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F.50. rum
Prev UpAppendix F. Additional Supplied ModulesHome Next

F.50. rum

F.50.1. Introduction

Therum module provides access method to work with theRUM indexes. It is based on theGIN access methods code.

GIN index allows to perform fast full-text search usingtsvector andtsquery types. However, full-text search withGIN index has the following drawbacks:

  • Slow ranking. Ranking requires positional information, butGIN 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 ofRUM is that it has slower build and insert time as compared toGIN becauseRUM stores additional information together with keys and uses generic WAL records.

F.50.2. Installation

rum is aPostgres Pro Standard extension and it has no special prerequisites.

Install extension as follows:

$ psqldbname -c "CREATE EXTENSION rum"

F.50.3. Common Operators

The operators provided by therum module are shown inTable F.112:

Table F.112. rum Operators

OperatorReturnsDescription
tsvector<=>tsqueryfloat4Returns distance betweentsvector andtsquery values.
timestamp<=>timestampfloat8Returns distance between twotimestamp values.
timestamp<=|timestampfloat8Returns distance only for ascendingtimestamp values.
timestamp|=>timestampfloat8Returns distance only for descendingtimestamp values.

F.50.4. Operator Classes

Therum extension provides the following operator classes:

rum_tsvector_ops

Storestsvector lexemes with positional information. Supports ordering by<=> operator and prefix search.

rum_tsvector_hash_ops

Stores hash oftsvector lexemes with positional information. Supports ordering by<=> operator, but does not support prefix search.

rum_tsvector_addon_ops

Storestsvector lexemes with additional data of any type supported byRUM.

rum_tsvector_hash_addon_ops

Storestsvector lexemes with additional data of any type supported byRUM. Does not support prefix search.

rum_tsquery_ops

Stores branches of query tree in additional information.

rum_anyarray_ops

Storesanyarray elements with length of the array. Supports ordering by <=> operator.

Indexable operators:&& @> <@ = %

rum_anyarray_addon_ops

Storesanyarray elements with additional data of any type supported byRUM.

rum_type_ops

Stores lexemes of the corresponding type with positional information. Thetype 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_type_ops supports ordering by<=>,<=|, and|=> operators. This operator class can be used together withrum_tsvector_addon_ops,rum_tsvector_hash_addon_ops, andrum_anyarray_addon_ops operator classes.

Supported indexable operators depend on the data type:

  • < <= = >= > <=> <=| |=> are supported forint2,int4,int8,float4,float8,money,oid,timestamp,timestamptz.

  • < <= = >= > are supported fortime,timetz,date,interval,macaddr,inet,cidr,text,varchar,char,bytea,bit,varbit,numeric.

Note

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.

F.50.5. Examples

F.50.5.1.  rum_tsvector_ops Example

Let's assume we have the following table:

CREATE TABLE test_rum(t text, a tsvector);CREATE TRIGGER tsvectorupdateBEFORE UPDATE OR INSERT ON test_rumFOR 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)

F.50.5.2.  rum_tsvector_addon_ops Example

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)

F.50.5.3.  rum_tsquery_ops Example

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)

F.50.5.4.  rum_anyarray_ops Example

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)

F.50.6. Authors

Alexander Korotkov

Oleg Bartunov

Teodor Sigaev


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