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
