anchor.diff:  . Run a MapReduce job to extract text and metadata from the raw content.
   . Run a MapReduce job to send the extracted content from HDFS to Solr using the SolrJ client. This implementation works with SolrJ's CloudServer Java client which is aware of where Solr is running via Zookeeper.
   
  -NOTE: Incremental indexing, where only changes to a document or directory are processed on successive job jar runs, is not supported. All three steps will be completed each time the job jar is run, regardless of whether the raw content hasn't changed.
  +NOTE: Incremental indexing, where only changes to a document or directory are processed on successive job jar runs, is not supported. All three steps will be completed each time the job jar is run, regardless of whether the original content has changed.
   
   The first step of the process converts the input content into a SequenceFile. In order to do this, the entire contents of that file must be read into memory so that it can be written out as a LWDocument in the SequenceFile. Thus, you should be careful to ensure that the system does not load into memory a file that is larger than the Java heap size of the process.
   
  
positive.code: 
  The job jar allows you to index many different types of content stored in HDFS to Solr. It uses MapReduce to leverage the scaling qualities of http://hadoop.apache.org[Apache Hadoop] while indexing content to Solr.
  
  NOTE: The job jar can be run from any location in your Hadoop cluster, but requires a Hadoop client if used on a server where Hadoop (`bin/hadoop)`) is not installed. The client allows the job jar to to submit the job to Hadoop. The name and availability of a client will vary depending on the Hadoop distribution vendor. See your vendor for more information about how to download a client and configure it if you need one.
  
  The job jar uses _ingest mappers_ to parse documents and prepare them to be added to a Solr index. The selection of the ingest mapper is one of the arguments provided to the job when it is started. Details of the available ingest mappers are included in the section <<Ingest Mappers>>.
  
  
negative.code: 
  `./gradlew clean shadowJar --info`
  
  This will produce one jar, named `{packageUser}-hadoop-job-{connectorVersion}.jar`, located in the a `solr-hadoop-job/build/libs` directory.
  
  If you rebuild the job jar at a later time (to take advantage of updates), remember to update the submodule with `git submodule update`.
  // end::build[]
  