The rum module provides access
method to work with the RUM indexes. It is based
on the GIN access methods code.
GIN index allows to perform fast full-text search
using tsvector and tsquery types.
However, full-text search with GIN index has the following drawbacks:
Slow ranking. Ranking requires positional information, but
GIN index does not store positions of lexemes.
So after the index scan we need an additional heap scan to retrieve
lexeme positions.
Slow phrase search. This problem is related to the previous one as phrase search also needs positional information.
Slow ordering by timestamp. GIN index cannot store
any additional information together with lexemes,
so it is necessary to perform a heap scan.
RUM solves these issues by storing additional
information in posting tree. In particular, it stores positional
information of lexemes or timestamps.
The drawback of RUM is that it has slower build and
insert time as compared to GIN because
RUM stores additional information together with keys and
uses generic WAL records.
rum is a Postgres Pro Standard
extension and it has no special prerequisites.
Install extension as follows:
$ psql dbname -c "CREATE EXTENSION rum"
The operators provided by the rum module are shown
in Table F.106:
Table F.106. rum Operators
| Operator | Returns | Description |
|---|---|---|
tsvector <=> tsquery | float4 | Returns distance between tsvector and tsquery values. |
timestamp <=> timestamp | float8 | Returns distance between two timestamp values. |
timestamp <=| timestamp | float8 | Returns distance only for ascending timestamp values. |
timestamp |=> timestamp | float8 | Returns distance only for descending timestamp values. |
The rum extension provides the following operator classes:
rum_tsvector_ops
Stores tsvector lexemes with positional information.
Supports ordering by <=> operator and prefix search.
rum_tsvector_hash_ops
Stores hash of tsvector lexemes with
positional information. Supports ordering by <=>
operator, but does not support prefix search.
rum_tsvector_addon_ops
Stores tsvector lexemes with additional data of any type supported by RUM.
rum_tsvector_hash_addon_ops
Stores tsvector lexemes with additional data of any type supported by RUM.
Does not support prefix search.
rum_tsquery_ops
Stores branches of query tree in additional information.
rum_anyarray_ops
Stores anyarray elements with length of the array.
Supports ordering by <=> operator.
Indexable operators: && @> <@ = %
rum_anyarray_addon_ops
Stores anyarray elements with additional data
of any type supported by RUM.
rum_type_ops
Stores lexemes of the corresponding type with positional information.
The type placeholder in the class name
must be substituted by one of the following type names:
int2, int4, int8,
float4, float8, money,
oid, timestamp, timestamptz,
time, timetz, date,
interval, macaddr, inet,
cidr, text, varchar,
char, bytea, bit,
varbit, numeric.
rum_ supports ordering by type_ops<=>, <=|,
and |=> operators. This operator class can be used together with
rum_tsvector_addon_ops, rum_tsvector_hash_addon_ops,
and rum_anyarray_addon_ops operator classes.
Supported indexable operators depend on the data type:
< <= = >= > <=> <=| |=> are supported for
int2, int4, int8,
float4, float8, money,
oid, timestamp, timestamptz.
< <= = >= > are supported for
time, timetz, date,
interval, macaddr, inet,
cidr, text, varchar,
char, bytea, bit,
varbit, numeric.
The following operator classes are now deprecated:
rum_tsvector_timestamp_ops,
rum_tsvector_timestamptz_ops,
rum_tsvector_hash_timestamp_ops,
rum_tsvector_hash_timestamptz_ops.
Let's assume we have the following table:
CREATE TABLE test_rum(t text, a tsvector);
CREATE TRIGGER tsvectorupdate
BEFORE UPDATE OR INSERT ON test_rum
FOR EACH ROW EXECUTE PROCEDURE tsvector_update_trigger('a', 'pg_catalog.english', 't');
INSERT INTO test_rum(t) VALUES ('The situation is most beautiful');
INSERT INTO test_rum(t) VALUES ('It is a beautiful');
INSERT INTO test_rum(t) VALUES ('It looks like a beautiful place');
Then we can create a new index:
CREATE INDEX rumidx ON test_rum USING rum (a rum_tsvector_ops);
And we can execute the following queries:
SELECT t, a <=> to_tsquery('english', 'beautiful | place') AS rank
FROM test_rum
WHERE a @@ to_tsquery('english', 'beautiful | place')
ORDER BY a <=> to_tsquery('english', 'beautiful | place');
t | rank
---------------------------------+-----------
The situation is most beautiful | 0.0303964
It is a beautiful | 0.0303964
It looks like a beautiful place | 0.0607927
(3 rows)
SELECT t, a <=> to_tsquery('english', 'place | situation') AS rank
FROM test_rum
WHERE a @@ to_tsquery('english', 'place | situation')
ORDER BY a <=> to_tsquery('english', 'place | situation');
t | rank
---------------------------------+-----------
The situation is most beautiful | 0.0303964
It looks like a beautiful place | 0.0303964
(2 rows)
Let's assume we have the following table:
CREATE TABLE tsts (id int, t tsvector, d timestamp);
\copy tsts from 'rum/data/tsts.data'
CREATE INDEX tsts_idx ON tsts USING rum (t rum_tsvector_addon_ops, d)
WITH (attach = 'd', to = 't');
Now we can execute the following queries:
EXPLAIN (costs off)
SELECT id, d, d <=> '2016-05-16 14:21:25' FROM tsts WHERE t @@ 'wr&qh' ORDER BY d <=> '2016-05-16 14:21:25' LIMIT 5;
QUERY PLAN
-----------------------------------------------------------------------------------
Limit
-> Index Scan using tsts_idx on tsts
Index Cond: (t @@ '''wr'' & ''qh'''::tsquery)
Order By: (d <=> 'Mon May 16 14:21:25 2016'::timestamp without time zone)
(4 rows)
SELECT id, d, d <=> '2016-05-16 14:21:25' FROM tsts WHERE t @@ 'wr&qh' ORDER BY d <=> '2016-05-16 14:21:25' LIMIT 5;
id | d | ?column?
-----+---------------------------------+---------------
355 | Mon May 16 14:21:22.326724 2016 | 2.673276
354 | Mon May 16 13:21:22.326724 2016 | 3602.673276
371 | Tue May 17 06:21:22.326724 2016 | 57597.326724
406 | Wed May 18 17:21:22.326724 2016 | 183597.326724
415 | Thu May 19 02:21:22.326724 2016 | 215997.326724
(5 rows)
Suppose we have the table:
CREATE TABLE query (q tsquery, tag text);
INSERT INTO query VALUES ('supernova & star', 'sn'),
('black', 'color'),
('big & bang & black & hole', 'bang'),
('spiral & galaxy', 'shape'),
('black & hole', 'color');
CREATE INDEX query_idx ON query USING rum(q);
We can execute the following fast query:
SELECT * FROM query
WHERE to_tsvector('black holes never exists before we think about them') @@ q;
q | tag
------------------+-------
'black' | color
'black' & 'hole' | color
(2 rows)
Let's assume we have the following table:
CREATE TABLE test_array (i int2[]);
INSERT INTO test_array VALUES ('{}'), ('{0}'), ('{1,2,3,4}'), ('{1,2,3}'), ('{1,2}'), ('{1}');
CREATE INDEX idx_array ON test_array USING rum (i rum_anyarray_ops);
Now we can execute the following query using index scan:
SET enable_seqscan TO off;
EXPLAIN (COSTS OFF) SELECT * FROM test_array WHERE i && '{1}' ORDER BY i <=> '{1}' ASC;
QUERY PLAN
------------------------------------------
Index Scan using idx_array on test_array
Index Cond: (i && '{1}'::smallint[])
Order By: (i <=> '{1}'::smallint[])
(3 rows)
SELECT * FROM test_array WHERE i && '{1}' ORDER BY i <=> '{1}' ASC;
i
-----------
{1}
{1,2}
{1,2,3}
{1,2,3,4}
(4 rows)
Alexander Korotkov
Oleg Bartunov
Teodor Sigaev