Hi Ken, It may be also related to Grid Engine job scheduling? If it is 16 core (virtual cores?), grid engine allocates 16 slots, If you use 'max' scheduling, it will send 16 processes sequentially to same machine, on the top of it each spark job has its own executors. Limit the number of jobs scheduled to the machine = number of physical cores of single CPU, it will solve the problem if it is related to GE. If you are sure it's related to Spark, please ignore.
-Sudhir Sent from my iPhone > On Jun 15, 2016, at 8:53 AM, Gene Pang <gene.p...@gmail.com> wrote: > > As Sven mentioned, you can use Alluxio to store RDDs in off-heap memory, and > you can then share that RDD across different jobs. If you would like to run > Spark on Alluxio, this documentation can help: > http://www.alluxio.org/documentation/master/en/Running-Spark-on-Alluxio.html > > Thanks, > Gene > >> On Tue, Jun 14, 2016 at 12:44 AM, agateaaa <agate...@gmail.com> wrote: >> Hi, >> >> I am seeing this issue too with pyspark (Using Spark 1.6.1). I have set >> spark.executor.cores to 1, but I see that whenever streaming batch starts >> processing data, see python -m pyspark.daemon processes increase gradually >> to about 5, (increasing CPU% on a box about 4-5 times, each pyspark.daemon >> takes up around 100 % CPU) >> >> After the processing is done 4 pyspark.daemon processes go away and we are >> left with one till the next batch run. Also sometimes the CPU usage for >> executor process spikes to about 800% even though spark.executor.core is set >> to 1 >> >> e.g. top output >> PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND >> 19634 spark 20 0 8871420 1.790g 32056 S 814.1 2.9 0:39.33 >> /usr/lib/j+ <--EXECUTOR >> >> 13897 spark 20 0 46576 17916 6720 S 100.0 0.0 0:00.17 python >> -m + <--pyspark.daemon >> 13991 spark 20 0 46524 15572 4124 S 98.0 0.0 0:08.18 python >> -m + <--pyspark.daemon >> 14488 spark 20 0 46524 15636 4188 S 98.0 0.0 0:07.25 python >> -m + <--pyspark.daemon >> 14514 spark 20 0 46524 15636 4188 S 94.0 0.0 0:06.72 python >> -m + <--pyspark.daemon >> 14526 spark 20 0 48200 17172 4092 S 0.0 0.0 0:00.38 python >> -m + <--pyspark.daemon >> >> >> >> Is there any way to control the number of pyspark.daemon processes that get >> spawned ? >> >> Thank you >> Agateaaa >> >>> On Sun, Mar 27, 2016 at 1:08 AM, Sven Krasser <kras...@gmail.com> wrote: >>> Hey Ken, >>> >>> 1. You're correct, cached RDDs live on the JVM heap. (There's an off-heap >>> storage option using Alluxio, formerly Tachyon, with which I have no >>> experience however.) >>> >>> 2. The worker memory setting is not a hard maximum unfortunately. What >>> happens is that during aggregation the Python daemon will check its process >>> size. If the size is larger than this setting, it will start spilling to >>> disk. I've seen many occasions where my daemons grew larger. Also, you're >>> relying on Python's memory management to free up space again once objects >>> are evicted. In practice, leave this setting reasonably small but make sure >>> there's enough free memory on the machine so you don't run into OOM >>> conditions. If the lower memory setting causes strains for your users, make >>> sure they increase the parallelism of their jobs (smaller partitions >>> meaning less data is processed at a time). >>> >>> 3. I believe that is the behavior you can expect when setting >>> spark.executor.cores. I've not experimented much with it and haven't looked >>> at that part of the code, but what you describe also reflects my >>> understanding. Please share your findings here, I'm sure those will be very >>> helpful to others, too. >>> >>> One more suggestion for your users is to move to the Pyspark DataFrame API. >>> Much of the processing will then happen in the JVM, and you will bump into >>> fewer Python resource contention issues. >>> >>> Best, >>> -Sven >>> >>> >>>> On Sat, Mar 26, 2016 at 1:38 PM, Carlile, Ken <carli...@janelia.hhmi.org> >>>> wrote: >>>> This is extremely helpful! >>>> >>>> I’ll have to talk to my users about how the python memory limit should be >>>> adjusted and what their expectations are. I’m fairly certain we bumped it >>>> up in the dark past when jobs were failing because of insufficient memory >>>> for the python processes. >>>> >>>> So just to make sure I’m understanding correctly: >>>> >>>> JVM memory (set by SPARK_EXECUTOR_MEMORY and/or SPARK_WORKER_MEMORY?) is >>>> where the RDDs are stored. Currently both of those values are set to 90GB >>>> spark.python.worker.memory controls how much RAM each python task can take >>>> maximum (roughly speaking. Currently set to 4GB >>>> spark.task.cpus controls how many java worker threads will exist and thus >>>> indirectly how many pyspark daemon processes will exist >>>> >>>> I’m also looking into fixing my cron jobs so they don’t stack up by >>>> implementing flock in the jobs and changing how teardowns of the spark >>>> cluster work as far as failed workers. >>>> >>>> Thanks again, >>>> —Ken >>>> >>>>> On Mar 26, 2016, at 4:08 PM, Sven Krasser <kras...@gmail.com> wrote: >>>>> >>>>> My understanding is that the spark.executor.cores setting controls the >>>>> number of worker threads in the executor in the JVM. Each worker thread >>>>> communicates then with a pyspark daemon process (these are not threads) >>>>> to stream data into Python. There should be one daemon process per worker >>>>> thread (but as I mentioned I sometimes see a low multiple). >>>>> >>>>> Your 4GB limit for Python is fairly high, that means even for 12 workers >>>>> you're looking at a max of 48GB (and it goes frequently beyond that). You >>>>> will be better off using a lower number there and instead increasing the >>>>> parallelism of your job (i.e. dividing the job into more and smaller >>>>> partitions). >>>>> >>>>>> On Sat, Mar 26, 2016 at 7:10 AM, Carlile, Ken >>>>>> <carli...@janelia.hhmi.org> wrote: >>>>>> Thanks, Sven! >>>>>> >>>>>> I know that I’ve messed up the memory allocation, but I’m trying not to >>>>>> think too much about that (because I’ve advertised it to my users as >>>>>> “90GB for Spark works!” and that’s how it displays in the Spark UI >>>>>> (totally ignoring the python processes). So I’ll need to deal with that >>>>>> at some point… esp since I’ve set the max python memory usage to 4GB to >>>>>> work around other issues! >>>>>> >>>>>> The load issue comes in because we have a lot of background cron jobs >>>>>> (mostly to clean up after spark…), and those will stack up behind the >>>>>> high load and keep stacking until the whole thing comes crashing down. I >>>>>> will look into how to avoid this stacking, as I think one of my >>>>>> predecessors had a way, but that’s why the high load nukes the nodes. I >>>>>> don’t have the spark.executor.cores set, but will setting that to say, >>>>>> 12 limit the pyspark threads, or will it just limit the jvm threads? >>>>>> >>>>>> Thanks! >>>>>> Ken >>>>>> >>>>>>> On Mar 25, 2016, at 9:10 PM, Sven Krasser <kras...@gmail.com> wrote: >>>>>>> >>>>>>> Hey Ken, >>>>>>> >>>>>>> I also frequently see more pyspark daemons than configured concurrency, >>>>>>> often it's a low multiple. (There was an issue pre-1.3.0 that caused >>>>>>> this to be quite a bit higher, so make sure you at least have a recent >>>>>>> version; see SPARK-5395.) >>>>>>> >>>>>>> Each pyspark daemon tries to stay below the configured memory limit >>>>>>> during aggregation (which is separate from the JVM heap as you note). >>>>>>> Since the number of daemons can be high and the memory limit is per >>>>>>> daemon (each daemon is actually a process and not a thread and >>>>>>> therefore has its own memory it tracks against the configured >>>>>>> per-worker limit), I found memory depletion to be the main source of >>>>>>> pyspark problems on larger data sets. Also, as Sea already noted the >>>>>>> memory limit is not firm and individual daemons can grow larger. >>>>>>> >>>>>>> With that said, a run queue of 25 on a 16 core machine does not sound >>>>>>> great but also not awful enough to knock it offline. I suspect >>>>>>> something else may be going on. If you want to limit the amount of work >>>>>>> running concurrently, try reducing spark.executor.cores (under normal >>>>>>> circumstances this would leave parts of your resources underutilized). >>>>>>> >>>>>>> Hope this helps! >>>>>>> -Sven >>>>>>> >>>>>>> >>>>>>>> On Fri, Mar 25, 2016 at 10:41 AM, Carlile, Ken >>>>>>>> <carli...@janelia.hhmi.org> wrote: >>>>>>>> Further data on this. >>>>>>>> I’m watching another job right now where there are 16 pyspark.daemon >>>>>>>> threads, all of which are trying to get a full core (remember, this is >>>>>>>> a 16 core machine). Unfortunately , the java process actually running >>>>>>>> the spark worker is trying to take several cores of its own, driving >>>>>>>> the load up. I’m hoping someone has seen something like this. >>>>>>>> >>>>>>>> —Ken >>>>>>>> >>>>>>>>> On Mar 21, 2016, at 3:07 PM, Carlile, Ken <carli...@janelia.hhmi.org> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>> No further input on this? I discovered today that the pyspark.daemon >>>>>>>>> threadcount was actually 48, which makes a little more sense (at >>>>>>>>> least it’s a multiple of 16), and it seems to be happening at reduce >>>>>>>>> and collect portions of the code. >>>>>>>>> >>>>>>>>> —Ken >>>>>>>>> >>>>>>>>>> On Mar 17, 2016, at 10:51 AM, Carlile, Ken >>>>>>>>>> <carli...@janelia.hhmi.org> wrote: >>>>>>>>>> >>>>>>>>>> Thanks! I found that part just after I sent the email… whoops. I’m >>>>>>>>>> guessing that’s not an issue for my users, since it’s been set that >>>>>>>>>> way for a couple of years now. >>>>>>>>>> >>>>>>>>>> The thread count is definitely an issue, though, since if enough >>>>>>>>>> nodes go down, they can’t schedule their spark clusters. >>>>>>>>>> >>>>>>>>>> —Ken >>>>>>>>>>> On Mar 17, 2016, at 10:50 AM, Ted Yu <yuzhih...@gmail.com> wrote: >>>>>>>>>>> >>>>>>>>>>> I took a look at docs/configuration.md >>>>>>>>>>> Though I didn't find answer for your first question, I think the >>>>>>>>>>> following pertains to your second question: >>>>>>>>>>> >>>>>>>>>>> <tr> >>>>>>>>>>> <td><code>spark.python.worker.memory</code></td> >>>>>>>>>>> <td>512m</td> >>>>>>>>>>> <td> >>>>>>>>>>> Amount of memory to use per python worker process during >>>>>>>>>>> aggregation, in the same >>>>>>>>>>> format as JVM memory strings (e.g. <code>512m</code>, >>>>>>>>>>> <code>2g</code>). If the memory >>>>>>>>>>> used during aggregation goes above this amount, it will spill >>>>>>>>>>> the data into disks. >>>>>>>>>>> </td> >>>>>>>>>>> </tr> >>>>>>>>>>> >>>>>>>>>>>> On Thu, Mar 17, 2016 at 7:43 AM, Carlile, Ken >>>>>>>>>>>> <carli...@janelia.hhmi.org> wrote: >>>>>>>>>>>> Hello, >>>>>>>>>>>> >>>>>>>>>>>> We have an HPC cluster that we run Spark jobs on using standalone >>>>>>>>>>>> mode and a number of scripts I’ve built up to dynamically schedule >>>>>>>>>>>> and start spark clusters within the Grid Engine framework. Nodes >>>>>>>>>>>> in the cluster have 16 cores and 128GB of RAM. >>>>>>>>>>>> >>>>>>>>>>>> My users use pyspark heavily. We’ve been having a number of >>>>>>>>>>>> problems with nodes going offline with extraordinarily high load. >>>>>>>>>>>> I was able to look at one of those nodes today before it went >>>>>>>>>>>> truly sideways, and I discovered that the user was running 50 >>>>>>>>>>>> pyspark.daemon threads (remember, this is a 16 core box), and the >>>>>>>>>>>> load was somewhere around 25 or so, with all CPUs maxed out at >>>>>>>>>>>> 100%. >>>>>>>>>>>> >>>>>>>>>>>> So while the spark worker is aware it’s only got 16 cores and >>>>>>>>>>>> behaves accordingly, pyspark seems to be happy to overrun >>>>>>>>>>>> everything like crazy. Is there a global parameter I can use to >>>>>>>>>>>> limit pyspark threads to a sane number, say 15 or 16? It would >>>>>>>>>>>> also be interesting to set a memory limit, which leads to another >>>>>>>>>>>> question. >>>>>>>>>>>> >>>>>>>>>>>> How is memory managed when pyspark is used? I have the spark >>>>>>>>>>>> worker memory set to 90GB, and there is 8GB of system overhead >>>>>>>>>>>> (GPFS caching), so if pyspark operates outside of the JVM memory >>>>>>>>>>>> pool, that leaves it at most 30GB to play with, assuming there is >>>>>>>>>>>> no overhead outside the JVM’s 90GB heap (ha ha.) >>>>>>>>>>>> >>>>>>>>>>>> Thanks, >>>>>>>>>>>> Ken Carlile >>>>>>>>>>>> Sr. Unix Engineer >>>>>>>>>>>> HHMI/Janelia Research Campus >>>>>>>>>>>> 571-209-4363 >>>>>>>>>> >>>>>>>>>> Т���������������������������������������������������������������������ХF� >>>>>>>>>> V�7V'67&�&R� R�� �â W6W"�V�7V'67&�&T 7 &�� 6�R��&pФf�" FF�F��� � >>>>>>>>>> 6��� �G2� R�� �â W6W"ֆV� 7 &�� 6�R��&pР >>>>>>>>> >>>>>>>>> --------------------------------------------------------------------- >>>>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For >>>>>>>>> additional commands, e-mail: user-h...@spark.apache.org >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> www.skrasser.com >>>>> >>>>> >>>>> >>>>> -- >>>>> www.skrasser.com >>> >>> >>> >>> -- >>> www.skrasser.com >
