## Uses of Class
org.apache.hadoop.mapreduce.JobContext

Packages that use JobContext
org.apache.hadoop.mapred: A software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) parallelly on large clusters (thousands of nodes) built of commodity hardware in a reliable, fault-tolerant manner.
org.apache.hadoop.mapreduce
org.apache.hadoop.mapreduce.lib.db
org.apache.hadoop.mapreduce.lib.input
org.apache.hadoop.mapreduce.lib.map
org.apache.hadoop.mapreduce.lib.output
org.apache.hadoop.mapreduce.lib.partition

Uses of JobContext in org.apache.hadoop.mapred

Subclasses of JobContext in org.apache.hadoop.mapred
`class` `JobContext`

Methods in org.apache.hadoop.mapred with parameters of type JobContext
`void` `OutputCommitter.abortJob(JobContext context, JobStatus.State runState)` This method implements the new interface by calling the old method.
`void` `OutputCommitter.cleanupJob(JobContext context)` Deprecated.
`void` `OutputCommitter.commitJob(JobContext context)` This method implements the new interface by calling the old method.
`static int` `LocalJobRunner.getLocalMaxRunningMaps(JobContext job)`
`static void` `LocalJobRunner.setLocalMaxRunningMaps(JobContext job, int maxMaps)` Set the max number of map tasks to run concurrently in the LocalJobRunner.
`void` `OutputCommitter.setupJob(JobContext jobContext)` This method implements the new interface by calling the old method.

Uses of JobContext in org.apache.hadoop.mapreduce

Subclasses of JobContext in org.apache.hadoop.mapreduce
`class` `Job` The job submitter's view of the Job.
`class` `MapContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT>` The context that is given to the `Mapper`.
`class` `Mapper.Context`
`class` `ReduceContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT>` The context passed to the `Reducer`.
`class` `Reducer.Context`
`class` `TaskAttemptContext` The context for task attempts.
`class` `TaskInputOutputContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT>` A context object that allows input and output from the task.

Methods in org.apache.hadoop.mapreduce with parameters of type JobContext
`void` `OutputCommitter.abortJob(JobContext jobContext, JobStatus.State state)` For aborting an unsuccessful job's output.
`abstract void` `OutputFormat.checkOutputSpecs(JobContext context)` Check for validity of the output-specification for the job.
`void` `OutputCommitter.cleanupJob(JobContext context)` Deprecated. use `OutputCommitter.commitJob(JobContext)` or `OutputCommitter.abortJob(JobContext, JobStatus.State)` instead
`void` `OutputCommitter.commitJob(JobContext jobContext)` For cleaning up the job's output after job completion.
`abstract List<InputSplit>` `InputFormat.getSplits(JobContext context)` Logically split the set of input files for the job.
`abstract void` `OutputCommitter.setupJob(JobContext jobContext)` For the framework to setup the job output during initialization

Uses of JobContext in org.apache.hadoop.mapreduce.lib.db

Methods in org.apache.hadoop.mapreduce.lib.db with parameters of type JobContext
`void` `DBOutputFormat.checkOutputSpecs(JobContext context)`
`List<InputSplit>` `DBInputFormat.getSplits(JobContext job)` Logically split the set of input files for the job.
`List<InputSplit>` `DataDrivenDBInputFormat.getSplits(JobContext job)` Logically split the set of input files for the job.

Uses of JobContext in org.apache.hadoop.mapreduce.lib.input

Methods in org.apache.hadoop.mapreduce.lib.input with parameters of type JobContext
`static PathFilter` `FileInputFormat.getInputPathFilter(JobContext context)` Get a PathFilter instance of the filter set for the input paths.
`static Path[]` `FileInputFormat.getInputPaths(JobContext context)` Get the list of input `Path`s for the map-reduce job.
`static long` `FileInputFormat.getMaxSplitSize(JobContext context)` Get the maximum split size.
`static long` `FileInputFormat.getMinSplitSize(JobContext job)` Get the minimum split size
`static int` `NLineInputFormat.getNumLinesPerSplit(JobContext job)` Get the number of lines per split
`List<InputSplit>` `DelegatingInputFormat.getSplits(JobContext job)`
`List<InputSplit>` `CombineFileInputFormat.getSplits(JobContext job)`
`List<InputSplit>` `NLineInputFormat.getSplits(JobContext job)` Logically splits the set of input files for the job, splits N lines of the input as one split.
`List<InputSplit>` `FileInputFormat.getSplits(JobContext job)` Generate the list of files and make them into FileSplits.
`protected boolean` `CombineFileInputFormat.isSplitable(JobContext context, Path file)`
`protected boolean` `TextInputFormat.isSplitable(JobContext context, Path file)`
`protected boolean` `FileInputFormat.isSplitable(JobContext context, Path filename)` Is the given filename splitable? Usually, true, but if the file is stream compressed, it will not be.
`protected boolean` `KeyValueTextInputFormat.isSplitable(JobContext context, Path file)`
`protected List<FileStatus>` `SequenceFileInputFormat.listStatus(JobContext job)`
`protected List<FileStatus>` `FileInputFormat.listStatus(JobContext job)` List input directories.

Uses of JobContext in org.apache.hadoop.mapreduce.lib.map

Methods in org.apache.hadoop.mapreduce.lib.map with parameters of type JobContext

`static

`<K1,V1,K2,V2> Class<Mapper<K1,V1,K2,V2>>`

`

`MultithreadedMapper.getMapperClass(JobContext job)`
Get the application's mapper class.

`static int` `MultithreadedMapper.getNumberOfThreads(JobContext job)` The number of threads in the thread pool that will run the map function.

Uses of JobContext in org.apache.hadoop.mapreduce.lib.output

Methods in org.apache.hadoop.mapreduce.lib.output with parameters of type JobContext
`void` `FileOutputCommitter.abortJob(JobContext context, JobStatus.State state)` Delete the temporary directory, including all of the work directories.
`void` `FileOutputFormat.checkOutputSpecs(JobContext job)`
`void` `SequenceFileAsBinaryOutputFormat.checkOutputSpecs(JobContext job)`
`void` `FilterOutputFormat.checkOutputSpecs(JobContext context)`
`void` `LazyOutputFormat.checkOutputSpecs(JobContext context)`
`void` `NullOutputFormat.checkOutputSpecs(JobContext context)`
`void` `FileOutputCommitter.cleanupJob(JobContext context)` Deprecated.
`void` `FileOutputCommitter.commitJob(JobContext context)` Delete the temporary directory, including all of the work directories.
`static boolean` `FileOutputFormat.getCompressOutput(JobContext job)` Is the job output compressed?
`static boolean` `MultipleOutputs.getCountersEnabled(JobContext job)` Returns if the counters for the named outputs are enabled or not.
`static SequenceFile.CompressionType` `SequenceFileOutputFormat.getOutputCompressionType(JobContext job)` Get the `SequenceFile.CompressionType` for the output `SequenceFile`.
`static Class<? extends CompressionCodec>` `FileOutputFormat.getOutputCompressorClass(JobContext job, Class<? extends CompressionCodec> defaultValue)` Get the `CompressionCodec` for compressing the job outputs.
`protected static String` `FileOutputFormat.getOutputName(JobContext job)` Get the base output name for the output file.
`static Path` `FileOutputFormat.getOutputPath(JobContext job)` Get the `Path` to the output directory for the map-reduce job.
`static Class<? extends WritableComparable>` `SequenceFileAsBinaryOutputFormat.getSequenceFileOutputKeyClass(JobContext job)` Get the key class for the `SequenceFile`
`static Class<? extends Writable>` `SequenceFileAsBinaryOutputFormat.getSequenceFileOutputValueClass(JobContext job)` Get the value class for the `SequenceFile`
`protected static void` `FileOutputFormat.setOutputName(JobContext job, String name)` Set the base output name for output file to be created.
`void` `FileOutputCommitter.setupJob(JobContext context)` Create the temporary directory that is the root of all of the task work directories.

Uses of JobContext in org.apache.hadoop.mapreduce.lib.partition

Methods in org.apache.hadoop.mapreduce.lib.partition with parameters of type JobContext
`static String` `KeyFieldBasedComparator.getKeyFieldComparatorOption(JobContext job)` Get the `KeyFieldBasedComparator` options
`String` `KeyFieldBasedPartitioner.getKeyFieldPartitionerOption(JobContext job)` Get the `KeyFieldBasedPartitioner` options

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