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HBASE-1698 Review documentation for o.a.h.h.mapreduce 

git-svn-id: https://svn.apache.org/repos/asf/hadoop/hbase/trunk@808144 13f79535-47bb-0310-9956-ffa450edef68
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Michael Stack 2009-08-26 18:13:04 +00:00
parent de546af646
commit 210a157a95
2 changed files with 22 additions and 168 deletions
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1
CHANGES.txt
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@ -8,6 +8,7 @@ Release 0.21.0 - Unreleased
HBASE-1737 Regions unbalanced when adding new node (recommit)
HBASE-1792 [Regression] Cannot save timestamp in the future
HBASE-1793 [Regression] HTable.get/getRow with a ts is broken
HBASE-1698 Review documentation for o.a.h.h.mapreduce
IMPROVEMENTS
HBASE-1760 Cleanup TODOs in HTable

189
src/java/org/apache/hadoop/hbase/mapreduce/package-info.java
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@ -33,41 +33,34 @@ Input/OutputFormats, a table indexing MapReduce job, and utility
<p>MapReduce jobs deployed to a MapReduce cluster do not by default have access
to the HBase configuration under <code>$HBASE_CONF_DIR</code> nor to HBase classes.
You could add <code>hbase-site.xml</code> to $HADOOP_HOME/conf and add
<code>hbase-X.X.X.jar</code> to the <code>$HADOOP_HOME/lib</code> and copy these
changes across your cluster but the cleanest means of adding hbase configuration
hbase jars to the <code>$HADOOP_HOME/lib</code> and copy these
changes across your cluster but a cleaner means of adding hbase configuration
and classes to the cluster <code>CLASSPATH</code> is by uncommenting
<code>HADOOP_CLASSPATH</code> in <code>$HADOOP_HOME/conf/hadoop-env.sh</code>
and adding the path to the hbase jar and <code>$HBASE_CONF_DIR</code> directory.
Then copy the amended configuration around the cluster.
You'll probably need to restart the MapReduce cluster if you want it to notice
the new configuration.
</p>
<p>For example, here is how you would amend <code>hadoop-env.sh</code> adding the
built hbase jar, hbase conf, and the <code>PerformanceEvaluation</code> class from
the built hbase test jar to the hadoop <code>CLASSPATH<code>:
adding hbase dependencies here. For example, here is how you would amend
<code>hadoop-env.sh</code> adding the
built hbase jar, zookeeper (needed by hbase client), hbase conf, and the
<code>PerformanceEvaluation</code> class from the built hbase test jar to the
hadoop <code>CLASSPATH<code>:
<blockquote><pre># Extra Java CLASSPATH elements. Optional.
# export HADOOP_CLASSPATH=
export HADOOP_CLASSPATH=$HBASE_HOME/build/test:$HBASE_HOME/build/hbase-X.X.X.jar:$HBASE_HOME/build/hbase-X.X.X-test.jar:$HBASE_HOME/conf</pre></blockquote>
export HADOOP_CLASSPATH=$HBASE_HOME/build/hbase-X.X.X.jar:$HBASE_HOME/build/hbase-X.X.X-test.jar:$HBASE_HOME/conf:${HBASE_HOME}/lib/zookeeper-X.X.X.jar</pre></blockquote>
<p>Expand <code>$HBASE_HOME</code> in the above appropriately to suit your
local environment.</p>
<p>After copying the above change around your cluster, this is how you would run
the PerformanceEvaluation MR job to put up 4 clients (Presumes a ready mapreduce
cluster):
<p>After copying the above change around your cluster (and restarting), this is
how you would run the PerformanceEvaluation MR job to put up 4 clients (Presumes
a ready mapreduce cluster):
<blockquote><pre>$HADOOP_HOME/bin/hadoop org.apache.hadoop.hbase.PerformanceEvaluation sequentialWrite 4</pre></blockquote>
The PerformanceEvaluation class wil be found on the CLASSPATH because you
added <code>$HBASE_HOME/build/test</code> to HADOOP_CLASSPATH
</p>
<p>Another possibility, if for example you do not have access to hadoop-env.sh or
are unable to restart the hadoop cluster, is bundling the hbase jar into a mapreduce
are unable to restart the hadoop cluster, is bundling the hbase jars into a mapreduce
job jar adding it and its dependencies under the job jar <code>lib/</code>
directory and the hbase conf into a job jar <code>conf/</code> directory.
directory and the hbase conf into the job jars top-level directory.
</a>
<h2><a name="sink">HBase as MapReduce job data source and sink</a></h2>
@ -79,7 +72,7 @@ Writing MapReduce jobs that read or write HBase, you'll probably want to subclas
{@link org.apache.hadoop.hbase.mapreduce.TableReducer TableReducer}. See the do-nothing
pass-through classes {@link org.apache.hadoop.hbase.mapreduce.IdentityTableMapper IdentityTableMapper} and
{@link org.apache.hadoop.hbase.mapreduce.IdentityTableReducer IdentityTableReducer} for basic usage. For a more
involved example, see {@link org.apache.hadoop.hbase.mapreduce.BuildTableIndex BuildTableIndex}
involved example, see {@link org.apache.hadoop.hbase.mapreduce.RowCounter}
or review the <code>org.apache.hadoop.hbase.mapreduce.TestTableMapReduce</code> unit test.
</p>
@ -106,162 +99,22 @@ to have lots of reducers so load is spread across the hbase cluster.</p>
currently existing regions. The
{@link org.apache.hadoop.hbase.mapreduce.HRegionPartitioner} is suitable
when your table is large and your upload is not such that it will greatly
alter the number of existing regions when done; other use the default
alter the number of existing regions when done; otherwise use the default
partitioner.
</p>
<h2><a name="examples">Example Code</a></h2>
<h3>Sample Row Counter</h3>
<p>See {@link org.apache.hadoop.hbase.mapreduce.RowCounter}. You should be able to run
<p>See {@link org.apache.hadoop.hbase.mapreduce.RowCounter}. This job uses
{@link org.apache.hadoop.hbase.mapreduce.TableInputFormat TableInputFormat} and
does a count of all rows in specified table.
You should be able to run
it by doing: <code>% ./bin/hadoop jar hbase-X.X.X.jar</code>. This will invoke
the hbase MapReduce Driver class. Select 'rowcounter' from the choice of jobs
offered. You may need to add the hbase conf directory to <code>$HADOOP_HOME/conf/hadoop-env.sh#HADOOP_CLASSPATH</code>
offered. This will emit rowcouner 'usage'. Specify tablename, column to count
and output directory. You may need to add the hbase conf directory to <code>$HADOOP_HOME/conf/hadoop-env.sh#HADOOP_CLASSPATH</code>
so the rowcounter gets pointed at the right hbase cluster (or, build a new jar
with an appropriate hbase-site.xml built into your job jar).
</p>
<h3>PerformanceEvaluation</h3>
<p>See org.apache.hadoop.hbase.PerformanceEvaluation from hbase src/test. It runs
a mapreduce job to run concurrent clients reading and writing hbase.
</p>
<h3>Sample MR Bulk Uploader</h3>
<p>A students/classes example based on a contribution by Naama Kraus with logs of
documentation can be found over in src/examples/mapred.
Its the <code>org.apache.hadoop.hbase.mapreduce.SampleUploader</code> class.
Just copy it under src/java/org/apache/hadoop/hbase/mapred to compile and try it
(until we start generating an hbase examples jar). The class reads a data file
from HDFS and per line, does an upload to HBase using TableReduce.
Read the class comment for specification of inputs, prerequisites, etc.
</p>
<h3>Example to bulk import/load a text file into an HTable
</h3>
<p>Here's a sample program from
<a href="http://www.spicylogic.com/allenday/blog/category/computing/distributed-systems/hadoop/hbase/">Allen Day</a>
that takes an HDFS text file path and an HBase table name as inputs, and loads the contents of the text file to the table
all up in the map phase.
</p>
<blockquote><pre>
package com.spicylogic.hbase;
package org.apache.hadoop.hbase.mapreduce;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.io.BatchUpdate;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.lib.NullOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* Class that adds the parsed line from the input to hbase
* in the map function. Map has no emissions and job
* has no reduce.
*&#x2f;
public class BulkImport implements Tool {
private static final String NAME = "BulkImport";
private Configuration conf;
public static class InnerMap extends MapReduceBase implements Mapper&lt;LongWritable, Text, Text, Text> {
private HTable table;
private HBaseConfiguration HBconf;
public void map(LongWritable key, Text value,
OutputCollector&lt;Text, Text> output, Reporter reporter)
throws IOException {
if ( table == null )
throw new IOException("table is null");
// Split input line on tab character
String [] splits = value.toString().split("\t");
if ( splits.length != 4 )
return;
String rowID = splits[0];
int timestamp = Integer.parseInt( splits[1] );
String colID = splits[2];
String cellValue = splits[3];
reporter.setStatus("Map emitting cell for row='" + rowID +
"', column='" + colID + "', time='" + timestamp + "'");
BatchUpdate bu = new BatchUpdate( rowID );
if ( timestamp > 0 )
bu.setTimestamp( timestamp );
bu.put(colID, cellValue.getBytes());
table.commit( bu );
}
public void configure(JobConf job) {
HBconf = new HBaseConfiguration(job);
try {
table = new HTable( HBconf, job.get("input.table") );
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}
public JobConf createSubmittableJob(String[] args) {
JobConf c = new JobConf(getConf(), BulkImport.class);
c.setJobName(NAME);
FileInputFormat.setInputPaths(c, new Path(args[0]));
c.set("input.table", args[1]);
c.setMapperClass(InnerMap.class);
c.setNumReduceTasks(0);
c.setOutputFormat(NullOutputFormat.class);
return c;
}
static int printUsage() {
System.err.println("Usage: " + NAME + " &lt;input> &lt;table_name>");
System.err.println("\twhere &lt;input> is a tab-delimited text file with 4 columns.");
System.err.println("\t\tcolumn 1 = row ID");
System.err.println("\t\tcolumn 2 = timestamp (use a negative value for current time)");
System.err.println("\t\tcolumn 3 = column ID");
System.err.println("\t\tcolumn 4 = cell value");
return -1;
}
public int run(@SuppressWarnings("unused") String[] args) throws Exception {
// Make sure there are exactly 3 parameters left.
if (args.length != 2) {
return printUsage();
}
JobClient.runJob(createSubmittableJob(args));
return 0;
}
public Configuration getConf() {
return this.conf;
}
public void setConf(final Configuration c) {
this.conf = c;
}
public static void main(String[] args) throws Exception {
int errCode = ToolRunner.run(new Configuration(), new BulkImport(), args);
System.exit(errCode);
}
}
</pre></blockquote>
*/
package org.apache.hadoop.hbase.mapreduce;