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
