Re: Can reduced parallelism lead to no shuffle spill?

I am just using the above example to understand how Spark handles partitions -- Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ - To unsubscribe e-mail: user-unsubscr...@spark.apache.org

Re: Can reduced parallelism lead to no shuffle spill?

eives the 30 GB partition will only need 14 * 3 + 30 = 72 > gb and hence won't spill to disk. So in this case will reduced parallelism > lead to no shuffle spill? > > > > -- > Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ > >

Can reduced parallelism lead to no shuffle spill?

30 GB partition will only need 14 * 3 + 30 = 72 gb and hence won't spill to disk. So in this case will reduced parallelism lead to no shuffle spill? -- Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ ---

Shuffle Spill to Disk

Hello, I am trying to measure how many bytes spill to disk in shuffle operation and I get always zero. This is not correct because the spark local disk is utilized. Can anyone explain me why the spill counter is zero? Thanks, Iacovos -

Re: Iterative rdd union + reduceByKey operations on small dataset leads to "No space left on device" error on account of lot of shuffle spill.

Yeah, you are right. I ran the experiments locally not on YARN. On Fri, Jul 27, 2018 at 11:54 PM, Vadim Semenov wrote: > `spark.worker.cleanup.enabled=true` doesn't work for YARN. > On Fri, Jul 27, 2018 at 8:52 AM dineshdharme > wrote: > > > > I am trying to do few (union + reduceByKey) operati

Re: Iterative rdd union + reduceByKey operations on small dataset leads to "No space left on device" error on account of lot of shuffle spill.

`spark.worker.cleanup.enabled=true` doesn't work for YARN. On Fri, Jul 27, 2018 at 8:52 AM dineshdharme wrote: > > I am trying to do few (union + reduceByKey) operations on a hiearchical > dataset in a iterative fashion in rdd. The first few loops run fine but on > the subsequent loops, the operat

Iterative rdd union + reduceByKey operations on small dataset leads to "No space left on device" error on account of lot of shuffle spill.

I am trying to do few (union + reduceByKey) operations on a hiearchical dataset in a iterative fashion in rdd. The first few loops run fine but on the subsequent loops, the operations ends up using the whole scratch space provided to it. I have set the spark scratch directory, i.e. SPARK_LOCAL_DI

Shuffle Spill (Memory) greater than Shuffle Spill (Disk)

Hello! In my spark job, I see that Shuffle Spill (Memory) is greater than Shuffle Spill (Disk). spark.shuffle.compress parameter is left to default(true?). I would expect the size on disk to be smaller which isn't the case here. I've been having some performance issues as well and I su

Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

d.com <mailto:tony@tendcloud.com> > > From: Sun Rui <mailto:sunrise_...@163.com> > Date: 2016-08-24 22:17 > To: Saisai Shao <mailto:sai.sai.s...@gmail.com> > CC: tony@tendcloud.com <mailto:tony@tendcloud.com>; user > <mailto:user@spark.apa

Re: Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

loud.com > > > *From:* Sun Rui > *Date:* 2016-08-24 22:17 > *To:* Saisai Shao > *CC:* tony@tendcloud.com; user > *Subject:* Re: Can we redirect Spark shuffle spill data to HDFS or > Alluxio? > Yes, I also tried FUSE before, it is not stable and I don’t recommend it &

Re: Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

@tendcloud.com From: Sun Rui Date: 2016-08-24 22:17 To: Saisai Shao CC: tony@tendcloud.com; user Subject: Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio? Yes, I also tried FUSE before, it is not stable and I don’t recommend it On Aug 24, 2016, at 22:15, Saisai Shao wrote

Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

data, spark will do shuffle and the shuffle >> data will write to local disk. Because we have limited capacity at local >> disk, the shuffled data will occupied all of the local disk and then will be >> failed. So is there a

Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

at >> local disk, the shuffled data will occupied all of the local disk and then >> will be failed. So is there a way we can write the shuffle spill data to >> HDFS? Or if we introduce alluxio in our system, can the shuffled data write &g

Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

, All, > When we run Spark on very large data, spark will do shuffle and the shuffle > data will write to local disk. Because we have limited capacity at local > disk, the shuffled data will occupied all of the local disk and then will be > failed. So is there a way we can write the

Re: Can we redirect Spark shuffle spill data to HDFS or Alluxio?

large data, spark will do shuffle and the > shuffle data will write to local disk. Because we have limited capacity at > local disk, the shuffled data will occupied all of the local disk and then > will be failed. So is there a way we can write the shuffle spill data to > HDFS? Or i

Can we redirect Spark shuffle spill data to HDFS or Alluxio?

Hi, All, When we run Spark on very large data, spark will do shuffle and the shuffle data will write to local disk. Because we have limited capacity at local disk, the shuffled data will occupied all of the local disk and then will be failed. So is there a way we can write the shuffle spill

Re: lost executor due to large shuffle spill memory

t;mailto:lishu...@gmail.com>> wrote: > > > > I have a task to remap the index to actual uuid in ALS prediction results. > > But it consistently fail due to lost executors. I noticed there's large > > shuffle spill memory but I don't know how to improve it. > &

Re: lost executor due to large shuffle spill memory

> I have a task to remap the index to actual uuid in ALS prediction > results. > > But it consistently fail due to lost executors. I noticed there's large > > shuffle spill memory but I don't know how to improve it. > > > > <http://apache-spark-user-lis

Re: lost executor due to large shuffle spill memory

remap the index to actual uuid in ALS prediction results. > But it consistently fail due to lost executors. I noticed there's large > shuffle spill memory but I don't know how to improve it. > > <http://apache-spark-user-list.1001560.n3.nabble.com/file/n26683/24.png> &g

lost executor due to large shuffle spill memory

I have a task to remap the index to actual uuid in ALS prediction results. But it consistently fail due to lost executors. I noticed there's large shuffle spill memory but I don't know how to improve it. <http://apache-spark-user-list.1001560.n3.nabble.com/file/n26683/24.png>

Re: How to avoid Spark shuffle spill memory?

your code to make is use less memory. David On Tue, Oct 6, 2015 at 3:19 PM, unk1102 wrote: > Hi I have a Spark job which runs for around 4 hours and it shared > SparkContext and runs many child jobs. When I see each job in UI I see > shuffle spill of around 30 to 40 GB and because of

How to avoid Spark shuffle spill memory?

Hi I have a Spark job which runs for around 4 hours and it shared SparkContext and runs many child jobs. When I see each job in UI I see shuffle spill of around 30 to 40 GB and because of that many times executors gets lost because of using physical memory beyond limits how do I avoid shuffle

Re: Shuffle Spill Memory and Shuffle Spill Disk

Hi Bijay, The Shuffle Spill (Disk) is the total number of bytes written to disk by records spilled during the shuffle. The Shuffle Spill (Memory) is the amount of space the spilled records occupied in memory before they were spilled. These differ because the serialized format is more compact

Shuffle Spill Memory and Shuffle Spill Disk

Hello, I am running TeraSort <https://github.com/ehiggs/spark-terasort> on 100GB of data. The final metrics I am getting on Shuffle Spill are: Shuffle Spill(Memory): 122.5 GB Shuffle Spill(Disk): 3.4 GB What's the difference and relation between these two metrics? Does these mean 1

Shuffle Spill

Hello, I have a few tasks in a stage with lots of tasks that have a large amount of shuffle spill. I scouted the web to understand shuffle spill, and I did not find any simple explanation of the spill mechanism. What I put together is: 1. the shuffle spill can happens when the shuffle is

Re: What is shuffle spill to memory?

Shuffle spill (memory) is the size of the deserialized form of the data in memory at the time when we spill it, whereas shuffle spill (disk) is the size of the serialized form of the data on disk after we spill it. This is why the latter tends to be much smaller than the former. Note that both

What is shuffle spill to memory?

Hi, in the Spark UI, one of the metrics is "shuffle spill (memory)". What is it exactly? Spilling to disk when the shuffle data doesn't fit in memory I get it, but what does it mean to spill to memory? Thanks, - Sebastien

Re: Shuffle Spill Issue

zer, > 240MB dataset yield to around 70MB shuffle data. Only that shuffle Spill ( > memory ) is abnormal, and sounds to me should not trigger at all. And, by > the way, this behavior only occurs in map out side, on reduce / shuffle > fetch side, this strange behavior won't happen. &g

RE: Shuffle Spill Issue

] I have no idea why shuffle spill is so large. But this might make it smaller: val addition = (a: Int, b: Int) => a + b val wordsCount = wordsPair.combineByKey(identity, addition, addition) This way only one entry per distinct word will end up in the shuffle for each partition, instead of o

Re: Shuffle Spill Issue

I have no idea why shuffle spill is so large. But this might make it smaller: val addition = (a: Int, b: Int) => a + b val wordsCount = wordsPair.combineByKey(identity, addition, addition) This way only one entry per distinct word will end up in the shuffle for each partition, instead of

RE: Shuffle Spill Issue

Task Time Total Tasks Failed Tasks Succeeded Tasks Shuffle ReadShuffle Write Shuffle Spill (Memory) Shuffle Spill (Disk) 10 sr437:48527 35 s8 0 8 0.0 B 2.5 MB 2.2 GB 1291.2 KB 12 sr437:46077 34 s8 0 8 0.0 B 2.5

Re: Shuffle Spill Issue

> As you can read from part of the task metrics as below, I noticed > that the shuffle spill part of metrics indicate that there are something > wrong. > > Executor ID Address Task Time Total Tasks Failed Tasks > Succeeded Tasks Shuffle ReadShuffle Write S

Shuffle Spill Issue

) I run a very small data set (2.4GB on HDFS on total) to confirm the problem here as below: As you can read from part of the task metrics as below, I noticed that the shuffle spill part of metrics indicate that there are something wrong. Executor ID Address Task Time Total