Re: pyspark streaming DStream compute

[Davies Liu]

On Tue, Sep 15, 2015 at 1:46 PM, Renyi Xiong  wrote:
> Can anybody help understand why pyspark streaming uses py4j callback to
> execute python code while pyspark batch uses worker.py?

There are two kind of callback in pyspark streaming:
1) one operate on RDDs, it take an RDD and return an new RDD, uses
py4j callback,
because SparkContext and RDDs are not accessible in worker.py
2) operate on records of RDD, it take an record and return new
records, uses worker.py

> regarding pyspark streaming, is py4j callback only used for DStream,
> worker.py still used for RDD?

Yes.

> thanks,
> Renyi.

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Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Pete Robbins]

Oops... I meant to say "The page size calculation is NOT the issue here"

On 16 September 2015 at 06:46, Pete Robbins  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  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 
>> 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 
>>> 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 
 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 
 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 
 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 
 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 
  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 

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Reynold Xin]

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  wrote:

> Oops... I meant to say "The page size calculation is NOT the issue here"
>
> On 16 September 2015 at 06:46, Pete Robbins  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  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 
>>> 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 
 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 
> 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 
> 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 
> 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 
> 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 <
> robbin...@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 

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Pete Robbins]

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  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 
> 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 
>> 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 
>>> 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 
>>> 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 
>>> 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 
>>> 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 
>>>  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

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Pete Robbins]

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  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  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 
>> 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?
>>>
>>
>>
>

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Pete Robbins]

Ok so it looks like the max number of active tasks reaches 30. I'm not
setting anything as it is a clean environment with clean spark code
checkout. I'll dig further to see why so many tasks are active.

Cheers,

On 15 September 2015 at 07:22, Reynold Xin  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 
> 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  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 
>>> 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 
 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?
>


>>>
>>
>

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Reynold Xin]

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  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  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  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 
>>> 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?

>>>
>>>
>>
>

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Pete Robbins]

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  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  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 
> 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  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 
> 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 
>  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
>

Predicate push-down bug?

[Ravi Ravi]

Turning on predicate pushdown for ORC datasources results in a
NoSuchElementException:

scala> val df = sqlContext.sql("SELECT name FROM people WHERE age < 15")
df: org.apache.spark.sql.DataFrame = [name: string]

scala> sqlContext.setConf("spark.sql.orc.filterPushdown", "*true*")

scala> df.explain
== Physical Plan ==
*java.util.NoSuchElementException*

Disabling the pushdown makes things work again:

scala> sqlContext.setConf("spark.sql.orc.filterPushdown", "*false*")

scala> df.explain
== Physical Plan ==
Project [name#6]
 Filter (age#7 < 15)
  Scan
OrcRelation[file:/home/mydir/spark-1.5.0-SNAPSHOT/test/people][name#6,age#7]

Have any of you run into this problem before? Is a fix available?

Thanks,
Ravi

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Reynold Xin]

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  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  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 
>> 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 
>> 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 
>> 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 
>> 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 
>>  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
>>
>
>

Re: Predicate push-down bug?

[Ram Sriharsha]

Hi Ravi

This does look like a bug.. I have created a JIRA to track it here:

https://issues.apache.org/jira/browse/SPARK-10623

Ram

On Tue, Sep 15, 2015 at 10:47 AM, Ram Sriharsha 
wrote:

> Hi Ravi
>
> Can you share more details? What Spark version are you running?
>
> Ram
>
> On Tue, Sep 15, 2015 at 10:32 AM, Ravi Ravi 
> wrote:
>
>> Turning on predicate pushdown for ORC datasources results in a
>> NoSuchElementException:
>>
>> scala> val df = sqlContext.sql("SELECT name FROM people WHERE age < 15")
>> df: org.apache.spark.sql.DataFrame = [name: string]
>>
>> scala> sqlContext.setConf("spark.sql.orc.filterPushdown", "*true*")
>>
>> scala> df.explain
>> == Physical Plan ==
>> *java.util.NoSuchElementException*
>>
>> Disabling the pushdown makes things work again:
>>
>> scala> sqlContext.setConf("spark.sql.orc.filterPushdown", "*false*")
>>
>> scala> df.explain
>> == Physical Plan ==
>> Project [name#6]
>>  Filter (age#7 < 15)
>>   Scan
>> OrcRelation[file:/home/mydir/spark-1.5.0-SNAPSHOT/test/people][name#6,age#7]
>>
>> Have any of you run into this problem before? Is a fix available?
>>
>> Thanks,
>> Ravi
>>
>>
>

Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Marcelo Vanzin]

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  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  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  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  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 
 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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Re: Unable to acquire memory errors in HiveCompatibilitySuite

[Pete Robbins]

This is the culprit:

https://issues.apache.org/jira/browse/SPARK-8406

"2.  Make `TestHive` use a local mode `SparkContext` with 32 threads to
increase parallelism

The major reason for this is that, the original parallelism of 2 is too
low to reproduce
the data loss issue.  Also, higher concurrency may potentially caught more
concurrency bugs
during testing phase. (It did help us spotted SPARK-8501.)"

Specific change:

http://git-wip-us.apache.org/repos/asf/spark/blob/0818fdec/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
--
diff --git
a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index f901bd8..ea325cc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -49,7 +49,7 @@ import scala.collection.JavaConversions._
 object TestHive
   extends TestHiveContext(
 new SparkContext(
-  System.getProperty("spark.sql.test.master", "local[2]"),
+  System.getProperty("spark.sql.test.master", "local[32]"),
   "TestSQLContext",
   new SparkConf()
 .set("spark.sql.test", "")



Setting that to local[8] to match my cores the HiveCompatibilitySuite
passes (and runs so much faster!) so maybe that should be changed to limit
threads to num cores?

Cheers,

On 15 September 2015 at 08:50, Pete Robbins  wrote:

> Ok so it looks like the max number of active tasks reaches 30. I'm not
> setting anything as it is a clean environment with clean spark code
> checkout. I'll dig further to see why so many tasks are active.
>
> Cheers,
>
> On 15 September 2015 at 07:22, Reynold Xin  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 
>> 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  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 
 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 
> 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

RE: And.eval short circuiting

[Zack Sampson]

I see. We're having problems with code like this (forgive my noob scala):

val df = Seq(("moose","ice"), (null,"fire")).toDF("animals", "elements")
df
  .filter($"animals".rlike(".*"))
  .filter(callUDF({(value: String) => value.length > 2}, BooleanType, 
$"animals"))
.collect()

This code throws a NPE because:
* Catalyst combines the filters with an AND
* the first filter passes returns null on the first input
* the second filter tries to read the length of that null

This feels weird. Reading that code, I wouldn't expect null to be passed to the 
second filter. Even weirder is that if you call collect() after the first 
filter you won't see nulls, and if you write the data to disk and reread it, 
the NPE won't happen.

It's bewildering! Is this the intended behavior?

From: Reynold Xin [r...@databricks.com]
Sent: Monday, September 14, 2015 10:14 PM
To: Zack Sampson
Cc: dev@spark.apache.org
Subject: Re: And.eval short circuiting

rxin=# select null and true;
 ?column?
--

(1 row)

rxin=# select null and false;
 ?column?
--
 f
(1 row)


null and false should return false.


On Mon, Sep 14, 2015 at 9:12 PM, Zack Sampson 
> wrote:
It seems like And.eval can avoid calculating right.eval if left.eval returns 
null. Is there a reason it's written like it is?


override def eval(input: Row): Any = {
  val l = left.eval(input)
  if (l == false) {
false
  } else {
val r = right.eval(input)
if (r == false) {
  false
} else {
  if (l != null && r != null) {
true
  } else {
null
  }
}
  }
}

pyspark streaming DStream compute

[Renyi Xiong]

Can anybody help understand why pyspark streaming uses py4j callback to
execute python code while pyspark batch uses worker.py?

regarding pyspark streaming, is py4j callback only used for
DStream, worker.py still used for RDD?

thanks,
Renyi.

Fwd: Null Value in DecimalType column of DataFrame

[Dirceu Semighini Filho]

Hi Yin, posted here because I think it's a bug.
So, it will return null and I can get a nullpointerexception, as I was
getting. Is this really the expected behavior? Never seen something
returning null in other Scala tools that I used.

Regards,


2015-09-14 18:54 GMT-03:00 Yin Huai :

> btw, move it to user list.
>
> On Mon, Sep 14, 2015 at 2:54 PM, Yin Huai  wrote:
>
>> A scale of 10 means that there are 10 digits at the right of the decimal
>> point. If you also have precision 10, the range of your data will be [0, 1)
>> and casting "10.5" to DecimalType(10, 10) will return null, which is
>> expected.
>>
>> On Mon, Sep 14, 2015 at 1:42 PM, Dirceu Semighini Filho <
>> dirceu.semigh...@gmail.com> wrote:
>>
>>> Hi all,
>>> I'm moving from spark 1.4 to 1.5, and one of my tests is failing.
>>> It seems that there was some changes in org.apache.spark.sql.types.
>>> DecimalType
>>>
>>> This ugly code is a little sample to reproduce the error, don't use it
>>> into your project.
>>>
>>> test("spark test") {
>>>   val file = 
>>> context.sparkContext().textFile(s"${defaultFilePath}Test.csv").map(f => 
>>> Row.fromSeq({
>>> val values = f.split(",")
>>> 
>>> Seq(values.head.toString.toInt,values.tail.head.toString.toInt,BigDecimal(values.tail.tail.head),
>>> values.tail.tail.tail.head)}))
>>>
>>>   val structType = StructType(Seq(StructField("id", IntegerType, false),
>>> StructField("int2", IntegerType, false), StructField("double",
>>>
>>>  DecimalType(10,10), false),
>>>
>>>
>>> StructField("str2", StringType, false)))
>>>
>>>   val df = context.sqlContext.createDataFrame(file,structType)
>>>   df.first
>>> }
>>>
>>> The content of the file is:
>>>
>>> 1,5,10.5,va
>>> 2,1,0.1,vb
>>> 3,8,10.0,vc
>>>
>>> The problem resides in DecimalType, before 1.5 the scala wasn't
>>> required. Now when using  DecimalType(12,10) it works fine, but using
>>> DecimalType(10,10) the Decimal values
>>> 10.5 became null, and the 0.1 works.
>>>
>>> Is there anybody working with DecimalType for 1.5.1?
>>>
>>> Regards,
>>> Dirceu
>>>
>>>
>>
>