining results from multiple batches A windowed stream with a window duration of 3 batches and a slide duration of 2 batches; every two time steps, we compute a result over the previous 3 time steps
  10. 10. 10.3.2 Stateless Transformations (cont.)  UpdateStateByKey transformation  updateStateByKey() maintains state across the batches in a DStream by providing access to a state variable for DStreams of key/value pairs  update(events, oldState)  returns a newState  events is a list of events that arrived in the current batch (may be empty)  oldState is an optional state object, stored within an Option; it might be missing if there was no previous state for the key  newState is also an Option; we can return an empty Option to specify that we want to delete the state
  11. 11. 10.4 Output Operations  Specify what needs to be done with the final transformed data in a stream  print()  save()  Saving DStream to text files in Scala ipAddressRequestCount.saveAsTextFiles("outputDir", "txt")  Saving SequenceFiles from a DStream in Scala val writableIpAddressRequestCount = { (ip, count) => (new Text(ip), new LongWritable(count)) } writableIpAddressRequestCount.saveAsHadoopFiles[ SequenceFileOutputFormat[Text, LongWritable]]("outputDir", "txt")
  12. 12. 10.5 Input Sources  Spark Streaming has built-in support for a number of different data sources.  “core” sources are built into the Spark Streaming Maven artifact  others are available through additional artifacts  Eg: spark-streaming-kafka.
  13. 13. 10.5.1 Core Sources  Stream of files  allows a stream to be created from files written in a directory of a Hadoop-compatible filesystem  needs to have a consistent date format for the directory names and the files have to be created atomically  Eg: Streaming text files written to a directory in Scala val logData = ssc.textFileStream(logDirectory)  Akka actor stream  allows using Akka actors as a source for streaming  To construct an actor stream:  create an Akka actor  implement the org.apache.spark.streaming.receiver.ActorHelper interface
  14. 14. 10.5.2 Additional Sources  Apache Kafka  Apache Plume  Push-based receiver  Pull-based receiver  Custom input sources
  15. 15. 10.5.3 Multiple Sources and Cluster Sizing  We can combine multiple DStreams using operations like union()  combine data from multiple input DStreams  The receivers are executed in the Spark cluster to use multiple ones  Each receiver runs as a long-running task within Spark’s executors, and hence occupies CPU cores allocated to the application  Note: Do not run Spark Streaming programs locally with master config‐ ured as "local" or "local[1]”
  16. 16. 10.6 “24/7” Operations  Spark provides strong fault tolerance guarantees.  As long as the input data is stored reliably, Spark Streaming will always compute the correct result from it, offering “exactly once” semantics, even if workers or the driver fail.  To run Spark Streaming applications 24/7 1. setting up checkpointing to a reliable storage system, such as HDFS or Amazon S3 2. worry about the fault tolerance of the driver program and of unreliable input sources
  17. 17. 10.6.1 Checkpointing  Main mechanism needs to be set up for fault tolerance  Allows periodically saving data about the application to a reliable storage system, such as HDFS or Amazon S3  for use in recovering  Two purposes:  Limiting the state that must be recomputed on failure  Providing fault tolerance for the driver
  18. 18. 10.6.2 Driver Fault Tolerance  Requires creating our StreamingContext, which takes in the checkpoint directory  use the StreamingContext.getOrCreate() function  Write initialization code using getOrCreate(), need to actually restart your driver program when it crashes
  19. 19. 10.6.3 Worker Fault Tolerance  Spark Streaming uses the same techniques as Spark for its fault tolerance.  All the data received from external sources is replicated among the Spark workers  All RDDs created through transformations of this replicated input data are tolerant to failure of a worker node, as the RDD lineage allows the system to recompute the lost data all the way from the surviving replica of the input data.
  20. 20. 10.6.4 Receiver Fault Tolerance  Spark Streaming restarts the failed receivers on other nodes in the cluster  Receivers provide the guarantees:  All data read from a reliable filesystem (e.g., with StreamingContext.hadoop Files) is reliable, because the underlying filesystem is replicated.  For unreliable sources such as Kafka, push-based Flume, or Twitter, Spark repli‐ cates the input data to other nodes, but it can briefly lose data if a receiver task is down.
  21. 21. 10.6.5 Processing Guarantees  Spark Streaming provide exactly- once semantics for all transformations  Even if a worker fails and some data gets reprocessed, the final transformed result (that is, the transformed RDDs) will be the same as if the data were processed exactly once.  When the transformed result is to be pushed to external systems using out‐ put operations, the task pushing the result may get executed multiple times due to failures, and some data can get pushed multiple times.
  22. 22. 10.7 Streaming UI  UI page that lets us look at what applications are doing. (typically http:// <driver>:4040)
  23. 23. 10.8 Performance Considerations  Batch in window sizes  Level of parallelism  Garbage Collection and Memory Usage
  24. 24. 10.8.1 Batch and Window Sizes  Minimum batch size Spark Streaming can use: 500 milliseconds  The best approach:  start with a larger batch size (around 10 seconds)  work your way down to a smaller batch size.  If the processing times reported in the Streaming UI remain consistent, then you can continue to decrease the batch size  Note: if they are increasing you may have reached the limit for your application.
  25. 25. 10.8.2 Level of Parallelism  Increasing the parallelism - a common way to reduce the processing time of batches  3 ways:  Increasing the number of receivers  Explicitly repartitioning received data  Increasing parallelism in aggregation
  26. 26. 10.8.3 Garbage Collection and Memory Usage  Java’s garbage collection - an aspect that can cause problems  To minimize large pauses due to GC  enabling Java’s Concurrent Mark- Sweep garbage collector.  The Concurrent Mark-Sweep garbage collector does consume more resources overall, but introduces fewer pauses.  To reduce GC pressure  Cache RDDs in serialized form  Use Kryo serialization  Use an LRU cache
  27. 27. Edx and Coursera Courses  Introduction to Big Data with Apache Spark  Spark Fundamentals I  Functional Programming Principles in Scala
  28. 28. 10.9 Conclusion  In this chapter, we have seen how to work with streaming data using DStreams.  Since DStreams are composed of RDDs, the techniques and knowledge you have gained from the earlier chapters remains applicable for streaming and real-time applications.  In the next chapter, we will look at machine learning with Spark.