hi sunfulin, As Kurt pointed out, if you use RocksDB state backend, maybe slow disk IO bound your job. You can check WindowOperator's latency metric to see how long it tasks to process an element. Hope this helps.
sunfulin <sunfulin0...@163.com> 于2020年1月8日周三 下午4:04写道： > Ah, I had checked resource usage and GC from flink dashboard. Seem that > the reason is not cpu or memory issue. Task heap memory usage is less then > 30%. Could you kindly tell that how I can see more metrics to help target > the bottleneck? > Really appreciated that. > > > > > > At 2020-01-08 15:59:17, "Kurt Young" <ykt...@gmail.com> wrote: > > Hi, > > Could you try to find out what's the bottleneck of your current job? This > would leads to > different optimizations. Such as whether it's CPU bounded, or you have too > big local > state thus stuck by too many slow IOs. > > Best, > Kurt > > > On Wed, Jan 8, 2020 at 3:53 PM 贺小令 <godfre...@gmail.com> wrote: > >> hi sunfulin, >> you can try with blink planner (since 1.9 +), which optimizes distinct >> aggregation. you can also try to enable >> *table.optimizer.distinct-agg.split.enabled* if the data is skew. >> >> best, >> godfreyhe >> >> sunfulin <sunfulin0...@163.com> 于2020年1月8日周三 下午3:39写道： >> >>> Hi, community, >>> I'm using Apache Flink SQL to build some of my realtime streaming apps. >>> With one scenario I'm trying to count(distinct deviceID) over about 100GB >>> data set in realtime, and aggregate results with sink to ElasticSearch >>> index. I met a severe performance issue when running my flink job. Wanner >>> get some help from community. >>> >>> >>> Flink version : 1.8.2 >>> Running on yarn with 4 yarn slots per task manager. My flink task >>> parallelism is set to be 10, which is equal to my kafka source partitions. >>> After running the job, I can observe high backpressure from the flink >>> dashboard. Any suggestions and kind of help is highly appreciated. >>> >>> >>> running sql is like the following: >>> >>> >>> INSERT INTO ES6_ZHANGLE_OUTPUT(aggId, pageId, ts, expoCnt, clkCnt) >>> >>> select aggId, pageId, statkey as ts, sum(cnt) as expoCnt, count(cnt) as >>> clkCnt from >>> >>> ( >>> >>> SELECT >>> >>> aggId, >>> >>> pageId, >>> >>> statkey, >>> >>> COUNT(DISTINCT deviceId) as cnt >>> >>> FROM >>> >>> ( >>> >>> SELECT >>> >>> 'ZL_005' as aggId, >>> >>> 'ZL_UV_PER_MINUTE' as pageId, >>> >>> deviceId, >>> >>> ts2Date(recvTime) as statkey >>> >>> from >>> >>> kafka_zl_etrack_event_stream >>> >>> ) >>> >>> GROUP BY aggId, pageId, statkey, MOD(hashCode(deviceId), 1024) >>> >>> ) as t1 >>> >>> group by aggId, pageId, statkey >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> Best >> >> > > > -- Benchao Li School of Electronics Engineering and Computer Science, Peking University Tel:+86-15650713730 Email: libenc...@gmail.com; libenc...@pku.edu.cn
