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I am facing some kafka source imbalance issue, can...
# troubleshooting
r
I am facing some kafka source imbalance issue, can someone tell me the strategy or point to code how flink decides to map which Topic’s partition<>subtask I am consuming from 2 topic and both have different inrates.. Below is what Flink is doing Topic 1.- 10 partitions Topic 2 - 5 partitons Question - Why subtask 4 is not consuming anything from Topic1?
z
hey @raghav tandon!
When consuming data from Kafka, Flink uses a partitioning strategy to determine which subtask should consume which partitions of the topic. The default partitioning strategy used by Flink is the "Round-Robin" partitioning strategy, which distributes the partitions of the topic evenly among all available subtasks.
This means that in your case, where you have 2 topics with 10 and 5 partitions respectively, Flink will distribute the 10 partitions of Topic 1 evenly among all subtasks, and the 5 partitions of Topic 2 evenly among all subtasks.
It's possible that the imbalance you are seeing with subtask 4 not consuming anything from Topic 1 is due to a difference in the rate of data being produced and consumed between the two topics. If Topic 2 has a higher rate of data being produced than Topic 1, the subtasks that are responsible for consuming Topic 2 may be processing data at a faster rate than the subtasks that are responsible for consuming Topic 1.
To avoid this, you can try the following strategies: 1. Increasing the parallelism of your Flink job: By increasing the number of subtasks, you can ensure that each topic is being consumed by more than one subtask. 2. Using a custom partitioning strategy: By implementing a custom partitioning strategy, you can control how the partitions of the topic are distributed among the subtasks. 3. Using a different consumer group for each topic: This will allow each subtask to consume data independently from the other subtasks and would ensure an even distribution of data. 4. Using Kafka's own consumer rebalancing logic: Kafka will rebalance the partitions among the consumers, this will be a useful option if you have a lot of topics with different partition count and you want to avoid custom partitioning.
You can find more information on how to implement custom partitioning strategies in the Flink documentation, or you can check the Flink Kafka source code to understand how the partitioning is done
r
@Zsombor Chikán Thanks for the reply, but as you said it should evenly distribute partitions in all subtasks, then i am not understanding that why subtask 4 has no partitions from Topic 1?
And as i already said inrate between Topic 1 and 2 is very different, so I understand why imbalance is there … But I want the answer to first part itself
z
It's possible that the imbalance you're seeing is due to the difference in the rate of incoming data between Topic1 and Topic2. Since subtask 4 is already busy consuming data from Topic2, it may not have capacity to consume more data from Topic1. Additionally, the round-robin partition assignment strategy can lead to imbalances if the number of partitions and subtasks do not match up evenly. For example, if there are 10 partitions and 5 subtasks, then some subtasks will be assigned 2 partitions and others will be assigned 1 partition. In this case, subtask 4 would be assigned 1 partition and the remaining 9 partitions will be assigned to the other subtasks. Another potential issue could be, if there is a failure or a shutdown of subtask 4 while running, then Flink will reassign the partitions from the failed subtask to other running subtasks, and the reassigned partitions may be from Topic 1 or Topic 2, so that might be the reason why subtask 4 is not consuming anything from Topic1. I would recommend checking the logs and metrics of your Flink job to see if there is any error or if subtask 4 is running and healthy, also check the overall partition assignment distribution among the subtasks and see if it's balanced or not.
r
Ok.. Thanks for the pointers..
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