Thank you guys that was really helpful in identifying the slow step, which in our case is the leftouterjoin. I'm checking with our admins to see if we have some sort of distributed system monitoring in place, which I'm sure we do.
Now just out of curiosity, what would be the rule of thumb or general guideline for the number of partitions and the number of reducers? Should it be some kind of factor of the number of cores available? Of nodes available? Should the number of partitions match the number of reducers or at least be some multiple of it for better performance? Thanks, Yann From: Evan Sparks <[email protected]<mailto:[email protected]>> Reply-To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Date: Wednesday, January 8, 2014 5:28 PM To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Cc: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Subject: Re: performance On this note - the ganglia web front end that runs on the master (assuming you're launching with the ec2 scripts) is great for this. Also, a common technique for diagnosing "which step is slow" is to run a '.cache' and a '.count' on the RDD after each step. This forces the RDD to be materialized, which subverts the lazy evaluation that causes such diagnosis to be hard sometimes. - Evan On Jan 8, 2014, at 2:57 PM, Andrew Ash <[email protected]<mailto:[email protected]>> wrote: My first thought on hearing that you're calling collect is that taking all the data back to the driver is intensive on the network. Try checking the basic systems stuff on the machines to get a sense of what's being heavily used: disk IO CPU network Any kind of distributed system monitoring framework should be able to handle these sorts of things. Cheers! Andrew On Wed, Jan 8, 2014 at 1:49 PM, Yann Luppo <[email protected]<mailto:[email protected]>> wrote: Hi, I have what I hope is a simple question. What's a typical approach to diagnostic performance issues on a Spark cluster? We've followed all the pertinent parts of the following document already: http://spark.incubator.apache.org/docs/latest/tuning.html But we seem to still have issues. More specifically we have a leftouterjoin followed by a flatmap and then a collect running a bit long. How would I go about determining the bottleneck operation(s) ? Is our leftouterjoin taking a long time? Is the function we send to the flatmap not optimized? Thanks, Yann
