Découvrir les types et fonctionnalités de données Redshift

Introduction à Redshift

Jason Myers

Principal Architect

Types de base

Types numériques

SMALLINT, INTEGER, BIGINT
DECIMAL / NUMERIC
DOUBLE PRECISION
REAL

Types date/heure

DATE
TIME, TIMETZ
TIMESTAMP, TIMESTAMPTZ

Types caractères

CHAR
VARCHAR

Type booléen

BOOLEAN

Types spéciaux

Type SUPER

Données semi-structurées
- Tableaux
- Tuples
- Structures imbriquées (p. ex. JSON)
Utilise PartiQL pour les traiter
Taille max de 16 Mo

Type VARBYTE

données binaires (BLOB)
Images et vidéos

Types PostgreSQL non pris en charge :

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

Afficher les colonnes et leurs 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';

Afficher les colonnes et leurs types (suite)

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

« Compatibilité » des types de données

Conversion implicite : affectations, comparaisons
Bien vérifier les résultats !

Type source	Types cibles
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

Conversion explicite des 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)

Fonctions TO

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)

Fonctions TO (suite)

-- 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)

Chaînes de format Date/heure et Numérique

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

Passons à la pratique !

Introduction à Redshift