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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-2.7.3.jar \ -input myInputDirs \ -output myOutputDir \ -mapper /bin/cat \ -reducer /usr/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 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 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.
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 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 |
You can supply a Java class as the mapper and/or the reducer.
hadoop jar hadoop-streaming-2.7.3.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.
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-2.7.3.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-2.7.3.jar \ -input myInputDirs \ -output myOutputDir \ -mapper myPythonScript.py \ -reducer /usr/bin/wc \ -file myPythonScript.py \ -file myDictionary.txt
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.
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.
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 |
You can specify additional configuration variables by using “-D <property>=<value>”.
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
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”.
To specify the number of reducers, for example two, use:
hadoop jar hadoop-streaming-2.7.3.jar \ -D mapreduce.job.reduces=2 \ -input myInputDirs \ -output myOutputDir \ -mapper /bin/cat \ -reducer /usr/bin/wc
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-2.7.3.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.
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.
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
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-2.7.3.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
Hadoop has a library class, 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-2.7.3.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 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 jar hadoop-streaming-2.7.3.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 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 jar hadoop-streaming-2.7.3.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[:-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 has a library class, 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-2.7.3.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).
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.
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.
See MapReduce Tutorial for details: Reducer
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-2.7.3.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
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.
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/…
You can specify multiple input directories with multiple ‘-input’ options:
hadoop jar hadoop-streaming-2.7.3.jar \
-input '/user/foo/dir1' -input '/user/foo/dir2' \
(rest of the command)
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.
You can specify your own custom class by packing them and putting the custom jar to $HADOOP_CLASSPATH.
You can use the record reader StreamXmlRecordReader to process XML documents.
hadoop jar hadoop-streaming-2.7.3.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.
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.
A streaming process can use the stderr to emit status information. To set a status, reporter:status:<message> should be sent to stderr.
See 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.