This content originally appeared on DEV Community and was authored by Uday Yadav
Data Types
Boolean Data
- TRUE
- FALSE
- NULL
TRUE | FALSE |
---|---|
TRUE | FALSE |
'true' | 'false' |
't' | 'f' |
'yes' | 'no' |
'y' | 'n' |
'1' | '0' |
CREATE TABLE booltable (
id SERIAL PRIMARY KEY ,
is_enable BOOLEAN NOT NULL
);
INSERT INTO booltable (is_enable) VALUES (TRUE), ('true'),
('y') , ('yes'), ('t'), ('1');
INSERT INTO booltable (is_enable) VALUES (FALSE), ('false'),
('n') , ('no'), ('f'), ('0');
SELECT * FROM booltable;
SELECT * FROM booltable WHERE is_enable = 'y';
SELECT * FROM booltable WHERE NOT is_enable;
Character Data
Character Type | Notes |
---|---|
CHARACTER (N), CHAR (N) | fixed-length, blank padded |
CHARACTER VARYING (N), VARCHAR(N) | variable length with length limit |
TEXT, VARCHAR | variable unlimited length, max 1GB |
- n is default to 1
-- INPUT
SELECT CAST('Uday' as character(10)) as "name";
-- OUTPUT
"Uday "
-- INPUT
SELECT 'Uday'::character(10) as "name";
-- OUTPUT
"Uday "
-- INPUT
SELECT 'uday'::varchar(10);
-- OUTPUT
"uday"
-- INPUT
SELECT 'lorem ipsum'::text;
-- OUTPUT
"lorem ipsum"
Numeric Data
Types | Notes |
---|---|
Integers | whole number, +ve and -ve |
Fixed-point, floating point | for fractions of whole nu |
type | size (bytes) | min | max |
---|---|---|---|
smallint | 2 | -32678 | 32767 |
integer | 4 | -2,147,483,648 | 2,147,483,647 |
bigint | 8 | -9223372036854775808 | 9223372036854775807 |
type | size | range |
---|---|---|
smallserial | 2 | 1 to 32767 |
serial | 4 | 1 to 2147483647 |
bigserial | 8 | 1 to 9223372036854775807 |
Fixed Point Data
numeric ( precision , scale ) | decimal ( precision , scale )
- precision : max number of digits to the left and right of the decimal point
- scale : number of digits allowable on the right of the decimal point
Floating Point Data
Type | Notes |
---|---|
Real | allows precision to six decimal digits |
Double precision | allows precision to 15 digits points of precision |
type | size | storage type | Range |
---|---|---|---|
numeric, decimal | variable | fixed point | 131072 digits before decimal point and 16383 digits after the decimal point |
real | 4 | floating point | 6 decimal digits precision |
double precision | 8 | floating point | 15 decimal digits precision |
CREATE TABLE table_numbers (
col_numeric numeric(20,5),
col_real real,
col_double double precision
);
INSERT INTO table_numbers (col_numeric,col_real,col_double)
VALUES (.9,.9,.9),
(3.34675,3.34675,3.34675),
(4.2345678910,4.2345678910,4.2345678910);
SELECT * FROM table_numbers;
-- OUTPUT
learning=# select * from table_numbers ;
col_numeric | col_real | col_double
------------------+----------+-------------
0.90000 | 0.9 | 0.9
3.34675 | 3.34675 | 3.34675
4.23457 | 4.234568 | 4.234567891
(3 rows)
Hierarchical order to SELECT best type : numeric > decimal > float
Date Time Data
type | stores | low | high |
---|---|---|---|
Date | date only | 4713 BC | 294276 AD |
Time | time only | 4713 BC | 5874897 AD |
Timestamp | date and time | 4713 BC | 294276 AD |
Timestampz |
date, time and timezone | 4713 BC | 294276 AD |
Interval | difference btw time |
Date type
CREATE TABLE table_dates (
id serial primary key,
employee_name varchar(100) not null,
hire_date DATE NOT NULL,
add_date DATE DEFAULT CURRENT_DATE
);
INSERT INTO table_dates (employee_name, hire_date)
VALUES ('uday','2020-02-02'),('another uday','2020-02-01');
SELECT *
FROM table_dates;
SELECT NOW();
Time type
CREATE TABLE table_time (
id serial primary key ,
class_name varchar(10) not null ,
start_time time not null ,
end_time time not null
);
INSERT INTO table_time (class_name, start_time, end_time)
VALUES ('maths','08:00:00','08:55:00'),
('chemistry','08:55:00','09:00:00');
SELECT * FROM table_time;
-- OUTPUT
id | class_name | start_time | end_time
---------+------------+------------+----------
1 | maths | 08:00:00 | 08:55:00
2 | chemistry | 08:55:00 | 09:00:00
(2 rows)
SELECT CURRENT_TIME;
current_time
-------------------------
07:21:00.163354+00
(1 row)
SELECT CURRENT_TIME(2);
current_time
---------------------
07:21:14.96+00
(1 row)
SELECT LOCALTIME;
localtime
----------------------
07:21:36.717509
(1 row)
SELECT time '12:10' - time '04:30' as RESULT;
result
---------------
07:40:00
(1 row)
-- format : interval 'n type'
-- n = number
-- type : second, minute, hours, day, month, year ....
SELECT CURRENT_TIME ,
CURRENT_TIME + INTERVAL '2 hours' as RESULT;
current_time | result
-------------------------+--------------------
07:22:06.241919+00 | 09:22:06.241919+00
(1 row)
SELECT CURRENT_TIME ,
CURRENT_TIME + INTERVAL '-2 hours' as RESULT;
current_time | result
-------------------------+--------------------
07:22:16.644727+00 | 05:22:16.644727+00
(1 row)
Timestamp and Timezone
-
timestamp
: stores time without time zone -
timestamptz
: timestamp with time zone , stored using UTC format - adding timestamp to timestamptz without mentioning the zone will result in server automatically assumes timezone to system's timezone
- Internally, PostgreSQL will store the timezoneaccurately but then OUTPUTting the data, will it be converted according to your timezone
SELECT name FROM pg_timezone_names
where name = 'posix/Asia/Calcutta';
SET TIMEZONE='Asia/Calcutta';
SELECT NOW()::TIMESTAMP;
now
---------------------------------
2021-08-12 12:53:03.971433
(1 row)
CREATE TABLE table_time_tz (
ts timestamp,
tstz timestamptz
);
INSERT INTO table_time_tz (ts, tstz)
VALUES ('2020-12-22 10:10:10',
'2020-12-22 10:10:10.009+05:30');
SELECT * FROM table_time_tz;
ts | tstz
--------------------------+-------------------------------
2020-12-22 10:10:10 | 2020-12-22 10:10:10.009+05:30
(1 row)
SELECT CURRENT_TIMESTAMP;
current_timestamp
--------------------------------------
2021-08-12 12:53:29.54762+05:30
(1 row)
SELECT timezone('Asia/Singapore','2020-01-01 00:00:00')
timezone
--------------------------
2020-01-01 02:30:00
(1 row)
UUID
- UUID : Universal Unique Identifier
- PostgreSQL doesn't provide internal function to generate UUID's, use
uuid-ossp
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
SELECT uuid_generate_v1();
uuid_generate_v1
-------------------------------------------
4d459e0c-fb3e-11eb-a638-0242ac110002
-- pure randomness
SELECT uuid_generate_v4();
uuid_generate_v4
-------------------------------------------
418f39e5-8a46-4da2-8cea-884904f45d6f
CREATE TABLE products_uuid (
id uuid default uuid_generate_v1(),
product_name varchar(100) not null
);
INSERT INTO products_uuid (product_name)
VALUES ('ice cream'),('cake'),('candies');
SELECT * FROM products_uuid;
id | product_name
-------------------------------------------+--------------
5cf1dbe0-fb3e-11eb-a638-0242ac110002 | ice cream
5cf1df28-fb3e-11eb-a638-0242ac110002 | cake
5cf1df46-fb3e-11eb-a638-0242ac110002 | candies
CREATE TABLE products_uuid_v4 (
id uuid default uuid_generate_v4(),
product_name varchar(100) not null
);
INSERT INTO products_uuid_v4 (product_name)
VALUES ('ice cream'),('cake'),('candies');
SELECT * FROM products_uuid_v4;
learning=# SELECT * FROM products_uuid_v4;
id | product_name
-------------------------------------------+--------------
83b74bed-2cf8-4e26-80b0-c7c7b2e5f3e7 | ice cream
ac563251-7a95-408d-966b-ed5ecc1f228d | cake
1079f6d3-b0c3-40ef-bd2e-da4467b63432 | candies
HSTORE
- stores data in key-value pairs
- key and VALUES are text string only
CREATE EXTENSION IF NOT EXISTS hstore;
CREATE TABLE table_hstore (
id SERIAL PRIMARY KEY ,
title varchar(100) not null,
book_info hstore
);
INSERT INTO table_hstore (title, book_info) VALUES
(
'Title 1', ' "publisher" => "ABC publisher" ,
"paper_cost" => "100" , "e_cost" => "5.85" '
);
SELECT * FROM table_hstore;
id | title | book_info
1 | Title 1 | "e_cost"=>"5.85", "publisher"=>"ABC publisher", "paper_cost"=>"100"
SELECT book_info -> 'publisher' as publisher
FROM table_hstore;
publisher
--------------------
ABC publisher
Json
- PostgreSQL supports both
- JSON
- BSON or JSONB ( Binary JSON )
- JSONB has full support for indexing
CREATE TABLE table_json (
id SERIAL PRIMARY KEY ,
docs json
);
INSERT INTO table_json (docs)
VALUES ('[1,2,3,4,5,6]'),('{"key":"value"}');
INSERT INTO table_json (docs)
VALUES ('[{"key":"value"},{"key2":"value2"}]');
SELECT * FROM table_json;
id | docs
---------+-------------------------------------
1 | [1,2,3,4,5,6]
2 | {"key":"value"}
3 | [{"key":"value"},{"key2":"value2"}]
ALTER TABLE table_json alter column docs type jsonb;
SELECT * FROM table_json where docs @> '2';
id | docs
---------+--------------------
1 | [1, 2, 3, 4, 5, 6]
CREATE index on table_json USING GIN (docs jsonb_path_ops);
Network Address Data Types
Name | Storage Size | Notes |
---|---|---|
cidr | 7 or 19 bytes | IPv4 and IPv6 networks |
inet | 7 or 19 bytes | IPv4 and IPv6 hosts and networks |
macaddr | 6 bytes | MAC addresses |
macaddr8 | 8 bytes | MAC addresses ( EUI 64-bit ) |
- It is better to use these types instead of plain text types of store network address, because these types offer input error checking and specialised operators and functions
- Supports indexing and advance operations
CREATE TABLE table_netaddr (
id SERIAL PRIMARY KEY ,
ip inet
);
INSERT INTO table_netaddr (ip)
VALUES ('148.77.50.74'),
('110.158.172.66'),
('176.103.251.175'),
('84.84.14.58'),
('141.122.225.161'),
('78.44.113.33'),
('81.236.254.9'),
('82.116.85.21'),
('54.64.79.223'),
('162.240.78.253');
SELECT * FROM table_netaddr LIMIT 5;
id | ip
---------+-----------------
1 | 148.77.50.74
2 | 110.158.172.66
3 | 176.103.251.175
4 | 84.84.14.58
5 | 141.122.225.161
SELECT
ip,
set_masklen(ip,24) as inet_24,
set_masklen(ip::cidr,24) as cidr_24 ,
set_masklen(ip::cidr,27) as cidr_27,
set_masklen(ip::cidr,28) as cidr_28
FROM
table_netaddr LIMIT 2;
ip | inet_24 | cidr_24 | cidr_27 | cidr_28
148.77.50.74 | 148.77.50.74/24 | 148.77.50.0/24 | 148.77.50.64/27 | 148.77.50.64/28
110.158.172.66 | 110.158.172.66/24 | 110.158.172.0/24 | 110.158.172.64/27 | 110.158.172.64/28
This content originally appeared on DEV Community and was authored by Uday Yadav
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Uday Yadav | Sciencx (2021-08-12T07:34:20+00:00) SQL Series : DataTypes. Retrieved from https://www.scien.cx/2021/08/12/sql-series-datatypes/
" » SQL Series : DataTypes." Uday Yadav | Sciencx - Thursday August 12, 2021, https://www.scien.cx/2021/08/12/sql-series-datatypes/
HARVARDUday Yadav | Sciencx Thursday August 12, 2021 » SQL Series : DataTypes., viewed ,<https://www.scien.cx/2021/08/12/sql-series-datatypes/>
VANCOUVERUday Yadav | Sciencx - » SQL Series : DataTypes. [Internet]. [Accessed ]. Available from: https://www.scien.cx/2021/08/12/sql-series-datatypes/
CHICAGO" » SQL Series : DataTypes." Uday Yadav | Sciencx - Accessed . https://www.scien.cx/2021/08/12/sql-series-datatypes/
IEEE" » SQL Series : DataTypes." Uday Yadav | Sciencx [Online]. Available: https://www.scien.cx/2021/08/12/sql-series-datatypes/. [Accessed: ]
rf:citation » SQL Series : DataTypes | Uday Yadav | Sciencx | https://www.scien.cx/2021/08/12/sql-series-datatypes/ |
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