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Re: [Improvement] Use Trie in place of HashMap to reduce memory footprint of Dictionary

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hexiaoqiao
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Nov 27, 2016; 9:27am

Re: [Improvement] Use Trie in place of HashMap to reduce memory footprint of Dictionary

hexiaoqiao
Hi Kumar Vishal,

I'll create task to trace this issue.
Thanks for your suggestions.

Regards，
He Xiaoqiao


On Sun, Nov 27, 2016 at 1:41 AM, Kumar Vishal <[hidden email]> wrote:
Hi Xiaoqiao He,

You can go ahead with DAT implementation, based on the result.
I will look forward for you PR.

Please let me know if you need any support:).

-Regards
KUmar Vishal

On Fri, Nov 25, 2016 at 11:22 PM, Xiaoqiao He <[hidden email]> wrote:

> Hi Liang, Kumar Vishal,
>
> I has done a standard benchmark about multiply data structures for
> Dictionary following your suggestions. Based on the test results, I think
> DAT may be the best choice for CarbonData.
>
> *1. Here are 2 test results:*
> -----------------------------------------------------------------------
> Benchmark about {HashMap,DAT,RadixTree,TrieDict} Structures for Dictionary
>   HashMap :                               java.util.HashMap
>   DAT (Double Array Trie):
> https://github.com/komiya-atsushi/darts-java
>   RadixTree:
> https://github.com/npgall/concurrent-trees
>   TrieDict (Dictionary in Kylin):
> http://kylin.apache.org/blog/2015/08/13/kylin-dictionary
> Dictionary Source (Traditional Chinese):
> https://raw.githubusercontent.com/fxsjy/jieba/master/extra_
> dict/dict.txt.big
> ================Test Result================
> a. Dictionary Size:584429
> --------
> b. Build Time (ms) :
>    DAT       : 5714
>    HashMap   : 110
>    RadixTree : 22044
>    TrieDict  : 855
> --------
> c. Memory footprint in 64-bit JVM (bytes) :
>    DAT       : 16779752
>    HashMap   : <a href="tel:32196592" value="+85232196592">32196592
>    RadixTree : 46130584
>    TrieDict  : 10443608
> --------
> d. Retrieval Performance for 9935293 query times (ms) :
>    DAT       : 585
>    HashMap   : 1010
>    RadixTree : 417639
>    TrieDict  : 8664
> ================Test Result================
>
> ================Test Result================
> a. Dictionary Size:584429
> --------
> b. Build Time (ms) :
>    DAT       : 5867
>    HashMap   : 100
>    RadixTree : 22082
>    TrieDict  : 840
> --------
> c. Memory footprint in 64-bit JVM (bytes) :
>    DAT       : 16779752
>    HashMap   : 32196592
>    RadixTree : 46130584
>    TrieDict  : 10443608
> --------
> d. Retrieval Performance for 9935293 query times (ms) :
>    DAT       : 593
>    HashMap   : 821
>    RadixTree : 422297
>    TrieDict  : 8752
> ================Test Result================
>
> *2. Conclusion:*
> a. TrieDict is good for building tree and less memory footprint overhead,
> but worst retrieval performance,
> b. DAT is a good tradeoff between memory footprint and retrieval
> performance,
> c. RadixTree has the worst performance in different aspects.
>
> *3. Result Analysis:*
> a. With Trie the memory footprint of the TrieDict mapping is kinda
> minimized if compared to HashMap, in order to improve performance there is
> a cache layer overlays on top of Trie.
> b. Because a large number of duplicate prefix data, the total memory
> footprint is more than trie, meanwhile i think calculating string hash code
> of traditional Chinese consume considerable time overhead, so the
> performance is not the best.
> c. DAT is a better tradeoff.
> d. I have no idea why RadixTree has the worst performance in terms of
> memory, retrieval and building tree.
>
>
> On Fri, Nov 25, 2016 at 11:28 AM, Liang Chen <[hidden email]>
> wrote:
>
> > Hi xiaoqiao
> >
> > ok, look forward to seeing your test result.
> > Can you take this task for this improvement? Please let me know if you
> need
> > any support :)
> >
> > Regards
> > Liang
> >
> >
> > hexiaoqiao wrote
> > > Hi Kumar Vishal,
> > >
> > > Thanks for your suggestions. As you said, choose Trie replace HashMap
> we
> > > can get better memory footprint and also good performance. Of course,
> DAT
> > > is not only choice, and I will do test about DAT vs Radix Trie and
> > release
> > > the test result as soon as possible. Thanks your suggestions again.
> > >
> > > Regards,
> > > Xiaoqiao
> > >
> > > On Thu, Nov 24, 2016 at 4:48 PM, Kumar Vishal &lt;
> >
> > > kumarvishal1802@
> >
> > > &gt;
> > > wrote:
> > >
> > >> Hi XIaoqiao He,
> > >> +1,
> > >> For forward dictionary case it will be very good optimisation, as our
> > >> case
> > >> is very specific storing byte array to int mapping[data to surrogate
> key
> > >> mapping], I think we will get much better memory footprint and
> > >> performance
> > >> will be also good(2x). We can also try radix tree(radix trie), it is
> > more
> > >> optimise for storage.
> > >>
> > >> -Regards
> > >> Kumar Vishal
> > >>
> > >> On Thu, Nov 24, 2016 at 12:12 PM, Liang Chen &lt;
> >
> > > chenliang6136@
> >
> > > &gt;
> > >> wrote:
> > >>
> > >> > Hi xiaoqiao
> > >> >
> > >> > For the below example, 600K dictionary data:
> > >> > It is to say that using "DAT" can save 36M memory against
> > >> > "ConcurrentHashMap", whereas the performance just lost less
> (1718ms) ?
> > >> >
> > >> > One more question:if increases the dictionary data size, what's the
> > >> > comparison results "ConcurrentHashMap" VS "DAT"
> > >> >
> > >> > Regards
> > >> > Liang
> > >> > ------------------------------------------------------------
> > >> > ------------------------------------------
> > >> > a. memory footprint (approximate quantity) in 64-bit JVM:
> > >> > ~104MB (*ConcurrentHashMap*) vs ~68MB (*DAT*)
> > >> >
> > >> > b. retrieval performance: total time(ms) of 500 million query:
> > >> > 12825 ms(*ConcurrentHashMap*) vs 14543 ms(*DAT*)
> > >> >
> > >> > Regards
> > >> > Liang
> > >> >
> > >> > hexiaoqiao wrote
> > >> > > hi Liang,
> > >> > >
> > >> > > Thanks for your reply, i need to correct the experiment result
> > >> because
> > >> > > it's
> > >> > > wrong order NO.1 column of result data table.
> > >> > >
> > >> > > In order to compare performance between Trie and HashMap, Two
> > >> different
> > >> > > structures are constructed using the same dictionary data which
> size
> > >> is
> > >> > > 600K and each item's length is between 2 and 50 bytes.
> > >> > >
> > >> > > ConcurrentHashMap (structure which is used in CarbonData
> currently)
> > >> vs
> > >> > > Double
> > >> > > Array Trie (one implementation of Trie Structures)
> > >> > >
> > >> > > a. memory footprint (approximate quantity) in 64-bit JVM:
> > >> > > ~104MB (*ConcurrentHashMap*) vs ~68MB (*DAT*)
> > >> > >
> > >> > > b. retrieval performance: total time(ms) of 500 million query:
> > >> > > 12825 ms(*ConcurrentHashMap*) vs 14543 ms(*DAT*)
> > >> > >
> > >> > > Regards,
> > >> > > He Xiaoqiao
> > >> > >
> > >> > >
> > >> > > On Thu, Nov 24, 2016 at 7:48 AM, Liang Chen &lt;
> > >> >
> > >> > > chenliang6136@
> > >> >
> > >> > > &gt; wrote:
> > >> > >
> > >> > >> Hi xiaoqiao
> > >> > >>
> > >> > >> This improvement looks great!
> > >> > >> Can you please explain the below data, what does it mean?
> > >> > >> ----------
> > >> > >> ConcurrentHashMap
> > >> > >> ~68MB 14543
> > >> > >> Double Array Trie
> > >> > >> ~104MB 12825
> > >> > >>
> > >> > >> Regards
> > >> > >> Liang
> > >> > >>
> > >> > >> <a href="tel:2016-11-24" value="+85220161124">2016-11-24 2:04 GMT+08:00 Xiaoqiao He &lt;
> > >> >
> > >> > > xq.he2009@
> > >> >
> > >> > > &gt;:
> > >> > >>
> > >> > >> >  Hi All,
> > >> > >> >
> > >> > >> > I would like to propose Dictionary improvement which using Trie
> > in
> > >> > >> place
> > >> > >> of
> > >> > >> > HashMap.
> > >> > >> >
> > >> > >> > In order to speedup aggregation, reduce run-time memory
> > footprint,
> > >> > >> enable
> > >> > >> > fast
> > >> > >> > distinct count etc, CarbonData encodes data using dictionary at
> > >> file
> > >> > >> level
> > >> > >> > or table level based on cardinality. It is a general and
> > efficient
> > >> way
> > >> > >> in
> > >> > >> > many big data systems, but when apply ConcurrentHashMap
> > >> > >> > to maintain Dictionary in CarbonData currently, memory overhead
> > of
> > >> > >> > Driver is very huge since it has to load whole Dictionary to
> > >> decode
> > >> > >> actual
> > >> > >> > data value, especially column cardinality is a large number.
> and
> > >> > >> CarbonData
> > >> > >> > will not do dictionary if cardinality > 1 million at default
> > >> behavior.
> > >> > >> >
> > >> > >> > I propose using Trie in place of HashMap for the following
> three
> > >> > >> reasons:
> > >> > >> > (1) Trie is a proper structure for Dictionary,
> > >> > >> > (2) Reduce memory footprint,
> > >> > >> > (3) Not impact retrieval performance
> > >> > >> >
> > >> > >> > The experimental results show that Trie is able to meet the
> > >> > >> requirement.
> > >> > >> > a. ConcurrentHashMap vs Double Array Trie
> > >> > >> > &lt;https://linux.thai.net/~thep/datrie/datrie.html&gt;(one
> > >> > >> implementation of
> > >> > >> > Trie Structures)
> > >> > >> > b. Dictionary size: 600K
> > >> > >> > c. Memory footprint and query time
> > >> > >> > - memory footprint (64-bit JVM) 500 million query time(ms)
> > >> > >> > ConcurrentHashMap
> > >> > >> > ~68MB 14543
> > >> > >> > Double Array Trie
> > >> > >> > ~104MB 12825
> > >> > >> >
> > >> > >> > Please share your suggestions about the proposed improvement of
> > >> > >> Dictionary.
> > >> > >> >
> > >> > >> > Regards
> > >> > >> > He Xiaoqiao
> > >> > >> >
> > >> > >>
> > >> > >>
> > >> > >>
> > >> > >> --
> > >> > >> Regards
> > >> > >> Liang
> > >> > >>
> > >> >
> > >> >
> > >> >
> > >> >
> > >> >
> > >> > --
> > >> > View this message in context: http://apache-carbondata-
> > >> > mailing-list-archive.1130556.n5.nabble.com/Improvement-Use-
> > >> > Trie-in-place-of-HashMap-to-reduce-memory-footprint-of-
> > >> > Dictionary-tp3132p3143.html
> > >> > Sent from the Apache CarbonData Mailing List archive mailing list
> > >> archive
> > >> > at Nabble.com.
> > >> >
> > >>
> >
> >
> >
> >
> >
> > --
> > View this message in context: http://apache-carbondata-
> > mailing-list-archive.1130556.n5.nabble.com/Improvement-Use-
> > Trie-in-place-of-HashMap-to-reduce-memory-footprint-of-
> > Dictionary-tp3132p3186.html
> > Sent from the Apache CarbonData Mailing List archive mailing list archive
> > at Nabble.com.
> >
>
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