Examples with MapReduceContext co.cask.cdap.api.mapreduce.MapReduceContext used on opensource projects

Example 11 with MapReduceContext

use of co.cask.cdap.api.mapreduce.MapReduceContext in project cdap by caskdata.

the class HitCounterProgram method initialize.

@Override
public void initialize() throws Exception {
    MapReduceContext context = getContext();
    Job job = context.getHadoopJob();
    job.setMapperClass(Emitter.class);
    job.setReducerClass(Counter.class);
    context.addInput(Input.ofStream(LogAnalysisApp.LOG_STREAM));
    context.addOutput(Output.ofDataset(LogAnalysisApp.HIT_COUNT_STORE));
}

Example 12 with MapReduceContext

use of co.cask.cdap.api.mapreduce.MapReduceContext in project cdap by caskdata.

the class DataCleansingMapReduce method initialize.

@Override
public void initialize() throws Exception {
    MapReduceContext context = getContext();
    partitionCommitter = PartitionBatchInput.setInput(context, DataCleansing.RAW_RECORDS, new KVTableStatePersistor(DataCleansing.CONSUMING_STATE, "state.key"));
    // Each run writes its output to a partition for the league
    Long timeKey = Long.valueOf(context.getRuntimeArguments().get(OUTPUT_PARTITION_KEY));
    PartitionKey outputKey = PartitionKey.builder().addLongField("time", timeKey).build();
    Map<String, String> metadataToAssign = ImmutableMap.of("source.program", "DataCleansingMapReduce");
    // set up two outputs - one for invalid records and one for valid records
    Map<String, String> invalidRecordsArgs = new HashMap<>();
    PartitionedFileSetArguments.setOutputPartitionKey(invalidRecordsArgs, outputKey);
    PartitionedFileSetArguments.setOutputPartitionMetadata(invalidRecordsArgs, metadataToAssign);
    context.addOutput(Output.ofDataset(DataCleansing.INVALID_RECORDS, invalidRecordsArgs));
    Map<String, String> cleanRecordsArgs = new HashMap<>();
    PartitionedFileSetArguments.setDynamicPartitioner(cleanRecordsArgs, TimeAndZipPartitioner.class);
    PartitionedFileSetArguments.setOutputPartitionMetadata(cleanRecordsArgs, metadataToAssign);
    context.addOutput(Output.ofDataset(DataCleansing.CLEAN_RECORDS, cleanRecordsArgs));
    Job job = context.getHadoopJob();
    job.setMapperClass(SchemaMatchingFilter.class);
    job.setNumReduceTasks(0);
    // simply propagate the schema (if any) to be used by the mapper
    String schemaJson = context.getRuntimeArguments().get(SCHEMA_KEY);
    if (schemaJson != null) {
        job.getConfiguration().set(SCHEMA_KEY, schemaJson);
    }
}