This is an execution with 80 executors
MetricMin25th percentileMedian75th percentileMax
Duration 31s 44s 50s 1.1min 2.6 min
GC Time 70ms 0.1s 0.3s 4s 53 s
Input 128.0MB 128.0MB 128.0MB 128.0MB 128.0MB
I executed as well with 40 executors
MetricMin25th percentileMedian75th percentileMax
Duration 26s 28s 28s 30s 35s
GC Time 54ms 60ms 66ms 80ms 0.4 s
Input 128.0MB 128.0MB 128.0MB 128.0MB 128.0 MB
I checked the %iowait and %steal in a worker it's all right in both of them
I understand the value of yarn.nodemanager.resource.memory-mb is for
each worker in the cluster and not the total value for YARN. it's
configured at 196GB right now. (I have 5 workers)
80executors x 4Gb = 320Gb, it shouldn't be a problem.
2015-02-06 10:03 GMT+01:00 Sandy Ryza <sandy.r...@cloudera.com>:
> Yes, having many more cores than disks and all writing at the same time can
> definitely cause performance issues. Though that wouldn't explain the high
> GC. What percent of task time does the web UI report that tasks are
> spending in GC?
>
> On Fri, Feb 6, 2015 at 12:56 AM, Guillermo Ortiz <konstt2...@gmail.com>
> wrote:
>>
>> Yes, It's surpressing to me as well....
>>
>> I tried to execute it with different configurations,
>>
>> sudo -u hdfs spark-submit --master yarn-client --class
>> com.mycompany.app.App --num-executors 40 --executor-memory 4g
>> Example-1.0-SNAPSHOT.jar hdfs://ip:8020/tmp/sparkTest/ file22.bin
>> parameters
>>
>> This is what I executed with different values in num-executors and
>> executor-memory.
>> What do you think there are too many executors for those HDDs? Could
>> it be the reason because of each executor takes more time?
>>
>> 2015-02-06 9:36 GMT+01:00 Sandy Ryza <sandy.r...@cloudera.com>:
>> > That's definitely surprising to me that you would be hitting a lot of GC
>> > for
>> > this scenario. Are you setting --executor-cores and --executor-memory?
>> > What are you setting them to?
>> >
>> > -Sandy
>> >
>> > On Thu, Feb 5, 2015 at 10:17 AM, Guillermo Ortiz <konstt2...@gmail.com>
>> > wrote:
>> >>
>> >> Any idea why if I use more containers I get a lot of stopped because
>> >> GC?
>> >>
>> >> 2015-02-05 8:59 GMT+01:00 Guillermo Ortiz <konstt2...@gmail.com>:
>> >> > I'm not caching the data. with "each iteration I mean,, each 128mb
>> >> > that a executor has to process.
>> >> >
>> >> > The code is pretty simple.
>> >> >
>> >> > final Conversor c = new Conversor(null, null, null,
>> >> > longFields,typeFields);
>> >> > SparkConf conf = new SparkConf().setAppName("Simple Application");
>> >> > JavaSparkContext sc = new JavaSparkContext(conf);
>> >> > JavaRDD<byte[]> rdd = sc.binaryRecords(path, c.calculaLongBlock());
>> >> >
>> >> > JavaRDD<String> rddString = rdd.map(new Function<byte[], String>() {
>> >> > @Override
>> >> > public String call(byte[] arg0) throws Exception {
>> >> > String result = c.parse(arg0).toString();
>> >> > return result;
>> >> > }
>> >> > });
>> >> > rddString.saveAsTextFile(url + "/output/" +
>> >> > System.currentTimeMillis()+
>> >> > "/");
>> >> >
>> >> > The parse function just takes an array of bytes and applies some
>> >> > transformations like,,,
>> >> > [0..3] an integer, [4...20] an String, [21..27] another String and so
>> >> > on.
>> >> >
>> >> > It's just a test code, I'd like to understand what it's happeing.
>> >> >
>> >> > 2015-02-04 18:57 GMT+01:00 Sandy Ryza <sandy.r...@cloudera.com>:
>> >> >> Hi Guillermo,
>> >> >>
>> >> >> What exactly do you mean by "each iteration"? Are you caching data
>> >> >> in
>> >> >> memory?
>> >> >>
>> >> >> -Sandy
>> >> >>
>> >> >> On Wed, Feb 4, 2015 at 5:02 AM, Guillermo Ortiz
>> >> >> <konstt2...@gmail.com>
>> >> >> wrote:
>> >> >>>
>> >> >>> I execute a job in Spark where I'm processing a file of 80Gb in
>> >> >>> HDFS.
>> >> >>> I have 5 slaves:
>> >> >>> (32cores /256Gb / 7physical disks) x 5
>> >> >>>
>> >> >>> I have been trying many different configurations with YARN.
>> >> >>> yarn.nodemanager.resource.memory-mb 196Gb
>> >> >>> yarn.nodemanager.resource.cpu-vcores 24
>> >> >>>
>> >> >>> I have tried to execute the job with different number of executors
>> >> >>> a
>> >> >>> memory (1-4g)
>> >> >>> With 20 executors takes 25s each iteration (128mb) and it never has
>> >> >>> a
>> >> >>> really long time waiting because GC.
>> >> >>>
>> >> >>> When I execute around 60 executors the process time it's about 45s
>> >> >>> and
>> >> >>> some tasks take until one minute because GC.
>> >> >>>
>> >> >>> I have no idea why it's calling GC when I execute more executors
>> >> >>> simultaneously.
>> >> >>> The another question it's why it takes more time to execute each
>> >> >>> block. My theory about the this it's because there're only 7
>> >> >>> physical
>> >> >>> disks and it's not the same 5 processes writing than 20.
>> >> >>>
>> >> >>> The code is pretty simple, it's just a map function which parse a
>> >> >>> line
>> >> >>> and write the output in HDFS. There're a lot of substrings inside
>> >> >>> of
>> >> >>> the function what it could cause GC.
>> >> >>>
>> >> >>> Any theory about?
>> >> >>>
>> >> >>>
>> >> >>> ---------------------------------------------------------------------
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>> >> >>>
>> >> >>
>> >
>> >
>
>
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