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Running Faunus on Amazon EC2

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Dan LaRocque edited this page Sep 5, 2014 · 34 revisions
This is the documentation for Faunus 0.4.
Faunus was merged into Titan and renamed Titan-Hadoop in version 0.5.
Documentation for the latest Titan version is available at http://s3.thinkaurelius.com/docs/titan/current.

Amazon EC2 and Whirr make it easy to set up a Hadoop compute cluster that can then be utilized by Faunus. This section of documentation will explain how to set up a Hadoop cluster on Amazon EC2 and execute Faunus scripts.

Setting Up Whirr

Apache Whirr is a set of libraries for running cloud services. Whirr provides a cloud-neutral way to run services (you don’t have to worry about the idiosyncrasies of each provider), a common service API (the details of provisioning are particular to the service), and smart defaults for services (you can get a properly configured system running quickly, while still being able to override settings as needed). You can also use Whirr as a command line tool for deploying clusters. — The Apache Whirr Homepage


Faunus provides a Whirr recipe for loading up a Hadoop cluster that is properly versioned for the Hadoop currently used by Faunus. This recipe is reproduced below. Please see the Whirr Quick Start for more information about the parameters and how to set up an Amazon EC2 account (e.g. ssh-keygen -t rsa -P '' and setting AWS keys as environmental variables).

whirr.cluster-name=faunuscluster
whirr.instance-templates=1 hadoop-jobtracker+hadoop-namenode,3 hadoop-datanode+hadoop-tasktracker
whirr.provider=aws-ec2
whirr.identity=${env:AWS_ACCESS_KEY_ID}
whirr.credential=${env:AWS_SECRET_ACCESS_KEY}
whirr.private-key-file=${sys:user.home}/.ssh/id_rsa
whirr.public-key-file=${sys:user.home}/.ssh/id_rsa.pub
whirr.hadoop.version=1.1.1

Once your Amazon EC2 keys and ssh key files have been properly set up, a Hadoop cluster can be launched. The recipe above creates a 4 node cluster.

faunus$ whirr launch-cluster --config bin/whirr.properties
Bootstrapping cluster
Configuring template
Configuring template
Starting 3 node(s) with roles [hadoop-datanode, hadoop-tasktracker]
Starting 1 node(s) with roles [hadoop-namenode, hadoop-jobtracker]
...

When logging into the Amazon EC2 Console, the cluster machines are visible. After running the Hadoop proxy shell script (in another window), the Hadoop cluster is ready for job submissions.

faunus$. ~/.whirr/faunuscluster/hadoop-proxy.sh
Running proxy to Hadoop cluster at ec2-23-20-32-211.compute-1.amazonaws.com. Use Ctrl-c to quit.

A simple check to ensure that the Hadoop cluster is working is to see if HDFS is available.

faunus$ export HADOOP_CONF_DIR=~/.whirr/faunuscluster
faunus$ hadoop fs -ls /
Found 3 items
drwxr-xr-x   - hadoop supergroup          0 2012-07-20 19:13 /hadoop
drwxrwxrwx   - hadoop supergroup          0 2012-07-20 19:13 /tmp
drwxrwxrwx   - hadoop supergroup          0 2012-07-20 19:13 /user

Recommended Map/Reduce Tasks Per Node

Below is the recommended number of tasks for each AWS EC2 machine type. These numbers are the default values used by Amazon’s Elastic MapReduce service

m1.small 
  mapred.tasktracker.map.tasks.maximum    = "2";
  mapred.tasktracker.reduce.tasks.maximum = "1";
c1.medium 
  mapred.tasktracker.map.tasks.maximum    = "4";
  mapred.tasktracker.reduce.tasks.maximum = "2";
m1.large 
  mapred.tasktracker.map.tasks.maximum    = "4";
  mapred.tasktracker.reduce.tasks.maximum = "2";
m1.xlarge
  mapred.tasktracker.map.tasks.maximum    = "8";
  mapred.tasktracker.reduce.tasks.maximum = "4";
c1.xlarge 
  mapred.tasktracker.map.tasks.maximum    = "8";
  mapred.tasktracker.reduce.tasks.maximum = "4";

This can be set in Whirr recipe using the following properties:

hadoop-mapreduce.mapred.tasktracker.map.tasks.maximum=8
hadoop-mapreduce.mapred.tasktracker.reduce.tasks.maximum=4

Note that it is typically best to go with less than this amount as when a Hadoop cluster is under heavy load, JVMs can start to fail. Moreover, even with less mappers/reducers, performance is not greatly effected as there is less OS time spent dealing with swapping threads in and out of processing. Given that EC2 is a virtual machine environment, CPU statistics can be inaccurate. A safe configuration to use is:

hadoop-mapreduce.mapred.tasktracker.jetty.cpu.check.enabled=false

Finally, if Oracle’s Java JVM is desired, newer version of Whirr support the following Java install function.

whirr.java.install-function=install_oab_java

Running a Faunus Script

Faunus can deploy jobs to the Amazon EC2 cluster. The first thing to do is to put a graph on HDFS. For this example, use the toy data/graph-of-the-gods.json file. Once the file is in HDFS, a faunus.sh derivation can be executed.

faunus$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(_)-oOOo-----
gremlin> hdfs.copyFromLocal('data/graph-of-the-gods.json','graph-of-the-gods.json')
==>null
gremlin> g = FaunusFactory.open('bin/faunus.properties')
==>faunusgraph[graphsoninputformat->graphsonoutputformat]
gremlin> g.V.out.type
12/09/18 00:29:34 INFO mapreduce.FaunusCompiler: Compiled to 2 MapReduce job(s)
12/09/18 00:29:34 INFO mapreduce.FaunusCompiler: Executing job 1 out of 2: MapSequence[com.thinkaurelius.faunus.mapreduce.transform.VerticesMap.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Map, com.thinkaurelius.faunus.mapreduce.transform.VerticesVerticesMapReduce.Reduce]
12/09/18 00:29:34 INFO mapreduce.FaunusCompiler: Job data location: output/job-0
...
==>titan
==>god
==>god
==>god
==>god
==>god
==>god
==>god
==>location
==>location
==>location
==>location
==>human
==>monster
==>monster
==>...
gremlin> hdfs.ls('output/*') 
==>rw-r--r-- ubuntu supergroup 0 _SUCCESS
==>rwxr-xr-x ubuntu supergroup 0 (D) _logs
==>rw-r--r-- ubuntu supergroup 0 _SUCCESS
==>rwxr-xr-x ubuntu supergroup 0 (D) _logs
==>rw-r--r-- ubuntu supergroup 435 graph-m-00000.bz2
==>rw-r--r-- ubuntu supergroup 75 sideeffect-m-00000.bz2

When the cluster is no longer needed, Whirr can be used to shutdown the cluster.

faunus$ whirr destroy-cluster --config bin/whirr.properties 
Starting to run scripts on cluster for phase destroyinstances: us-east-1/i-16376a6e, us-east-1/i-0c376a74, us-east-1/i-0a376a72
Running destroy phase script on: us-east-1/i-16376a6e
Running destroy phase script on: us-east-1/i-0c376a74
Starting to run scripts on cluster for phase destroyinstances: us-east-1/i-08376a70
Running destroy phase script on: us-east-1/i-0a376a72
Running destroy phase script on: us-east-1/i-08376a70
destroy phase script run completed on: us-east-1/i-0c376a74
destroy phase script run completed on: us-east-1/i-0a376a72
destroy phase script run completed on: us-east-1/i-16376a6e
Successfully executed destroy script: [output=, error=, exitCode=0]
destroy phase script run completed on: us-east-1/i-08376a70
Successfully executed destroy script: [output=, error=, exitCode=0]
Successfully executed destroy script: [output=, error=, exitCode=0]
Successfully executed destroy script: [output=, error=, exitCode=0]
Finished running destroy phase scripts on all cluster instances
Destroying faunuscluster cluster
Cluster faunuscluster destroyed
faunus$
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