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.
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 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.
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>{
public Bar process(Foo foo) throws Exception {
//Perform simple transformation, convert a Foo to a Bar
return new Bar(foo);
}
}
public class BarWriter implements ItemWriter<Bar>{
public void write(List<? extends Bar> bars) throws Exception {
//write bars
}
}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>
<chunk reader="fooReader" processor="fooProcessor" writer="barWriter"
commit-interval="2"/>
</tasklet>
</step>
</job>Performing a single transformation is useful in many scenarios,
but what if you want to 'chain' together multiple
ItemProcessors? 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>{
public Bar process(Foo foo) throws Exception {
//Perform simple transformation, convert a Foo to a Bar
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
}
}A FooProcessor and
BarProcessor 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>
<chunk reader="fooReader" processor="compositeProcessor" writer="foobarWriter"
commit-interval="2"/>
</tasklet>
</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>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 skipping; 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.
Both ItemReaders and
ItemWriters 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.
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>
<chunk reader="fooReader" processor="fooProcessor" writer="compositeItemWriter"
commit-interval="2">
<streams>
<stream ref="barWriter" />
</streams>
</chunk>
</tasklet>
</step>
</job>
<bean id="compositeItemWriter" class="...CompositeItemWriter">
<property name="delegate" ref="barWriter" />
</bean>
<bean id="barWriter" class="...BarWriter" />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.
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.
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:
Table 6.1. FlatFileItemReader Properties
| Property | Type | Description |
|---|---|---|
| comments | String[] | Specifies line prefixes that indicate comment rows |
| encoding | String | Specifies what text encoding to use - default is "ISO-8859-1" |
| lineMapper | LineMapper | Converts a String
to an Object representing the
item. |
| linesToSkip | int | Number of lines to ignore at the top of the file |
| recordSeparatorPolicy | RecordSeparatorPolicy | Used to determine where the line endings are and do things like continue over a line ending if inside a quoted string. |
| resource | Resource | The resource from which to read. |
| skippedLinesCallback | 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. |
| strict | boolean | In strict mode, the reader will throw an exception on ExecutionContext if the input resource does not exist. |
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.
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
LineTokenizers should be used on a
particular line by checking against a pattern.
The FieldSetMapper interface defines a
single method, mapFieldSet, 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.
Now that the basic interfaces for reading in flat files have been defined, it becomes clear that three basic steps are required:
Read one line from the file.
Pass the string line into the
LineTokenizer#tokenize() method, in
order to retrieve a FieldSet.
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.
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.
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 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;
}
}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.
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:
ISIN: Unique identifier for the item being order - 12 characters long.
Quantity: Number of this item being ordered - 3 characters long.
Price: Price of the item - 5 characters long.
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.
Supporting the above syntax for ranges requires that a
specialized property editor,
RangeArrayPropertyEditor, be configured in
the ApplicationContext. However, this bean
is automatically declared in an
ApplicationContext where the batch
namespace is used.
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 receive the correct items. The
PatternMatchingCompositeLineMapper makes this
easy by allowing maps of patterns to
LineTokenizers and patterns to
FieldSetMappers to be configured:
<bean id="orderFileLineMapper"
class="org.spr...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
LineTokenizers 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 patterns 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 pattern can be found in Section 11.5, “Multi-Line Records”.
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 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.
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.
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.
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.
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.
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.
Now that the LineAggregator interface and
its most basic implementation,
PassThroughLineAggregator, have been defined,
the basic flow of writing can be explained:
The object to be written is passed to the
LineAggregator in order to obtain a
String.
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.spr...FlatFileItemWriter">
<property name="resource" value="file:target/test-outputs/output.txt" />
<property name="lineAggregator">
<bean class="org.spr...PassThroughLineAggregator"/>
</property>
</bean>The above example may be useful for the most basic uses of a
writing to a file. However, most users of the
FlatFileItemWriter 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:
Read one line from the file.
Pass the string line into the
LineTokenizer#tokenize() method, in
order to retrieve a FieldSet
Pass the FieldSet returned from
tokenizing to a FieldSetMapper, returning
the result from the ItemReader#read()
method
File writing has similar, but inverse steps:
Pass the item to be written to the writer
convert the fields on the item into an array
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.
There are many cases where a collection, such as an array,
Collection, or
FieldSet, needs to be written out.
"Extracting" an array from a one of these collection types is very
straightforward: simply convert the collection to an array.
Therefore, the PassThroughFieldExtractor
should be used in this scenario. It should be noted, that if the
object passed in is not a type of collection, then the
PassThroughFieldExtractor will return an
array containing solely the item to be extracted.
As with the BeanWrapperFieldSetMapper
described in the file reading section, it is often preferable 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.
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.spr...DelimitedLineAggregator">
<property name="delimiter" value=","/>
<property name="fieldExtractor">
<bean class="org.spr...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.
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.spr...FormatterLineAggregator">
<property name="fieldExtractor">
<bean class="org.spr...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.
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.
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.
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.
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.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.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.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 {
System.out.println(credit);
}
}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.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("data/outputFile.xml")
Map aliases = new HashMap();
aliases.put("customer","org.springframework.batch.sample.domain.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);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.spr...MultiResourceItemReader">
<property name="resources" value="classpath:data/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.
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. If a SQL statement returns 1 million rows, the result set probably holds 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.
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 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.spr...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.CustomerCreditRowMapper"/>
</property>
</bean>Because there are so many varying options for opening a cursor
in Java, there are many properties on the
JdbcCustorItemReader that can be set:
Table 6.2. 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. |
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>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.
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
SqlPagingQueryProviderFactoryBean 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 SqlPagingQueryProviderFactoryBean
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.spr...JdbcPagingItemReader">
<property name="dataSource" ref="dataSource"/>
<property name="queryProvider">
<bean class="org.spr...SqlPagingQueryProviderFactoryBean">
<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.
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.spr...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.
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.spr...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>
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.
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" />
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.
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.spr...JdbcCursorItemReader">
<property name="dataSource" ref="dataSource" />
<property name="rowMapper">
<bean class="org.springframework.batch.sample.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.
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.
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());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 ItemStreams 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.
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;
}
}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.