org.apache.beam.sdk.io.hadoop.inputformat

Class HadoopInputFormatIO

java.lang.Object

org.apache.beam.sdk.io.hadoop.inputformat.HadoopInputFormatIO

@Experimental(value=SOURCE_SINK)
public class HadoopInputFormatIO
extends java.lang.Object

A HadoopInputFormatIO is a Transform for reading data from any source which implements Hadoop InputFormat. For example- Cassandra, Elasticsearch, HBase, Redis, Postgres etc. HadoopInputFormatIO has to make several performance trade-offs in connecting to InputFormat, so if there is another Beam IO Transform specifically for connecting to your data source of choice, we would recommend using that one, but this IO Transform allows you to connect to many data sources that do not yet have a Beam IO Transform.

You will need to pass a Hadoop Configuration with parameters specifying how the read will occur. Many properties of the Configuration are optional, and some are required for certain InputFormat classes, but the following properties must be set for all InputFormats:

• mapreduce.job.inputformat.class: The InputFormat class used to connect to your data source of choice.
• key.class: The key class returned by the InputFormat in mapreduce.job.inputformat.class.
• value.class: The value class returned by the InputFormat in mapreduce.job.inputformat.class.

For example:

 {
   Configuration myHadoopConfiguration = new Configuration(false);
   // Set Hadoop InputFormat, key and value class in configuration
   myHadoopConfiguration.setClass("mapreduce.job.inputformat.class",
      MyDbInputFormatClass, InputFormat.class);
   myHadoopConfiguration.setClass("key.class", MyDbInputFormatKeyClass, Object.class);
   myHadoopConfiguration.setClass("value.class",
      MyDbInputFormatValueClass, Object.class);
 }
 

You will need to check to see if the key and value classes output by the InputFormat have a Beam Coder available. If not, you can use withKeyTranslation/withValueTranslation to specify a method transforming instances of those classes into another class that is supported by a Beam Coder. These settings are optional and you don't need to specify translation for both key and value. If you specify a translation, you will need to make sure the K or V of the read transform match the output type of the translation.

You will need to set appropriate InputFormat key and value class (i.e. "key.class" and "value.class") in Hadoop Configuration. If you set different InputFormat key or value class than InputFormat's actual key or value class then, it may result in an error like "unexpected extra bytes after decoding" while the decoding process of key/value object happens. Hence, it is important to set appropriate InputFormat key and value class.

Reading using HadoopInputFormatIO

 
 Pipeline p = ...; // Create pipeline.
 // Read data only with Hadoop configuration.
 p.apply("read",
     HadoopInputFormatIO.<InputFormatKeyClass, InputFormatKeyClass>read()
              .withConfiguration(myHadoopConfiguration);
 
 // Read data with configuration and key translation (Example scenario: Beam Coder is not
 available for key class hence key translation is required.).
 SimpleFunction<InputFormatKeyClass, MyKeyClass> myOutputKeyType =
       new SimpleFunction<InputFormatKeyClass, MyKeyClass>() {
         public MyKeyClass apply(InputFormatKeyClass input) {
           // ...logic to transform InputFormatKeyClass to MyKeyClass
         }
 };
 

 
 p.apply("read",
     HadoopInputFormatIO.<MyKeyClass, InputFormatKeyClass>read()
              .withConfiguration(myHadoopConfiguration)
              .withKeyTranslation(myOutputKeyType);
 
 

// Read data with configuration and value translation (Example scenario: Beam Coder is not available for value class hence value translation is required.).

 
 SimpleFunction<InputFormatValueClass, MyValueClass> myOutputValueType =
      new SimpleFunction<InputFormatValueClass, MyValueClass>() {
          public MyValueClass apply(InputFormatValueClass input) {
            // ...logic to transform InputFormatValueClass to MyValueClass
          }
  };
 
 

 
 p.apply("read",
     HadoopInputFormatIO.<InputFormatKeyClass, MyValueClass>read()
              .withConfiguration(myHadoopConfiguration)
              .withValueTranslation(myOutputValueType);
 
 

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HadoopInputFormatIO.HadoopInputFormatBoundedSource<K,V> Bounded source implementation for HadoopInputFormatIO.
static class	HadoopInputFormatIO.Read<K,V> A PTransform that reads from any data source which implements Hadoop InputFormat.
static class	HadoopInputFormatIO.SerializableSplit A wrapper to allow Hadoop InputSplit to be serialized using Java's standard serialization mechanisms.

Constructor Summary

Constructors

Constructor and Description
HadoopInputFormatIO()

Method Summary

Modifier and Type	Method and Description
static <K,V> HadoopInputFormatIO.Read<K,V>	read() Creates an uninitialized HadoopInputFormatIO.Read.

Methods inherited from class java.lang.Object

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

Constructor Detail

HadoopInputFormatIO

public HadoopInputFormatIO()

Method Detail

read

public static <K,V> HadoopInputFormatIO.Read<K,V> read()

Creates an uninitialized HadoopInputFormatIO.Read. Before use, the Read must be initialized with a HadoopInputFormatIO.Read#withConfiguration(HadoopConfiguration) that specifies the source. A key/value translation may also optionally be specified using HadoopInputFormatIO.Read.withKeyTranslation(org.apache.beam.sdk.transforms.SimpleFunction<?, K>)/ HadoopInputFormatIO.Read.withValueTranslation(org.apache.beam.sdk.transforms.SimpleFunction<?, V>).