How to Integrate SQuirreL with Phoenix

If you’re looking to use a client GUI, or graphical user interface, to interact with Phoenix, you might want to give SQuirreL a try. SQuirreL is a graphical java program that lets you see the structure of a database, browse the data in tables, and perform SQL queries.

Installing SQuirreL

The first thing you’ll want to do is install SQuirreL. To do this, go here and select the appropriate download for your operating system.

Once you have downloaded the file, open a new terminal window. Navigate to wherever you downloaded the file to, and run this code:

java -jar squirrel-sql-3.6-MACOSX-install.jar

You may have to modify that code slightly based on the name of the file you downloaded (version and operating system).

Once the installer dialog pops up, follow the instructions to install SQuirreL onto your system. You can choose to select optional installs if you like. For now, I chose to just do the base and standard install:

Installing SQuirrelSQL

SQuirrelSQL is now installed on your local machine, but we’re not done yet. We still need to set it up so we can use it with Phoenix.

Configuring SQuirrelSQL for Phoenix

Before we get started, make sure you have your Hortonworks sandbox VM up and running. You should have already followed this guide to set up Phoenix on your sandbox.

Step 1 – Move Phoenix client jar to SQuirreL lib directory

In your VM, navigate to the folder where Phoenix is installed. For me that folder is /usr/hdp/ You need to move the phoenix client jar from that folder to your local machine. If you have a shared folder set up between your vm and your computer, run code similar to this to copy it to your local machine:

cp /usr/hdp/ /media/sf_hdp_shared_folder/

Once you’ve got the file on your local machine, add it to SQuirrelSQL’s lib directory. On a Mac, you do this by navigating to Applications then right clicking on SQuirrelSQL and clicking ‘Show Package Contents’:

Finding SQuirreL's lib folder

From there, navigate to Contents > Resources > Java > lib. Copy the phoenix client jar to that directory:

Adding the phoenix jar to SQuirreL's lib folder

Step 2 – Switch your VM’s network adapter to Host-only

To make things easier for us, we’re going to switch the VM’s network adapter to Host-only. This gets rid of some bugs that can pop up if you try to connect to the VM when it’s using a NAT adapter. If your VM’s network adapter is already in Host-only mode, skip ahead to step 3.

To switch to Host-only mode, first power off your Hortonworks sandbox. Open up the VirtualBox Manager, click on your Hortonworks Sandbox, and select Settings > Network. Disable your NAT adapter, or any other adapters, if they are enabled. Then, with an empty adapter spot click Enable Network Adapter. Attach it to ‘Host-only Adapter’, then select any of the available options for its Name. Leave the Advanced Settings as they are:

Host-only network settings

Troubleshooting – What if there is no option for a Host-only Adapter after the Name field?

In this case, you need to edit your VirtualBox Manager settings. Close out of the Hortonworks settings and go to VirtualBox > Preferences > Network. Select the Host-only Networks tab, then click the plus icon:

Adding a new host-only network

A new Host-only network should now be available for you to select for your Hortonworks sandbox. Make sure you go back into the settings for your Hortonworks sandbox and switch your VM’s network adapter to your new Host-only adapter. When you have done that, move on to Step 3.

Step 3 – Add hortonworks.hbase.vm to your hosts file

If you haven’t already updated your local machine’s hosts file with the ip address of your sandbox, we’re going to do that in this step. If you have already done that, skip ahead to step 4.

To add hortonworks.hbase.vm to your hosts file, open up a terminal window. On a Mac, type this code:

sudo nano /etc/hosts

You’ll be asked to enter your password, then nano (a text editor) will open. Add this line at the end of your hosts file: hortonworks.hbase.vm

This is what my hosts file currently looks like:

Hosts file

Some important notes

  • The ip address you enter may be different than mine. To check it, type this code in your Hortonworks sandbox:
  • You can call your sandbox whatever you like in the hosts file. I chose to go with hortonworks.hbase.vm, but you can call it if you like. Just remember what you called it because we’ll be using that name later.

Step 4 – Add Phoenix driver to SQuirreL

Open up SQuirrel, click the Drivers tab on the left side of the window, and click the plus button to create a new driver. Enter this information into the driver creation window:

  • Name: Phoenix
  • Example URL: jdbc:phoenix:hortonworks.hbase.vm:2181:/hbase-unsecure
  • Website URL: [Blank. Do not write anything here]
  • Class Name: org.apache.phoenix.jdbc.PhoenixDriver

It should look like this:

Phoenix Driver Settings

Click OK. You should get a message which reads “Driver class org.apache.phoenix.jdbc.PhoenixDriver successfully registered for driver definition: Phoenix”

Step 4.5 – Ensure your sandbox and HBase are running

Before continuing, make sure both your Hortonworks sandbox and HBase are running. Recall that you can check if HBase is running in Ambari at your-sandbox-ip:8080. If you’re having trouble starting HBase or getting warnings that don’t disappear after a minute or two, check out the ‘Starting HBase on the sandbox’ section of this guide.

Step 5 – Create an Alias

Switch to the Aliases tab and click the plus button to create a new alias. Enter this information in the alias creation window:

  • Name – Alias name (ex: hortonworksSandbox, whatever you want)
  • Driver – Phoenix
  • URL – This should be auto-populated when you select your driver with jdbc:phoenix:hortonworks.hbase.vm:2181:/hbase-unsecure
  • User Name – Whatever you like (ex: admin)
  • Password – Whatever you like (ex: admin)

It should look like this:

Phoenix Alias

Once you’ve filled out the above information, click Test then select Connect. A box should pop up which says “Connection successful”. Click OK then OK again to create the alias.

Step 6 – Connect

We’re almost done! Double click on your newly created alias and click Connect. You should now be successfully connected and able to run SQL queries.

For a very short guide to using SQuirreL, check out this link.

How to Integrate Apache Phoenix with HBase

If you’re looking to get started with using Apache Phoenix, the open source SQL skin for HBase, the first thing you’ll want to do is install it. This guide will show you how to do that on the Hortonworks virtual sandbox.

If you’re running your setup on a machine that isn’t the Hortonworks sandbox, the installation guide over on the Phoenix website should help. Hortonworks also has an installation guide for both unsecure and secure hadoop clusters. In this guide we’ll be setting up Phoenix on an unsecure cluster (sandbox).

What is Apache Phoenix?

Before we start, let’s talk briefly about what Phoenix is and what it can do for you. As previously mentioned, Phoenix is an open source SQL skin for HBase. This means that it takes your SQL queries and transforms them into a series of HBase scans. The transformations are all done under the hood. For the most part, you can run SQL queries over HBase as if you were merely using a relational database like MySQL or SQLite.

What are some use cases for Phoenix?

Phoenix can be used for a few different use cases:

  • At SiftScience, they use Phoenix for ad-hoc queries and exposing data insights.
  • At Alibaba, they use Phoenix for queries where there is a large dataset with a relatively small result (10,000 records or so), or for complicated queries over large dataset with a large result (millions of records).
  • At Ebay, they use Phoenix for Path or Flow analysis, as well as for real time data trends.

To see more use cases, go here.

Where to learn more

If you’re inclined to learn more about Phoenix before we get started, check out the FAQ, learn about which SQL statements are supported (a lot), or simply check out the project home page.

Installing Phoenix

Ready to get started? We’re going to be using the open-source, package management utility yum (Yellowdog Updater, Modified) to install Phoenix. To start the installation run:

yum install phoenix

Possible installation errors (and their fixes)

There’s a good chance that code will fail if you haven’t used yum before.

If it fails with the error message Couldn’t resolve host, the issue most likely stems from your network adapter. Check your network adapter settings: Machine > Settings > Network. You should have a network adapter enabled that is attached to NAT. Make sure no other network adapters are enabled. If you don’t have a NAT adapter enabled, power off your machine. Once it’s powered off you can return to the same Machine > Settings > Network menu to add or enable a NAT adapter. The default settings should be fine:

Enable NAT Adapter

If you receive the message Error: Cannot retrieve metalink for respository : epel, you will have to run this code in your VM:

sudo sed -i "s/mirrorlist=https/mirrorlist=http/" /etc/yum.repos.d/epel.repo

It will update yum’s repository to use http instead of https.

Installing Phoenix with Yum

With the above fixes in place, you should be ready to install Phoenix with yum. Run this code:

yum install phoenix

Once the installation finishes, find your Phoenix core jar file. For me it was located at /usr/hdp/ Link the Phoenix core jar file to the HBase Master and Region servers. Here was the code I used to link it:

ln -sf /usr/hdp/ /usr/hdp/

Change the version numbers if you have different versions of hadoop (hdp) or phoenix.

Edit the hbase-site.xml

The next step is editing the hbase-site.xml. Run this:

vi /usr/hdp/

Again, change version numbers in that code as necessary. Now that you’re in vi, a linux text editor, hit to change from command mode to insert mode. Insert mode will let you make changes to the text in the file, while command mode lets you cause actions that will be taken on the file. Place this code between the two configuration tags:


The file should look like this when you’re done:

hbase-site.xml file

Notice that ‘:wq’ will allow you to save the file and exit

Save the file by pressing ‘ESC’ to change from Insert Mode to Command mode, then hit ‘:wq’ to save and quit.

Start HBase

If HBase isn’t running yet, you need to start it. Similarly, if HBase is already running, you need to restart it.

Log into Ambari in your browser at with username/password admin/admin. If that doesn’t work, check which ip address to use by typing this code in your terminal:


Once you’re logged in, start HBase by clicking HBase on the left panel

Starting HBase in Ambari

then Service Actions > Start:

HBase tab in Ambari







Give it a minute or two to start if you get a red alert when it first starts up. If the alert persists, you may have to stop another service to free up memory on your sandbox. I chose to stop MapReduce2 for now. You can always enable it later.

Phoenix should now be installed and ready for use.

Testing your new Phoenix installation

To test your new Phoenix installation, navigate to phoenix’s bin folder:

cd /usr/hdp/

Let’s run the program:

python localhost:2181:/hbase-unsecure

It may take a minute or two to start up. If it hangs for too long go check Ambari to make sure HBase is still running.

Once the program starts, enter these commands:

create table test (mykey integer not null primary key, mycolumn varchar);
upsert into test values (1,'Hello');
upsert into test values (2,'World!');
select * from test;

The first command creates a table called test with an integer (numeric) key and a varchar (text) column. The next two commands insert rows into the table. In this case, the third command selects all rows from the table and prints them to the screen:

Phoenix results

That’s it for now! You’ve successfully integrated Apache Phoenix with HBase used it to create a simple table. If you’d like to use a GUI to interact with Phoenix, go check out this guide. To dive deeper into Phoenix, check out the quick start guide, or the FAQ. And as always, if you have any questions feel free to reach out to me.

HBase Development in Java

If you want to learn how to do some simple HBase operations in Java, this guide is for you.

Some important notes before we get started:

  • I’ll be using HBase’s 2.0.0 API. There are some differences between this API and other, older versions of the API. At the time of this writing, not many tutorials or guides exist for the the 2.0 version of the API. So if you run into issues trying to get something to work, looking at the API docs are probably your best bet.
  • To test the code you’ll be developing, you’ll want to use the Hortonworks Sandbox.
  • If you haven’t configured Eclipse for big data development, give this guide a look. It will cover how to set up your IDE with Maven, connect to Github, and set up a shared folder between your computer and the Hortonworks sandbox.

Ready to begin?

Starting HBase on the sandbox

The first thing you’ll want to do is start HBase on your sandbox. If you haven’t already, fire up your virtual machine and point your browser to If that doesn’t work, check your sandbox’s IP address using this code in your sandbox:


Log in to Ambari using the username ‘admin’ and password ‘admin’.

Once you reach your dashboard, click on HBase. Then click Service Actions > Start.

If your virtual box doesn’t have enough RAM available, you might have to stop some other running services to ensure that all the HBase servers are able to start. In my case, I stopped MapReduce2. You can always go back and enable it again later. Also, if you see any alerts when MapReduce2 is turned off, you can click Service Actions > Turn On Maintenance Mode.

Creating a table and inserting data into it

Now it’s time to start developing in Eclipse.

Open Eclipse and create a new Java project. If you don’t have Maven set up yet, give this guide a look. Once you’ve created your Java project., right click it and hit Configure > Convert to Maven Project.

If you read the code here, check out the comments, and add the necessary jars to your pom.xml file (hbase-client 1.1.1) it should be fairly simple to get the code running. You’ll have to uncomment a line in the main method in order to insert records. Export it like you did in the MapReduce tutorial, upload it to your VM, and run it using this code:

yarn jar BasicTableTransactions.jar BasicTableTransactions

Don’t forget to change the permissions of your uploaded jar before trying to run it:

chmod 777 BasicTableTransactions.jar

The code will create a table for you and insert a few records into it.

You can check that the code works by opening up your Hortonworks sandbox and typing:

hbase shell

Once the shell starts, you can view your table, count the number of records in it, and perform scans over the data:

describe 'business_data'
count 'business_data'
scan 'business_data',{COLUMNS => 'artist_data'}

For further clarification about the code on Github or the code mentioned above, feel free to reach out to me.

Schema design

If you want to dip your feet into creating well designed schemas in HBase, give this article a look. It’s a good introduction that covers some important design considerations. For further reading, try HBase’s suggestions in the schema section of their reference guide.

Remotely connecting to HBase

Being able to debug your code is a very important part of software development. One of the issues I ran into with testing my HBase code was a lack of clear direction as to where the log files were stored. When testing my code I tried to print logs to ensure that the program was running as I expected, but I was unable to figure out where to log files were being printed. From trying to find the job in the yarn application master (port 16010), which it seems HBase jobs don’t show up in, to reading through all the logs I could get my hands on, my log statements just weren’t showing up. If you know how or where to access the logs Java sends, please let me know and I’ll update this guide accordingly.

Back to connecting to HBase remotely.

Follow this guide. It includes source code and instructions on setting up your sandbox correctly. I skipped step 4 and started HBase via Ambari instead (port 8080). Once you switch to a host-only network adapter, the IP address you’ll use to log in will likely change. To check what it is, use this code in your Hortonworks sandbox VM:


In step 5, the author is talking about the hosts file of your local machine, not your sandbox.

Once you’re done with the guide, run your code. You should be able to successfully connect to HBase.

Customizing log events / print statements

It’s nice to use System.out.println statements to try to track what’s going on in your code, but it’s better to use log events. Log events will let you track what’s going on at a much more granular level than simple print statements would.

Ready to make the switch to log events?

The first thing you need to do is find your file. For me, this was located at

/Users/myname/Development/Hadoop/hadoop-2.7.1/share/hadoop/tools/sls/sample-conf/ If that’s too long and complicated for you, you could just make your own and place it wherever you like. Here’s what is in my file:

# Define the root logger with appender X:

log4j.rootLogger = INFO, consoleAppender

## Set the appender named X to be a console appender:
log4j.appender.consoleAppender = org.apache.log4j.ConsoleAppender

# Define the layout for console Appender appender
log4j.appender.consoleAppender.layout = org.apache.log4j.PatternLayout
log4j.appender.consoleAppender.layout.ConversionPattern=%-4r [%t] %-5p %c %x - %m%n

Notice that I set the level of the logger to be INFO. This allows the logger to ignore DEBUG events instead of printing them to screen. If you would like to see debug events, simply change INFO to DEBUG.

Now that you have your file set up, it’s time to use it. Create a new logger using this code:

private static final Log LOG = LogFactory.getLog(BasicTableTransactions.class);

The above code should be put within your class, outside of any methods. Now, put this code in your main method:

//configure log4j so it can run
Properties props = new Properties();
props.load(new FileInputStream("/Users/myname/Development/Hadoop/hadoop-2.7.1/share/hadoop/tools/sls/sample-conf/"));

Replace the path I have up there with whatever path you have your file in.

We’re almost done. Now all you have to do is log events. To do this, place this code wherever you want to log something:"Here's a log statement I want to send to the console");

If you want to change the level of the log statement (more on logging levels and their hierarchy here), change ‘info’ to whichever level you prefer. You can see how my code is using logging here and here.

Deleting all records from an HBase table

Say you ran the code above, accidentally inserted a ton of near-duplicate rows, and want to restart with an empty table. To do that, log onto your Hortonworks sandbox’s shell and type the following to launch the HBase shell:

hbase shell

Now, run this code:

truncate 'business_data'

The above code will remove your table and create a new one with the same settings. If you want to remove your table altogether, use:

disable 'business_data'
drop 'business_data'

That’s it for the guide for now. Stay tuned for updates.

MapReduce & Eclipse: a Quick Start Guide for Java Developers

Want to start developing MapReduce programs in Java using Eclipse? This guide will get you up to speed.

It will walk you through setting up Maven (a great build manager) with Eclipse, setting up Github (a great version control system) with Eclipse, setting up a shared folder between your computer and the Hortonworks sandbox, and conclude with an example MapReduce application written in Java for you to learn from.

Setting up Maven with Eclipse

When developing MapReduce programs in Eclipse, a lot of the code you’ll be using requires you to have certain .jar files on your system. One way to do this is to download the jar files yourself. A better way to do it is to use Maven. Maven helps you manage your project builds.

Installing M2Eclipse

To use Maven with Eclipse, we’ll be using the plugin M2Eclipse. Open Eclipse and navigate to Help > Install New Software. Enter ‘’ in the form after Work with: and click Add. Once the download options appear, select Maven Integration for Eclipse.

Finish the installation and restart Eclipse.

Converting a project to a Maven project

To convert your project to a Maven project, right click on it and select Configure > Convert to Maven Project. The default settings it brings up should be fine. Click Finish.

You now have a Maven Project.

Adding dependencies to your Maven project

To add dependencies (jar files) to your Maven project, click on the pom.xml. Click Dependencies, then click Add. You’ll need to input the Group ID, Artifact ID, and Version for each dependency you add. For a MapReduce program on Hortonwork’s sandbox running Hadoop version 2.7.1 I include hadoop-client 2.7.1 and commons-logging 1.1.1. The former has the Group ID org.apache.hadoop, the Artifact ID hadoop-client, and the version 2.7.1. The latter has the Group ID commons-logging, the Artifact ID commons-logging, and the version 1.1.1.

You could search the internet for the specific group, artifact, and versions you want, or you could connect Maven to a repository and do the search right from the Add popup.

Connecting to the Hortonworks repository

By now, you have M2Eclipse and a Maven project set up in Eclipse, but you still don’t have any searchable repositories. To get those, you’ll need to edit or create the settings.xml file in the .m2 folder (${HOME}/.m2/). This folder might be hidden on your computer.

Creating or Editing your settings.xml file

If you don’t have a settings.xml file, you’ll need to create it. Using your favorite text editor (Sublime Text is a nice one) paste the code from here. If you already have a settings.xml file, simply add the standard-extra-repos profile. Save your new settings as settings.xml in the .m2 folder (${HOME}/.m2/).

Now that you have a settings.xml file, go back into Eclipse. Select Eclipse > Preferences > Maven > User Settings. Update your settings so that it points to your new settings.xml file. Click Update Settings and Apply. Restart Eclipse and you should now be able to search for Maven dependencies.

This concludes the section on setting up Maven. If you run into any issues Google search will be your friend; there’s lots of people who have probably run into the bug you’re having before. If you’re still stuck, feel free to email me.

Connecting Eclipse with your Github account

Github, if you haven’t heard of it, is a popular version control system which uses Git. To learn more about it, check out the short explanation here, medium explanation here, and longer tutorials here. It’s a great way to collaborate with teammates in a distributed fashion.

Let’s get started. If you don’t already have an account at Github, create one.

Installing Egit and Jgit

Have an account? Time to set up Eclipse to work seamlessly with Github. Open Eclipse and navigate to Help > Install New Software. Enter ‘‘ in the form after Work with: and click Add. Once the download options appear, select Eclipse Team Provider and JGit.

Egit and Jgit Installation for Eclipse

Finish the installation and restart Eclipse.

Creating a repository

Now, before we can do anything in Eclipse, we need a repository to upload our project to. Go to Github in your browser, log in, and click the ‘+ New Repository’ button. Give it a name, set it to Public or Private, and initialize it with a README. You can edit the README if you like by clicking on it and hitting the ‘Edit this file’ button/pencil.

Next, copy the clone URL of your repository by clicking the copy to clipboard button right below where it says ‘HTTPS clone URL’. It’s on the right side of your repository in your browser, towards the bottom of the screen.

Cloning the repository in Eclipse

Head back to Eclipse and make sure the Git Repositories view is showing: Window > Show View > Other… > Git > Git Repositories. Click Clone a Git Repository in the new window that appeared. Egit should automatically fill out URI, Host, and Repository path. Fill out your username and password in the Authentication section. Store it in the secure store if you don’t want to keep typing it in. Now, select your Master branch, hit Next, pick where you want to store it, and hit Finish.

Sharing your project to Eclipse

The next step is sharing your project to Eclipse. To do this, right click on your project and navigate to Team > Share Project. Select the repository you just created and hit Finish.

We’re not done yet. Right click on your project and hit ‘Team > Add to Index’. This will allow you to start tracking changes. Next, create a .gitignore file so that your bin won’t be tracked by Github (tracking the bin leads to file conflicts).

Egit - adding .gitignore file


If a .gitignore already exists but you cannot see it in your project files, try clicking on the white, downward facing arrow in the Navigator pane. Select Filters… and uncheck .* resources. Make sure /bin/ and /target/ are in the .gitignore file.

You’re not ready to commit your project. Right click your project, select Team > Commit. Put in a comment about the commit and select ‘Commit and Push’.

Check your repository on Github. It should now contain your project.

You have successfully connected Eclipse with your Github account. If you’d like to learn more about git and using Github, refer to the references linked to at the start of this section.

Setting up a shared folder between your computer and the Hortonworks sandbox

Having a shared folder between your computer and the Hortonworks sandbox can help speed up your development process. To set one up using VMWare fusion follow these steps:

  1. Pause your sandbox if it is already running
  2. Navigate to the sandbox settings > sharing
  3. Click the + icon and select a folder to share between your computer and your sandbox
  4. You’re done! Your shared folder will be accessible in your sandbox at /mnt/hgfs/hdp_shared_folder/ where hdp_shared_folder is the name of your shared folder

To share a folder in VirtualBox the process is similar:

  1. Pause your sandbox if it is already running. You might have to power it off.
  2. Navigate to Settings > Shared Folders
  3. Click the + icon and select a folder to share between your computer and your sandbox
  4. Select the auto-mount option
  5. You’re done! Your shared folder will be accessible in your sandbox at /media/sf_hdp_shared_folder where hdp_shared_folder is the name of your shared folder

To copy files out of your shared folder to the current directory you’re working in use (make sure you include the dot at the end):

cp /mnt/hgfs/hdp_shared_folder/filename.txt .

Coding Your MapReduce Program

Now that you’re development environment is set up, it’s time to start developing. A great way to get started is to do the classic word count example. There’s plenty of tutorials out there to guide you through that though, so I’m going to walk you through a new kind of MapReduce program. This one will use one MapReduce job to sum the individual characters of a text file, followed by another MapReduce job to sort the output by value in descending order of occurrences.

To get started, go check out the source code, located here.

Next, either Fork the repository (which essentially creates a copy of the project under your Github account so you can modify it as you please) or simply copy and paste the source code into new, properly named class files in Eclipse.

To run the code you’ll need to export it as a runnable jar. Right click, Export > Java > Runnable Jar. Make sure JobChainer is set as the launch configuration, and export the file.

If you are unable to select JobChainer as the launch configuration, you will have to change the .classpath in your project. To do that, you must first be able to see the .classpath file in Eclipse.

Click on the white down arrow in the package explorer, then Filters. After that, make sure *.resources is unchecked:


Enable viewing of dot files in Eclipse

Classpath file







Click okay. You should now be able to view the .classpath file in your project.

In the classpath file, look for the two Hadoop dependencies which have a reference to:


Classpath dependencies

Change those references to point to the corresponding Hadoop files on your system.

When you are done, you should be able to successfully export JobChainer as a runnable jar. If you are still having issues, try running the JobChainer class in Eclipse. It should generate the necessary launch configuration for you.

Next, upload the jar to your sandbox and run it using:

hadoop jar CharCount.jar JobChainer /test/input.txt /test/output/


  • You will have to create the input directory and put an input file in there
  • You need to choose a new output folder each time you run the script
  • You may have to change the file permissions of the CharCount.jar file
    • chmod 777 CharCount.jar

Once the MapReduce job finishes, check that there is an output file:

hadoop fs -ls /test/output/

Then take a look inside the output file for your results:

hadoop fs -cat /test/output/part-r-00000

That’s it for the quick-start guide. If you have any questions, feel free to send me an email or leave a comment.

Getting Started With Apache Giraph on CDH 5.1.2

Wondering how to set up a working version of Apache Giraph on CDH 5.1.2? This guide will get you started.

Building Giraph

1. Clone Giraph from GitHub: ‘git clone’

2. Modify the hadoop_2 profile in the pom.xml contained in the giraph folder you just cloned

  • Change the hadoop.version to read ‘2.3.0-cdh5.1.2’

3. Compile, package and install Giraph: ‘mvn -Phadoop_2 -fae -DskipTests clean install’

  • Giraph is now located in giraph/giraph-core/target

Running an Example

Now that you’ve built Giraph, it’s time to run an example.

Create a simple graph text file to use as input. For example:


I called the graph tiny_graph.txt. Next, create a shell script to take care of running the example:

#remove everything from the folder called giraph/output in the hadoop file system
hadoop fs -rm -r giraph/output/*
#remove all text file from the giraph/input folder
hadoop fs -rm giraph/input/*.txt
#put the 2nd argument to this script (located at /path/) into the hdfs folder giraph/input
hadoop fs -put /path/$2 giraph/input/
#change path and ClusterURL:Port as neccesary. $1 = name of example to run. $3 = num workers
hadoop jar /path/giraph/giraph-examples/target/giraph-examples-1.1.0-SNAPSHOT-for-hadoop-2.3.0-cdh5.1.2-jar-with-dependencies.jar org.apache.giraph.GiraphRunner -D"-Xms10240m -Xmx15360m" -D mapred.job.tracker="ClusterURL:Port" -D giraph.zkList="ClusterURL:Port" org.apache.giraph.examples.$1 -vif -vip giraph/input/$2 -vof -op giraph/output/lcc -w $3 -ca giraph.SplitMasterWorker=false
rm -f part-m-00001

That’s it! You should now be able to run it with ‘sh ExampleName InputFileName.txt NumWorkers’. Thank you to Abdul Quamar, who wrote the shell script mine is based on.