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Giraph - Quick Start


This is a step-by-step guide on getting started with Giraph. The guide is targeted towards those who want to write and test patches or run Giraph jobs on a small input. It is not intended for production-class deployment.

In what follows, we will deploy a single-node, pseudo-distributed Hadoop cluster on one physical machine. This node will act as both master/slave. That is, it will run NameNode, SecondaryNameNode, JobTracker, DataNode, and TaskTracker Java processes. We will also deploy Giraph on this node. The deployment uses the following software/configuration:

  • Ubuntu Server 12.04.2 (64-bit) with the following configuration:
    • Hardware: Dual-core 2GHz CPU (64-bit arch), 4GB RAM, 80GB HD, 100 Mbps NIC
    • Admin account: hdamin
    • Hostname: hdnode01
    • IP address:
    • Network mask:
  • Apache Hadoop
  • Apache Giraph 1.2.0-SNAPSHOT

Deploying Hadoop

We will now deploy a signle-node, pseudo-distributed Hadoop cluster. First, install Java 1.6 or later and validate the installation:

sudo apt-get install openjdk-7-jdk
java -version

You should see your Java version information. Notice that the complete JDK is installed in /usr/lib/jvm/java-7-openjdk-amd64, where you can find Java's bin and lib directories. After that, create a dedicated hadoop group, a new user account hduser, and then add this user account to the newly created group:

sudo addgroup hadoop
sudo adduser --ingroup hadoop hduser

Next, download and extract hadoop- from Apache archives (this is the default version assumed in Giraph):

su - hdadmin
cd /usr/local
sudo wget
sudo tar xzf hadoop-
sudo mv hadoop- hadoop
sudo chown -R hduser:hadoop hadoop

After installation, swich to user account hduser and edit the account's $HOME/.bashrc with the following:

export HADOOP_HOME=/usr/local/hadoop
export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64

This will set Hadoop/Java related environment variables. After that, edit $HADOOP_HOME/conf/ with the following:

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64

The second line will force Hadoop to use IPv4 instead of IPv6, even if IPv6 is configured on the machine. As Hadoop stores temporary files during its computation, you need to create a base temporary directorty for local FS and HDFS files as follows:

su - hdadmin
sudo mkdir -p /app/hadoop/tmp
sudo chown hduser:hadoop /app/hadoop/tmp
sudo chmod 750 /app/hadoop/tmp

Make sure the /etc/hosts file has the following lines (if not, add/update them):       localhost   hdnode01

Even though we can use localhost for all communication within this single-node cluster, using the hostname is generally a better practice (e.g., you might add a new node and convert your single-node, pseudo-distributed cluster to multi-node, distributed cluster).

Now, edit Hadoop configuration files core-site.xml, mapred-site.xml, and hdfs-site.xml under $HADOOP_HOME/conf to reflect the current setup. Add the new lines between <configuration>...</configuration>, as specified below:

  • Edit core-site.xml with:
  • Edit mapred-site.xml with:
    By default, Hadoop allows 2 mappers to run at once. Giraph's code, however, assumes that we can run 4 mappers at the same time. Accordingly, for this single-node, pseudo-distributed deployment, we need to add the last two properties in mapred-site.xml to reflect this requirement. Otherwise, some of Giraph's unittests will fail.
  • Edit hdfs-site.xml with:
    Notice that you just set the replication service to make only 1 copy of the files stored in HDFS. This is because you have only one data nodes. The default value is 3 and you will receive run-time exceptions if you do not change it!

Next, set up SSH for user account hduser so that you do not have to enter a passcode every time an SSH connection is started:

su - hduser
ssh-keygen -t rsa -P ""
cat $HOME/.ssh/ >> $HOME/.ssh/authorized_keys

And then SSH to hdnode01 under user account hduser (this must be to hdnode01, as we used the node's hostname in Hadoop configuration). You will be asked for a password if this is the first time you SSH to the node under this user account. When prompted, do store the public RSA key into $HOME/.ssh/known_hosts. Once you make sure you can SSH without a passcode/password, edit $HADOOP_HOME/conf/masters with this line:


Similarly, edit $HADOOP_HOME/conf/slaves with the following two lines:


These edits set a single-node, pseudo-distributed Hadoop cluster consisting of a single master and a single slave on the same physical machine. Note that if you want to deploy a multi-node, distributed Hadoop cluster, you should add other data nodes (e.g., hdnode02, hdnode03, ...) in the $HADOOP_HOME/conf/slaves file after following all of the steps above on each new node with minor changes. You can find more details on this at Apache Hadoop website.

Let us move on. To initialize HDFS, format it by running the following command:

$HADOOP_HOME/bin/hadoop namenode -format

And then start the HDFS and the map/reduce daemons in the following order:


Make sure that all necessary Java processes are running on both hdnode01 by running this command:


Which should output the following (ignore process IDs):

9079 NameNode
9560 JobTracker
9263 DataNode
9453 SecondaryNameNode
16316 Jps
9745 TaskTracker

To stop the daemons, run the equivelent $HADOOP_HOME/bin/stop-*.sh scripts in a reversed order. This is important so that you will not lose your date. You are done with deploying a single-node, pseudo-distributed Hadoop cluster.

Running a map/reduce job

Now that we have a running Hadoop cluster, we can run map/reduce jobs. We will use the WordCount example job which reads text files and counts how often words occur. The input is text files and the output is text files, each line of which contains a word and the count of how often it occurred, separated by a tab. This example is archived in $HADOOP_HOME/hadoop-examples- Let us get started. First, download a large UTF-8 text into a temporary directory, copy it to HDFS, and then make sure it is was copied successfully:

cd /tmp/
$HADOOP_HOME/bin/hadoop dfs -copyFromLocal /tmp/pg132.txt /user/hduser/input/pg132.txt
$HADOOP_HOME/bin/hadoop dfs -ls /user/hduser/input

After that, you can run the wordcount example. To launch a map/reduce job, you use the $HADOOP_HOME/bin/hadoop jar command as follows:

$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-examples- wordcount /user/hduser/input/pg132.txt /user/hduser/output/wordcount

You can monitor the progress of your job and other cluster info using the web UI for the running daemons:

Once the job is completed, you can check the output by running:

$HADOOP_HOME/bin/hadoop dfs -cat /user/hduser/output/wordcount/p* | less

Deploying Giraph

We will now deploy Giraph. In order to build Giraph from the repository, you need first to install Git and Maven 3 by running the following commands:

su - hdadmin
sudo apt-get install git
sudo apt-get install maven
mvn -version

Make sure that you have installed Maven 3 or higher. Giraph uses the Munge plugin, which requires Mave 3, to support multiple versions of Hadoop. Also, the web site plugin requires Maven 3. You can now clone Giraph from its Github mirror:

cd /usr/local/
sudo git clone
sudo chown -R hduser:hadoop giraph
su - hduser

After that, edit $HOME/.bashrc for user account hduser with the following line:

export GIRAPH_HOME=/usr/local/giraph

Save and close the file, and then validate, compile, test (if required), and then package Giraph into JAR files by running the following commands:

source $HOME/.bashrc
mvn package -DskipTests

The argument -DskipTests will skip the testing phase. This may take a while on the first run because Maven is downloading the most recent artifacts (plugin JARs and other files) into your local repository. You may also need to execute the command a couple of times before it succeeds. This is because the remote server may time out before your downloads are complete. Once the packaging is successful, you will have the Giraph core JAR $GIRAPH_HOME/giraph-core/target/giraph-1.2.0-SNAPSHOT-for-hadoop- and Giraph examples JAR $GIRAPH_HOME/giraph-examples/target/giraph-examples-1.1.0-SNAPSHOT-for-hadoop- You are done with deploying Giraph.

Running a Giraph job

With Giraph and Hadoop deployed, you can run your first Giraph job. We will use the SimpleShortestPathsComputation example job which reads an input file of a graph in one of the supported formats and computes the length of the shortest paths from a source node to all other nodes. The source node is always the first node in the input file. We will use JsonLongDoubleFloatDoubleVertexInputFormat input format. First, create an example graph under /tmp/tiny_graph.txt with the follwing:


Save and close the file. Each line above has the format [source_id,source_value,[[dest_id, edge_value],...]]. In this graph, there are 5 nodes and 12 directed edges. Copy the input file to HDFS:

$HADOOP_HOME/bin/hadoop dfs -copyFromLocal /tmp/tiny_graph.txt /user/hduser/input/tiny_graph.txt
$HADOOP_HOME/bin/hadoop dfs -ls /user/hduser/input

We will use IdWithValueTextOutputFormat output file format, where each line consists of source_id length for each node in the input graph (the source node has a length of 0, by convention). You can now run the example by:

$HADOOP_HOME/bin/hadoop jar $GIRAPH_HOME/giraph-examples/target/giraph-examples-1.2.0-SNAPSHOT-for-hadoop- org.apache.giraph.GiraphRunner org.apache.giraph.examples.SimpleShortestPathsComputation -vif -vip /user/hduser/input/tiny_graph.txt -vof -op /user/hduser/output/shortestpaths -w 1

Notice that the job is computed using a single worker using the argument -w. To get more information about running a Giraph job, run the following command:

$HADOOP_HOME/bin/hadoop jar $GIRAPH_HOME/giraph-examples/target/giraph-examples-1.2.0-SNAPSHOT-for-hadoop- org.apache.giraph.GiraphRunner -h

This will output the following:

usage: org.apache.giraph.utils.ConfigurationUtils [-aw <arg>] [-c <arg>]
       [-ca <arg>] [-cf <arg>] [-eif <arg>] [-eip <arg>] [-eof <arg>]
       [-esd <arg>] [-h] [-jyc <arg>] [-la] [-mc <arg>] [-op <arg>] [-pc
       <arg>] [-q] [-th <arg>] [-ve <arg>] [-vif <arg>] [-vip <arg>] [-vof
       <arg>] [-vsd <arg>] [-vvf <arg>] [-w <arg>] [-wc <arg>] [-yh <arg>]
       [-yj <arg>]
 -aw,--aggregatorWriter <arg>           AggregatorWriter class
 -c,--messageCombiner <arg>             Message messageCombiner class
 -ca,--customArguments <arg>            provide custom arguments for the
                                        job configuration in the form: -ca
                                        -ca <param3>=<value3> etc. It
                                        can appear multiple times, and the
                                        last one has effect for the sameparam.
 -cf,--cacheFile <arg>                  Files for distributed cache
 -eif,--edgeInputFormat <arg>           Edge input format
 -eip,--edgeInputPath <arg>             Edge input path
 -eof,--vertexOutputFormat <arg>               Edge output format
 -esd,--edgeSubDir <arg>                subdirectory to be used for the
                                        edge output
 -h,--help                              Help
 -jyc,--jythonClass <arg>               Jython class name, used if
                                        computation passed in is a python
 -la,--listAlgorithms                   List supported algorithms
 -mc,--masterCompute <arg>              MasterCompute class
 -op,--outputPath <arg>                 Vertex output path
 -pc,--partitionClass <arg>             Partition class
 -q,--quiet                             Quiet output
 -th,--typesHolder <arg>                Class that holds types. Needed
                                        only if Computation is not set
 -ve,--outEdges <arg>                   Vertex edges class
 -vif,--vertexInputFormat <arg>         Vertex input format
 -vip,--vertexInputPath <arg>           Vertex input path
 -vof,--vertexOutputFormat <arg>        Vertex output format
 -vsd,--vertexSubDir <arg>              subdirectory to be used for the
                                        vertex output
 -vvf,--vertexValueFactoryClass <arg>   Vertex value factory class
 -w,--workers <arg>                     Number of workers
 -wc,--workerContext <arg>              WorkerContext class
 -yh,--yarnheap <arg>                   Heap size, in MB, for each Giraph
                                        task (YARN only.) Defaults to
                                        giraph.yarn.task.heap.mb => 1024
                                        (integer) MB.
 -yj,--yarnjars <arg>                   comma-separated list of JAR
                                        filenames to distribute to Giraph
                                        tasks and ApplicationMaster. YARN
                                        only. Search order: CLASSPATH,
                                        HADOOP_HOME, user current dir.

You can monitor the progress of your Giraph job from the JobTracker web GUI. Once the job is completed, you can check the results by:

$HADOOP_HOME/bin/hadoop dfs -cat /user/hduser/output/shortestpaths/p* | less

Getting involved

Giraph is an open-source project and external contributions are extremely appreciated. There are many ways to get involved:

  • Subscribe to the mailing lists, particularly the user and developer lists, where you can get a feel for the state of the project and what the community is working on.
  • Try out more examples and play with Giraph on your cluster. Be sure to ask questions on the user list or file an issue if you run into problems with your particular configuration.
  • Browse the existing issues to find something you may be interested in working on. Take a look at the section on generating patches for detailed instructions on contributing your changes.
  • Make Giraph more accessable to new comers by updating this and other site documentation.

Optional: Setting up a virtual machine

You do not have a spare physical machine for deployment? No big deal, you can follow all of the steps above on a Virtual Machine (VM)! First, install Oracle VM VirtualBox Manager 4.2 or newer then create a new VM using the software/hardware configuration specified in the Overview section.

By default, VirtualBox sets up one network adapter attached to NAT for new VMs. This will enable the VM to access external networks but not other VMs or the host OS. To allow VM-to-VM and VM-to-host communication, we need to set up a new network adapter attached to a host-only adapter. To do this, go to File > Preferences > Network in VirtualBox Manager and then add a new host-only network using the defauly settings. The default IP address is with network mask and name vboxnet0. Next, for the Hadoop/Giraph VM, go to Settings > Network, enable Adapter 2, and then attach it to the host-only adapter vboxnet0. Finally, we need to configure the second adapter in the guest OS. To do this, boot the VM into the guest OS and then edit /etc/network/interfaces with the following:

auto eth1
iface eth1 inet static

Save and close the file. You now have two interfaces: eth0 for Adapter 1 (NAT, IP dynamically assigned), and eth1 for Adapter 2 (host-only, with an IP address that can reach vboxnet0 on the host OS). Finally, fire up the new interface by running:

sudo ifup eth1

In order to avoid using IP addresses and use hostnames instead, update /etc/hosts file on the VM and the host OS with the following:       localhost    vboxnet0   hdnode01

Now you can ping to the VM using its hostname instead of its IP address. You are done with setting up the VM.