The aggregator processor has the following options:

By default the aggregator processor uses: - HeaderAttributeCorrelationStrategy(IntegrationMessageHeaderAccessor.CORRELATION_ID) - for correlation; - SequenceSizeReleaseStrategy - for release; - DefaultAggregatingMessageGroupProcessor - for aggregation; - SimpleMessageStore - for messageStoreType.

The aggregator application can be configured for persistent MessageGroupStore implementations. The configuration for target technology is fully based on the Spring Boot auto-configuration. But default JDBC, MongoDb and Redis auto-configurations are excluded. They are @Import ed basing on the aggregator.messageStoreType configuration property. Consult Spring Boot Reference Manual for auto-configuration for particular technology you use for aggregator.

The JDBC JdbcMessageStore requires particular tables in the target data base. You can find schema scripts for appropriate RDBMS vendors in the org.springframework.integration.jdbc package of the spring-integration-jdbc jar. Those scripts can be used for automatic data base initialization via Spring Boot.

For example:

java -jar aggregator-rabbit-1.0.0.RELEASE.jar
               --aggregator.message-store-type=jdbc
               --spring.datasource.url=jdbc:h2:mem:test
               --spring.datasource.schema=org/springframework/integration/jdbc/schema-h2.sql

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one of the folders and build it:

$ ./mvnw clean package

java -jar aggregator_processor.jar
               --aggregator.message-store-type=jdbc
               --spring.datasource.url=jdbc:h2:mem:test
               --spring.datasource.schema=org/springframework/integration/jdbc/schema-h2.sql

java -jar aggregator_processor.jar
               --spring.data.mongodb.port=0
               --aggregator.correlation=T(Thread).currentThread().id
               --aggregator.release="!#this.?[payload == 'bar'].empty"
               --aggregator.aggregation="#this.?[payload == 'foo'].![payload]"
               --aggregator.message-store-type=mongodb
               --aggregator.message-store-entity=aggregatorTest

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to [email protected].

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar bridge-processor.jar

The filter processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar filter-processor.jar --expression="payload"

The groovy-filter processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar groovy-filter-processor.jar --script=script.groovy

The groovy-transform processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar groovy-transform-processor.jar --script=script.groovy

The header-enricher processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar header-enricher-processor.jar --headers='foo=payload.someProperty \n bar=payload.otherProperty'

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to [email protected].

The httpclient processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

$ java -jar httpclient-processor.jar --httpclient.url=http://someurl  --httpclient.http-method=POST --httpclient.headers-expression="{'Content-Type':'application/json'}"

$ java -jar httpclient-processor.jar --httpclient.url=http://someurl  --httpclient.reply-expression="statusCode.name()"

The pmml processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one of the folders and build it:

$ ./mvnw clean package

java -jar pmml-processor.jar --pmml.modelLocation= --pmml.modelName="
java -jar pmml-processor.jar --pmml.modelLocation= --pmml.modelNameExpression="

The python-http processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

See httpclient processor for more examples on httpclient properties.

$java -jar python-http-processor.jar --wrapper.script=/local/directory/build-json.py --httpclient.url=http://someurl
--httpclient.http-method=POST --httpclient.headers-expression="{'Content-Type':'application/json'}"

$java -jar python-http-processor.jar --git.uri=https://github.com/some-repo --wrapper.script=some-script.py --wrapper
.variables=foo=0.45,bar=0.55 --httpclient.url=http://someurl

The jython processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

$java -jar python-jython-processor.jar --jython.script=/local/directory/to_uppercase.py

$java -jar python-jython-processor.jar --git.uri=https://github.com/some-repo --jython
.script=map-tweet-sentiments.py --jython.variables=neutral=0.45,positive=0.55

The scriptable-transform processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar scriptable-transform-processor.jar --language=ruby --script="return ""#{payload.upcase}"""

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.

	Caution
	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.

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one of the folders and build it:

$ ./mvnw clean package

java -jar splitter-processor.jar --splitter.expression=expression
java -jar splitter-processor.jar --splitter.delimiters=delimiters

The tcp-client processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar tcp-client-processor.jar --tcp.decoder=LF --tcp.encoder=LF

The transform processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one one of the folders and build it:

$ ./mvnw clean package

java -jar transform-processor.jar --expression=payload.toUpperCase()

This transform will convert all message payloads to upper case.

The tensorflow processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one of the folders and build it:

$ ./mvnw clean package

java -jar tensorflow-processor.jar --model= --modelFetch= --mode="

The twitter-sentiment processor has the following options:

$ ./mvnw clean install -PgenerateApps
$ cd apps

You can find the corresponding binder based projects here. You can then cd into one of the folders and build it:

$ ./mvnw clean package

java -jar twitter-sentiment-processor.jar --tensorflow.twitter.vocabulary= --tensorflow.model= \
    --tensorflow.modelFetch= --tensorflow.mode="

And here is a sample pipeline that computes sentiments for json tweets coming from the twitterstream source and using the pre-build minimal_graph.proto and vocab.csv:

tweets=twitterstream --access-token-secret=xxx --access-token=xxx --consumer-secret=xxx --consumer-key=xxx \
| filter --expression=#jsonPath(payload,'$.lang')=='en' \
| twitter-sentimet --vocabulary='http://dl.bintray.com/big-data/generic/vocab.csv' \
   --output-name=output/Softmax --model='http://dl.bintray.com/big-data/generic/minimal_graph.proto' \
   --model-fetch=output/Softmax \
| log