Using HBase Row Decoder with Pentaho MapReduce

The HBase Row Decoder step is designed specifically for use in MapReduce transformations to decode the key and value data that is output by the TableInputFormat. The key output is the row key from HBase. The value is an HBase result object containing all the column values for the row.

The following use case shows you how to configure Pentaho MapReduce to use the TableInputFormat for reading data from HBase. It also shows you how to configure a MapReduce transformation to process that data using the HBase Row Decoder step.

Note: To process HBase data using incoming key and value data to produce a specified mapping, you will need to configure Hadoop to access HBase.

First, create a Pentaho MapReduce job entry that includes a transformation which uses a MapReduce Input step and an HBase row decoder step, as shown below:

In the transformation, open the MapReduce Input step. Configure the Key field and Value field to produce a serialized result by selecting Serializable in the Type field:

Next, open the HBase row decoder step and set the Key field to use the key and the HBase result field to use the value produced by the MapReduce Input step.

Then, define or load a mapping in the Create/Edit mappings tab. Note that once defined (or loaded), this mapping is captured in the transformation metadata.

Next, configure Pentaho MapReduce job entry to ensure that input splits are created using the TableInputFormat. Define the Input Path and Input format fields in the Job Setup tab, as shown below.

Finally, in the User Defined tab, assign a Name and Value for each property shown in the table below to configure the scan performed by the TableInputFormat:

When you execute the job, the output is the row key from HBase and the value is an HBase result object containing all the column values for that row.