Problem Overview: I am working on a Flink application that allows users to design dataflows dynamically. The core engine is built around stages, where a DataStream is passed sequentially through these stages. Each stage processes the stream and outputs it, which is then passed to the next stage.
Now, I need to implement a switch stage that enables routing of the DataStream into multiple routes. Each route has:
A case (condition): A specific value to check against a field in the records of the DataStream. A pipeline of stages: Each route can have its own unique sequence of stages to process the data that matches its case. The main goal is to:
Dynamically route the DataStream based on the field values in each record. Ensure that a record enters a route only when its condition matches the case for that route. The Challenge: Flink uses lazy evaluation by default, which means the execution plan is built first, and no data is processed until the job starts. Due to this:
If the routing logic is placed outside the processElement() function, it executes before any data is processed, causing all routes to be entered prematurely. If the routing logic is placed inside the processElement() function, I can correctly route individual records, but: I cannot pass the resulting routed DataStream to the subsequent stages. processElement() only works on single records, so it doesn’t allow me to handle complete DataStream transformations dynamically for each route. Requirements for the Solution: The routing logic must execute at runtime based on the actual data in the DataStream, not during the job's execution plan creation. Each route must have its own independent pipeline of stages, which should only process the data that matches the route's condition. The solution should ensure that lazy evaluation does not prematurely execute all routes, and processing only occurs when the data arrives. Current Attempts: Routing Logic Outside processElement():
This approach executes all route pipelines before the data is processed because Flink evaluates the transformation logic upfront due to lazy evaluation. As a result, all routes are entered, which is not the desired behavior. Routing Logic Inside processElement():
By moving the routing logic inside processElement(), I can correctly identify which route a record belongs to. However, processElement() operates on individual records and does not allow me to dynamically transform or pass the resulting routed DataStream to its respective pipeline of stages. Example Use Case: Switch Stage Configuration:
Field to check: field Routes: Route A: field = "case1" Route B: field = "case2" Expected Behavior:
For each record in the DataStream: If the value of field equals "case1", the record should be routed to Route A and processed through its stages. If the value of field equals "case2", the record should be routed to Route B and processed through its stages. If no match is found, the record should continue to the default pipeline. Issues Faced:
Eager Evaluation: All route pipelines (e.g., stages for Route A and Route B) are executed before any data arrives. Single-Record Processing: Placing the logic in processElement() allows me to handle individual records, but I cannot dynamically pass the resulting routed DataStream to the subsequent stage pipelines. Desired Solution: A mechanism that allows dynamic routing of the DataStream based on record field values at runtime. Each route should have its own pipeline of stages that only processes the data matching its case. Avoid premature execution of the route pipelines during Flink's lazy evaluation.
Example For Clarification:
My code depends on stages, each stage could be a source, transformation, or sink and each stage contains initialize function and execute function.
Now I added a Switch stage that determines which route should the dataflow should take. this is configured as follows:
stages=source:source1,rules:rules1,switch:switch1
switch1.type=switch
switch1.routes=routeA,routeB
switch1.field=user_id
routeA.case=1
routeA.stages=source:source2,rules:rules2,target:target1
routeB.case=2
routeB.stages=source:source3,rules:rules3,target:target2
I fixed my datastream to always contain a user_id that is equal to 1, so now it should always enter routeA. But it always enters both routes.
This is my switch stage class:
https://docs.google.com/document/d/1pJUulzAmcMnYfawZqH7RUHvsWDb7ahGcxigYoab-D5M/edit?usp=sharing
all logs are printed before the data is arrived, and the code enters the initialization and execution of all routes, it doesn't enter the RouteSplitterFunction first to decide which route it should take.
I hope this clarifies my problem.
With Flink, the topology needs to be static -- it's determined at compile time -- but with the DataStream API you can route events dynamically by using a process function with side outputs.
Consider an example with records having different priorities:
{"id":1,"data":"NL31PJMQ9001080613","priority":"MAJOR"}
{"id":2,"data":"AD5934360143N91217Gfa7hA","priority":"MINOR"}
{"id":3,"data":"ST35710189607894938826568","priority":"CRITICAL"}
{"id":4,"data":"IT90R5678911215DpWA54OpHM17","priority":"CRITICAL"}
{"id":5,"data":"RS93089245532951564296","priority":"MAJOR"}
To use side outputs to split this stream, you first define an OutputTag for each side output stream:
final Map<Event.Priority, OutputTag<Event>> tagsByPriority =
Map.ofEntries(
entry(Event.Priority.CRITICAL, new OutputTag<>("critical") {}),
entry(Event.Priority.MAJOR, new OutputTag<>("major") {}),
entry(Event.Priority.MINOR, new OutputTag<>("minor") {}));
Then you can use a process function to emit each record to the appropriate side output stream:
final SingleOutputStreamOperator<Event> process =
source.process(
new ProcessFunction<>()
@Override
public void processElement(
Event value,
ProcessFunction<Event, Event>.Context ctx,
Collector<Event> out) {
final OutputTag<Event> selectedOutput =
tagsByPriority.get(value.priority);
ctx.output(selectedOutput, value);
}
});
For a more complete example, see the recipe on splitting/routing event streams in the Flink Cookbook.
The same pattern can also be achieved with Flink SQL:
EXECUTE STATEMENT SET BEGIN
INSERT INTO critical SELECT id, data FROM input WHERE priority = 'CRITICAL';
INSERT INTO major SELECT id, data FROM input WHERE priority = 'MAJOR';
INSERT INTO minor SELECT id, data FROM input WHERE priority = 'MINOR';
END;
By putting the three statements into a statement set, the input will only be read once, and then fanned out.