# ItemReaders and ItemWriters

All batch processing can be described in its most simple form as reading in large amounts of data, performing some type of calculation or transformation, and writing the result out. Spring Batch provides three key interfaces to help perform bulk reading and writing: `ItemReader`, `ItemProcessor` and `ItemWriter`.

## ItemReader

Although a simple concept, an `ItemReader` is the means for providing data from many different types of input. The most general examples include:

- Flat File- Flat File Item Readers read lines of data from a flat file that typically describe records with fields of data defined by fixed positions in the file or delimited by some special character (e.g. Comma).

- XML - XML ItemReaders process XML independently of technologies used for parsing, mapping and validating objects. Input data allows for the validation of an XML file against an XSD schema.

- Database - A database resource is accessed to return resultsets which can be mapped to objects for processing. The default SQL ItemReaders invoke a `RowMapper` to return objects, keep track of the current row if restart is required, store basic statistics, and provide some transaction enhancements that will be explained later.

There are many more possibilities, but we'll focus on the basic ones for this chapter. A complete list of all available ItemReaders can be found in Appendix A.

`ItemReader` is a basic interface for generic input operations:

    public interface ItemReader<T> {

      T read() throws Exception, UnexpectedInputException, ParseException;

    }

The `read` method defines the most essential contract of the `ItemReader`; calling it returns one Item or null if no more items are left. An item might represent a line in a file, a row in a database, or an element in an XML file. It is generally expected that these will be mapped to a usable domain object (i.e. Trade, Foo, etc) but there is no requirement in the contract to do so.

It is expected that implementations of the `ItemReader` interface will be forward only. However, if the underlying resource is transactional (such as a JMS queue) then calling read may return the same logical item on subsequent calls in a rollback scenario. It is also worth noting that a lack of items to process by an `ItemReader` will not cause an exception to be thrown. For example, a database `ItemReader` that is configured with a query that returns 0 results will simply return null on the first invocation of `read`.

## ItemWriter

`ItemWriter` is similar in functionality to an `ItemReader`, but with inverse operations. Resources still need to be located, opened and closed but they differ in that an `ItemWriter` writes out, rather than reading in. In the case of databases or queues these may be inserts, updates, or sends. The format of the serialization of the output is specific to each batch job.

As with `ItemReader`, `ItemWriter` is a fairly generic interface:

    public interface ItemWriter<T> {

      void write(List<? extends T> items) throws Exception;

    }

As with `read` on `ItemReader`, `write` provides the basic contract of `ItemWriter`; it will attempt to write out the list of items passed in as long as it is open. Because it is generally expected that items will be 'batched' together into a chunk and then output, the interface accepts a list of items, rather than an item by itself. After writing out the list, any flushing that may be necessary can be performed before returning from the write method. For example, if writing to a Hibernate DAO, multiple calls to write can be made, one for each item. The writer can then call close on the hibernate Session before returning.

## ItemProcessor

The `ItemReader` and `ItemWriter` interfaces are both very useful for their specific tasks, but what if you want to insert business logic before writing? One option for both reading and writing is to use the composite pattern: create an `ItemWriter` that contains another `ItemWriter`, or an `ItemReader` that contains another `ItemReader`. For example:

      public class CompositeItemWriter<T> implements ItemWriter<T> {

        ItemWriter<T> itemWriter;

        public CompositeItemWriter(ItemWriter<T> itemWriter) {
          this.itemWriter = itemWriter;
        }

        public void write(List<? extends T> items) throws Exception {

          //Add business logic here

          itemWriter.write(item);
        }

        public void setDelegate(ItemWriter<T> itemWriter){
          this.itemWriter = itemWriter;
        }
    }

The class above contains another `ItemWriter` to which it delgates after having provided some business logic. This pattern could easily be used for an `ItemReader` as well, perhaps to obtain more reference data based upon the input that was provided by the main `ItemReader`. It is also useful if you need to control the call to `write` yourself. However, if you only want to 'transform' the item passed in for writing before it is actually written, there isn't much need to call `write` yourself: you just want to modify the item. For this scenario, Spring Batch provides the `ItemProcessor` interface:

      public interface ItemProcessor<I, O> {

      O process(I item) throws Exception;
    }

An `ItemProcessor` is very simple; given one object, transform it and return another. The provided object may or may not be of the same type. The point is that business logic may be applied within process, and is completely up to the developer to create. An `ItemProcessor` can be wired directly into a step, For example, assuming an `ItemReader` provides a class of type Foo, and it needs to be converted to type Bar before being written out. An `ItemProcessor` can be written that performs the conversion:

      public class Foo {}

      public class Bar {
        public Bar(Foo foo) {}
      }

      public class FooProcessor implements ItemProcessor<Foo,Bar>{

        //Perform simple transformation, convert a Foo to a Bar
        public Bar process(Foo foo) throws Exception {
          return new Bar(foo);
        }
      }

      public class BarWriter implements ItemWriter<Bar>{

        public void write(List<? extends Bar> bars) throws Exception {
          //write bars
        }

        //rest of class ommitted for clarity
      }

In the very simple example above, there is a class `Foo`, a class `Bar`, and a class `FooProcessor` that adheres to the `ItemProcessor` interface. The transformation is simple, but any type of transformation could be done here. The `BarWriter` will be used to write out `Bar` objects, throwing an exception if any other type is provided. Similarly, the `FooProcessor` will throw an exception if anything but a `Foo` is provided. The `FooProcessor` can then be injected into a `Step`:

      <job id="ioSampleJob">
        <step name="step1">
          <tasklet reader="fooReader" processor="fooProcessor" writer="barWriter" commit-interval="2"/>
        </step>
      </job>

### Chaining ItemProcessors

Performing a single transformation is useful in many scenarios, but what if you want to 'chain' together multiple `ItemProcessor`s? This can be accomplished using the composite pattern mentioned previously. To update the previous, single transformation, example, `Foo` will be transformed to `Bar`, which will be transformed to `Foobar` and written out:

      public class Foo {}

      public class Bar {
        public Bar(Foo foo) {}
      }

      public class Foobar{
        public Foobar(Bar bar){}
      }

      public class FooProcessor implements ItemProcessor<Foo,Bar>{

        //Perform simple transformation, convert a Foo to a Bar
        public Bar process(Foo foo) throws Exception {
          return new Bar(foo);
        }
      }

      public class BarProcessor implements ItemProcessor<Bar,FooBar>{

        public FooBar process(Bar bar) throws Exception {
          return new Foobar(bar);
        }
      }

      public class FoobarWriter implements ItemWriter<FooBar>{

        public void write(List<? extends FooBar> items) throws Exception {
          //write items
        }

        //rest of class ommitted for clarity
      }

A `FooTransformer` and `BarTransformer` can be 'chained' together to give the resultant `Foobar`:

      CompositeItemProcessor<Foo,Foobar> compositeProcessor = new CompositeItemProcessor<Foo,Foobar>();
      List itemProcessors = new ArrayList();
      itemProcessors.add(new FooTransformer());
      itemProcessors.add(new BarTransformer());
      compositeProcessor.setItemProcessors(itemProcessors);

Just as with the previous example, the composite processor can be configured into the `Step`:

      <job id="ioSampleJob">
        <step name="step1">
          <tasklet reader="fooReader" processor="compositeProcessor" writer="foobarWriter" commit-interval="2"/>
        </step>
      </job>

      <bean id="compositeItemProcessor"
            class="org.springframework.batch.item.support.CompositeItemProcessor">
        <property name="itemProcessors">
          <list>
            <bean class="..FooProcessor" />
            <bean class="..BarProcessor" />
          </list>
        </property>
      </bean>

### Filtering Records

One typical use for an item processor is to filter out records before they are passed to the ItemWriter. Filtering is an action distinct from skpping; skipping indicates that a record is invalid whereas filtering simply indicates that a record should not be written.

For example, consider a batch job that reads a file containing three different types of records: records to insert, records to update, and records to delete. If record deletion is not supported by the system, then we would not want to send any "delete" records to the `ItemWriter`. But, since these records are not actually bad records, we would want to filter them out, rather than skip. As a result, the ItemWriter would receive only "insert" and "update" records.

To filter a record, one simply returns "null" from the `ItemProcessor`. The framework will detect that the result is "null" and avoid adding that item to the list of records delivered to the `ItemWriter`. As usual, an exception thrown from the `ItemProcessor` will result in a skip.

## ItemStream

Both `ItemReader`s and `ItemWriter`s serve their individual purposes well, but there is a common concern among both of them that necessitates another interface. In general, as part of the scope of a batch job, readers and writers need to be opened, closed, and require a mechanism for persisting state:

    public interface ItemStream {

      void open(ExecutionContext executionContext) throws ItemStreamException;

      void update(ExecutionContext executionContext) throws ItemStreamException;

      void close() throws ItemStreamException;
    }

Before describing each method, we should mention the `ExecutionContext`. Clients of an `ItemReader` that also implement `ItemStream` should call `open` before any calls to `read` in order to open any resources such as files or to obtain connections. A similar restriction applies to an `ItemWriter` that implements `ItemStream`. As mentioned in Chapter 2, if expected data is found in the `ExecutionContext`, it may be used to start the `ItemReader` or `ItemWriter` at a location other than its initial state. Conversely, `close` will be called to ensure that any resources allocated during `open` will be released safely. `update` is called primarily to ensure that any state currently being held is loaded into the provided `ExecutionContext`. This method will be called before committing, to ensure that the current state is persisted in the database before commit.

In the special case where the client of an `ItemStream` is a `Step` (from the Spring Batch Core), an `ExecutionContext` is created for each `StepExecution` to allow users to store the state of a particular execution, with the expectation that it will be returned if the same `JobInstance` is started again. For those familiar with Quartz, the semantics are very similar to a Quartz `JobDataMap`.

## The Delegate Pattern and Registering with the Step

Note that the `CompositeItemWriter` is an example of the delegation pattern, which is common in Spring Batch. The delegates themselves might implement callback interfaces like `ItemStream` or `StepListener`. If they do, and they are being used in conjunction with Spring Batch Core as part of a `Step` in a `Job`, then they almost certainly need to be registered manually with the `Step`. A reader, writer, or processor that is directly wired into the Step will be registered automatically if it implements `ItemStream` or a `StepListener` interface. But because the delegates are not known to the `Step`, they need to be injected as listeners or streams (or both if appropriate):

      <job id="ioSampleJob">
        <step name="step1">
          <tasklet reader="fooReader" processor="fooProcessor" writer="compositeItemWriter"
                   commit-interval="2"/>
          <streams>
            <stream ref="barWriter" />
          </streams>
        </step>
      </job>

      <bean id="compositeItemWriter"
            class="...CompositeItemWriter" >
        <property name="delegate" ref="barWriter" />
      </bean>

      <bean id="barWriter" class="...BarWriter" />

## Flat Files

One of the most common mechanisms for interchanging bulk data has always been the flat file. Unlike XML, which has an agreed upon standard for defining how it is structured (XSD), anyone reading a flat file must understand ahead of time exactly how the file is structured. In general, all flat files fall into two types: Delimited and Fixed Length. Delimited files are those in which fields are separated by a delimiter, such as a comma. Fixed Length files have fields that are a set length.

### The FieldSet

When working with flat files in Spring Batch, regardless of whether it is for input or output, one of the most important classes is the `FieldSet`. Many architectures and libraries contain abstractions for helping you read in from a file, but they usually return a String or an array of Strings. This really only gets you halfway there. A `FieldSet` is Spring Batch’s abstraction for enabling the binding of fields from a file resource. It allows developers to work with file input in much the same way as they would work with database input. A `FieldSet` is conceptually very similar to a Jdbc `ResultSet`. FieldSets only require one argument, a `String` array of tokens. Optionally, you can also configure in the names of the fields so that the fields may be accessed either by index or name as patterned after `ResultSet`:

      String[] tokens = new String[]{"foo", "1", "true"};
      FieldSet fs = new DefaultFieldSet(tokens);
      String name = fs.readString(0);
      int value = fs.readInt(1);
      boolean booleanValue = fs.readBoolean(2);

There are many more options on the `FieldSet` interface, such as `Date`, long, `BigDecimal`, etc. The biggest advantage of the `FieldSet` is that it provides consistent parsing of flat file input. Rather than each batch job parsing differently in potentially unexpected ways, it can be consistent, both when handling errors caused by a format exception, or when doing simple data conversions.

### FlatFileItemReader

A flat file is any type of file that contains at most two-dimensional (tabular) data. Reading flat files in the Spring Batch framework is facilitated by the class `FlatFileItemReader`, which provides basic functionality for reading and parsing flat files. The two most important required dependencies of `FlatFileItemReader` are `Resource` and `LineMapper.` The `LineMapper` interface will be explored more in the next sections. The resource property represents a Spring Core `Resource`. Documentation explaining how to create beans of this type can be found in Spring Framework, Chapter 4.Resources. Therefore, this guide will not go into the details of creating `Resource` objects. However, a simple example of a file system resource can be found below:

      Resource resource = new FileSystemResource("resources/trades.csv");

In complex batch environments the directory structures are often managed by the EAI infrastructure where drop zones for external interfaces are established for moving files from ftp locations to batch processing locations and vice versa. File moving utilities are beyond the scope of the spring batch architecture but it is not unusual for batch job streams to include file moving utilities as steps in the job stream. It is sufficient that the batch architecture only needs to know how to locate the files to be processed. Spring Batch begins the process of feeding the data into the pipe from this starting point. However, Spring Integration provides many of these types of services.

The other properties in `FlatFileItemReader` allow you to further specify how your data will be interpreted:

#### Flat File Item Reader Properties

Property | Type | Description
--- | --- | ---
encoding | String | Specifies what text encoding to use - default is "ISO-8859-1"
comments | String[] | Specifies line prefixes that indicate comment rows
linesToSkip | int | Number of lines to ignore at the top of the file
skippedLinesCallbackHandler | LineCallbackHandler | Interface which passes the raw line content of the lines in the file to be skipped. If linesToSkip is set to 2, then this interface will be called twice.
firstLineIsHeader | boolean | Indicates that the first line of the file is a header containing field names. If the column names have not been set yet and the tokenizer extends AbstractLineTokenizer, field names will be set automatically from this line
recordSeparatorPolicy | RecordSeparatorPolicy | Used to determine where the line endings are and do things like continue over a line ending if inside a quoted string.

#### LineMapper

As with `RowMapper`, which takes a low level construct such as `ResultSet` and returns an `Object`, flat file processing requires the same construct to convert a `String` line into an `Object`:

      public interface LineMapper<T> {

        T mapLine(String line, int lineNumber) throws Exception;
      }

The basic contract is that, given the current line and the line number with which it is associated, the mapper should return a resulting domain object. This is similar to `RowMapper` in that each line is associated with its line number, just as each row in a `ResultSet` is tied to its row number. This allows the line number to be tied to the resulting domain object for identity comparison or for more informative logging. However, unlike `RowMapper`, the `LineMapper` is given a raw line which, as discussed above, only gets you halfway there. The line must be tokenized into a `FieldSet`, which can then be mapped to an object, as described below.

#### LineTokenizer

An abstraction for turning a line of input into a line into a `FieldSet` is necessary because there can be many formats of flat file data that need to be converted to a `FieldSet`. In Spring Batch, this interface is the `LineTokenizer`:

      public interface LineTokenizer {

        FieldSet tokenize(String line);

      }

The contract of a `LineTokenizer` is such that, given a line of input (in theory the `String` could encompass more than one line), a `FieldSet` representing the line will be returned. This `FieldSet` can then be passed to a `FieldSetMapper`. Spring Batch contains the following `LineTokenizer` implementations:

- `DelmitedLineTokenizer` - Used for files where fields in a record are separated by a delimiter. The most common delimiter is a comma, but pipes or semicolons are often used as well.

- `FixedLengthTokenizer` - Used for files where fields in a record are each a 'fixed width'. The width of each field must be defined for each record type.

- `PatternMatchingCompositeLineTokenizer` - Determines which among a list of `LineTokenizer`s should be used on a particular line by checking against a pattern.

#### FieldSetMapper

The `FieldSetMapper` interface defines a single method, `mapLine`, which takes a `FieldSet` object and maps its contents to an object. This object may be a custom DTO, a domain object, or a simple array, depending on the needs of the job. The `FieldSetMapper` is used in conjunction with the `LineTokenizer` to translate a line of data from a resource into an object of the desired type:

      public interface FieldSetMapper<T> {

        T mapFieldSet(FieldSet fieldSet);

      }

The pattern used is the same as the `RowMapper` used by `JdbcTemplate`.

#### DefaultLineMapper

Now that the basic interfaces for reading in flat files have been defined, it becomes clear that three basic steps are required:

1. Read one line from the file.

2. Pass the string line into the `LineTokenizer#tokenize`() method, in order to retrieve a `FieldSet`.

3. Pass the `FieldSet` returned from tokenizing to a `FieldSetMapper`, returning the result from the `ItemReader#read`() method.

The two interfaces described above represent two separate tasks: converting a line into a `FieldSet`, and mapping a `FieldSet` to a domain object. Because the input of a `LineTokenizer` matches the input of the `LineMapper` (a line), and the output of a `FieldSetMapper` matches the output of the `LineMapper`, a default implementation that uses both a `LineTokenizer` and `FieldSetMapper` is provided. The `DefaultLineMapper` represents the behavior most users will need:

      public class DefaultLineMapper<T> implements LineMapper<T>, InitializingBean {

        private LineTokenizer tokenizer;

        private FieldSetMapper<T> fieldSetMapper;

        public T mapLine(String line, int lineNumber) throws Exception {
          return fieldSetMapper.mapFieldSet(tokenizer.tokenize(line));
        }

        public void setLineTokenizer(LineTokenizer tokenizer) {
          this.tokenizer = tokenizer;
        }

        public void setFieldSetMapper(FieldSetMapper<T> fieldSetMapper) {
          this.fieldSetMapper = fieldSetMapper;
        }
      }

The above functionality is provided in a default implementation, rather than being built into the reader itself (as was done in previous versions of the framework) in order to allow users greater flexibility in controlling the parsing process, especially if access to the raw line is needed.

#### Simple Delimited File Reading Example

The following example will be used to illustrate this using an actual domain scenario. This particular batch job reads in football players from the following file:

      ID,lastName,firstName,position,birthYear,debutYear
      "AbduKa00,Abdul-Jabbar,Karim,rb,1974,1996",
      "AbduRa00,Abdullah,Rabih,rb,1975,1999",
      "AberWa00,Abercrombie,Walter,rb,1959,1982",
      "AbraDa00,Abramowicz,Danny,wr,1945,1967",
      "AdamBo00,Adams,Bob,te,1946,1969",
      "AdamCh00,Adams,Charlie,wr,1979,2003"

The contents of this file will be mapped to the following `Player` domain object:

      public class Player implements Serializable {

      private String ID;
      private String lastName;
      private String firstName;
      private String position;
      private int birthYear;
      private int debutYear;

        public String toString() {

          return "PLAYER:ID=" + ID + ",Last Name=" + lastName +
            ",First Name=" + firstName + ",Position=" + position +
            ",Birth Year=" + birthYear + ",DebutYear=" +
            debutYear;
        }

        // setters and getters...
      }

In order to map a `FieldSet` into a `Player` object, a `FieldSetMapper` that returns players needs to be defined:

      protected static class PlayerFieldSetMapper implements FieldSetMapper<Player> {
        public Player mapFieldSet(FieldSet fieldSet) {
          Player player = new Player();

          player.setID(fieldSet.readString(0));
          player.setLastName(fieldSet.readString(1));
          player.setFirstName(fieldSet.readString(2));
          player.setPosition(fieldSet.readString(3));
          player.setBirthYear(fieldSet.readInt(4));
          player.setDebutYear(fieldSet.readInt(5));

          return player;
        }
      }

The file can then be read by correctly constructing a `FlatFileItemReader` and calling `read`:

      FlatFileItemReader<Player> itemReader = new FlatFileItemReader<Player>();
      itemReader.setResource(new FileSystemResource("resources/players.csv"));
      //DelimitedLineTokenizer defaults to comma as its delimiter
      LineMapper<Player> lineMapper = new DefaultLineMapper<Player>();
      lineMapper.setLineTokenizer(new DelimitedLineTokenizer());
      lineMapper.setFieldSetMapper(new PlayerFieldSetMapper());
      itemReader.setLineMapper(lineMapper);
      itemReader.open(new ExecutionContext());
      Player player = itemReader.read();

Each call to `read` will return a new Player object from each line in the file. When the end of the file is reached, null will be returned.

#### Mapping fields by name

There is one additional piece of functionality that is allowed by both `DelimitedLineTokenizer` and `FixedLengthTokenizer` that is similar in function to a Jdbc `ResultSet`. The names of the fields can be injected into either of these `LineTokenizer` implementations to increase the readability of the mapping function. First, the column names of all fields in the flat file are injected into the tokenizer:

      tokenizer.setNames(new String[] {"ID", "lastName","firstName","position","birthYear","debutYear"});

a `FieldSetMapper` can this use this information as follows:

        public class PlayerMapper implements FieldSetMapper<Player> {
            public Player mapFieldSet(FieldSet fs) {

               if(fs == null){
                  return null;
               }

               Player player = new Player();
               player.setID(fs.readString("ID"));
               player.setLastName(fs.readString("lastName"));
               player.setFirstName(fs.readString("firstName"));
               player.setPosition(fs.readString("position"));
               player.setDebutYear(fs.readInt("debutYear"));
               player.setBirthYear(fs.readInt("birthYear"));

               return player;
            }
       }

#### Automapping FieldSets to Domain Objects

For many, having to write a specific `FieldSetMapper` is equally as cumbersome as writing a specific `RowMapper` for a `JdbcTemplate`. Spring Batch makes this easier by providing a `FieldSetMapper` that automatically maps fields by matching a field name with a setter on the object using the JavaBean specification. Again using the football example, the `BeanWrapperFieldSetMapper` configuration looks like the following:

      <bean id="fieldSetMapper"
            class="org.springframework.batch.item.file.mapping.BeanWrapperFieldSetMapper">
        <property name="prototypeBeanName" value="player" />
      </bean>

      <bean id="player"
            class="org.springframework.batch.sample.domain.Player"
            scope="prototype" />

For each entry in the `FieldSet`, the mapper will look for a corresponding setter on a new instance of the `Player` object (for this reason, prototype scope is required) in the same way the Spring container will look for setters matching a property name. Each available field in the `FieldSet` will be mapped, and the resultant `Player` object will be returned, with no code required.

#### Fixed Length file formats

So far only delimited files have been discussed in much detail, however, they represent only half of the file reading picture. Many organizations that use flat files use fixed length formats. An example fixed length file is below:

      UK21341EAH4121131.11customer1
      UK21341EAH4221232.11customer2
      UK21341EAH4321333.11customer3
      UK21341EAH4421434.11customer4
      UK21341EAH4521535.11customer5

While this looks like one large field, it actually represent 4 distinct fields:

1. ISIN: Unique identifier for the item being order - 12 characters long.

2. Quantity: Number of this item being ordered - 3 characters long.

3. Price: Price of the item - 5 characters long.

4. Customer: Id of the customer ordering the item - 9 characters long.

When configuring the `FixedLengthLineTokenizer`, each of these lengths must be provided in the form of ranges:

      <bean id="fixedLengthLineTokenizer"
            class="org.springframework.batch.io.file.transform.FixedLengthTokenizer">
        <property name="names" value="ISIN, Quantity, Price, Customer" />
        <property name="columns" value="1-12, 13-15, 16-20, 21-29" />
      </bean>

Because the `FixedLengthLineTokenizer` uses the same `LineTokenizer` interface as discussed above, it will return the same `FieldSet` as if a delimiter had been used. This allows the same approaches to be used in handling its output, such as using the `BeanWrapperFieldSetMapper`.

It should be noted that supporting the above ranges requires a specialized property editor be configured anywhere in the `ApplicationContext`:

      <bean id="customEditorConfigurer"
            class="org.springframework.beans.factory.config.CustomEditorConfigurer">
        <property name="customEditors">
          <map>
            <entry key="org.springframework.batch.item.file.transform.Range[]">
              <bean class="org.springframework.batch.item.file.transform.RangeArrayPropertyEditor" />
            </entry>
          </map>
        </property>
      </bean>

#### Multiple record types within a single file

All of the file reading examples up to this point have all made a key assumption for simplicity's sake: all of the records in a file have the same format. However, this may not always be the case. It is very common that a file might have records with different formats that need to be tokenized differently and mapped to different objects. The following excerpt from a file illustrates this:

      USER;Smith;Peter;;T;20014539;F
      LINEA;1044391041ABC037.49G201XX1383.12H
      LINEB;2134776319DEF422.99M005LI

In this file we have three types of records, "USER", "LINEA", and "LINEB". A "USER" line corresponds to a User object. "LINEA" and "LINEB" both correspond to Line objects, though a "LINEA" has more information than a "LINEB".

The `ItemReader` will read each line individually, but we must specify different `LineTokenizer` and `FieldSetMapper` objects so that the `ItemWriter` will recieve the correct items. The `PatternMatchingCompositeLineMapper` makes this easy by allowing maps of patterns to `LineTokenizer`s and patterns to `FieldSetMapper`s to be configured:

      <bean id="orderFileLineMapper"
            class="org.springframework.batch.item.file.mapping.PatternMatchingCompositeLineMapper">
        <property name="tokenizers">
          <map>
            <entry key="USER*" value-ref="userTokenizer" />
            <entry key="LINEA*" value-ref="lineATokenizer" />
            <entry key="LINEB*" value-ref="lineBTokenizer" />
          </map>
        </property>
        <property name="fieldSetMappers">
          <map>
            <entry key="USER*" value-ref="userFieldSetMapper" />
            <entry key="LINE*" value-ref="lineFieldSetMapper" />
          </map>
        </property>
      </bean>

In this example, "LINEA" and "LINEB" have separate `LineTokenizer`s but they both use the same `FieldSetMapper`.

The `PatternMatchingCompositeLineMapper` makes use of the `PatternMatcher`'s `match` method in order to select the correct delegate for each line. The `PatternMatcher` allows for two wildcard characters with special meaning: the question mark ("?") will match exactly one character, while the asterisk ("*") will match zero or more characters. Note that in the configuration above, all paterns end with an asterisk, making them effectively prefixes to lines. The `PatternMatcher` will always match the most specific pattern possible, regardless of the order in the configuration. So if "LINE*" and "LINEA*" were both listed as patterns, "LINEA" would match pattern "LINEA*", while "LINEB" would match pattern "LINE*". Additionally, a single asterisk ("*") can serve as a default by matching any line not matched by any other pattern.

     <entry key="*" value-ref="defaultLineTokenizer" />

There is also a `PatternMatchingCompositeLineTokenizer` that can be used for tokenization alone.

It is also common for a flat file to contain records that each span multiple lines. To handle this situation, a more complex strategy is required. A demonstration of this common patter can be found in .

#### Exception Handling in flat files

There are many scenarios when tokenizing a line may cause exceptions to be thrown. Many flat files are imperfect and contain records that aren't formatted correctly. Many users choose to skip these erroneous lines, logging out the issue, original line, and line number. These logs can later be inspected manually or or by another batch job. For this reason, Spring Batch provides a hierarchy of exceptions for handling parse exceptions: `FlatFileParseException` and `FlatFileFormatException`. `FlatFileParseException` is thrown by the `FlatFileItemReader` when any errors are encountered while trying to read a file. `FlatFileFormatException` is thrown by implementations of the `LineTokenizer` interface, and indicates a more specific error encountered while tokenizing.

##### IncorrectTokenCountException

Both `DelimitedLineTokenizer` and `FixedLengthLineTokenizer` have the ability to specify column names that can be used for creating a `FieldSet`. However, if the number of column names doesn't match the number of columns found while tokenizing a line the `FieldSet` can't be created, and a `IncorrectTokenCountException` is thrown, which contains the number of tokens encountered, and the number expected:

      tokenizer.setNames(new String[] {"A", "B", "C", "D"});

      try{
        tokenizer.tokenize("a,b,c");
      }
      catch(IncorrectTokenCountException e){
        assertEquals(4, e.getExpectedCount());
        assertEquals(3, e.getActualCount());
      }

Because the tokenizer was configured with 4 column names, but only 3 tokens were found in the file, an `IncorrectTokenCountException` was thrown.

##### IncorrectLineLengthException

Files formatted in a fixed length format have additional requirements when parsing because, unlike a delimited format, each column must strictly adhere to its predefined width. If the total line length doesn't add up to the widest value of this column, an exception is thrown:

      tokenizer.setColumns(new Range[] { new Range(1, 5), new Range(6, 10), new Range(11, 15) });
      try {
        tokenizer.tokenize("12345");
        fail("Expected IncorrectLineLengthException");
      }
      catch (IncorrectLineLengthException ex) {
        assertEquals(15, ex.getExpectedLength());
        assertEquals(5, ex.getActualLength());
      }

The configured ranges for the tokenizer above are: 1-5, 6-10, and 11-15, thus the total length of the line expected is 15. However, in this case a line of length 5 was passed in, causing an `IncorrectLineLengthException` to be thrown. Throwing an exception here rather than only mapping the first column allows the processing of the line to fail earlier, and with more information than it would if it failed while trying to read in column 2 in a `FieldSetMapper`. However, there are scenarios where the length of the line isn't always constant. For this reason, validation of line length can be turned off via the 'strict' property:

      tokenizer.setColumns(new Range[] { new Range(1, 5), new Range(6, 10) });
      tokenizer.setStrict(false);
      FieldSet tokens = tokenizer.tokenize("12345");
      assertEquals("12345", tokens.readString(0));
      assertEquals("", tokens.readString(1));

The above example is almost identical to the one before it, except that tokenizer.setStrict(false) was called. This setting tells the tokenizer to not enforce line lengths when tokenizing the line. A `FieldSet` is now correctly created and returned. However, it will only contain empty tokens for the remaining values.

### FlatFileItemWriter

Writing out to flat files has the same problems and issues that reading in from a file must overcome. A step must be able to write out in either delimited or fixed length formats in a transactional manner.

#### LineAggregator

Just as the `LineTokenizer` interface is necessary to take an item and turn it into a `String`, file writing must have a way to aggregate multiple fields into a single string for writing to a file. In Spring Batch this is the `LineAggregator`:

      public interface LineAggregator<T> {

        public String aggregate(T item);

      }

The `LineAggregator` is the opposite of a `LineTokenizer`. `LineTokenizer` takes a `String` and returns a `FieldSet`, whereas `LineAggregator` takes an `item` and returns a `String`.

##### PassThroughLineAggregator

The most basic implementation of the LineAggregator interface is the `PassThroughLineAggregator`, which simply assumes that the object is already a string, or that its string representation is acceptable for writing:

      public class PassThroughLineAggregator<T> implements LineAggregator<T> {

        public String aggregate(T item) {
          return item.toString();
        }
      }

The above implementation is useful if direct control of creating the string is required, but the advantages of a `FlatFileItemWriter`, such as transaction and restart support, are necessary.

#### Simplified File Writing Example

Now that the `LineAggregator` interface and its most basic implementation, `PassThroughLineAggregator`, have been defined, the basic flow of writing can be explained:

1. The object to be written is passed to the `LineAggregator` in order to obtain a `String`.

2. The returned `String` is written to the configured file.

The following excerpt from the `FlatFileItemWriter` expresses this in code:

      public void write(T item) throws Exception {
        write(lineAggregator.aggregate(item) + LINE_SEPARATOR);
      }

A simple configuration would look like the following:

      <bean id="itemWriter"
            class="org.springframework.batch.io.file.FlatFileItemWriter">
        <property name="resource"
                  value="file:target/test-outputs/20070122.testStream.multilineStep.txt" />
        <property name="lineAggregator">
          <bean class="org.springframework.batch.item.file.transform.PassThroughLineAggregator"/>
        </property>
      </bean>

#### FieldExtractor

The above example may be useful for the most basic uses of a writing to a file. However, most users of the `FlatFileItemWriter` will will have a domain object that needs to be written out, and thus must be converted into a line. In file reading, the following was required:

1. Read one line from the file.

2. Pass the string line into the `LineTokenizer#tokenize`() method, in order to retrieve a `FieldSet`

3. Pass the `FieldSet` returned from tokenizing to a `FieldSetMapper`, returning the result from the `ItemReader#read`() method

File writing has similar, but inverse steps:

1. Pass the item to be written to the writer

2. convert the fields on the item into an array

3. aggregate the resulting array into a line

Because there is no way for the framework to know which fields from the object need to be written out, a `FieldExtractor` must be written to accomplish the task of turning the item into an array:

      public interface FieldExtractor<T> {

        Object[] extract(T item);

      }

Implementations of the `FieldExtractor` interface should create an array from the fields of the provided object, which can then be written out with a delimiter between the elements, or as part of a field-width line.

##### PassThroughFieldExtractor

There are many cases where an array or something that can be converted to an array, such as a `Collection`, needs to be written out. For example, a `List` could be passed through, in which case it only needs to be converted to an `Object` array to be written out. For this type of scenario the `PassThroughFieldExtractor` can be used. It should be noted, that if the object passed in is not an array, and not a `Collection`, then an `Object` array containing solely the item will be returned.

##### BeanWrapperFieldExtractor

As with the `BeanWrapperFieldSetMapper` described in the file reading section, it is often preferrable to configure how to convert a domain object to an object array, rather than writing the conversion yourself. The `BeanWrapperFieldExtractor` provides just this type of functionality:

      BeanWrapperFieldExtractor<Name> extractor = new BeanWrapperFieldExtractor<Name>();
      extractor.setNames(new String[] { "first", "last", "born" });

      String first = "Alan";
      String last = "Turing";
      int born = 1912;

      Name n = new Name(first, last, born);
      Object[] values = extractor.extract(n);

      assertEquals(first, values[0]);
      assertEquals(last, values[1]);
      assertEquals(born, values[2]);

This extractor implementation has only one required property, the names of the fields to map. Just as the `BeanWrapperFieldSetMapper` needs field names to map fields on the `FieldSet` to setters on the provided object, the `BeanWrapperFieldExtractor` needs names to map to getters for creating an object array. It is worth noting that the order of the names determines the order of the fields within the array.

#### Delimited File Writing Example

The most basic flat file format is one in which all fields are separated by a delimiter. This can be accomplished using a `DelimitedLineAggregator`. The example below writes out a simple domain object that represents a credit to a customer account:

      public class CustomerCredit {

        private int id;
        private String name;
        private BigDecimal credit;

        //getters and setters removed for clarity
      }

Because a domain object is being used, an implementation of the FieldExtractor interface must be provided, along with the delimiter to use:

      <bean id="itemWriter" class="org.springframework.batch.item.file.FlatFileItemWriter">
        <property name="resource" ref="outputResource" />
        <property name="lineAggregator">
          <bean class="org.springframework.batch.item.file.transform.DelimitedLineAggregator">
            <property name="delimiter" value=","/>
            <property name="fieldExtractor">
              <bean class="org.springframework.batch.item.file.transform.BeanWrapperFieldExtractor">
                <property name="names" value="name,credit"/>
              </bean>
            </property>
          </bean>
        </property>
      </bean>

In this case, the `BeanWrapperFieldExtractor` described earlier in this chapter is used to turn the name and credit fields within `CustomerCredit` into an object array, which is then written out with commas between each field.

#### Fixed Width File Writing Example

Delimited is not the only type of flat file format. Many prefer to use a set width for each column to delineate between fields, which is usually referred to as 'fixed width'. Spring Batch supports this in file writing via the `FormatterLineAggregator`. Using the same `CustomerCredit` domain object described above, it can be configured as follows:

      <bean id="itemWriter" class="org.springframework.batch.item.file.FlatFileItemWriter">
        <property name="resource" ref="outputResource" />
        <property name="lineAggregator">
          <bean class="org.springframework.batch.item.file.transform.FormatterLineAggregator">
            <property name="fieldExtractor">
              <bean class="org.springframework.batch.item.file.transform.BeanWrapperFieldExtractor">
                <property name="names" value="name,credit" />
              </bean>
            </property>
            <property name="format" value="%-9s%-2.0f" />
          </bean>
        </property>
      </bean>

Most of the above example should look familiar. However, the value of the format property is new:

      <property name="format" value="%-9s%-2.0f" />

The underlying implementation is built using the same `Formatter` added as part of Java 5. The Java `Formatter` is based on the `printf` functionality of the C programming language. Most details on how to configure a formatter can be found in the javadoc of Formatter.

#### Handling file creation

`FlatFileItemReader` has a very simple relationship with file resources. When the reader is initialized, it opens the file if it exists, and throws an exception if it does not. File writing isn't quite so simple. At first glance it seems like a similar straight forward contract should exist for `FlatFileItemWriter`: if the file already exists, throw an exception, and if it does not, create it and start writing. However, potentially restarting a `Job` can cause issues. In normal restart scenarios, the contract is reversed: if the file exists, start writing to it from the last known good position, and if it does not, throw an exception. However, what happens if the file name for this job is always the same? In this case, you would want to delete the file if it exists, unless it's a restart. Because of this possibility, the `FlatFileItemWriter` contains the property, `shouldDeleteIfExists`. Setting this property to true will cause an existing file with the same name to be deleted when the writer is opened.

## XML Item Readers and Writers

Spring Batch provides transactional infrastructure for both reading XML records and mapping them to Java objects as well as writing Java objects as XML records.

### Constraints on streaming XML

The StAX API is used for I/O as other standard XML parsing APIs do not fit batch processing requirements (DOM loads the whole input into memory at once and SAX controls the parsing process allowing the user only to provide callbacks).

Lets take a closer look how XML input and output works in Spring Batch. First, there are a few concepts that vary from file reading and writing but are common across Spring Batch XML processing. With XML processing, instead of lines of records (FieldSets) that need to be tokenized, it is assumed an XML resource is a collection of 'fragments' corresponding to individual records:

Figure 3.1: XML Input

The 'trade' tag is defined as the 'root element' in the scenario above. Everything between '<trade>' and '</trade>' is considered one 'fragment'. Spring Batch uses Object/XML Mapping (OXM) to bind fragments to objects. However, Spring Batch is not tied to any particular XML binding technology. Typical use is to delegate to Spring OXM, which provides uniform abstraction for the most popular OXM technologies. The dependency on Spring OXM is optional and you can choose to implement Spring Batch specific interfaces if desired. The relationship to the technologies that OXM supports can be shown as the following:

Figure 3.2: OXM Binding

Now with an introduction to OXM and how one can use XML fragments to represent records, let's take a closer look at readers and writers.

### StaxEventItemReader

The `StaxEventItemReader` configuration provides a typical setup for the processing of records from an XML input stream. First, lets examine a set of XML records that the `StaxEventItemReader` can process.

    <?xml version="1.0" encoding="UTF-8"?>
    <records>
      <trade xmlns="http://springframework.org/batch/sample/io/oxm/domain">
        <isin>XYZ0001</isin>
        <quantity>5</quantity>
        <price>11.39</price>
        <customer>Customer1</customer>
      </trade>
      <trade xmlns="http://springframework.org/batch/sample/io/oxm/domain">
        <isin>XYZ0002</isin>
        <quantity>2</quantity>
        <price>72.99</price>
        <customer>Customer2c</customer>
      </trade>
      <trade xmlns="http://springframework.org/batch/sample/io/oxm/domain">
        <isin>XYZ0003</isin>
        <quantity>9</quantity>
        <price>99.99</price>
        <customer>Customer3</customer>
      </trade>
    </records>

To be able to process the XML records the following is needed:

- Root Element Name - Name of the root element of the fragment that constitutes the object to be mapped. The example configuration demonstrates this with the value of trade.

- Resource - Spring Resource that represents the file to be read.

- `FragmentDeserializer` - Unmarshalling facility provided by Spring OXM for mapping the XML fragment to an object.

      <bean id="itemReader" class="org.springframework.batch.item.xml.StaxEventItemReader">
        <property name="fragmentRootElementName" value="customer" />
        <property name="resource" value="data/iosample/input/input.xml" />
        <property name="unmarshaller" ref="customerCreditMarshaller" />
      </bean>

      <bean id="customerCreditMarshaller" class="org.springframework.oxm.xstream.XStreamMarshaller">
        <property name="aliases">
          <util:map id="aliases">
            <entry key="customer"
                   value="org.springframework.batch.sample.domain.trade.CustomerCredit" />
            <entry key="price" value="java.math.BigDecimal" />
            <entry key="name" value="java.lang.String" />
          </util:map>
        </property>
      </bean>

Notice that in this example we have chosen to use an `XStreamMarshaller` that requires an alias passed in as a map with the first key and value being the name of the fragment (i.e. root element) and the object type to bind. Then, similar to a `FieldSet`, the names of the other elements that map to fields within the object type are described as key/value pairs in the map. In the configuration file we can use a Spring configuration utility to describe the required alias as follows:

      <bean id="itemReader" class="org.springframework.batch.item.xml.StaxEventItemReader">
        <property name="fragmentRootElementName" value="customer" />
        <property name="resource" value="data/iosample/input/input.xml" />
        <property name="unmarshaller" ref="customerCreditMarshaller" />
      </bean>

      <bean id="customerCreditMarshaller" class="org.springframework.oxm.xstream.XStreamMarshaller">
        <property name="aliases">
          <util:map id="aliases">
            <entry key="customer"
                   value="org.springframework.batch.sample.domain.trade.CustomerCredit" />
            <entry key="price" value="java.math.BigDecimal" />
            <entry key="name" value="java.lang.String" />
          </util:map>
        </property>
      </bean>

On input the reader reads the XML resource until it recognizes that a new fragment is about to start (by matching the tag name by default). The reader creates a standalone XML document from the fragment (or at least makes it appear so) and passes the document to a deserializer (typically a wrapper around a Spring OXM `Unmarshaller`) to map the XML to a Java object.

In summary, this procedure is analogous to the following scripted Java code which uses the injection provided by the Spring configuration:

          StaxEventItemReader xmlStaxEventItemReader = new StaxEventItemReader()
          Resource resource = new ByteArrayResource(xmlResource.getBytes())

          Map aliases = new HashMap();
          aliases.put("customer","org.springframework.batch.sample.domain.trade.CustomerCredit");
          aliases.put("price","java.math.BigDecimal");
          aliases.put("name","java.lang.String");
          Marshaller marshaller = new XStreamMarshaller();
          marshaller.setAliases(aliases);
          xmlStaxEventItemReader.setUnmarshaller(marshaller);
          xmlStaxEventItemReader.setResource(resource);
          xmlStaxEventItemReader.setFragmentRootElementName("customer");
          xmlStaxEventItemReader.open(new ExecutionContext());

          boolean hasNext = true

          CustomerCredit credit = null;

          while (hasNext) {
              credit = xmlStaxEventItemReader.read();
              if (credit == null) {
                  hasNext = false;
              } else {
                  println trade;
              }
          }

### StaxEventItemWriter

Output works symmetrically to input. The `StaxEventItemWriter` needs a `Resource`, a serializer, and a rootTagName. A Java object is passed to a serializer (typically a wrapper around Spring OXM `Marshaller`) which writes to a `Resource` using a custom event writer that filters the `StartDocument` and `EndDocument` events produced for each fragment by the OXM tools. We'll show this in an example using the `MarshallingEventWriterSerializer`. The Spring configuration for this setup looks as follows:

      <bean id="itemWriter" class="org.springframework.batch.item.xml.StaxEventItemWriter">
        <property name="resource" ref="outputResource" />
        <property name="marshaller" ref="customerCreditMarshaller" />
        <property name="rootTagName" value="customers" />
        <property name="overwriteOutput" value="true" />
      </bean>

The configuration sets up the three required properties and optionally sets the overwriteOutput=true, mentioned earlier in the chapter for specifying whether an existing file can be overwritten. It should be noted the marshaller used for the writer is the exact same as the one used in the reading example from earlier in the chapter:

      <bean id="customerCreditMarshaller" class="org.springframework.oxm.xstream.XStreamMarshaller">
        <property name="aliases">
          <util:map id="aliases">
            <entry key="customer"
                   value="org.springframework.batch.sample.domain.trade.CustomerCredit" />
            <entry key="price" value="java.math.BigDecimal" />
            <entry key="name" value="java.lang.String" />
          </util:map>
        </property>
      </bean>

To summarize with a Java example, the following code illustrates all of the points discussed, demonstrating the programmatic setup of the required properties:

         StaxEventItemWriter staxItemWriter = new StaxEventItemWriter()
         FileSystemResource resource = new FileSystemResource(File.createTempFile("StaxEventWriterOutputSourceTests", "xml"))

         Map aliases = new HashMap();
         aliases.put("customer","org.springframework.batch.sample.domain.trade.CustomerCredit");
         aliases.put("price","java.math.BigDecimal");
         aliases.put("name","java.lang.String");
         Marshaller marshaller = new XStreamMarshaller();
         marshaller.setAliases(aliases);

         staxItemWriter.setResource(resource);
         staxItemWriter.setMarshaller(marshaller);
         staxItemWriter.setRootTagName("trades");
         staxItemWriter.setOverwriteOutput(true);

         ExecutionContext executionContext = new ExecutionContext();
         staxItemWriter.open(executionContext);
         CustomerCredit Credit = new CustomerCredit();
         trade.setPrice(11.39);
         credit.setName("Customer1");
         staxItemWriter.write(trade);

## Multi-File Input

It is a common requirement to process multiple files within a single `Step`. Assuming the files all have the same formatting, the `MultiResourceItemReader` supports this type of input for both XML and flat file processing. Consider the following files in a directory:

    file-1.txt  file-2.txt  ignored.txt

file-1.txt and file-2.txt are formatted the same and for business reasons should be processed together. The `MuliResourceItemReader` can be used to read in both files by using wildcards:

      <bean id="multiResourceReader" class="org.springframework.batch.item.file.MultiResourceItemReader">
        <property name="resources" value="classpath:data/multiResourceJob/input/file-*.txt" />
        <property name="delegate" ref="flatFileItemReader" />
      </bean>

The referenced delegate is a simple `FlatFileItemReader`. The above configuration will read input from both files, handling rollback and restart scenarios. It should be noted that, as with any `ItemReader`, adding extra input (in this case a file) could cause potential issues when restarting. It is recommended that batch jobs work with their own individual directories until completed successfully.

## Database

Like most enterprise application styles, a database is the central storage mechanism for batch. However, batch differs from other application styles due to the sheer size of the datasets with which the system must work. The Spring Core `JdbcTemplate` illustrates this problem well. If you use `JdbcTemplate` with a `RowMapper`, the `RowMapper` will be called once for every result returned from the provided query. This causes few issues in scenarios where the dataset is small, but the large datasets often necessary for batch processing would cause any JVM to crash quickly. If the SQL statement returns 1 million rows, the `RowMapper` will be called 1 million times, holding all returned results in memory until all rows have been read. Spring Batch provides two types of solutions for this problem: Cursor and Paging database ItemReaders.

### Cursor Based ItemReaders

Using a database cursor is generally the default approach of most batch developers, because it is the database's solution to the problem of 'streaming' relational data. The Java `ResultSet` class is essentially an object orientated mechanism for manipulating a cursor. A `ResultSet` maintains a cursor to the current row of data. Calling `next` on a `ResultSet` moves this cursor to the next row. Spring Batch cursor based ItemReaders open the a cursor on initialization, and move the cursor forward one row for every call to `read`, returning a mapped object that can be used for processing. The `close` method will then be called to ensure all resources are freed up. The Spring core `JdbcTemplate` gets around this problem by using the callback pattern to completely map all rows in a `ResultSet` and close before returning control back to the method caller. However, in batch this must wait until the step is complete. Below is a generic diagram of how a cursor based `ItemReader` works, and while a SQL statement is used as an example since it is so widely known, any technology could implement the basic approach:

This example illustrates the basic pattern. Given a 'FOO' table, which has three columns: ID, NAME, and BAR, select all rows with an ID greater than 1 but less than 7. This puts the beginning of the cursor (row 1) on ID 2. The result of this row should be a completely mapped Foo object. Calling `read`() again moves the cursor to the next row, which is the Foo with an ID of 3. The results of these reads will be written out after each `read`, thus allowing the objects to be garbage collected (assuming no instance variables are maintaining references to them).

#### JdbcCursorItemReader

`JdbcCursorItemReader` is the Jdbc implementation of the cursor based technique. It works directly with a `ResultSet` and requires a SQL statement to run against a connection obtained from a `DataSource`. The following database schema will be used as an example:

      CREATE TABLE CUSTOMER (
       ID BIGINT IDENTITY PRIMARY KEY,
       NAME VARCHAR(45),
       CREDIT FLOAT
      );

Many people prefer to use a domain object for each row, so we'll use an implementation of the `RowMapper` interface to map a `CustomerCredit` object:

      public class CustomerCreditRowMapper implements RowMapper {

        public static final String ID_COLUMN = "id";
        public static final String NAME_COLUMN = "name";
        public static final String CREDIT_COLUMN = "credit";

        public Object mapRow(ResultSet rs, int rowNum) throws SQLException {
            CustomerCredit customerCredit = new CustomerCredit();

            customerCredit.setId(rs.getInt(ID_COLUMN));
            customerCredit.setName(rs.getString(NAME_COLUMN));
            customerCredit.setCredit(rs.getBigDecimal(CREDIT_COLUMN));

            return customerCredit;
        }
    }

Because `JdbcTemplate` is so familiar to users of Spring, and the `JdbcCursorItemReader` shares key interfaces with it, it is useful to see an example of how to read in this data with `JdbcTemplate`, in order to contrast it with the `ItemReader`. For the purposes of this example, let's assume there are 1,000 rows in the CUSTOMER database. The first example will be using `JdbcTemplate`:

      //For simplicity sake, assume a dataSource has already been obtained
      JdbcTemplate jdbcTemplate = new JdbcTemplate(dataSource);
      List customerCredits = jdbcTemplate.query("SELECT ID, NAME, CREDIT from CUSTOMER", new CustomerCreditRowMapper());

After running this code snippet the customerCredits list will contain 1,000 `CustomerCredit` objects. In the query method, a connection will be obtained from the `DataSource`, the provided SQL will be run against it, and the `mapRow` method will be called for each row in the `ResultSet`. Let's contrast this with the approach of the `JdbcCursorItemReader`:

      JdbcCursorItemReader itemReader = new JdbcCursorItemReader();
      itemReader.setDataSource(dataSource);
      itemReader.setSql("SELECT ID, NAME, CREDIT from CUSTOMER");
      itemReader.setRowMapper(new CustomerCreditRowMapper());
      int counter = 0;
      ExecutionContext executionContext = new ExecutionContext();
      itemReader.open(executionContext);
      Object customerCredit = new Object();
      while(customerCredit != null){
        customerCredit = itemReader.read();
        counter++;
      }
      itemReader.close(executionContext);

After running this code snippet the counter will equal 1,000. If the code above had put the returned customerCredit into a list, the result would have been exactly the same as with the `JdbcTemplate` example. However, the big advantage of the `ItemReader` is that it allows items to be 'streamed'. The `read` method can be called once, and the item written out via an `ItemWriter`, and then the next item obtained via `read`. This allows item reading and writing to be done in 'chunks' and committed periodically, which is the essence of high performance batch processing. Furthermore, it is very easily configured for injection into a Spring Batch `Step`:

      <bean id="itemReader" class="org.springframework.batch.item.database.JdbcCursorItemReader">
        <property name="dataSource" ref="dataSource"/>
        <property name="sql" value="select ID, NAME, CREDIT from CUSTOMER"/>
        <property name="rowMapper">
          <bean class="org.springframework.batch.sample.domain.trade.internal.CustomerCreditRowMapper"/>
        </property>
      </bean>

##### Additional Properties

Because there are so many varying options for opening a cursor in Java, there are many properties on the `JdbcCustorItemReader` that can be set:

###### JdbcCursorItemReader Properties

ignoreWarnings | Determines whether or not SQLWarnings are logged or cause an exception - default is true
fetchSize | Gives the Jdbc driver a hint as to the number of rows that should be fetched from the database when more rows are needed by the `ResultSet` object used by the `ItemReader`. By default, no hint is given.
maxRows | Sets the limit for the maximum number of rows the underlying `ResultSet` can hold at any one time.
queryTimeout | Sets the number of seconds the driver will wait for a `Statement` object to execute to the given number of seconds. If the limit is exceeded, a `DataAccessEception` is thrown. (Consult your driver vendor documentation for details).
verifyCursorPosition | Because the same `ResultSet` held by the `ItemReader` is passed to the `RowMapper`, it is possible for users to call `ResultSet.next`() themselves, which could cause issues with the reader's internal count. Setting this value to true will cause an exception to be thrown if the cursor position is not the same after the `RowMapper` call as it was before.
saveState | Indicates whether or not the reader's state should be saved in the `ExecutionContext` provided by `ItemStream#update`(`ExecutionContext`) The default value is false.
driverSupportsAbsolute | Defaults to false. Indicates whether the Jdbc driver supports setting the absolute row on a `ResultSet`. It is recommended that this is set to true for Jdbc drivers that supports `ResultSet.absolute`() as it may improve performance, especially if a step fails while working with a large data set.

setUseSharedExtendedConnection

Defaults to false. Indicates whether the connection used for the cursor should be used by all other processing thus sharing the same transaction. If this is set to false, which is the default, then the cursor will be opened using its own connection and will not participate in any transactions started for the rest of the step processing. If you set this flag to true then you must wrap the `DataSource` in an `ExtendedConnectionDataSourceProxy` to prevent the connection from being closed and released after each commit. When you set this option to true then the statement used to open the cursor will be created with both 'READ_ONLY' and 'HOLD_CUSORS_OVER_COMMIT' options. This allows holding the cursor open over transaction start and commits performed in the step processing. To use this feature you need a database that supports this and a Jdbc driver supporting Jdbc 3.0 or later.

#### HibernateCursorItemReader

Just as normal Spring users make important decisions about whether or not to use ORM solutions, which affect whether or not they use a `JdbcTemplate` or a `HibernateTemplate`, Spring Batch users have the same options. `HibernateCursorItemReader` is the Hibernate implementation of the cursor technique. Hibernate's usage in batch has been fairly controversial. This has largely been because Hibernate was originally developed to support online application styles. However, that doesn't mean it can't be used for batch processing. The easiest approach for solving this problem is to use a `StatelessSession` rather than a standard session. This removes all of the caching and dirty checking hibernate employs that can cause issues in a batch scenario. For more information on the differences between stateless and normal hibernate sessions, refer to the documentation of your specific hibernate release. The `HibernateCursorItemReader` allows you to declare an HQL statement and pass in a `SessionFactory`, which will pass back one item per call to `read` in the same basic fashion as the `JdbcCursorItemReader`. Below is an example configuration using the same 'customer credit' example as the JDBC reader:

      HibernateCursorItemReader itemReader = new HibernateCursorItemReader();
      itemReader.setQueryString("from CustomerCredit");
      //For simplicity sake, assume sessionFactory already obtained.
      itemReader.setSessionFactory(sessionFactory);
      itemReader.setUseStatelessSession(true);
      int counter = 0;
      ExecutionContext executionContext = new ExecutionContext();
      itemReader.open(executionContext);
      Object customerCredit = new Object();
      while(customerCredit != null){
        customerCredit = itemReader.read();
        counter++;
      }
      itemReader.close(executionContext);

This configured `ItemReader` will return `CustomerCredit` objects in the exact same manner as described by the `JdbcCursorItemReader`, assuming hibernate mapping files have been created correctly for the Customer table. The 'useStatelessSession' property defaults to true, but has been added here to draw attention to the ability to switch it on or off. It is also worth noting that the fetchSize of the underlying cursor can be set via the setFetchSize property. As with `JdbcCursorItemReader`, configuration is straightforward:

      <bean id="itemReader"
            class="org.springframework.batch.item.database.HibernateCursorItemReader">
        <property name="sessionFactory" ref="sessionFactory" />
        <property name="queryString" value="from CustomerCredit" />
      </bean>

### Paging ItemReaders

An alternative to using a database cursor is executing multiple queries where each query is bringing back a portion of the results. We refer to this portion as a page. Each query that is executed must specify the starting row number and the number of rows that we want returned for the page.

#### JdbcPagingItemReader

One implementation of a paging `ItemReader` is the `JdbcPagingItemReader`. The `JdbcPagingItemReader` needs a `PagingQueryProvider` responsible for providing the SQL queries used to retrieve the rows making up a page. Since each database has its own strategy for providing paging support, we need to use a different `PagingQueryProvider` for each supported database type. There is also the `SimpleDelegatingPagingQueryProvider` that will auto-detect the database that is being used and determine the appropriate `PagingQueryProvider` implementation. This simplifies the configuration and is the recommended best practice.

The `SimpleDelegatingPagingQueryProvider` requires that you specify a select clause and a from clause. You can also provide an optional where clause. These clauses will be used to build an SQL statement combined with the required sortKey.

After the reader has been opened, it will pass back one item per call to `read` in the same basic fashion as any other `ItemReader`. The paging happens behind the scenes when additional rows are needed.

Below is an example configuration using a similar 'customer credit' example as the cursor based ItemReaders above:

        <bean id="itemReader"
            class="org.springframework.batch.item.database.JdbcPagingItemReader">
            <property name="dataSource" ref="dataSource"/>
            <property name="queryProvider">
                <bean class="org.springframework.batch.item.database.support.SimpleDelegatingPagingQueryProvider">
                    <property name="selectClause" value="select id, name, credit"/>
                    <property name="fromClause" value="from customer"/>
                    <property name="whereClause" value="where status=:status"/>
                    <property name="sortKey" value="id"/>
                </bean>
            </property>
            <property name="parameterValues">
                <map>
                    <entry key="status" value="NEW"/>
                </map>
            </property>
            <property name="pageSize" value="1000"/>
            <property name="rowMapper" ref="customerMapper"/>
        </bean>

This configured `ItemReader` will return `CustomerCredit` objects using the `RowMapper` that must be specified. The 'pageSize' property determines the number of entities read from the database for each query execution.

The 'parameterValues' property can be used to specify a Map of parameter values for the query. If you use named parameters in the where clause the key for each entry should match the name of the named parameter. If you use a traditional '?' placeholder then the key for each entry should be the number of the placeholder, starting with 1.

#### JpaPagingItemReader

Another implementation of a paging `ItemReader` is the `JpaPagingItemReader`. JPA doesn't have a concept similar to the Hibernate `StatelessSession` so we have to use other features provided by the JPA specification. Since JPA supports paging, this is a natural choice when it comes to using JPA for batch processing. After each page is read, the entities will become detached and the persistence context will be cleared in order to allow the entities to be garbage collected once the page is processed.

The `JpaPagingItemReader` allows you to declare a JPQL statement and pass in a `EntityManagerFactory`. It will then pass back one item per call to `read` in the same basic fashion as any other `ItemReader`. The paging happens behind the scenes when additional entities are needed. Below is an example configuration using the same 'customer credit' example as the JDBC reader above:

      <bean id="itemReader"
        class="org.springframework.batch.item.database.JpaPagingItemReader">
        <property name="entityManagerFactory" ref="entityManagerFactory"/>
        <property name="queryString" value="select c from CustomerCredit c"/>
        <property name="pageSize" value="1000"/>
      </bean>

This configured `ItemReader` will return `CustomerCredit` objects in the exact same manner as described by the `JdbcPagingItemReader` above, assuming the Customer object has the correct JPA annotations or ORM mapping file. The 'pageSize' property determines the number of entities read from the database for each query execution.

#### IbatisPagingItemReader

If you use IBATIS for your data access then you can use the `IbatisPagingItemReader` which, as the name indicates, is an implementation of a paging `ItemReader`. IBATIS doesn't have direct support for reading rows in pages but by providing a couple of standard variables you can add paging support to your IBATIS queries.

Here is an example of a configuration for a `IbatisPagingItemReader` reading CustomerCredits as in the examples above:

      <bean id="itemReader"
        class="org.springframework.batch.item.database.IbatisPagingItemReader">
        <property name="sqlMapClient" ref="sqlMapClient"/>
        <property name="queryId" value="getPagedCustomerCredits"/>
        <property name="pageSize" value="1000"/>
      </bean>

The `IbatisPagingItemReader` configuration above references an IBATIS query called "getPagedCustomerCredits". Here is an example of what that query should look like for MySQL.

      <select id="getPagedCustomerCredits" resultMap="customerCreditResult">
          select id, name, credit from customer order by id asc LIMIT #_skiprows#, #_pagesize#
      </select>

The `_skiprows` and `_pagesize` variables are provided by the `IbatisPagingItemReader` and there is also a `_page` variable that can be used if necessary. The syntax for the paging queries varies with the database used. Here is an example for Oracle (unfortunately we need to use CDATA for some operators since this belongs in an XML document):

      <select id="getPagedCustomerCredits" resultMap="customerCreditResult">
        select * from (
         select * from (
          select t.id, t.name, t.credit, ROWNUM ROWNUM_ from customer t order by id
         ) where ROWNUM_ <![CDATA[ > ]]> ( #_page# * #_pagesize# )
        ) where ROWNUM <![CDATA[ <= ]]> #_pagesize#
      </select>

### Database ItemWriters

While both Flat Files and XML have specific ItemWriters, there is no exact equivalent in the database world. This is because transactions provide all the functionality that is needed. ItemWriters are necessary for files because they must act as if they're transactional, keeping track of written items and flushing or clearing at the appropriate times. Databases have no need for this functionality, since the write is already contained in a transaction. Users can create their own DAOs that implement the `ItemWriter` interface or use one from a custom `ItemWriter` that's written for generic processing concerns, either way, they should work without any issues. One thing to look out for is the performance and error handling capabilities that are provided by batching the outputs. This is most common when using hibernate as an `ItemWriter`, but could have the same issues when using Jdbc batch mode. Batching database output doesn't have any inherent flaws, assuming we are careful to flush and there are no errors in the data. However, any errors while writing out can cause confusion because there is no way to know which individual item caused an exception, or even if any individual item was responsible, as illustrated below:

If items are buffered before being written out, any errors encountered will not be thrown until the buffer is flushed just before a commit. For example, let's assume that 20 items will be written per chunk, and the 15th item throws a DataIntegrityViolationException. As far as the Step is concerned, all 20 item will be written out successfully, since there's no way to know that an error will occur until they are actually written out. Once `Session#``flush`() is called, the buffer will be emptied and the exception will be hit. At this point, there's nothing the `Step` can do, the transaction must be rolled back. Normally, this exception might cause the Item to be skipped (depending upon the skip/retry policies), and then it won't be written out again. However, in the batched scenario, there's no way for it to know which item caused the issue, the whole buffer was being written out when the failure happened. The only way to solve this issue is to flush after each item:

This is a common use case, especially when using Hibernate, and the simple guideline for implementations of `ItemWriter`, is to flush on each call to `write()`. Doing so allows for items to be skipped reliably, with Spring Batch taking care internally of the granularity of the calls to `ItemWriter` after an error.

## Reusing Existing Services

Batch systems are often used in conjunction with other application styles. The most common is an online system, but it may also support integration or even a thick client application by moving necessary bulk data that each application style uses. For this reason, it is common that many users want to reuse existing DAOs or other services within their batch jobs. The Spring container itself makes this fairly easy by allowing any necessary class to be injected. However, there may be cases where the existing service needs to act as an `ItemReader` or `ItemWriter`, either to satisfy the dependency of another Spring Batch class, or because it truly is the main `ItemReader` for a step. It is fairly trivial to write an adaptor class for each service that needs wrapping, but because it is such a common concern, Spring Batch provides implementations: `ItemReaderAdapter` and `ItemWriterAdapter`. Both classes implement the standard Spring method invoking the delegate pattern and are fairly simple to set up. Below is an example of the reader:

      <bean id="itemReader" class="org.springframework.batch.item.adapter.ItemReaderAdapter">
        <property name="targetObject" ref="fooService" />
        <property name="targetMethod" value="generateFoo" />
      </bean>

      <bean id="fooService" class="org.springframework.batch.item.sample.FooService" />

One important point to note is that the contract of the targetMethod must be the same as the contract for `read`: when exhausted it will return null, otherwise an `Object`. Anything else will prevent the framework from knowing when processing should end, either causing an infinite loop or incorrect failure, depending upon the implementation of the `ItemWriter`. The `ItemWriter` implementation is equally as simple:

      <bean id="itemWriter" class="org.springframework.batch.item.adapter.ItemWriterAdapter">
        <property name="targetObject" ref="fooService" />
        <property name="targetMethod" value="processFoo" />
      </bean>

      <bean id="fooService" class="org.springframework.batch.item.sample.FooService" />

## Validating Input

During the course of this chapter, multiple approaches to parsing input have been discussed. Each major implementation will throw an exception if it is not 'well-formed'. The `FixedLengthTokenizer` will throw an exception if a range of data is missing. Similarly, attempting to access an index in a `RowMapper` of `FieldSetMapper` that doesn't exist or is in a different format than the one expected will cause an exception to be thrown. All of these types of exceptions will be thrown before `read` returns. However, they don't address the issue of whether or not the returned item is valid. For example, if one of the fields is an age, it obviously cannot be negative. It will parse correctly, because it existed and is a number, but it won't cause an exception. Since there are already a plethora of Validation frameworks, Spring Batch does not attempt to provide yet another, but rather provides a very simple interface that can be implemented by any number of frameworks:

      public interface Validator {

        void validate(Object value) throws ValidationException;

      }

The contract is that the `validate` method will throw an exception if the object is invalid, and return normally if it is valid. Spring Batch provides an out of the box `ItemProcessor:`

      <bean class="org.springframework.batch.item.validator.ValidatingItemProcessor">
        <property name="validator" ref="validator" />
      </bean>

      <bean id="validator"
            class="org.springframework.batch.item.validator.SpringValidator">
        <property name="validator">
          <bean id="orderValidator"
                class="org.springmodules.validation.valang.ValangValidator">
            <property name="valang">
              <value>
                <![CDATA[
                  { orderId : ? > 0 AND ? <= 9999999999 : 'Incorrect order ID' : 'error.order.id' }
                  { totalLines : ? = size(lineItems) : 'Bad count of order lines'
                                                     : 'error.order.lines.badcount'}
                  { customer.registered : customer.businessCustomer = FALSE OR ? = TRUE
                                        : 'Business customer must be registered'
                                        : 'error.customer.registration'}
                  { customer.companyName : customer.businessCustomer = FALSE OR ? HAS TEXT
                                         : 'Company name for business customer is mandatory'
                                         :'error.customer.companyname'}
              ]]>
              </value>
            </property>
          </bean>
        </property>
      </bean>

This simple example shows a simple `ValangValidator` that is used to validate an order object. The intent is not to show Valang functionality as much as to show how a validator could be added.

## Preventing state persistence

By default, all of the `ItemReader` and `ItemWriter` implementations store their current state in the `ExecutionContext` before it is committed. However, this may not always be the desired behavior. For example, many developers choose to make their database readers 'rerunnable' by using a process indicator. An extra column is added to the input data to indicate whether or not it has been processed. When a particular record is being read (or written out) the processed flag is flipped from false to true. The SQL statement can then contain an extra statement in the where clause, such as "where PROCESSED_IND = false", thereby ensuring that only unprocessed records will be returned in the case of a restart. In this scenario, it is preferable to not store any state, such as the current row number, since it will be irrelevant upon restart. For this reason, all readers and writers include the 'saveState' property:

      <bean id="playerSummarizationSource"
            class="org.springframework.batch.item.database.JdbcCursorItemReader">
        <property name="dataSource" ref="dataSource" />
        <property name="rowMapper">
          <bean class="org.springframework.batch.sample.mapping.PlayerSummaryMapper" />
        </property>
        <property name="saveState" value="false" />
        <property name="sql">
          <value>
            SELECT games.player_id, games.year_no, SUM(COMPLETES),
            SUM(ATTEMPTS), SUM(PASSING_YARDS), SUM(PASSING_TD),
            SUM(INTERCEPTIONS), SUM(RUSHES), SUM(RUSH_YARDS),
            SUM(RECEPTIONS), SUM(RECEPTIONS_YARDS), SUM(TOTAL_TD)
            from games, players where players.player_id =
            games.player_id group by games.player_id, games.year_no
          </value>
        </property>
      </bean>

The `ItemReader` configured above will not make any entries in the `ExecutionContext` for any executions in which it participates.

## Creating Custom ItemReaders and ItemWriters

So far in this chapter the basic contracts that exist for reading and writing in Spring Batch and some common implementations have been discussed. However, these are all fairly generic, and there are many potential scenarios that may not be covered by out of the box implementations. This section will show, using a simple example, how to create a custom `ItemReader` and `ItemWriter` implementation and implement their contracts correctly. The `ItemReader` will also implement `ItemStream`, in order to illustrate how to make a reader or writer restartable.

### Custom ItemReader Example

For the purpose of this example, a simple `ItemReader` implementation that reads from a provided list will be created. We'll start out by implementing the most basic contract of `ItemReader`, `read`:

      public class CustomItemReader<T> implements ItemReader<T>{

        List<T> items;

        public CustomItemReader(List<T> items) {
          this.items = items;
        }

        public T read() throws Exception, UnexpectedInputException,
           NoWorkFoundException, ParseException {

          if (!items.isEmpty()) {
            return items.remove(0);
          }
          return null;
        }
      }

This very simple class takes a list of items, and returns them one at a time, removing each from the list. When the list is empty, it returns null, thus satisfying the most basic requirements of an `ItemReader`, as illustrated below:

      List<String> items = new ArrayList<String>();
      items.add("1");
      items.add("2");
      items.add("3");

      ItemReader itemReader = new CustomItemReader<String>(items);
      assertEquals("1", itemReader.read());
      assertEquals("2", itemReader.read());
      assertEquals("3", itemReader.read());
      assertNull(itemReader.read());

#### Making the `ItemReader` restartable

The final challenge now is to make the `ItemReader` restartable. Currently, if the power goes out, and processing begins again, the `ItemReader` must start at the beginning. This is actually valid in many scenarios, but it is sometimes preferable that a batch job starts where it left off. The key discriminant is often whether the reader is stateful or stateless. A stateless reader does not need to worry about restartability, but a stateful one has to try and reconstitute its last known state on restart. For this reason, we recommend that you keep custom readers stateless if possible, so you don't have to worry about restartability.

If you do need to store state, then the `ItemStream` interface should be used:

      public class CustomItemReader<T> implements ItemReader<T>, ItemStream {

        List<T> items;
        int currentIndex = 0;
        private static final String CURRENT_INDEX = "current.index";

        public CustomItemReader(List<T> items) {
          this.items = items;
        }

        public T read() throws Exception, UnexpectedInputException,
            ParseException {

          if (currentIndex < items.size()) {
            return items.get(currentIndex++);
          }

          return null;
        }

        public void open(ExecutionContext executionContext) throws ItemStreamException {
          if(executionContext.containsKey(CURRENT_INDEX)){
            currentIndex = new Long(executionContext.getLong(CURRENT_INDEX)).intValue();
          }
          else{
            currentIndex = 0;
          }
        }

        public void update(ExecutionContext executionContext) throws ItemStreamException {
          executionContext.putLong(CURRENT_INDEX, new Long(currentIndex).longValue());
        };

        public void close() throws ItemStreamException {}
      }

On each call to the `ItemStream` `update` method, the current index of the `ItemReader` will be stored in the provided `ExecutionContext` with a key of 'current.index'. When the `ItemStream` `open` method is called, the `ExecutionContext` is checked to see if it contains an entry with that key. If the key is found, then the current index is moved to that location. This is a fairly trivial example, but it still meets the general contract:

      ExecutionContext executionContext = new ExecutionContext();
      ((ItemStream)itemReader).open(executionContext);
      assertEquals("1", itemReader.read());
      ((ItemStream)itemReader).update(executionContext);

      List<String> items = new ArrayList<String>();
      items.add("1");
      items.add("2");
      items.add("3");
      itemReader = new CustomItemReader<String>(items);

      ((ItemStream)itemReader).open(executionContext);
      assertEquals("2", itemReader.read());

Most ItemReaders have much more sophisticated restart logic. The `JdbcCursorItemReader`, for example, stores the row id of the last processed row in the Cursor.

It is also worth noting that the key used within the `ExecutionContext` should not be trivial. That is because the same `ExecutionContext` is used for all `ItemStream`s within a `Step`. In most cases, simply prepending the key with the class name should be enough to guarantee uniqueness. However, in the rare cases where two of the same type of `ItemStream` are used in the same step (which can happen if two files are need for output) then a more unique name will be needed. For this reason, many of the Spring Batch `ItemReader` and `ItemWriter` implementations have a `setName`() property that allows this key name to be overridden.

### Custom ItemWriter Example

Implementing a Custom `ItemWriter` is similar in many ways to the `ItemReader` example above, but differs in enough ways as to warrant its own example. However, adding restartability is essentially the same, so it won't be covered in this example. As with the `ItemReader` example, a `List` will be used in order to keep the example as simple as possible:

      public class CustomItemWriter<T> implements ItemWriter<T> {

        List<T> output = TransactionAwareProxyFactory.createTransactionalList();

        public void write(List<? extends T> items) throws Exception {
          output.addAll(items);
        }

        public List<T> getOutput() {
          return output;
        }
      }

#### Making the `ItemWriter` restartable

To make the ItemWriter restartable we would follow the same process as for the `ItemReader`, adding and implementing the `ItemStream` interface to synchronize the execution context. In the example we might have to count the number of items processed and add that as a footer record. If we needed to do that, we could implement `ItemStream` in our `ItemWriter` so that the counter was reconstituted from the execution context if the stream was re-opened.

In many realistic cases, custom ItemWriters also delegate to another writer that itself is restartable (e.g. when writing to a file), or else it writes to a transactional resource so doesn't need to be restartable because it is stateless. When you have a stateful writer you should probably also be sure to implement `ItemStream` as well as `ItemWriter`. Remember also that the client of the writer needs to be aware of the `ItemStream`, so you may need to register it as a stream in the configuration xml.