Tables and Data

Tables are where you store your data.

Tables are similar to excel spreadsheets. They contain columns and rows. For example, this table has 3 "columns" (id, name, description) and 4 "rows" of data:

`id`	`name`	`description`
1	The Phantom Menace	Two Jedi escape a hostile blockade to find allies and come across a young boy who may bring balance to the Force.
2	Attack of the Clones	Ten years after the invasion of Naboo, the Galactic Republic is facing a Separatist movement.
3	Revenge of the Sith	As Obi-Wan pursues a new threat, Anakin acts as a double agent between the Jedi Council and Palpatine and is lured into a sinister plan to rule the galaxy.
4	Star Wars	Luke Skywalker joins forces with a Jedi Knight, a cocky pilot, a Wookiee and two droids to save the galaxy from the Empire's world-destroying battle station.

There are a few important differences from a spreadsheet, but it's a good starting point if you're new to Relational databases.

Creating Tables#

When creating a table, it's best practice to add columns at the same time.

You must define the "data type" of each column when it is created. You can add and remove columns at any time after creating a table.

Supabase provides several options for creating tables. You can use the Dashboard or create them directly using SQL. We provide a SQL editor within the Dashboard, or you can connect to your database and run the SQL queries yourself.

Go to the Table Editor page in the Dashboard.
Click New Table and create a table with the name todos.
Click Save.
Click New Column and create a column with the name task and type text.
Click Save.

note

When naming tables, use lowercase and underscores instead of spaces (e.g., table_name, not Table Name).

Columns#

You must define the "data type" when you create a column.

Data types#

Every column is a predefined type. PostgreSQL provides many default types, and you can even design your own (or use extensions) if the default types don't fit your needs. You can use any data type that Postgres supports via the SQL editor. We only support a subset of these in the Table Editor in an effort to keep the experience simple for people with less experience with databases.

Show/Hide default data types

`Name`	`Aliases`	`Description`
bigint	int8	signed eight-byte integer
bigserial	serial8	autoincrementing eight-byte integer
bit		fixed-length bit string
bit varying	varbit	variable-length bit string
boolean	bool	logical Boolean (true/false)
box		rectangular box on a plane
bytea		binary data (“byte array”)
character	char	fixed-length character string
character varying	varchar	variable-length character string
cidr		IPv4 or IPv6 network address
circle		circle on a plane
date		calendar date (year, month, day)
double precision	float8	double precision floating-point number (8 bytes)
inet		IPv4 or IPv6 host address
integer	int, int4	signed four-byte integer
interval [ fields ]		time span
json		textual JSON data
jsonb		binary JSON data, decomposed
line		infinite line on a plane
lseg		line segment on a plane
macaddr		MAC (Media Access Control) address
macaddr8		MAC (Media Access Control) address (EUI-64 format)
money		currency amount
numeric	decimal	exact numeric of selectable precision
path		geometric path on a plane
pg_lsn		PostgreSQL Log Sequence Number
pg_snapshot		user-level transaction ID snapshot
point		geometric point on a plane
polygon		closed geometric path on a plane
real	float4	single precision floating-point number (4 bytes)
smallint	int2	signed two-byte integer
smallserial	serial2	autoincrementing two-byte integer
serial	serial4	autoincrementing four-byte integer
text		variable-length character string
time [ without time zone ]		time of day (no time zone)
time with time zone	timetz	time of day, including time zone
timestamp [ without time zone ]		date and time (no time zone)
timestamp with time zone	timestamptz	date and time, including time zone
tsquery		text search query
tsvector		text search document
txid_snapshot		user-level transaction ID snapshot (deprecated; see pg_snapshot)
uuid		universally unique identifier
xml		XML data

You can "cast" columns from one type to another, however there can be some incompatibilities between types. For example, if you cast a timestamp to a date, you will lose all the time information that was previously saved.

Primary Keys#

A table can have a "primary key" - a unique identifier for every row of data. A few tips for Primary Keys:

It's recommended to create a Primary Key for every table in your database.
You can use any column as a primary key, as long as it is unique for every row.
It's common to use a uuid type or a numbered identity column as your primary key.

create table movies (
  id bigint generated always as identity primary key
);

In the example above, we have:

created a column called id
assigned the data type bigint
instructed the database that this should be generated always as identity, which means that Postgres will automatically assign a unique number to this column.
Because it's unique, we can also use it as our primary key.

We could also use generated by default as identity, which would allow us to insert our own unique values.

create table movies (
  id bigint generated by default as identity primary key
);

Loading data#

There are several ways to load data in Supabase. You can load data directly into the database or using the APIs. Use the "Bulk Loading" instructions if you are loading large data sets.

Basic data loading#

insert into movies
  (name, description)
values
  (
    'The Empire Strikes Back',
    'After the Rebels are brutally overpowered by the Empire on the ice planet Hoth, Luke Skywalker begins Jedi training with Yoda.'
  ),
  (
    'Return of the Jedi',
    'After a daring mission to rescue Han Solo from Jabba the Hutt, the Rebels dispatch to Endor to destroy the second Death Star.'
  );

Bulk data loading#

When inserting large data sets it's best to use PostgreSQL's COPY command. This loads data directly from a file into a table. There are several file formats available for copying data: text, csv, binary, JSON, etc.

For example, if you wanted to load a CSV file into your movies table:

./movies.csv

"The Empire Strikes Back", "After the Rebels are brutally overpowered by the Empire on the ice planet Hoth, Luke Skywalker begins Jedi training with Yoda."
"Return of the Jedi", "After a daring mission to rescue Han Solo from Jabba the Hutt, the Rebels dispatch to Endor to destroy the second Death Star."

You would connect to your database directly and load the file with the COPY command:

psql -h DATABASE_URL -p 5432 -d postgres -U postgres \
  -c "\COPY movies FROM './movies.csv';"

Additionally use the DELIMITER, HEADER and FORMAT options as defined in the PostgreSQL COPY docs.

psql -h DATABASE_URL -p 5432 -d postgres -U postgres \
  -c "\COPY movies FROM './movies.csv' WITH DELIMITER ',' CSV HEADER"

If you receive an error FATAL: password authentication failed for user "postgres", reset your database password in the Database Settings and try again.

Joining tables with Foreign Keys#

Tables can be "joined" together using Foreign Keys.

This is where the "Relational" naming comes from, as data typically forms some sort of relationship.

In our "movies" example above, we might want to add a "category" for each movie (for example, "Action", or "Documentary"). Let's create a new table called categories and "link" our movies table.

create table categories (
  id bigint generated always as identity primary key,
  name text -- category name
);
alter table movies
  add column category_id bigint references categories;

You can also create "many-to-many" relationships by creating a "join" table. For example if you had the following situations:

You have a list of movies.
A movie can have several actors.
An actor can perform in several movies.

Schemas#

Tables belong to schemas. Schemas are a way of organizing your tables, often for security reasons.

If you don't explicitly pass a schema when creating a table, Postgres will assume that you want to create the table in the public schema.

We can create schemas for organizing tables. For example, we might want a private schema which is hidden from our API:

create schema private;

Now we can create tables inside the private schema:

create table private.salaries (
  id bigint generated by default as identity primary key,
  salary bigint not null,
  actor_id bigint not null references public.actors
);

Views#

A View is a convenient shortcut to a query. Creating a view does not involve new tables or data. When run, an underlying query is executed, returning its results to the user.

caution

By default, PostgreSQL views bypass Row Level Security unless you change their owner (see https://github.com/supabase/supabase/discussions/901). PostgreSQL v15 (coming soon) will have a more intuitive control for this through security invoker views and the previous step won't be needed.

Say we have the following tables from a database of a university:

students

id	name	type
1	Princess Leia	undergraduate
2	Yoda	graduate
3	Anakin Skywalker	graduate

courses

id	title	code
1	Introduction to Postgres	PG101
2	Authentication Theories	AUTH205
3	Fundamentals of Supabase	SUP412

grades

id	student_id	course_id	result
1	1	1	B+
2	1	3	A+
3	2	2	A
4	3	1	A-
5	3	2	A
6	3	3	B-

Creating a view consisting of all the three tables will look like this:

create view transcripts as
    select
        students.name,
        students.type,
        courses.title,
        courses.code,
        grades.result
    from grades
    left join students on grades.student_id = students.id
    left join courses on grades.course_id = courses.id;
alter view transcripts owner to authenticated;

Once done, we can now access the underlying query with:

select * from transcripts;

When to use views#

Views provide the several benefits:

Simplicity
Consistency
Logical Organization
Security

Simplicity

As a query becomes more complex, it can be a hassle to call it over and over - especially when we run it regularly. In the example above, instead of repeatedly running:

select
  students.name,
  students.type,
  courses.title,
  courses.code,
  grades.result
from
  grades
  left join students on grades.student_id = students.id
  left join courses on grades.course_id = courses.id;

We can run this instead:

select * from transcripts;

Additionally, a view behaves like a typical table. We can safely use it in table JOINs or even create new views using existing views.

Consistency

Views ensure that the likelihood of mistakes decreases when repeatedly executing a query. In our example above, we may decide that we want to exclude the course Introduction to Postgres. The query would become:

select
  students.name,
  students.type,
  courses.title,
  courses.code,
  grades.result
from
  grades
  left join students on grades.student_id = students.id
  left join courses on grades.course_id = courses.id
where courses.code != 'PG101';

Without a view, we would need to go into every dependent query to add the new rule. This would increase in the likelihood of errors and inconsistencies, as well as introducing a lot of effort for a developer. With views, we can alter just the underlying query in the view transcripts. The change will be applied to all applications using this view.

Logical Organization

With views, we can give our query a name. This is extremely useful for teams working with the same database. Instead of guessing what a query is supposed to do, a well-named view can easily explain it. For example, by looking at the name of the view transcripts, we can infer that the underlying query might involve the students, courses, and grades tables.

Security

Views can restrict the amount and type of data presented to a user. Instead of allowing a user direct access to a set of tables, we provide them a view instead. We can prevent them from reading sensitive columns by excluding them from the underlying query.

Materialized Views#

A materialized view is a form of view but it also stores the results to disk. In subsequent reads of a materialized view, the time taken to return its results would be much faster than a conventional view. This is because the data is readily available for a materialized view while the conventional view executes the underlying query each time it is called.

Using our example above, a materialized view can be created like this:

create materialized view transcripts as
  select
    students.name,
    students.type,
    courses.title,
    courses.code,
    grades.result
  from
    grades
    left join students on grades.student_id = students.id
    left join courses on grades.course_id = courses.id;

Reading from the materialized view is the same as a conventional view:

select * from transcripts;

Refreshing materialized views#

Unfortunately, there is a trade-off - data in materialized views are not always up to date. We need to refresh it regularly to prevent the data from becoming too stale. To do so:

refresh materialized view transcripts;

It's up to you how regularly refresh your materialized views, and it's probably different for each view depending on its use-case.

Materialized views vs Conventional views#

Materialized views are useful when execution times for queries or views are too slow. These could likely occur in views or queries involving multiple tables and billions of rows. When using such a view, however, there should be tolerance towards data being outdated. Some use-cases for materialized views are internal dashboards and analytics.

Creating a materialized view is not a solution to inefficient queries. You should always seek to optimize a slow running query even if you are implementing a materialized view.

Resources#

Edit this page on GitHub