oozie-工作流Map-Reduce行为

Map-Reduce行为

A map-reduce action can be configured to perform file system cleanup and directory creation before starting the map reduce job. This capability enables Oozie to retry a Hadoop job in the situation of a transient failure (Hadoop checks the non-existence of the job output directory and then creates it when the Hadoop job is starting, thus a retry without cleanup of the job output directory would fail).

The workflow job will wait until the Hadoop map/reduce job completes before continuing to the next action in the workflow execution path.

The counters of the Hadoop job and job exit status (=FAILED=, KILLED or SUCCEEDED ) must be available to the workflow job after the Hadoop jobs ends. This information can be used from within decision nodes and other actions configurations.

The map-reduce action has to be configured with all the necessary Hadoop JobConf properties to run the Hadoop map/reduce job.

Hadoop JobConf properties can be specified in a JobConf XML file bundled with the workflow application or they can be indicated inline in the map-reduce action configuration.

The configuration properties are loaded in the following order, streaming , job-xml and configuration , and later values override earlier values.

Streaming and inline property values can be parameterized (templatized) using EL expressions.

The Hadoop mapred.job.tracker and fs.default.name properties must not be present in the job-xml and inline configuration.

3.2.2.1 Adding Files and Archives for the Job

The file , archive elements make available, to map-reduce jobs, files and archives. If the specified path is relative, it is assumed the file or archiver are within the application directory, in the corresponding sub-path. If the path is absolute, the file or archive it is expected in the given absolute path.

Files specified with the file element, will be symbolic links in the home directory of the task.

If a file is a native library (an '.so' or a '.so.#' file), it will be symlinked as and '.so' file in the task running directory, thus available to the task JVM.

To force a symlink for a file on the task running directory, use a '#' followed by the symlink name. For example 'mycat.sh#cat'.

Refer to Hadoop distributed cache documentation for details more details on files and archives.

3.2.2.2 Streaming

Streaming information can be specified in the streaming element.

The mapper and reducer elements are used to specify the executable/script to be used as mapper and reducer.

User defined scripts must be bundled with the workflow application and they must be declared in the files element of the streaming configuration. If the are not declared in the files element of the configuration it is assumed they will be available (and in the command PATH) of the Hadoop slave machines.

Some streaming jobs require Files found on HDFS to be available to the mapper/reducer scripts. This is done using the file and archive elements described in the previous section.

The Mapper/Reducer can be overridden by a mapred.mapper.class or mapred.reducer.class properties in the job-xml file or configuration elements.

3.2.2.3 Pipes

Pipes information can be specified in the pipes element.

A subset of the command line options which can be used while using the Hadoop Pipes Submitter can be specified via elements - map , reduce , inputformat , partitioner , writer , program .

The program element is used to specify the executable/script to be used.

User defined program must be bundled with the workflow application.

Some pipe jobs require Files found on HDFS to be available to the mapper/reducer scripts. This is done using the file and archive elements described in the previous section.

Pipe properties can be overridden by specifying them in the job-xml file or configuration element.

3.2.2.4 Syntax

<workflow-app name="[WF-DEF-NAME]" xmlns="uri:oozie:workflow:0.1">
    ...
    <action name="[NODE-NAME]">
        <map-reduce>
            <job-tracker>[JOB-TRACKER]</job-tracker>
            <name-node>[NAME-NODE]</name-node>
            <prepare>
                <delete path="[PATH]"/>
                ...
                <mkdir path="[PATH]"/>
                ...
            </prepare>
            <streaming>
                <mapper>[MAPPER-PROCESS]</mapper>
                <reducer>[REDUCER-PROCESS]</reducer>
                <record-reader>[RECORD-READER-CLASS]</record-reader>
                <record-reader-mapping>[NAME=VALUE]</record-reader-mapping>
                ...
                <env>[NAME=VALUE]</env>
                ...
            </streaming>

            <pipes>
                <map>[MAPPER]</map>
                <reduce>[REDUCER]</reducer>
                <inputformat>[INPUTFORMAT]</inputformat>
                <partitioner>[PARTITIONER]</partitioner>
                <writer>[OUTPUTFORMAT]</writer>
                <program>[EXECUTABLE]</program>
            </pipes>
            <job-xml>[JOB-XML-FILE]</job-xml>
            <configuration>
                <property>
                    <name>[PROPERTY-NAME]</name>
                    <value>[PROPERTY-VALUE]</value>
                </property>
                ...
            </configuration>
            <file>[FILE-PATH]</file>
            ...
            <archive>[FILE-PATH]</archive>
            ...
        </map-reduce>        <ok to="[NODE-NAME]"/>
        <error to="[NODE-NAME]"/>
    </action>
    ...
</workflow-app>
The prepare element, if present, indicates a list of path do delete before starting the job. This should be used exclusively for directory cleanup for the job to be executed. The delete operation will be performed in the fs.default.name filesystem.

The job-xml element, if present, must refer to a Hadoop JobConf job.xml file bundled in the workflow application. The job-xml element is optional and if present it can be only one.

The configuration element, if present, contains JobConf properties for the Hadoop job.

Properties specified in the configuration element override properties specified in the file specified in the job-xml element.

The file element, if present, must specify the target sybolic link for binaries by separating the original file and target with a # (file#target-sym-link). This is not required for libraries.

The mapper and reducer process for streaming jobs, should specify the executable command with URL encoding. e.g. '%' should be replaced by '%25'.

Example:

<workflow-app name="foo-wf" xmlns="uri:oozie:workflow:0.1">
    ...
    <action name="myfirstHadoopJob">
        <map-reduce>
            <job-tracker>foo:9001</job-tracker>
            <name-node>bar:9000</name-node>
            <prepare>
                <delete path="hdfs://foo:9000/usr/tucu/output-data"/>
            </prepare>
            <job-xml>/myfirstjob.xml</job-xml>
            <configuration>
                <property>
                    <name>mapred.input.dir</name>
                    <value>/usr/tucu/input-data</value>
                </property>
                <property>
                    <name>mapred.output.dir</name>
                    <value>/usr/tucu/input-data</value>
                </property>
                <property>
                    <name>mapred.reduce.tasks</name>
                    <value>${firstJobReducers}</value>
                </property>
            </configuration>
        </map-reduce>
        <ok to="myNextAction"/>
        <error to="errorCleanup"/>
    </action>
    ...
</workflow-app>
In the above example, the number of Reducers to be used by the Map/Reduce job has to be specified as a parameter of the workflow job configuration when creating the workflow job.

Streaming Example:

<workflow-app name="sample-wf" xmlns="uri:oozie:workflow:0.1">
    ...
    <action name="firstjob">
        <map-reduce>
            <job-tracker>foo:9001</job-tracker>
            <name-node>bar:9000</name-node>
            <prepare>
                <delete path="${output}"/>
            </prepare>
            <streaming>
                <mapper>/bin/bash testarchive/bin/mapper.sh testfile</mapper>
                <reducer>/bin/bash testarchive/bin/reducer.sh</reducer>
            </streaming>
            <configuration>
                <property>
                    <name>mapred.input.dir</name>
                    <value>${input}</value>
                </property>
                <property>
                    <name>mapred.output.dir</name>
                    <value>${output}</value>
                </property>
                <property>
                    <name>stream.num.map.output.key.fields</name>
                    <value>3</value>
                </property>
            </configuration>
            <file>/users/blabla/testfile.sh#testfile</file>
            <archive>/users/blabla/testarchive.jar#testarchive</archive>
        </map-reduce>
        <ok to="end"/>
        <error to="kill"/>
    </action>
...
</workflow-app>
Pipes Example:

<workflow-app name="sample-wf" xmlns="uri:oozie:workflow:0.1">
    ...
    <action name="firstjob">
        <map-reduce>
            <job-tracker>foo:9001</job-tracker>
            <name-node>bar:9000</name-node>
            <prepare>
                <delete path="${output}"/>
            </prepare>
            <pipes>
                <program>bin/wordcount-simple#wordcount-simple</program>
            </pipes>
            <configuration>
                <property>
                    <name>mapred.input.dir</name>
                    <value>${input}</value>
                </property>
                <property>
                    <name>mapred.output.dir</name>
                    <value>${output}</value>
                </property>
            </configuration>
            <archive>/users/blabla/testarchive.jar#testarchive</archive>
        </map-reduce>
        <ok to="end"/>
        <error to="kill"/>
    </action>
...
</workflow-app>

oozie-工作流Map-Reduce行为

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