org.apache.hadoop.mapreduce

类 InputFormat<K,V>

java.lang.Object

org.apache.hadoop.mapreduce.InputFormat<K,V>

直接已知子类:

DBInputFormat, DelegatingInputFormat, DynamicInputFormat, FileInputFormat, UniformSizeInputFormat

public abstract class InputFormat<K,V>
extends Object

InputFormat describes the input-specification for a Map-Reduce job.

The Map-Reduce framework relies on the InputFormat of the job to:

1. Validate the input-specification of the job.
2. Split-up the input file(s) into logical InputSplits, each of which is then assigned to an individual Mapper.
3. Provide the RecordReader implementation to be used to glean input records from the logical InputSplit for processing by the Mapper.

The default behavior of file-based InputFormats, typically sub-classes of FileInputFormat, is to split the input into logical InputSplits based on the total size, in bytes, of the input files. However, the FileSystem blocksize of the input files is treated as an upper bound for input splits. A lower bound on the split size can be set via mapred.min.split.size.

Clearly, logical splits based on input-size is insufficient for many applications since record boundaries are to respected. In such cases, the application has to also implement a RecordReader on whom lies the responsibility to respect record-boundaries and present a record-oriented view of the logical InputSplit to the individual task.

另请参阅:

InputSplit, RecordReader, FileInputFormat

构造器概要

构造器

构造器和说明
InputFormat()

方法概要

方法

限定符和类型	方法和说明
abstract RecordReader<K,V>	createRecordReader(InputSplit split, TaskAttemptContext context) Create a record reader for a given split.
abstract List<InputSplit>	getSplits(JobContext context) Logically split the set of input files for the job.

从类继承的方法 java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

构造器详细资料

InputFormat

public InputFormat()

方法详细资料

getSplits

public abstract List<InputSplit> getSplits(JobContext context)
                                    throws IOException,
                                           InterruptedException

Logically split the set of input files for the job.

Each InputSplit is then assigned to an individual Mapper for processing.

Note: The split is a logical split of the inputs and the input files are not physically split into chunks. For e.g. a split could be <input-file-path, start, offset> tuple. The InputFormat also creates the RecordReader to read the InputSplit.

参数:

context - job configuration.

返回:

an array of InputSplits for the job.

抛出:

IOException

InterruptedException

createRecordReader

public abstract RecordReader<K,V> createRecordReader(InputSplit split,
                                   TaskAttemptContext context)
                                              throws IOException,
                                                     InterruptedException

Create a record reader for a given split. The framework will call RecordReader.initialize(InputSplit, TaskAttemptContext) before the split is used.

参数:

split - the split to be read

context - the information about the task

返回:

a new record reader

抛出:

IOException

InterruptedException