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Pig and Hbase integration


The Hadoop ecosystem contains a lot of sub project. Hbase and Pig are just some of them.

Hbase is the Hadoop database, allowing to manage your data in a table way more than in a file way.

Pig is a scripting language that will generate on the fly map reduce job to get the data you need. It is very compact compared to hand writing map reduce job.

One of the nice thing between Pig and Hbase is that they can be integrated. Thanks to recent patch committed.

The documentation is not well updated yet (currently almost relate to the patch itself) some can be found on some post like here but they all lack of details explanation. Even the Cloudera distribution CDH3 indicates support for this integration but no sample can be found.

Below I describe the installation and configuration steps to make the integration works, provide and example and finally expose some of the limits of the current release (0.8)

  1. First, install the map reduce components (Job tracker and Task tracker). One Job tracker and many task tracker as you have data nodes. Each distribution may provide different procedure for the installation, I’m using the Cloudera CDH3 distrib, which for the map reduce installation is well documented.
  2. Now proceed with the Pig installation,  it is also easy as long you are not trying the integration with Hbase.  You need only to install pig on the client side, you do not need to install it on each Data Node neither on the Name Node, but just on the machine where you want to run the pig program.
  3. Check your installation by entering the the grunt shell (just enter ‘pig’ from the shell).
  4. Now the tricky part – In order to use Pig/Hbase integration you in fact need to make Map Reduce jobs aware of Hbase classes, otherwise you will have “ClassNotFoundException” or worst the zookeeper exception like “org.apache.zookeeper.KeeperException$ConnectionLossException: KeeperErrorCode = ConnectionLoss for /hbase” during execution. The way to perform this easily without coping the hbase configurations into your hadoop configuration dir, is by using hadoop-env.sh and hbase to print its own classpath.
    So add to your hadoop-env.sh file file the following

    #define the location of hbase
    export HBASE_HOME=/usr/lib/hbase
    #Customize the classpath to make map reduce job aware of Hbase
    export HADOOP_CLASSPATH="`${HBASE_HOME}/bin/hbase classpath`:$HADOOP_CLASSPATH"
    </span>
  5. You will also need pig to be aware of Hbase configuration, for this you can use the HBASE_CONF_DIR environment variable (for CDH release), which is configured by default to be /etc/hbase/conf,

Ok your installation should be fine now, so let’s do an example…. For this example let assume we have stored in HBase a schema named TestTable, and column family named A, we have also several fields named field0, field1,…, and we want to extract this information and store it into ‘results/extract’.  In this case the pig script will looks like:

my_data = LOAD 'hbase://TestTable' USING org.apache.pig.backend.hadoop.hbase.HBaseStorage('A:field0 A:field1', '-loadKey=true') as (id, field0, field1);

store my_data into 'results/extract' using PigStorage(';');

So the above script indicate that the my_data relation will contains the fields “field0, field1″ and the ID (due to the -loadKey parameter). These fields will be stored as id, field0, field1 under the ‘result/extract’ folder and values will be separated by semicolon.

You can also use some comparison operator on the key. The current operator supported are lt, lte, gt, gte for lower than, lower than or equal, greater than and greather than or equal.

 Note: There is no support for logical operator, you can use more than one comparison operator which are chained as AND.

Limitations:

The current HBaseStorage, does not allow the usage of wildcard, that is if you need all the fields in a row, you need to enumerate them. Wildcard are supported in version 0.9.

You can use HBaseStorage to store back the records in HBase nevertheless the HBase usage is incosistent a bug was already opened on this.

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  1. February 17, 2013 at 17:43

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