Thank you Xiaoqiao for looking into this issue and sharing your result!

Have you tried varied dictionary size for comparison among all the 
alternatives? 

And please pay closer attention to the license of DAT implementation, as they 
are under LGPL, generally speaking, it is not legally allowed to be included.

Jihong

-----Original Message-----
From: Xiaoqiao He [mailto:xq.he2...@gmail.com] 
Sent: Friday, November 25, 2016 9:52 AM
To: dev@carbondata.incubator.apache.org
Subject: Re: [Improvement] Use Trie in place of HashMap to reduce memory 
footprint of Dictionary

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   : 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 <chenliang6...@gmail.com>
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
> >> > >>
> >> > >> 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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