If the number of combinations between partition and schemaID is limited
then the subtask index could actually improve the distribution of values.
In any case, the easiest way to do this is to add a RichMapFunction
after the flatMap, or modify the flatMap, to also include the subtask index.
Typically this would be done by creating a Tuple2 containing the index
and value.
On 18.04.2017 15:43, Kamil Dziublinski wrote:
I am not sure if you really need a keyby, your load will be
distributed among your map function without it. But could you explain
a bit what is your sink doing?
As for setting parallelism on the consumer remember that you wont have
higher parallelism than number of partitions in your topic.
If you have 240 partitions that's fine, but if you have less than
other subtasks will be idle. Only one task can read from one partition
in parallel.
On Tue, Apr 18, 2017 at 3:38 PM Telco Phone <[email protected]
<mailto:[email protected]>> wrote:
I am trying to use the task number as a keyby value to help fan
out the work load reading from kafka.
Given:
DataStream<SchemaRecord> stream =
env.addSource(new
FlinkKafkaConsumer010<SchemaRecord>("topicA", schema, properties)
).setParallelism(240).flatMap(new
SchemaRecordSplit()).setParallelism(240).
name("TopicA splitter").keyBy("partition",
"keyByHelper", "schemaId");
stream.addSink(new CustomMaprFsSink()).name("TopicA
Sink").setParallelism(240);
In the DeserialClass I am trying to get to the
getRuntimeContext().getIndexOfThisSubtask();
Which is only avaliable in the RichSinkFunction
The above is partition (by hour) , schemaID (avro schemaId) and I
would like to add the task number so that all 240 readers /
writers have something to do.
Any ideas ?
