org.springframework.batch.core.step.item

## Class FaultTolerantChunkProcessor<I,O>

- java.lang.Object
- - org.springframework.batch.core.step.item.SimpleChunkProcessor<I,O>
  - - org.springframework.batch.core.step.item.FaultTolerantChunkProcessor<I,O>

- All Implemented Interfaces:
    ChunkProcessor<I>, org.springframework.beans.factory.InitializingBean

      public class FaultTolerantChunkProcessor<I,O>
      extends SimpleChunkProcessor<I,O>

  FaultTolerant implementation of the `ChunkProcessor` interface, that allows for skipping or retry of items that cause exceptions during writing.

- - ### Constructor Summary

    Constructors
    Constructor and Description
    `FaultTolerantChunkProcessor(ItemProcessor<? super I,? extends O> itemProcessor, ItemWriter<? super O> itemWriter, BatchRetryTemplate batchRetryTemplate)`

  - ### Method Summary

    All Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `protected Chunk<O>` | `getAdjustedOutputs(Chunk<I> inputs, Chunk<O> outputs)` Extension point for subclasses that want to adjust the outputs based on additional saved data in the inputs.
    `protected int` | `getFilterCount(Chunk<I> inputs, Chunk<O> outputs)` Extension point for subclasses to calculate the filter count.
    `protected void` | `initializeUserData(Chunk<I> inputs)` Extension point for subclasses to allow them to memorise the contents of the inputs, in case they are needed for accounting purposes later.
    `protected boolean` | `isComplete(Chunk<I> inputs)` Extension point for subclasses that want to store additional data in the inputs.
    `void` | `setBuffering(boolean buffering)` A flag to indicate that items have been buffered and therefore will always come back as a chunk after a rollback.
    `void` | `setChunkMonitor(ChunkMonitor chunkMonitor)`
    `void` | `setKeyGenerator(KeyGenerator keyGenerator)` The `KeyGenerator` to use to identify failed items across rollback.
    `void` | `setProcessorTransactional(boolean processorTransactional)` Flag to say that the `ItemProcessor` is transactional (defaults to true).
    `void` | `setProcessSkipPolicy(SkipPolicy SkipPolicy)`
    `void` | `setRollbackClassifier(org.springframework.classify.Classifier<java.lang.Throwable,java.lang.Boolean> rollbackClassifier)` A classifier that can distinguish between exceptions that cause rollback (return true) or not (return false).
    `void` | `setWriteSkipPolicy(SkipPolicy SkipPolicy)`
    `protected Chunk<O>` | `transform(StepContribution contribution, Chunk<I> inputs)`
    `protected void` | `write(StepContribution contribution, Chunk<I> inputs, Chunk<O> outputs)` Simple implementation delegates to the `SimpleChunkProcessor.doWrite(List)` method and increments the write count in the contribution.

    - ### Methods inherited from class org.springframework.batch.core.step.item.SimpleChunkProcessor

      `afterPropertiesSet, doAfterWrite, doOnWriteError, doProcess, doWrite, getListener, process, registerListener, setItemProcessor, setItemWriter, setListeners, writeItems`

    - ### Methods inherited from class java.lang.Object

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

- - ### Constructor Detail

    - #### FaultTolerantChunkProcessor

          public FaultTolerantChunkProcessor(ItemProcessor<? super I,? extends O> itemProcessor,
                                             ItemWriter<? super O> itemWriter,
                                             BatchRetryTemplate batchRetryTemplate)

  - ### Method Detail

    - #### setKeyGenerator

          public void setKeyGenerator(KeyGenerator keyGenerator)

      The `KeyGenerator` to use to identify failed items across rollback. Not used in the case of the `buffering flag` being true (the default).
      Parameters:
        `keyGenerator` - the `KeyGenerator` to set

    - #### setProcessSkipPolicy

          public void setProcessSkipPolicy(SkipPolicy SkipPolicy)

      Parameters:
        `SkipPolicy` - the `SkipPolicy` for item processing

    - #### setWriteSkipPolicy

          public void setWriteSkipPolicy(SkipPolicy SkipPolicy)

      Parameters:
        `SkipPolicy` - the `SkipPolicy` for item writing

    - #### setRollbackClassifier

          public void setRollbackClassifier(org.springframework.classify.Classifier<java.lang.Throwable,java.lang.Boolean> rollbackClassifier)

      A classifier that can distinguish between exceptions that cause rollback (return true) or not (return false).
      Parameters:
        `rollbackClassifier` - classifier

    - #### setChunkMonitor

          public void setChunkMonitor(ChunkMonitor chunkMonitor)

      Parameters:
        `chunkMonitor` - monitor

    - #### setBuffering

          public void setBuffering(boolean buffering)

      A flag to indicate that items have been buffered and therefore will always come back as a chunk after a rollback. Otherwise things are more complicated because after a rollback the new chunk might or might not contain items from the previous failed chunk.
      Parameters:
        `buffering` - true if items will be buffered

    - #### setProcessorTransactional

          public void setProcessorTransactional(boolean processorTransactional)

      Flag to say that the `ItemProcessor` is transactional (defaults to true). If false then the processor is only called once per item per chunk, even if there are rollbacks with retries and skips.
      Parameters:
        `processorTransactional` - the flag value to set

    - #### initializeUserData

          protected void initializeUserData(Chunk<I> inputs)

      Description copied from class: `SimpleChunkProcessor`
      Extension point for subclasses to allow them to memorise the contents of the inputs, in case they are needed for accounting purposes later. The default implementation sets up some user data to remember the original size of the inputs. If this method is overridden then some or all of `SimpleChunkProcessor.isComplete(Chunk)`, `SimpleChunkProcessor.getFilterCount(Chunk, Chunk)` and `SimpleChunkProcessor.getAdjustedOutputs(Chunk, Chunk)` might also need to be, to ensure that the user data is handled consistently.
      Overrides:
        `initializeUserData` in class `SimpleChunkProcessor<I,O>`
      Parameters:
        `inputs` - the inputs for the process

    - #### getFilterCount

          protected int getFilterCount(Chunk<I> inputs,
                                       Chunk<O> outputs)

      Description copied from class: `SimpleChunkProcessor`
      Extension point for subclasses to calculate the filter count. Defaults to the difference between input size and output size.
      Overrides:
        `getFilterCount` in class `SimpleChunkProcessor<I,O>`
      Parameters:
        `inputs` - the inputs after transformation
        `outputs` - the outputs after transformation
      Returns:
        the difference in sizes
      See Also:
        `SimpleChunkProcessor.initializeUserData(Chunk)`

    - #### isComplete

          protected boolean isComplete(Chunk<I> inputs)

      Description copied from class: `SimpleChunkProcessor`
      Extension point for subclasses that want to store additional data in the inputs. Default just checks if inputs are empty.
      Overrides:
        `isComplete` in class `SimpleChunkProcessor<I,O>`
      Parameters:
        `inputs` - the input chunk
      Returns:
        true if it is empty
      See Also:
        `SimpleChunkProcessor.initializeUserData(Chunk)`

    - #### getAdjustedOutputs

          protected Chunk<O> getAdjustedOutputs(Chunk<I> inputs,
                                                Chunk<O> outputs)

      Description copied from class: `SimpleChunkProcessor`
      Extension point for subclasses that want to adjust the outputs based on additional saved data in the inputs. Default implementation just returns the outputs unchanged.
      Overrides:
        `getAdjustedOutputs` in class `SimpleChunkProcessor<I,O>`
      Parameters:
        `inputs` - the inputs for the transformation
        `outputs` - the result of the transformation
      Returns:
        the outputs unchanged
      See Also:
        `SimpleChunkProcessor.initializeUserData(Chunk)`

    - #### transform

          protected Chunk<O> transform(StepContribution contribution,
                                       Chunk<I> inputs)
                                throws java.lang.Exception

      Overrides:
        `transform` in class `SimpleChunkProcessor<I,O>`
      Throws:
        `java.lang.Exception`

    - #### write

          protected void write(StepContribution contribution,
                               Chunk<I> inputs,
                               Chunk<O> outputs)
                        throws java.lang.Exception

      Description copied from class: `SimpleChunkProcessor`
      Simple implementation delegates to the `SimpleChunkProcessor.doWrite(List)` method and increments the write count in the contribution. Subclasses can handle more complicated scenarios, e.g.with fault tolerance. If output items are skipped they should be removed from the inputs as well.
      Overrides:
        `write` in class `SimpleChunkProcessor<I,O>`
      Parameters:
        `contribution` - the current step contribution
        `inputs` - the inputs that gave rise to the outputs
        `outputs` - the outputs to write
      Throws:
        `java.lang.Exception` - if there is a problem