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.

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

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. Postgres 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

Postgres 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.

1create table movies (2  id bigint generated always as identity primary key3);

In the example above, we have:

1. created a column called id
2. assigned the data type bigint
3. instructed the database that this should be generated always as identity, which means that Postgres will automatically assign a unique number to this column.
4. 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.

1create table movies (2  id bigint generated by default as identity primary key3);

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#

1insert into movies2  (name, description)3values4  (5    'The Empire Strikes Back',6    'After the Rebels are brutally overpowered by the Empire on the ice planet Hoth, Luke Skywalker begins Jedi training with Yoda.'7  ),8  (9    'Return of the Jedi',10    'After a daring mission to rescue Han Solo from Jabba the Hutt, the Rebels dispatch to Endor to destroy the second Death Star.'11  );

Bulk data loading#

When inserting large data sets it's best to use Postgres'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:

1"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."2"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:

1psql -h DATABASE_URL -p 5432 -d postgres -U postgres \2  -c "\COPY movies FROM './movies.csv';"

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

1psql -h DATABASE_URL -p 5432 -d postgres -U postgres \2  -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.

1create table categories (2  id bigint generated always as identity primary key,3  name text -- category name4);56alter table movies7  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:

1create schema private;

Now we can create tables inside the private schema:

1create table private.salaries (2  id bigint generated by default as identity primary key,3  salary bigint not null,4  actor_id bigint not null references public.actors5);

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.

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:

1create view transcripts as2    select3        students.name,4        students.type,5        courses.title,6        courses.code,7        grades.result8    from grades9    left join students on grades.student_id = students.id10    left join courses on grades.course_id = courses.id;1112grant all on table transcripts to authenticated;

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

1select * from transcripts;

View security#

By default, views are accessed with their creator's permission ("security definer"). If a privileged role creates a view, others accessing it will use that role's elevated permissions. To enforce row level security policies, define the view with the "security invoker" modifier.

1-- alter a security_definer view to be security_invoker2alter view <view name>3set (security_invoker = true);45-- create a view with the security_invoker modifier6create view <view name> with(security_invoker=true) as (7  select * from <some table>8);

When to use views#

Views provide 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:

1select2  students.name,3  students.type,4  courses.title,5  courses.code,6  grades.result7from8  grades9  left join students on grades.student_id = students.id10  left join courses on grades.course_id = courses.id;

We can run this instead:

1select * 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:

1select2  students.name,3  students.type,4  courses.title,5  courses.code,6  grades.result7from8  grades9  left join students on grades.student_id = students.id10  left join courses on grades.course_id = courses.id11where 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 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:

1create materialized view transcripts as2  select3    students.name,4    students.type,5    courses.title,6    courses.code,7    grades.result8  from9    grades10    left join students on grades.student_id = students.id11    left join courses on grades.course_id = courses.id;

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

1select * 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:

1refresh 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#

• Official Docs: Create table
• Official Docs: Create view
• Postgres Tutorial: Create tables
• Postgres Tutorial: Add column
• Postgres Tutorial: Views