Supabase Cheat Sheet

Updated 2026-04-29

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Supabase is an open-source Backend-as-a-Service (BaaS) platform built on PostgreSQL, providing instant APIs, authentication, real-time subscriptions, storage, and serverless functions. It combines a full PostgreSQL database with modern BaaS features, offering a developer-friendly alternative to Firebase with the flexibility of SQL. The platform auto-generates RESTful and GraphQL APIs via PostgREST and pg_graphql, supports Row Level Security for granular access control, and provides a complete ecosystem — including Edge Functions, queues, branching, and an MCP server — for building production-ready applications. As of 2026, tables in the public schema are no longer exposed to the Data API by default; explicit GRANT statements are required for new tables.

What This Cheat Sheet Covers

This topic spans 23 focused tables and 196 indexed concepts, 111 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Database OperationsTable 2: Query FiltersTable 3: Query ModifiersTable 4: Relationships and JoinsTable 5: Authentication MethodsTable 6: Row Level Security (RLS)Table 7: Storage OperationsTable 8: Storage BucketsTable 9: Realtime FeaturesTable 10: Edge FunctionsTable 11: TypeScript Type GenerationTable 12: Database ExtensionsTable 13: CLI CommandsTable 14: Database Functions and TriggersTable 15: Database ViewsTable 16: Connection ManagementTable 17: Backup and RecoveryTable 18: Auth HooksTable 19: Error HandlingTable 20: Supabase Queues (pgmq)Table 21: Database TestingTable 22: Platform FeaturesTable 23: Advanced Patterns

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Table 1: Database Operations

The everyday CRUD verbs you chain off supabase.from('table') — the core of working with Postgres through the auto-generated API. Each one maps to a familiar SQL operation, with rpc reaching into stored functions when you need real server-side logic. Note the recurring safety rule: update and delete demand a filter, or they touch every row.

Method	Example	Description
select	`await supabase.from('users').select('*')`	• Fetches rows from a table • use `.select('col1, col2')` for specific columns.
insert	`await supabase.from('users').insert({ name: 'John', email: 'j@x.com' })`	• Inserts one or more rows • accepts single object or array • chain `.select()` to return inserted data.
update	`await supabase.from('users').update({ name: 'Jane' }).eq('id', 5)`	• Modifies existing rows • requires a filter to avoid updating all rows.
upsert	`await supabase.from('users').upsert({ id: 5, name: 'Jane' })`	• Inserts or updates based on primary key or unique constraint • use `onConflict` option to specify the column.
delete	`await supabase.from('users').delete().eq('id', 5)`	• Removes rows • requires filter to avoid deleting all rows • chain `.select()` to return deleted data.
rpc	`await supabase.rpc('increment_view_count', { post_id: 42 })`	• Calls a PostgreSQL stored function • allows complex server-side logic without exposing raw SQL.

Table 2: Query Filters

Narrowing a result set is all about chaining the right filter onto your select. These operators cover the obvious comparisons (eq, gt, lt) plus the ones people forget exist — case-insensitive search with ilike, array membership with in and contains, and or/not/filter for the conditions that don't fit a single method.

Operator	Example	Description
eq	`.eq('status', 'active')`	Equals — matches exact value (case-sensitive for strings).

The rest of this section is part of the full cheat sheet

Table 3: Query Modifiers

Once a query has filtered down to the right rows, modifiers shape how those rows come back — sorted, paged, capped, or coerced into a single object. The pair worth memorizing is single versus maybeSingle: the first throws when there isn't exactly one row, the second quietly returns null.

Modifier	Example	Description
order	`.order('created_at', { ascending: false })`	• Sorts results by column • supports multiple columns with chained calls.

The rest of this section is part of the full cheat sheet

Table 4: Relationships and Joins

Because PostgREST reads your foreign keys, you can pull related tables into a nested object straight from select — no manual JOIN syntax. These patterns show how to embed a parent, filter on the embedded table, force an inner join, disambiguate when two foreign keys point at the same table, and even count related rows instead of fetching them.

Pattern	Example	Description
Nested select	`.select(', author:users()')`	• Joins related table via foreign key • automatically detects relationships • returns nested object.

The rest of this section is part of the full cheat sheet

Table 1: Database Operations

Method	Example	Description
select	`await supabase.from('users').select('*')`	• Fetches rows from a table • use `.select('col1, col2')` for specific columns.
insert	`await supabase.from('users').insert({ name: 'John', email: 'j@x.com' })`	• Inserts one or more rows • accepts single object or array • chain `.select()` to return inserted data.
update	`await supabase.from('users').update({ name: 'Jane' }).eq('id', 5)`	• Modifies existing rows • requires a filter to avoid updating all rows.
upsert	`await supabase.from('users').upsert({ id: 5, name: 'Jane' })`	• Inserts or updates based on primary key or unique constraint • use `onConflict` option to specify the column.
delete	`await supabase.from('users').delete().eq('id', 5)`	• Removes rows • requires filter to avoid deleting all rows • chain `.select()` to return deleted data.
rpc	`await supabase.rpc('increment_view_count', { post_id: 42 })`	• Calls a PostgreSQL stored function • allows complex server-side logic without exposing raw SQL.

Table 2: Query Filters

Operator	Example	Description
eq	`.eq('status', 'active')`	Equals — matches exact value (case-sensitive for strings).

Table 3: Query Modifiers

Modifier	Example	Description
order	`.order('created_at', { ascending: false })`	• Sorts results by column • supports multiple columns with chained calls.

Table 4: Relationships and Joins

Pattern	Example	Description
Nested select	`.select(', author:users()')`	• Joins related table via foreign key • automatically detects relationships • returns nested object.