|From:||Anastasia Lubennikova <a(dot)lubennikova(at)postgrespro(dot)ru>|
|Subject:||Re: [WIP] Effective storage of duplicates in B-tree index.|
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31.08.2015 10:41, Anastasia Lubennikova:
> Hi, hackers!
> I'm going to begin work on effective storage of duplicate keys in
> B-tree index.
> The main idea is to implement posting lists and posting trees for
> B-tree index pages as it's already done for GIN.
> In a nutshell, effective storing of duplicates in GIN is organised as
> Index stores single index tuple for each unique key. That index tuple
> points to posting list which contains pointers to heap tuples (TIDs).
> If too many rows having the same key, multiple pages are allocated for
> the TIDs and these constitute so called posting tree.
> You can find wonderful detailed descriptions in gin readme
> and articles <http://www.cybertec.at/gin-just-an-index-type/>.
> It also makes possible to apply compression algorithm to posting
> list/tree and significantly decrease index size. Read more in
> presentation (part 1)
> Now new B-tree index tuple must be inserted for each table row that we
> It can possibly cause page split. Because of MVCC even unique index
> could contain duplicates.
> Storing duplicates in posting list/tree helps to avoid superfluous splits.
I'd like to share the progress of my work. So here is a WIP patch.
It provides effective duplicate handling using posting lists the same
way as GIN does it.
Layout of the tuples on the page is changed in the following way:
TID (ip_blkid, ip_posid) + key, TID (ip_blkid, ip_posid) + key, TID
(ip_blkid, ip_posid) + key
TID (N item pointers, posting list offset) + key, TID (ip_blkid,
ip_posid), TID (ip_blkid, ip_posid), TID (ip_blkid, ip_posid)
It seems that backward compatibility works well without any changes. But
I haven't tested it properly yet.
Here are some test results. They are obtained by test functions
test_btbuild and test_ginbuild, which you can find in attached sql file.
i - number of distinct values in the index. So i=1 means that all rows
have the same key, and i=10000000 means that all keys are different.
The other columns contain the index size (MB).
i B-tree Old B-tree New GIN
1 214,234375 87,7109375 10,2109375
10 214,234375 87,7109375 10,71875
100 214,234375 87,4375 15,640625
1000 214,234375 86,2578125 31,296875
10000 214,234375 78,421875 104,3046875
100000 214,234375 65,359375 49,078125
1000000 214,234375 90,140625 106,8203125
10000000 214,234375 214,234375 534,0625
You can note that the last row contains the same index sizes for B-tree,
which is quite logical - there is no compression if all the keys are
Other cases looks really nice to me.
Next thing to say is that I haven't implemented posting list compression
yet. So there is still potential to decrease size of compressed btree.
I'm almost sure, there are still some tiny bugs and missed functions,
but on the whole, the patch is ready for testing.
I'd like to get a feedback about the patch testing on some real
datasets. Any bug reports and suggestions are welcome.
Here is a couple of useful queries to inspect the data inside the index
create extension pageinspect;
select * from bt_metap('idx');
select bt.* from generate_series(1,1) as n, lateral bt_page_stats('idx',
n) as bt;
select n, bt.* from generate_series(1,1) as n, lateral
bt_page_items('idx', n) as bt;
And at last, the list of items I'm going to complete in the near future:
1. Add storage_parameter 'enable_compression' for btree access method
which specifies whether the index handles duplicates. default is 'off'
2. Bring back microvacuum functionality for compressed indexes.
3. Improve insertion speed. Insertions became significantly slower with
compressed btree, which is obviously not what we do want.
4. Clean the code and comments, add related documentation.
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