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From Reynold Xin <r...@databricks.com>
Subject Re: Unable to acquire memory errors in HiveCompatibilitySuite
Date Wed, 16 Sep 2015 16:27:30 GMT
SparkEnv for the driver was created in SparkContext. The default
parallelism field is set to the number of slots (max number of active
tasks). Maybe we can just use the default parallelism to compute that in
local mode.

On Wednesday, September 16, 2015, Pete Robbins <robbinspg@gmail.com> wrote:

> so forcing the ShuffleMemoryManager to assume 32 cores and therefore
> calculate a pagesize of 1MB passes the tests.
>
> How can we determine the correct value to use in getPageSize rather than
> Runtime.getRuntime.availableProcessors()?
>
> On 16 September 2015 at 10:17, Pete Robbins <robbinspg@gmail.com
> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>> wrote:
>
>> I see what you are saying. Full stack trace:
>>
>> java.io.IOException: Unable to acquire 4194304 bytes of memory
>>       at
>> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
>>       at
>> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPageIfNecessary(UnsafeExternalSorter.java:349)
>>       at
>> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.insertKVRecord(UnsafeExternalSorter.java:478)
>>       at
>> org.apache.spark.sql.execution.UnsafeKVExternalSorter.insertKV(UnsafeKVExternalSorter.java:138)
>>       at
>> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.switchToSortBasedAggregation(TungstenAggregationIterator.scala:489)
>>       at
>> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:379)
>>       at
>> org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622)
>>       at
>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$
>> 1.org
>> $apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110)
>>       at
>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
>>       at
>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
>>       at
>> org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:63)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:99)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:63)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:63)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>>       at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>>       at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>>       at
>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>       at org.apache.spark.scheduler.Task.run(Task.scala:88)
>>       at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>       at
>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1153)
>>       at
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
>>       at java.lang.Thread.run(Thread.java:785)
>>
>> On 16 September 2015 at 09:30, Reynold Xin <rxin@databricks.com
>> <javascript:_e(%7B%7D,'cvml','rxin@databricks.com');>> wrote:
>>
>>> Can you paste the entire stacktrace of the error? In your original email
>>> you only included the last function call.
>>>
>>> Maybe I'm missing something here, but I still think the bad heuristics
>>> is the issue.
>>>
>>> Some operators pre-reserve memory before running anything in order to
>>> avoid starvation. For example, imagine we have an aggregate followed by a
>>> sort. If the aggregate is very high cardinality, and uses up all the memory
>>> and even starts spilling (falling back to sort-based aggregate), there
>>> isn't memory available at all for the sort operator to use. To work around
>>> this, each operator reserves a page of memory before they process any data.
>>>
>>> Page size is computed by Spark using:
>>>
>>> the total amount of execution memory / (maximum number of active tasks *
>>> 16)
>>>
>>> and then rounded to the next power of 2, and cap between 1MB and 64MB.
>>>
>>> That is to say, in the worst case, we should be able to reserve at least
>>> 8 pages (16 rounded up to the next power of 2).
>>>
>>> However, in your case, the max number of active tasks is 32 (set by test
>>> env), while the page size is determined using # cores (8 in your case). So
>>> it is off by a factor of 4. As a result, with this page size, we can only
>>> reserve at least 2 pages. That is to say, if you have more than 3 operators
>>> that need page reservation (e.g. an aggregate followed by a join on the
>>> group by key followed by a shuffle - which seems to be the case of
>>> join31.q), the task can fail to reserve memory before running anything.
>>>
>>>
>>> There is a 2nd problem (maybe this is the one you were trying to point
>>> out?) that is tasks running at the same time can be competing for memory
>>> with each other.  Spark allows each task to claim up to 2/N share of
>>> memory, where N is the number of active tasks. If a task is launched before
>>> others and hogs a lot of memory quickly, the other tasks that are launched
>>> after it might not be able to get enough memory allocation, and thus will
>>> fail. This is not super ideal, but probably fine because tasks can be
>>> retried, and can succeed in retries.
>>>
>>>
>>> On Wed, Sep 16, 2015 at 1:07 AM, Pete Robbins <robbinspg@gmail.com
>>> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>> wrote:
>>>
>>>> ok so let me try again ;-)
>>>>
>>>> I don't think that the page size calculation matters apart from hitting
>>>> the allocation limit earlier if the page size is too large.
>>>>
>>>> If a task is going to need X bytes, it is going to need X bytes. In
>>>> this case, for at least one of the tasks, X > maxmemory/no_active_tasks
at
>>>> some point during execution. A smaller page size may use the memory more
>>>> efficiently but would not necessarily avoid this issue.
>>>>
>>>> The next question would be: Is the memory limit per task of
>>>> max_memory/no_active_tasks reasonable? It seems fair but if this limit is
>>>> reached currently an exception is thrown, maybe the task could wait for
>>>> no_active_tasks to decrease?
>>>>
>>>> I think what causes my test issue is that the 32 tasks don't execute as
>>>> quickly on my 8 core box so more are active at any one time.
>>>>
>>>> I will experiment with the page size calculation to see what effect it
>>>> has.
>>>>
>>>> Cheers,
>>>>
>>>>
>>>>
>>>> On 16 September 2015 at 06:53, Reynold Xin <rxin@databricks.com
>>>> <javascript:_e(%7B%7D,'cvml','rxin@databricks.com');>> wrote:
>>>>
>>>>> It is exactly the issue here, isn't it?
>>>>>
>>>>> We are using memory / N, where N should be the maximum number of
>>>>> active tasks. In the current master, we use the number of cores to
>>>>> approximate the number of tasks -- but it turned out to be a bad
>>>>> approximation in tests because it is set to 32 to increase concurrency.
>>>>>
>>>>>
>>>>> On Tue, Sep 15, 2015 at 10:47 PM, Pete Robbins <robbinspg@gmail.com
>>>>> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>> wrote:
>>>>>
>>>>>> Oops... I meant to say "The page size calculation is NOT the issue
>>>>>> here"
>>>>>>
>>>>>> On 16 September 2015 at 06:46, Pete Robbins <robbinspg@gmail.com
>>>>>> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>> wrote:
>>>>>>
>>>>>>> The page size calculation is the issue here as there is plenty
of
>>>>>>> free memory, although there is maybe a fair bit of wasted space
in some
>>>>>>> pages. It is that when we have a lot of tasks each is only allowed
to reach
>>>>>>> 1/n of the available memory and several of the tasks bump in
to that limit.
>>>>>>> With tasks 4 times the number of cores there will be some contention
and so
>>>>>>> they remain active for longer.
>>>>>>>
>>>>>>> So I think this is a test case issue configuring the number of
>>>>>>> executors too high.
>>>>>>>
>>>>>>> On 15 September 2015 at 18:54, Reynold Xin <rxin@databricks.com
>>>>>>> <javascript:_e(%7B%7D,'cvml','rxin@databricks.com');>>
wrote:
>>>>>>>
>>>>>>>> Maybe we can change the heuristics in memory calculation
to use
>>>>>>>> SparkContext.defaultParallelism if it is local mode.
>>>>>>>>
>>>>>>>>
>>>>>>>> On Tue, Sep 15, 2015 at 10:28 AM, Pete Robbins <robbinspg@gmail.com
>>>>>>>> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>>
wrote:
>>>>>>>>
>>>>>>>>> Yes and at least there is an override by setting
>>>>>>>>> spark.sql.test.master to local[8] , in fact local[16]
worked on my 8 core
>>>>>>>>> box.
>>>>>>>>>
>>>>>>>>> I'm happy to use this as a workaround but the 32 hard-coded
will
>>>>>>>>> fail running build/tests on a clean checkout if you only
have 8 cores.
>>>>>>>>>
>>>>>>>>> On 15 September 2015 at 17:40, Marcelo Vanzin <vanzin@cloudera.com
>>>>>>>>> <javascript:_e(%7B%7D,'cvml','vanzin@cloudera.com');>>
wrote:
>>>>>>>>>
>>>>>>>>>> That test explicitly sets the number of executor
cores to 32.
>>>>>>>>>>
>>>>>>>>>> object TestHive
>>>>>>>>>>   extends TestHiveContext(
>>>>>>>>>>     new SparkContext(
>>>>>>>>>>       System.getProperty("spark.sql.test.master",
"local[32]"),
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Mon, Sep 14, 2015 at 11:22 PM, Reynold Xin <
>>>>>>>>>> rxin@databricks.com
>>>>>>>>>> <javascript:_e(%7B%7D,'cvml','rxin@databricks.com');>>
wrote:
>>>>>>>>>> > Yea I think this is where the heuristics is
failing -- it uses
>>>>>>>>>> 8 cores to
>>>>>>>>>> > approximate the number of active tasks, but
the tests somehow
>>>>>>>>>> is using 32
>>>>>>>>>> > (maybe because it explicitly sets it to that,
or you set it
>>>>>>>>>> yourself? I'm
>>>>>>>>>> > not sure which one)
>>>>>>>>>> >
>>>>>>>>>> > On Mon, Sep 14, 2015 at 11:06 PM, Pete Robbins
<
>>>>>>>>>> robbinspg@gmail.com
>>>>>>>>>> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>>
wrote:
>>>>>>>>>> >>
>>>>>>>>>> >> Reynold, thanks for replying.
>>>>>>>>>> >>
>>>>>>>>>> >> getPageSize parameters: maxMemory=515396075,
numCores=0
>>>>>>>>>> >> Calculated values: cores=8, default=4194304
>>>>>>>>>> >>
>>>>>>>>>> >> So am I getting a large page size as I only
have 8 cores?
>>>>>>>>>> >>
>>>>>>>>>> >> On 15 September 2015 at 00:40, Reynold Xin
<
>>>>>>>>>> rxin@databricks.com
>>>>>>>>>> <javascript:_e(%7B%7D,'cvml','rxin@databricks.com');>>
wrote:
>>>>>>>>>> >>>
>>>>>>>>>> >>> Pete - can you do me a favor?
>>>>>>>>>> >>>
>>>>>>>>>> >>>
>>>>>>>>>> >>>
>>>>>>>>>> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala#L174
>>>>>>>>>> >>>
>>>>>>>>>> >>> Print the parameters that are passed
into the getPageSize
>>>>>>>>>> function, and
>>>>>>>>>> >>> check their values.
>>>>>>>>>> >>>
>>>>>>>>>> >>> On Mon, Sep 14, 2015 at 4:32 PM, Reynold
Xin <
>>>>>>>>>> rxin@databricks.com
>>>>>>>>>> <javascript:_e(%7B%7D,'cvml','rxin@databricks.com');>>
wrote:
>>>>>>>>>> >>>>
>>>>>>>>>> >>>> Is this on latest master / branch-1.5?
>>>>>>>>>> >>>>
>>>>>>>>>> >>>> out of the box we reserve only 16%
(0.2 * 0.8) of the memory
>>>>>>>>>> for
>>>>>>>>>> >>>> execution (e.g. aggregate, join)
/ shuffle sorting. With a
>>>>>>>>>> 3GB heap, that's
>>>>>>>>>> >>>> 480MB. So each task gets 480MB /
32 = 15MB, and each
>>>>>>>>>> operator reserves at
>>>>>>>>>> >>>> least one page for execution. If
your page size is 4MB, it
>>>>>>>>>> only takes 3
>>>>>>>>>> >>>> operators to use up its memory.
>>>>>>>>>> >>>>
>>>>>>>>>> >>>> The thing is page size is dynamically
determined -- and in
>>>>>>>>>> your case it
>>>>>>>>>> >>>> should be smaller than 4MB.
>>>>>>>>>> >>>>
>>>>>>>>>> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala#L174
>>>>>>>>>> >>>>
>>>>>>>>>> >>>> Maybe there is a place that in the
maven tests that we
>>>>>>>>>> explicitly set
>>>>>>>>>> >>>> the page size (spark.buffer.pageSize)
to 4MB? If yes, we
>>>>>>>>>> need to find it and
>>>>>>>>>> >>>> just remove it.
>>>>>>>>>> >>>>
>>>>>>>>>> >>>>
>>>>>>>>>> >>>> On Mon, Sep 14, 2015 at 4:16 AM,
Pete Robbins <
>>>>>>>>>> robbinspg@gmail.com
>>>>>>>>>> <javascript:_e(%7B%7D,'cvml','robbinspg@gmail.com');>>
>>>>>>>>>> >>>> wrote:
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> I keep hitting errors running
the tests on 1.5 such as
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> - join31 *** FAILED ***
>>>>>>>>>> >>>>>   Failed to execute query using
catalyst:
>>>>>>>>>> >>>>>   Error: Job aborted due to
stage failure: Task 9 in stage
>>>>>>>>>> 3653.0
>>>>>>>>>> >>>>> failed 1 times, most recent
failure: Lost task 9.0 in stage
>>>>>>>>>> 3653.0 (TID
>>>>>>>>>> >>>>> 123363, localhost): java.io.IOException:
Unable to acquire
>>>>>>>>>> 4194304 bytes of
>>>>>>>>>> >>>>> memory
>>>>>>>>>> >>>>>       at
>>>>>>>>>> >>>>>
>>>>>>>>>> org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.acquireNewPage(UnsafeExternalSorter.java:368)
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> This is using the command
>>>>>>>>>> >>>>> build/mvn -Pyarn -Phadoop-2.2
-Phive -Phive-thriftserver
>>>>>>>>>> test
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> I don't see these errors in
any of the amplab jenkins
>>>>>>>>>> builds. Do those
>>>>>>>>>> >>>>> builds have any configuration/environment
that I may be
>>>>>>>>>> missing? My build is
>>>>>>>>>> >>>>> running with whatever defaults
are in the top level
>>>>>>>>>> pom.xml, eg -Xmx3G.
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> I can make these tests pass
by setting
>>>>>>>>>> spark.shuffle.memoryFraction=0.6
>>>>>>>>>> >>>>> in the HiveCompatibilitySuite
rather than the default 0.2
>>>>>>>>>> value.
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> Trying to analyze what is going
on with the test it is
>>>>>>>>>> related to the
>>>>>>>>>> >>>>> number of active tasks, which
seems to rise to 32, and so
>>>>>>>>>> the
>>>>>>>>>> >>>>> ShuffleMemoryManager allows
less memory per task even
>>>>>>>>>> though most of those
>>>>>>>>>> >>>>> tasks do not have any memory
allocated to them.
>>>>>>>>>> >>>>>
>>>>>>>>>> >>>>> Has anyone seen issues like
this before?
>>>>>>>>>> >>>>
>>>>>>>>>> >>>>
>>>>>>>>>> >>>
>>>>>>>>>> >>
>>>>>>>>>> >
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> Marcelo
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>

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