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Indexes in PostgreSQL (10)

The document presents a detailed overview of PostgreSQL indexes, focusing on the improvements and new features introduced in version 10, including parallelism, hash indexing, and support for sp-gist on inet data types. It discusses various access methods, their structures, and performance characteristics, as well as execution plans and their optimizations. The presentation also highlights the logging of hash indexes and enhancements in handling inet data with sp-gist methods.

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PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Indexes in PostgreSQL (10)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The outlineThe outline • Indexes in PostgreSQL • What’s new in v10: – Parallelism – Hash indexing – New supports for SP-GiST (inet data) – Summarization of BRINs
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com ~$ whoami~$ whoami Giuseppe BroccoloGiuseppe Broccolo - data engineer at- data engineer at - member of- member of @giubro gbroccolo7 gbroccolo gemini__81 g.broccolo.7@gmail.com
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com PostgreSQL indexesPostgreSQL indexes • AKA Access Methods
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com PostgreSQL indexesPostgreSQL indexes • AKA Access Methods – allow concurrent changes (MVCC compliant) – persist the information (WAL) – speed up access to data: • links to data blocks (sometimes can be avoided) • Indexes’ blocks live in shared buffers AWA data blocks 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB WALWALWAL sharedbuffers
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the treesThe default AMs – the trees • binary structure hierarchically sorted – nodes (values, link to pointed nodes, etc.) – pointing depends from hierarchical criteria – allow to skip orders of values • N~O(an ) n~O(logN)→
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the treesThe default AMs – the trees balanced • binary structure hierarchically sorted – nodes (values, link to pointed nodes, etc.) – pointing depends from hierarchical criteria – allow to skip orders of values • N~O(an ) n~O(logN)→ • balanced structures speed up punctual searches
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the treesThe default AMs – the trees balanced unbalanced • binary structure hierarchically sorted – nodes (values, link to pointed nodes, etc.) – pointing depends from hierarchical criteria – allow to skip orders of values • N~O(an ) n~O(logN)→ • balanced structures speed up punctual searches • unbalanced ones are quite faster for range searches
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the hashesThe default AMs – the hashes • binary maps (k: v) – k: the hash of the search key - bucket – v: the address where the key is stored – just one kind of search: = – complexity: • ~O(1) – like trees, their sizes are comparable with the indexed dataset • ~O(N) search key k: value... hashing N complexity ~O(logN) ... ~O(1)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the BRINsThe default AMs – the BRINs • Block Range Indexes: – À. Herrera, S. Riggs, H. Linnakangas (PG 9.5) – Range: summarization of adjacent-on-disk blocks – complexity: • ~O(N/K), K~10/100 • really small indexes,faster creation • ~O(N/K’), K’~1000/10000 • can be used for low-selectivity queries • low performance for “dynamic” data 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB range 0 range 1 range 2 range 3 range 7range 6range 5range 4 Summarization: blk n. xxxxx range X blk n. yyyyy blk n. zzzzz ......
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMsThe default AMs • B-tree, GIN, GiST, SP-GiST, Hash, BRIN • can add user defined new access methods – fully supported since 9.6 (thanks to postgrespro & 2ndQuadrant) • CREATE ACCESS METHOD sortable generalized balanced unbalanced trees
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Extend AMs to datatypes: the OpClassesExtend AMs to datatypes: the OpClasses • access methods use operator classes (opclass) • • • • define: – operators for the needed types – support functions depending on the access method • can be extended to specific datatypes CREATE INDEX idx_name USING method ON table (column opclass_name) WITH (opt=value); • CREATE OPERATOR CLASS opclass_name FOR TYPE datatype USING method OPERATOR $$(), [...], FUNCTION func1(), [...]
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Execution plansExecution plans • IndexScan need to inspect data pages for row visibility • IndexOnlyScan just index pages, use visibility map (PG9.2) • BitmapIndexScan BitmapHeapScan 1) reduce # of accesses using a bitmap 2) used by BRIN to inspect block ranges N complexity ~O(logN)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com What’s new in PG 10 ?What’s new in PG 10 ?
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Parallelization in index scansParallelization in index scans • parallelization is not new in PG (9.6), see G. Ciolli later – parallel B-tree index scans – parallel BitmapHeapScan (different areas of the heap are processed by parallel workers) – R. Syed, A. Kapila, R. Haas, R. Sabih, D. Kumar, R. Haas, J. Rouhaud
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Parallelization in Index ScansParallelization in Index Scans • for B-tree – Workers inspect leaf pages in parallel gather node gather node worker #1 worker #2 worker #N ... • for bitmap heap scan – Workers inspect heap chunks in parallel
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Parallelization in Index ScansParallelization in Index Scans • The parameters: – max_parallel_workers (included in max_worker_processes) – max_parallel_workers_per_gather (included in max_parallel_workers) – min_parallel_index_scan_size (512kB) • heuristic: # workers / index size > 512kB * 3# workers – parallel_setup_cost (1000.0) – parallel_tuple_cost (0.1) – force_parallel_mode (false) • tune them basing on underlying HW!
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • Ex. IndexOnlyScan on B-tree • table/B-tree ~O(300MB) =# CREATE TABLE test AS =# SELECT generate_series(1,10000000) t(i); CREATE =# CREATE INDEX btree_idx ON test USING btree (i); CREATE
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When parallelization is disabled:When parallelization is disabled: • Ex. IndexOnlyScan on B-tree: =# EXPLAIN ANALYZE SELECT * FROM test WHERE i=5; QUERY PLAN ---------------------------------------------------------- Index Only Scan using btree_id on test (cost=0.43..8.45 rows=1 width=4) (actual time=0.433..0.434 rows=1 loops=1) Index Cond: (i = 5) Heap Fetches: 1 Planning time: 0.525 ms Execution time: 0.461 ms (5 rows)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • Setup parallel executions: • • • • Plan does not change!! Force parallelization... =# SET max_parallel_workers TO 8; SET =# SET max_parallel_workers_per_gather TO 8; -- up to 6 workers SET =# SET force_parallel_mode TO true; SET
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • Ex. IndexOnlyScan on B-tree =# EXPLAIN ANALYZE SELECT * FROM test WHERE i=5; QUERY PLAN ---------------------------------------------------------- Gather (cost=1000.43..1008.45 rows=1 width=4) (actual time=2.523..2.579 rows=1 loops=1) Workers Planned: 6 Workers Launched: 6 Single Copy: true -> Index Only Scan using btree_id on test (cost=0.43..8.45 rows=1 width=4) (actual time=0.030..0.032 rows=1 loops=1) Index Cond: (i = 5) Heap Fetches: 0 Planning time: 0.063 ms Execution time: 3.934 ms (9 rows)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • try to “trick” the planner with lower tuple costs: • the same plan is obtained – and it is still disadvantageous! – costs parameters are (almost) always fine – parallelization costs are sustainable in case of (real) big data =# SET force_parallel_mode TO false; SET =# SET parallel_tuple_cost TO 0.01; SET
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Hash indexes are now logged!Hash indexes are now logged! 8kB8kB8kB8kB WALWALWAL • Hash AMs did not define how index changes had to be logged into WALs: – Hashes lived just in shared buffers – no crash safe! – Hashes could not be phisically replicated • Hashes AMs now include WAL logging (R. Haas, G. Ghosh, A. Kapila,A. Sharma)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Hash indexes are now logged!Hash indexes are now logged! • Ex. physical replication, with pre-existing hash index before 1st base backup: hot standby =# d hash_example Table "public.hash_example" Column | Type | Modifiers --------+---------+----------- i | integer | Indexes: "hash_idx" hash (i) master =# d hash_example Table "public.hash_example" Column | Type | Modifiers --------+---------+----------- i | integer | Indexes: "hash_idx" hash (i) WALWAL WALWALWALWAL
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Hash indexes are now logged!Hash indexes are now logged! • pre PostgreSQL 10: hot standby =# explain analyze select * from =# hash_example where i = 123; QUERY PLAN ----------------------------------------- Index Scan using hash_idx on hash_example (cost=0.00..8.02 rows=1 width=21) (actual time=1.526..1.529 rows=1 loops=1) [...] master =# explain analyze select * from =# hash_example where i = 123; ERROR: could not read block 0 in file "base/16402/458955269": read only 0 of 8192 byte =# SET enable_index_scan TO false; SET WALWAL WALWALWALWAL
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Unbalanced indexes perform better in case of inclusion searches: – Ex. Quad-tree && bbox • H. Hesegeli extended the use case to IPv4/IPv6 addresses (inet, 7 Bytes/19 Bytes): – defined the OpClass for inet to be interfaced with SP-GiST AMs • inet_ops → && >> >>= > >= <> << <<= < <= = – important improvement in SP-GiST AM: # of child nodes is limited
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. =# CREATE TABLE network_a AS SELECT ((random() * 255)::int::text || '.' || =# (random() * 255)::int::text || '.' || =# (random() * 255)::int::text || '.' || =# (random() * 255)::int::text || '/' || =# (random() * 32)::int::text)::inet as addr =# FROM generate_series(1, 1000); CREATE =# CREATE INDEX gist_idx ON network_a USING gist (addr inet_ops); CREATE =# CREATE INDEX spgist_idx_a ON network_a USING spgist (addr inet_ops); CREATE =# CREATE TABLE network_b AS ( =# SELECT * FROM network_a ORDER BY random() LIMIT 100); CREATE
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. no indexes =# EXPLAIN ANALYZE SELECT * FROM network_a a JOIN network_b b ON b.addr && a.addr; QUERY PLAN ----------------------------------------------------------------------------------- Nested Loop (cost=0.00..15032.50 rows=78724 width=14) (actual time=0.017..185.134 rows=94973 loops=1) Join Filter: (a.addr && b.addr) Rows Removed by Join Filter: 905027 -> Seq Scan on network_a a (cost=0.00..15.00 rows=1000 width=7) (actual time=0.008..0.187 rows=1000 loops=1) -> Materialize (cost=0.00..20.00 rows=1000 width=7) (actual time=0.000..0.061 rows=1000 loops=1000) -> Seq Scan on network_b b (cost=0.00..15.00 rows=1000 width=7) (actual time=0.005..0.083 rows=1000 loops=1) Planning time: 0.522 ms Execution time: 190.120 ms (8 rows)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. GiST index =# EXPLAIN ANALYZE SELECT * FROM network_a a JOIN network_b b ON b.addr && a.addr; QUERY PLAN ----------------------------------------------------------------------------------- Nested Loop (cost=0.14..631.40 rows=13600 width=39) (actual time=0.048..112.023 rows=94973 loops=1) -> Seq Scan on network_b b (cost=0.00..23.60 rows=1360 width=32) (actual time=0.016..0.153 rows=1000 loops=1) -> Index Only Scan using gist_idx_a on network_a a (cost=0.14..0.35 rows=10 width=7) (actual time=0.018..0.093 rows=95 loops=1000) Index Cond: (addr && a.addr) Heap Fetches: 94973 Planning time: 0.111 ms Execution time: 119.433 ms (7 rows)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. SP-GiST index =# EXPLAIN ANALYZE SELECT * FROM network_a a JOIN network_b b ON b.addr && a.addr; QUERY PLAN ----------------------------------------------------------------------------------- Nested Loop (cost=0.14..667.40 rows=13600 width=39) (actual time=0.034..58.196 rows=94973 loops=1) -> Seq Scan on network_b b (cost=0.00..23.60 rows=1360 width=32) (actual time=0.009..0.105 rows=1000 loops=1) -> Index Only Scan using spgist_idx_a on network_a a (cost=0.14..0.37 rows=10 width=7) (actual time=0.008..0.042 rows=95 loops=1000) Index Cond: (addr && a.addr) Heap Fetches: 94973 Planning time: 0.109 ms Execution time: 63.562 ms (7 rows)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com BRIN summarization for newBRIN summarization for new INSERTINSERTss • pre PG 10: perform VACUUM, or call brin_summarize_new_value() • NOW (Á. Herrera): – autovacuum daemon is now able to summarize now data in present ranges: • CREATE INDEX ON table USING brin (column) WITH (autosummarize=on); – It is possible to summarize/desummarized single blocks (bigint): • brin_summarize_range / brin_desummarize_range • BRIN are (still) not able to “shrinks” summarized data – if you update/delete boundary data, need to REINDEX
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Other features about indexesOther features about indexes • Improve hash index performance (A. Kapila, M. Cy, A. Sharma) • Improve accuracy in determining if a BRIN index scan is beneficial (D. Rowley, E. Hasegeli) • Allow faster GiST INSERTs/UPDATEs by reusing index space efficiently (A. Borodin) • Reduce page locking during vacuuming of GIN indexes (A. Borodin)
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The future of indexes in PostgreSQLThe future of indexes in PostgreSQL • Allow compression/decompression AM functions in SP-GiST OpClasses (good for PostGIS!) • CREATE GLOBAL INDEX
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com ConclusionsConclusions • PostgreSQL has a long tradition in indexes development • different types for different goals • an eye to the future
PGDay.IT 2017 - 11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Creative Commons licenseCreative Commons license This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License https://creativecommons.org/licenses/by-nc-sa/4.0/ © 2017 Giuseppe Broccolo, ITPUG – www.itpug.org/

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Indexes in PostgreSQL (10)

  • 1.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Indexes in PostgreSQL (10)
  • 2.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The outlineThe outline • Indexes in PostgreSQL • What’s new in v10: – Parallelism – Hash indexing – New supports for SP-GiST (inet data) – Summarization of BRINs
  • 3.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com ~$ whoami~$ whoami Giuseppe BroccoloGiuseppe Broccolo - data engineer at- data engineer at - member of- member of @giubro gbroccolo7 gbroccolo gemini__81 g.broccolo.7@gmail.com
  • 4.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com PostgreSQL indexesPostgreSQL indexes • AKA Access Methods
  • 5.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com PostgreSQL indexesPostgreSQL indexes • AKA Access Methods – allow concurrent changes (MVCC compliant) – persist the information (WAL) – speed up access to data: • links to data blocks (sometimes can be avoided) • Indexes’ blocks live in shared buffers AWA data blocks 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB WALWALWAL sharedbuffers
  • 6.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the treesThe default AMs – the trees • binary structure hierarchically sorted – nodes (values, link to pointed nodes, etc.) – pointing depends from hierarchical criteria – allow to skip orders of values • N~O(an ) n~O(logN)→
  • 7.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the treesThe default AMs – the trees balanced • binary structure hierarchically sorted – nodes (values, link to pointed nodes, etc.) – pointing depends from hierarchical criteria – allow to skip orders of values • N~O(an ) n~O(logN)→ • balanced structures speed up punctual searches
  • 8.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the treesThe default AMs – the trees balanced unbalanced • binary structure hierarchically sorted – nodes (values, link to pointed nodes, etc.) – pointing depends from hierarchical criteria – allow to skip orders of values • N~O(an ) n~O(logN)→ • balanced structures speed up punctual searches • unbalanced ones are quite faster for range searches
  • 9.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the hashesThe default AMs – the hashes • binary maps (k: v) – k: the hash of the search key - bucket – v: the address where the key is stored – just one kind of search: = – complexity: • ~O(1) – like trees, their sizes are comparable with the indexed dataset • ~O(N) search key k: value... hashing N complexity ~O(logN) ... ~O(1)
  • 10.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMs – the BRINsThe default AMs – the BRINs • Block Range Indexes: – À. Herrera, S. Riggs, H. Linnakangas (PG 9.5) – Range: summarization of adjacent-on-disk blocks – complexity: • ~O(N/K), K~10/100 • really small indexes,faster creation • ~O(N/K’), K’~1000/10000 • can be used for low-selectivity queries • low performance for “dynamic” data 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB 8kB8kB8kB8kB range 0 range 1 range 2 range 3 range 7range 6range 5range 4 Summarization: blk n. xxxxx range X blk n. yyyyy blk n. zzzzz ......
  • 11.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The default AMsThe default AMs • B-tree, GIN, GiST, SP-GiST, Hash, BRIN • can add user defined new access methods – fully supported since 9.6 (thanks to postgrespro & 2ndQuadrant) • CREATE ACCESS METHOD sortable generalized balanced unbalanced trees
  • 12.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Extend AMs to datatypes: the OpClassesExtend AMs to datatypes: the OpClasses • access methods use operator classes (opclass) • • • • define: – operators for the needed types – support functions depending on the access method • can be extended to specific datatypes CREATE INDEX idx_name USING method ON table (column opclass_name) WITH (opt=value); • CREATE OPERATOR CLASS opclass_name FOR TYPE datatype USING method OPERATOR $$(), [...], FUNCTION func1(), [...]
  • 13.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Execution plansExecution plans • IndexScan need to inspect data pages for row visibility • IndexOnlyScan just index pages, use visibility map (PG9.2) • BitmapIndexScan BitmapHeapScan 1) reduce # of accesses using a bitmap 2) used by BRIN to inspect block ranges N complexity ~O(logN)
  • 14.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com What’s new in PG 10 ?What’s new in PG 10 ?
  • 15.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Parallelization in index scansParallelization in index scans • parallelization is not new in PG (9.6), see G. Ciolli later – parallel B-tree index scans – parallel BitmapHeapScan (different areas of the heap are processed by parallel workers) – R. Syed, A. Kapila, R. Haas, R. Sabih, D. Kumar, R. Haas, J. Rouhaud
  • 16.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Parallelization in Index ScansParallelization in Index Scans • for B-tree – Workers inspect leaf pages in parallel gather node gather node worker #1 worker #2 worker #N ... • for bitmap heap scan – Workers inspect heap chunks in parallel
  • 17.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Parallelization in Index ScansParallelization in Index Scans • The parameters: – max_parallel_workers (included in max_worker_processes) – max_parallel_workers_per_gather (included in max_parallel_workers) – min_parallel_index_scan_size (512kB) • heuristic: # workers / index size > 512kB * 3# workers – parallel_setup_cost (1000.0) – parallel_tuple_cost (0.1) – force_parallel_mode (false) • tune them basing on underlying HW!
  • 18.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • Ex. IndexOnlyScan on B-tree • table/B-tree ~O(300MB) =# CREATE TABLE test AS =# SELECT generate_series(1,10000000) t(i); CREATE =# CREATE INDEX btree_idx ON test USING btree (i); CREATE
  • 19.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When parallelization is disabled:When parallelization is disabled: • Ex. IndexOnlyScan on B-tree: =# EXPLAIN ANALYZE SELECT * FROM test WHERE i=5; QUERY PLAN ---------------------------------------------------------- Index Only Scan using btree_id on test (cost=0.43..8.45 rows=1 width=4) (actual time=0.433..0.434 rows=1 loops=1) Index Cond: (i = 5) Heap Fetches: 1 Planning time: 0.525 ms Execution time: 0.461 ms (5 rows)
  • 20.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • Setup parallel executions: • • • • Plan does not change!! Force parallelization... =# SET max_parallel_workers TO 8; SET =# SET max_parallel_workers_per_gather TO 8; -- up to 6 workers SET =# SET force_parallel_mode TO true; SET
  • 21.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • Ex. IndexOnlyScan on B-tree =# EXPLAIN ANALYZE SELECT * FROM test WHERE i=5; QUERY PLAN ---------------------------------------------------------- Gather (cost=1000.43..1008.45 rows=1 width=4) (actual time=2.523..2.579 rows=1 loops=1) Workers Planned: 6 Workers Launched: 6 Single Copy: true -> Index Only Scan using btree_id on test (cost=0.43..8.45 rows=1 width=4) (actual time=0.030..0.032 rows=1 loops=1) Index Cond: (i = 5) Heap Fetches: 0 Planning time: 0.063 ms Execution time: 3.934 ms (9 rows)
  • 22.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com When is parallelization used ?When is parallelization used ? • try to “trick” the planner with lower tuple costs: • the same plan is obtained – and it is still disadvantageous! – costs parameters are (almost) always fine – parallelization costs are sustainable in case of (real) big data =# SET force_parallel_mode TO false; SET =# SET parallel_tuple_cost TO 0.01; SET
  • 23.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Hash indexes are now logged!Hash indexes are now logged! 8kB8kB8kB8kB WALWALWAL • Hash AMs did not define how index changes had to be logged into WALs: – Hashes lived just in shared buffers – no crash safe! – Hashes could not be phisically replicated • Hashes AMs now include WAL logging (R. Haas, G. Ghosh, A. Kapila,A. Sharma)
  • 24.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Hash indexes are now logged!Hash indexes are now logged! • Ex. physical replication, with pre-existing hash index before 1st base backup: hot standby =# d hash_example Table "public.hash_example" Column | Type | Modifiers --------+---------+----------- i | integer | Indexes: "hash_idx" hash (i) master =# d hash_example Table "public.hash_example" Column | Type | Modifiers --------+---------+----------- i | integer | Indexes: "hash_idx" hash (i) WALWAL WALWALWALWAL
  • 25.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Hash indexes are now logged!Hash indexes are now logged! • pre PostgreSQL 10: hot standby =# explain analyze select * from =# hash_example where i = 123; QUERY PLAN ----------------------------------------- Index Scan using hash_idx on hash_example (cost=0.00..8.02 rows=1 width=21) (actual time=1.526..1.529 rows=1 loops=1) [...] master =# explain analyze select * from =# hash_example where i = 123; ERROR: could not read block 0 in file "base/16402/458955269": read only 0 of 8192 byte =# SET enable_index_scan TO false; SET WALWAL WALWALWALWAL
  • 26.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Unbalanced indexes perform better in case of inclusion searches: – Ex. Quad-tree && bbox • H. Hesegeli extended the use case to IPv4/IPv6 addresses (inet, 7 Bytes/19 Bytes): – defined the OpClass for inet to be interfaced with SP-GiST AMs • inet_ops → && >> >>= > >= <> << <<= < <= = – important improvement in SP-GiST AM: # of child nodes is limited
  • 27.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. =# CREATE TABLE network_a AS SELECT ((random() * 255)::int::text || '.' || =# (random() * 255)::int::text || '.' || =# (random() * 255)::int::text || '.' || =# (random() * 255)::int::text || '/' || =# (random() * 32)::int::text)::inet as addr =# FROM generate_series(1, 1000); CREATE =# CREATE INDEX gist_idx ON network_a USING gist (addr inet_ops); CREATE =# CREATE INDEX spgist_idx_a ON network_a USING spgist (addr inet_ops); CREATE =# CREATE TABLE network_b AS ( =# SELECT * FROM network_a ORDER BY random() LIMIT 100); CREATE
  • 28.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. no indexes =# EXPLAIN ANALYZE SELECT * FROM network_a a JOIN network_b b ON b.addr && a.addr; QUERY PLAN ----------------------------------------------------------------------------------- Nested Loop (cost=0.00..15032.50 rows=78724 width=14) (actual time=0.017..185.134 rows=94973 loops=1) Join Filter: (a.addr && b.addr) Rows Removed by Join Filter: 905027 -> Seq Scan on network_a a (cost=0.00..15.00 rows=1000 width=7) (actual time=0.008..0.187 rows=1000 loops=1) -> Materialize (cost=0.00..20.00 rows=1000 width=7) (actual time=0.000..0.061 rows=1000 loops=1000) -> Seq Scan on network_b b (cost=0.00..15.00 rows=1000 width=7) (actual time=0.005..0.083 rows=1000 loops=1) Planning time: 0.522 ms Execution time: 190.120 ms (8 rows)
  • 29.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. GiST index =# EXPLAIN ANALYZE SELECT * FROM network_a a JOIN network_b b ON b.addr && a.addr; QUERY PLAN ----------------------------------------------------------------------------------- Nested Loop (cost=0.14..631.40 rows=13600 width=39) (actual time=0.048..112.023 rows=94973 loops=1) -> Seq Scan on network_b b (cost=0.00..23.60 rows=1360 width=32) (actual time=0.016..0.153 rows=1000 loops=1) -> Index Only Scan using gist_idx_a on network_a a (cost=0.14..0.35 rows=10 width=7) (actual time=0.018..0.093 rows=95 loops=1000) Index Cond: (addr && a.addr) Heap Fetches: 94973 Planning time: 0.111 ms Execution time: 119.433 ms (7 rows)
  • 30.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com SP-GiST support forSP-GiST support for inetinet • Ex. SP-GiST index =# EXPLAIN ANALYZE SELECT * FROM network_a a JOIN network_b b ON b.addr && a.addr; QUERY PLAN ----------------------------------------------------------------------------------- Nested Loop (cost=0.14..667.40 rows=13600 width=39) (actual time=0.034..58.196 rows=94973 loops=1) -> Seq Scan on network_b b (cost=0.00..23.60 rows=1360 width=32) (actual time=0.009..0.105 rows=1000 loops=1) -> Index Only Scan using spgist_idx_a on network_a a (cost=0.14..0.37 rows=10 width=7) (actual time=0.008..0.042 rows=95 loops=1000) Index Cond: (addr && a.addr) Heap Fetches: 94973 Planning time: 0.109 ms Execution time: 63.562 ms (7 rows)
  • 31.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com BRIN summarization for newBRIN summarization for new INSERTINSERTss • pre PG 10: perform VACUUM, or call brin_summarize_new_value() • NOW (Á. Herrera): – autovacuum daemon is now able to summarize now data in present ranges: • CREATE INDEX ON table USING brin (column) WITH (autosummarize=on); – It is possible to summarize/desummarized single blocks (bigint): • brin_summarize_range / brin_desummarize_range • BRIN are (still) not able to “shrinks” summarized data – if you update/delete boundary data, need to REINDEX
  • 32.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Other features about indexesOther features about indexes • Improve hash index performance (A. Kapila, M. Cy, A. Sharma) • Improve accuracy in determining if a BRIN index scan is beneficial (D. Rowley, E. Hasegeli) • Allow faster GiST INSERTs/UPDATEs by reusing index space efficiently (A. Borodin) • Reduce page locking during vacuuming of GIN indexes (A. Borodin)
  • 33.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com The future of indexes in PostgreSQLThe future of indexes in PostgreSQL • Allow compression/decompression AM functions in SP-GiST OpClasses (good for PostGIS!) • CREATE GLOBAL INDEX
  • 34.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com ConclusionsConclusions • PostgreSQL has a long tradition in indexes development • different types for different goals • an eye to the future
  • 35.
    PGDay.IT 2017 -11th edition Milan October, 13th 2017 Giuseppe Broccolo g.broccolo.7@gmail.com Viralize.com Creative Commons licenseCreative Commons license This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License https://creativecommons.org/licenses/by-nc-sa/4.0/ © 2017 Giuseppe Broccolo, ITPUG – www.itpug.org/