PostgreSQL Indexs

-

Postgresql Index:

HEAP:
Storage area for storing the whole row of the table.
This is divided into multiple pages (as shown in the above picture) and each page size is by default 8KB. Within each page, each item pointer (e.g. 0,1, 2, ….) points to data within the page.

Index Storage:
This storage stores only key values i.e. columns value contained by index.
This is also divided into multiple pages and each page size is by default 8KB.

Tuple Identifier (TID):
TID is 6 bytes number which consists of two parts.
The first part is 4-byte page number and remaining 2 bytes tuple index inside the page.
The combination of these two numbers uniquely points to the storage location for a particular tuple.
========================================================================
postgresql index types:
=======================

  1. B-tree 
  2. Hash 
  3. GiST 
  4. sp-Gist 
  5. GIN 
  6. BRIN

=============================================

B-Tree Index:

B-Tree is the default and the most commonly used index type. Specifying a primary key or a unique within a CREATE TABLE statement causes PostgreSQL to create B-Tree indexes. CREATE INDEX statements without the USING clause will also create B-Tree indexes:

example:   
Before Index:

 product=# explain analyze select * from t1 where id=5;
                                              QUERY PLAN
------------------------------------------------------------------------------------------------------
 Seq Scan on t1  (cost=0.00..179057.19 rows=1 width=11) (actual time=47.515..6802.694 rows=1 loops=1)
   Filter: (id = 5)
   Rows Removed by Filter: 9999999
 Planning Time: 0.491 ms
 Execution Time: 6802.792 ms
(5 rows)

After Index:

 product=# create index in_t1_id on t1 using btree(id);
CREATE INDEX
product=# explain analyze select * from t1 where id=5;
                                                   QUERY PLAN
----------------------------------------------------------------------------------------------------------------
 Index Scan using in_t1_id on t1  (cost=0.43..8.45 rows=1 width=11) (actual time=25.031..25.039 rows=1 loops=1)
   Index Cond: (id = 5)
 Planning Time: 5.143 ms
 Execution Time: 25.102 ms
(4 rows)

B-tree index features introduced in v12:
----------------------------------------
    Reduce locking overhead for B-tree index inserts for improved performance.
    Introduce REINDEX CONCURRENTLY to make it easier to rebuild an index without down-time.
    Improve performance for index-only scans on indexes with many attributes.
    Add a view pg_stat_progress_create_index to report progress for CREATE INDEX and REINDEX.

Hash Index:

Hash index operations are not presently WAL-logged, so hash indexes might need to be rebuilt with REINDEX  after a database crash if there were unwritten changes. Also, changes to hash indexes are not replicated over streaming or file-based replication after the initial base backup, so they give wrong answers to queries that subsequently use them. For these reasons, hash index use is presently discouraged.
example:   create index in_t1_id on t1 using hash(id);

GIN Index:

GIN stands for Generalized Inverted Index, commonly referred to as GIN, are most useful when you have data types that contain multiple values in a single column. GIN is designed for handling cases where the items to be indexed are composite values, and the queries to be handled by the index need to search for element values that appear within the composite items.

The most common data types that fall into this bucket are:

hStore
Arrays
Range types
JSONB
tsvector
tsquery
which supports indexed queries using these operators:

<@
@>
=
&&

Example:

 db=# create table ts(doc text, doc_tsv tsvector);
CREATE TABLE
db=# insert into ts(doc) values
db-#   ('Can a sheet slitter slit sheets?'),
db-#   ('How many sheets could a sheet slitter slit?'),
db-#   ('I slit a sheet, a sheet I slit.'),
db-#   ('Upon a slitted sheet I sit.'),
db-#   ('Whoever slit the sheets is a good sheet slitter.'),
db-#   ('I am a sheet slitter.'),
db-#   ('I slit sheets.'),
db-#   ('I am the sleekest sheet slitter that ever slit sheets.'),
db-#   ('She slits the sheet she sits on.');
INSERT 0 9
db=# update ts set doc_tsv = to_tsvector(doc);
UPDATE 9

db=# explain analyze select doc from ts where doc_tsv @@ to_tsquery('many & slitter');
                                                   QUERY PLAN
-----------------------------------------------------------------------------------------------------------------
 Seq Scan on ts  (cost=10000000000.00..10000000241.00 rows=1 width=32) (actual time=0.104..0.161 rows=1 loops=1)
   Filter: (doc_tsv @@ to_tsquery('many & slitter'::text))
   Rows Removed by Filter: 8
 Planning Time: 0.136 ms
 Execution Time: 0.180 ms
(5 rows)


db=# create index on ts using gin(doc_tsv);
CREATE INDEX
db=# explain analyze select doc from ts where doc_tsv @@ to_tsquery('many & slitter');
                                                       QUERY PLAN                                                       ------------------------------------------------------------------------------------------------------------------------ Bitmap Heap Scan on ts  (cost=12.25..16.51 rows=1 width=32) (actual time=0.066..0.067 rows=1 loops=1)
   Recheck Cond: (doc_tsv @@ to_tsquery('many & slitter'::text))
   Heap Blocks: exact=1
   ->  Bitmap Index Scan on ts_doc_tsv_idx  (cost=0.00..12.25 rows=1 width=0) (actual time=0.057..0.057 rows=1 loops=1)
         Index Cond: (doc_tsv @@ to_tsquery('many & slitter'::text))
 Planning Time: 2.390 ms
 Execution Time: 0.125 ms
(7 rows)

Gist Index:

GiST stands for Generalized Search Tree.
GiST indexes allow a building of general tree structures.
GiST indexes are useful in indexing geometric data types and full-text search.

example:point,line,box,circle

which support indexed queries using these operators:
<<
&<
&>
>>
<<|
&<|
|&>
|>>
@>
<@
~=
&&

example::
db=# create table points(p point);
CREATE TABLE
db=# insert into points(p) values
db-#   (point '(1,1)'), (point '(3,2)'), (point '(6,3)'),
db-#   (point '(5,5)'), (point '(7,8)'), (point '(8,6)');
INSERT 0 6
db=# explain analyze select * from points where p <@ box '(2,1),(7,4)';
                                            QUERY PLAN
--------------------------------------------------------------------------------------------------
 Seq Scan on points  (cost=0.00..33.13 rows=2 width=16) (actual time=0.047..0.049 rows=2 loops=1)
   Filter: (p <@ '(7,4),(2,1)'::box)
   Rows Removed by Filter: 4
 Planning Time: 1.621 ms
 Execution Time: 0.082 ms
(5 rows)


db=# create index on points using gist(p);
CREATE INDEX
db=# explain analyze select * from points where p <@ box '(2,1),(7,4)';
                                           QUERY PLAN
-------------------------------------------------------------------------------------------------
 Seq Scan on points  (cost=0.00..1.07 rows=1 width=16) (actual time=0.021..0.023 rows=2 loops=1)
   Filter: (p <@ '(7,4),(2,1)'::box)
   Rows Removed by Filter: 4
 Planning Time: 3.361 ms
 Execution Time: 0.048 ms
(5 rows)


db=# set enable_seqscan=off;
SET
db=# explain analyze select * from points where p <@ box '(2,1),(7,4)';
                                                        QUERY PLAN                                                   
---------------------------------------------------------------------------------------------------------------------------
 Index Only Scan using points_p_idx on points  (cost=0.13..8.15 rows=1 width=16) (actual time=0.269..0.271 rows=2 loops=1)
   Index Cond: (p <@ '(7,4),(2,1)'::box)
   Heap Fetches: 2
 Planning Time: 0.145 ms
 Execution Time: 0.313 ms
(5 rows)

db=# explain (costs off) select * from points where p <@ box '(2,1),(7,4)';
                  QUERY PLAN
----------------------------------------------
 Index Only Scan using points_p_idx on points
   Index Cond: (p <@ '(7,4),(2,1)'::box)
(2 rows)
example:point,line,box,circle

sp-gist:

SP-GiST is an abbreviation for space-partitioned GiST. SP-GiST supports partitioned search trees,
which facilitate development of a wide range of different non-balanced data structures, such as quad-trees, k-d trees, and radix trees (tries).

which support indexed queries using these operators:
<<
>>
~=
<@
<^
>^
example::
==========
postgres=# create table points(p point);
CREATE TABLE
postgres=# insert into points(p) values
postgres-#   (point '(1,1)'), (point '(3,2)'), (point '(6,3)'),
postgres-#   (point '(5,5)'), (point '(7,8)'), (point '(8,6)');
INSERT 0 6
postgres=# select amop.amopopr::regoperator, amop.amopstrategy
postgres-# from pg_opclass opc, pg_opfamily opf, pg_am am, pg_amop amop
postgres-# where opc.opcname = 'quad_point_ops'
postgres-# and opf.oid = opc.opcfamily
postgres-# and am.oid = opf.opfmethod
postgres-# and amop.amopfamily = opc.opcfamily
postgres-# and am.amname = 'spgist'
postgres-# and amop.amoplefttype = opc.opcintype;
     amopopr     | amopstrategy
-----------------+--------------
 <<(point,point) |            1
 >>(point,point) |            5
 ~=(point,point) |            6
 <^(point,point) |           10
 >^(point,point) |           11
 <@(point,box)   |            8
(6 rows)


postgres=# explain (costs off) select * from points where p >^ point '(2,7)';
           QUERY PLAN
---------------------------------
 Seq Scan on points
   Filter: (p >^ '(2,7)'::point)
(2 rows)


postgres=# set enable_seqscan = off;
SET
postgres=# explain (costs off) select * from points where p >^ point '(2,7)';
           QUERY PLAN
---------------------------------
 Seq Scan on points
   Filter: (p >^ '(2,7)'::point)
(2 rows)


postgres=# create index points_quad_idx on points using spgist(p);
CREATE INDEX
postgres=# explain (costs off) select * from points where p >^ point '(2,7)';
                   QUERY PLAN
-------------------------------------------------
 Index Only Scan using points_quad_idx on points
   Index Cond: (p >^ '(2,7)'::point)
(2 rows)

Brin Index:

BRIN stands for Block Range Index. BRIN is designed for handling very large tables.

example: create index in_t1_id on t1 using brin(id);

Comments

Post a Comment

Popular posts from this blog

PostgreSQL pg_pool-II Installation and Configuration

-
Image
Pgpool-II Pgpool-II is a proxy software that sits between PostgreSQL servers and a PostgreSQL database client.  It provides the following features: 1.Connection Pooling:     Pgpool-II maintains established connections to the PostgreSQL servers, and reuses them whenever a new connection with the same properties (i.e. user name, database, protocol version, and other connection parameters if any) comes in. It reduces the connection overhead, and improves system's overall throughput.   2.Load Balancing:     If a database is replicated (because running in either replication mode or master/slave mode), performing a SELECT query on any server will return the same result. Pgpool-II takes advantage of the replication feature in order to reduce the load on each PostgreSQL server.  It does that by distributing SELECT queries among available servers, improving the system's overall throughput. In an ideal scenario, read performance could improve proportionally to the number of PostgreSQ
Read more

PostgreSQL Migration Using MTK

-
Enterprisedb Migration Toolkit: EDB Migration Toolkit is a powerful command-line tool that offers granular control of the migration process. Migration Toolkit facilitates migration of database objects and data to an EDB Postgres Advanced Server (Advanced Server) or PostgreSQL database from: Oracle MySQL Microsoft SQL Server Sybase which objects migrate to postgres Oracle Objects: Schemas   Tables    List-Partitioned Tables         Range-Partitioned Table         Constraints   Indexes   Triggers         Table Data   Views     Materialized Views         Packages         Procedures   Functions       Sequences       Users/Roles   Profiles   Object Types   Object Type Methods   Database Links     Queues       MySQL Objects: Schemas Tables Constraints Indexes Table Data Microsoft SQL Server Objects: Schemas Tables Constraints Indexes Table Data Sybase Obj
Read more

PostgreSQL Pages and Tuples

-
Image
INTERNAL LAYOUT: a tuple or an item is a synonym for a row a relation is a synonym for a table a filenode is an id which represent a reference to a table or an index. a block and page are equals and they represent a 8kb segment information the file storing the table. a heap refer to heap file . Heap files are lists of unordered records of variable size. Although sharing a similar name, heap files are different from heap data structure. CTID represent the physical location of the row version within its table. CTID is also a special column available for every tables but not visible unless specifically mentioned. It consists of a page number and the index of an item identifier. OID stands for Object Identifier. database cluster , we call a database cluster the storage area on disk. A database cluster is a collection of databases that is managed by a single instance of a running database server. Page:            Inside the data file (heap table and index, as well as the free space ma
Read more