Author: omalley
Date: Thu Dec 6 11:04:03 2007
New Revision: 601817
URL: http://svn.apache.org/viewvc?rev=601817&view=rev
Log:
HADOOP-1327. Adding user documentation for streaming.
Added:
lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/streaming.xml
Modified:
lucene/hadoop/trunk/CHANGES.txt
lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/site.xml
Modified: lucene/hadoop/trunk/CHANGES.txt
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/CHANGES.txt?rev=601817&r1=601816&r2=601817&view=diff
==============================================================================
--- lucene/hadoop/trunk/CHANGES.txt (original)
+++ lucene/hadoop/trunk/CHANGES.txt Thu Dec 6 11:04:03 2007
@@ -103,6 +103,9 @@
Earlier, it was a random backoff from an interval.
(Amar Kamat via ddas)
+ HADOOP-1327. Include website documentation for streaming. (Rob Weltman
+ via omalley)
+
OPTIMIZATIONS
HADOOP-1898. Release the lock protecting the last time of the last stack
Modified: lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/site.xml
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/site.xml?rev=601817&r1=601816&r2=601817&view=diff
==============================================================================
--- lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/site.xml
(original)
+++ lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/site.xml Thu
Dec 6 11:04:03 2007
@@ -22,6 +22,7 @@
<setup label="Cluster Setup" href="cluster_setup.html" />
<hdfs label="HDFS Architecture" href="hdfs_design.html" />
<mapred label="Map-Reduce Tutorial" href="mapred_tutorial.html" />
+ <streaming label="Streaming" href="streaming.html" />
<api label="API Docs" href="ext:api/index" />
<wiki label="Wiki" href="ext:wiki" />
<faq label="FAQ" href="ext:faq" />
Added:
lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/streaming.xml
URL:
http://svn.apache.org/viewvc/lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/streaming.xml?rev=601817&view=auto
==============================================================================
--- lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/streaming.xml
(added)
+++ lucene/hadoop/trunk/src/docs/src/documentation/content/xdocs/streaming.xml
Thu Dec 6 11:04:03 2007
@@ -0,0 +1,542 @@
+<?xml version="1.0"?>
+
+<!DOCTYPE document PUBLIC "-//APACHE//DTD Documentation V2.0//EN"
+ "http://forrest.apache.org/dtd/document-v20.dtd";>
+
+
+<document>
+<header>
+<title>Hadoop Streaming</title>
+<meta name="http-equiv">Content-Type</meta>
+<meta name="content">text/html;</meta>
+<meta name="charset">utf-8</meta>
+</header>
+<body>
+<section>
+<title>Hadoop Streaming</title>
+
+<p>
+Hadoop streaming is a utility that comes with the Hadoop distribution. The
utility allows you to create and run map/reduce jobs with any executable or
script as the mapper and/or the reducer. For example:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper /bin/cat \
+ -reducer /bin/wc
+</source>
+</section>
+
+<section>
+<title>How Does Streaming Work </title>
+<p>
+In the above example, both the mapper and the reducer are executables that
read the input from stdin (line by line) and emit the output to stdout. The
utility will create a map/reduce job, submit the job to an appropriate cluster,
and monitor the progress of the job until it completes.
+</p><p>
+ When an executable is specified for mappers, each mapper task will launch
the executable as a separate process when the mapper is initialized. As the
mapper task runs, it converts its inputs into lines and feed the lines to the
stdin of the process. In the meantime, the mapper collects the line oriented
outputs from the stdout of the process and converts each line into a key/value
pair, which is collected as the output of the mapper. By default, the
+ <em>prefix of a line up to the first tab character</em> is the
<strong>key</strong> and the the rest of the line (excluding the tab character)
will be the <strong>value</strong>. However, this can be customized, as <a
href="#Customizing_the_Way_to_Split_Lin">discussed later</a>.
+</p>
+<p>
+When an executable is specified for reducers, each reducer task will launch
the executable as a separate process then the reducer is initialized. As the
reducer task runs, it converts its input key/values pairs into lines and feeds
the lines to the stdin of the process. In the meantime, the reducer collects
the line oriented outputs from the stdout of the process, converts each line
into a key/value pair, which is collected as the output of the reducer. By
default, the prefix of a line up to the first tab character is the key and the
the rest of the line (excluding the tab character) is the value. However, this
can be customized, as <a href="#Customizing_the_Way_to_Split_Lin">discussed
later</a>.
+</p><p>
+This is the basis for the communication protocol between the map/reduce
framework and the streaming mapper/reducer.
+</p><p>
+You can supply a Java class as the mapper and/or the reducer. The above
example is equivalent to:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
+ -reducer /bin/wc
+</source>
+</section>
+
+<section>
+<title>Package Files With Job Submissions</title>
+<p>
+You can specify any executable as the mapper and/or the reducer. The
executables do not need to pre-exist on the machines in the cluster; however,
if they don't, you will need to use "-file" option to tell the framework to
pack your executable files as a part of job submission. For example:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper myPythonScript.py \
+ -reducer /bin/wc \
+ -file myPythonScript.py
+</source>
+<p>
+The above example specifies a user defined Python executable as the mapper.
The option "-file myPythonScript.py" causes the python executable shipped to
the cluster machines as a part of job submission.
+</p>
+<p>
+In addition to executable files, you can also package other auxiliary files
(such as dictionaries, configuration files, etc) that may be used by the mapper
and/or the reducer. For example:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper myPythonScript.py \
+ -reducer /bin/wc \
+ -file myPythonScript.py \
+ -file myDictionary.txt
+</source>
+</section>
+
+<section>
+<title>Streaming Options and Usage </title>
+
+<section>
+<title>Mapper-Only Jobs </title>
+<p>
+Often, you may want to process input data using a map function only. To do
this, simply set mapred.reduce.tasks to zero. The map/reduce framework will not
create any reducer tasks. Rather, the outputs of the mapper tasks will be the
final output of the job.
+</p><p>
+To be backward compatible, Hadoop Streaming also supports the "-reduce NONE"
option, which is equivalent to "-jobconf mapred.reduce.tasks=0".
+</p>
+</section>
+
+<section>
+<title>Specifying Other Plugins for Jobs </title>
+<p>
+Just as with a normal map/reduce job, you can specify other plugins for a
streaming job:
+</p>
+<source>
+ -inputformat JavaClassName
+ -outputformat JavaClassName
+ -partitioner JavaClassName
+ -combiner JavaClassName
+</source>
+<p>
+The class you supply for the input format should return key/value pairs of
Text class. If you do not specify an input format class, the TextInputFormat is
used as the default. Since the TextInputFormat returns keys of LongWritable
class, which are actually not part of the input data, the keys will be
discarded; only the values will be piped to the streaming mapper.
+</p><p>
+The class you supply for the output format is expected to take key/value pairs
of Text class. If you do not specify an output format class, the
TextOutputFormat is used as the default.
+</p>
+</section>
+
+<section>
+<title>Large files and archives in Hadoop Streaming </title>
+
+<p>
+The -cacheFile and -cacheArchive options allow you to make files and archives
available to the tasks. The argument is a URI to the file or archive that you
have already uploaded to HDFS. These files and archives are cached across jobs.
You can retrieve the host and fs_port values from the fs.default.name config
variable.
+</p>
+<p>
+Here are examples of the -cacheFile option:
+</p>
+<source>
+-cacheFile hdfs://host:fs_port/user/testfile.txt#testlink
+</source>
+<p>
+In the above example, the part of the url after # is used as the symlink name
that is created in the current working directory of tasks. So the tasks will
have a symlink called testlink in the cwd that points to a local copy of
testfile.txt. Multiple entries can be specified as:
+</p>
+<source>
+-cacheFile hdfs://host:fs_port/user/testfile1.txt#testlink1 -cacheFile
hdfs://host:fs_port/user/testfile2.txt#testlink2
+</source>
+<p>
+The -cacheArchive option allows you to copy jars locally to the cwd of tasks
and automatically unjar the files. For example:
+</p>
+<source>
+-cacheArchive hdfs://host:fs_port/user/testfile.jar#testlink3
+</source>
+<p>
+In the example above, a symlink testlink3 is created in the current working
directory of tasks. This symlink points to the directory that stores the
unjarred contents of the uploaded jar file.
+</p>
+<p>
+Here's another example of the -cacheArchive option. Here, the input.txt file
has two lines specifying the names of the two files: testlink/cache.txt and
testlink/cache2.txt. "testlink" is a symlink to the archived directory, which
has the files "cache.txt" and "cache2.txt".
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input "/user/me/samples/cachefile/input.txt" \
+ -mapper "xargs cat" \
+ -reducer "cat" \
+ -output "/user/me/samples/cachefile/out" \
+ -cacheArchive
'hdfs://hadoop-nn1.example.com:8020/user/me/samples/cachefile/cachedir.jar#testlink'
\
+ -jobconf mapred.map.tasks=1 \
+ -jobconf mapred.reduce.tasks=1 \
+ -jobconf mapred.job.name="Experiment"
+
+$ ls test_jar/
+cache.txt cache2.txt
+
+$ jar cvf cachedir.jar -C test_jar/ .
+added manifest
+adding: cache.txt(in = 30) (out= 29)(deflated 3%)
+adding: cache2.txt(in = 37) (out= 35)(deflated 5%)
+
+$ hadoop dfs -put cachedir.jar samples/cachefile
+
+$ hadoop dfs -cat /user/me/samples/cachefile/input.txt
+testlink/cache.txt
+testlink/cache2.txt
+
+$ cat test_jar/cache.txt
+This is just the cache string
+
+$ cat test_jar/cache2.txt
+This is just the second cache string
+
+$ hadoop dfs -ls /user/me/samples/cachefile/out
+Found 1 items
+/user/me/samples/cachefile/out/part-00000 <r 3> 69
+
+$ hadoop dfs -cat /user/me/samples/cachefile/out/part-00000
+This is just the cache string
+This is just the second cache string
+
+</source>
+</section>
+
+<section>
+<title>Specifying Additional Configuration Variables for Jobs </title>
+<p>
+You can specify additional configuration variables by using "-jobconf
<n>=<v>". For example:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper org.apache.hadoop.mapred.lib.IdentityMapper\
+ -reducer /bin/wc \
+ -jobconf mapred.reduce.tasks=2
+</source>
+<p>
+The -jobconf mapred.reduce.tasks=2 in the above example specifies to use two
reducers for the job.
+</p>
+<p>
+For more details on the jobconf parameters see: <a
href="http://wiki.apache.org/lucene-hadoop/JobConfFile";>http://wiki.apache.org/lucene-hadoop/JobConfFile</a></p>
+</section>
+
+<section>
+<title>Other Supported Options </title>
+<p>
+Other options you may specify for a streaming job are described here:
+</p>
+<table>
+<tr><th>Parameter</th><th>Optional/Required </th><th>Description </th></tr>
+<tr><td> -cluster name </td><td> Optional </td><td> Switch between local
Hadoop and one or more remote clusters </td></tr>
+
+<tr><td> -dfs host:port or local </td><td> Optional </td><td> Override the
DFS configuration for the job </td></tr>
+<tr><td> -jt host:port or local </td><td> Optional </td><td> Override the
JobTracker configuration for the job </td></tr>
+<tr><td> -additionalconfspec specfile </td><td> Optional </td><td> Specifies a
set of configuration variables in an XML file like hadoop-site.xml, instead of
using multiple options of type "-jobconf name=value" </td></tr>
+
+<tr><td> -cmdenv name=value </td><td> Optional </td><td> Pass env var to
streaming commands </td></tr>
+<tr><td> -cacheFile fileNameURI </td><td> Optional </td><td> Specify a file to
be uploaded to the HDFS </td></tr>
+<tr><td> -cacheArchive fileNameURI </td><td> Optional </td><td> Specify a jar
file to be uploaded to the HDFS. This jar file is unjarred automatically in the
cwd of the task </td></tr>
+
+<tr><td> -inputreader JavaClassName </td><td> Optional </td><td> For
backwards-compatibility: specifies a record reader class (instead of an input
format class) </td></tr>
+<tr><td> -verbose </td><td> Optional </td><td> Verbose output </td></tr>
+</table>
+<p>
+To switch between "local" Hadoop and one or more remote Hadoop clusters use
-cluster <name>.
+By default, hadoop-default.xml and hadoop-site.xml are used. The -cluster
<name> option will cause $HADOOP_HOME/conf/hadoop-<name>.xml to be
used instead.
+</p>
+<p>
+To change the local temp directory use:
+</p>
+<source>
+ -jobconf dfs.data.dir=/tmp
+</source>
+<p>
+To specify additional local temp directories use:
+</p>
+<source>
+ -jobconf mapred.local.dir=/tmp/local
+ -jobconf mapred.system.dir=/tmp/system
+ -jobconf mapred.temp.dir=/tmp/temp
+</source>
+<p>
+For more details on jobconf parameters see: <a
href="http://wiki.apache.org/lucene-hadoop/JobConfFile";>http://wiki.apache.org/lucene-hadoop/JobConfFile</a>
+</p><p>
+To set an environment variable in a streaming command use:
+</p>
+<source>
+-cmdenv EXAMPLE_DIR=/home/example/dictionaries/
+</source>
+</section>
+</section>
+
+<section>
+<title>More usage examples </title>
+
+<section>
+<title>Customizing the Way to Split Lines into Key/Value Pairs </title>
+<p>
+As noted earlier, when the map/reduce framework reads a line from the stdout
of the mapper, it splits the line into a key/value pair. By default, the prefix
of the line up to the first tab character is the key and the the rest of the
line (excluding the tab character) is the value.
+</p>
+<p>
+However, you can customize this default. You can specify a field separator
other than the tab character (the default), and you can specify the nth (n >=
1) character rather than the first character in a line (the default) as the
separator between the key and value. For example:
+</p>
+
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
+ -reducer org.apache.hadoop.mapred.lib.IdentityReducer \
+ -jobconf stream.map.output.field.separator=. \
+ -jobconf stream.num.map.output.key.fields=4
+</source>
+<p>
+In the above example, "-jobconf stream.map.output.field.separator=." specifies
"." as the field separator for the map outputs, and the prefix up to the fourth
"." in a line will be the key and the rest of the line (excluding the fourth
".") will be the value. If a line has less than four "."s, then the whole line
will be the key and the value will be an empty Text object (like the one
created by new Text("")).
+</p><p>
+Similarly, you can use "-jobconf stream.reduce.output.field.separator=SEP" and
"-jobconf stream.num.reduce.output.fields=NUM" to specify the nth field
separator in a line of the reduce outputs as the separator between the key and
the value.
+</p>
+</section>
+
+
+<section>
+<title>A Useful Partitioner Class (secondary sort, the -partitioner
org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner option) </title>
+<p>
+Hadoop has a library class,
org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner, that is useful for many
applications. This class allows the map/reduce framework to partition the map
outputs based on prefixes of keys, not the whole keys. For example:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
+ -reducer org.apache.hadoop.mapred.lib.IdentityReducer \
+ -partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner \
+ -jobconf stream.map.output.field.separator=. \
+ -jobconf stream.num.map.output.key.fields=4 \
+ -jobconf map.output.key.field.separator=. \
+ -jobconf num.key.fields.for.partition=2 \
+ -jobconf mapred.reduce.tasks=12
+</source>
+<p>
+Here, <em>-jobconf stream.map.output.field.separator=.</em> and <em>-jobconf
stream.num.map.output.key.fields=4</em> are as explained in previous example.
The two variables are used by streaming to identify the key/value pair of
mapper.
+</p><p>
+The map output keys of the above map/reduce job normally have four fields
separated by ".". However, the map/reduce framework will partition the map
outputs by the first two fields of the keys using the <em>-jobconf
num.key.fields.for.partition=2</em> option. Here, <em>-jobconf
map.output.key.field.separator=.</em> specifies the separator for the
partition. This guarantees that all the key/value pairs with the same first two
fields in the keys will be partitioned into the same reducer.
+</p><p>
+<em>This is effectively equivalent to specifying the first two fields as the
primary key and the next two fields as the secondary. The primary key is used
for partitioning, and the combination of the primary and secondary keys is used
for sorting.</em> A simple illustration is shown here:
+</p>
+<p>
+Output of map (the keys)</p><source>
+11.12.1.2
+11.14.2.3
+11.11.4.1
+11.12.1.1
+11.14.2.2
+
+</source>
+<p>
+Partition into 3 reducers (the first 2 fields are used as keys for
partition)</p><source>
+11.11.4.1
+-----------
+11.12.1.2
+11.12.1.1
+-----------
+11.14.2.3
+11.14.2.2
+</source>
+<p>
+Sorting within each partition for the reducer(all 4 fields used for
sorting)</p><source>
+11.11.4.1
+-----------
+11.12.1.1
+11.12.1.2
+-----------
+11.14.2.2
+11.14.2.3
+</source>
+</section>
+
+<section>
+<title>Working with the Hadoop Aggregate Package (the -reduce aggregate
option) </title>
+<p>
+Hadoop has a library package called "Aggregate" (<a
href="https://svn.apache.org/repos/asf/lucene/hadoop/trunk/src/java/org/apache/hadoop/mapred/lib/aggregate";>https://svn.apache.org/repos/asf/lucene/hadoop/trunk/src/java/org/apache/hadoop/mapred/lib/aggregate</a>).
Aggregate provides a special reducer class and a special combiner class, and
a list of simple aggregators that perform aggregations such as "sum", "max",
"min" and so on over a sequence of values. Aggregate allows you to define a
mapper plugin class that is expected to generate "aggregatable items" for each
input key/value pair of the mappers. The combiner/reducer will aggregate those
aggregatable items by invoking the appropriate aggregators.
+</p><p>
+To use Aggregate, simply specify "-reducer aggregate":
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper myAggregatorForKeyCount.py \
+ -reducer aggregate \
+ -file myAggregatorForKeyCount.py \
+ -jobconf mapred.reduce.tasks=12
+</source>
+<p>
+The python program myAggregatorForKeyCount.py looks like:
+</p>
+<source>
+#!/usr/bin/python
+
+import sys;
+
+def generateLongCountToken(id):
+ return "LongValueSum:" + id + "\t" + "1"
+
+def main(argv):
+ line = sys.stdin.readline();
+ try:
+ while line:
+ line = line[:-1];
+ fields = line.split("\t");
+ print generateLongCountToken(fields[0]);
+ line = sys.stdin.readline();
+ except "end of file":
+ return None
+if __name__ == "__main__":
+ main(sys.argv)
+</source>
+</section>
+
+<section>
+<title>Field Selection ( similar to unix 'cut' command) </title>
+<p>
+Hadoop has a library class,
org.apache.hadoop.mapred.lib.FieldSelectionMapReduce, that effectively allows
you to process text data like the unix "cut" utility. The map function defined
in the class treats each input key/value pair as a list of fields. You can
specify the field separator (the default is the tab character). You can select
an arbitrary list of fields as the map output key, and an arbitrary list of
fields as the map output value. Similarly, the reduce function defined in the
class treats each input key/value pair as a list of fields. You can select an
arbitrary list of fields as the reduce output key, and an arbitrary list of
fields as the reduce output value. For example:
+</p>
+<source>
+$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input myInputDirs \
+ -output myOutputDir \
+ -mapper org.apache.hadoop.mapred.lib.FieldSelectionMapReduce\
+ -reducer org.apache.hadoop.mapred.lib.FieldSelectionMapReduce\
+ -partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner \
+ -jobconf map.output.key.field.separa=. \
+ -jobconf num.key.fields.for.partition=2 \
+ -jobconf mapred.data.field.separator=. \
+ -jobconf map.output.key.value.fields.spec=6,5,1-3:0- \
+ -jobconf reduce.output.key.value.fields.spec=0-2:5- \
+ -jobconf mapred.reduce.tasks=12
+</source>
+<p>
+The option "-jobconf map.output.key.value.fields.spec=6,5,1-3:0-" specifies
key/value selection for the map outputs. Key selection spec and value selection
spec are separated by ":". In this case, the map output key will consist of
fields 6, 5, 1, 2, and 3. The map output value will consist of all fields (0-
means field 0 and all
+the subsequent fields).
+</p><p>
+The option "-jobconf reduce.output.key.value.fields.spec=0-2:0-" specifies
key/value selection for the reduce outputs. In this case, the reduce output key
will consist of fields 0, 1, 2 (corresponding to the original fields 6, 5, 1).
The reduce output value will consist of all fields starting from field 5
(corresponding to all the original fields).
+</p>
+</section>
+</section>
+
+<section>
+<title>Frequently Asked Questions </title>
+
+<section>
+<title>How do I use Hadoop Streaming to run an arbitrary set of
(semi-)independent tasks? </title>
+<p>
+Often you do not need the full power of Map Reduce, but only need to run
multiple instances of the same program - either on different parts of the data,
or on the same data, but with different parameters. You can use Hadoop
Streaming to do this.
+</p>
+
+</section>
+
+<section>
+<title>How do I process files, one per map? </title>
+<p>
+As an example, consider the problem of zipping (compressing) a set of files
across the hadoop cluster. You can achieve this using either of these methods:
+</p><ol>
+<li> Hadoop Streaming and custom mapper script:<ul>
+ <li> Generate a file containing the full DFS path of the input files. Each
map task would get one file name as input.</li>
+ <li> Create a mapper script which, given a filename, will get the file to
local disk, gzip the file and put it back in the desired output directory</li>
+</ul></li>
+<li>The existing Hadoop Framework:<ul>
+ <li>Add these commands to your main function:
+<source>
+ OutputFormatBase.setCompressOutput(conf, true);
+ OutputFormatBase.setOutputCompressorClass(conf,
org.apache.hadoop.io.compress.GzipCodec.class);
+ conf.setOutputFormat(NonSplitableTextInputFormat.class);
+ conf.setNumReduceTasks(0);
+</source></li>
+ <li>Write your map function:
+<source>
+
+ public void map(WritableComparable key, Writable value,
+ OutputCollector output,
+ Reporter reporter) throws IOException {
+ output.collect((Text)value, null);
+ }
+</source></li>
+ <li>Note that the output filename will not be the same as the original
filename</li>
+</ul></li>
+</ol>
+</section>
+
+<section>
+<title>How many reducers should I use? </title>
+<p>
+See the Hadoop Wiki for details: <a
href="http://wiki.apache.org/lucene-hadoop/HowManyMapsAndReduces";>http://wiki.apache.org/lucene-hadoop/HowManyMapsAndReduces</a>
+</p>
+</section>
+
+<section>
+<title>If I set up an alias in my shell script, will that work after -mapper,
i.e. say I do: alias c1='cut -f1'. Will -mapper "c1" work? </title>
+<p>
+Using an alias will not work, but variable substitution is allowed as shown in
this example:
+</p>
+<source>
+$ hadoop dfs -cat samples/student_marks
+alice 50
+bruce 70
+charlie 80
+dan 75
+
+$ c2='cut -f2'; $HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
+ -input /user/me/samples/student_marks
+ -mapper \"$c2\" -reducer 'cat'
+ -output /user/me/samples/student_out
+ -jobconf mapred.job.name='Experiment'
+
+$ hadoop dfs -ls samples/student_out
+Found 1 items/user/me/samples/student_out/part-00000 <r 3> 16
+
+$ hadoop dfs -cat samples/student_out/part-00000
+50
+70
+75
+80
+</source>
+</section>
+
+<section>
+<title>Can I use UNIX pipes? For example, will -mapper "cut -f1 | sed
s/foo/bar/g" work?</title>
+<p>
+Currently this does not work and gives an "java.io.IOException: Broken pipe"
error. This is probably a bug that needs to be investigated.
+</p>
+</section>
+
+<section>
+<title>When I run a streaming job by <strong>distributing large
executables</strong> (for example, 3.6G) through the -file option, I get a "No
space left on device" error. What do I do? </title>
+<p>
+The jar packaging happens in a directory pointed to by the configuration
variable stream.tmpdir. The default value of stream.tmpdir is /tmp. Set the
value to a directory with more space:
+</p>
+<source>
+-jobconf stream.tmpdir=/export/bigspace/...
+</source>
+</section>
+
+<section>
+<title>How do I specify multiple input directories? </title>
+<p>
+You can specify multiple input directories with multiple '-input' options:
+</p><source>
+ hadoop jar hadoop-streaming.jar -input '/user/foo/dir1' -input
'/user/foo/dir2'
+</source>
+</section>
+
+<section>
+<title>How do I generate output files with gzip format? </title>
+<p>
+Instead of plain text files, you can generate gzip files as your generated
output. Pass '-jobconf mapred.output.compress=true -jobconf
mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCode' as
option to your streaming job.
+</p>
+</section>
+
+<section>
+<title>How do I provide my own input/output format with streaming? </title>
+<p>
+At least as late as version 0.14, Hadoop does not support multiple jar files.
So, when specifying your own custom classes you will have to pack them along
with the streaming jar and use the custom jar instead of the default hadoop
streaming jar.
+</p>
+</section>
+
+<section>
+<title>How do I parse XML documents using streaming? </title>
+<p>
+You can use the record reader StreamXmlRecordReader to process XML documents.
+</p>
+<source>
+hadoop jar hadoop-streaming.jar -inputreader
"StreamXmlRecord,begin=BEGIN_STRING,end=END_STRING" ..... (rest of the command)
+</source>
+<p>
+Anything found between BEGIN_STRING and END_STRING would be treated as one
record for map tasks.
+</p>
+</section>
+</section>
+</body>
+</document>
