Hi,
We used both flink versions 1.9.1 and 1.10.1
We used rocksDB default configuration.
The streaming pipeline is very simple.
1. Kafka consumer
2. Process function
3. Kafka producer
The code of the process function is listed below:
private transient MapState<String, Object> testMapState;
@Override
public void processElement(Map<String, Object> value, Context ctx,
Collector<Map<String, Object>> out) throws Exception {
if (testMapState.isEmpty()) {
testMapState.putAll(value);
out.collect(value);
testMapState.clear();
}
}
We used the same code with ValueState and observed the same results.
BR,
Nick
בתאריך יום ג׳, 16 ביוני 2020 ב-11:56 מאת Yun Tang <myas...@live.com
>:
> Hi Nick
>
> It's really strange that performance could improve when checkpoint is
> enabled.
> In general, enable checkpoint might bring a bit performance downside to
> the whole job.
>
> Could you give more details e.g. Flink version, configurations of RocksDB
> and simple code which could reproduce this problem.
>
> Best
> Yun Tang
> ------------------------------
> *From:* nick toker <nick.toker....@gmail.com>
> *Sent:* Tuesday, June 16, 2020 15:44
> *To:* user@flink.apache.org <user@flink.apache.org>
> *Subject:* Improved performance when using incremental checkpoints
>
> Hello,
>
> We are using RocksDB as the backend state.
> At first we didn't enable the checkpoints mechanism.
>
> We observed the following behaviour and we are wondering why ?
>
> When using the rocksDB *without* checkpoint the performance was very
> extremely bad.
> And when we enabled the checkpoint the performance was improved by a*
> factor of 10*.
>
> Could you please explain if this behaviour is expected ?
> Could you please explain why enabling the checkpoint significantly
> improves the performance ?
>
> BR,
> Nick
>
