Apache Spark Streaming: Architecture and Fault Tolerance

© 2015 IBM Corporation
Apache Hadoop Day 2015
Paranth Thiruvengadam – Architect @ IBM
Sachin Aggarwal – Developer @ IBM

Spark Streaming
 Features of Spark Streaming
 High Level API (joins, windows etc.)
 Fault – Tolerant (exactly once semantics achievable)
 Deep Integration with Spark Ecosystem (MLlib, SQL, GraphX
etc.)

Architecture

High Level Overview

Receiving Data
Driver
RECEIVER
Input
Source
Executor
Executor
Data Blocks
Data Blocks
Data Blocks
Are replicated
To another
Executor
Driver runs
Receiver as
Long running
tasks
Receiver divides
Streams into
Blocks and
keeps in
memory

Processing Data
Driver
RECEIVER
Executor
Executor
Data Blocks
Data Blocks
Every batch
Internal Driver
Launches tasks
To process the
blocks
Data
Store
results
results

What’s different from other
Streaming applications?

Traditional Stream Processing

Load Balancing…

Node failure / Stragglers…

Word Count with Kafka

Fault Tolerance

Fault Tolerance
 Why Care?
 Different guarantees for Data Loss
 Atleast Once
 Exactly Once
 What all can fail?
 Driver
 Executor

What happens when executor fails?

What happens when Driver fails?

Recovering Driver – Checkpointing

Driver restart

Driver restart – ToDO List
 Configure automatic driver restart
 Spark Standalone
 YARN
 Set Checkpoint in HDFS compatible file system
streamingContext.checkpiont(hdfsDirectory)
 Ensure the Code uses checkpoints for recovery
Def setupStreamingContext() : StreamingContext = {
Val context = new StreamingContext(…)
Val lines = KafkaUtils.createStream(…)
…
Context.checkpoint(hdfsDir)
Val context = StreamingContext.getOrCreate(hdfsDir, setupStreamingContext)
Context.start()

WAL for no data loss

Recover using WAL

Configuration – Enabling WAL
 Enable Checkpointing.
 Enable WAL in Spark Configuration
 sparkConf.set(“spark.streaming.receiver.writeAheadLog.en
able”, “true”)
 Receiver should acknowledge the input source after data
written to WAL
 Disable in-memory replication

Normal Processing

Restarting Failed Driver

Fault-Tolerant Semantics
Exactly Once, If Outputs are Idempotent or transactional
Exactly Once, as long as received data is not lost
Aleast Once, with Checkpointing / WAL
Source
Receiving
Transforming
Outputting
Sink

Fault-Tolerant Semantics
Exactly Once, If Outputs are Idempotent or transactional
Exactly Once, as long as received data is not lost
Exactly Once, with Kafka Direct API
Source
Receiving
Transforming
Outputting
Sink

How to achieve “exactly once”
guarantee?

Before Kafka Direct API

Kafka Direct API
• Simplified Parallelism
• Less Storage Need
• Exactly Once Semantics
Benefits of this approach

DISCRETIZED STREAMS (DSTREAMS)
• Dstream is basic abstraction in Spark Streaming.
• It is represented by a continuous series of RDDs(of the
same type).
• Each RDD in a DStream contains data from a certain
interval
• DStreams can either be created from live data (such as,
data from TCP sockets, Kafka, Flume, etc.) using a
Streaming Context or it can be generated by
transforming existing DStreams using operations such
as `map`, `window` and `reduceByKeyAndWindow`.

DISCRETIZED STREAMS (DSTREAMS)

WORD COUNT
val sparkConf = new SparkConf()
.setMaster("local[2]”)
.setAppName("WordCount")
val sc = new SparkContext(
sparkConf)
val file = sc.textFile(“filePath”)
val words = file
.flatMap(_.split(" "))
Val pairs = words
.map(x => (x, 1))
val wordCounts =pairs
.reduceByKey(_ + _)
wordCounts.saveAsTextFile(args(1))
val conf = new SparkConf()
.setMaster("local[2]")
.setAppName("SocketStreaming")
val ssc = new StreamingContext(
conf, Seconds(2))
val lines = ssc
.socketTextStream("localhost", 9998)
val words = lines
val pairs = words
.map(word => (word, 1))
val wordCounts = pairs
.reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()

KAFKA STREAM
val lines = ssc
.socketTextStream("localh
ost", 9998)
val words = lines
val pairs = words
.map(word => (word, 1))
val wordCounts = pairs
.reduceByKey(_ + _)
val zkQuorum="localhost:2181”;
val group="test";
val topics="test";
val numThreads="1";
val topicMap = topics
.split(",")
.map((_, numThreads.toInt))
.toMap
val lines = KafkaUtils
.createStream(
ssc, zkQuorum, group, topicMap)
.map(_._2)
val words = lines
……..

OPERATIONS
• Repartition
• Operation on RDD
(Example print partition count
of each RDD)
Val re_lines=lines
.repartition(5)
re_lines
.foreachRDD(x =>fun(x))
def fun (rdd:RDD[String]) ={
print("partition count”
+ rdd.partitions.length)
}

STATELESS TRANSFORMATIONS
• map() Apply a function to each element in the DStream and return a DStream of the result.
• ds.map(x => x + 1)
• flatMap() Apply a function to each element in the DStream and return a DStream of the contents
of the iterators returned.
• ds.flatMap(x => x.split(" "))
• filter() Return a DStream consisting of only elements that pass the condition passed to filter.
• ds.filter(x => x != 1)
• repartition() Change the number of partitions of the DStream.
• ds.repartition(10)
• reduceBy Combine values with the same Key() key in each batch.
• ds.reduceByKey( (x,y)=>x+y)
• groupBy Group values with the same Key() key in each batch.
• ds.groupByKey()

STATEFUL TRANSFORMATIONS
Stateful transformations require checkpointing to be
enabled in your StreamingContext for fault tolerance
• Windowed transformations: windowed computations
allow you to apply transformations over a sliding window
of data
• UpdateStateByKey transformation: Enables this by
providing access to a state variable for DStreams of
key/value pairs

WINDOW OPERATIONS
This shows that any window operation needs to specify two
parameters.
• window length - The duration of the window.
• sliding interval - The interval at which the window
operation is performed.
These two parameters must be multiples of the batch
interval of the source Dstream

WINDOWED TRANSFORMATIONS
• window(windowLength, slideInterval)
• Return a new Dstream, computed based on windowed batches of the source Dstream.
• countByWindow(windowLength, slideInterval)
• Return a sliding window count of elements in the stream.
• val totalWordCount= words.countByWindow(Seconds(30), Seconds(10))
• reduceByWindow(func, windowLength, slideInterval)
• Return a new single-element stream, created by aggregating elements in the stream over a sliding
interval using func.
• The function should be associative so that it can be computed correctly in parallel.
• val totalWordCount= pairs.reduceByWindow({(x, y) => x + y},{(x, y) => x – y} Seconds(10)
• reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks])
• Returns a new DStream of (K, V) pairs where the values for each key are aggregated using the
given reduce function func over batches in a sliding window
• val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30),
Seconds(10))
• countByValueAndWindow(windowLength, slideInterval)
• Returns a new DStream of (K, Long) pairs where the value of each key is its frequency within a
sliding window.
• val EachWordCount= word.countByValueAndWindow(Seconds(30), Seconds(10))

UPDATE STATE BY KEY
TRANSFORMATION
• updateStateByKey()
• Enables this by providing access to a state variable for DStreams of
key/value pairs
• User provide a function updateFunc(events, oldState) and initialRDD
• val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1),
("world", 1)))
• val updateFunc = (values: Seq[Int], state: Option[Int]) => {
val currentCount = values.foldLeft(0)(_ + _)
val previousCount = state.getOrElse(0)
Some(currentCount + previousCount)
}
• val stateCount= pairs.updateStateByKey[Int](updateFunc)

TRANSFORM OPERATION
• The transform operation allows arbitrary RDD-to-RDD
functions to be applied on a DStream.
• It can be used to apply any RDD operation that is not
exposed in the DStream API.
• For example, the functionality of joining every batch in a
data stream with another dataset is not directly exposed
in the DStream API.
• val cleanedDStream = wordCounts.transform(rdd => {
rdd.join(data)
})

JOIN OPERATIONS
• Stream-stream joins:
• Streams can be very easily joined with other streams.
• val stream1: DStream[String, String] = ...
• val stream2: DStream[String, String] = ...
• val joinedStream = stream1.join(stream2)
• Windowed join
• val windowedStream1 = stream1.window(Seconds(20))
• val windowedStream2 = stream2.window(Minutes(1))
• val joinedStream = windowedStream1.join(windowedStream2)
• Stream-dataset joins
• val dataset: RDD[String, String] = ...
• val windowedStream = stream.window(Seconds(20))...
• val joinedStream = windowedStream.transform { rdd => rdd.join(dataset) }

USING FOREACHRDD()
• foreachRDD is a powerful primitive that allows data to be sent out to
external systems.
• dstream.foreachRDD { rdd =>
rdd.foreachPartition { partitionOfRecords =>
val connection = ConnectionPool.getConnection()
partitionOfRecords.foreach(record => connection.send(record))
ConnectionPool.returnConnection(connection)
}
}
• Using foreachRDD, Each RDD is converted to a DataFrame, registered
as a temporary table and then queried using SQL.
• words.foreachRDD { rdd =>
val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)
import sqlContext.implicits._
val wordsDataFrame = rdd.toDF("word")
wordsDataFrame.registerTempTable("words")
val wordCountsDataFrame =
sqlContext.sql("select word, count(*) as total from words group by word")
wordCountsDataFrame.show()
}

DSTREAMS (SPARK CODE)
• DStreams internally is characterized by a few basic properties:
• A list of other DStreams that the DStream depends on
• A time interval at which the DStream generates an RDD
• A function that is used to generate an RDD after each time interval
• Methods that should be implemented by subclasses of Dstream
• Time interval after which the DStream generates a RDD
• def slideDuration: Duration
• List of parent DStreams on which this DStream depends on
• def dependencies: List[DStream[_]]
• Method that generates a RDD for the given time
• def compute(validTime: Time): Option[RDD[T]]
• This class contains the basic operations available on all DStreams, such as
`map`, `filter` and `window`. In addition, PairDStreamFunctions contains
operations available only on DStreams of key-value pairs, such as
`groupByKeyAndWindow` and `join`. These operations are automatically
available on any DStream of pairs (e.g., DStream[(Int, Int)] through implicit
conversions.

Apache Spark Streaming: Architecture and Fault Tolerance

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Apache Spark Streaming: Architecture and Fault Tolerance

Similaire à Apache Spark Streaming: Architecture and Fault Tolerance (20)

Dernier

Dernier (20)

Apache Spark Streaming: Architecture and Fault Tolerance

Notes de l'éditeur