Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

So I think ramdisk is simple way to try. Besides I think Reynold's suggestion is quite valid, with such high-end machine, putting everything in memory might not improve the performance a lot as assumed. Since bottleneck will be shifted, like memory bandwidth, NUMA, CPU efficiency

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

Yes we see it on final write. Our preference is to eliminate this. On Fri, Apr 1, 2016, 7:25 PM Saisai Shao wrote: > Hi Michael, shuffle data (mapper output) have to be materialized into disk > finally, no matter how large memory you have, it is the design purpose of >

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

Hi Michael, shuffle data (mapper output) have to be materialized into disk finally, no matter how large memory you have, it is the design purpose of Spark. In you scenario, since you have a big memory, shuffle spill should not happen frequently, most of the disk IO you see might be final shuffle

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

Shuffling a 1tb set of keys and values (aka sort by key) results in about 500gb of io to disk if compression is enabled. Is there any way to eliminate shuffling causing io? On Fri, Apr 1, 2016, 6:32 PM Reynold Xin wrote: > Michael - I'm not sure if you actually read my

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

r a test writing to RAM Disk if that configuration is > available. > > Thanks > > Yong > > -- > From: r...@databricks.com > Date: Fri, 1 Apr 2016 15:32:23 -0700 > Subject: Re: Eliminating shuffle write and spill disk IO reads/writes in > Spark > To:

RE: Eliminating shuffle write and spill disk IO reads/writes in Spark

00 Subject: Re: Eliminating shuffle write and spill disk IO reads/writes in Spark To: slavi...@gmail.com CC: mri...@gmail.com; d...@spark.apache.org; user@spark.apache.org Michael - I'm not sure if you actually read my email, but spill has nothing to do with the shuffle fil

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

Michael - I'm not sure if you actually read my email, but spill has nothing to do with the shuffle files on disk. It was for the partitioning (i.e. sorting) process. If that flag is off, Spark will just run out of memory when data doesn't fit in memory. On Fri, Apr 1, 2016 at 3:28 PM, Michael

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

RAMdisk is a fine interim step but there is a lot of layers eliminated by keeping things in memory unless there is need for spillover. At one time there was support for turning off spilling. That was eliminated. Why? On Fri, Apr 1, 2016, 6:05 PM Mridul Muralidharan wrote:

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

I think Reynold's suggestion of using ram disk would be a good way to test if these are the bottlenecks or something else is. For most practical purposes, pointing local dir to ramdisk should effectively give you 'similar' performance as shuffling from memory. Are there concerns with taking that

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

I totally disagree that it’s not a problem. - Network fetch throughput on 40G Ethernet exceeds the throughput of NVME drives. - What Spark is depending on is Linux’s IO cache as an effective buffer pool This is fine for small jobs but not for jobs with datasets in the TB/node range. - On

Re: Eliminating shuffle write and spill disk IO reads/writes in Spark

spark.shuffle.spill actually has nothing to do with whether we write shuffle files to disk. Currently it is not possible to not write shuffle files to disk, and typically it is not a problem because the network fetch throughput is lower than what disks can sustain. In most cases, especially with