MapReduceIndexerTool input splits

Different from some other indexing tools, the MapReduceIndexerTool does not operate on HDFS blocks as input splits. This means that when indexing a smaller number of large files, fewer hosts may be involved. For example, indexing two files that are each one GB results in two hosts acting as mappers. If these files were stored on a system with a 128 MB block size, other mappers might divide the work on the two files among 16 mappers, corresponding to the 16 HDFS blocks that store the two files.

This intentional design choice aligns with MapReduceIndexerTool supporting indexing non-splittable file formats such as JSON, XML, jpg, or log4j.

In theory, this could result in inefficient use of resources when a single host indexes a large file while many other hosts sit idle. In reality, this indexing strategy typically results in satisfactory performance in production environments because in most cases the number of files is large enough that work is spread throughout the cluster.

While dividing tasks by input splits does not present problems in most cases, users may still want to divide indexing tasks along HDFS splits. In that case, use the CrunchIndexerTool, which can work with Hadoop input splits using the input-file-format option.