Item reading and writing
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 describes records with fields of data defined by fixed positions in the file or delimited by some special character (such as a comma).
-
XML: XML
ItemReadersprocess 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
ItemReaderimplementations invoke aRowMapperto return objects, keep track of the current row if restart is required, store basic statistics, and provide some transaction enhancements that are explained later.
There are many more possibilities, but we focus on the basic ones for this chapter. A
complete list of all available ItemReader implementations can be found in
Appendix A.
ItemReader is a basic interface for generic
input operations, as shown in the following interface definition:
public interface ItemReader<T> {
T read() throws Exception, UnexpectedInputException, ParseException, NonTransientResourceException;
}
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 are mapped to a usable domain object (such as Trade, Foo, or others), but there
is no requirement in the contract to do so.
It is expected that implementations of the ItemReader interface are 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 does not cause an
exception to be thrown. For example, a database ItemReader that is configured with a
query that returns 0 results returns 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 operations 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, as shown in the following interface definition:
public interface ItemWriter<T> {
void write(Chunk<? extends T> items) throws Exception;
}
As with read on ItemReader, write provides the basic contract of ItemWriter. It
attempts to write out the list of items passed in as long as it is open. Because it is
generally expected that items are '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 flush on the hibernate session before
returning.
ItemStream
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. The ItemStream interface serves that purpose,
as shown in the following example:
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 is called to ensure that any resources allocated during open are released safely.
update is called primarily to ensure that any state currently being held is loaded into
the provided ExecutionContext. This method is 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 is 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,
such as StepListener. If they do and if 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 gets registered automatically if it implements ItemStream or a
StepListener interface. However, because the delegates are not known to the Step,
they need to be injected as listeners or streams (or both if appropriate).
The following example shows how to inject a delegate as a stream in XML:
<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="...CustomCompositeItemWriter">
<property name="delegate" ref="barWriter" />
</bean>
<bean id="barWriter" class="...BarWriter" />The following example shows how to inject a delegate as a stream in XML:
@Bean
public Job ioSampleJob(JobRepository jobRepository) {
return new JobBuilder("ioSampleJob", jobRepository)
.start(step1())
.build();
}
@Bean
public Step step1(JobRepository jobRepository, PlatformTransactionManager transactionManager) {
return new StepBuilder("step1", jobRepository)
.<String, String>chunk(2, transactionManager)
.reader(fooReader())
.processor(fooProcessor())
.writer(compositeItemWriter())
.stream(barWriter())
.build();
}
@Bean
public CustomCompositeItemWriter compositeItemWriter() {
CustomCompositeItemWriter writer = new CustomCompositeItemWriter();
writer.setDelegate(barWriter());
return writer;
}
@Bean
public BarWriter barWriter() {
return new 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 String objects. 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 similar to a JDBC
ResultSet. A FieldSet requires only one argument: a String array of tokens.
Optionally, you can also configure the names of the fields so that the fields may be
accessed either by index or name as patterned after ResultSet, as shown in the following
example:
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, and so on. 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 called
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 is 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 5. Resources. Therefore, this guide does not go into the details of
creating Resource objects beyond showing the following simple example:
Resource resource = new FileSystemResource("resources/trades.csv");
In complex batch environments, the directory structures are often managed by the Enterprise Application Integration (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. 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 let you further specify how your data is
interpreted, as described in the following table:
| Property | Type | Description |
|---|---|---|
comments |
String[] |
Specifies line prefixes that indicate comment rows. |
encoding |
String |
Specifies what text encoding to use. The default value is |
lineMapper |
|
Converts a |
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 |
|
The resource from which to read. |
skippedLinesCallback |
LineCallbackHandler |
Interface that passes the raw line content of
the lines in the file to be skipped. If |
strict |
boolean |
In strict mode, the reader throws an exception on |
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, as shown in the following interface definition:
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 later in this document.
LineTokenizer
An abstraction for turning a line of input 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 is
returned. This FieldSet can then be passed to a FieldSetMapper. Spring Batch contains
the following LineTokenizer implementations:
-
DelimitedLineTokenizer: 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 whichLineTokenizeramong a list of tokenizers should be used on a particular line by checking against a pattern.
FieldSetMapper
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 an 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, as shown in the following interface definition:
public interface FieldSetMapper<T> {
T mapFieldSet(FieldSet fieldSet) throws BindException;
}
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:
-
Read one line from the file.
-
Pass the
Stringline into theLineTokenizer#tokenize()method to retrieve aFieldSet. -
Pass the
FieldSetreturned from tokenizing to aFieldSetMapper, returning the result from theItemReader#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 a FieldSetMapper is provided. The DefaultLineMapper,
shown in the following class definition, represents the behavior most users need:
public class DefaultLineMapper<T> implements LineMapper<>, 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) 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 illustrates how to read a flat file with 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 are 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...
}
To map a FieldSet into a Player object, a FieldSetMapper that returns players needs
to be defined, as shown in the following example:
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, as shown in the following example:
FlatFileItemReader<Player> itemReader = new FlatFileItemReader<>();
itemReader.setResource(new FileSystemResource("resources/players.csv"));
DefaultLineMapper<Player> lineMapper = new DefaultLineMapper<>();
//DelimitedLineTokenizer defaults to comma as its delimiter
lineMapper.setLineTokenizer(new DelimitedLineTokenizer());
lineMapper.setFieldSetMapper(new PlayerFieldSetMapper());
itemReader.setLineMapper(lineMapper);
itemReader.open(new ExecutionContext());
Player player = itemReader.read();
Each call to read returns a new
Player object from each line in the file. When the end of the file is
reached, null is returned.
Mapping Fields by Name
There is one additional piece of functionality that is allowed by both
DelimitedLineTokenizer and FixedLengthTokenizer and 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,
as shown in the following example:
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;
}
}
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 snippet in XML:
<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" />Again using the football example, the BeanWrapperFieldSetMapper configuration looks like
the following snippet in Java:
@Bean
public FieldSetMapper fieldSetMapper() {
BeanWrapperFieldSetMapper fieldSetMapper = new BeanWrapperFieldSetMapper();
fieldSetMapper.setPrototypeBeanName("player");
return fieldSetMapper;
}
@Bean
@Scope("prototype")
public Player player() {
return new Player();
}
For each entry in the FieldSet, the mapper looks 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 looks for setters matching a property name. Each available
field in the FieldSet is mapped, and the resultant Player object is 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 follows:
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 ordered - 12 characters long.
-
Quantity: Number of the 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.
The following example shows how to define ranges for the FixedLengthLineTokenizer in
XML:
<bean id="fixedLengthLineTokenizer"
class="org.springframework.batch.item.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 earlier, it returns 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 preceding syntax for ranges requires that a specialized property editor,
|
The following example shows how to define ranges for the FixedLengthLineTokenizer in
Java:
@Bean
public FixedLengthTokenizer fixedLengthTokenizer() {
FixedLengthTokenizer tokenizer = new FixedLengthTokenizer();
tokenizer.setNames("ISIN", "Quantity", "Price", "Customer");
tokenizer.setColumns(new Range(1, 12),
new Range(13, 15),
new Range(16, 20),
new Range(21, 29));
return tokenizer;
}
Because the FixedLengthLineTokenizer uses the same LineTokenizer interface as
discussed above, it returns the same FieldSet as if a delimiter had been used. This
lets the same approaches be used in handling its output, such as using the
BeanWrapperFieldSetMapper.
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 reads each line individually, but we must specify different
LineTokenizer and FieldSetMapper objects so that the ItemWriter receives the
correct items. The PatternMatchingCompositeLineMapper makes this easy by allowing maps
of patterns to LineTokenizers and patterns to FieldSetMappers to be configured.
The following example shows how to define ranges for the FixedLengthLineTokenizer in
XML:
<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>@Bean
public PatternMatchingCompositeLineMapper orderFileLineMapper() {
PatternMatchingCompositeLineMapper lineMapper =
new PatternMatchingCompositeLineMapper();
Map<String, LineTokenizer> tokenizers = new HashMap<>(3);
tokenizers.put("USER*", userTokenizer());
tokenizers.put("LINEA*", lineATokenizer());
tokenizers.put("LINEB*", lineBTokenizer());
lineMapper.setTokenizers(tokenizers);
Map<String, FieldSetMapper> mappers = new HashMap<>(2);
mappers.put("USER*", userFieldSetMapper());
mappers.put("LINE*", lineFieldSetMapper());
lineMapper.setFieldSetMappers(mappers);
return lineMapper;
}
In this example, "LINEA" and "LINEB" have separate LineTokenizer instances, but they both use
the same FieldSetMapper.
The PatternMatchingCompositeLineMapper uses the PatternMatcher#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 ("?") matches exactly one
character, while the asterisk ("*") matches zero or more characters. Note that, in the
preceding configuration, all patterns end with an asterisk, making them effectively
prefixes to lines. The PatternMatcher always matches 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.
The following example shows how to match a line not matched by any other pattern in XML:
<entry key="*" value-ref="defaultLineTokenizer" />The following example shows how to match a line not matched by any other pattern in Java:
...
tokenizers.put("*", 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 the multiLineRecords sample.
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 incorrectly formatted records. Many users choose to
skip these erroneous lines while logging the issue, the original line, and the 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.
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 does not match the number of columns found while tokenizing a line, the FieldSet
cannot be created, and an IncorrectTokenCountException is thrown, which contains the
number of tokens encountered, and the number expected, as shown in the following example:
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 does not equal the widest value of this column, an exception is thrown, as shown in the following example:
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. Consequently,
the total length of the line is 15. However, in the preceding example, 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 contain if it failed while
trying to read in column 2 in a FieldSetMapper. However, there are scenarios where the
length of the line is not always constant. For this reason, validation of line length can
be turned off via the 'strict' property, as shown in the following example:
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 preceding 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 contains only 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 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, shown in the
following interface definition:
public interface LineAggregator<T> {
public String aggregate(T item);
}
The LineAggregator is the logical opposite of 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 assumes that the object is already a string or that
its string representation is acceptable for writing, as shown in the following code:
public class PassThroughLineAggregator<T> implements LineAggregator<T> {
public String aggregate(T item) {
return item.toString();
}
}
The preceding 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:
-
The object to be written is passed to the
LineAggregatorin order to obtain aString. -
The returned
Stringis 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);
}
In XML, a simple example of configuration might 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>In Java, a simple example of configuration might look like the following:
@Bean
public FlatFileItemWriter itemWriter() {
return new FlatFileItemWriterBuilder<Foo>()
.name("itemWriter")
.resource(new FileSystemResource("target/test-outputs/output.txt"))
.lineAggregator(new PassThroughLineAggregator<>())
.build();
}
FieldExtractor
The preceding example may be useful for the most basic uses of a writing to a file.
However, most users of the FlatFileItemWriter 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 line into the
LineTokenizer#tokenize()method, in order to retrieve aFieldSet. -
Pass the
FieldSetreturned from tokenizing to aFieldSetMapper, returning the result from theItemReader#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, as shown in the following interface definition:
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 fixed-width line.
PassThroughFieldExtractor
There are many cases where a collection, such as an array, Collection, or FieldSet,
needs to be written out. "Extracting" an array from one of these collection types is very
straightforward. To do so, 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
returns an array containing solely the item to be extracted.
BeanWrapperFieldExtractor
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 this
functionality, as shown in the following example:
BeanWrapperFieldExtractor<Name> extractor = new BeanWrapperFieldExtractor<>();
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 following example 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.
The following example shows how to use the FieldExtractor with a delimiter in XML:
<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>The following example shows how to use the FieldExtractor with a delimiter in Java:
@Bean
public FlatFileItemWriter<CustomerCredit> itemWriter(Resource outputResource) throws Exception {
BeanWrapperFieldExtractor<CustomerCredit> fieldExtractor = new BeanWrapperFieldExtractor<>();
fieldExtractor.setNames(new String[] {"name", "credit"});
fieldExtractor.afterPropertiesSet();
DelimitedLineAggregator<CustomerCredit> lineAggregator = new DelimitedLineAggregator<>();
lineAggregator.setDelimiter(",");
lineAggregator.setFieldExtractor(fieldExtractor);
return new FlatFileItemWriterBuilder<CustomerCredit>()
.name("customerCreditWriter")
.resource(outputResource)
.lineAggregator(lineAggregator)
.build();
}
In the previous example, 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.
It is also possible to use the FlatFileItemWriterBuilder.DelimitedBuilder to
automatically create the BeanWrapperFieldExtractor and DelimitedLineAggregator
as shown in the following example:
@Bean
public FlatFileItemWriter<CustomerCredit> itemWriter(Resource outputResource) throws Exception {
return new FlatFileItemWriterBuilder<CustomerCredit>()
.name("customerCreditWriter")
.resource(outputResource)
.delimited()
.delimiter("|")
.names(new String[] {"name", "credit"})
.build();
}
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 with the FormatterLineAggregator.
Using the same CustomerCredit domain object described above, it can be configured as
follows in XML:
<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>Using the same CustomerCredit domain object described above, it can be configured as
follows in Java:
@Bean
public FlatFileItemWriter<CustomerCredit> itemWriter(Resource outputResource) throws Exception {
BeanWrapperFieldExtractor<CustomerCredit> fieldExtractor = new BeanWrapperFieldExtractor<>();
fieldExtractor.setNames(new String[] {"name", "credit"});
fieldExtractor.afterPropertiesSet();
FormatterLineAggregator<CustomerCredit> lineAggregator = new FormatterLineAggregator<>();
lineAggregator.setFormat("%-9s%-2.0f");
lineAggregator.setFieldExtractor(fieldExtractor);
return new FlatFileItemWriterBuilder<CustomerCredit>()
.name("customerCreditWriter")
.resource(outputResource)
.lineAggregator(lineAggregator)
.build();
}
Most of the preceding example should look familiar. However, the value of the format property is new.
The following example shows the format property in XML:
<property name="format" value="%-9s%-2.0f" />The following example shows the format property in Java:
...
FormatterLineAggregator<CustomerCredit> lineAggregator = new FormatterLineAggregator<>();
lineAggregator.setFormat("%-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.
It is also possible to use the FlatFileItemWriterBuilder.FormattedBuilder to
automatically create the BeanWrapperFieldExtractor and FormatterLineAggregator
as shown in following example:
@Bean
public FlatFileItemWriter<CustomerCredit> itemWriter(Resource outputResource) throws Exception {
return new FlatFileItemWriterBuilder<CustomerCredit>()
.name("customerCreditWriter")
.resource(outputResource)
.formatted()
.format("%-9s%-2.0f")
.names(new String[] {"name", "credit"})
.build();
}
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
straightforward 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 causes 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 by allowing the user to provide only callbacks). |
We need to consider 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 (FieldSet instances) that need
to be tokenized, it is assumed an XML resource is a collection of 'fragments'
corresponding to individual records, as shown in the following image:
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 is shown in the following image:
With an introduction to OXM and how one can use XML fragments to represent records, we can now more closely examine readers and writers.
StaxEventItemReader
The StaxEventItemReader configuration provides a typical setup for the processing of
records from an XML input stream. First, consider the following set of XML records that
the StaxEventItemReader can process:
<?xml version="1.0" encoding="UTF-8"?>
<records>
<trade xmlns="https://springframework.org/batch/sample/io/oxm/domain">
<isin>XYZ0001</isin>
<quantity>5</quantity>
<price>11.39</price>
<customer>Customer1</customer>
</trade>
<trade xmlns="https://springframework.org/batch/sample/io/oxm/domain">
<isin>XYZ0002</isin>
<quantity>2</quantity>
<price>72.99</price>
<customer>Customer2c</customer>
</trade>
<trade xmlns="https://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: The 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: A Spring Resource that represents the file to read.
-
Unmarshaller: An unmarshalling facility provided by Spring OXM for mapping the XML fragment to an object.
The following example shows how to define a StaxEventItemReader that works with a root
element named trade, a resource of data/iosample/input/input.xml, and an unmarshaller
called tradeMarshaller in XML:
<bean id="itemReader" class="org.springframework.batch.item.xml.StaxEventItemReader">
<property name="fragmentRootElementName" value="trade" />
<property name="resource" value="org/springframework/batch/item/xml/domain/trades.xml" />
<property name="unmarshaller" ref="tradeMarshaller" />
</bean>The following example shows how to define a StaxEventItemReader that works with a root
element named trade, a resource of data/iosample/input/input.xml, and an unmarshaller
called tradeMarshaller in Java:
@Bean
public StaxEventItemReader itemReader() {
return new StaxEventItemReaderBuilder<Trade>()
.name("itemReader")
.resource(new FileSystemResource("org/springframework/batch/item/xml/domain/trades.xml"))
.addFragmentRootElements("trade")
.unmarshaller(tradeMarshaller())
.build();
}
Note that, in this example, we have chosen to use an XStreamMarshaller, which accepts
an alias passed in as a map with the first key and value being the name of the fragment
(that is, a 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.
The following example shows how to describe the alias in XML:
<bean id="tradeMarshaller"
class="org.springframework.oxm.xstream.XStreamMarshaller">
<property name="aliases">
<util:map id="aliases">
<entry key="trade"
value="org.springframework.batch.sample.domain.trade.Trade" />
<entry key="price" value="java.math.BigDecimal" />
<entry key="isin" value="java.lang.String" />
<entry key="customer" value="java.lang.String" />
<entry key="quantity" value="java.lang.Long" />
</util:map>
</property>
</bean>The following example shows how to describe the alias in Java:
@Bean
public XStreamMarshaller tradeMarshaller() {
Map<String, Class> aliases = new HashMap<>();
aliases.put("trade", Trade.class);
aliases.put("price", BigDecimal.class);
aliases.put("isin", String.class);
aliases.put("customer", String.class);
aliases.put("quantity", Long.class);
XStreamMarshaller marshaller = new XStreamMarshaller();
marshaller.setAliases(aliases);
return marshaller;
}
On input, the reader reads the XML resource until it recognizes that a new fragment is
about to start. By default, the reader matches the element name to recognize that a new
fragment is about to start. The reader creates a standalone XML document from the
fragment 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 Java code, which uses the injection provided by the Spring configuration:
StaxEventItemReader<Trade> xmlStaxEventItemReader = new StaxEventItemReader<>();
Resource resource = new ByteArrayResource(xmlResource.getBytes());
Map aliases = new HashMap();
aliases.put("trade","org.springframework.batch.sample.domain.trade.Trade");
aliases.put("price","java.math.BigDecimal");
aliases.put("customer","java.lang.String");
aliases.put("isin","java.lang.String");
aliases.put("quantity","java.lang.Long");
XStreamMarshaller unmarshaller = new XStreamMarshaller();
unmarshaller.setAliases(aliases);
xmlStaxEventItemReader.setUnmarshaller(unmarshaller);
xmlStaxEventItemReader.setResource(resource);
xmlStaxEventItemReader.setFragmentRootElementName("trade");
xmlStaxEventItemReader.open(new ExecutionContext());
boolean hasNext = true;
Trade trade = null;
while (hasNext) {
trade = xmlStaxEventItemReader.read();
if (trade == null) {
hasNext = false;
}
else {
System.out.println(trade);
}
}
StaxEventItemWriter
Output works symmetrically to input. The StaxEventItemWriter needs a Resource, a
marshaller, and a rootTagName. A Java object is passed to a marshaller (typically a
standard Spring OXM Marshaller) which writes to a Resource by using a custom event
writer that filters the StartDocument and EndDocument events produced for each
fragment by the OXM tools.
The following XML example uses the MarshallingEventWriterSerializer:
<bean id="itemWriter" class="org.springframework.batch.item.xml.StaxEventItemWriter">
<property name="resource" ref="outputResource" />
<property name="marshaller" ref="tradeMarshaller" />
<property name="rootTagName" value="trade" />
<property name="overwriteOutput" value="true" />
</bean>The following Java example uses the MarshallingEventWriterSerializer:
@Bean
public StaxEventItemWriter itemWriter(Resource outputResource) {
return new StaxEventItemWriterBuilder<Trade>()
.name("tradesWriter")
.marshaller(tradeMarshaller())
.resource(outputResource)
.rootTagName("trade")
.overwriteOutput(true)
.build();
}
The preceding configuration sets up the three required properties and sets the optional
overwriteOutput=true attrbute, mentioned earlier in this chapter for specifying whether
an existing file can be overwritten.
The following XML example uses the same marshaller as the one used in the reading example shown 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.Trade" />
<entry key="price" value="java.math.BigDecimal" />
<entry key="isin" value="java.lang.String" />
<entry key="customer" value="java.lang.String" />
<entry key="quantity" value="java.lang.Long" />
</util:map>
</property>
</bean>The following Java example uses the same marshaller as the one used in the reading example shown earlier in the chapter:
@Bean
public XStreamMarshaller customerCreditMarshaller() {
XStreamMarshaller marshaller = new XStreamMarshaller();
Map<String, Class> aliases = new HashMap<>();
aliases.put("trade", Trade.class);
aliases.put("price", BigDecimal.class);
aliases.put("isin", String.class);
aliases.put("customer", String.class);
aliases.put("quantity", Long.class);
marshaller.setAliases(aliases);
return marshaller;
}
To summarize with a Java example, the following code illustrates all of the points discussed, demonstrating the programmatic setup of the required properties:
FileSystemResource resource = new FileSystemResource("data/outputFile.xml")
Map aliases = new HashMap();
aliases.put("trade","org.springframework.batch.sample.domain.trade.Trade");
aliases.put("price","java.math.BigDecimal");
aliases.put("customer","java.lang.String");
aliases.put("isin","java.lang.String");
aliases.put("quantity","java.lang.Long");
Marshaller marshaller = new XStreamMarshaller();
marshaller.setAliases(aliases);
StaxEventItemWriter staxItemWriter =
new StaxEventItemWriterBuilder<Trade>()
.name("tradesWriter")
.marshaller(marshaller)
.resource(resource)
.rootTagName("trade")
.overwriteOutput(true)
.build();
staxItemWriter.afterPropertiesSet();
ExecutionContext executionContext = new ExecutionContext();
staxItemWriter.open(executionContext);
Trade trade = new Trade();
trade.setPrice(11.39);
trade.setIsin("XYZ0001");
trade.setQuantity(5L);
trade.setCustomer("Customer1");
staxItemWriter.write(trade);
JSON Item Readers And Writers
Spring Batch provides support for reading and Writing JSON resources in the following format:
[
{
"isin": "123",
"quantity": 1,
"price": 1.2,
"customer": "foo"
},
{
"isin": "456",
"quantity": 2,
"price": 1.4,
"customer": "bar"
}
]It is assumed that the JSON resource is an array of JSON objects corresponding to individual items. Spring Batch is not tied to any particular JSON library.
JsonItemReader
The JsonItemReader delegates JSON parsing and binding to implementations of the
org.springframework.batch.item.json.JsonObjectReader interface. This interface
is intended to be implemented by using a streaming API to read JSON objects
in chunks. Two implementations are currently provided:
To be able to process JSON records, the following is needed:
-
Resource: A Spring Resource that represents the JSON file to read. -
JsonObjectReader: A JSON object reader to parse and bind JSON objects to items
The following example shows how to define a JsonItemReader that works with the
previous JSON resource org/springframework/batch/item/json/trades.json and a
JsonObjectReader based on Jackson:
@Bean
public JsonItemReader<Trade> jsonItemReader() {
return new JsonItemReaderBuilder<Trade>()
.jsonObjectReader(new JacksonJsonObjectReader<>(Trade.class))
.resource(new ClassPathResource("trades.json"))
.name("tradeJsonItemReader")
.build();
}
JsonFileItemWriter
The JsonFileItemWriter delegates the marshalling of items to the
org.springframework.batch.item.json.JsonObjectMarshaller interface. The contract
of this interface is to take an object and marshall it to a JSON String.
Two implementations are currently provided:
To be able to write JSON records, the following is needed:
-
Resource: A SpringResourcethat represents the JSON file to write -
JsonObjectMarshaller: A JSON object marshaller to marshall objects to JSON format
The following example shows how to define a JsonFileItemWriter:
@Bean
public JsonFileItemWriter<Trade> jsonFileItemWriter() {
return new JsonFileItemWriterBuilder<Trade>()
.jsonObjectMarshaller(new JacksonJsonObjectMarshaller<>())
.resource(new ClassPathResource("trades.json"))
.name("tradeJsonFileItemWriter")
.build();
}
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 MultiResourceItemReader can be used to read in both files by
using wildcards.
The following example shows how to read files with wildcards in XML:
<bean id="multiResourceReader" class="org.spr...MultiResourceItemReader">
<property name="resources" value="classpath:data/input/file-*.txt" />
<property name="delegate" ref="flatFileItemReader" />
</bean>The following example shows how to read files with wildcards in Java:
@Bean
public MultiResourceItemReader multiResourceReader() {
return new MultiResourceItemReaderBuilder<Foo>()
.delegate(flatFileItemReader())
.resources(resources())
.build();
}
The referenced delegate is a simple FlatFileItemReader. The above configuration reads
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.
Input resources are ordered by using MultiResourceItemReader#setComparator(Comparator)
to make sure resource ordering is preserved between job runs in restart scenario.
|
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. 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-based ItemReader Implementations
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 oriented 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. The Spring Batch cursor-based ItemReader
implementation opens a cursor on initialization and moves the cursor forward one row for
every call to read, returning a mapped object that can be used for processing. The
close method is then 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. The following image shows a
generic diagram of how a cursor-based ItemReader works. Note that, while the example
uses SQL (because SQL 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 are written out after each
read, 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 an SQL statement to run against a connection
obtained from a DataSource. The following database schema is 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 the following example uses an
implementation of the RowMapper interface to map a CustomerCredit object:
public class CustomerCreditRowMapper implements RowMapper<CustomerCredit> {
public static final String ID_COLUMN = "id";
public static final String NAME_COLUMN = "name";
public static final String CREDIT_COLUMN = "credit";
public CustomerCredit 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 JdbcCursorItemReader shares key interfaces with JdbcTemplate, 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, assume there are 1,000 rows in
the CUSTOMER database. The first example uses 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 the preceding code snippet, the customerCredits list contains 1,000
CustomerCredit objects. In the query method, a connection is obtained from the
DataSource, the provided SQL is run against it, and the mapRow method is called for
each row in the ResultSet. Contrast this with the approach of the
JdbcCursorItemReader, shown in the following example:
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();
After running the preceding code snippet, the counter equals 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, the item
can be written out by an ItemWriter, and then the next item can be obtained with
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 easily configured for injection into a Spring Batch Step.
The following example shows how to inject an ItemReader into a Step in XML:
<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>The following example shows how to inject an ItemReader into a Step in Java:
@Bean
public JdbcCursorItemReader<CustomerCredit> itemReader() {
return new JdbcCursorItemReaderBuilder<CustomerCredit>()
.dataSource(this.dataSource)
.name("creditReader")
.sql("select ID, NAME, CREDIT from CUSTOMER")
.rowMapper(new CustomerCreditRowMapper())
.build();
}
Additional Properties
Because there are so many varying options for opening a cursor in Java, there are many
properties on the JdbcCursorItemReader that can be set, as described in the following
table:
ignoreWarnings |
Determines whether or not SQLWarnings are logged or cause an exception.
The default is |
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 |
maxRows |
Sets the limit for the maximum number of rows the underlying |
queryTimeout |
Sets the number of seconds the driver waits for a |
verifyCursorPosition |
Because the same |
saveState |
Indicates whether or not the reader’s state should be saved in the
|
driverSupportsAbsolute |
Indicates whether the JDBC driver supports
setting the absolute row on a |
setUseSharedExtendedConnection |
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 |
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
does not mean it cannot 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 and 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 lets you 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. The following example configuration uses 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();
This configured ItemReader returns 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 fetch size of the underlying cursor can be set with the
setFetchSize property. As with JdbcCursorItemReader, configuration is
straightforward.
The following example shows how to inject a Hibernate ItemReader in XML:
<bean id="itemReader"
class="org.springframework.batch.item.database.HibernateCursorItemReader">
<property name="sessionFactory" ref="sessionFactory" />
<property name="queryString" value="from CustomerCredit" />
</bean>The following example shows how to inject a Hibernate ItemReader in Java:
@Bean
public HibernateCursorItemReader itemReader(SessionFactory sessionFactory) {
return new HibernateCursorItemReaderBuilder<CustomerCredit>()
.name("creditReader")
.sessionFactory(sessionFactory)
.queryString("from CustomerCredit")
.build();
}
StoredProcedureItemReader
Sometimes it is necessary to obtain the cursor data by using a stored procedure. The
StoredProcedureItemReader works like the JdbcCursorItemReader, except that, instead
of running a query to obtain a cursor, it runs a stored procedure that returns a cursor.
The stored procedure can return the cursor in three different ways:
-
As a returned
ResultSet(used by SQL Server, Sybase, DB2, Derby, and MySQL). -
As a ref-cursor returned as an out parameter (used by Oracle and PostgreSQL).
-
As the return value of a stored function call.
The following XML example configuration uses the same 'customer credit' example as earlier examples:
<bean id="reader" class="o.s.batch.item.database.StoredProcedureItemReader">
<property name="dataSource" ref="dataSource"/>
<property name="procedureName" value="sp_customer_credit"/>
<property name="rowMapper">
<bean class="org.springframework.batch.sample.domain.CustomerCreditRowMapper"/>
</property>
</bean>The following Java example configuration uses the same 'customer credit' example as earlier examples:
@Bean
public StoredProcedureItemReader reader(DataSource dataSource) {
StoredProcedureItemReader reader = new StoredProcedureItemReader();
reader.setDataSource(dataSource);
reader.setProcedureName("sp_customer_credit");
reader.setRowMapper(new CustomerCreditRowMapper());
return reader;
}The preceding example relies on the stored procedure to provide a ResultSet as a
returned result (option 1 from earlier).
If the stored procedure returned a ref-cursor (option 2), then we would need to provide
the position of the out parameter that is the returned ref-cursor.
The following example shows how to work with the first parameter being a ref-cursor in XML:
<bean id="reader" class="o.s.batch.item.database.StoredProcedureItemReader">
<property name="dataSource" ref="dataSource"/>
<property name="procedureName" value="sp_customer_credit"/>
<property name="refCursorPosition" value="1"/>
<property name="rowMapper">
<bean class="org.springframework.batch.sample.domain.CustomerCreditRowMapper"/>
</property>
</bean>The following example shows how to work with the first parameter being a ref-cursor in Java:
@Bean
public StoredProcedureItemReader reader(DataSource dataSource) {
StoredProcedureItemReader reader = new StoredProcedureItemReader();
reader.setDataSource(dataSource);
reader.setProcedureName("sp_customer_credit");
reader.setRowMapper(new CustomerCreditRowMapper());
reader.setRefCursorPosition(1);
return reader;
}
If the cursor was returned from a stored function (option 3), we would need to set the
property "function" to true. It defaults to false.
The following example shows property to true in XML:
<bean id="reader" class="o.s.batch.item.database.StoredProcedureItemReader">
<property name="dataSource" ref="dataSource"/>
<property name="procedureName" value="sp_customer_credit"/>
<property name="function" value="true"/>
<property name="rowMapper">
<bean class="org.springframework.batch.sample.domain.CustomerCreditRowMapper"/>
</property>
</bean>The following example shows property to true in Java:
@Bean
public StoredProcedureItemReader reader(DataSource dataSource) {
StoredProcedureItemReader reader = new StoredProcedureItemReader();
reader.setDataSource(dataSource);
reader.setProcedureName("sp_customer_credit");
reader.setRowMapper(new CustomerCreditRowMapper());
reader.setFunction(true);
return reader;
}
In all of these cases, we need to define a RowMapper as well as a DataSource and the
actual procedure name.
If the stored procedure or function takes in parameters, then they must be declared and
set by using the parameters property. The following example, for Oracle, declares three
parameters. The first one is the out parameter that returns the ref-cursor, and the
second and third are in parameters that takes a value of type INTEGER.
The following example shows how to work with parameters in XML:
<bean id="reader" class="o.s.batch.item.database.StoredProcedureItemReader">
<property name="dataSource" ref="dataSource"/>
<property name="procedureName" value="spring.cursor_func"/>
<property name="parameters">
<list>
<bean class="org.springframework.jdbc.core.SqlOutParameter">
<constructor-arg index="0" value="newid"/>
<constructor-arg index="1">
<util:constant static-field="oracle.jdbc.OracleTypes.CURSOR"/>
</constructor-arg>
</bean>
<bean class="org.springframework.jdbc.core.SqlParameter">
<constructor-arg index="0" value="amount"/>
<constructor-arg index="1">
<util:constant static-field="java.sql.Types.INTEGER"/>
</constructor-arg>
</bean>
<bean class="org.springframework.jdbc.core.SqlParameter">
<constructor-arg index="0" value="custid"/>
<constructor-arg index="1">
<util:constant static-field="java.sql.Types.INTEGER"/>
</constructor-arg>
</bean>
</list>
</property>
<property name="refCursorPosition" value="1"/>
<property name="rowMapper" ref="rowMapper"/>
<property name="preparedStatementSetter" ref="parameterSetter"/>
</bean>The following example shows how to work with parameters in Java:
@Bean
public StoredProcedureItemReader reader(DataSource dataSource) {
List<SqlParameter> parameters = new ArrayList<>();
parameters.add(new SqlOutParameter("newId", OracleTypes.CURSOR));
parameters.add(new SqlParameter("amount", Types.INTEGER);
parameters.add(new SqlParameter("custId", Types.INTEGER);
StoredProcedureItemReader reader = new StoredProcedureItemReader();
reader.setDataSource(dataSource);
reader.setProcedureName("spring.cursor_func");
reader.setParameters(parameters);
reader.setRefCursorPosition(1);
reader.setRowMapper(rowMapper());
reader.setPreparedStatementSetter(parameterSetter());
return reader;
}
In addition to the parameter declarations, we need to specify a PreparedStatementSetter
implementation that sets the parameter values for the call. This works the same as for
the JdbcCursorItemReader above. All the additional properties listed in
Additional Properties apply to the StoredProcedureItemReader as well.
Paging ItemReader Implementations
An alternative to using a database cursor is running multiple queries where each query fetches a portion of the results. We refer to this portion as a page. Each query must specify the starting row number and the number of rows that we want returned in 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 SqlPagingQueryProviderFactoryBean
that auto-detects 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 and the
required sortKey are used to build an SQL statement.
It is important to have a unique key constraint on the sortKey to guarantee that
no data is lost between executions.
|
After the reader has been opened, it passes 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.
The following XML example configuration uses a similar 'customer credit' example as the
cursor-based ItemReaders shown previously:
<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>The following Java example configuration uses a similar 'customer credit' example as the
cursor-based ItemReaders shown previously:
@Bean
public JdbcPagingItemReader itemReader(DataSource dataSource, PagingQueryProvider queryProvider) {
Map<String, Object> parameterValues = new HashMap<>();
parameterValues.put("status", "NEW");
return new JdbcPagingItemReaderBuilder<CustomerCredit>()
.name("creditReader")
.dataSource(dataSource)
.queryProvider(queryProvider)
.parameterValues(parameterValues)
.rowMapper(customerCreditMapper())
.pageSize(1000)
.build();
}
@Bean
public SqlPagingQueryProviderFactoryBean queryProvider() {
SqlPagingQueryProviderFactoryBean provider = new SqlPagingQueryProviderFactoryBean();
provider.setSelectClause("select id, name, credit");
provider.setFromClause("from customer");
provider.setWhereClause("where status=:status");
provider.setSortKey("id");
return provider;
}
This configured ItemReader returns CustomerCredit objects using the RowMapper,
which must be specified. The 'pageSize' property determines the number of entities read
from the database for each query run.
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 does
not 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 become detached and the persistence context is cleared, to allow the entities to
be garbage collected once the page is processed.
The JpaPagingItemReader lets you declare a JPQL statement and pass in a
EntityManagerFactory. It then passes 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.
The following XML example configuration uses the same 'customer credit' example as the JDBC reader shown previously:
<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>The following Java example configuration uses the same 'customer credit' example as the JDBC reader shown previously:
@Bean
public JpaPagingItemReader itemReader() {
return new JpaPagingItemReaderBuilder<CustomerCredit>()
.name("creditReader")
.entityManagerFactory(entityManagerFactory())
.queryString("select c from CustomerCredit c")
.pageSize(1000)
.build();
}
This configured ItemReader returns CustomerCredit objects in the exact same manner as
described for the JdbcPagingItemReader above, assuming the CustomerCredit 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.
Database ItemWriters
While both flat files and XML files have a specific ItemWriter instance, there is no exact equivalent
in the database world. This is because transactions provide all the needed functionality.
ItemWriter implementations 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 does not have any inherent flaws, assuming we
are careful to flush and there are no errors in the data. However, any errors while
writing 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 in the
following image:
If items are buffered before being written, any errors are not thrown until the buffer is
flushed just before a commit. For example, assume that 20 items are written per chunk,
and the 15th item throws a DataIntegrityViolationException. As far as the Step
is concerned, all 20 item are written successfully, since there is no way to know that an
error occurs until they are actually written. Once Session#flush() is called, the
buffer is emptied and the exception is hit. At this point, there is 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 is not written
again. However, in the batched scenario, there is no way to know which item caused the
issue. The whole buffer was being written when the failure happened. The only way to
solve this issue is to flush after each item, as shown in the following image:
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 internally taking care 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 adapter 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 by invoking the delegate pattern and are fairly simple to set up.
The following XML example uses the ItemReaderAdapter:
<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" />The following Java example uses the ItemReaderAdapter:
@Bean
public ItemReaderAdapter itemReader() {
ItemReaderAdapter reader = new ItemReaderAdapter();
reader.setTargetObject(fooService());
reader.setTargetMethod("generateFoo");
return reader;
}
@Bean
public FooService fooService() {
return new 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 returns null. Otherwise, it returns an
Object. Anything else prevents the framework from knowing when processing should end,
either causing an infinite loop or incorrect failure, depending upon the implementation
of the ItemWriter.
The following XML example uses the ItemWriterAdapter:
<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" />The following Java example uses the ItemWriterAdapter:
@Bean
public ItemWriterAdapter itemWriter() {
ItemWriterAdapter writer = new ItemWriterAdapter();
writer.setTargetObject(fooService());
writer.setTargetMethod("processFoo");
return writer;
}
@Bean
public FooService fooService() {
return new FooService();
}
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) 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 are 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 is irrelevant upon restart. For this reason, all readers and writers include the
'saveState' property.
The following bean definition shows how to prevent state persistence in XML:
<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 following bean definition shows how to prevent state persistence in Java:
@Bean
public JdbcCursorItemReader playerSummarizationSource(DataSource dataSource) {
return new JdbcCursorItemReaderBuilder<PlayerSummary>()
.dataSource(dataSource)
.rowMapper(new PlayerSummaryMapper())
.saveState(false)
.sql("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")
.build();
}
The ItemReader configured above does not make any entries in the ExecutionContext for
any executions in which it participates.
Creating Custom ItemReaders and ItemWriters
So far, this chapter has discussed the basic contracts of reading and writing in Spring
Batch and some common implementations for doing so. 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 shows, by using a simple example, how to create a custom
ItemReader and ItemWriter implementation and implement their contracts correctly. The
ItemReader also implements ItemStream, in order to illustrate how to make a reader or
writer restartable.
Custom ItemReader Example
For the purpose of this example, we create a simple ItemReader implementation that
reads from a provided list. We start by implementing the most basic contract of
ItemReader, the read method, as shown in the following code:
public class CustomItemReader<T> implements ItemReader<T> {
List<T> items;
public CustomItemReader(List<T> items) {
this.items = items;
}
public T read() throws Exception, UnexpectedInputException,
NonTransientResourceException, ParseException {
if (!items.isEmpty()) {
return items.remove(0);
}
return null;
}
}
The preceding 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 in the following test code:
List<String> items = new ArrayList<>();
items.add("1");
items.add("2");
items.add("3");
ItemReader itemReader = new CustomItemReader<>(items);
assertEquals("1", itemReader.read());
assertEquals("2", itemReader.read());
assertEquals("3", itemReader.read());
assertNull(itemReader.read());
Making the ItemReader Restartable
The final challenge is to make the ItemReader restartable. Currently, if processing is
interrupted and 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
restarts 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 to reconstitute its last known state on restart. For this reason,
we recommend that you keep custom readers stateless if possible, so you need not 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, NonTransientResourceException {
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
is 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<>();
items.add("1");
items.add("2");
items.add("3");
itemReader = new CustomItemReader<>(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 needed for
output), a more unique name is needed. For this reason, many of the Spring Batch
ItemReader and ItemWriter implementations have a setName() property that lets this
key name 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 is not covered in this example. As with the
ItemReader example, a List is 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(Chunk<? 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 (for example, when writing to a file), or else it writes to a
transactional resource and so does not need to be restartable, because it is stateless.
When you have a stateful writer you should probably 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.
Item Reader and Writer Implementations
In this section, we will introduce you to readers and writers that have not already been discussed in the previous sections.
Decorators
In some cases, a user needs specialized behavior to be appended to a pre-existing
ItemReader. Spring Batch offers some out of the box decorators that can add
additional behavior to to your ItemReader and ItemWriter implementations.
Spring Batch includes the following decorators:
SynchronizedItemStreamReader
When using an ItemReader that is not thread safe, Spring Batch offers the
SynchronizedItemStreamReader decorator, which can be used to make the ItemReader
thread safe. Spring Batch provides a SynchronizedItemStreamReaderBuilder to construct
an instance of the SynchronizedItemStreamReader.
For example, the FlatFileItemReader is not thread-safe and cannot be used in
a multi-threaded step. This reader can be decorated with a SynchronizedItemStreamReader
in order to use it safely in a multi-threaded step. Here is an example of how to decorate
such a reader:
@Bean
public SynchronizedItemStreamReader<Person> itemReader() {
FlatFileItemReader<Person> flatFileItemReader = new FlatFileItemReaderBuilder<Person>()
// set reader properties
.build();
return new SynchronizedItemStreamReaderBuilder<Person>()
.delegate(flatFileItemReader)
.build();
}
SingleItemPeekableItemReader
Spring Batch includes a decorator that adds a peek method to an ItemReader. This peek
method lets the user peek one item ahead. Repeated calls to the peek returns the same
item, and this is the next item returned from the read method. Spring Batch provides a
SingleItemPeekableItemReaderBuilder to construct an instance of the
SingleItemPeekableItemReader.
| SingleItemPeekableItemReader’s peek method is not thread-safe, because it would not be possible to honor the peek in multiple threads. Only one of the threads that peeked would get that item in the next call to read. |
SynchronizedItemStreamWriter
When using an ItemWriter that is not thread safe, Spring Batch offers the
SynchronizedItemStreamWriter decorator, which can be used to make the ItemWriter
thread safe. Spring Batch provides a SynchronizedItemStreamWriterBuilder to construct
an instance of the SynchronizedItemStreamWriter.
For example, the FlatFileItemWriter is not thread-safe and cannot be used in
a multi-threaded step. This writer can be decorated with a SynchronizedItemStreamWriter
in order to use it safely in a multi-threaded step. Here is an example of how to decorate
such a writer:
@Bean
public SynchronizedItemStreamWriter<Person> itemWriter() {
FlatFileItemWriter<Person> flatFileItemWriter = new FlatFileItemWriterBuilder<Person>()
// set writer properties
.build();
return new SynchronizedItemStreamWriterBuilder<Person>()
.delegate(flatFileItemWriter)
.build();
}
MultiResourceItemWriter
The MultiResourceItemWriter wraps a ResourceAwareItemWriterItemStream and creates a new
output resource when the count of items written in the current resource exceeds the
itemCountLimitPerResource. Spring Batch provides a MultiResourceItemWriterBuilder to
construct an instance of the MultiResourceItemWriter.
ClassifierCompositeItemWriter
The ClassifierCompositeItemWriter calls one of a collection of ItemWriter
implementations for each item, based on a router pattern implemented through the provided
Classifier. The implementation is thread-safe if all delegates are thread-safe. Spring
Batch provides a ClassifierCompositeItemWriterBuilder to construct an instance of the
ClassifierCompositeItemWriter.
ClassifierCompositeItemProcessor
The ClassifierCompositeItemProcessor is an ItemProcessor that calls one of a
collection of ItemProcessor implementations, based on a router pattern implemented
through the provided Classifier. Spring Batch provides a
ClassifierCompositeItemProcessorBuilder to construct an instance of the
ClassifierCompositeItemProcessor.
Messaging Readers And Writers
Spring Batch offers the following readers and writers for commonly used messaging systems:
AmqpItemReader
The AmqpItemReader is an ItemReader that uses an AmqpTemplate to receive or convert
messages from an exchange. Spring Batch provides a AmqpItemReaderBuilder to construct
an instance of the AmqpItemReader.
AmqpItemWriter
The AmqpItemWriter is an ItemWriter that uses an AmqpTemplate to send messages to
an AMQP exchange. Messages are sent to the nameless exchange if the name not specified in
the provided AmqpTemplate. Spring Batch provides an AmqpItemWriterBuilder to
construct an instance of the AmqpItemWriter.
JmsItemReader
The JmsItemReader is an ItemReader for JMS that uses a JmsTemplate. The template
should have a default destination, which is used to provide items for the read()
method. Spring Batch provides a JmsItemReaderBuilder to construct an instance of the
JmsItemReader.
JmsItemWriter
The JmsItemWriter is an ItemWriter for JMS that uses a JmsTemplate. The template
should have a default destination, which is used to send items in write(List). Spring
Batch provides a JmsItemWriterBuilder to construct an instance of the JmsItemWriter.
KafkaItemReader
The KafkaItemReader is an ItemReader for an Apache Kafka topic. It can be configured
to read messages from multiple partitions of the same topic. It stores message offsets
in the execution context to support restart capabilities. Spring Batch provides a
KafkaItemReaderBuilder to construct an instance of the KafkaItemReader.
Database Readers
Spring Batch offers the following database readers:
Neo4jItemReader
The Neo4jItemReader is an ItemReader that reads objects from the graph database Neo4j
by using a paging technique. Spring Batch provides a Neo4jItemReaderBuilder to
construct an instance of the Neo4jItemReader.
MongoItemReader
The MongoItemReader is an ItemReader that reads documents from MongoDB by using a
paging technique. Spring Batch provides a MongoItemReaderBuilder to construct an
instance of the MongoItemReader.
HibernateCursorItemReader
The HibernateCursorItemReader is an ItemStreamReader for reading database records
built on top of Hibernate. It executes the HQL query and then, when initialized, iterates
over the result set as the read() method is called, successively returning an object
corresponding to the current row. Spring Batch provides a
HibernateCursorItemReaderBuilder to construct an instance of the
HibernateCursorItemReader.
HibernatePagingItemReader
The HibernatePagingItemReader is an ItemReader for reading database records built on
top of Hibernate and reading only up to a fixed number of items at a time. Spring Batch
provides a HibernatePagingItemReaderBuilder to construct an instance of the
HibernatePagingItemReader.
Database Writers
Spring Batch offers the following database writers:
Neo4jItemWriter
The Neo4jItemWriter is an ItemWriter implementation that writes to a Neo4j database.
Spring Batch provides a Neo4jItemWriterBuilder to construct an instance of the
Neo4jItemWriter.
MongoItemWriter
The MongoItemWriter is an ItemWriter implementation that writes to a MongoDB store
using an implementation of Spring Data’s MongoOperations. Spring Batch provides a
MongoItemWriterBuilder to construct an instance of the MongoItemWriter.
RepositoryItemWriter
The RepositoryItemWriter is an ItemWriter wrapper for a CrudRepository from Spring
Data. Spring Batch provides a RepositoryItemWriterBuilder to construct an instance of
the RepositoryItemWriter.
HibernateItemWriter
The HibernateItemWriter is an ItemWriter that uses a Hibernate session to save or
update entities that are not part of the current Hibernate session. Spring Batch provides
a HibernateItemWriterBuilder to construct an instance of the HibernateItemWriter.
Specialized Readers
Spring Batch offers the following specialized readers:
LdifReader
The LdifReader reads LDIF (LDAP Data Interchange Format) records from a Resource,
parses them, and returns a LdapAttribute object for each read executed. Spring Batch
provides a LdifReaderBuilder to construct an instance of the LdifReader.
MappingLdifReader
The MappingLdifReader reads LDIF (LDAP Data Interchange Format) records from a
Resource, parses them then maps each LDIF record to a POJO (Plain Old Java Object).
Each read returns a POJO. Spring Batch provides a MappingLdifReaderBuilder to construct
an instance of the MappingLdifReader.
AvroItemReader
The AvroItemReader reads serialized Avro data from a Resource.
Each read returns an instance of the type specified by a Java class or Avro Schema.
The reader may be optionally configured for input that embeds an Avro schema or not.
Spring Batch provides an AvroItemReaderBuilder to construct an instance of the AvroItemReader.
Specialized Writers
Spring Batch offers the following specialized writers: