It should only apply to the map operator.
On 19.09.2017 17:38, AndreaKinn wrote:
If I apply a sharing slot as in the example:
DataStream LTzAccStream = env
.addSource(new FlinkKafkaConsumer010<>("topic",
new
CustomDeserializer(), properties))
If I apply a sharing slot as in the example:
DataStream LTzAccStream = env
.addSource(new FlinkKafkaConsumer010<>("topic",
new
CustomDeserializer(), properties))
.assignTimestampsAndWatermarks(new
CustomTimestampExtractor())
There is no notion of "full" in Flink except that one slot will run at most
one subtask of each operator.
The scheduling depends on the structure of the job, the parallelism of the
operators, and the number of slots per TM.
It's hard to tell without knowing the details.
2017-09-19 11:57 GMT+02:00
So Flink use the other nodes just if one is completely "full" ?
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Hi,
Flink's scheduling aims to co-located tasks to reduce network communication
and ease the reasoning about resource/slot consumption.
A slot can execute one subtask of each operator of a program, i.e, a
parallel slice of the program.
You can control the scheduling of tasks by specifying resourc
Hi,
I'm experimenting a bit with the cluster.
I didn't set any options about sharing slots and chains hoping that Flink
decided autonomously how to balance the load through the nodes of the
cluster. My cluster is composed by one job and task manager and two task
manager.
I noted that every time
