Real time data processing with kafla spark integration

1© Copyright 2014 EMC Corporation. All rights reserved.
Real Time Data Streaming
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Speakers:
Sumit Gupta, Data Intelligene Engineer, EMC
Kartikeya Putturaya, Data Intelligence Engineer, EMC
ChandraSekarRao Venkata, Data Intelligence Engineer, EMC

Data Engineering at EMC IT
Stack
Distributed Frameworks: Apache Spark, Pivotal Hadoop, Apache Storm
Messaging Systems: Rabbit MQ, Apache Kafka
Relation Store: Greenplum
A glimpse on what we do
Predictive Maintenance of Exchange Servers - Monitoring over 145 exchange
servers in real time, with an analytics engine running on a 8 node cluster, processing
data volumes of ~100MB per 2 minutes
User Behavior Analytics for Network Threat Detection – Real time monitoring of
EMC’s internal networks and performing user behavior pattern analysis for threats,
again on a 8 node cluster, processing a stream of ~150MB of data any point of time

Predictive Maintenance of Exchange Servers

User Behavior Analytics for Network Threat Detection

Apache Kafka

Overview
An apache project initially developed at LinkedIn
Distributed publish-subscribe messaging system
• Designed for processing of real time activity stream data e.g. logs, metrics collections
• Written in Scala
• Does not follow JMS Standards, neither uses JMS APIs
Features
Persistent messaging
High-throughput
Supports both queue and topic semantics
Uses Zookeeper for forming a cluster of nodes (producer/consumer/broker)
and many more…
http://kafka.apache.org/

How it works

Real time transfer
Broker does not Push messages to Consumer, Consumer Polls messages from Broker.

Kafka maintains a feed of messages in categories called topics. For each topic Kafka cluster maintains a
partitioned log

Kafka Installation
Download
http://kafka.apache.org/downloads.html
Untar it
> tar -xzf kafka_<version>.tgz
> cd kafka_<version>

Start Servers
Start the Zookeeper server
> bin/zookeeper-server-start.sh config/zookeeper.properties
Pre-requisite: Zookeeper should be up and running.
Now Start the Kafka Server
> bin/kafka-server-start.sh config/server.properties

Create a topic
> bin/kafka-topics.sh --create --zookeeper
localhost:2181 --replication-factor 1 --partitions 1 --
topic test
List down all topics
> bin/kafka-topics.sh --list --zookeeper
localhost:2181
Output: test
Create/List Topics

Producer
Send some Messages
> bin/kafka-console-producer.sh --broker-list
localhost:9092 --topic test
Now type on console:
This is a message
This is another message

Consumer
Receive some Messages
> bin/kafka-console-consumer.sh --zookeeper
localhost:2181 --topic test --from-beginning
This is a message
This is another message

Copy configs
> cp config/server.properties config/server-1.properties
> cp config/server.properties config/server-2.properties
Changes in the config files.
config/server-1.properties:
broker.id=1
port=9093
log.dir=/tmp/kafka-logs-1
config/server-2.properties:
broker.id=2
port=9094
log.dir=/tmp/kafka-logs-2
Multi-Broker Cluster

Start other Nodes with new configs
> bin/kafka-server-start.sh config/server-1.properties &
> bin/kafka-server-start.sh config/server-2.properties &
Create a new topic with replication factor as 3
> bin/kafka-topics.sh --create --zookeeper localhost:2181 --
replication-factor 3 --partitions 1 --topic my-replicated-topic
List down the all topics
> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-
replicated-topic
Topic:my-replicated-topic PartitionCount:1 ReplicationFactor:3 Configs:
Topic: my-replicated-topic Partition: 0 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0
Start with New Nodes

Spark Streaming
Makes it easy to build scalable fault-tolerant streaming
applications.
Ease of Use
Fault Tolerance
Combine streaming with batch and interactive queries.

Spark Steaming Programming Model
Spark streaming provides a high level abstraction called
Discretized Stream or DStream
- represents a stream of data
- implemented as a sequence of RDDS

Spark Streaming + Kafka
There are two approaches to receive the data from Kafka for spark streaming
• Receiver based approach
• Direct approach

#import Streaming Context and KafkaUtils
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
sc = SparkContext(appName="PythonStreamingKafkaWordCount")
ssc = StreamingContext(sc, 1)
#create KafkaStream by passing zookeeper server address and topic SparkStreaming
kvs = KafkaUtils.createStream(ssc, "localhost:2181",
"spark-streaming-consumer", {“sparkStream":1})
#lines Dstream from KafkaStream
lines = kvs.map(lambda x: x[1])
#count Dstream from lines Dstream
counts = lines.flatMap(lambda line: line.split(" ")) .map(lambda word: (word, 1))
.reduceByKey(lambda a, b: a+b)
counts.pprint()
ssc.start() ssc.awaitTermination()

from pyspark.streaming.kafka import KafkaUtils
directKafkaStream = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list":
brokers})
offsetRanges = []
def storeOffsetRanges(rdd):
global offsetRanges
offsetRanges = rdd.offsetRanges()
return rdd
def printOffsetRanges(rdd):
for o in offsetRanges:
print "%s %s %s %s" % (o.topic, o.partition, o.fromOffset, o.untilOffset)
directKafkaStream
.transform(storeOffsetRanges)
.foreachRDD(printOffsetRanges)

Thank You

Real time data processing with kafla spark integration

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