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Monday, June 06. 2011


PostgreSQL 9.1 Trigrams teaching LIKE and ILIKE new tricks

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There once existed programmers who were asked to explain this snippet of code: 1 + 2

  • The C programmer explained "It's a common mathematical expression."
  • The C++, Java, C# and other impure object-oriented programmers said "We concur. It's a common mathematical expression."
  • The Smalltalk programmer explained "1 adds 2."
  • The Lisp programmer stood up, a bit in disgust, and said, "No no! You are doing it all wrong!"
    The Lisp Programmer then pulled out a Polish calculator, punched in + 1 2 ,and with a very serious face, explained
    "+ should be pushing those other two around."

I find this episode interesting because while the Lisp programmer I feel is more right, the Smalltalk programmer has managed to follow the rest of the crowd and still stick to her core principle. This brings us to what does this have to do with trigrams in PostgreSQL 9.1. Well just like 1 + 2 being a common mathematical expression, abc LIKE '%b%' is a common logical relational database expression that we have long taken for granted as not an indexable operation in most databases (not any other database to I can think of) until PostgreSQL 9.1, which can utilize trigram indices (the Lisp programmer behind the curtain) to make it fast.

There are 2 main enhancements happening with trigrams in PostgreSQL 9.1 both of which depesz has already touched on in FASTER LIKE/ILIKE and KNNGIST. This means you can have an even faster trigram search than you ever have had before and you can do it in such a fashion that doesn't require any PostgreSQL trigram specific syntactical expressions. So while PostgreSQL 9.1 might be understanding LIKE much like all the other databases you work with, if you have a trigram index in place, it will just be doing it a little faster and sometimes a lot faster using the more clever PostgreSQL 9.1 planner. This is one example of how you can use applications designed for many databases and still be able to utilize advanced features in your database of choice. In this article we'll demonstrate.

For this example we'll use a table of 490,000 someodd records consisting of Massachusetts street segments and their names excerpted from TIGER 2010 data. You can download the trimmed data set from here if you want to play along.

The old story of using Btree Indexes

Normally if you plan to do LIKE searches, you would create a btree ops on the column or functional index on the upper/lower of the column. So here is the basic old in action:

CREATE INDEX idx_featnames_short_fullname_btree_ops
   ON featnames_short USING btree (fullname varchar_pattern_ops);
   
vacuum analyze featnames_short;
-- takes 13ms returns 8 rows, uses btree ops index
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname LIKE 'Devonshire%';
 
EXPLAIN (ANALYZE true, FORMAT yaml) 
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname LIKE 'Devonshire%';

- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 8.45
    Total Cost: 8.47
    Plan Rows: 2
    Plan Width: 11
    Actual Startup Time: 0.120
    Actual Total Time: 0.121
    Actual Rows: 8
    Actual Loops: 1
    Plans: 
      - Node Type: "Index Scan"
        Parent Relationship: "Outer"
        Scan Direction: "Forward"
        Index Name: "idx_featnames_short_fullname_btree_ops"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 0.00
        Total Cost: 8.34
        Plan Rows: 41
        Plan Width: 11
        Actual Startup Time: 0.024
        Actual Total Time: 0.093
        Actual Rows: 48
        Actual Loops: 1
        Index Cond: "(((fullname)::text ~>=~ 'Devonshire'::text) AND ((fullname)::text ~<~ 'Devonshirf'::text))"
        Filter: "((fullname)::text ~~ 'Devonshire%'::text)"
  Triggers: 
  Total Runtime: 0.161

btree plan with LIKE
-- takes 178ms returns 8 rows, uses no index
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname LIKE '%Devonshire%';
 
EXPLAIN (ANALYZE true, FORMAT yaml) 
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname LIKE '%Devonshire%';
 
- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 9768.55
    Total Cost: 9768.57
    Plan Rows: 2
    Plan Width: 11
    Actual Startup Time: 125.773
    Actual Total Time: 125.774
    Actual Rows: 8
    Actual Loops: 1
    Plans: 
      - Node Type: "Seq Scan"
        Parent Relationship: "Outer"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 0.00
        Total Cost: 9768.45
        Plan Rows: 40
        Plan Width: 11
        Actual Startup Time: 24.995
        Actual Total Time: 125.708
        Actual Rows: 48
        Actual Loops: 1
        Filter: "((fullname)::text ~~ '%Devonshire%'::text)"
  Triggers: 
  Total Runtime: 125.823

btree plan with LIKE no index
-- takes 869 ms returns 8 rows uses no index
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname ILIKE '%Devonshire%';
 
EXPLAIN (ANALYZE true, FORMAT yaml) 
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname ILIKE '%Devonshire%';
 
- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 9768.55
    Total Cost: 9768.57
    Plan Rows: 2
    Plan Width: 11
    Actual Startup Time: 842.275
    Actual Total Time: 842.276
    Actual Rows: 8
    Actual Loops: 1
    Plans: 
      - Node Type: "Seq Scan"
        Parent Relationship: "Outer"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 0.00
        Total Cost: 9768.45
        Plan Rows: 40
        Plan Width: 11
        Actual Startup Time: 113.184
        Actual Total Time: 842.190
        Actual Rows: 48
        Actual Loops: 1
        Filter: "((fullname)::text ~~* '%Devonshire%'::text)"
  Triggers: 
  Total Runtime: 842.320

The new way

Well in the new way the btree is sometimes faster for some LIKE scenarios, but can't be employed in all where as the trigram works for all. In the new way we supplement our btree with a trigram index or use a trigram index instead to do most ILIKE searches and LIKE '%me%' like searches. Here is the same exercise repeated with adding on a trigram index.

-- Install trigram module if you don't have installed already
CREATE EXTENSION pg_trgm;
 
-- Add trigram index (seems to take much longer to build if have btree 28 secs))
 CREATE INDEX idx_featnames_short_fullname_trgm_gist
   ON featnames_short USING gist (fullname gist_trgm_ops);
   
vacuum analyze featnames_short;

-- repeat the exercise
--takes  13 ms returns 8 rows (uses btree like other)
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname LIKE 'Devonshire%';
 
EXPLAIN (ANALYZE true, FORMAT yaml) 
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname LIKE 'Devonshire%';
 
- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 8.44
    Total Cost: 8.46
    Plan Rows: 2
    Plan Width: 11
    Actual Startup Time: 0.121
    Actual Total Time: 0.123
    Actual Rows: 8
    Actual Loops: 1
    Plans: 
      - Node Type: "Index Scan"
        Parent Relationship: "Outer"
        Scan Direction: "Forward"
        Index Name: "idx_featnames_short_fullname_btree_ops"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 0.00
        Total Cost: 8.34
        Plan Rows: 40
        Plan Width: 11
        Actual Startup Time: 0.024
        Actual Total Time: 0.092
        Actual Rows: 48
        Actual Loops: 1
        Index Cond: "(((fullname)::text ~>=~ 'Devonshire'::text) AND ((fullname)::text ~<~ 'Devonshirf'::text))"
        Filter: "((fullname)::text ~~ 'Devonshire%'::text)"
  Triggers: 
  Total Runtime: 0.167
  
--takes  13 ms returns 8 rows - uses gist
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname ILIKE 'Devonshire%';
 
EXPLAIN (ANALYZE true, FORMAT yaml) 
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname ILIKE 'Devonshire%';
 
- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 152.77
    Total Cost: 152.79
    Plan Rows: 2
    Plan Width: 11
    Actual Startup Time: 3.647
    Actual Total Time: 3.649
    Actual Rows: 8
    Actual Loops: 1
    Plans: 
      - Node Type: "Bitmap Heap Scan"
        Parent Relationship: "Outer"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 4.76
        Total Cost: 152.67
        Plan Rows: 40
        Plan Width: 11
        Actual Startup Time: 3.403
        Actual Total Time: 3.613
        Actual Rows: 48
        Actual Loops: 1
        Recheck Cond: "((fullname)::text ~~* 'Devonshire%'::text)"
        Plans: 
          - Node Type: "Bitmap Index Scan"
            Parent Relationship: "Outer"
            Index Name: "idx_featnames_short_fullname_trgm_gist"
            Startup Cost: 0.00
            Total Cost: 4.75
            Plan Rows: 40
            Plan Width: 0
            Actual Startup Time: 3.378
            Actual Total Time: 3.378
            Actual Rows: 48
            Actual Loops: 1
            Index Cond: "((fullname)::text ~~* 'Devonshire%'::text)"
  Triggers: 
  Total Runtime: 3.720
  
-- takes 14-18 ms returns 8 rows uses gist bitmap index scan with heap scan
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname ILIKE '%Devonshire%';
 
EXPLAIN (ANALYZE true, FORMAT yaml) 
SELECT DISTINCT fullname 
 FROM featnames_short 
 WHERE fullname ILIKE '%Devonshire%';
 
- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 152.77
    Total Cost: 152.79
    Plan Rows: 2
    Plan Width: 11
    Actual Startup Time: 5.586
    Actual Total Time: 5.588
    Actual Rows: 8
    Actual Loops: 1
    Plans: 
      - Node Type: "Bitmap Heap Scan"
        Parent Relationship: "Outer"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 4.76
        Total Cost: 152.67
        Plan Rows: 40
        Plan Width: 11
        Actual Startup Time: 5.351
        Actual Total Time: 5.553
        Actual Rows: 48
        Actual Loops: 1
        Recheck Cond: "((fullname)::text ~~* '%Devonshire%'::text)"
        Plans: 
          - Node Type: "Bitmap Index Scan"
            Parent Relationship: "Outer"
            Index Name: "idx_featnames_short_fullname_trgm_gist"
            Startup Cost: 0.00
            Total Cost: 4.75
            Plan Rows: 40
            Plan Width: 0
            Actual Startup Time: 5.325
            Actual Total Time: 5.325
            Actual Rows: 48
            Actual Loops: 1
            Index Cond: "((fullname)::text ~~* '%Devonshire%'::text)"
  Triggers: 
  Total Runtime: 5.655

Some old rules still apply

If your queries are writtern something like below then the below query will not use an index: 


-- This will not use an index -- 
 SELECT  fullname 
 FROM featnames_short 
 WHERE upper(fullname) LIKE '%DEVONSHIRE%';

There is no index above that will work for this, however if you put in an index on:

-- But if you do this the above uses an index again (14 ms - 8 rows)--
 CREATE INDEX idx_featnames_short_ufullname_trgm_gist
   ON featnames_short USING gist (upper(fullname) gist_trgm_ops);

What about citext and case insensitive LIKE found in MySQL and SQL Server

Sadly, we couldn't figure out how to get citext to use an index regardless of what we did. Though the hack we described a while ago of turning your PostgreSQL varchar() columns into case insensitive columns and putting a trigram gist index on upper(fullname) seems to work just dandy.

--finishes in 13-17ms and returns 9 rows 
--(using CASE INSENSITIVE OPERATORS HACK for varchar columns) --
SELECT DISTINCT fullname FROM featnames_short WHERE fullname LIKE '%devonshire%';

Case insensitive 
- Plan: 
    Node Type: "Aggregate"
    Strategy: "Hashed"
    Startup Cost: 4113.74
    Total Cost: 4114.62
    Plan Rows: 88
    Plan Width: 12
    Actual Startup Time: 2.848
    Actual Total Time: 2.850
    Actual Rows: 9
    Actual Loops: 1
    Plans: 
      - Node Type: "Bitmap Heap Scan"
        Parent Relationship: "Outer"
        Relation Name: "featnames_short"
        Alias: "featnames_short"
        Startup Cost: 138.38
        Total Cost: 4106.67
        Plan Rows: 2825
        Plan Width: 12
        Actual Startup Time: 2.725
        Actual Total Time: 2.826
        Actual Rows: 51
        Actual Loops: 1
        Recheck Cond: "(upper((fullname)::text) ~~ 'DEVONSHIRE%'::text)"
        Plans: 
          - Node Type: "Bitmap Index Scan"
            Parent Relationship: "Outer"
            Index Name: "idx_featnames_short_ufullname_trgm_gist"
            Startup Cost: 0.00
            Total Cost: 137.67
            Plan Rows: 2825
            Plan Width: 0
            Actual Startup Time: 2.710
            Actual Total Time: 2.710
            Actual Rows: 51
            Actual Loops: 1
            Index Cond: "(upper((fullname)::text) ~~ 'DEVONSHIRE%'::text)"
  Triggers: 
  Total Runtime: 2.883

Be case insensitive (with a varchar datatype), at the expense of making all your varchars in the database case insensitive.

Posted by Leo Hsu and Regina Obe in 9.1, basics, contrib spotlight, intermediate, pgtrgm, postgresql versions at 01:23 | Comment (1) | Trackbacks (0)

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I managed to get trigrams working with citext. Assuming fullname is citext:

CREATE INDEX idx_featnames_short_ufullname_trgm_gist
ON featnames_short USING gist (cast(fullname as text) gist_trgm_ops);

SELECT fullname
FROM featnames_short
WHERE fullname::text ILIKE '%DEVONSHIRE%';
#1 Ka Chun Leung on 2011-09-26 12:17 (Reply)

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