Kafka pours and Spark resolves

1. Kafka pours and Spark resolves! Alexey Zinovyev, Java/BigData Trainer in EPAM

2. About With IT since 2007 With Java since 2009 With Hadoop since 2012 With Spark since 2014 With EPAM since 2015

3. 3Spark Streaming from Zinoviev Alexey Contacts E-mail : Alexey_Zinovyev@epam.com Twitter : @zaleslaw @BigDataRussia Facebook: https://www.facebook.com/zaleslaw vk.com/big_data_russia Big Data Russia vk.com/java_jvm Java & JVM langs

4. 4Spark Streaming from Zinoviev Alexey Spark Family

5. 5Spark Streaming from Zinoviev Alexey Spark Family

6. 6Spark Streaming from Zinoviev Alexey Pre-summary • Before RealTime • Spark + Cassandra • Sending messages with Kafka • DStream Kafka Consumer • Structured Streaming in Spark 2.1 • Kafka Writer in Spark 2.2

7. 7Spark Streaming from Zinoviev Alexey < REAL-TIME

8. 8Spark Streaming from Zinoviev Alexey Modern Java in 2016Big Data in 2014

9. 9Spark Streaming from Zinoviev Alexey Batch jobs produce reports. More and more..

10. 10Spark Streaming from Zinoviev Alexey But customer can wait forever (ok, 1h)

11. 11Spark Streaming from Zinoviev Alexey Big Data in 2017

12. 12Spark Streaming from Zinoviev Alexey Machine Learning EVERYWHERE

13. 13Spark Streaming from Zinoviev Alexey Data Lake in promotional brochure

14. 14Spark Streaming from Zinoviev Alexey Data Lake in production

15. 15Spark Streaming from Zinoviev Alexey Simple Flow in Reporting/BI systems

16. 16Spark Streaming from Zinoviev Alexey Let’s use Spark. It’s fast!

17. 17Spark Streaming from Zinoviev Alexey MapReduce vs Spark

18. 18Spark Streaming from Zinoviev Alexey MapReduce vs Spark

19. 19Spark Streaming from Zinoviev Alexey Simple Flow in Reporting/BI systems with Spark

20. 20Spark Streaming from Zinoviev Alexey Spark handles last year logs with ease

21. 21Spark Streaming from Zinoviev Alexey Where can we store events?

22. 22Spark Streaming from Zinoviev Alexey Let’s use Cassandra to store events!

23. 23Spark Streaming from Zinoviev Alexey Let’s use Cassandra to read events!

24. 24Spark Streaming from Zinoviev Alexey Cassandra CREATE KEYSPACE mySpace WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1 }; USE test; CREATE TABLE logs ( application TEXT, time TIMESTAMP, message TEXT, PRIMARY KEY (application, time));

25. 25Spark Streaming from Zinoviev Alexey Cassandra to Spark val dataSet = sqlContext .read .format("org.apache.spark.sql.cassandra") .options(Map( "table" -> "logs", "keyspace" -> "mySpace" )) .load() dataSet .filter("message = 'Log message'") .show()

26. 26Spark Streaming from Zinoviev Alexey Simple Flow in Pre-Real-Time systems

27. 27Spark Streaming from Zinoviev Alexey Spark cluster over Cassandra Cluster

28. 28Spark Streaming from Zinoviev Alexey More events every second!

29. 29Spark Streaming from Zinoviev Alexey SENDING MESSAGES

30. 30Spark Streaming from Zinoviev Alexey Your Father’s Messaging System

31. 31Spark Streaming from Zinoviev Alexey Your Father’s Messaging System

32. 32Spark Streaming from Zinoviev Alexey Your Father’s Messaging System InitialContext ctx = new InitialContext(); QueueConnectionFactory f = (QueueConnectionFactory)ctx.lookup(“qCFactory"); QueueConnection con = f.createQueueConnection(); con.start();

33. 33Spark Streaming from Zinoviev Alexey KAFKA

34. 34Spark Streaming from Zinoviev Alexey Kafka • messaging system

35. 35Spark Streaming from Zinoviev Alexey Kafka • messaging system • distributed

36. 36Spark Streaming from Zinoviev Alexey Kafka • messaging system • distributed • supports Publish-Subscribe model

37. 37Spark Streaming from Zinoviev Alexey Kafka • messaging system • distributed • supports Publish-Subscribe model • persists messages on disk

38. 38Spark Streaming from Zinoviev Alexey Kafka • messaging system • distributed • supports Publish-Subscribe model • persists messages on disk • replicates within the cluster (integrated with Zookeeper)

39. 39Spark Streaming from Zinoviev Alexey The main benefits of Kafka Scalability with zero down time Zero data loss due to replication

40. 40Spark Streaming from Zinoviev Alexey Kafka Cluster consists of … • brokers (leader or follower) • topics ( >= 1 partition) • partitions • partition offsets • replicas of partition • producers/consumers

41. 41Spark Streaming from Zinoviev Alexey Kafka Components with topic “messages” #1 Producer Thread #1 Producer Thread #2 Producer Thread #3 Topic: Messages Data Data Data Zookeeper

42. 42Spark Streaming from Zinoviev Alexey Kafka Components with topic “messages” #2 Producer Thread #1 Producer Thread #2 Producer Thread #3 Topic: Messages Part #1 Part #2 Data Data Data Broker #1 Zookeeper Part #1 Part #2 Leader Leader

43. 43Spark Streaming from Zinoviev Alexey Kafka Components with topic “messages” #3 Producer Thread #1 Producer Thread #2 Producer Thread #3 Topic: Messages Part #1 Part #2 Data Data Data Broker #1 Broker #2 Zookeeper Part #1 Part #1 Part #2 Part #2

44. 44Spark Streaming from Zinoviev Alexey Why do we need Zookeeper?

45. 45Spark Streaming from Zinoviev Alexey Kafka Demo

46. 46Spark Streaming from Zinoviev Alexey REAL TIME WITH DSTREAMS

47. 47Spark Streaming from Zinoviev Alexey RDD Factory

48. 48Spark Streaming from Zinoviev Alexey From socket to console with DStreams

49. 49Spark Streaming from Zinoviev Alexey DStream val conf = new SparkConf().setMaster("local[2]") .setAppName("NetworkWordCount") val ssc = new StreamingContext(conf, Seconds(1)) val lines = ssc.socketTextStream("localhost", 9999) val words = lines.flatMap(_.split(" ")) val pairs = words.map(word => (word, 1)) val wordCounts = pairs.reduceByKey(_ + _) wordCounts.print() ssc.start() ssc.awaitTermination()

54. 54Spark Streaming from Zinoviev Alexey Kafka as a main entry point for Spark

55. 55Spark Streaming from Zinoviev Alexey DStreams Demo

56. 56Spark Streaming from Zinoviev Alexey How to avoid DStreams with RDD-like API?

57. 57Spark Streaming from Zinoviev Alexey SPARK 2.2 DISCUSSION

58. 58Spark Streaming from Zinoviev Alexey Continuous Applications

59. 59Spark Streaming from Zinoviev Alexey Continuous Applications cases • Updating data that will be served in real time • Extract, transform and load (ETL) • Creating a real-time version of an existing batch job • Online machine learning

60. 60Spark Streaming from Zinoviev Alexey The main concept of Structured Streaming You can express your streaming computation the same way you would express a batch computation on static data.

61. 61Spark Streaming from Zinoviev Alexey Batch Spark 2.2 // Read JSON once from S3 logsDF = spark.read.json("s3://logs") // Transform with DataFrame API and save logsDF.select("user", "url", "date") .write.parquet("s3://out")

62. 62Spark Streaming from Zinoviev Alexey Real Time Spark 2.2 // Read JSON continuously from S3 logsDF = spark.readStream.json("s3://logs") // Transform with DataFrame API and save logsDF.select("user", "url", "date") .writeStream.parquet("s3://out") .start()

63. 63Spark Streaming from Zinoviev Alexey WordCount from Socket val lines = spark.readStream .format("socket") .option("host", "localhost") .option("port", 9999) .load() val words = lines.as[String].flatMap(_.split(" ")) val wordCounts = words.groupBy("value").count()

66. 66Spark Streaming from Zinoviev Alexey WordCount from Socket val lines = spark.readStream .format("socket") .option("host", "localhost") .option("port", 9999) .load() val words = lines.as[String].flatMap(_.split(" ")) val wordCounts = words.groupBy("value").count() Don’t forget to start Streaming

67. 67Spark Streaming from Zinoviev Alexey Unlimited Table

68. 68Spark Streaming from Zinoviev Alexey WordCount with Structured Streaming [Complete Mode]

69. 69Spark Streaming from Zinoviev Alexey Kafka -> Structured Streaming -> Console

70. 70Spark Streaming from Zinoviev Alexey Kafka To Console Demo

71. 71Spark Streaming from Zinoviev Alexey OPERATIONS

72. 72Spark Streaming from Zinoviev Alexey You can … • filter • sort • aggregate • join • foreach • explain

73. 73Spark Streaming from Zinoviev Alexey Operators Demo

74. 74Spark Streaming from Zinoviev Alexey How it works?

75. 75Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset

76. 76Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset Logical Plan

77. 77Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset Logical Plan Optimized Logical Plan

78. 78Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset Logical Plan Optimized Logical Plan Logical Plan Logical Plan Logical PlanPhysical Plan

79. 79Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset Logical Plan Optimized Logical Plan Logical Plan Logical Plan Logical PlanPhysical Plan Selected Physical Plan Planner

80. 80Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset Logical Plan Optimized Logical Plan Logical Plan Logical Plan Logical PlanPhysical Plan Selected Physical Plan

81. 81Spark Streaming from Zinoviev Alexey Deep Diving in Spark Internals Dataset Logical Plan Optimized Logical Plan Logical Plan Logical Plan Logical PlanPhysical Plan Selected Physical Plan Incremental #1 Incremental #2 Incremental #3

82. 82Spark Streaming from Zinoviev Alexey Incremental Execution: Planner polls Planner

83. 83Spark Streaming from Zinoviev Alexey Incremental Execution: Planner runs Planner Offset: 1 - 5 Incremental #1 R U N Count: 5

84. 84Spark Streaming from Zinoviev Alexey Incremental Execution: Planner runs #2 Planner Offset: 1 - 5 Incremental #1 Incremental #2 Offset: 6 - 9 R U N Count: 5 Count: 4

85. 85Spark Streaming from Zinoviev Alexey Aggregation with State Planner Offset: 1 - 5 Incremental #1 Incremental #2 Offset: 6 - 9 R U N Count: 5 Count: 5+4=9 5

86. 86Spark Streaming from Zinoviev Alexey DataSet.explain() == Physical Plan == Project [avg(price)#43,carat#45] +- SortMergeJoin [color#21], [color#47] :- Sort [color#21 ASC], false, 0 : +- TungstenExchange hashpartitioning(color#21,200), None : +- Project [avg(price)#43,color#21] : +- TungstenAggregate(key=[cut#20,color#21], functions=[(avg(cast(price#25 as bigint)),mode=Final,isDistinct=false)], output=[color#21,avg(price)#43]) : +- TungstenExchange hashpartitioning(cut#20,color#21,200), None : +- TungstenAggregate(key=[cut#20,color#21], functions=[(avg(cast(price#25 as bigint)),mode=Partial,isDistinct=false)], output=[cut#20,color#21,sum#58,count#59L]) : +- Scan CsvRelation(-----) +- Sort [color#47 ASC], false, 0 +- TungstenExchange hashpartitioning(color#47,200), None +- ConvertToUnsafe +- Scan CsvRelation(----)

87. 87Spark Streaming from Zinoviev Alexey What’s the difference between Complete and Append output modes?

88. 88Spark Streaming from Zinoviev Alexey COMPLETE, APPEND & UPDATE

89. 89Spark Streaming from Zinoviev Alexey There are two main modes and one in future • append (default)

90. 90Spark Streaming from Zinoviev Alexey There are two main modes and one in future • append (default) • complete

91. 91Spark Streaming from Zinoviev Alexey There are two main modes and one in future • append (default) • complete • update [in dreams]

92. 92Spark Streaming from Zinoviev Alexey Aggregation with watermarks

93. 93Spark Streaming from Zinoviev Alexey SOURCES & SINKS

94. 94Spark Streaming from Zinoviev Alexey Spark Streaming is a brick in the Big Data Wall

95. 95Spark Streaming from Zinoviev Alexey Let's save to Parquet files

98. 98Spark Streaming from Zinoviev Alexey Can we write to Kafka?

99. 99Spark Streaming from Zinoviev Alexey Nightly Build

100. 100Spark Streaming from Zinoviev Alexey Kafka-to-Kafka

101. 101Spark Streaming from Zinoviev Alexey J-K-S-K-S-C

102. 102Spark Streaming from Zinoviev Alexey Pipeline Demo

103. 103Spark Streaming from Zinoviev Alexey I didn’t find sink/source for XXX…

104. 104Spark Streaming from Zinoviev Alexey Console Foreach Sink import org.apache.spark.sql.ForeachWriter val customWriter = new ForeachWriter[String] { override def open(partitionId: Long, version: Long) = true override def process(value: String) = println(value) override def close(errorOrNull: Throwable) = {} } stream.writeStream .queryName(“ForeachOnConsole") .foreach(customWriter) .start

105. 105Spark Streaming from Zinoviev Alexey Pinch of wisdom • check checkpointLocation • don’t use MemoryStream • think about GC pauses • be careful about nighty builds • use .groupBy.count() instead count() • use console sink instead .show() function

106. 106Spark Streaming from Zinoviev Alexey We have no ability… • join two streams • work with update mode • make full outer join • take first N rows • sort without pre-aggregation

107. 107Spark Streaming from Zinoviev Alexey Roadmap 2.2 • Support other data sources (not only S3 + HDFS) • Transactional updates • Dataset is one DSL for all operations • GraphFrames + Structured MLLib • KafkaWriter • TensorFrames

108. 108Spark Streaming from Zinoviev Alexey IN CONCLUSION

109. 109Spark Streaming from Zinoviev Alexey Final point Scalable Fault-Tolerant Real-Time Pipeline with Spark & Kafka is ready for usage

110. 110Spark Streaming from Zinoviev Alexey A few papers about Spark Streaming and Kafka Introduction in Spark + Kafka http://bit.ly/2mJjE4i

111. 111Spark Streaming from Zinoviev Alexey Source code from Demo Spark Tutorial https://github.com/zaleslaw/Spark-Tutorial

112. 112Spark Streaming from Zinoviev Alexey Contacts E-mail : Alexey_Zinovyev@epam.com Twitter : @zaleslaw @BigDataRussia Facebook: https://www.facebook.com/zaleslaw vk.com/big_data_russia Big Data Russia vk.com/java_jvm Java & JVM langs

113. 113Spark Streaming from Zinoviev Alexey Any questions?