Sometimes instead of applying a pre-defined transformation like this:

simple transformation

you want to be able to dynamically alter some aspects of the transformation – by streaming in thresholds, or rules, or other parameters. The pattern in Flink that supports this is something called connected streams, wherein a single operator has two input streams, like this:

connected streams

Connected streams can also used for implementing streaming joins, a topic that’s covered in a later exercises.


In this example a control stream is used to specify words which must be filtered out of the streamOfWords. A RichCoFlatMapFunction called ControlFunction is applied to the connected streams to get this done.

public static void main(String[] args) throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

	DataStream<String> control = env.fromElements("DROP", "IGNORE").keyBy(x -> x);
	DataStream<String> streamOfWords = env.fromElements("data", "DROP", "artisans", "IGNORE").keyBy(x -> x);
		.flatMap(new ControlFunction())


Note that the two streams being connected must be keyed in compatible ways – either both streams are not keyed, or both are keyed, and if they are both keyed, the key values have to be the same. In this case the streams are both of type DataStream<String>, and both streams are keyed by the string. As you will see below, this RichCoFlatmap is storing a Boolean value in keyed state, and this Boolean is shared by the two streams.

public static class ControlFunction extends RichCoFlatMapFunction<String, String, String> {
	private ValueState<Boolean> blocked;
	public void open(Configuration config) {
	    blocked = getRuntimeContext().getState(new ValueStateDescriptor<>("blocked", Boolean.class));
	public void flatMap1(String control_value, Collector<String> out) throws Exception {
	public void flatMap2(String data_value, Collector<String> out) throws Exception {
	    if (blocked.value() == null) {

A RichCoFlatMapFunction is a kind of FlatMapFunction that can be applied to a pair of connected streams, and has access to the rich function interface – which we will take advantage of in this case to make it stateful.

The blocked Boolean is being used to remember the keys (or the words) that have been mentioned on the control stream, and those words are being filtered from the streamOfWords stream. This is keyed state, and it is shared between the two streams, which is why the two streams have to share the same keyspace.

flatMap1 and flatMap2 are called by the Flink runtime with elements from each of the two connected streams – in our case, elements from the control stream are passed into flatMap1, and elements from streamOfWords are passed into flatMap2. This was determined by the order in which we connected the two streams via control.connect(datastreamOfWords).

It is important to recognize that you have no control over the order in which the flatMap1 and flatMap2 callbacks are called. These two input streams are racing against each other, and the Flink runtime will do what it wants to regarding consuming events from one stream or the other. In cases where timing and/or ordering matter, you may find it necessary to buffer events in managed Flink state until your application is ready to process them.