First could you check whether the added filter conditions are executed before join operators? If they are already pushed down and executed before join, it's should be some real join keys generating data skew.
Best, Kurt On Tue, Jan 14, 2020 at 5:09 AM Eva Eva <eternalsunshine2...@gmail.com> wrote: > Hi Kurt, > > Assuming I'm joining two tables, "latestListings" and "latestAgents" like > below: > > "SELECT * FROM latestListings l " + > "LEFT JOIN latestAgents aa ON l.listAgentKeyL = aa.ucPKA " + > "LEFT JOIN latestAgents ab ON l.buyerAgentKeyL = ab.ucPKA " + > "LEFT JOIN latestAgents ac ON l.coListAgentKeyL = ac.ucPKA " + > > > In order to avoid joining on NULL keys, are you suggesting that I change > the query as below: > > "SELECT * FROM latestListings l " + > "LEFT JOIN latestAgents aa ON l.listAgentKeyL = aa.ucPKA AND > l.listAgentKeyL IS NOT NULL " + > > "LEFT JOIN latestAgents ab ON l.buyerAgentKeyL = ab.ucPKA AND > l.buyerAgentKeyL IS NOT NULL " + > > "LEFT JOIN latestAgents ac ON l.coListAgentKeyL = ac.ucPKA AND > l.coListAgentKeyL IS NOT NULL" + > > > I tried this but noticed that it didn't work as the data skew (and heavy load > on one task) continued. Could you please let me know if I missed anything? > > > Thanks, > > Eva > > > On Sun, Jan 12, 2020 at 8:44 PM Kurt Young <ykt...@gmail.com> wrote: > >> Hi, >> >> You can try to filter NULL values with an explicit condition like "xxxx >> is not NULL". >> >> Best, >> Kurt >> >> >> On Sat, Jan 11, 2020 at 4:10 AM Eva Eva <eternalsunshine2...@gmail.com> >> wrote: >> >>> Thank you both for the suggestions. >>> I did a bit more analysis using UI and identified at least one >>> problem that's occurring with the job rn. Going to fix it first and then >>> take it from there. >>> >>> *Problem that I identified:* >>> I'm running with 26 parallelism. For the checkpoints that are expiring, >>> one of a JOIN operation is finishing at 25/26 (96%) progress with >>> corresponding SubTask:21 has "n/a" value. For the same operation I also >>> noticed that the load is distributed poorly with heavy load being fed to >>> SubTask:21. >>> My guess is bunch of null values are happening for this JOIN operation >>> and being put into the same task. >>> Currently I'm using SQL query which gives me limited control on handling >>> null values so I'll try to programmatically JOIN and see if I can avoid >>> JOIN operation whenever the joining value is null. This should help with >>> better load distribution across subtasks. And may also fix expiring >>> checkpointing issue. >>> >>> Thanks for the guidance. >>> Eva. >>> >>> On Fri, Jan 10, 2020 at 7:44 AM Congxian Qiu <qcx978132...@gmail.com> >>> wrote: >>> >>>> Hi >>>> >>>> For expired checkpoint, you can find something like " Checkpoint xxx of >>>> job xx expired before completing" in jobmanager.log, then you can go to the >>>> checkpoint UI to find which tasks did not ack, and go to these tasks to see >>>> what happened. >>>> >>>> If checkpoint was been declined, you can find something like "Decline >>>> checkpoint xxx by task xxx of job xxx at xxx." in jobmanager.log, in this >>>> case, you can go to the task directly to find out why the checkpoint >>>> failed. >>>> >>>> Best, >>>> Congxian >>>> >>>> >>>> Yun Tang <myas...@live.com> 于2020年1月10日周五 下午7:31写道： >>>> >>>>> Hi Eva >>>>> >>>>> If checkpoint failed, please view the web UI or jobmanager log to see >>>>> why checkpoint failed, might be declined by some specific task. >>>>> >>>>> If checkpoint expired, you can also access the web UI to see which >>>>> tasks did not respond in time, some hot task might not be able to respond >>>>> in time. Generally speaking, checkpoint expired is mostly caused by back >>>>> pressure which led the checkpoint barrier did not arrive in time. Resolve >>>>> the back pressure could help the checkpoint finished before timeout. >>>>> >>>>> I think the doc of monitoring web UI for checkpoint [1] and back >>>>> pressure [2] could help you. >>>>> >>>>> [1] >>>>> https://ci.apache.org/projects/flink/flink-docs-release-1.9/monitoring/checkpoint_monitoring.html >>>>> [2] >>>>> https://ci.apache.org/projects/flink/flink-docs-release-1.9/monitoring/back_pressure.html >>>>> >>>>> Best >>>>> Yun Tang >>>>> ------------------------------ >>>>> *From:* Eva Eva <eternalsunshine2...@gmail.com> >>>>> *Sent:* Friday, January 10, 2020 10:29 >>>>> *To:* user <user@flink.apache.org> >>>>> *Subject:* Please suggest helpful tools >>>>> >>>>> Hi, >>>>> >>>>> I'm running Flink job on 1.9 version with blink planner. >>>>> >>>>> My checkpoints are timing out intermittently, but as state grows they >>>>> are timing out more and more often eventually killing the job. >>>>> >>>>> Size of the state is large with Minimum=10.2MB and Maximum=49GB (this >>>>> one is accumulated due to prior failed ones), Average=8.44GB. >>>>> >>>>> Although size is huge, I have enough space on EC2 instance in which >>>>> I'm running job. I'm using RocksDB for checkpointing. >>>>> >>>>> *Logs does not have any useful information to understand why >>>>> checkpoints are expiring/failing, can someone please point me to tools >>>>> that >>>>> can be used to investigate and understand why checkpoints are failing.* >>>>> >>>>> Also any other related suggestions are welcome. >>>>> >>>>> >>>>> Thanks, >>>>> Reva. >>>>> >>>>
