Use the filter module in a stream to determine whether a Message should be passed to the output channel.
The filter processor has the following options:
payload.toString())The simplest way to use the filter processor is to pass a SpEL expression when creating the stream. The expression should evaluate the message and return true or false. For example:
dataflow:> stream create --name filtertest --definition "http --server.port=9000 | filter --expression=payload=='good' | log" --deploy
This filter will only pass Messages to the log sink if the payload is the word "good". Try sending "good" to the HTTP endpoint and you should see it in the Spring Cloud Data Flow logs:
dataflow:> http post --target http://localhost:9000 --data "good"
Alternatively, if you send the word "bad" (or anything else), you shouldn’t see the log entry.
A Processor module that retains or discards messages according to a predicate, expressed as a Groovy script.
The groovy-filter processor has the following options:
A processor module that makes requests to an HTTP resource and emits the response body as a message payload. This processor can be combined, e.g., with a time source module to periodically poll results from a HTTP resource.
The httpclient processor has the following options:
A Processor module that transforms messages using a Groovy script.
The groovy-transform processor has the following options:
Use the transform module in a stream to convert a Message’s content or structure.
The transform processor has the following options:
payload.toString())The simplest way to use the transform processor is to pass a SpEL expression when creating the stream. The expression should return the modified message or payload. For example:
dataflow:> stream create --name transformtest --definition "http --server.port=9003 | transform --expression=payload.toUpperCase() | log" --deploy
This transform will convert all message payloads to upper case. If sending the word "foo" to the HTTP endpoint and you should see "FOO" in the Spring Cloud Data Flow logs:
dataflow:> http post --target http://localhost:9003 --data "foo"
As part of the SpEL expression you can make use of the pre-registered JSON Path function. The syntax is #jsonPath(payload,'<json path expression>')
The splitter module builds upon the concept of the same name in Spring Integration and allows the splitting of a single message into several distinct messages.
null)null)null)null)true)When no expression, fileMarkers, or charset is provided, a DefaultMessageSplitter is configured with (optional) delimiters.
When fileMarkers or charset is provided, a FileSplitter is configured (you must provide either a fileMarkers
or charset to split files, which must be text-based - they are split into lines).
Otherwise, an ExpressionEvaluatingMessageSplitter is configured.
When splitting File payloads, the sequenceSize header is zero because the size cannot be determined at the beginning.
Ambiguous properties are not allowed.
As part of the SpEL expression you can make use of the pre-registered JSON Path function. The syntax is
#jsonPath(payload, '<json path expression>').
For example, consider the following JSON:
{ "store": { "book": [ { "category": "reference", "author": "Nigel Rees", "title": "Sayings of the Century", "price": 8.95 }, { "category": "fiction", "author": "Evelyn Waugh", "title": "Sword of Honour", "price": 12.99 }, { "category": "fiction", "author": "Herman Melville", "title": "Moby Dick", "isbn": "0-553-21311-3", "price": 8.99 }, { "category": "fiction", "author": "J. R. R. Tolkien", "title": "The Lord of the Rings", "isbn": "0-395-19395-8", "price": 22.99 } ], "bicycle": { "color": "red", "price": 19.95 } }}
and an expression #jsonPath(payload, '$.store.book'); the result will be 4 messages, each with a Map payload
containing the properties of a single book.