Learning about Redshift data types and features

Introduction to Redshift

Jason Myers

Principal Architect

Basic data types

Numeric types

SMALLINT, INTEGER, BIGINT
DECIMAL / NUMERIC
DOUBLE PRECISION
REAL

Datetime types

DATE
TIME, TIMETZ
TIMESTAMP, TIMESTAMPTZ

Character types

CHAR
VARCHAR

Boolean type

BOOLEAN

Special data types

SUPER type

Semistructured data
- Arrays
- Tuples
- Nested structures (e.g. JSON)
Uses PartiQL to handle them
16MB max size

VARBYTE type

binary data (BLOBs)
Images and Videos

Unsupported PostgreSQL types:

PostgreSQL Type	Redshift Types
DATETIME	TIMESTAMP, TIMESTAMPTZ
SERIAL	INTEGER, BIGINT
UUID	VARCHAR
JSON	SUPER, VARCHAR
ARRAY	SUPER, VARCHAR
BIT	BOOLEAN, SMALLINT, VARCHAR

Viewing columns and their data types

-- View the column details
SELECT column_name, 
       data_type, 
       character_maximum_length AS character_max_len,
       numeric_precision, 
       numeric_scale
  -- Using a view with both internal and external schema's columns
  FROM SVV_ALL_COLUMNS 
 -- Only in the spectrumdb schema
 WHERE schema_name = 'spectrumdb'
   -- For the ecommerce_sales table
   AND table_name = 'ecommerce_sales';

Viewing columns and their data types (cont)

column_name                  | data_type         | character_max_len | numeric_precision | numeric_scale
=============================|===================|===================|===================|==============
year_qtr                     | character varying | 100               | null              | null
total_sales                  | integer           | null              | 32                | 0
ecom_sales                   | integer           | null              | 32                | 0
percent_ecom                 | real              | null              | null              | null
percent_change_quarter_total | real              | null              | null              | null
percent_change_quarter_ecom  | real              | null              | null              | null
percent_change_year_total    | real              | null              | null              | null
percent_change_year_ecom     | real              | null              | null              | null

Data type "compatibility"

Implicit conversion: assignments, comparisons
Make sure to double check outcomes!

From Type	To Types
CHAR	VARCHAR
DATE	CHAR, VARCHAR, TIMESTAMP, TIMESTAMPTZ
TIMESTAMP	CHAR, DATE, VARCHAR, TIMESTAMPTZ
BIGINT	BOOLEAN, CHAR, DECIMAL, DOUBLE PRECISION, INTEGER, REAL, SMALLINT, VARCHAR
DECIMAL	BIGINT, CHAR, DOUBLE PRECISION, INTEGER, REAL, SMALLINT, VARCHAR

¹ https://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html#r_Type_conversion

Explicitly casting data types

CAST

-- Casting a decimal to a integer and aliasing it
SELECT CAST(2.00 AS INTEGER) AS our_int;

our_int
========
2
(1 row)

TO functions

TO_CHAR, TO_DATE, TO_NUMBER

-- Convert the string to a date
SELECT CAST('14-01-2024 02:36:48' AS DATE) AS out_date;

date/time field value out of range: "14-01-2024 02:36:48"
HINT:  Perhaps you need a different "datestyle" setting.

-- Parse the string to a date
SELECT TO_DATE('14-01-2024 02:36:48', 'DD-MM-YYYY') AS our_date;

our_date
========
2024-01-14
(1 row)

TO functions (cont)

-- Get the name of the month in a date
SELECT TO_CHAR(date '2024-01-14', 'MONTH') AS month_name;

month_name
========
JANUARY
(1 row)

Datetime and Numeric format strings

¹ https://docs.aws.amazon.com/redshift/latest/dg/r_FORMAT_strings.html

Let's practice!

Introduction to Redshift