How to develop Big Data Pipelines for Hadoop, by Costin Leau

Big Data Pipelines for Hadoop
Costin Leau

@costinl – SpringSource/VMware

Agenda

 Spring Ecosystem
 Spring Hadoop
• Simplifying Hadoop programming
 Use Cases
• Configuring and invoking Hadoop in your applications
• Event-driven applications
• Hadoop based workflows

Applications (Reporting/Web/…)

Analytics
Data Structured
Collection Data

Data copy
MapReduce
HDFS
3

Spring Ecosystem

 Spring Framework
• Widely deployed Apache 2.0 open source application framework
• “More than two thirds of Java developers are either using Spring today or plan to do so
within the next 2 years.“ – Evans Data Corp (2012)
• Project started in 2003
• Features: Web MVC, REST, Transactions, JDBC/ORM, Messaging, JMX
• Consistent programming and configuration model
• Core Values – “simple but powerful’
• Provide a POJO programming model
• Allow developers to focus on business logic, not infrastructure concerns
• Enable testability
 Family of projects
• Spring Security • Spring Integration
• Spring Data • Spring Batch
• Spring Hadoop (NEW!)
4

Relationship of Spring Projects

Spring Batch

On and Off Hadoop workflows

Spring Integration
Spring
Event-driven applications Hadoop

Simplify Hadoop
Spring Data programming

Redis, MongoDB, Neo4j, Gemfire

Spring Framework

Web, Messaging Applications

5

Spring Hadoop

 Simplify creating Hadoop applications
• Provides structure through a declarative configuration model
• Parameterization based on through placeholders and an expression language
• Support for environment profiles
 Start small and grow
 Features – Milestone 1
• Create, configure and execute all type of Hadoop jobs
• MR, Streaming, Hive, Pig, Cascading
• Client side Hadoop configuration and templating
• Easy HDFS, FsShell, DistCp operations though JVM scripting
• Use Spring Integration to create event-driven applications around Hadoop
• Spring Batch integration
• Hadoop jobs and HDFS operations can be part of workflow

6

Configuring and invoking Hadoop in your
applications
Simplifying Hadoop Programming

7

Hello World – Use from command line

 Running a parameterized job from the command line
applicationContext.xml
<context:property-placeholder location="hadoop-${env}.properties"/>

<hdp:configuration>
fs.default.name=${hd.fs}
</hdp:configuration>

<hdp:job id="word-count-job"
input-path=“${input.path}"
output-path="${output.path}"
mapper="org.apache.hadoop.examples.WordCount.TokenizerMapper"
reducer="org.apache.hadoop.examples.WordCount.IntSumReducer"/>

<bean id="runner" class="org.springframework.data.hadoop.mapreduce.JobRunner"
p:jobs-ref="word-count-job"/>

hadoop-dev.properties
input.path=/user/gutenberg/input/word/
output.path=/user/gutenberg/output/word/
hd.fs=hdfs://localhost:9000

java –Denv=dev –jar SpringLauncher.jar applicationContext.xml

8

Hello World – Use in an application

 Use Dependency Injection to obtain reference to Hadoop Job
• Perform additional runtime configuration and submit
public class WordService {

@Inject
private Job mapReduceJob;

public void processWords() {
mapReduceJob.submit();
}
}

9

Hive

 Create a Hive Server and Thrift Client
<hive-server port="${hive.port}" >
someproperty=somevalue
hive.exec.scratchdir=/tmp/mydir
</hive-server/>

<hive-client host="${hive.host}" port="${hive.port}"/>b

 Create Hive JDBC Client and use with Spring JdbcTemplate
• No need for connection/statement/resultset resource management
<bean id="hive-driver" class="org.apache.hadoop.hive.jdbc.HiveDriver"/>

<bean id="hive-ds"
class="org.springframework.jdbc.datasource.SimpleDriverDataSource"
c:driver-ref="hive-driver" c:url="${hive.url}"/>

<bean id="template" class="org.springframework.jdbc.core.JdbcTemplate"
c:data-source-ref="hive-ds"/>

String result = jdbcTemplate.query("show tables", new ResultSetExtractor<String>() {
public String extractData(ResultSet rs) throws SQLException, DataAccessException {
// extract data from result set
}
});

10

Pig

 Create a Pig Server with properties and specify scripts to run
• Default is mapreduce mode
<pig job-name="pigJob" properties-location="pig.properties">
pig.tmpfilecompression=true
pig.exec.nocombiner=true

<script location="org/company/pig/script.pig">
<arguments>electric=sea</arguments>
</script>

<script>
A = LOAD 'src/test/resources/logs/apache_access.log' USING PigStorage() AS
(name:chararray, age:int);
B = FOREACH A GENERATE name;
DUMP B;
</script>
</pig>

11

HDFS and FileSystem (FS) shell operations
<hdp:script id="inlined-groovy" language=“groovy">
 Use Spring File System Shell
name = UUID.randomUUID().toString()
API to invoke familiar scriptName = "src/test/resources/test.properties"
fs.copyFromLocalFile(scriptName, name)
“bin/hadoop fs” commands
// use the shell (made available under variable fsh)
• mkdir, chmod, .. dir = "script-dir"
if (!fsh.test(dir)) {
 Call using Java or JVM fsh.mkdir(dir); fsh.cp(name, dir); fsh.chmod(700, dir)
}
scripting languages println fsh.ls(dir).toString()
fsh.rmr(dir)
 Variable replacement inside <hdp:script/>
scripts

<script id="inlined-js" language="javascript">

importPackage(java.util);
importPackage(org.apache.hadoop.fs);
 Use FileSystem API to call println("${hd.fs}")
copyFromFocalFile name = UUID.randomUUID().toString()
scriptName = "src/test/resources/test.properties“
// use the file system (made available under variable fs)
fs.copyFromLocalFile(scriptName, name)
// return the file length
fs.getLength(name)

</script>
12

Hadoop DistributedCache

 Distribute and cache
• Files to Hadoop nodes
• Add them to the classpath of the child-jvm
<cache create-symlink="true">
<classpath value="/cp/some-library.jar#library.jar" />
<classpath value="/cp/some-zip.zip" />
<cache value="/cache/some-archive.tgz#main-archive" />
<cache value="/cache/some-resource.res" />
</cache>

13

Cascading

 Spring supports a type safe, Java based configuration model
 Alternative or complement to XML
 Good fit for Cascading configuration
@Configuration
public class CascadingConfig {
@Value("${cascade.sec}") private String sec;

@Bean public Pipe tsPipe() {
DateParser dateParser = new DateParser(new Fields("ts"),
"dd/MMM/yyyy:HH:mm:ss Z");
return new Each("arrival rate", new Fields("time"), dateParser);
}

@Bean public Pipe tsCountPipe() {
Pipe tsCountPipe = new Pipe("tsCount", tsPipe());
tsCountPipe = new GroupBy(tsCountPipe, new Fields("ts"));
}
}

<bean class="org.springframework.data.hadoop.cascading.CascadingConfig "/>

<bean id="cascade"
class="org.springframework.data.hadoop.cascading.HadoopFlowFactoryBean"
p:configuration-ref="hadoop-configuration" p:tail-ref="tsCountPipe" />

14

Mixing Technologies

15

Hello World + Scheduling

 Schedule a job in a standalone or web application
• Support for Spring Scheduler and Quartz Scheduler
 Submit a job every ten minutes
• Use PathUtil’s helper class to generate time based output directory
• e.g. /user/gutenberg/results/2011/2/29/10/20

<task:scheduler id="myScheduler"/>

<task:scheduled-tasks scheduler="myScheduler">
<task:scheduled ref=“mapReduceJob" method=“submit" cron="10 * * * * *"/>
</task:scheduled-tasks>

<hdp:job id="mapReduceJob" scope=“prototype"
input-path="${input.path}"
output-path="#{@pathUtils.getTimeBasedPathFromRoot()}"

<bean name="pathUtils" class="org.springframework.data.hadoop.PathUtils"
p:rootPath="/user/gutenberg/results"/>

16

Hello World + MongoDB

 Combine Hadoop and MongoDB in a single application
• Increment a counter in a MongoDB document for each user runnning a job
• Submit Hadoop job
<hdp:job id="mapReduceJob"
input-path="${input.path}" output-path="${output.path}"

<mongo:mongo host=“${mongo.host}" port=“${mongo.port}"/>

<bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
<constructor-arg ref="mongo"/>
<constructor-arg name="databaseName" value=“wcPeople"/>
</bean>

public class WordService {

@Inject private Job mapReduceJob;
@Inject private MongoTemplate mongoTemplate;

public void processWords(String userName) {

mongoTemplate.upsert(query(where(“userName”).is(userName)), update().inc(“wc”,1), “userColl”);

mapReduceJob.submit();
}
}

17

Event-driven applications

18

Enterprise Application Integration (EAI)

 EAI Starts with Messaging
 Why Messaging
•Logical Decoupling
•Physical Decoupling
• Producer and Consumer are not aware of one
another
 Easy to build event-driven applications
• Integration between existing and new applications
• Pipes and Filter based architecture

19

Pipes and Filters Architecture

 Endpoints are connected through Channels and exchange Messages

File
Producer
Endpoint
JMS Consumer
Endpoint
TCP
Route

Channel

$> cat foo.txt | grep the | while read l; do echo $l ; done

20

Spring Integration Components

 Channels  Adapters
• Point-to-Point • File, FTP/SFTP
• Publish-Subscribe • Email, Web Services, HTTP
• Optionally persisted by a • TCP/UDP, JMS/AMQP
MessageStore • Atom, Twitter, XMPP
 Message Operations • JDBC, JPA
• Router, Transformer • MongoDB, Redis
• Filter, Resequencer • Spring Batch
• Splitter, Aggregator • Tail, syslogd, HDFS
 Management
• JMX
• Control Bus

21

Spring Integration

 Implementation of Enterprise Integration Patterns
• Mature, since 2007
• Apache 2.0 License
 Separates integration concerns from processing logic
• Framework handles message reception and method invocation
• e.g. Polling vs. Event-driven
• Endpoints written as POJOs
• Increases testability

Endpoint Endpoint

22

Spring Integration – Polling Log File example

 Poll a directory for files, files are rolled over every 10 seconds.
 Copy files to staging area
 Copy files to HDFS
 Use an aggregator to wait for “all 6 files in 1 minute interval” to
launch MR job

23

Spring Integration – Configuration and Tooling

 Behind the scenes, configuration is XML or Scala DSL based

<file:inbound-channel-adapter id="filesInAdapter" channel="filInChannel"
directory="#{systemProperties['user.home']}/input">
<integration:poller fixed-rate="5000"/>
</file:inbound-channel-adapter>

 Integration with Eclipse

24

Spring Integration – Streaming data from a Log File

 Tail the contents of a file
 Transformer categorizes messages
 Route to specific channels based on category
 One route leads to HDFS write and filtered data stored in Redis

25

Spring Integration – Multi-node log file example

 Spread log collection across multiple machines
 Use TCP Adapters
• Retries after connection failure
• Error channel gets a message in case of failure
• Can startup when application starts or be controlled via Control Bus
• Send(“@tcpOutboundAdapter.retryConnection()”), or stop, start, isConnected.

26

Hadoop Based Workflows

27

Spring Batch

 Enables development of customized enterprise batch applications
essential to a company’s daily operation
 Extensible Batch architecture framework
• First of its kind in JEE space, Mature, since 2007, Apache 2.0 license
• Developed by SpringSource and Accenture
• Make it easier to repeatedly build quality batch jobs that employ best practices
• Reusable out of box components
• Parsers, Mappers, Readers, Processors, Writers, Validation Language
• Support batch centric features
• Automatic retries after failure
• Partial processing, skipping records
• Periodic commits
• Workflow – Job of Steps – directed graph, parallel step execution, tracking, restart, …
• Administrative features – Command Line/REST/End-user Web App
• Unit and Integration test friendly

28

Off Hadoop Workflows

 Client, Scheduler, or SI calls job launcher
to start job execution
 Job is an application component
representing a batch process
 Job contains a sequence of steps.
• Steps can execute sequentially, non-
sequentially, in parallel
• Job of jobs also supported
 Job repository stores execution metadata
 Steps can contain item processing flow
<step id="step1">
<tasklet>
<chunk reader="flatFileItemReader" processor="itemProcessor" writer=“jdbcItemWriter"
commit-interval="100" retry-limit="3"/>
</chunk>
</tasklet>
</step>

 Listeners for Job/Step/Item processing
29


<step id="step1">
<tasklet>
<chunk reader="flatFileItemReader" processor="itemProcessor" writer=“mongoItemWriter"
</chunk>
</tasklet>
</step>

30


<step id="step1">
<tasklet>
<chunk reader="flatFileItemReader" processor="itemProcessor" writer=“hdfsItemWriter"
</chunk>
</tasklet>
</step>

31

On Hadoop Workflows

 Reuse same infrastructure for
Hadoop based workflows

HDFS

PIG

 Step can any Hadoop job type
MR Hive
or HDFS operation

HDFS

32

Spring Batch Configuration
<job id="job1">
<step id="import" next="wordcount">
<tasklet ref=“import-tasklet"/>
</step>

<step id="wordcount" next="pig">
<tasklet ref="wordcount-tasklet" />
</step>

<step id="pig">
<tasklet ref="pig-tasklet"
</step>

<split id="parallel" next="hdfs">
<flow>
<step id="mrStep">
<tasklet ref="mr-tasklet"/>
</step>
</flow>
<flow>
<step id="hive">
<tasklet ref="hive-tasklet"/>
</step>
</flow>
</split>

<step id="hdfs">
<tasklet ref="hdfs-tasklet"/>
</step>
</job>

33

Spring Batch Configuration

 Additional XML configuration behind the graph
 Reuse previous Hadoop job definitions
• Start small, grow

<script-tasklet id=“import-tasklet">
<script location="clean-up-wordcount.groovy"/>
</script-tasklet>

<tasklet id="wordcount-tasklet" job-ref="wordcount-job"/>

<job id=“wordcount-job" scope=“prototype"
input-path="${input.path}"
output-path="#{@pathUtils.getTimeBasedPathFromRoot()}"

<pig-tasklet id="pig-tasklet">
<script location="org/company/pig/handsome.pig" />
</pig-tasklet>

<hive-tasklet id="hive-script">
<script location="org/springframework/data/hadoop/hive/script.q" />
</hive-tasklet>

34

Questions

 At milestone 1 – welcome feedback
 Project Page: http://www.springsource.org/spring-data/hadoop
 Source Code: https://github.com/SpringSource/spring-hadoop
 Forum: http://forum.springsource.org/forumdisplay.php?87-Hadoop
 Issue Tracker: https://jira.springsource.org/browse/SHDP
 Blog: http://blog.springsource.org/2012/02/29/introducing-spring-
hadoop/
 Books

35

How to develop Big Data Pipelines for Hadoop, by Costin Leau

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