Schema

A schema controls a database’s structure and behavior.

Fauna Schema Language

In Fauna, you define schema using Fauna Schema Language (FSL). You use FSL to create and update schema for:

Fauna stores each schema as an FQL document in a related system collection. Collectively, these constitute the database schema.

Manage schema as .fsl files

You can manage FSL schemas using Fauna Dashboard or as .fsl files using the Fauna CLI.

Using the CLI lets you:

For more information, see Manage schema as .fsl files.

Collection schema

Reference: FSL collection schema

A collection schema defines the structure and behavior of a collection and its documents. It can include:

You create and manage collection schema in FSL:

collection Product {
  // Field definitions.
  // Define the structure of the collection's documents.
  name: String?
  description: String?
  price: Int = 0
  stock: Int = 0
  creationTime: Time = Time.now()
  creationTimeEpoch: Int?
  typeConflicts: { *: Any }?

  // Wildcard constraint.
  // Allows or disallows arbitrary ad hoc fields.
  *: Any

  // Migrations block.
  // Used for schema migrations.
  // Instructs Fauna how to handle updates to a collection's
  // field definitions and wildcard constraint.
  // Contains imperative migration statements.
  migrations {
    add .typeConflicts
    add .stock
    add_wildcard
    backfill .stock = 0
    drop .internalDesc
    move_conflicts .typeConflicts
    move .desc -> .description
    split .creationTime -> .creationTime, .creationTimeEpoch
  }

  // Index definition.
  // You use indexes to filter and sort documents
  // in a performant way.
  index byName {
    terms [.name]
    values [desc(.stock), desc(mva(.categories))]
  }

  // Unique constraint.
  // Ensures a field value or combination of field values
  // is unique for each document in the collection.
  // Supports multivalue attribute (`mva`) fields, such as Arrays.
  unique [.name, .description, mva(.categories)]

  // Check constraint.
  // Ensures a field value meets provided criteria
  // before writes. Written as FQL predicate functions.
  check posStock ((doc) => doc.stock >= 0)

  // Computed field.
  // A document field that derives its value from a
  // user-defined, read-only FQL function that runs on every read.
  compute InventoryValue: Number = (.stock * .price)

  // Controls whether you can write to the `ttl` field for collection
  // documents. If the collection schema doesn't contain field
  // definitions, `document_ttls` defaults to `true`. Otherwise,
  // `document_ttls` defaults to `false`.
  document_ttls true

  // Sets the default `ttl` for documents in days from their creation
  // timestamp. You can override the default ttl` during document
  // creation.
  ttl_days 5

  // Controls document history retention.
  history_days 3
}

Document type definitions

A collection’s schema can include a document type definition. The definition controls what fields are accepted in a collection’s documents. You define a document’s type using:

Field definitions

Reference: FSL collection schema: Field definitions

Field definitions define fields for a collection’s documents. A field definition consists of:

  • A field name

  • Accepted data types for the field’s values

  • An optional default value

You can use field definitions to:

  • Ensure each document in a collection contains a specific field

  • Limit a field’s values to specific types

  • Set a default value for documents missing a field

  • Enumerate accepted values

collection Product {
  // `name` is optional (nullable).
  // Accepts `String` or `null` values.
  name: String?  // Equivalent to `name: String | Null`

  // `price` is optional (nullable).
  // Accepts `Int` or `null` values.
  price: Int?

  // `stock` is non-nullable.
  // Accepts only `Int` values.
  // If missing, defaults to `0`.
  stock: Int = 0

  // `creationTime` is non-nullable.
  // Accepts only `Time` or `Number` values.
  // If missing, defaults to the current time.
  creationTime: Time | Number = Time.now()

  // `category` is non-nullable.
  // Accepts only the enumerated "grocery",
  // "pharmacy", or "home goods" values.
  // If missing, defaults to "grocery".
  category: "grocery" | "pharmacy" | "home goods" = "grocery"
}

Wildcard constraint

Reference: Wildcard constraints

An ad hoc field is an arbitrary document field that doesn’t have a field definition.

You can use a collection schema’s wildcard constraint to allow or disallow ad hoc fields in the collection’s documents.

collection Product {
  name: String?  // Equivalent to `name: String | Null`
  ...

  // Wildcard constraint.
  // This example accepts ad hoc fields of any type.
  *: Any
}

Computed fields

Reference: FSL collection schema: Computed field definitions

Computed fields derive their field value from a provided function. They let you create new fields based on existing data or calculations.

You can use a computed field to:

  • Combine or transform other field values

  • Assign a value based on an if ... else expression

  • Assign a value based on one or more ranges

Computed fields aren’t part of the original document or persistently stored. Instead, the field’s value is computed on each read.

Document type enforcement

Fauna rejects attempts to write documents that don’t conform to a collection’s field definitions and wildcard constraint.

You can use the collection’s field definitions and wildcard constraint to adjust how strictly you enforce a predefined structure on collection documents:

Strategy Description Field definitions Wildcard constraint

Schemaless

Accepts ad hoc fields of any type. No fields are predefined.

No field definitions

No wildcard constraint
OR
A wildcard constraint of
*: Any

Permissive

Accepts ad hoc fields and predefined fields. Fields must conform to the structure of their definitions.

One or more field definitions

A wildcard constraint

Strict

Only accepts predefined fields

One or more field definitions

No wildcard constraint

Schemaless by default

If a collection has no field definitions, it’s schemaless by default. It implicitly accepts ad hoc fields of any type.

Progressive enforcement

Using permissive document types is often helpful earlier in an application’s development. Allowing ad hoc fields lets you add fields as needed.

As your data evolves, you can use zero-downtime migrations to add field definitions for ad hoc fields and normalize field values. This lets you move from a permissive document type to strict one (or the reverse).

Tutorial: Progressively enforce a document type

Zero-downtime schema migrations

A schema migration is an update to a collection schema’s field definitions or wildcard constraint. Schema migrations require no downtime or locks on your database.

Migrations block

Reference: FSL collection schema: Migrations block

To handle migrations, you include a migrations block in the collection schema. The block contains one or more imperative migration statements.

The statements instruct Fauna on how to migrate from the collection’s current field definitions and wildcard constraint to the new ones.

collection Product {
  ...
  *: Any

  migrations {
    // Applied 2099-05-06
    add .typeConflicts
    add .stock
    move_conflicts .typeConflicts
    backfill .stock = 0
    drop .internalDesc
    move .desc -> .description
    split .creationTime -> .creationTime, .creationTimeEpoch

    // Applied 2099-05-20
    // Make `price` a required field.
    split .price -> .price, .tempPrice
    drop .tempPrice
    backfill .price = 1
  }
}

Run a schema migration

A typical schema migration involves the following steps:

  1. Update the field definitions and wildcard constraint in the collection schema.

  2. Add one or more related migration statements to the collection schema’s migrations block. Include comments to group and annotate statements related to the same migration.

    Fauna runs each new migration statement sequentially from top to bottom. Fauna ignores unchanged migration statements from previous migrations.

  3. Commit the updated collection schema to Fauna with a staged schema change using the Fauna Dashboard or the Fauna CLI.

    You can’t use a staged schema change to delete or rename schema. Instead, delete or rename the schema in a separate unstaged schema change.

    To run a staged schema change using the CLI:

    1. Use fauna schema push to stage the schema changes. fauna schema push stages schema changes by default:

      fauna schema push

      This allows Fauna to build indexes for the updated schema before applying the schema to the database.

      A database can have one staged schema change at a time. You can update staged schema using fauna schema push.

      When a database has staged schema, any access or updates done using FQL’s schema commands on related system collections interact with the staged schema, not the database’s active schema.

      For example, when schema changes are staged, Collection.all() returns Collection documents for the staged collection schema, not the database’s Collection documents.

      If a database has staged schema, you can’t edit the database’s active schema using FQL, the Dashboard, or an unstaged schema change. You must first abandon the staged schema change.

    2. Use fauna schema status to check the status of the staged schema:

      fauna schema status

      Possible statuses:

      Staged status Description

      pending

      Changes are being processed. New indexes are still being built.

      ready

      All indexes have been built. Changes are ready to commit.

      failed

      There was an error during the staging process.

    3. When the status is ready, use fauna schema commit to apply the staged schema to the database:

      fauna schema commit

      You can only commit staged schema with a status of ready.

      If you no longer wish to apply the staged schema or if the status is failed, use fauna schema abandon to unstage the schema:

      fauna schema abandon

Once committed, changes from the migration are immediately visible in any subsequent queries.

Migration errors

When you submit a collection schema, Fauna checks the schema’s field definitions and migration statements for potential conflicts.

If a change could conflict with the collection’s data, Fauna rejects the schema with an error message. The check doesn’t require a read or scan of the collection’s documents.

Index definitions

An index stores, or covers, specific document field values for quick retrieval. Using indexes can significantly improve query performance and reduce costs, especially for large datasets.

See Indexes

Unique constraints

Reference: FSL collection schema: Unique constraint definitions

Unique constraints ensure a field value or combination of field values is unique for each document in a collection. Fauna rejects document writes that don’t meet the constraint.

Check constraints

Reference: FSL collection schema: Check constraint definitions

Check constraints ensure field values meet a pre-defined rule. For example, you can check that field values are in an allowed range.

You define a check constraint as a read-only FQL predicate. Fauna only allows document writes if the predicate evaluates to true.

Document time-to-live (TTL)

A document can include an optional ttl (time-to-live) field that contains the document’s expiration timestamp. After the ttl timestamp passes, Fauna permanently deletes the document.

You can use a collection schema’s ttl_days field to set a default ttl for collection documents. See Set a default ttl.

You can use a collection schema’s document_ttls field to control whether you can write to the ttl field for collection documents. See Enable or disable ttl writes.

See Document time-to-live (TTL)

Document history

Fauna stores snapshots of each document’s history. Fauna creates these snapshots each time the document receives a write.

You can use a collection schema’s history_days field to set how many days of document history Fauna retains for a collection’s documents.

See Document history

Protected mode

Protected mode is a database setting that prohibits destructive operations on a database’s collections.

When you create a database in the Fauna Dashboard, you select one of the following Protected Mode options:

Option Description

Disabled

Default. No operations are prohibited.

Enabled

Prohibits destructive operations. See Prohibited operations.

Inherit

Sets the database’s Protected Mode setting to the nearest ancestor’s Protected Mode setting that is not Inherit. This option is only available for child databases.

Prohibited operations

When Protected Mode is Enabled, the following operations are prohibited.

Resource or field Prohibited operations Exceptions

Collection

Delete

Index definitions

Change
Remove

history_days

Decrease
Remove

ttl_days

Decrease
Add

Unique constraints

Change
Remove

Database schema validation

When you write to a schema, Fauna parses and validates the entire database schema in a single transaction.

Concurrent schema writes in the same database can cause contended transactions, even if the changes affect different resources. To avoid errors, perform schema changes sequentially instead.

Schema version

Fauna maintains a schema_version for the database schema that’s returned in Query HTTP API responses. Fauna increments this version when you write to any schema for the database.

The schema number acts as a comparative value to help clients determine the minimum schema version used for query execution.

Considerations

Keep the following in mind when working with the schema_version:

  • The schema_version is cached and may change without schema modifications.

  • The schema_version is not permanently persisted in Fauna.

  • You should only use the schema_version to verify that a query request ran against a specific minimum schema version. Do not rely on schema_version being consistent across requests.

Client drivers

Fauna’s client drivers include the schema_version values in a query info or query response class. This class is used for both successful query responses and errors:

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