9.8. Bulk Loading

9.8.1. Overview

HBase includes several methods of loading data into tables. The most straightforward method is to either use the TableOutputFormat class from a MapReduce job, or use the normal client APIs; however, these are not always the most efficient methods.

The bulk load feature uses a MapReduce job to output table data in HBase's internal data format, and then directly loads the generated StoreFiles into a running cluster. Using bulk load will use less CPU and network resources than simply using the HBase API.

9.8.2. Bulk Load Architecture

The HBase bulk load process consists of two main steps. Preparing data via a MapReduce job

The first step of a bulk load is to generate HBase data files (StoreFiles) from a MapReduce job using HFileOutputFormat. This output format writes out data in HBase's internal storage format so that they can be later loaded very efficiently into the cluster.

In order to function efficiently, HFileOutputFormat must be configured such that each output HFile fits within a single region. In order to do this, jobs whose output will be bulk loaded into HBase use Hadoop's TotalOrderPartitioner class to partition the map output into disjoint ranges of the key space, corresponding to the key ranges of the regions in the table.

HFileOutputFormat includes a convenience function, configureIncrementalLoad(), which automatically sets up a TotalOrderPartitioner based on the current region boundaries of a table. Completing the data load

After the data has been prepared using HFileOutputFormat, it is loaded into the cluster using completebulkload. This command line tool iterates through the prepared data files, and for each one determines the region the file belongs to. It then contacts the appropriate Region Server which adopts the HFile, moving it into its storage directory and making the data available to clients.

If the region boundaries have changed during the course of bulk load preparation, or between the preparation and completion steps, the completebulkloads utility will automatically split the data files into pieces corresponding to the new boundaries. This process is not optimally efficient, so users should take care to minimize the delay between preparing a bulk load and importing it into the cluster, especially if other clients are simultaneously loading data through other means.

9.8.3. Importing the prepared data using the completebulkload tool

After a data import has been prepared, either by using the importtsv tool with the "importtsv.bulk.output" option or by some other MapReduce job using the HFileOutputFormat, the completebulkload tool is used to import the data into the running cluster.

The completebulkload tool simply takes the output path where importtsv or your MapReduce job put its results, and the table name to import into. For example:

$ hadoop jar hbase-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable

The -c config-file option can be used to specify a file containing the appropriate hbase parameters (e.g., hbase-site.xml) if not supplied already on the CLASSPATH (In addition, the CLASSPATH must contain the directory that has the zookeeper configuration file if zookeeper is NOT managed by HBase).

Note: If the target table does not already exist in HBase, this tool will create the table automatically.

This tool will run quickly, after which point the new data will be visible in the cluster.

9.8.4. See Also

For more information about the referenced utilities, see Section 14.1.9, “ImportTsv” and Section 14.1.10, “CompleteBulkLoad”.

9.8.5. Advanced Usage

Although the importtsv tool is useful in many cases, advanced users may want to generate data programatically, or import data from other formats. To get started doing so, dig into ImportTsv.java and check the JavaDoc for HFileOutputFormat.

The import step of the bulk load can also be done programatically. See the LoadIncrementalHFiles class for more information.

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