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HCL Schema

Atlas schemas can be defined in SQL, external ORMs and programs, or by using the Atlas HCL language. The HCL-based language allows developers to describe database schemas in a declarative manner, and it supports all SQL features supported by Atlas. The main advantages of using HCL are that it enables developers to manage their database schemas like regular code, facilitates sharing and reusing files between projects, allows variable injection, and provides the ability to attach annotations to objects, such as PII or sensitive data.

Editor Support​

The Atlas HCL language provides plugins for popular editors like VSCode and JetBrains to enhance your day-to-day editing, navigation, formatting, and testing experience.

VSCode extension

Schema​

The schema object describes a database schema. A DATABASE in MySQL and SQLite, or a SCHEMA in PostgreSQL. An HCL file can contain 1 or more schema objects.

In MySQL and MariaDB, the schema resource can contain the charset and collate attributes. Read more about them in MySQL or MariaDB websites.

# Schema with attributes.
schema "market" {
  charset = "utf8mb4"
  collate = "utf8mb4_0900_ai_ci"
  comment = "A schema comment"
}

# Schema without attributes.
schema "orders" {}

Table​

A table describes a table in a SQL database. A table hold its columns, indexes, constraints, and additional attributes that are supported by the different drivers.

table "users" {
  schema = schema.public
  column "id" {
    type = int
  }
  column "name" {
    type = varchar(255)
  }
  column "manager_id" {
    type = int
  }
  primary_key {
    columns = [
      column.id
    ]
  }
  index "idx_name" {
    columns = [
      column.name
    ]
    unique = true
  }
  foreign_key "manager_fk" {
    columns = [column.manager_id]
    ref_columns = [column.id]
    on_delete = CASCADE
    on_update = NO_ACTION
  }
}

Check​

A check is a child resource of a table that describes a CHECK constraint.

table "products" {
  column "price" {
    type = float
  }
  check "positive price" {
    expr = "price > 0"
  }
}

Partitions​

The partition option is a PostgreSQL-specific option that allows defining table partitioning. Table partitioning refers to splitting logical large tables into smaller physical ones.

table "logs" {
  schema = schema.public
  column "date" {
    type = date
  }
  column "text" {
    type = integer
  }
  partition {
    type = RANGE
    columns = [column.date]
  }
}

table "metrics" {
  schema = schema.public
  column "x" {
    type = integer
  }
  column "y" {
    type = integer
  }
  partition {
    type = RANGE
    by {
      column = column.x
    }
    by {
      expr = "floor(y)"
    }
  }
}

Foreign Tables​

The foreign_table option is a PostgreSQL-specific block that allows defining a foreign table. A foreign table is a table that is not local to the current database but is defined in another database.

foreign_table "table_1" {
  schema = schema.public
  column "id" {
    type = integer
  }
  // If the server is defined in the
  // same schema, we can reference it.
  server  = server.test_server
  options = {
    schema_name = "public"
    table_name  = "tablex_1"
  }
}

foreign_table "table_2" {
  schema = schema.public
  column "id" {
    type = integer
  }
  column "name" {
    type = integer
    options = {
      column_name = "other_name"
    }
  }
  // If the server is not defined in the same
  // schema, we can define its name as a string.
  server = "external_server"
}

server "test_server" {
  fdw     = extension.postgres_fdw
  comment = "test server"
  options = {
    dbname = "postgres"
    host   = "localhost"
    port   = "5429"
  }
}

Row Level Security​

The row_security option is a PostgreSQL-specific option that allows enabling row-level security policies for a table.

table "users" {
  schema = schema.public
  column "id" {
    type = int
  }
  row_security {
    enabled  = true // ENABLE ROW LEVEL SECURITY
    enforced = true // FORCE ROW LEVEL SECURITY
  }
}
Defining Policies

To define row-level security policies for a table, refer to the policy example.

Full-Text Index​

The fulltext option is a SQL Server-specific option that allows enabling full-text index for a table.

table "t1" {
  schema = schema.dbo
  column "c1" {
    null = false
    type = bigint
    identity {
      seed      = 701
      increment = 1000
    }
  }
  column "c2" {
    null = false
    type = varbinary(-1)
  }
  column "c3" {
    null = false
    type = char(3)
  }
  index "uq_t1" {
    unique  = true
    columns = [column.c1]
  }
  fulltext {
    unique_key = index.uq_t1
    filegroup  = "PRIMARY"
    catalog    = "FT_CD"
    on {
      column   = column.c2
      type     = column.c3
      language = "English"
    }
  }
}
schema "dbo" {
}

To able to use this feature, you need to enable the Full-Text Search feature in SQL Server both on the production server and the devdb. Bellow is example of enabling the feature on the devdb, with custom dockerfile.

docker "sqlserver" "dev" {
  image = "mssql:2017-fts"
  build {
    context  = "."
    platform = ["linux/amd64"] # SQL Server 2017 only supports amd64 architecture.
  }
  database = "awesome"          # Can't use full-text search in master database.
  collate  = "Vietnamese_CI_AS" # Collation for the database.
  # We need create the default catalog for full-text search on the database.
  baseline = <<SQL
    CREATE FULLTEXT CATALOG [FT_CD] ON FILEGROUP [PRIMARY] AS DEFAULT;
  SQL
}

env {
  name = atlas.env
  dev  = docker.sqlserver.dev.url
  src  = "file://schema.sql"
}

Then we can run atlas schema inspect --env local --url "env://src" to get the schema in HCL format.

Table Qualification​

In some cases, an Atlas DDL document may contain multiple tables of the same name. This usually happens when the same table name appears in two different schemas. In these cases, the table names must be disambiguated by using resource qualifiers. The following document describes a database that contains two schemas named a and b, and both of them contain a table named users.

schema "a" {}
schema "b" {}

table "a" "users" {
  schema = schema.a
  // .. columns
}
table "b" "users" {
  schema = schema.b
  // .. columns
}

Storage Engine​

The engine attribute allows for overriding the default storage engine of the table. Supported by MySQL, MariaDB and ClickHouse.

table "users" {
  schema = schema.public
  engine = MyISAM
}

table "posts" {
  schema = schema.public
  engine = InnoDB
}

table "orders" {
  schema = schema.public
  engine = "MyRocks"
}

System-Versioned Tables​

Atlas Pro Feature

System-Versioned tables are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The system_versioned block allows marking a table as a system-versioned temporal table. Supported by SQL Server. This block can be used to define the history table, retention period, and the period unit.

The period block defines the period columns for the system-versioned table. It requires two columns: start and end, which are used to define the period of validity for each row. These columns must be of type datetime2, non-nullable, and have the generated always as row start and row end respectively.

schema "dbo" {}
table "t1" {
  schema = schema.dbo
  column "c1" {
    type = int
    null = false
  }
  column "c2" {
    type = money
    null = false
  }
  column "c3" {
    type = datetime2(7)
    null = false
    generated_always {
      as = ROW_START
    }
  }
  column "c4" {
    type = datetime2(7)
    null = false
    generated_always {
      as = ROW_END
    }
  }
  primary_key {
    on {
      column = column.c1
      desc   = true
    }
  }
  period "system_time" {
    type  = SYSTEM_TIME
    start = column.c3
    end   = column.c4
  }
  system_versioned {
    history_table  = "dbo.t1_History"
    retention      = 3
    retention_unit = MONTH
  }
}

Distribution​

The distribution block is a Redshift-specific option that allows specifying the distribution method of the table.

table "users" {
  schema = schema.public
  column "id" {
    type = int
  }
  distribution {
    style = KEY     // EVEN | ALL | AUTO
    key = column.id // only for KEY style
  }
}

Sorting​

The sort block is a Redshift-specific option that allows specifying the sorting method of the table.

table "users" {
  schema = schema.public
  column "id" {
    type = int
  }
  sort {
    style = COMPOUND // INTERLEAVED | COMPOUND | AUTO
    columns = [column.id]
  }
}

Sorting Style AUTO

Redshift restricts user access to certain external tables which are used to inspect the sort style. Therefore, Atlas will ignore differences when changing the style to AUTO. You will need to manually adjust the sort style on your target Redshift database after modifying it in the Atlas schema.

To change the sort style to AUTO, run the following SQL command:

ALTER TABLE "my_table" ALTER SORTKEY AUTO;

View​

A view is a virtual table in the database, defined by a statement that queries rows from one or more existing tables or views.

Atlas Pro Feature

Views are currently available only to Atlas Pro users. To use this feature, run:

atlas login
view "clean_users" {
  schema = schema.public
  column "id" {
    type = int
  }
  column "name" {
    type = text
  }
  as         = <<-SQL
  SELECT u.id, u.name
    FROM ${table.users.name} AS u
    JOIN ${view.active_users.name} AS au USING (id)
  SQL
  depends_on = [table.users, view.t1]
  comment    = "A view to active users without sensitive data"
}

view "comedies" {
  schema = schema.public
  column "id" {
    type = int
  }
  column "name" {
    type = text
  }
  as           = "SELECT id, name FROM films WHERE kind = 'Comedy'"
  depends_on   = [table.films]
  check_option = CASCADED
  security     = INVOKER // DEFINER | INVOKER (MySQL/MariaDB only).
}
Testing Views

Atlas's testing framework allows you to write unit tests for your views. The following example demonstrates how to write tests for the clean_users view defined above. For more detail, read the schema testing docs or see the full example.

schema.test.hcl
test "schema" "view" {
  # Seeding to test view.
  exec {
    sql = "INSERT INTO users (id, name) VALUES (1, 'Alice'), (2, 'Bob'), (3, 'Charlie');"
  }
  log {
    message = "Seeded the database"
  }
  # Expected exec to pass.
  exec {
    sql = <<SQL
    SELECT id, name
    FROM clean_users;
    SQL
  }
  log {
    message = "Tested the view"
  }
  # Validates data.
  exec {
    sql = "SELECT id, name FROM clean_users;"
    format = table
    output = <<TAB
 id |  name
----+---------
 1  | Alice
 2  | Bob
 3  | Charlie
TAB
  }
  log {
    message = "Table is as expected"
  }
}

Materialized View​

A materialized view is a table-like structure that holds the results of a query. Unlike a regular view, the results of a materialized view are stored in the database and can be refreshed periodically to reflect changes in the underlying data.

Atlas Pro Feature

Materialized views are currently available only to Atlas Pro users. To use this feature, run:

atlas login
materialized "mat_view" {
  schema = schema.public
  column "total" {
    null = true
    type = numeric
  }
  index "idx_expr" {
    unique = true
    on {
      expr = "((total > (0)::numeric))"
    }
  }
  index "idx_pred" {
    unique  = true
    columns = [column.total]
    where   = "(total < (0)::numeric)"
  }
  as         = <<-SQL
   SELECT sum(total) AS total
     FROM m1;
  SQL
  depends_on = [materialized.m1]
}

Column​

A column is a child resource of a table.

column "name" {
  type = text
  null = false
}

column "age" {
  type = integer
  default = 42
}

column "active" {
  type = tinyint(1)
  default = true
}

Properties​

NameKindTypeDescription
nullattributeboolDefines whether the column is nullable.
typeattribute*schemahcl.TypeDefines the type of data that can be stored in the column.
defaultattribute*schemahcl.LiteralValueDefines the default value of the column.

Generated Columns​

Generated columns are columns whose their values are computed using other columns or by deterministic expressions.

table "users" {
  schema = schema.test
  column "a" {
    type = int
  }
  column "b" {
    type = int
    # In MySQL, generated columns are VIRTUAL by default.
    as = "a * 2"
  }
  column "c" {
    type = int
    as {
      expr = "a * b"
      type = STORED
    }
  }
}
info

Note, it is recommended to use the --dev-url option when generated columns are used.

Encodings​

Encodings are used to define the compression algorithm for the column data. Supported by ClickHouse and Redshift.

table "users" {
  schema = schema.public
  column "name" {
    type = text
    encode = LZ4 // AZ64 | RAW | LZ4 | ZSTD
  }
}

Column Types​

The SQL dialects supported by Atlas (Postgres, MySQL, MariaDB, and SQLite) vary in the types they support. At this point, the Atlas DDL does not attempt to abstract away the differences between various databases. This means that the schema documents are tied to a specific database engine and version. This may change in a future version of Atlas as we plan to add "Virtual Types" support. This section lists the various types that are supported in each database.

For a full list of supported column types, click here.

Primary Key​

A primary_key is a child resource of a table, and it defines the table's primary key.

Example​

primary_key {
  columns = [column.id]
}

Properties​

NameKindTypeDescription
columnsattributereference (list)A list of references to columns that comprise the primary key.

Foreign Key​

Foreign keys are child resources of a table, and it defines some columns in the table as references to columns in other tables.

Example​

schema.hcl
table "users" {
  schema = schema.public
  column "id" {
    type = integer
  }
  primary_key {
    columns = [column.id]
  }
}

table "orders" {
  schema = schema.market
  // ...
  column "owner_id" {
    type = integer
  }
  foreign_key "owner_id" {
    columns     = [column.owner_id]
    ref_columns = [table.users.column.id]
    on_update   = NO_ACTION
    on_delete   = NO_ACTION
  }
}

Referencing Qualified Tables​

If a foreign key references a column in a qualified table, it is referenced using table.<qualifier>.<table_name>.column.<column_name>:

schema.hcl
table "public" "users" {
  schema = schema.public
  column "id" {
    type = integer
  }
  primary_key {
    columns = [column.id]
  }
}

table "admin" "users" {
  schema = schema.admin
  // ...
  column "external_id" {
    type = integer
  }
  foreign_key "external_id" {
    columns     = [column.external_id]
    ref_columns = [table.admin.users.column.id]
    on_update   = NO_ACTION
    on_delete   = NO_ACTION
  }
}

Properties​

NameKindTypeDescription
columnsattributereference (list)The columns that reference other columns.
ref_columnsattributereference (list)The referenced columns.
on_updateattributeschema.ReferenceOptionDefines what to do on update.
on_deleteattributeschema.ReferenceOptionDefines what to do on delete.

Index​

Indexes are child resources of a table, and it defines an index on the table.

Example​

# Columns only.
index "idx_name" {
  unique = true
  columns = [column.name]
}

# Columns and order.
index "idx_name" {
  unique = true
  on {
    column = column.rank
  }
  on {
    column = column.score
    desc = true
  }
}

# Custom index type.
index "idx_name" {
  type = GIN
  columns = [column.data]
}

# Include non-key columns.
index "idx_include" {
  columns = [column.range]
  include = [column.version]
}

# Define operator class.
index "idx_operator_class" {
  type = GIN
  on {
    column = column.j
    ops    = jsonb_path_ops
  }
}

# Full-text index with ngram parser.
index "index_parser" {
  type    = FULLTEXT
  columns = [column.text]
  parser  = ngram
}

# Postgres-specific NULLS [NOT] DISTINCT option.
index "index_nulls_not_distinct" {
  unique         = true
  columns        = [column.text]
  nulls_distinct = false
}

Properties​

NameKindTypeDescription
uniqueattributebooleanDefines whether a uniqueness constraint is set on the index.
typeattributeIndexType (enum)Defines the index type. e.g. HASH, GIN, FULLTEXT.
columnsattributereference (list)The columns that comprise the index.
onresourceschema.IndexPart (list)The index parts that comprise the index.
optionsattributeschema.AttrAdditional driver specific attributes. e.g. storage_params

Index Expressions​

Index expressions allow setting indexes over functions or computed expressions. Supported by PostgreSQL, SQLite and MySQL8.

table "t" {
  schema = schema.test
  column "c1" {
    type = int
  }
  column "c2" {
    type = int
  }
  index "i" {
    on {
      expr = "c1 - c2"
    }
    on {
      expr = "c2 - c1"
    }
  }
}
info

Note, it is recommended to use the --dev-url option when index expressions are used.

Partial Indexes​

Partial indexes allow setting indexes over subset of the table. Supported by PostgreSQL and SQLite.

table "t" {
  schema = schema.public
  column "b" {
    type = bool
  }
  column "c" {
    type = int
  }
  index "i" {
    columns = [column.c]
    where = "b AND c > 0"
  }
}
info

Note, it is recommended to use the --dev-url option when partial indexes are used.

Index Prefixes​

Index prefixes allow setting an index on the first N characters of string columns. Supported by MySQL and MariaDB.

table "users" {
  schema = schema.test
  column "name" {
    type = varchar(255)
  }
  index "user_name" {
    on {
      column = column.name
      prefix = 128
    }
  }
}

Unique Constraints​

The unique block allows defining a unique constraint supported by PostgreSQL:

# Columns only.
unique "name" {
  columns = [column.name]
}

# Include non-key columns.
unique "name_include_version" {
  columns = [column.name]
  include = [column.version]
}
Adding unique constraints concurrently

In order to add a unique constraint in non-blocking mode, the index supporting the constraint needs to be created concurrently first and then converted to a unique constraint. To achieve this, follow the steps below:

  1. Define a unique index block on the desired table.
  2. Ensure a Diff Policy is used to instruct Atlas to create the index concurrently.
  3. Apply the migration and ensure the index was created.
  4. Replace the index block with a unique block to create a new unique constraint using the existing index.

Exclude Constraints​

The exclude block allows defining a exclusion constraint supported by PostgreSQL:

exclude "excl_speaker_during" {
  type = GIST
  on {
    column = column.speaker
    op     = "="
  }
  on {
    column = column.during
    op     = "&&"
  }
}

# Include non-key columns.
exclude "excl_speaker_during" {
  type = GIST
  on {
    column = column.speaker
    op     = "="
  }
  on {
    column = column.during
    op     = "&&"
  }
  include = [column.another]
}

Storage Parameters​

The storage_params block allows configuring the storage parameters of the index. Supported by PostgreSQL.

Atlas supports defining IVFFlat indexes when the vector extension is defined in the schema, enabling efficient similarity searches by organizing vectors into lists and searching only those closest to the query vector. 

index "index_name" {
  type = "IVFFlat"
  on {
    column = column.embedding
    ops    = "vector_l2_ops"
  }
  storage_params {
    lists = 100
  }
}

extension "vector" {
  schema  = schema.public
}

Trigger​

Atlas Pro Feature

Triggers are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The trigger block allows defining SQL triggers in HCL format.

function "audit_orders" {
  schema = schema.public
  lang   = PLpgSQL
  return = trigger
  as     = <<-SQL
  BEGIN
    INSERT INTO orders_audit(order_id, operation) VALUES (NEW.order_id, TG_OP);
    RETURN NEW;
  END;
  SQL
}
trigger "trigger_orders_audit" {
  on = table.orders
  after {
    insert    = true
    update_of = [table.orders.column.amount]
  }
  execute {
    function = function.audit_orders
  }
}

Computed Triggers​

To configure the same trigger for multiple tables/views, users can utilize the for_each meta-argument. By setting it up, a trigger block will be computed for each item in the provided value. Note that for_each accepts either a map or a set of references.

schema.pg.hcl
trigger "audit_log_trigger" {
  for_each = [table.users, table.orders, table.payments]
  on       = each.value
  after {
    insert = true
    update = true
    delete = true
  }
  execute  {
    function = function.audit_log_table
  }
}

Event Trigger​

Atlas Pro Feature

Event Triggers are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The event_trigger block allows defining PostgreSQL event trigger functions that automatically execute in response to specific events within the database system, like table creation or schema modifications.

# Block table rewrites.
event_trigger "block_table_rewrite" {
  on      = table_rewrite
  execute = function.no_rewrite_allowed
}

# Filter specific events.
event_trigger "record_table_creation" {
  on      = ddl_command_start
  tags    = ["CREATE TABLE"]
  execute = function.record_table_creation
}

Function​

Atlas Pro Feature

Functions are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The function block allows defining functions in HCL format. The lang attribute specifies the language of the function. For example, PLpgSQL, SQL, CRL, etc.

function "positive" {
  schema = schema.public
  lang   = SQL
  arg "v" {
    type = integer
  }
  return = boolean
  as     = "SELECT v > 0"
}

function "sql_body1" {
  schema = schema.public
  lang   = SQL
  arg "v" {
    type = integer
  }
  return = integer
  as = <<-SQL
   BEGIN ATOMIC
    SELECT v;
   END
  SQL
}

function "sql_body2" {
  schema = schema.public
  lang   = SQL
  arg {
    type = integer
  }
  return     = integer
  as         = "RETURN $1"
  volatility = IMMUTABLE // STABLE | VOLATILE
  leakproof  = true      // NOT LEAKPROOF | LEAKPROOF
  strict     = true      // (CALLED | RETURNS NULL) ON NULL INPUT
  security   = INVOKER   // DEFINER | INVOKER
}
Testing Functions

Atlas's testing framework allows you to write unit tests for your functions. The following example demonstrates how to write tests for the positive function defined above. For more detail, read the schema testing docs or see the full example.

schema.test.hcl
test "schema" "simple_test" {
  parallel = true
  assert {
    sql = "SELECT positive(1)"
  }
  log {
    message = "First assertion passed"
  }
  assert {
    sql = <<SQL
SELECT NOT positive(0);
SELECT NOT positive(-1);
SQL
  }
}

Aggregate Functions​

The aggregate block defines a function that computes a single result from a set of values. Supported by PostgreSQL.

aggregate "sum_of_squares" {
  schema = schema.public
  arg {
    type = double_precision
  }
  state_type = double_precision
  state_func = function.sum_squares_sfunc
}

function "sum_squares_sfunc" {
  schema = schema.public
  lang   = PLpgSQL
  arg "state" {
    type = double_precision
  }
  arg "value" {
    type = double_precision
  }
  return = double_precision
  as     = <<-SQL
  BEGIN
      RETURN state + value * value;
  END;
  SQL
}

Procedure​

Atlas Pro Feature

Procedures are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The procedure block allows defining SQL procedure in HCL format.

procedure "proc" {
  schema = schema.public
  lang   = SQL
  arg "a" {
    type = integer
  }
  arg "b" {
    type = text
  }
  arg "c" {
    type    = integer
    default = 100
  }
  as = <<-SQL
   INSERT INTO t1 VALUES(a, b);
   INSERT INTO t2 VALUES(c, b);
  SQL
}
Testing Procedures

Atlas's testing framework allows you to write unit tests for your procedures. The following example demonstrates how to write tests for a stored procedure, archive_old_sales, that moves old sales from the sales table to the archive_sales table according to a specified cutoff date. For more detail, read the schema testing docs or see the full example.

schema.test.hcl
test "schema" "procedure" {
  # Seed data
  exec {
    sql = <<-SQL
      INSERT INTO sales (id, sale_amount, sale_date) VALUES
      (1, 150.00, '2024-07-18'),
      (2, 200.00, '2024-06-20'),
      (1, 350.00, '2024-07-10');
    SQL
  }
  # Execute the procedure with a specific cutoff date
  exec {
    sql = "CALL archive_old_sales('2024-07-18')"  # Archive sales before this date
  }
  # Verify data in archive_sales table
  exec {
    sql = "SELECT COUNT(*) FROM archive_sales WHERE sale_date < '2024-07-18'"
    output = "2" # Expect 2 archived sales
  }
  # Verify data in sales table
  exec {
    sql = "SELECT COUNT(*) FROM sales"
    output = "1"  # Expect 1 sale remaining in the sales table after cutoff date
  }
}

Domain​

Atlas Pro Feature

Domains are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The domain type is a user-defined data type that is based on an existing data type but with optional constraints and default values. Supported by PostgreSQL.

domain "us_postal_code" {
  schema = schema.public
  type   = text
  null   = true
  check "us_postal_code_check" {
    expr = "((VALUE ~ '^\\d{5}$'::text) OR (VALUE ~ '^\\d{5}-\\d{4}$'::text))"
  }
}

domain "username" {
  schema = schema.public
  type    = text
  null    = false
  default = "anonymous"
  check "username_length" {
    expr = "(length(VALUE) > 3)"
  }
}

table "users" {
  schema = schema.public
  column "name" {
    type = domain.username
  }
  column "zip" {
    type = domain.us_postal_code
  }
}

schema "public" {
  comment = "standard public schema"
}
Testing Domains

Atlas's testing framework allows you to write unit tests for your domains. The following example demonstrates how to write tests for the us_postal_code domain defined above. For more detail, read the schema testing docs or see the full example.

schema.test.hcl
test "schema" "postal" {
  parallel = true
  exec {
    sql = "select '12345'::us_postal_code"
  }
  catch {
    sql = "select 'hello'::us_postal_code"
  }
}

Composite Type​

Atlas Pro Feature

Composite types are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The composite type is a user-defined data type that represents the structure of a row or record. Supported by PostgreSQL.

composite "address" {
  schema = schema.public
  field "street" {
    type = text
  }
  field "city" {
    type = text
  }
}

table "users" {
  schema = schema.public
  column "address" {
    type = composite.address
  }
}

schema "public" {
  comment = "standard public schema"
}

Policies​

Atlas Pro Feature

Policies are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The policy block allows defining row-level security policies. Supported by PostgreSQL.

schema.pg.hcl
policy "sales_rep_access" {
  on    = table.orders
  for   = SELECT
  to    = [PUBLIC]
  using = "(sales_rep_id = (CURRENT_USER)::integer)"
}

policy "restrict_sales_rep_updates" {
  on      = table.orders
  as      = RESTRICTIVE
  for     = UPDATE
  to      = ["custom_role"]
  check   = "(sales_rep_id = (CURRENT_USER)::integer)"
  comment = "This is a restrictive policy"
}
Enabling Row-Level Security

To enable and force row-level security on a table, refer to the table row-level security example.

Computed Policies​

To configure the same policy for multiple tables, users can utilize the for_each meta-argument. By setting it up, a policy block will be computed for each item in the provided value. Note that for_each accepts either a map or a set of references.

schema.pg.hcl
policy "tenant_access_policy" {
  for_each = [table.users, table.orders, table.payments]
  on       = each.value
  as       = RESTRICTIVE
  using    = "tenant_isolation_policy()"
}

Sequence​

Atlas Pro Feature

Sequences are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The sequence block allows defining sequence number generator. Supported by PostgreSQL and SQL Server.

Note, a sequence block is printed by Atlas on inspection, or it may be manually defined in the schema only if it represents a PostgreSQL sequence that is not implicitly created by the database for identity or serial columns.

# Simple sequence with default values.
sequence "s1" {
  schema = schema.public
}

# Sequence with custom configuration.
sequence "s2" {
  schema    = schema.public
  type      = smallint
  start     = 100
  increment = 2
  min_value = 100
  max_value = 1000
}

# Sequence that is owned by a column.
sequence "s3" {
  schema  = schema.public
  owner   = table.t2.column.id
  comment = "Sequence with column owner"
}

# The sequences created by this table are not printed on inspection.
table "users" {
  schema = schema.public
  column "id" {
    type = int
    identity {
        generated = ALWAYS
        start = 10000
    }
  }
  column "serial" {
    type = serial
  }
  primary_key  {
    columns = [column.id]
  }
}

table "t2" {
  schema = schema.public
  column "id" {
    type = int
  }
}

schema "public" {
  comment = "standard public schema"
}

Enum​

The enum type allows storing a set of enumerated values. Supported by PostgreSQL.

enum "status" {
  schema = schema.test
  values = ["on", "off"]
}

table "t1" {
  schema = schema.test
  column "c1" {
    type = enum.status
  }
}

table "t2" {
  schema = schema.test
  column "c1" {
    type = enum.status
  }
}

Extension​

Atlas Pro Feature

Extensions are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The extension block allows the definition of PostgreSQL extensions to be loaded into the database. The following arguments are supported:

  • schema (Optional) - The schema in which to install the extension's objects, given that the extension allows its contents to be relocated.
  • version (Optional) - The version of the extension to install. Defaults to the version specified in the extension's control file.
  • comment (Read-only) - The description of the extension. This field is populated in atlas inspect output.
extension "adminpack" {
  version = "2.1"
  comment = "administrative functions for PostgreSQL"
}
extension "postgis" {
  schema  = schema.public
  version = "3.4.1"
  comment = "PostGIS geometry and geography spatial types and functions"
}
extension "pgcrypto" {
  schema  = schema.public
  version = "1.3"
  comment = "cryptographic functions"
}
schema "public" {
  comment = "standard public schema"
}
Extensions work in database-scope only

Although the schema argument is supported, it only indicates where the extension's objects will be installed. However, the extension itself is installed at the database level and cannot be loaded multiple times into different schemas.

Therefore, to avoid conflicts with other schemas, when working with extensions, the scope of the migration should be set to the database, where objects are qualified with the schema name. To learn more about the difference between database and schema scopes, visit this doc.

Foreign Servers​

Atlas Pro Feature

Foreign-servers are currently available only to Atlas Pro users. To use this feature, run:

atlas login

The server block defines a foreign server in the database, containing the connection details needed to access an external data source via a foreign-data wrapper. This feature is supported by PostgreSQL.

extension "postgres_fdw"  {
  schema = schema.public
}

server "test_server" {
  fdw     = extension.postgres_fdw
  comment = "test server"
  options = {
    dbname = "postgres"
    host   = "localhost"
    port   = "5432"
  }
}
Foreign Servers work in database-scope only

Foreign servers are defined at the database level, and their names must be unique within the database.

Therefore, when working with foreign servers, the scope of the migration should be set to the database, where bjects are qualified with the schema name. To learn more about the difference between database and schema scopes, visit this doc.

Comment​

The comment attribute is an attribute of schema, table, column, and index.

schema "public" {
  comment = "A schema comment"
}

table "users" {
  schema = schema.public
  column "name" {
    type    = text
    comment = "A column comment"
  }
  index "name_idx" {
    columns = [column.name]
  }
  comment = "A table comment"
}

Charset and Collation​

The charset and collate are attributes of schema, table and column and supported by MySQL, MariaDB and PostgreSQL. Read more about them in MySQL, MariaDB and PostgreSQL websites.

schema "public" {
    charset = "utf8mb4"
    collate = "utf8mb4_0900_ai_ci"
}

table "products" {
    column "name" {
        type    = text
        collate = "binary"
    }
    collate = "utf8_general_ci"
}

Auto Increment​

AUTO_INCREMENT and IDENTITY columns are attributes of the column and table resource, and can be used to generate a unique identity for new rows.

In MySQL/MariaDB the auto_increment attribute can be set on columns and tables.

table "users" {
  schema = schema.public
  column "id" {
    null = false
    type = bigint
    auto_increment = true
  }
  primary_key  {
    columns = [column.id]
  }
}

The auto_increment column can be set on the table to configure a start value other than 1.

table "users" {
  schema = schema.public
  column "id" {
    null = false
    type = bigint
    auto_increment = true
  }
  primary_key  {
    columns = [column.id]
  }
  auto_increment = 100
}