[Spark meetup] Spark Streaming Overview

Jan 21, 201511 likes2,461 views

Spark Streaming allows real-time processing of live data streams. It works by dividing the streaming data into batches called DStreams, which are then processed using Spark's batch API. Common sources of data include Kafka, files, and sockets. Transformations like map, reduce, join and window can be applied to DStreams. Stateful operations like updateStateByKey allow updating persistent state. Checkpointing to reliable storage like HDFS provides fault-tolerance.

Spark
SQL
Spark
Streaming
MLlib
(machine
learning)
GraphX
(graph)
SPARK STREAMING OVERVIEW

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•
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• Kafka provides seamless integration between information of producers and consumers without blocking the producers of the
information, and without letting producers know who the final consumers are.
• Each consumer keeps control of its own offset (read)
• On demand topic creation
SPARK STREAMING OVERVIEW

• ETL and ELT, wide catalog of sources and sinks
• Flexible design of topologies and agent deployment strategies.
• Data transformation, thanks to interceptors.
•
•
SPARK STREAMING OVERVIEW

readClob
readCSV
readLine
readMultiLine
readAvro
readJson
addCurrentTime
addLocalHost
geoIP
findReplace
Split
generateUUID
decompress
If
extractJsonPaths
detectMimeType
xquery
extractURIComponents
xslt
Grok (regular
expressions)
exec
spooling
logger
SPARK STREAMING OVERVIEW

CASSANDRA
Kafka
STRATIO DEEP
STRATIO DEEP
•
•
•
•
•
•
•
SPARK STREAMING OVERVIEW

•
•
•
•
•
Shark
(SQL)
Spark
Streaming
Mllib
(machine
learning)
GraphX
(graph)
SPARK STREAMING OVERVIEW

RDD, what is that?
SPARK STREAMING OVERVIEW

Spark Streaming: Overall view
SPARK STREAMING OVERVIEW

SPARK STREAMING OVERVIEW
Spark Streaming: Overall view

Discretized Stream or DStream.
SPARK STREAMING OVERVIEW

Input DStreams and Receivers.
• Basic (distributed with Spark Streaming).
• Advanced (available as dependency).
SPARK STREAMING OVERVIEW

Basic sources
• File Stream.
• Sockets.
• Actors (Akka).
• Queue RDDs (Testing).
SPARK STREAMING OVERVIEW

Advanced sources
SPARK STREAMING OVERVIEW

Do It Yourself
• Code onStart()
• Code onStop()
• Code receive()
• Custom Receiver ready!
SPARK STREAMING OVERVIEW

• map(func), flatMap(func),
filter(func), count()
• repartition(numPartitions)
• union(otherStream)
• reduce(func),countByValue
(), reduceByKey(func,
[numTasks])
• join(otherStream,
[numTasks]), cogroup
(otherStream, [numTasks])
• transform(func)
• updateStateByKey(func)
• window(windowLength,
slideInterval)
• countByWindow(windowLength
, slideInterval)
• reduceByWindow(func,
windowLength, slideInterval)
• reduceByKeyAndWindow(func,
windowLength, slideInterval,
[numTasks])
• countByValueAndWindow(wind
owLength, slideInterval,
[numTasks])
• print()
• foreachRDD(func)
• saveAsObjectFiles(prefix,
[suffix])
• saveAsTextFiles(prefix,
[suffix])
• saveAsHadoopFiles(prefix,
[suffix])
SPARK STREAMING OVERVIEW

• Stateful transformations (updateStateByKey,
reduceByKeyAndWindow).
• As fault-tolerance mechanism, when driver crashes.
HDFS is mandatory if you are going to use operations that requires checkpointing.
SPARK STREAMING OVERVIEW

Configuration parameters
• spark.streaming.receiver.maxRate
• spark.streaming.concurrentJobs
• spark.streaming.receiver.writeAheadLogs.enable
• spark.streaming.unpersist
SPARK STREAMING OVERVIEW

each node has mutable state and
for each record they have to update
state & send new records
SPARK STREAMING OVERVIEW

Spark Streaming allows real-time processing of live data streams using the Spark engine. It discretizes streams into batches represented as RDDs, on which transformations like maps, filters and reductions can be applied. Receivers bring in data from sources like Kafka, Flume and files. Windows allow aggregating data over time periods like counting words in the last 60 seconds every 10 seconds. Combined with Spark's machine learning and graph processing libraries, it enables applications like Twitter sentiment analysis.

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