There are many input and output formats supported in hadoop out of the box and we will explore the same in this article.
Hadoop can process many different types of data formats, from flat text files to databases. In this section, we explore the different formats available.
Controlling the maximum line length
If you are using one of the text input formats discussed here, you can set a maximum expected line length to safeguard against corrupted files. Corruption in a file can manifest itself as a very long line, which can cause out-ofmemory errors and then task failure. By setting mapreduce.input.linerecordreader.line.maxlength to a value in bytes that fits in memory (and is comfortably greater than the length of lines in your input data), you ensure that the record reader will skip the (long) corrupt lines without the task failing.
TextInputFormat – TextInputFormat is the default InputFormat. Each record is a line of input. The key, a LongWritable, is the byte offset within the file of the beginning of the line. The value is the contents of the line, excluding any line terminators.
KeyValueTextInputFormat – TextInputFormat’s keys, being simply the offsets within the file, are not normally very useful. It is common for each line in a file to be a key-value pair, separated by a delimiter such as a tab character. For example, this is the kind of output produced by TextOut putFormat, Hadoop’s default OutputFormat. To interpret such files correctly, KeyValueTextInputFormat is appropriate.
You can specify the separator via the mapreduce.input.keyvaluelinerecordreader.key.value.separator property. It is a tab character by default.
NLineInputFormat – With TextInputFormat and KeyValueTextInputFormat, each mapper receives a variable number of lines of input. The number depends on the size of the split and the length of the lines. If you want your mappers to receive a fixed number of lines of input, then NLineInputFormat is the InputFormat to use. Like with TextInputFormat, the keys are the byte offsets within the file and the values are the lines themselves. N refers to the number of lines of input that each mapper receives. With N set to 1 (the default), each mapper receives exactly one line of input. The mapreduce.input.lineinputformat.linespermap property controls the value of N.
StreamInputFormat – Hadoop comes with a InputFormat for streaming which can be used outside streaming and can be used for processing XML documents. You can use it by setting your input format to StreamInputFormat and setting the stream.recordreader.class property to org.apache.hadoop.streaming.mapreduce.StreamXmlRecordReader. The reader is configured by setting job configuration properties to tell it the patterns for the start and end tags.
XmlInputFormat courtesy of the Lucene sub-project: Mahout is been recommended in many blogging sites to be working for xml parsing and can be considered for xml reading. Link here XmlInputFormat.java
SequenceFileInputFormat – Hadoop MapReduce is not restricted to processing textual data. It has support for binary formats, too. Hadoop’s sequence file format stores sequences of binary key-value pairs. Sequence files are well suited as a format for MapReduce data because they are splittable (they have sync points so that readers can synchronize with record boundaries from an arbitrary point in the file, such as the start of a split), they support compression as a part of the format, and they can store arbitrary types using a variety of serialization frameworks.
SequenceFileAsTextInputFormat – SequenceFileAsTextInputFormat is a variant of SequenceFileInputFormat that converts the sequence file’s keys and values to Text objects. The conversion is performed by calling toString() on the keys and values. This format makes sequence files suitable input for Streaming
SequenceFileAsBinaryInputFormat – SequenceFileAsBinaryInputFormat is a variant of SequenceFileInputFormat that retrieves the sequence file’s keys and values as opaque binary objects. They are encapsulated as BytesWritable objects, and the application is free to interpret the underlying byte array as it pleases.
FixedLengthInputFormat – FixedLengthInputFormat is for reading fixed-width binary records from a file, when the records are not separated by delimiters. The record size must be set via fixedlengthinputformat.record.length.
Multiple Inputs – Although the input to a MapReduce job may consist of multiple input files (constructed by a combination of file globs, filters, and plain paths), all of the input is interpreted by a single InputFormat and a single Mapper. What often happens, however, is that the data format evolves over time, so you have to write your mapper to cope with all of your legacy formats. Or you may have data sources that provide the same type of data but in different formats. This arises in the case of performing joins of different datasets . For instance, one might be tab-separated plain text, and the other a binary sequence file. Even if they are in the same format, they may have
different representations, and therefore need to be parsed differently. These cases are handled elegantly by using the MultipleInputs class, which allows you to specify which InputFormat and Mapper to use on a per-path basis.
Database Input – DBInputFormat is an input format for reading data from a relational database, using JDBC. Because it doesn’t have any sharding capabilities, you need to be careful not to overwhelm the database from which you are reading by running too many mappers.
For this reason, it is best used for loading relatively small datasets, perhaps for joining with larger datasets from HDFS using MultipleInputs. The corresponding output format is DBOutputFormat, which is useful for dumping job outputs (of modest size)
into a database.
Hadoop has output data formats that correspond to the input formats.
Text Output – The default output format, TextOutputFormat, writes records as lines of text. Its keys and values may be of any type, since TextOutputFormat turns them to strings by calling toString() on them. Each key-value pair is separated by a tab character, although that may be changed using the mapreduce.output.textoutputformat.separator property.
SequenceFileOutputFormat – its for writing binary Output. As the name indicates, SequenceFileOutputFormat writes sequence files for its output. This is a good choice of output if it forms the input to a further MapReduce job, since it is compact and is readily compressed.
SequenceFileAsBinaryOutputFormat – Is the counterpart to SequenceFileAsBinaryInputFormat writes keys and values in raw binary format into a sequence file container.
MapFileOutputFormat – MapFileOutputFormat writes map files as output. The keys in a MapFile must be added in order, so you need to ensure that your reducers emit keys in sorted order.
Multiple Outputs – Sometimes there is a need to have more control over the naming of the files or to produce multiple files per reducer. MapReduce comes with the MultipleOutputs class to help you do this.
Lazy Output – FileOutputFormat subclasses will create output (part-r-nnnnn) files, even if they are empty. Some applications prefer that empty files not be created, which is where Lazy OutputFormat helps. It is a wrapper output format that ensures that the output file is created only when the first record is emitted for a given partition. To use it, call its setOutputFormatClass() method with the JobConf and the underlying output format.
Database Output – The output formats for writing to relational databases and to HBase.