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From "Till Westmann" <ti...@apache.org>
Subject Re: Implement an SerializableVector in Hyracks
Date Wed, 06 Apr 2016 17:14:47 GMT
Not really. Just talked with Yingyi as well and we agree with Jianfeng 
that
taking the data structure memory usage into account is probably the most
important improvement here.

Cheers,
Till

On 6 Apr 2016, at 9:21, Chen Li wrote:

> @Till: do you have any thoughts on this subject before we close it?
>
> On Fri, Apr 1, 2016 at 2:08 PM, Jianfeng Jia <jianfeng.jia@gmail.com> 
> wrote:
>
>> Dear devs,
>>
>> Xi has implemented the SerializableVector(SVector) which allocated 
>> memory
>> increasingly rather than a long java int array upfront.
>> We did some tests but it seems not a good enough answer to solve the 
>> OOM
>> issue caused by long java array allocation.
>>
>> The first experiment was to test if the standalone SVector approach 
>> would
>> get more memory comparing to the long int array. With the heap size 
>> of
>> -Xmx2g on a single machine, the SVector could allocate up to 1280M 
>> data,
>> while the original Java array can allocate up to 1080M data. It can
>> allocate about 18% more space.
>>
>> When we move to the AsterixDB, we replace the int array which stores 
>> the
>> (FrameId, TupleId) pair with the SVector. Then we gradually 
>> increasing the
>> "compiler.sortmemory" to increase the in-memory buffer for external 
>> sort.
>> Both master and SVector succeeded in 1150M case and failed at 1250M 
>> case.
>> Furthermore, the master branch succeeded in 1200M case but the 
>> SVector
>> failed in that case.
>>
>> Given that, we may think that allocate the memory chunk by chunk 
>> doesn't
>> win too much space comparing to allocate a big one directly.
>> I think we need to take the structure memory usage into the spilling 
>> case
>> calculation, but we may not need an extra structure to chop the 
>> memory.
>>
>> Any insights?
>> If you are interested, the code sample Xi wrote is here:
>> https://github.com/xixZ/JVMExperiment/tree/master/src/testing
>>
>>
>> On Tue, Feb 2, 2016 at 11:01 PM, Jianfeng Jia 
>> <jianfeng.jia@gmail.com>
>> wrote:
>>
>>> Thanks Ted for the elaborate introduction!
>>> I did some reading about it. Based on my understanding, the main
>> advantage
>>> of ValueVector is its column-wise design. In that case, the 
>>> per-record
>>> based metadata, e.g. indexes or hash keys, can be directly added as 
>>> an
>>> column along with the existing record. Thus, the sorting within one 
>>> row
>>> group should be easily addressed. One question still not very clear 
>>> to me
>>> is how to generate the sorted result across several row groups?
>>> If you can get somebody to talk more about it that will be great. 
>>> Thank
>>> you!
>>>
>>> On Feb 1, 2016, at 6:03 PM, Ted Dunning <ted.dunning@gmail.com> 
>>> wrote:
>>>
>>> So there are several key points for ValueVectors that I can 
>>> describe, but
>>> for the authoritative voice, others would have to speak.
>>>
>>> The first point is that in Drill at least (and this is not required)
>>> ValueVectors are off-heap.  This helps enormously in managing the
>>> life-cycle since vectors can be associated with queries and when the
>> query
>>> ends, all associated vectors can be deallocated quickly.  That also
>> allows
>>> the memory footprint of Drill to be adjusted both up and down while
>>> running.
>>>
>>> Secondly, ValueVectors are stored column-wise, not record-wise.  
>>> Most
>>> manipulations do not require copies. Projection simply requires 
>>> ignoring
>>> some columns. New columns can be added without disturbing old ones.
>>> Filtering is done using a row selection bitmap. Sorting is often 
>>> done
>> using
>>> an index column.
>>>
>>> The assumption is also that you will have a row group with something
>> like a
>>> hundred thousand rows in it. This means that the size of a single 
>>> group
>>> isn't usually astronomical although very large data structures in a
>> single
>>> row can make the regulation of the size of row groups more 
>>> difficult.
>>>
>>> Of particular interest is the fact that processing code can be 
>>> generated
>> in
>>> Java that avoids almost all object creation so that most SQL-like 
>>> queries
>>> don't require any object cons'ing at all during the row scans. 
>>> Moreover,
>>> the code generated can even be rendered by the JIT into vectorized 
>>> low
>>> level instructions because the access patterns on ValueVectors are 
>>> so
>>> simple.
>>>
>>> Nested data structures are handled using invisible marking columns
>> similar
>>> to the way that nesting is marked in Dremel or Parquet. This means 
>>> that
>> you
>>> get uniformly typed pseudo columns that represent a flattened view 
>>> of the
>>> nested content. Many restructuring operations can be done by simply
>>> re-interpreting the nested data without any copying at all.
>>>
>>> If more detail is desired we should probably get somebody who is 
>>> more
>>> active in the Drill implementation to talk about how this all works 
>>> and
>> how
>>> it will be extracted into Apache Arrow.
>>>
>>> More information can be found here:
>>>
>>> https://drill.apache.org/docs/value-vectors/
>>>
>>>
>>>
>>>
>>> On Mon, Feb 1, 2016 at 5:08 PM, Jianfeng Jia 
>>> <jianfeng.jia@gmail.com>
>>> wrote:
>>>
>>> If I understand correctly, it seems very similar to the IFrame in 
>>> Hyrack,
>>> which is also a container to store a sequence of record into the
>>> ByteBuffer.
>>>
>>> I’m not clear about how records are manipulated inside the 
>>> ValueVectors.
>>> In Hyracks case, we store the pointer(usually a pair of (frameID,
>>> recordID)) in one java array and manipulate the pointers instead of 
>>> the
>>> original records. We mainly want to break the that one array into
>> multiple
>>> small ByteBuffer-based arrays. By doing so, we can reduce the risk 
>>> of OOM
>>> for a large array and we may also take the memory usage of those 
>>> pointers
>>> into account for the flush decisions.  @Ted, could you share some
>> insights
>>> about how the ValueVectors handles manipulations? e.g. sort, hashing 
>>> …
>> etc.
>>>
>>> On Feb 1, 2016, at 3:16 PM, Ted Dunning <ted.dunning@gmail.com> 
>>> wrote:
>>>
>>> Have you guys looked at the Drill ValueVectors?
>>>
>>> This structure is being spun out as Apache Arrow with multiple 
>>> interfaces
>>> and language bindings.
>>>
>>> On Mon, Feb 1, 2016 at 9:56 AM, Jianfeng Jia 
>>> <jianfeng.jia@gmail.com>
>>>
>>> wrote:
>>>
>>>
>>> Hi,
>>>
>>> We plan to implement an append-only array at first. The main reason 
>>> is
>>> this is how the auxiliary data structure be used so far. Then the
>>> implementation is straightforward.
>>>
>>> The tree-structured Vector in Scala can save a lot in updating case
>>>
>>> mainly
>>>
>>> because of their immutable requirement. It can saving unnecessary 
>>> data
>>> copies comparing to other immutable list when updating. We only 
>>> allow
>>> in-place update. The tree design may be overkill for us.
>>>
>>> Xi made on detailed design doc is here:
>>>
>>>
>>>
>> https://docs.google.com/document/d/1bs3JBCxmvuJZmBq_gKzUDt0FZdM9j3d3MPAmC29ZoSA/edit?usp=sharing
>>>
>>> Any thoughts or comments?
>>>
>>>
>>> On Jan 18, 2016, at 9:52 AM, Chen Li <chenli@gmail.com> wrote:
>>>
>>> @Xi and Jianfeng: after we come up with the design, let's share it 
>>> with
>>>
>>> the
>>>
>>> group for an approval before the implementation.
>>>
>>> Chen
>>>
>>> On Fri, Jan 15, 2016 at 11:48 AM, Mike Carey <dtabass@gmail.com>
>>>
>>> wrote:
>>>
>>>
>>> The accounting is just as critical as the chunking - we should do 
>>> both
>>> (together).
>>>
>>>
>>> On 1/15/16 9:00 AM, Till Westmann wrote:
>>>
>>> I don’t have relevant experience on the subject. But I think that 
>>> it
>>> sounds good to avoid arbitrarily long chunks of memory. Especially -
>>>
>>> as
>>>
>>> Jianfeng wrote - it would be good to be able to a) account for this
>>>
>>> memory
>>>
>>> and b) to manage it.
>>> An interesting question for me would be what the overhead of such a
>>> Vector is compared to a simple Java array and as a result where it
>>>
>>> should
>>>
>>> be used to replace arrays. (The comparison in [3] only compares
>>>
>>> different
>>>
>>> Scala collections, but doesn’t look at plain arrays.)
>>>
>>> Cheers,
>>> Till
>>>
>>> On 14 Jan 2016, at 22:05, Chen Li wrote:
>>>
>>> Before we ask Xi to work on this project, it will be good to know if
>>>
>>> other people have seen similar problems and agree with this plan.
>>> @Till: can you share some tips?
>>>
>>> Chen
>>>
>>> On Wed, Jan 13, 2016 at 4:27 PM, Jianfeng Jia <
>>>
>>> jianfeng.jia@gmail.com
>>>
>>>
>>> wrote:
>>>
>>> Hi Devs,
>>>
>>> First of all, Xi Zhang is a Master student at UCI wants to work
>>>
>>> with
>>>
>>> us
>>>
>>> for a while. Welcome Xi!
>>>
>>> We are thinking of making a Frame-based, memory-bound
>>> SerializableVector at first. We expect this vector can solve some
>>> occasionally Java.Heap.OutOfMemory exceptions in Hyracks.
>>> Though we did a good job on organizing the record-located memory,
>>>
>>> the
>>>
>>> OOM exception can still happen while operating the auxiliary data
>>> structure. For example in the sort run generator, instead of moving
>>>
>>> record
>>>
>>> around we are creating an reference “pointer" array to the 
>>> original
>>>
>>> record.
>>>
>>> However, if the record is small and the size of that int array will
>>>
>>> be
>>>
>>> large, then the OOM exception will occur, which is the case of
>>>
>>> issue
>>>
>>> [1].
>>>
>>>
>>> One way to solve this problem is to put auxiliary data structures
>>>
>>> into
>>>
>>> the memory-bounded frame as well. In general, it will be much
>>>
>>> easier
>>>
>>> to ask
>>>
>>> for multiple small memory blocks than one big chunk of memory. I
>>>
>>> guess that
>>>
>>> was the same reason why we have “SerializableHashTable” for
>>>
>>> HashJoin. It
>>>
>>> will be nice to have a more general structure that can be used by
>>>
>>> all the
>>>
>>> operators.
>>>
>>> The Frame based Vector idea is inspired by the Scala Vector[2]
>>>
>>> which
>>>
>>> looks like a List, but internally it is implemented as a 32-ary
>>>
>>> tree. The
>>>
>>> performance of it is very stable for variety size of object[3]. It
>>>
>>> will
>>>
>>> have all the benefits of ArrayList and the LinkedList. In addition,
>>>
>>> we can
>>>
>>> take the memory usage of the auxiliary structure into the
>>>
>>> calculation. We
>>>
>>> will work on the detailed design doc later if we are agree on this
>>> direction.
>>>
>>> Any thoughts or suggestions? Thank you!
>>>
>>>
>>> [1]
>>>
>>>
>>>
>>>
>> https://code.google.com/p/asterixdb/issues/detail?id=934&can=1&q=last%20straw&colspec=ID%20Type%20Status%20Priority%20Milestone%20Owner%20Summary%20ETA%20Severity
>>>
>>> <
>>>
>>>
>>>
>>>
>> https://code.google.com/p/asterixdb/issues/detail?id=934&can=1&q=last%20straw&colspec=ID%20Type%20Status%20Priority%20Milestone%20Owner%20Summary%20ETA%20Severity
>>>
>>>
>>>
>>> [2] https://bitbucket.org/astrieanna/bitmapped-vector-trie <
>>> https://bitbucket.org/astrieanna/bitmapped-vector-trie>
>>> [3]
>>>
>>> http://danielasfregola.com/2015/06/15/which-immutable-scala-collection/
>>>
>>> <
>>>
>>> http://danielasfregola.com/2015/06/15/which-immutable-scala-collection/
>>>
>>>
>>>
>>>
>>> Best,
>>>
>>> Jianfeng Jia
>>> PhD Candidate of Computer Science
>>> University of California, Irvine
>>>
>>>
>>>
>>>
>>>
>>>
>>> Best,
>>>
>>> Jianfeng Jia
>>> PhD Candidate of Computer Science
>>> University of California, Irvine
>>>
>>>
>>>
>>>
>>>
>>> Best,
>>>
>>> Jianfeng Jia
>>> PhD Candidate of Computer Science
>>> University of California, Irvine
>>>
>>>
>>>
>>>
>>>
>>> Best,
>>>
>>> Jianfeng Jia
>>> PhD Candidate of Computer Science
>>> University of California, Irvine
>>>
>>>
>>
>>
>> --
>>
>> -----------------
>> Best Regards
>>
>> Jianfeng Jia
>> Ph.D. Candidate of Computer Science
>> University of California, Irvine
>>

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