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<workflow-app xmlns="uri:oozie:workflow:0.3"
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name="test-core_examples_javamapreduce_cloner_with_multiple_output_without_reducer_with_explicit_schema">
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<!-- This example writes to 2 datastores: person and documents.
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The class responsible for writing multiple datastores is:
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eu.dnetlib.iis.core.examples.javamapreduce.PersonClonerMapperMultipleOutput. -->
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<start to="data_producer" />
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<action name="data_producer">
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<java>
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<job-tracker>${jobTracker}</job-tracker>
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<name-node>${nameNode}</name-node>
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<!-- The data generated by this node is deleted in this section -->
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<prepare>
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<delete path="${nameNode}${workingDir}/data_producer" />
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<mkdir path="${nameNode}${workingDir}/data_producer" />
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</prepare>
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<configuration>
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<property>
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<name>mapred.job.queue.name</name>
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<value>${queueName}</value>
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</property>
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</configuration>
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<!-- This is simple wrapper for the Java code -->
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<main-class>eu.dnetlib.iis.core.java.ProcessWrapper</main-class>
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<!-- The business Java code that gets to be executed -->
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<arg>eu.dnetlib.iis.core.examples.java.SampleDataProducer</arg>
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<!-- All input and output ports have to be bound to paths in HDFS -->
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<arg>-Operson=${workingDir}/data_producer/person</arg>
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<arg>-Odocument=${workingDir}/data_producer/document</arg>
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</java>
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<ok to="mr_cloner" />
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<error to="fail" />
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</action>
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<action name="mr_cloner">
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<map-reduce>
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<job-tracker>${jobTracker}</job-tracker>
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<name-node>${nameNode}</name-node>
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<!-- The data generated by this node in the previous run is
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deleted in this section -->
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<prepare>
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<delete path="${nameNode}${workingDir}/mr_cloner" />
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</prepare>
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<!-- That's a multiple output MapReduce job, so no need to
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create mr_cloner directory, since it will be created by
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MapReduce /> -->
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<configuration>
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<!-- # Standard set of options that stays the same regardless
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of a concrete definition of map-reduce job -->
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<!-- ## Various options -->
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<!--This property seems to not be needed -->
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<!--<property> <name>mapred.job.queue.name</name> <value>${queueName}</value>
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</property> -->
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<property>
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<name>mapreduce.inputformat.class</name>
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<value>org.apache.avro.mapreduce.AvroKeyInputFormat</value>
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</property>
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<!-- The output format is not needed since there is no Reduce phase -->
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<!-- <property>
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<name>mapreduce.outputformat.class</name>
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<value>eu.dnetlib.iis.core.javamapreduce.hack.KeyOutputFormat</value>
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</property>-->
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<property>
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<name>mapred.mapoutput.key.class</name>
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<value>org.apache.avro.mapred.AvroKey</value>
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</property>
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<property>
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<name>mapred.mapoutput.value.class</name>
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<value>org.apache.avro.mapred.AvroValue</value>
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</property>
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<property>
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<name>mapred.output.key.class</name>
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<value>org.apache.avro.mapred.AvroKey</value>
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</property>
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<property>
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<name>mapred.output.value.class</name>
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<value>org.apache.avro.mapred.AvroValue</value>
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</property>
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<property>
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<name>mapred.output.key.comparator.class</name>
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<value>org.apache.avro.hadoop.io.AvroKeyComparator</value>
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</property>
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<property>
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<name>io.serializations</name>
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<value>org.apache.hadoop.io.serializer.WritableSerialization,org.apache.hadoop.io.serializer.avro.AvroSpecificSerialization,org.apache.hadoop.io.serializer.avro.AvroReflectSerialization,org.apache.avro.hadoop.io.AvroSerialization
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</value>
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</property>
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<property>
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<name>mapred.output.value.groupfn.class</name>
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<value>org.apache.avro.hadoop.io.AvroKeyComparator</value>
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</property>
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<property>
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<name>rpc.engine.org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolPB
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</name>
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<value>org.apache.hadoop.ipc.ProtobufRpcEngine</value>
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</property> <!-- This directory does not correspond to a data store. In fact,
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this directory only contains multiple data stores.-->
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<!-- ## This is required for new MapReduce API usage -->
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<property>
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<name>mapred.mapper.new-api</name>
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<value>true</value>
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</property>
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<property>
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<name>mapred.reducer.new-api</name>
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<value>true</value>
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</property>
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<!-- # Job-specific options -->
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<!-- Since there is no reduce phase, there should be no
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reduce tasks -->
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<property>
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<name>mapred.reduce.tasks</name>
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<value>0</value>
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</property>
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<!-- ## Names of all output ports -->
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<property>
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<name>avro.mapreduce.multipleoutputs</name>
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<value>person age</value>
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</property>
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<!-- ## Output classes for all output ports -->
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<property>
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<name>avro.mapreduce.multipleoutputs.namedOutput.person.format
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</name>
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<value>org.apache.avro.mapreduce.AvroKeyOutputFormat</value>
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</property>
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<property>
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<name>avro.mapreduce.multipleoutputs.namedOutput.age.format
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</name>
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<value>org.apache.avro.mapreduce.AvroKeyOutputFormat</value>
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</property>
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<!-- ## Classes of mapper and reducer -->
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<property>
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<name>mapreduce.map.class</name>
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<value>eu.dnetlib.iis.core.examples.javamapreduce.MultipleOutputPersonClonerMapper</value>
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</property>
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<!-- No reducer -->
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<!-- ## Schemas -->
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<!-- ### Shema of the data ingested by the mapper. To be more precise,
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it's the schema of Avro data passed as template parameter of the AvroKey
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object passed to mapper. -->
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<property>
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<name>avro.schema.input.key</name>
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<value>{
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"type" : <!-- This directory does not correspond to a data store. In fact,
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this directory only contains multiple data stores.--> "record",
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"name" : "Person",
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"namespace" : "eu.dnetlib.iis.core.examples.schemas.documentandauthor",
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"fields" : [ {
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"name" : "id",
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"type" : "int"
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}, {
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"name" : "name",
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"type" : "string"
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}, {
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"name" : "age",
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"type" : "int"
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} ]
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}
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</value>
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</property>
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<!-- ### Schemas of the data produced by the mapper -->
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<!-- #### Schema of the key produced by the mapper. To be more precise,
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it's the schema of Avro data produced by the mapper and passed forward as
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template paramter of AvroKey object. (it has to have the same value for the
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"*.reader.schema" and "*.writer.schema"). -->
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<!-- As a convention, we're setting "null" values
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since mapper does not produce any standard data in this example
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(probably any other valid Avro schema would do as well).-->
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<property>
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<name>avro.serialization.key.reader.schema</name>
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<value>"null"</value>
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</property>
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<property>
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<name>avro.serialization.key.writer.schema</name>
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<value>"null"</value>
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</property>
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<!-- #### Schema of the value produced by the mapper. To be more precise,
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it's the schema of Avro data produced by the mapper and passed forward as
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template paramter of AvroValue object. (it has to have the same value for
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the "*.reader.schema" and "*.writer.schema") -->
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<!-- As a convention, we're setting "null" values
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since mapper does not produce any standard data in this example
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(probably any other valid Avro schema would do as well).-->
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<property>
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<name>avro.serialization.value.reader.schema</name>
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<value>"null"</value>
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</property>
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<property>
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<name>avro.serialization.value.writer.schema</name>
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<value>"null"</value>
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</property>
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213
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214
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<!-- ### Shema of multiple output ports. -->
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215
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<property>
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<name>avro.mapreduce.multipleoutputs.namedOutput.person.keyschema
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</name>
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<value>{
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"type" : "record",
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"name" : "Person",
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"namespace" : "eu.dnetlib.iis.core.examples.schemas.documentandauthor",
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"fields" : [ {
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"name" : "id",
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"type" : "int"
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}, {
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"name" : "name",
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"type" : "string"
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229
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}, {
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"name" : "age",
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"type" : "int"
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232
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} ]
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233
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}
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234
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</value>
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235
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</property>
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236
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237
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<property>
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238
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<name>avro.mapreduce.multipleoutputs.namedOutput.age.keyschema
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</name>
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240
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<value>{
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241
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"type" : "record",
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"name" : "PersonAge",
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"namespace" : "eu.dnetlib.iis.core.examples.schemas.documentandauthor",
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244
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"fields" : [ {
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245
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"name" : "age",
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"type" : "int"
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247
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} ]
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248
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}
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249
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</value>
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250
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</property>
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251
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|
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252
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<!-- ## Specification of the input and output data store -->
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253
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254
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<property>
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255
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<name>mapred.input.dir</name>
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256
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<value>${workingDir}/data_producer/person</value>
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257
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</property>
|
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258
|
<!-- This directory does not correspond to a data store. In fact,
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259
|
this directory only contains multiple data stores. It has to
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260
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be set to the name of the workflow node.-->
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261
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<property>
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262
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<name>mapred.output.dir</name>
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263
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<value>${workingDir}/mr_cloner</value>
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264
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</property>
|
|
265
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|
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266
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<!-- ## Workflow node parameters -->
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267
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268
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<property>
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269
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<name>copiesCount</name>
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270
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<value>2</value>
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271
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</property>
|
|
272
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</configuration>
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273
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</map-reduce>
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274
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<ok to="cloner" />
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275
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<error to="fail" />
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|
276
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</action>
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|
277
|
|
|
278
|
<!-- cloner works on duplicated data -->
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279
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<action name="cloner">
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280
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<java>
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281
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<job-tracker>${jobTracker}</job-tracker>
|
|
282
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<name-node>${nameNode}</name-node>
|
|
283
|
<!-- The data generated by this node is deleted in this section -->
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|
284
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<prepare>
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|
285
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<delete path="${nameNode}${workingDir}/cloner" />
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|
286
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<mkdir path="${nameNode}${workingDir}/cloner" />
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|
287
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</prepare>
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|
288
|
<configuration>
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|
289
|
<property>
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|
290
|
<name>mapred.job.queue.name</name>
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|
291
|
<value>${queueName}</value>
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|
292
|
</property>
|
|
293
|
</configuration>
|
|
294
|
<!-- This is simple wrapper for the Java code -->
|
|
295
|
<main-class>eu.dnetlib.iis.core.java.ProcessWrapper</main-class>
|
|
296
|
<!-- The business Java code that gets to be executed -->
|
|
297
|
<arg>eu.dnetlib.iis.core.examples.java.PersonCloner</arg>
|
|
298
|
<!-- All input and output ports have to be bound to paths in HDFS -->
|
|
299
|
<arg>-Iperson=${workingDir}/mr_cloner/person</arg>
|
|
300
|
<arg>-Operson=${workingDir}/cloner/person</arg>
|
|
301
|
</java>
|
|
302
|
<ok to="end" />
|
|
303
|
<error to="fail" />
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|
304
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</action>
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|
305
|
<kill name="fail">
|
|
306
|
<message>Unfortunately, the process failed -- error message:
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|
307
|
[${wf:errorMessage(wf:lastErrorNode())}]
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|
308
|
</message>
|
|
309
|
</kill>
|
|
310
|
<end name="end" />
|
|
311
|
</workflow-app>
|