Real Time Data Processing Using Spark Streaming

1© Cloudera, Inc. All rights reserved.

Hari Shreedharan, Software Engineer @ Cloudera

Committer/PMC Member, Apache Flume

Committer, Apache Sqoop

Contributor, Apache Spark

Author, Using Flume (O’Reilly)

Real Time Data Processing using Spark Streaming


Motivation for Real-Time Stream Processing

Data is being created at unprecedented rates

• Exponential data growth from mobile, web, social• Connected devices: 9B in 2012 to 50B by 2020• Over 1 trillion sensors by 2020• Datacenter IP traffic growing at CAGR of 25%

How can we harness it data in real-time?• Value can quickly degrade → capture value immediately• From reactive analysis to direct operational impact• Unlocks new competitive advantages• Requires a completely new approach...


Use Cases Across Industries

CreditIdentifyfraudulent transactions as soon as they occur.

TransportationDynamicRe-routingOf traffic orVehicle Fleet.

Retail• Dynamic InventoryManagement• Real-timeIn-storeOffers and recommendations

Consumer Internet &MobileOptimize userengagement basedon user’s currentbehavior.

HealthcareContinuouslymonitor patientvital stats and proactively identifyat-risk patients.

Manufacturing• Identifyequipmentfailures and react instantly• PerformProactivemaintenance.

SurveillanceIdentifythreatsand intrusionsIn real-time

Digital Advertising& MarketingOptimize and personalize content based on real-time information.


From Volume and Variety to Velocity

Present

Batch + Stream Processing

Time to Insight of Seconds

Big-Data = Volume + Variety

Big-Data = Volume + Variety + Velocity

PastPresent

Hadoop Ecosystem evolves as well…

Past

Big Data has evolved

Batch Processing

Time to insight of Hours


Key Components of Streaming Architectures

Data Ingestion & TransportationService

Real-Time Stream Processing Engine

Kafka Flume

System Management

Security

Data Management & Integration

Real-TimeData Serving


Canonical Stream Processing Architecture

Kafka

Data Ingest

App 1

App 2

.

.

.

Kafka Flume

HDFS HBase

Data Sources


Spark: Easy and Fast Big Data

•Easy to Develop•Rich APIs in Java, Scala, Python

• Interactive shell

•Fast to Run•General execution graphs

• In-memory storage

2-5× less codeUp to 10× faster on disk,

100× in memory


Spark Architecture

Driver

Worker

Worker

Worker

Data

RAM

Data

RAM

Data

RAM


RDDs

RDD = Resilient Distributed Datasets• Immutable representation of data• Operations on one RDD creates a new one• Memory caching layer that stores data in a distributed, fault-tolerant cache• Created by parallel transformations on data in stable storage• Lazy materialization

Two observations:a. Can fall back to disk when data-set does not fit in memoryb. Provides fault-tolerance through concept of lineage


Spark Streaming

Extension of Apache Spark’s Core API, for Stream Processing.

The Framework Provides

Fault Tolerance

Scalability

High-Throughput


Spark Streaming

• Incoming data represented as Discretized Streams (DStreams)

• Stream is broken down into micro-batches

• Each micro-batch is an RDD – can share code between batch and streaming


val tweets = ssc.twitterStream()

val hashTags = tweets.flatMap (status => getTags(status))

hashTags.saveAsHadoopFiles("hdfs://...")

flatMap flatMap flatMap

save save save

batch @ t+1batch @ t batch @ t+2tweets DStream

hashTags DStream

Stream composed of small (1-10s) batch

computations

“Micro-batch” Architecture


Use DStreams for Windowing Functions


Spark Streaming

• Runs as a Spark job

• YARN or standalone for scheduling

• YARN has KDC integration

• Use the same code for real-time Spark Streaming and for batch Spark jobs.

• Integrates natively with messaging systems such as Flume, Kafka, Zero MQ….

• Easy to write “Receivers” for custom messaging systems.


Sharing Code between Batch and Streaming

def filterErrors (rdd: RDD[String]): RDD[String] = {

rdd.filter(s => s.contains(“ERROR”))

}

Library that filters “ERRORS”

• Streaming generates RDDs periodically

• Any code that operates on RDDs can therefore be used in streaming as well


Sharing Code between Batch and Streaming

val lines = sc.textFile(…)

val filtered = filterErrors(lines)

filtered.saveAsTextFile(...)

Spark:

val dStream = FlumeUtils.createStream(ssc, "34.23.46.22", 4435)

val filtered = dStream.foreachRDD((rdd: RDD[String], time: Time) => {

filterErrors(rdd)

}))

filtered.saveAsTextFiles(…)

Spark Streaming:


Reliability

• Received data automatically persisted to HDFS Write Ahead Log to prevent data loss

• set spark.streaming.receiver.writeAheadLog.enable=true in spark conf

• When AM dies, the application is restarted by YARN

• Received, ack-ed and unprocessed data replayed from WAL (data that made it into blocks)

• Reliable Receivers can replay data from the original source, if required

• Un-acked data replayed from source.

• Kafka, Flume receivers bundled with Spark are examples

• Reliable Receivers + WAL = No data loss on driver or receiver failure!


Kafka Connectors

• Reliable Kafka DStream

• Stores received data to Write Ahead Log on HDFS for replay

• No data loss

• Stable and supported!

• Direct Kafka DStream

• Uses low level API to pull data from Kafka

• Replays from Kafka on driver failure

• No data loss

• Experimental


Flume Connector

• Flume Polling DStream

• Use Spark sink from Maven to Flume’s plugin directory

• Flume Polling Receiver polls the sink to receive data

• Replays received data from WAL on HDFS

• No data loss

• Stable and Supported!


Spark Streaming Use-Cases

• Real-time dashboards

• Show approximate results in real-time

• Reconcile periodically with source-of-truth using Spark

• Joins of multiple streams

• Time-based or count-based “windows”

• Combine multiple sources of input to produce composite data

• Re-use RDDs created by Streaming in other Spark jobs.


What is coming?

• Run on Secure YARN for more than 7 days!

• Better Monitoring and alerting

• Batch-level and task-level monitoring

• SQL on Streaming

• Run SQL-like queries on top of Streaming (medium – long term)

• Python!

• Limited support coming in Spark 1.3


Current Spark project status

• 400+ contributors and 50+ companies contributing

• Includes: Databricks, Cloudera, Intel, Yahoo! etc

• Dozens of production deployments

• Spark Streaming Survived Netflix Chaos Monkey – production ready!

• Included in CDH!


More Info..

• CDH Docs: http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH5/latest/CDH5-Installation-Guide/cdh5ig_spark_installation.html

• Cloudera Blog: http://blog.cloudera.com/blog/category/spark/

• Apache Spark homepage: http://spark.apache.org/

• Github: https://github.com/apache/spark


Thank you

[email protected]

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Real Time Data Processing Using Spark Streaming

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