Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.sai.msu.su/~megera/postgres/talks/pgcon-2014-vodka.pdf Äàòà èçìåíåíèÿ: Fri May 23 22:32:40 2014 Äàòà èíäåêñèðîâàíèÿ: Mon Apr 11 08:02:06 2016 Êîäèðîâêà:

CREATE INDEX ... USING VODKA
An efcient indexing of nested structures Oleg Bartunov (MSU), Teodor Sigaev (MSU), Alexander Korotkov (MEPhI)


Oleg Bartunov, Teodor Sigaev
· Locale support · Extendability (indexing)
· GiST, GIN, SP-GiST

· Extensions:
· · · · · intarray pg_trgm ltree hstore, hstore v2.0 jsonb plantuner
htps://www.facebook.com/oleg.bartunov obartunov@gmail.com, teodor@sigaev.ru

· Full Text Search (FTS) · KNN-GiST


Alexander Korotkov
· · · · · Indexed regexp search GIN compression & fast scan Fast GiST build Range types indexing Split for GiST

aekorotkov@gmail.com


Agenda
· · · · Introducton to jsonb indexing Jsquery - Jsonb Query Language Exercises on jsonb GIN opclasses with Jsquery support VODKA access method


Schema-less data in PostgreSQL
key-value model document-based model
· · · · · First unpublished version of hstore (May 16, 2003) Dec 05, 2006 - hstore is a part of PostgreSQL 8.2 May 23, 2007 - GIN index for hstore, PostgreSQL 8.3 Sep, 20, 2010 - Andrew Gierth improved hstore, PostgreSQL 9.0 Json data type (text), PostgreSQL 9.2

· One step forward true json data type.Nested hstore with arrays support - PGCon-2013 · Binary storage for nested data structuresand applicaton to hstore data type -PGConf-2013

- 9.4dev, Mar 23, 2014 · A lot of community work on improving jsonb -- stll ...
· pgsql: Introduce jsonb, a structured format for storing json.


Jsonb vs Json
SELECT '{"c":0, "a":2,"a":1}'::json, '{"c":0, json | jsonb -----------------------+-----------------{"c":0, "a":2,"a":1} | {"a": 1, "c": 0} (1 row) "a":2,"a":1}'::jsonb;

· · · ·

jso jso jso jso

n: nb: nb: nb:

textual storage «as is» no whitespaces no duplicate keys, last key win keys are sorted


Jsonb vs Json
· Data · 1,252,973 Delicious bookmarks · Server · MBA, 8 GB RAM, 256 GB SSD · Test · Input performance - copy data to table · Access performance - get value by key · Search performance contains @> operator


Jsonb vs Json
· Data · 1,252,973 bookmarks from Delicious in json format (js) · The same bookmarks in jsonb format (jb) · The same bookmarks as text (tx)
=# \dt+ Schema | Name --------+-----public | jb public | js public | tx | +| | | List Type | ------+table | table | table | of relations Owner | Size | Description ---------+---------+------------postgres | 1374 MB | overhead is < 4% postgres | 1322 MB | postgres | 1322 MB |


Jsonb vs Json
· Input performance (parser) Copy data (1,252,973 rows) as text, json,jsonb
copy t from '/path/to/test.dump'

Text: 34 s Json: 37 s Jsonb: 43 s

- as is - json validaton - json validaton, binary storage


Jsonb vs Json (binary storage)
· Access performance -- get value by key
· Base: · Jsonb: · Json: SELECT js FROM js; SELECT j->>'updated' FROM jb; SELECT j->>'updated' FROM js;

Base: Jsonb: Json:

0.6 s 1s 9.6 s

0.4 9

Jsonb ~ 20X faster Json


Jsonb vs Json
EXPLAIN ANALYZE SELECt count(*) FROM js WHERE js #>>'{tags,0,term}' = 'NYC'; QUERY PLAN ---------------------------------------------------------------------------Aggregate (cost=187812.38..187812.39 rows=1 width=0) (actual time=10054.602..10054.602 rows=1 loops=1) -> Seq Scan on js (cost=0.00..187796.88 rows=6201 width=0) (actual time=0.030..10054.426 rows=123 loops=1) Filter: ((js #>> '{tags,0,term}'::text[]) = 'NYC'::text) Rows Removed by Filter: 1252850 Planning time: 0.078 ms Execution runtime: 10054.635 ms (6 rows)

Json: no contains @> operator, search frst array element


Jsonb vs Json (binary storage)
EXPLAIN ANALYZE SELECT count(*) FROM jb WHERE jb @> '{"tags":[{"term":"NYC"}]}'::jsonb; QUERY PLAN --------------------------------------------------------------------------------------Aggregate (cost=191521.30..191521.31 rows=1 width=0) (actual time=1263.201..1263.201 rows=1 loops=1) -> Seq Scan on jb (cost=0.00..191518.16 rows=1253 width=0) (actual time=0.007..1263.065 rows=285 loops=1) Filter: (jb @> '{"tags": [{"term": "NYC"}]}'::jsonb) Rows Removed by Filter: 1252688 Planning time: 0.065 ms Execution runtime: 1263.225 ms Execution runtime: 10054.635 ms (6 rows)

Jsonb ~ 10X faster Json


Jsonb vs Json (GIN: key && value)
CREATE INDEX gin_jb_idx ON jb USING gin(jb);
EXPLAIN ANALYZE SELECT count(*) FROM jb WHERE jb @> '{"tags":[{"term":"NYC"}]}'::jsonb; QUERY PLAN --------------------------------------------------------------------------------------Aggregate (cost=4772.72..4772.73 rows=1 width=0) (actual time=8.486..8.486 rows=1 loops=1) -> Bitmap Heap Scan on jb (cost=73.71..4769.59 rows=1253 width=0) (actual time=8.049..8.462 rows=285 loops=1) Recheck Cond: (jb @> '{"tags": [{"term": "NYC"}]}'::jsonb) Heap Blocks: exact=285 -> Bitmap Index Scan on gin_jb_idx (cost=0.00..73.40 rows=1253 width=0) (actual time=8.014..8.014 rows=285 loops=1) Index Cond: (jb @> '{"tags": [{"term": "NYC"}]}'::jsonb) Planning time: 0.115 ms Execution runtime: 8.515 ms Execution runtime: 10054.635 ms (8 rows)

Jsonb ~ 150X faster Json


Jsonb vs Json (GIN: hash path.value)
CREATE INDEX gin_jb_path_idx ON jb USING gin(jb jsonb_path_ops);
EXPLAIN ANALYZE SELECT count(*) FROM jb WHERE jb @> '{"tags":[{"term":"NYC"}]}'::jsonb; QUERY PLAN --------------------------------------------------------------------------------------Aggregate (cost=4732.72..4732.73 rows=1 width=0) (actual time=0.644..0.644 rows=1 loops=1) -> Bitmap Heap Scan on jb (cost=33.71..4729.59 rows=1253 width=0) (actual time=0.102..0.620 rows=285 loops=1) Recheck Cond: (jb @> '{"tags": [{"term": "NYC"}]}'::jsonb) Heap Blocks: exact=285 -> Bitmap Index Scan on gin_jb_path_idx (cost=0.00..33.40 rows=1253 width=0) (actual time=0.062..0.062 rows=285 loops=1) Index Cond: (jb @> '{"tags": [{"term": "NYC"}]}'::jsonb) Planning time: 0.056 ms Execution runtime: 0.668 ms Execution runtime: 10054.635 ms (8 rows)

Jsonb ~ 1800X faster Json


MongoDB 2.6.0
· Load data - ~13 min SLOW !
mongoimport 2014-04-08T2 ... 2014-04-08T2 2014-04-08T2 2014-04-08T2 3:00:36.050+0400 3:00:36.565+0400 check 9 1252973 3:00:36.566+0400 imported 1252973 objects

Jsonb 43 s
1233/second 1252000 1547/second

--host localhost -c js --type json < delicious-rss-1250k 2:47:10.014+0400 3700

· Search - ~ 1s (seqscan) THE SAME
db.js.fnd({tags: {$elemMatch:{ term: "NYC"}}}).count() 285 -- 980 ms

· Search - ~ 1ms (indexscan) Jsonb 0.7ms
db.js.ensureIndex( {"tags.term" : 1} ) db.js.fnd({tags: {$elemMatch:{ term: "NYC"}}}).


Summary: PostgreSQL 9.4 vs Mongo 2.6.0
· Operator contains @>
· · · · json jsonb jsonb mongo : : : : 10 8.5 0.7 1.0 s ms ms ms seqscan GIN jsonb_ops GIN jsonb_path_ops btree index - 636 - 295 - 44 m) - 387 - 100

·Table size

· Index size
· jsonb_ops jsonb_path_ops · jsonb_path_ops (tags) jsonb_path_ops (tags.ter · mongo (tags) mongo (tags.term)

· Text : · Json : · Jsonb : Mb (no compression, 815Mb) · mongo : Mb Mb USING gin((jb->'tags') jsonb_path_ops 1.6 Mb Mb Mb

·Input performance:
34 37 43 13 s s s m

·postgres : 1.3Gb ·mongo : 1.8Gb


Jsonb query
· Currently, one can search jsonb data using
· Contains operators - jsonb @> jsonb, jsonb <@ jsonb (GIN indexes)
jb @> '{"tags":[{"term":"NYC"}]}'::jsonb

Keys should be specifed from root Equivalence operator -- jsonb = jsonb (GIN indexes) · Exists operators -- jsonb ? text, jsonb ?! text[], jsonb ?& text[] (GIN indexes) Only root keys supported · Operators on jsonb parts (functonal indexes)
jb WHERE jb ?| '{tags,links}'

SELECT ('{"a": {"b":5}}'::jsonb -> 'a'->>'b')::int > 2; CREATE INDEX ....USING BTREE ( (jb->'a'->>'b')::int);

Very cumbersome, too many functonal indexes


Jsonb query
· Need Jsonb query language
· · · · More operators on keys, values Types support Schema support (constraints on keys, values) Indexes support

· Introduce Jsquery - textual data type and @@ match operator

jsonb @@ jsquery


Jsonb query language (Jsquery)
expr ::= path value_expr | path '(' expr ')' | '(' expr ')' | '!' expr | expr '&' expr | expr '|' expr value_expr ::= '=' scalar_value | IN '(' value_list ')' | '=' array | '=' '*' | '<' NUMERIC | '<' '=' NUMERIC | '>' NUMERIC | '>' '=' NUMERIC | '@' '>' array | '<' '@' array | '&' '&' array path ::= path_elem | path '.' path_elem value_list ::= scalar_value | value_list ',' scalar_value array ::= '[' value_list ']'

path_elem ::= '*' | '#' | '%' | '$' | key key ::= STRING | true | false | NUMERIC | null | IN

scalar_value ::= null | STRING | IN | true | false | NUMERIC


Jsonb query language (Jsquery)
· # - any element array
SELECT '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b.# = 2';

path

::= path_elem | path '.' path_elem

· % - any key
SELECT '{"a": {"b": [1,2,3]}}'::jsonb @@ '%.b.# = 2';

· * - anything
SELECT '{"a": {"b": [1,2,3]}}'::jsonb @@ '*.# = 2';

path_elem ::= '*' | '#' | '%' | '$' | key key ::= STRING | true | false | NUMERIC | null | IN

· $ - current element
select '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b.# ($ = 2 | $ < 3)';

· Use "double quotes" for key !
select 'a1."12222" < 111'::jsquery;


Jsonb query language (Jsquery)
· Scalar
select '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b.# IN (1,2,5)';

· Test for key existence
select '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b = *';

· Array overlap
select '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b && [1,2,5]';

· Array contains
select '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b @> [1,2]';

value_expr ::= '=' scalar_value | IN '(' value_list ')' | '=' array | '=' '*' | '<' NUMERIC | '<' '=' NUMERIC | '>' NUMERIC | '>' '=' NUMERIC | '@' '>' array | '<' '@' array | '&' '&' array

· Array contained
select '{"a": {"b": [1,2,3]}}'::jsonb @@ 'a.b <@ [1,2,3,4,5]';


Jsonb query language (Jsquery)
· How many products are similar to "B000089778" and have
product_sales_rank in range between 10000-20000 ?

· SQL

SELECT count(*) FROM jr WHERE (jr->>'product_sales_rank')::int > 10000 and (jr->> 'product_sales_rank')::int < 20000 and ....boring stuff SELECT count(*) FROM jr WHERE jr @@ ' similar_product_ids && ["B000089778"] & product_sales_rank( $ > 10000 & $ < 20000)'

· Jsquery · Mongodb
db.reviews.fnd( { $and :[ {similar_product_ids: { $in ["B000089778"]}}, {product_sales_rank:{$gt:10000, $lt:20000}}] } ).count()


Jsonb query language (Jsquery)
explain( analyze, buffers) select count(*) from jb where jb @> '{"tags":[{"term":"NYC"}]}'::jsonb; QUERY PLAN --------------------------------------------------------------------------------------------------------------Aggregate (cost=191517.30..191517.31 rows=1 width=0) (actual time=1039.422..1039.423 rows=1 loops=1) Buffers: shared hit=97841 read=78011 -> Seq Scan on jb (cost=0.00..191514.16 rows=1253 width=0) (actual time=0.006..1039.310 rows=285 loops=1) Filter: (jb @> '{"tags": [{"term": "NYC"}]}'::jsonb) Rows Removed by Filter: 1252688 Buffers: shared hit=97841 read=78011 Planning time: 0.074 ms

Execution time: 1039.444 ms
explain( analyze,costs off) select count(*) from jb where jb @@ 'tags.#.term = "NYC"'; QUERY PLAN -------------------------------------------------------------------Aggregate (actual time=891.707..891.707 rows=1 loops=1) -> Seq Scan on jb (actual time=0.010..891.553 rows=285 loops=1) Filter: (jb @@ '"tags".#."term" = "NYC"'::jsquery) Rows Removed by Filter: 1252688

Execution time: 891.745 ms


Jsquery (indexes)
· GIN opclasses with jsquery support
· jsonb_value_path_ops -- use Bloom fltering for key matching {"a":{"b":{"c":10}}} 10.( bloom(a) or bloom(b) or bloom(c) )
· Good for key matching (wildcard support) , not good for range query

· jsonb_path_value_ops -- hash path (like jsonb_path_ops) {"a":{"b":{"c":10}}} hash(a.b.c).10
· No wildcard support, no problem with ranges
List of relations Schema | Name | Type | Owner | Table | Size | Description --------+-------------------------+-------+----------+--------------+---------+------------public | jb | table | postgres | | 1374 MB | public | jb_value_path_idx | index | postgres | jb | 306 MB | public | jb_gin_idx | index | postgres | jb | 544 MB | public | jb_path_value_idx | index | postgres | jb | 306 MB | public | jb_path_idx | index | postgres | jb | 251 MB |


Jsquery (indexes)
explain( analyze,costs off) select count(*) from jb where jb @@ 'tags.#.term = "NYC"'; QUERY PLAN ------------------------------------------------------------------------------------------------Aggregate (actual time=0.609..0.609 rows=1 loops=1) -> Bitmap Heap Scan on jb (actual time=0.115..0.580 rows=285 loops=1) Recheck Cond: (jb @@ '"tags".#."term" = "NYC"'::jsquery) Heap Blocks: exact=285 -> Bitmap Index Scan on jb_value_path_idx (actual time=0.073..0.073 rows=285 loops=1) Index Cond: (jb @@ '"tags".#."term" = "NYC"'::jsquery) Execution time: 0.634 ms (7 rows)


Jsquery (indexes)
explain( analyze,costs off) select count(*) from jb where jb @@ '*.term = "NYC"'; QUERY PLAN ------------------------------------------------------------------------------------------------Aggregate (actual time=0.688..0.688 rows=1 loops=1) -> Bitmap Heap Scan on jb (actual time=0.145..0.660 rows=285 loops=1) Recheck Cond: (jb @@ '*."term" = "NYC"'::jsquery) Heap Blocks: exact=285 -> Bitmap Index Scan on jb_value_path_idx (actual time=0.113..0.113 rows=285 loops=1) Index Cond: (jb @@ '*."term" = "NYC"'::jsquery) Execution time: 0.716 ms (7 rows)


Citus dataset
· 3023162 reviews from Citus 1998-2000 years · 1573 MB

{ "customer_id": "AE22YDHSBFYIP", "product_category": "Business & Investing", "product_group": "Book", "product_id": "1551803542", "product_sales_rank": 11611, "product_subcategory": "General", "product_title": "Start and Run a Coffee Bar (Start & Run a)", "review_date": { "$date": 31363200000 }, "review_helpful_votes": 0, "review_rating": 5, "review_votes": 10, "similar_product_ids": [ "0471136174", "0910627312", "047112138X", "0786883561", "0201570483" ] }


Jsquery (indexes)
explain (analyze, costs off) select count(*) from jr where jr @@ ' similar_product_ids && ["B000089778"]'; QUERY PLAN -----------------------------------------------------------------------------------------------Aggregate (actual time=0.359..0.359 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=0.084..0.337 rows=185 loops=1) Recheck Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery) Heap Blocks: exact=107 -> Bitmap Index Scan on jr_path_value_idx (actual time=0.057..0.057 rows=185 loops=1) Index Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery) Execution time: 0.394 ms (7 rows)


Jsquery (indexes)
· No statstcs, no planning :(
explain (analyze, costs off) select count(*) from jr where jr @@ ' similar_product_ids && ["B000089778"] & product_sales_rank( $ > 10000 & $ < 20000)'; QUERY PLAN -------------------------------------------------------------------------------------------------------------------------------------Aggregate (actual time=126.149..126.149 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=126.057..126.143 rows=45 loops=1) Recheck Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "product_sales_rank"($ > 10000 & $ < 20000))'::jsquery) Heap Blocks: exact=45 -> Bitmap Index Scan on jr_path_value_idx (actual time=126.029..126.029 rows=45 loops=1) Index Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "product_sales_rank"($ > 10000 & $ < 20000))'::jsquery) Execution time: 129.309 ms !!! No statistics (7 rows)


MongoDB 2.6.0
db.reviews.fnd( { $and :[ {similar_product_ids: { $in:["B000089778"]}}, {product_sales_rank:{$gt:10000, $lt:20000}}] } ) .explain() { "n" : 45, .................... "millis" : 7, "indexBounds" : { "similar_product_ids" : [ index size = 400 MB just for similar_product_ids !!! [ "B000089778", "B000089778" ] ] }, }


Jsquery (indexes)
· Need statstcs and planner support !
explain (analyze,costs off) select count(*) from jr where jr @@ ' similar_product_ids && ["B000089778"]' and (jr->>'product_sales_rank')::int>10000 and (jr->>'product_sales_rank')::int<20000; ----------------------------------------------------------------------------------------------------------------------------------------Aggregate (actual time=0.479..0.479 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=0.079..0.472 rows=45 loops=1) Recheck Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery) Filter: ((((jr ->> 'product_sales_rank'::text))::integer > 10000) AND (((jr ->> 'product_sales_rank'::text))::integer < 20000)) Rows Removed by Filter: 140 Heap Blocks: exact=107 -> Bitmap Index Scan on jr_path_value_idx (actual time=0.041..0.041 rows=185 loops=1) Index Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery) Execution time: 0.506 ms (9 rows)


Contrib/jsquery
· PostgreSQL has potental to execute jsquery for 0.5 ms vs Mongo 7 ms !
· Need statstcs · Need planner

· Availability
· Jsquery + opclasses are available as extensions · Grab it from htps://github.com/akorotkov/jsquery (branch master) , we need your feedback ! · We will release it afer PostgreSQL 9.4 release · Need real sample data and queries !


Beter indexing ...
· GIN is a proven and efectve index access method · Need indexing for jsonb with operatons on paths (no hash!) and values
· B-tree in entry tree is not good - length limit, no prefx compression
List of relations Schema | Name | Type | Owner | Table | Size | Description --------+-----------------------------+-------+----------+---------------+---------+------------public | jb | table | postgres | | 1374 MB | public | jb_uniq_paths | table | postgres | | 912 MB | public | jb_uniq_paths_btree_idx | index | postgres | jb_uniq_paths | 885 MB |text_pattern_ops public | jb_uniq_paths_spgist_idx | index | postgres | jb_uniq_paths | 598 MB |now much less !


Beter indexing ...
· Provide interface to change hardcoded B-tree in Entry tree
· Use spgist opclass for storing paths and values as is (strings hashed in values)

· We may go further - provide interface to change hardcoded B-tree in postng tree
· GIS aware full text search !

· New index access method

CREATE INDEX ... USING VODKA


GIN History
· Introduced at PostgreSQL Anniversary Meetng in Toronto, Jul 7-8, 2006 by Oleg Bartunov and Teodor Sigaev


GIN History
· Introduced at PostgreSQL Anniversary Meetng in Toronto, Jul 7-8, 2006 by Oleg Bartunov and Teodor Sigaev · Supported by JFG Networks (France) · «Gin stands for Generalized Inverted iNdex and should be considered as a genie, not a drink.» · Alexander Korotkov, Heikki Linnakangas have joined GIN++ development in 2013


GIN History
· From GIN Readme, posted in -hackers, 2006-04-26
TODO ---Nearest future: * Opclasses for all types (no programming, just many catalog changes). Distant future: * Replace B-tree of entries to something like GiST (VODKA ! 2014) * Add multicolumn support * Optimize insert operations (background index insertion)


GIN index structure for jsonb
{ }, { } "product_group": "Book", "product_sales_rank": 15000 "product_group": "Music", "product_sales_rank": 25000


Vodka index structure for jsonb
{ }, { } "product_group": "Book", "product_sales_rank": 15000 "product_group": "Music", "product_sales_rank": 25000


Vodka distlling instructons
· confg -- confgures parameters
· entry tree opclass · equality operator

· compare -- compares entry tree parameters (as in GIN) · extract value -- decompose datum into entries (as in GIN) · extract query -- decompose query into keys:
· operator to scan entry tree · argument to scan entry tree

· consistent -- check if item satsfes query (as in GIN) · triconsistent -- check if item satsfes query in ternary logic (as in GIN)


CREATE INDEX ... USING VODKA
· Delicious bookmarks, mostly text data
set maintenance_work_mem = '1GB'; List of relations Schema | Name | Type | Owner | Table | Size | Description --------+--------------------+-------+----------+-------+---------+------------public | jb | table | postgres | | 1374 MB | 1252973 rows public | jb_value_path_idx | index | postgres | jb | 306 MB | 98769.096 public | jb_gin_idx | index | postgres | jb | 544 MB | 129860.859 public | jb_path_value_idx | index | postgres | jb | 306 MB | 100560.313 public | jb_path_idx | index | postgres | jb | 251 MB | 68880.320 public | jb_vodka_idx | index | postgres | jb | 409 MB | 185362.865 public | jb_vodka_idx5 | index | postgres | jb | 325 MB | 174627.234 new spgist (6 rows)


CREATE INDEX ... USING VODKA
select count(*) from jb where jb @@ 'tags.#.term = "NYC"'; ------------------------------------------------------------------------------------------Aggregate (actual time=0.423..0.423 rows=1 loops=1) -> Bitmap Heap Scan on jb (actual time=0.146..0.404 rows=285 loops=1) Recheck Cond: (jb @@ '"tags".#."term" = "NYC"'::jsquery) Heap Blocks: exact=285 -> Bitmap Index Scan on jb_vodka_idx (actual time=0.108..0.108 rows=285 loops=1) Index Cond: (jb @@ '"tags".#."term" = "NYC"'::jsquery)

Execution time: 0.456 ms (0.634 ms, GIN jsonb_value_path_ops)
select count(*) from jb where jb @@ '*.term = "NYC"'; ------------------------------------------------------------------------------------------Aggregate (actual time=0.495..0.495 rows=1 loops=1) -> Bitmap Heap Scan on jb (actual time=0.245..0.474 rows=285 loops=1) Recheck Cond: (jb @@ '*."term" = "NYC"'::jsquery) Heap Blocks: exact=285 -> Bitmap Index Scan on jb_vodka_idx (actual time=0.214..0.214 rows=285 loops=1) Index Cond: (jb @@ '*."term" = "NYC"'::jsquery)

Execution time: 0.526 ms (0.716 ms, GIN jsonb_path_value_ops)


CREATE INDEX ... USING VODKA
· CITUS data, text and numeric
set maintenance_work_mem = '1GB'; List of relations Schema | Name | Type | Owner | Table | Size | Description --------+--------------------+-------+----------+-------+---------+------------public | jr | table | postgres | | 1573 MB | 3023162 rows public | jr_value_path_idx | index | postgres | jr | 196 MB | 79180.120 public | jr_gin_idx | index | postgres | jr | 235 MB | 111814.929 public | jr_path_value_idx | index | postgres | jr | 196 MB | 73369.713 public | jr_path_idx | index | postgres | jr | 180 MB | 48981.307 public | jr_vodka_idx3 | index | postgres | jr | 240 MB | 155714.777 public | jr_vodka_idx4 | index | postgres | jr | 211 MB | 169440.130 new spgist (6 rows)


CREATE INDEX ... USING VODKA
explain (analyze, costs off) select count(*) from jr where jr @@ ' similar_product_ids && ["B000089778"]'; QUERY PLAN ------------------------------------------------------------------------------------------Aggregate (actual time=0.200..0.200 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=0.090..0.183 rows=185 loops=1) Recheck Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery) Heap Blocks: exact=107 -> Bitmap Index Scan on jr_vodka_idx (actual time=0.077..0.077 rows=185 loops=1) Index Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery)

Execution time: 0.237 ms (0.394 ms, GIN jsonb_path_value_idx)
(7 rows)


CREATE INDEX ... USING VODKA
· No statstcs, no planning :(
select count(*) from jr where jr @@ ' similar_product_ids && ["B000089778"] & product_sales_rank( $ > 10000 & $ < 20000)'; QUERY PLAN -------------------------------------------------------------------------------------------------------------------------------------Aggregate (actual time=127.471..127.471 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=127.416..127.461 rows=45 loops=1) Recheck Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "product_sales_rank"($ > 10000 & $ < 20000))'::jsquery) Heap Blocks: exact=45 -> Bitmap Index Scan on jr_vodka_idx (actual time=127.400..127.400 rows=45 loops=1) Index Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "product_sales_rank"($ > 10000 & $ < 20000))'::jsquery) Execution time: 130.051 ms (7 rows)


CREATE INDEX ... USING VODKA
· No statstcs, no planning :(
select count(*) from jr where jr @@ ' similar_product_ids && ["B000089778"]' and (jr->>'product_sales_rank')::int>10000 and (jr->>'product_sales_rank')::int<20000; QUERY PLAN ----------------------------------------------------------------------------------------------------------------------------------------Aggregate (actual time=0.401..0.401 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=0.109..0.395 rows=45 loops=1) Recheck Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery) Filter: ((((jr ->> 'product_sales_rank'::text))::integer > 10000) AND (((jr ->> 'product_sales_rank'::text))::integer < 20000)) Rows Removed by Filter: 140 Heap Blocks: exact=107 -> Bitmap Index Scan on jr_vodka_idx (actual time=0.079..0.079 rows=185 loops=1) Index Cond: (jr @@ '"similar_product_ids" && ["B000089778"]'::jsquery)

Execution time: 0.431 ms (7 ms, MongoDB)
(9 rows)

BIG Potental !


CREATE INDEX ... USING VODKA
select count(*) from jr where jr @@ 'similar_product_ids && ["B000089778"] & review_rating($> 3 & $<5)'; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------Aggregate (actual time=98.313..98.314 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=98.273..98.307 rows=32 loops=1) Recheck Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "review_rating"($ > 3 & $ < 5))'::jsquery) Heap Blocks: exact=16 -> Bitmap Index Scan on jr_path_value_idx (actual time=98.254..98.254 rows=32 loops=1) Index Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "review_rating"($ > 3 & $ < 5))'::jsquery)

Execution time: 99.873 ms
------------------------------------------------------------------------------------------------------------------------Aggregate (actual time=1.521..1.521 rows=1 loops=1) -> Bitmap Heap Scan on jr (actual time=1.503..1.515 rows=32 loops=1) Recheck Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "review_rating"($ > 3 & $ < 5))'::jsquery) Heap Blocks: exact=16 -> Bitmap Index Scan on jr_vodka_idx (actual time=1.498..1.498 rows=32 loops=1) Index Cond: (jr @@ '("similar_product_ids" && ["B000089778"] & "review_rating"($ > 3 & $ < 5))'::jsquery)

Execution time: 1.550 ms (FAST SCAN !)


Need positonal informaton for both GIN and VODKA
[{"f1": 23, "f2": 46}, {"f1": 42, "f2": 24}, {"f1": 98, "f2": 2}, {"f1": 62, "f2": 70}, {"f1": 66, "f2": 41}, {"f1": 32, "f2": 95}, {"f1": 11, "f2": 64}, {"f1": 20, "f2": 27}, {"f1": 45, "f2": 42}, {"f1": 14, "f2": 53}] # explain analyze select * from test where v @@ '#(f1 = 10 & f2 = 20)'; - HUGE RECHECK!
QUERY PLAN --------------------------------------------------------------------------------------------------------------------------Bitmap Heap Scan on test (cost=191.75..3812.68 rows=1000 width=32) (actual time=14.576..35.039 rows=1005 loops=1) Recheck Cond: (v @@ '#("f1" = 10 & "f2" = 20)'::jsquery) Rows Removed by Index Recheck: 7998 Heap Blocks: exact=8416 -> Bitmap Index Scan on test_idx (cost=0.00..191.50 rows=1000 width=0) (actual time=13.396..13.396 rows=9003 loops=1) Index Cond: (v @@ '#("f1" = 10 & "f2" = 20)'::jsquery) Execution time: 35.329 ms


There are can be diferent favors of Vodka


Spaghet indexing ...

Find twirled spaghetti


Spaghet indexing ...

R-tree fails here -- bounding box of each separate spaghetti is the same


Spaghet indexing ...

R-tree fails here -- bounding box of each separate spaghetti is the same


Otawa downtown: York and George streets


Idea: Use multple boxes


Rtree Vodka


Rtree Vodka
· R-tree based on GiST as Entry tree · An algorithm for covering polygons with rectangles ? · Need support -- POSTGIS ?


Vodka problems
· How to update ItemPointer in entry tree? Insert new then vacuum is weird and expensive. amupdateiptr? · Now it's hard to get OIDs of extension opclass or operator. · Storing entry tree in separate fle: is it correct? What infrastructure should handle it? · TODO: replacing postng tree with arbitrary access method. Have to share multple indexes in same fle. How am interface can handle it? Pass root block number to am? How to vacuum?


Summary
· contrib/jsquery for 9.4
· Jsquery - Jsonb Query Language · Two GIN opclasses with jsquery support · Grab it from htps://github.com/akorotkov/jsquery (branch master)

· Prototype of VODKA access method · Plans for improving indexing infrastructure · This work was supported by


Meet us in Madrid with beter VODK A !

!


VODKA Optimized Dendriform Keys Array