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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -input myInputDirs \
    -output myOutputDir \
    -mapper /bin/cat \
    -reducer /bin/wc

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, 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, as discussed later.

This is the basis for the communication protocol between the Map/Reduce framework and the streaming mapper/reducer.

You can supply a Java class as the mapper and/or the reducer. The above example is equivalent to:

$HADOOP_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -input myInputDirs \
    -output myOutputDir \
    -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
    -reducer /bin/wc

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 supports 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.

bin/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 or JavaClassName	Optional	Combiner executable for map output
-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 output.collect (or 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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -input myInputDirs \
    -output myOutputDir \
    -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
    -reducer /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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -input myInputDirs \
    -output myOutputDir \
    -mapper myPythonScript.py \
    -reducer /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_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

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/

Streaming supports 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.

bin/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
-jt host:port or local	Optional	Specify a job tracker
-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 jobconf parameters see: mapred-default.html

Specifying Map-Only Jobs

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.

    -D mapred.reduce.tasks=0
 

To be backward compatible, Hadoop Streaming also supports the "-reduce NONE" option, which is equivalent to "-D mapred.reduce.tasks=0".

Specifying the Number of Reducers

To specify the number of reducers, for example two, use:

 $HADOOP_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -D mapred.reduce.tasks=2 \
    -input myInputDirs \
    -output myOutputDir \
    -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
    -reducer /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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -D stream.map.output.field.separator=. \
    -D stream.num.map.output.key.fields=4 \
    -input myInputDirs \
    -output myOutputDir \
    -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
    -reducer org.apache.hadoop.mapred.lib.IdentityReducer 
 

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. For an example, see The -archives Option. Also see Other Supported Options.

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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
                  -archives 'hdfs://hadoop-nn1.example.com/user/me/samples/cachefile/cachedir.jar' \  
                  -D mapred.map.tasks=1 \
                  -D mapred.reduce.tasks=1 \ 
                  -D mapred.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%)

$ hadoop dfs -put cachedir.jar samples/cachefile

$ hadoop 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

$ 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
 

Hadoop Partitioner Class

Hadoop has a library class, KeyFieldBasedPartitioner, p> 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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -D stream.map.output.field.separator=. \
    -D stream.num.map.output.key.fields=4 \
    -D map.output.key.field.separator=. \
    -D mapred.text.key.partitioner.options=-k1,2 \
    -D mapred.reduce.tasks=12 \
    -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 

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, KeyFieldBasedComparator, that is useful for many applications. This class provides a subset of features provided by the Unix/GNU Sort. For example:

$HADOOP_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -D mapred.output.key.comparator.class=org.apache.hadoop.mapred.lib.KeyFieldBasedComparator \
    -D stream.map.output.field.separator=. \
    -D stream.num.map.output.key.fields=4 \
    -D map.output.key.field.separator=. \
    -D mapred.text.key.comparator.options=-k2,2nr \
    -D mapred.reduce.tasks=12 \
    -input myInputDirs \
    -output myOutputDir \
    -mapper org.apache.hadoop.mapred.lib.IdentityMapper \
    -reducer org.apache.hadoop.mapred.lib.IdentityReducer 

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 mapred.text.key.comparator.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 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_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -D mapred.reduce.tasks=12 \
    -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[:-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)

Hadoop Field Selection Class

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:

$HADOOP_HOME/bin/hadoop  jar $HADOOP_HOME/hadoop-streaming.jar \
    -D map.output.key.field.separa=. \
    -D mapred.text.key.partitioner.options=-k1,2 \
    -D mapred.data.field.separator=. \
    -D map.output.key.value.fields.spec=6,5,1-3:0- \
    -D reduce.output.key.value.fields.spec=0-2:5- \
    -D mapred.reduce.tasks=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 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 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).

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 using either of these methods:

1. 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
2. The existing Hadoop Framework:
   • Add these commands to your main function:

         FileOutputFormat.setCompressOutput(conf, true);
         FileOutputFormat.setOutputCompressorClass(conf, org.apache.hadoop.io.compress.GzipCodec.class);
         conf.setOutputFormat(NonSplitableTextInputFormat.class);
         conf.setNumReduceTasks(0);
     

   • Write your map function:

         public void map(WritableComparable key, 
                         Writable value, 
                         OutputCollector output, 
                         Reporter reporter) 
             throws IOException {
                 output.collect((Text)value, null);
             }
     

   • Note that the output filename will not be the same as the original filename

How many reducers should I use?

See the Hadoop Wiki for details: 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:

$ hadoop dfs -cat samples/student_marks
alice   50
bruce   70
charlie 80
dan     75

$ c2='cut -f2'; $HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \
    -D mapred.job.name='Experiment'
    -input /user/me/samples/student_marks 
    -output /user/me/samples/student_out 
    -mapper \"$c2\" -reducer 'cat'  
    
$ 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

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.jar -input '/user/foo/dir1' -input '/user/foo/dir2' 

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 mapred.output.compress=true -D mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec' as option to your streaming job.

How do I provide my own input/output format with streaming?

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.

How do I parse XML documents using streaming?

You can use the record reader StreamXmlRecordReader to process XML documents.

hadoop jar hadoop-streaming.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 JobConf variables in a streaming job's mapper/reducer?

See Configured Parameters. During the execution of a streaming job, the names of the "mapred" parameters are transformed. The dots ( . ) become underscores ( _ ). For example, mapred.job.id becomes mapred_job_id and mapred.jar becomes mapred_jar. In your code, use the parameter names with the underscores.

How do I get the JobConf variables in a streaming job's mapper/reducer?

See the Configured Parameters. During the execution of a streaming job, the names of the "mapred" parameters are transformed. The dots ( . ) become underscores ( _ ). For example, mapred.job.id becomes mapred_job_id and mapred.jar becomes mapred_jar. In your code, use the parameter names with the underscores.