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MAPREDUCE-5637. Convert Hadoop Streaming document to APT (Akira AJISAKA via jeagles) 

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@ -43,6 +43,9 @@ Release 2.5.0 - UNRELEASED
MAX_CHUNKS_IDEAL, MIN_RECORDS_PER_CHUNK and SPLIT_RATIO to be configurable. MAX_CHUNKS_IDEAL, MIN_RECORDS_PER_CHUNK and SPLIT_RATIO to be configurable.
(Tsuyoshi OZAWA via szetszwo) (Tsuyoshi OZAWA via szetszwo)
MAPREDUCE-5637. Convert Hadoop Streaming document to APT (Akira AJISAKA via
jeagles)
OPTIMIZATIONS OPTIMIZATIONS
BUG FIXES BUG FIXES

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@ -0,0 +1,792 @@
~~ Licensed under the Apache License, Version 2.0 (the "License");
~~ you may not use this file except in compliance with the License.
~~ You may obtain a copy of the License at
~~
~~ http://www.apache.org/licenses/LICENSE-2.0
~~
~~ Unless required by applicable law or agreed to in writing, software
~~ distributed under the License is distributed on an "AS IS" BASIS,
~~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~~ See the License for the specific language governing permissions and
~~ limitations under the License. See accompanying LICENSE file.
---
Hadoop Streaming
---
---
${maven.build.timestamp}
Hadoop Streaming
%{toc|section=1|fromDepth=0|toDepth=4}
* Hadoop Streaming
Hadoop streaming is a utility that comes with the Hadoop distribution. The
utility allows you to create and run Map/Reduce jobs with any executable or
script as the mapper and/or the reducer. For example:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-input myInputDirs \
-output myOutputDir \
-mapper /bin/cat \
-reducer /usr/bin/wc
+---+
* How Streaming Works
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.
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 <prefix of a line up to the first tab character> is the <<<key>>> and the
rest of the line (excluding the tab character) will be the <<<value>>>. If
there is no tab character in the line, then entire line is considered as key
and the value is null. However, this can be customized by setting
<<<-inputformat>>> command option, as discussed later.
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 rest of the line (excluding the tab character) is the value.
However, this can be customized by setting <<<-outputformat>>> command
option, as discussed later.
This is the basis for the communication protocol between the Map/Reduce
framework and the streaming mapper/reducer.
User can specify <<<stream.non.zero.exit.is.failure>>> as <<<true>>> or
<<<false>>> to make a streaming task that exits with a non-zero status to be
<<<Failure>>> or <<<Success>>> respectively. By default, streaming tasks
exiting with non-zero status are considered to be failed tasks.
* Streaming Command Options
Streaming supports streaming command options as well as
{{{Generic_Command_Options}generic command options}}. The general command
line syntax is shown below.
<<Note:>> Be sure to place the generic options before the streaming options,
otherwise the command will fail. For an example, see
{{{Making_Archives_Available_to_Tasks}Making Archives Available to Tasks}}.
+---+
hadoop command [genericOptions] [streamingOptions]
+---+
The Hadoop streaming command options are listed here:
*-------------*--------------------*------------------------------------------*
|| Parameter || Optional/Required || Description |
*-------------+--------------------+------------------------------------------+
| -input directoryname or filename | Required | Input location for mapper
*-------------+--------------------+------------------------------------------+
| -output directoryname | Required | Output location for reducer
*-------------+--------------------+------------------------------------------+
| -mapper executable or JavaClassName | Required | Mapper executable
*-------------+--------------------+------------------------------------------+
| -reducer executable or JavaClassName | Required | Reducer executable
*-------------+--------------------+------------------------------------------+
| -file filename | Optional | Make the mapper, reducer, or combiner executable
| | | available locally on the compute nodes
*-------------+--------------------+------------------------------------------+
| -inputformat JavaClassName | Optional | Class you supply should return
| | | key/value pairs of Text class. If not
| | | specified, TextInputFormat is used as
| | | the default
*-------------+--------------------+------------------------------------------+
| -outputformat JavaClassName | Optional | Class you supply should take
| | | key/value pairs of Text class. If
| | | not specified, TextOutputformat is
| | | used as the default
*-------------+--------------------+------------------------------------------+
| -partitioner JavaClassName | Optional | Class that determines which reduce a
| | | key is sent to
*-------------+--------------------+------------------------------------------+
| -combiner streamingCommand | Optional | Combiner executable for map output
| or JavaClassName | |
*-------------+--------------------+------------------------------------------+
| -cmdenv name=value | Optional | Pass environment variable to streaming
| | | commands
*-------------+--------------------+------------------------------------------+
| -inputreader | Optional | For backwards-compatibility: specifies a record
| | | reader class (instead of an input format class)
*-------------+--------------------+------------------------------------------+
| -verbose | Optional | Verbose output
*-------------+--------------------+------------------------------------------+
| -lazyOutput | Optional | Create output lazily. For example, if the output
| | | format is based on FileOutputFormat, the output file
| | | is created only on the first call to Context.write
*-------------+--------------------+------------------------------------------+
| -numReduceTasks | Optional | Specify the number of reducers
*-------------+--------------------+------------------------------------------+
| -mapdebug | Optional | Script to call when map task fails
*-------------+--------------------+------------------------------------------+
| -reducedebug | Optional | Script to call when reduce task fails
*-------------+--------------------+------------------------------------------+
** Specifying a Java Class as the Mapper/Reducer
You can supply a Java class as the mapper and/or the reducer.
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-input myInputDirs \
-output myOutputDir \
-inputformat org.apache.hadoop.mapred.KeyValueTextInputFormat \
-mapper org.apache.hadoop.mapred.lib.IdentityMapper \
-reducer /usr/bin/wc
+---+
You can specify <<<stream.non.zero.exit.is.failure>>> as <<<true>>> or
<<<false>>> to make a streaming task that exits with a non-zero status to be
<<<Failure>>> or <<<Success>>> respectively. By default, streaming tasks
exiting with non-zero status are considered to be failed tasks.
** Packaging Files With Job Submissions
You can specify any executable as the mapper and/or the reducer. The
executables do not need to pre-exist on the machines in the cluster; however,
if they don't, you will need to use "-file" option to tell the framework to
pack your executable files as a part of job submission. For example:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-input myInputDirs \
-output myOutputDir \
-mapper myPythonScript.py \
-reducer /usr/bin/wc \
-file myPythonScript.py
+---+
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.
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:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-input myInputDirs \
-output myOutputDir \
-mapper myPythonScript.py \
-reducer /usr/bin/wc \
-file myPythonScript.py \
-file myDictionary.txt
+---+
** Specifying Other Plugins for Jobs
Just as with a normal Map/Reduce job, you can specify other plugins for a
streaming job:
+---+
-inputformat JavaClassName
-outputformat JavaClassName
-partitioner JavaClassName
-combiner streamingCommand or JavaClassName
+---+
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.
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.
** Setting Environment Variables
To set an environment variable in a streaming command use:
+---+
-cmdenv EXAMPLE_DIR=/home/example/dictionaries/
+---+
* Generic Command Options
Streaming supports {{{Streaming_Command_Options}streaming command options}}
as well as generic command options. The general command line syntax is shown
below.
<<Note:>> Be sure to place the generic options before the streaming options,
otherwise the command will fail. For an example, see
{{{Making_Archives_Available_to_Tasks}Making Archives Available to Tasks}}.
+---+
hadoop command [genericOptions] [streamingOptions]
+---+
The Hadoop generic command options you can use with streaming are listed
here:
*-------------*--------------------*------------------------------------------*
|| Parameter || Optional/Required || Description |
*-------------+--------------------+------------------------------------------+
| -conf configuration_file | Optional | Specify an application configuration
| | | file
*-------------+--------------------+------------------------------------------+
| -D property=value | Optional | Use value for given property
*-------------+--------------------+------------------------------------------+
| -fs host:port or local | Optional | Specify a namenode
*-------------+--------------------+------------------------------------------+
| -files | Optional | Specify comma-separated files to be copied to the
| | | Map/Reduce cluster
*-------------+--------------------+------------------------------------------+
| -libjars | Optional | Specify comma-separated jar files to include in the
| | | classpath
*-------------+--------------------+------------------------------------------+
| -archives | Optional | Specify comma-separated archives to be unarchived on
| | | the compute machines
*-------------+--------------------+------------------------------------------+
** Specifying Configuration Variables with the -D Option
You can specify additional configuration variables by using
"-D \<property\>=\<value\>".
*** Specifying Directories
To change the local temp directory use:
+---+
-D dfs.data.dir=/tmp
+---+
To specify additional local temp directories use:
+---+
-D mapred.local.dir=/tmp/local
-D mapred.system.dir=/tmp/system
-D mapred.temp.dir=/tmp/temp
+---+
<<Note:>> For more details on job configuration parameters see:
{{{./mapred-default.xml}mapred-default.xml}}
*** Specifying Map-Only Jobs
Often, you may want to process input data using a map function only. To do
this, simply set <<<mapreduce.job.reduces>>> 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.
+---+
-D mapreduce.job.reduces=0
+---+
To be backward compatible, Hadoop Streaming also supports the "-reducer NONE"
option, which is equivalent to "-D mapreduce.job.reduces=0".
*** Specifying the Number of Reducers
To specify the number of reducers, for example two, use:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-D mapreduce.job.reduces=2 \
-input myInputDirs \
-output myOutputDir \
-mapper /bin/cat \
-reducer /usr/bin/wc
+---+
*** Customizing How Lines are Split into Key/Value Pairs
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 rest of
the line (excluding the tab character) is the value.
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:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-D stream.map.output.field.separator=. \
-D stream.num.map.output.key.fields=4 \
-input myInputDirs \
-output myOutputDir \
-mapper /bin/cat \
-reducer /bin/cat
+---+
In the above example, "-D 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("")).
Similarly, you can use "-D stream.reduce.output.field.separator=SEP" and
"-D 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.
Similarly, you can specify "stream.map.input.field.separator" and
"stream.reduce.input.field.separator" as the input separator for Map/Reduce
inputs. By default the separator is the tab character.
** Working with Large Files and Archives
The -files and -archives 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.
<<Note:>> The -files and -archives options are generic options. Be sure to
place the generic options before the command options, otherwise the command
will fail.
*** Making Files Available to Tasks
The -files option creates a symlink in the current working directory of the
tasks that points to the local copy of the file.
In this example, Hadoop automatically creates a symlink named testfile.txt in
the current working directory of the tasks. This symlink points to the local
copy of testfile.txt.
+---+
-files hdfs://host:fs_port/user/testfile.txt
+---+
User can specify a different symlink name for -files using #.
+---+
-files hdfs://host:fs_port/user/testfile.txt#testfile
+---+
Multiple entries can be specified like this:
+---+
-files hdfs://host:fs_port/user/testfile1.txt,hdfs://host:fs_port/user/testfile2.txt
+---+
*** Making Archives Available to Tasks
The -archives option allows you to copy jars locally to the current working
directory of tasks and automatically unjar the files.
In this example, Hadoop automatically creates a symlink named testfile.jar in
the current working directory of tasks. This symlink points to the directory
that stores the unjarred contents of the uploaded jar file.
+---+
-archives hdfs://host:fs_port/user/testfile.jar
+---+
User can specify a different symlink name for -archives using #.
+---+
-archives hdfs://host:fs_port/user/testfile.tgz#tgzdir
+---+
In this example, the input.txt file has two lines specifying the names of the
two files: cachedir.jar/cache.txt and cachedir.jar/cache2.txt. "cachedir.jar"
is a symlink to the archived directory, which has the files "cache.txt" and
"cache2.txt".
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-archives 'hdfs://hadoop-nn1.example.com/user/me/samples/cachefile/cachedir.jar' \
-D mapreduce.job.maps=1 \
-D mapreduce.job.reduces=1 \
-D mapreduce.job.name="Experiment" \
-input "/user/me/samples/cachefile/input.txt" \
-output "/user/me/samples/cachefile/out" \
-mapper "xargs cat" \
-reducer "cat"
$ 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%)
$ hdfs dfs -put cachedir.jar samples/cachefile
$ hdfs dfs -cat /user/me/samples/cachefile/input.txt
cachedir.jar/cache.txt
cachedir.jar/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
$ hdfs dfs -ls /user/me/samples/cachefile/out
Found 2 items
-rw-r--r-- 1 me supergroup 0 2013-11-14 17:00 /user/me/samples/cachefile/out/_SUCCESS
-rw-r--r-- 1 me supergroup 69 2013-11-14 17:00 /user/me/samples/cachefile/out/part-00000
$ hdfs dfs -cat /user/me/samples/cachefile/out/part-00000
This is just the cache string
This is just the second cache string
+---+
* More Usage Examples
** Hadoop Partitioner Class
Hadoop has a library class,
{{{../../api/org/apache/hadoop/mapred/lib/KeyFieldBasedPartitioner.html}
KeyFieldBasedPartitioner}}, that is useful for many applications. This class
allows the Map/Reduce framework to partition the map outputs based on certain
key fields, not the whole keys. For example:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-D stream.map.output.field.separator=. \
-D stream.num.map.output.key.fields=4 \
-D map.output.key.field.separator=. \
-D mapreduce.partition.keypartitioner.options=-k1,2 \
-D mapreduce.job.reduces=12 \
-input myInputDirs \
-output myOutputDir \
-mapper /bin/cat \
-reducer /bin/cat \
-partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner
+---+
Here, <-D stream.map.output.field.separator=.> and
<-D stream.num.map.output.key.fields=4> are as explained in previous example.
The two variables are used by streaming to identify the key/value pair of
mapper.
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
<-D mapred.text.key.partitioner.options=-k1,2> option. Here,
<-D map.output.key.field.separator=.> 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.
<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.> A simple illustration is shown here:
Output of map (the keys)
+---+
11.12.1.2
11.14.2.3
11.11.4.1
11.12.1.1
11.14.2.2
+---+
Partition into 3 reducers (the first 2 fields are used as keys for partition)
+---+
11.11.4.1
-----------
11.12.1.2
11.12.1.1
-----------
11.14.2.3
11.14.2.2
+---+
Sorting within each partition for the reducer(all 4 fields used for sorting)
+---+
11.11.4.1
-----------
11.12.1.1
11.12.1.2
-----------
11.14.2.2
11.14.2.3
+---+
** Hadoop Comparator Class
Hadoop has a library class,
{{{../../api/org/apache/hadoop/mapreduce/lib/partition/KeyFieldBasedComparator.html}
KeyFieldBasedComparator}}, that is useful for many applications. This class
provides a subset of features provided by the Unix/GNU Sort. For example:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-D mapreduce.job.output.key.comparator.class=org.apache.hadoop.mapreduce.lib.partition.KeyFieldBasedComparator \
-D stream.map.output.field.separator=. \
-D stream.num.map.output.key.fields=4 \
-D mapreduce.map.output.key.field.separator=. \
-D mapreduce.partition.keycomparator.options=-k2,2nr \
-D mapreduce.job.reduces=1 \
-input myInputDirs \
-output myOutputDir \
-mapper /bin/cat \
-reducer /bin/cat
+---+
The map output keys of the above Map/Reduce job normally have four fields
separated by ".". However, the Map/Reduce framework will sort the outputs by
the second field of the keys using the
<-D mapreduce.partition.keycomparator.options=-k2,2nr> option. Here, <-n>
specifies that the sorting is numerical sorting and <-r> specifies that the
result should be reversed. A simple illustration is shown below:
Output of map (the keys)
+---+
11.12.1.2
11.14.2.3
11.11.4.1
11.12.1.1
11.14.2.2
+---+
Sorting output for the reducer (where second field used for sorting)
+---+
11.14.2.3
11.14.2.2
11.12.1.2
11.12.1.1
11.11.4.1
+---+
** Hadoop Aggregate Package
Hadoop has a library package called
{{{../../org/apache/hadoop/mapred/lib/aggregate/package-summary.html}
Aggregate}}. 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.
To use Aggregate, simply specify "-reducer aggregate":
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-input myInputDirs \
-output myOutputDir \
-mapper myAggregatorForKeyCount.py \
-reducer aggregate \
-file myAggregatorForKeyCount.py \
+---+
The python program myAggregatorForKeyCount.py looks like:
+---+
#!/usr/bin/python
import sys;
def generateLongCountToken(id):
return "LongValueSum:" + id + "\t" + "1"
def main(argv):
line = sys.stdin.readline();
try:
while line:
line = line&#91;:-1];
fields = line.split("\t");
print generateLongCountToken(fields&#91;0]);
line = sys.stdin.readline();
except "end of file":
return None
if __name__ == "__main__":
main(sys.argv)
+---+
** Hadoop Field Selection Class
Hadoop has a library class,
{{{../../api/org/apache/hadoop/mapred/lib/FieldSelectionMapReduce.html}
FieldSelectionMapReduce}}, that effectively allows you to process text data
like the unix "cut" utility. The map function defined in the class treats
each input key/value pair as a list of fields. You can specify the field
separator (the default is the tab character). You can select an arbitrary
list of fields as the map output key, and an arbitrary list of fields as the
map output value. Similarly, the reduce function defined in the class treats
each input key/value pair as a list of fields. You can select an arbitrary
list of fields as the reduce output key, and an arbitrary list of fields as
the reduce output value. For example:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-D mapreduce.map.output.key.field.separator=. \
-D mapreduce.partition.keypartitioner.options=-k1,2 \
-D mapreduce.fieldsel.data.field.separator=. \
-D mapreduce.fieldsel.map.output.key.value.fields.spec=6,5,1-3:0- \
-D mapreduce.fieldsel.reduce.output.key.value.fields.spec=0-2:5- \
-D mapreduce.map.output.key.class=org.apache.hadoop.io.Text \
-D mapreduce.job.reduces=12 \
-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
+---+
The option "-D
mapreduce.fieldsel.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).
The option "-D mapreduce.fieldsel.reduce.output.key.value.fields.spec=0-2:5-"
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).
* Frequently Asked Questions
** How do I use Hadoop Streaming to run an arbitrary set of (semi) independent
tasks?
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.
** How do I process files, one per map?
As an example, consider the problem of zipping (compressing) a set of files
across the hadoop cluster. You can achieve this by using Hadoop Streaming
and custom mapper script:
* Generate a file containing the full HDFS path of the input files. Each map
task would get one file name as input.
* 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.
** How many reducers should I use?
See MapReduce Tutorial for details: {{{./MapReduceTutorial.html#Reducer}
Reducer}}
** If I set up an alias in my shell script, will that work after -mapper?
For example, say I do: alias c1='cut -f1'. Will -mapper "c1" work?
Using an alias will not work, but variable substitution is allowed as shown
in this example:
+---+
$ hdfs dfs -cat /user/me/samples/student_marks
alice 50
bruce 70
charlie 80
dan 75
$ c2='cut -f2'; hadoop jar hadoop-streaming-${project.version}.jar \
-D mapreduce.job.name='Experiment' \
-input /user/me/samples/student_marks \
-output /user/me/samples/student_out \
-mapper "$c2" -reducer 'cat'
$ hdfs dfs -cat /user/me/samples/student_out/part-00000
50
70
75
80
+---+
** Can I use UNIX pipes?
For example, will -mapper "cut -f1 | sed s/foo/bar/g" work?
Currently this does not work and gives an "java.io.IOException: Broken pipe"
error. This is probably a bug that needs to be investigated.
** What do I do if I get the "No space left on device" error?
For example, when I run a streaming job by distributing large executables
(for example, 3.6G) through the -file option, I get a "No space left on
device" error.
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:
+---+
-D stream.tmpdir=/export/bigspace/...
+---+
** How do I specify multiple input directories?
You can specify multiple input directories with multiple '-input' options:
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-input '/user/foo/dir1' -input '/user/foo/dir2' \
(rest of the command)
+---+
** How do I generate output files with gzip format?
Instead of plain text files, you can generate gzip files as your generated
output. Pass '-D mapreduce.output.fileoutputformat.compress=true -D
mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.GzipCodec'
as option to your streaming job.
** How do I provide my own input/output format with streaming?
You can specify your own custom class by packing them and putting the custom
jar to \$\{HADOOP_CLASSPATH\}.
** How do I parse XML documents using streaming?
You can use the record reader StreamXmlRecordReader to process XML documents.
+---+
hadoop jar hadoop-streaming-${project.version}.jar \
-inputreader "StreamXmlRecord,begin=BEGIN_STRING,end=END_STRING" \
(rest of the command)
+---+
Anything found between BEGIN_STRING and END_STRING would be treated as one
record for map tasks.
** How do I update counters in streaming applications?
A streaming process can use the stderr to emit counter information.
<<<reporter:counter:\<group\>,\<counter\>,\<amount\>>>> should be sent to
stderr to update the counter.
** How do I update status in streaming applications?
A streaming process can use the stderr to emit status information. To set a
status, <<<reporter:status:\<message\>>>> should be sent to stderr.
** How do I get the Job variables in a streaming job's mapper/reducer?
See {{{./MapReduceTutorial.html#Configured_Parameters}
Configured Parameters}}. During the execution of a streaming job, the names
of the "mapred" parameters are transformed. The dots ( . ) become underscores
( _ ). For example, mapreduce.job.id becomes mapreduce_job_id and
mapreduce.job.jar becomes mapreduce_job_jar. In your code, use the parameter
names with the underscores.

1
hadoop-project/src/site/site.xml
View File

@ -92,6 +92,7 @@
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<item name="Pluggable Shuffle/Sort" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/PluggableShuffleAndPluggableSort.html"/> <item name="Pluggable Shuffle/Sort" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/PluggableShuffleAndPluggableSort.html"/>
<item name="Distributed Cache Deploy" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/DistributedCacheDeploy.html"/> <item name="Distributed Cache Deploy" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/DistributedCacheDeploy.html"/>
<item name="Hadoop Streaming" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/HadoopStreaming.html"/>
<item name="Hadoop Archives" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/HadoopArchives.html"/> <item name="Hadoop Archives" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/HadoopArchives.html"/>
<item name="DistCp" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/DistCp.html"/> <item name="DistCp" href="hadoop-mapreduce-client/hadoop-mapreduce-client-core/DistCp.html"/>
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