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

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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

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Spark
Family

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Spark
Family

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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

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< REAL-TIME

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Modern Java in 2016Big Data in 2014

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Batch jobs produce reports. More and more..

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But customer can wait forever (ok, 1h)

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Big Data in 2017

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Machine Learning EVERYWHERE

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Data Lake in promotional brochure

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Data Lake in production

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Simple Flow in Reporting/BI systems

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Let’s use Spark. It’s fast!

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MapReduce vs Spark

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MapReduce vs Spark

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Simple Flow in Reporting/BI systems with Spark

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Spark handles last year logs with ease

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Where can we store events?

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Let’s use Cassandra to store events!

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Let’s use Cassandra to read events!

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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));

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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()

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Simple Flow in Pre-Real-Time systems

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Spark cluster over Cassandra Cluster

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More events every second!

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SENDING MESSAGES

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Your Father’s Messaging System

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Your Father’s Messaging System

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

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KAFKA

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Kafka
• messaging system

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Kafka
• messaging system
• distributed

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Kafka
• messaging system
• distributed
• supports Publish-Subscribe model

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Kafka
• messaging system
• distributed
• supports Publish-Subscribe model
• persists messages on disk

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Kafka
• messaging system
• distributed
• supports Publish-Subscribe model
• persists messages on disk
• replicates within the cluster (integrated with Zookeeper)

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The main benefits of Kafka
Scalability with zero down time
Zero data loss due to replication

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Kafka Cluster consists of …
• brokers (leader or follower)
• topics ( >= 1 partition)
• partitions
• partition offsets
• replicas of partition
• producers/consumers

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Kafka Components with topic “messages” #1
Producer
Thread #1
Producer
Thread #2
Producer
Thread #3
Topic: Messages
Data
Data
Data
Zookeeper

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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

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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

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Why do we need Zookeeper?

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Kafka
Demo

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REAL TIME WITH
DSTREAMS

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RDD Factory

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From socket to console with DStreams

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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()

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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()

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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()

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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()

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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()

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Kafka as a main entry point for Spark

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DStreams
Demo

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How to avoid DStreams with RDD-like API?

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SPARK 2.2 DISCUSSION

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Continuous Applications

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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

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

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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")

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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()

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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()

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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()

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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()

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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

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Unlimited Table

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WordCount with Structured Streaming [Complete Mode]

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Kafka -> Structured Streaming -> Console

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Kafka To
Console
Demo

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OPERATIONS

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You can …
• filter
• sort
• aggregate
• join
• foreach
• explain

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Operators
Demo

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How it works?

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Deep Diving in Spark Internals
Dataset

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Deep Diving in Spark Internals
Dataset Logical Plan

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Deep Diving in Spark Internals
Dataset Logical Plan
Optimized
Logical Plan

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Deep Diving in Spark Internals
Dataset Logical Plan
Optimized
Logical Plan
Logical Plan
Logical Plan
Logical PlanPhysical
Plan

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Deep Diving in Spark Internals
Dataset Logical Plan
Optimized
Logical Plan
Logical Plan
Logical Plan
Logical PlanPhysical
Plan
Selected
Physical Plan
Planner

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Deep Diving in Spark Internals
Dataset Logical Plan
Optimized
Logical Plan
Logical Plan
Logical Plan
Logical PlanPhysical
Plan
Selected
Physical Plan

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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

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Incremental Execution: Planner polls
Planner

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Incremental Execution: Planner runs
Planner
Offset: 1 - 5
Incremental
#1
R
U
N
Count: 5

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Incremental Execution: Planner runs #2
Planner
Offset: 1 - 5
Incremental
#1
Incremental
#2
Offset: 6 - 9
R
U
N
Count: 5
Count: 4

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Aggregation with State
Planner
Offset: 1 - 5
Incremental
#1
Incremental
#2
Offset: 6 - 9
R
U
N
Count: 5
Count: 5+4=9
5

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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(----)

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What’s the difference between
Complete and Append
output modes?

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COMPLETE, APPEND &
UPDATE

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There are two main modes and one in future
• append (default)

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There are two main modes and one in future
• append (default)
• complete

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There are two main modes and one in future
• append (default)
• complete
• update [in dreams]

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Aggregation with watermarks

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SOURCES & SINKS

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Spark Streaming is a brick in the Big Data Wall

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Let's save to Parquet files

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Let's save to Parquet files

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Let's save to Parquet files

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Can we write to Kafka?

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Nightly Build

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Kafka-to-Kafka

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J-K-S-K-S-C

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Pipeline
Demo

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I didn’t find sink/source for XXX…

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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

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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

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We have no ability…
• join two streams
• work with update mode
• make full outer join
• take first N rows
• sort without pre-aggregation

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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

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IN CONCLUSION

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Final point
Scalable Fault-Tolerant Real-Time Pipeline with
Spark & Kafka
is ready for usage

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

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Source code from Demo
Spark Tutorial
https://github.com/zaleslaw/Spark-Tutorial

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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

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Any questions?