F.57. rum
F.57.1. Introduction
The rum module provides access method to work with the RUM indexes. It is based on the GIN access method code.
GIN index allows you to perform fast full-text search using tsvector and tsquery types. However, full-text search with GIN index has some performance issues because positional and other additional information is not stored.
RUM solves these issues by storing additional information in a posting tree. As compared to GIN, RUM index has the following benefits:
Faster ranking. Ranking requires positional information. And after the index scan we do not need an additional heap scan to retrieve lexeme positions because RUM index stores them.
Faster phrase search. This improvement is related to the previous one as phrase search also needs positional information.
Faster ordering by timestamp. RUM index stores additional information together with lexemes, so it is not necessary to perform a heap scan.
A possibility to perform depth-first search and therefore return first results immediately.
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.
F.57.2. Installation
rum is a Postgres Pro Standard extension and it has no special prerequisites.
Install extension as follows:
$ psql dbname -c "CREATE EXTENSION rum"
F.57.3. Common Operators
The operators provided by the rum module are shown in Table F.35:
Table F.35. 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. |
Note
rum introduces its own ranking function that is executed inside the <=> operator. It calculates the score (inverted distance) using the specified normalization method. This function is a combination of ts_rank and ts_rank_cd (see Section 9.13 for details). While ts_rank does not support logical operators and ts_rank_cd works poorly with OR queries, the rum-specific ranking function overcomes these drawbacks.
F.57.4. Operator Classes
The rum extension provides the following operator classes:
rum_tsvector_opsStores
tsvectorlexemes with positional information. Supports ordering by<=>operator and prefix search.rum_tsvector_hash_opsStores hash of
tsvectorlexemes with positional information. Supports ordering by<=>operator, but does not support prefix search.rum_tsvector_addon_opsStores
tsvectorlexemes with additional data of any type supported by RUM.rum_tsvector_hash_addon_opsStores
tsvectorlexemes with additional data of any type supported by RUM. Does not support prefix search.rum_tsquery_opsStores branches of query tree in additional information.
rum_anyarray_opsStores
anyarrayelements with length of the array. Supports ordering by <=> operator.Indexable operators:
&& @> <@ = %rum_anyarray_addon_opsStores
anyarrayelements with additional data of any type supported by RUM.rum_type_opsStores lexemes of the corresponding type with positional information. The
typeplaceholder 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 bytype_ops<=>,<=|, and|=>operators. This operator class can be used together withrum_tsvector_addon_ops,rum_tsvector_hash_addon_ops, andrum_anyarray_addon_opsoperator 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.57.5. Functions
The RUM index provides functions for low-level inspection of all its page types.
-
rum_metapage_info(rel_nametext,blk_numint4) returnsrecord Returns information about a RUM index metapage. For example:
SELECT * FROM rum_metapage_info('rum_index', 0); -[ RECORD 1 ]----+----------- pending_head | 4294967295 pending_tail | 4294967295 tail_free_size | 0 n_pending_pages | 0 n_pending_tuples | 0 n_total_pages | 87 n_entry_pages | 80 n_data_pages | 6 n_entries | 1650 version | 0xC0DE0002-
rum_page_opaque_info(rel_nametext,blk_numint4) returnsrecord Returns information about a RUM index opaque area, such as
leftlinkandrightlink,maxoff, andfreespace. Themaxoffparameter is the number of elements stored in the page, it is used differently for different types of pages. Thefreespacecolumn represents the amount of free space in the page. For example:SELECT * FROM rum_page_opaque_info('rum_index', 10); leftlink | rightlink | maxoff | freespace | flags ----------+-----------+--------+-----------+-------- 6 | 11 | 0 | 0 | {leaf} (1 row)-
rum_internal_entry_page_items(rel_nametext,blk_numint4) returnssetof record Returns information stored in internal pages of the entry tree. This information is extracted from
IndexTuple. For example:SELECT * FROM rum_internal_entry_page_items('rum_index', 1); key | attrnum | category | down_link ---------------------------------+---------+------------------+----------- 3d | 1 | RUM_CAT_NORM_KEY | 3 6k | 1 | RUM_CAT_NORM_KEY | 2 a8 | 1 | RUM_CAT_NORM_KEY | 4 ... Tue May 10 21:21:22.326724 2016 | 2 | RUM_CAT_NORM_KEY | 83 Sat May 14 19:21:22.326724 2016 | 2 | RUM_CAT_NORM_KEY | 84 Wed May 18 17:21:22.326724 2016 | 2 | RUM_CAT_NORM_KEY | 85 +inf | | | 86 (79 rows)The RUM index, just like GIN, packs downlinks and keys in pairs of the
(P_n, K_{n+1})type on internal pages of the entry tree. Note that there is no key forP_0, it is assumed to be equal to-inf. Also, there is no downlink for the last keyK_{n+1}, it is assumed to be the largest key, or high key, in the subtree to which theP_nlink leads. In the rightmost page of each internal level of the entry tree, the key related toP_ndoes not have any value and is assumed to be equal to+inf.-
rum_leaf_entry_page_items(rel_nametext,blk_numint4) returnssetof record Returns information that is stored in leaf pages of the entry tree. This information is extracted from compressed posting lists. For example:
SELECT * FROM rum_leaf_entry_page_items('rum_index', 10); key | attrnum | category | tuple_id | add_info_is_null | add_info | is_posting_tree | posting_tree_root -----+---------+------------------+----------+------------------+----------+------------------+-------------------- ay | 1 | RUM_CAT_NORM_KEY | (0,16) | t | | f | ay | 1 | RUM_CAT_NORM_KEY | (0,23) | t | | f | ay | 1 | RUM_CAT_NORM_KEY | (2,1) | t | | f | ... az | 1 | RUM_CAT_NORM_KEY | (0,15) | t | | f | az | 1 | RUM_CAT_NORM_KEY | (0,22) | t | | f | az | 1 | RUM_CAT_NORM_KEY | (1,4) | t | | f | ... b9 | 1 | RUM_CAT_NORM_KEY | | | | t | 7 ... (1602 rows)Each posting list is an
IndexTuplethat stores the key value and a compressed list of TIDs. When therum_leaf_entry_page_items()function is called, the key value is attached to each TID for convenience, but in the page it is stored separately.If the number of TIDs is too large, then a posting tree is used for storage rather than a posting list. In the example above, a posting tree is created for the key with the
b9value. The key in the posting tree is the TID. In this case, the magic number and the page number, which is the root of the posting tree, are stored insideIndexTupleinstead of the posting list.-
rum_internal_data_page_items(rel_nametext,blk_numint4) returnssetof record Returns information that is stored in internal pages of the posting tree. This information is extracted from arrays of the
RumPostingItemstructures. For example:SELECT * FROM rum_internal_data_page_items('rum_index', 7); is_high_key | block_number | tuple_id | add_info_is_null | add_info -------------+--------------+----------+------------------+---------- t | | (0,0) | t | f | 9 | (138,79) | t | f | 8 | (0,0) | t | (3 rows)Each internal page element of the posting tree contains the high key (TID) value for the child page and a link to this child page as well as additional information if it was added when creating the index.
At the beginning of am internal page of the posting tree, the high key of this page is always stored if it has the
(0,0)value, this is equivalent to+inf. This is always the case if the page is the rightmost.At the moment, RUM does not support storing data of the
varlenadata types in internal pages of the posting tree. These types of data can be stored only on leaf pages. Therefore, the following output is possible:is_high_key | block_number | tuple_id | add_info_is_null | add_info -------------+--------------+----------+------------------+------------------------------------------------ ... f | 23 | (39,43) | f | varlena types in posting tree is not supported f | 22 | (74,9) | f | varlena types in posting tree is not supported ...
-
rum_leaf_data_page_items(rel_nametext,blk_numint4) returnssetof record Returns information that is stored in leaf pages of the posting tree. This information is extracted from compressed posting lists. For example:
SELECT * FROM rum_leaf_data_page_items('rum_idx', 9); is_high_key | tuple_id | add_info_is_null | add_info -------------+-----------+------------------+---------- t | (138,79) | t | f | (0,9) | t | f | (1,23) | t | f | (3,5) | t | f | (3,22) | t |Unlike leaf pages of the entry tree, compressed posting lists are not stored in
IndexTuplein the posting tree leaf pages. The high key is the largest key on the page.
F.57.6. Examples
F.57.6.1. rum_tsvector_ops Example
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)
F.57.6.2. 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.57.6.3. 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.57.6.4. 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.57.7. Authors
Alexander Korotkov
Oleg Bartunov
Teodor Sigaev