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HCL Column Types

The following guide describes the column types supported by Atlas HCL, and how to use them.

MySQL​

Bit​

The bit type allows creating BIT columns. An optional size attribute allows controlling the number of bits stored in a column, ranging from 1 to 64.

table "t" {
  schema = schema.test
  column "c1" {
    type = bit
  }
  column "c2" {
    type = bit(4)
  }
}

Binary​

The varbinary and binary types allow storing binary byte strings.

table "t" {
  schema = schema.test
  column "c1" {
    // Equals to binary(1).
    type = binary
  }
  column "c2" {
    type = binary(10)
  }
  column "c3" {
    type = varbinary(255)
  }
}

Blob​

The tinyblob, mediumblob, blob and longblob types allow storing binary large objects.

table "t" {
  schema = schema.test
  column "c1" {
    type = tinyblob
  }
  column "c2" {
    type = mediumblob
  }
  column "c3" {
    type = blob
  }
  column "c4" {
    type = longblob
  }
}

Boolean​

The bool and boolean types are mapped to tinyint(1) in MySQL. Still, Atlas allows maintaining columns of type bool in the schema for simplicity reasons.

table "t" {
  schema = schema.test
  column "c1" {
    type = bool
  }
  column "c2" {
    type = boolean
  }
}

Learn more about the motivation for these types in the MySQL website.

Date and Time​

Atlas supports the standard MySQL types for storing date and time values: time, timestamp, date, datetime, and year.

table "t" {
  schema = schema.test
  column "c1" {
    type = time
  }
  column "c2" {
    type = timestamp
  }
  column "c3" {
    type = date
  }
  column "c4" {
    type = datetime
  }
  column "c5" {
    type = year
  }
  column "c6" {
    type = time(1)
  }
  column "c7" {
    type = timestamp(2)
  }
  column "c8" {
    type = datetime(4)
  }
}

Fixed Point (Decimal)​

The decimal and numeric types are supported for storing exact numeric values. Note that in MySQL the two types are identical.

table "t" {
  schema = schema.test
  column "c1" {
    // Equals to decimal(10) as the
    // default precision is 10.
    type = decimal
  }
  column "c2" {
    // Equals to decimal(5,0).
    type = decimal(5)
  }
  column "c3" {
    type = decimal(5,2)
  }
  column "c4" {
    type     = numeric
    unsigned = true
  }
}

Floating Point (Float)​

The float and double types are supported for storing approximate numeric values.

table "t" {
  schema = schema.test
  column "c1" {
    type = float
  }
  column "c2" {
    type = double
  }
  column "c3" {
    type     = float
    unsigned = true
  }
  column "c4" {
    type     = double
    unsigned = true
  }
}

Enum​

The enum type allows storing a set of enumerated values.

table "t" {
  schema = schema.test
  column "c1" {
    type = enum("a", "b")
  }
  column "c2" {
    type = enum(
      "c",
      "d",
    )
  }
}

Integer​

The tinyint, smallint, int, mediumint, bigint integer types are support by Atlas.

table "t" {
  schema = schema.test
  column "c1" {
    type = int
  }
  column "c2" {
    type = tinyint
  }
  column "c3" {
    type = smallint
  }
  column "c4" {
    type = mediumint
  }
  column "c5" {
    type = bigint
  }
}

Integer Attributes​

The auto_increment, and unsigned attributes are also supported by integer types.

table "t" {
  schema = schema.test
  column "c1" {
    type     = tinyint
    unsigned = true
  }
  column "c2" {
    type           = smallint
    auto_increment = true
  }
  primary_key {
    columns = [column.c2]
  }
}

JSON​

The json type allows storing JSON objects.

table "t" {
  schema = schema.test
  column "c1" {
    type = json
  }
}

Set​

The set type allows storing a set of values.

table "t" {
  schema = schema.test
  column "c1" {
    type = set("a", "b")
  }
  column "c2" {
    type = set(
      "c",
      "d",
    )
  }
}

String​

Atlas supports the standard MySQL types for storing string values. varchar, char, tinytext, mediumtext, text and longtext.

table "t" {
  schema = schema.test
  column "c1" {
    type = varchar(255)
  }
  column "c2" {
    type = char(1)
  }
  column "c3" {
    type = tinytext
  }
  column "c4" {
    type = mediumtext
  }
  column "c5" {
    type = text
  }
  column "c6" {
    type = longtext
  }
}

Spatial​

The geometry, point, multipoint, linestring and the rest of the MySQL spatial types are supported by Atlas.

table "t" {
  schema = schema.test
  column "c1" {
    type = geometry
  }
  column "c2" {
    type = point
  }
  column "c3" {
    type = multipoint
  }
  column "c4" {
    type = linestring
  }
}

PostgreSQL​

Array​

Atlas supports defining PostgreSQL array types using the sql function.

table "t" {
  schema = schema.test
  column "c1" {
    type = sql("int[]")
  }
  column "c2" {
    type = sql("text[]")
  }
  column "c3" {
    type = sql("int ARRAY")
  }
  column "c4" {
    type = sql("varchar(255)[]")
  }
  column "c5" {
    // The current PostgreSQL implementation
    // ignores any supplied array size limits.
    type = sql("point[4][4]")
  }
}

Bit​

The bit and bit varying types allow creating bit string columns.

table "t" {
  schema = schema.test
  column "c1" {
    // Equals to bit(1).
    type = bit
  }
  column "c2" {
    type = bit(2)
  }
  column "c3" {
    // Unlimited length.
    type = bit_varying
  }
  column "c4" {
    type = bit_varying(1)
  }
}

Boolean​

The boolean type allows creating standard SQL boolean columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = boolean
  }
  column "c2" {
    type    = boolean
    default = true
  }
}

Binary​

The bytea type allows creating binary string columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = bytea
  }
}

Date, Time and Interval​

Atlas supports the standard PostgreSQL types for creating date, time and interval columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = date
  }
  column "c2" {
    // Equals to "time without time zone".
    type = time
  }
  column "c3" {
    // Equals to "time with time zone".
    type = timetz
  }
  column "c4" {
    // Equals "timestamp without time zone".
    type = timestamp
  }
  column "c5" {
    // Equals "timestamp with time zone".
    type = timestamptz
  }
  column "c6" {
    type = timestamp(4)
  }
  column "c7" {
    type = interval
  }
}

Domain​

The domain type is a user-defined data type that is based on an existing data type but with optional constraints and default values. Learn more about it in the PostgreSQL website.

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
  }
}

Enum​

The enum type allows storing a set of enumerated values. Learn more about it in the PostgreSQL website.

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
  }
}

Fixed Point (Decimal)​

The decimal and numeric types are supported for storing exact numeric values. Note that in PostgreSQL the two types are identical.

table "t" {
  schema = schema.test
  column "c1" {
    // Equals to decimal.
    type = numeric
  }
  column "c2" {
    // Equals to decimal(5).
    type = numeric(5)
  }
  column "c3" {
    // Equals to decimal(5,2).
    type = numeric(5,2)
  }
}

Floating Point (Float)​

The real and double_precision types are supported for storing approximate numeric values.

table "t" {
  schema = schema.test
  column "c1" {
    type = real
  }
  column "c2" {
    type = double_precision
  }
  column "c3" {
    // Equals to real when precision is between 1 to 24.
    type = float(10)
  }
  column "c2" {
    // Equals to double_precision when precision is between 1 to 24.
    type = float(30)
  }
}

Geometric​

Atlas supports the standard PostgreSQL types for creating geometric columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = circle
  }
  column "c2" {
    type = line
  }
  column "c3" {
    type = lseg
  }
  column "c4" {
      type = box
  }
  column "c5" {
      type = path
  }
  column "c6" {
      type = polygon
  }
  column "c7" {
      type = point
  }
}

Integer​

The smallint, integer / int, bigint types allow creating integer types.

table "t" {
  schema = schema.test
  column "c1" {
    type = smallint
  }
  column "c2" {
    type = integer
  }
  column "c3" {
    type = int
  }
  column "c4" {
    type    = bigint
    default = 1
  }
}

JSON​

The json and jsonb types allow creating columns for storing JSON objects.

table "t" {
  schema = schema.test
  column "c1" {
    type = json
  }
  column "c2" {
    type = jsonb
  }
}

Money​

The money data type allows creating columns for storing currency amount with a fixed fractional precision.

table "t" {
  schema = schema.test
  column "c1" {
    type = money
  }
}

Network Address​

The inet, cidr, macaddr and macaddr8 types allow creating network address columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = inet
  }
  column "c2" {
    type = cidr
  }
  column "c3" {
    type = macaddr
  }
  column "c4" {
    type = macaddr8
  }
}

Range​

PostgreSQL supports the creation of range types for storing range of values of some element type. Learn more about them in the PostgreSQL website.

table "t" {
  schema = schema.test
  column "r1" {
    type = int4range
  }
  column "r2" {
    type = int8range
  }
  column "r3" {
    type = numrange
  }
  column "r4" {
    type = tsrange
  }
  column "r5" {
    type = tstzrange
  }
  column "r6" {
    type = daterange
  }
  column "r7" {
    type = int4multirange
  }
  column "r8" {
    type = int8multirange
  }
  column "r9" {
    type = nummultirange
  }
  column "r10" {
    type = tsmultirange
  }
  column "r11" {
    type = tstzmultirange
  }
  column "r12" {
    type = datemultirange
  }
}

Serial​

PostgreSQL supports creating columns of types smallserial, serial, and bigserial. Note that these types are not actual types, but more like "macros" for creating non-nullable integer columns with sequences attached.

table "t" {
  schema = schema.test
  column "c1" {
      type = smallserial
  }
  column "c2" {
      type = serial
  }
  column "c3" {
      type = bigserial
  }
}

String​

The varchar, char, character_varying, character and text types allow creating string columns.

table "t" {
  schema = schema.test
  column "c1" {
    // Unlimited length.
    type = varchar
  }
  column "c2" {
    // Alias to character_varying(255).
    type = varchar(255)
  }
  column "c3" {
    // Equals to char(1).
    type = char
  }
  column "c4" {
    // Alias to character(5).
    type = char(5)
  }
  column "c5" {
    type = text
  }
}

Text Search​

The tsvector and tsquery data types are designed to store and query full text search. Learn more about them in the PostgreSQL website.

table "t" {
  schema = schema.test
  column "tsv" {
    type = tsvector
  }
  column "tsq" {
    type = tsquery
  }
}

UUID​

The uuid data type allows creating columns for storing Universally Unique Identifiers (UUID).

table "t" {
  schema = schema.test
  column "c1" {
    type = uuid
  }
  column "c2" {
    type    = uuid
    default = sql("gen_random_uuid()")
  }
}

XML​

The xml data type allows creating columns for storing XML data.

table "t" {
  schema = schema.test
  column "c1" {
    type = xml
  }
}

SQLite​

Values in SQLite are stored in one of the four native types: BLOB, INTEGER, NULL, TEXT and REAL. Still, Atlas supports variety of data types that are commonly used by ORMs. These types are mapped to column affinities based on the rules described in SQLite website.

Blob​

The blob data type allows creating columns with BLOB type affinity.

table "t" {
  schema = schema.main
  column "c" {
    type = blob
  }
}

Integer​

The int and integer data types allow creating columns with INTEGER type affinity.

table "t" {
  schema = schema.main
  column "c" {
    type = int
  }
}

Numeric​

The numeric and decimal data types allow creating columns with NUMERIC type affinity.

table "t" {
  schema = schema.main
  column "c" {
    type = decimal
  }
}

Text​

The text, varchar, clob, character and varying_character data types allow creating columns with text type affinity. i.e. stored as text strings.

table "t" {
  schema = schema.main
  column "c" {
    type = text
  }
}

Real​

The real, double, double_precision, and float data types allow creating columns with real type affinity.

table "t" {
  schema = schema.main
  column "c" {
    type = real
  }
}

Additional Types​

As mentioned above, Atlas supports variety of data types that are commonly used by ORMs. e.g. Ent.

table "t" {
  schema = schema.main
  column "c1" {
    type = bool
  }
  column "c2" {
    type = date
  }
  column "c3" {
    type = datetime
  }
  column "c4" {
    type = uuid
  }
  column "c5" {
    type = json
  }
}

SQL Server​

Bit​

The bit type allows creating BIT columns.

table "t" {
  schema = schema.dbo
  column "c1" {
    type = bit
  }
}

Binary strings​

The varbinary and binary types allow storing binary byte strings.

table "t" {
  schema = schema.dbo
  column "c1" {
    // Equals to binary(1).
    type = binary
  }
  column "c2" {
    type = binary(10)
  }
  column "c3" {
    type = varbinary(255)
  }
  column "c4" {
    // Max length: 8,000 bytes.
    type = varbinary(MAX)
  }
}

Date and Time​

Atlas supports the standard SQL Server types for storing date and time values: date, datetime, datetime2, datetimeoffset, smalldatetime and time. The document on Microsoft website has more information on date and time types.

table "t" {
  schema = schema.dbo
  column "c1" {
    type = date
  }
  column "c2" {
    type = datetime
  }
  column "c3" {
    type = datetime2
  }
  column "c4" {
    type = datetimeoffset
  }
  column "c5" {
    type = smalldatetime
  }
  column "c6" {
    // Equals to time(7).
    type = time
  }
  column "c7" {
    type = time(1)
  }
  column "c8" {
    type = time(2)
  }
  column "c9" {
    type = time(3)
  }
  column "c10" {
    type = time(4)
  }
  column "c11" {
    type = time(5)
  }
  column "c12" {
    type = time(6)
  }
}

Integer​

The int, bigint, smallint, and tinyint integer types are support by Atlas. See document on Microsoft website for more information on integer types.

table "t" {
  schema = schema.dbo
  column "c1" {
    type = int
  }
  column "c2" {
    type = tinyint
  }
  column "c3" {
    type = smallint
  }
  column "c4" {
    type = bigint
  }
}

Integer Blocks​

The identity block can be used to create an identity column.

table "t" {
  schema = schema.dbo
  column "c1" {
    type = tinyint
  }
  column "c2" {
    type = bigint
    identity {
      seed      = 701
      increment = 1000
    }
  }
  primary_key {
    columns = [column.c2]
  }
}

Fixed Point (Decimal)​

The decimal and numeric types are supported for storing exact numeric values. Note that in SQL Server the two types are identical.

table "t" {
  schema = schema.dbo
  column "c1" {
    // Equals to decimal(18, 0) as the
    // default precision is 18.
    type = decimal
  }
  column "c2" {
    // Equals to decimal(5,0).
    type = decimal(5)
  }
  column "c3" {
    type = decimal(5,2)
  }
  column "c4" {
    type = numeric
  }
}

Floating Point (Float)​

The float and real types are supported for storing approximate numeric values. The document on Microsoft website has more information on float types.

table "t" {
  schema = schema.dbo
  column "c1" {
    // Equals to float(53).
    type = float
  }
  column "c2" {
    // float(n) is between 1 and 53.
    type = float(12)
  }
  column "c3" {
    // The ISO synonym for real is `float(24)`.
    type = real
  }
}

Money​

The money and smallmoney data types allows creating columns for storing currency amount with a fixed fractional precision.

table "t" {
  schema = schema.dbo
  column "c1" {
    type = money
  }
  column "c2" {
    type = smallmoney
  }
}

Character strings​

The char, and varchar types allow creating string columns. The document on Microsoft website has more information on string types.

table "t" {
  schema = schema.dbo
  column "c1" {
    // Equals to varchar(1).
    type = varchar
  }
  column "c2" {
    type = varchar(255)
  }
  column "c3" {
    type = varchar(MAX)
  }
  column "c4" {
    // Equals to char(1).
    type = char
  }
  column "c5" {
    type = char(5)
  }
}

Unicode character strings​

The nchar, and nvarchar types allow creating string columns. The document on Microsoft website has more information on unicode string types.

table "t" {
  schema = schema.dbo
  column "c1" {
    // Equals to nvarchar(1).
    type = nvarchar
  }
  column "c2" {
    type = nvarchar(255)
  }
  column "c3" {
    type = nvarchar(MAX)
  }
  column "c4" {
    // Equals to nchar(1).
    type = nchar
  }
  column "c5" {
    type = nchar(5)
  }
}

ntext, text and image​

Atlas supports some deprecated types for backward compatibility. The document on Microsoft website has more information on ntext, text and image types.

table "t" {
  schema = schema.dbo
  column "c1" {
    type = ntext
  }
  column "c2" {
    type = text
  }
  column "c3" {
    type = image
  }
}

User-defined types​

There are two types of user-defined types are supported by Atlas: Alias Types and Table Types.

The CLR user-defined types are not supported by Atlas.

Alias Types​

The type_alias type allows creating columns with user-defined types.

type_alias "ssn" {
  schema = schema.dbo
  type   = varchar(11)
  null   = false
}
type_alias "age" {
  schema = schema.dbo
  type   = smallint
  null   = false
}
table "t" {
  schema = schema.dbo
  column "ssn" {
    type = type_alias.ssn
  }
  column "age" {
    type = type_alias.age
  }
}

Table Types​

The type_table type allows the creation of columns with user-defined table types. The User-Defined table type only allows to use of functions/procedures arguments and not on table columns.

type_table "address" {
  schema = schema.dbo
  column "ssn" {
    type = type_alias.ssn
  }
  column "street" {
    type = varchar(255)
  }
  column "city" {
    type = varchar(255)
  }
  column "state" {
    type = varchar(2)
  }
  column "zip" {
    type = type_alias.zip
  }
  index {
    unique  = true
    columns = [column.ssn]
  }
  check "zip_check" {
    expr = "len(zip) = 5"
  }
}
function "fn1" {
  schema = schema.dbo
  lang   = SQL
  arg "@a1" {
    type     = type_table.address
    readonly = true // The table type is readonly argument.
  }
  arg "@zip" {
    type = type_alias.zip
  }
  return = int
  as     = <<-SQL
  BEGIN
    RETURN (SELECT COUNT(1) FROM @a1 WHERE zip = @zip);
  END
  SQL
}
type_alias "ssn" {
  schema = schema.foo
  type   = varchar(11)
  null   = false
}
type_alias "zip" {
  schema = schema.foo
  type   = varchar(5)
  null   = false
}
note

SQL Server doesn't support creating a named unique constraint on a user-defined table type. Atlas was unable to handle duplicate unique constraints (the unique constraints on the same columns) on table types. The below example will cause schema diff for every time it applies schema.

CREATE TYPE [typ1] AS TABLE (
  [c1] int NOT NULL UNIQUE ([c1] DESC),
  UNIQUE ([c1] ASC)
);

ClickHouse​

Array​

Atlas supports defining ClickHouse array types using the sql function.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    type = sql("Array(Int32)")
  }
  column "c2" {
    type = sql("Array(String)")
  }
  column "c3" {
    type = sql("Array(Array(Int32))")
  }
}

Boolean​

The Bool type allows creating standard SQL boolean columns.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    type = Bool
  }
  column "c2" {
    type    = Bool
    default = true
  }
}

Date and Time​

Atlas supports the standard ClickHouse types for creating date and time columns: Date, DateTime, DateTime32 DateTime64.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null     = false
    type     = Date
  }
  column "c2" {
    null    = false
    type    = Date32
  }
  column "c3" {
    null    = false
    type    = DateTime
  }
  column "c4" {
    null    = false
    type    = DateTime("America/New_York")
  }
  column "c5" {
    null    = false
    type    = DateTime
  }
  column "c6" {
    null    = false
    type    = DateTime32("America/New_York")
  }
  column "c7" {
    null    = false
    type    = DateTime64(3)
  }
  column "c8" {
    null    = false
    type    = DateTime64(3, "America/New_York")
  }
}

Fixed Point (Decimal)​

The Decimal type allows creating columns for storing exact numeric values. The precision and scale are specified as below.

  • Decimal Precision: 9, Scale: 0
  • Decimal32(Scale) Precision: 9, Scale: Scale
  • Decimal64(Scale) Precision: 18, Scale: Scale
  • Decimal128(Scale) Precision: 38, Scale: Scale
  • Decimal256(Scale) Precision: 76, Scale: Scale
  • Decimal(Precision, Scale) Precision: Precision, Scale: Scale
table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = Decimal
  }
  column "c2" {
    null = false
    type = Decimal32(2)
  }
  column "c3" {
    null = false
    type = Decimal64(2)
  }
  column "c4" {
    null = false
    type = Decimal128(2)
  }
  column "c5" {
    null = false
    type = Decimal256(2)
  }
  column "c6" {
    null = false
    type = Decimal(11, 2)
  }
}

Enum​

The Enum type allows storing a set of enumerated values and supports defining ClickHouse enum types using the sql function.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = Enum("a", "b")
  }
  column "c2" {
    null = false
    type = Enum8("a", "b")
  }
  column "c3" {
    null = false
    type = Enum16("a", "b")
  }
}

Fixed String​

The FixedString type allows creating columns for storing fixed-length string values.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = FixedString(10)
  }
}

Floating Point (Float)​

The Float32 and Float64 types are supported for storing approximate numeric values. The aliases for these types are Float and Double.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = Float
  }
  column "c2" {
    null = false
    type = Double
  }
}

Integer​

The Int8, Int16, Int32, Int64, Int128, Int256 types allow creating integer types. The aliases for these types are Tinyint, Smallint, Int, Bigint.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = Tinyint
  }
  column "c2" {
    null = false
    type = Smallint
  }
  column "c3" {
    null = false
    type = Int
  }
  column "c4" {
    null = false
    type = Bigint
  }
  column "c5" {
    null = false
    type = Int128
  }
  column "c6" {
    null = false
    type = Int256
  }
}

Integer Attributes​

The Unsigned attribute is also supported by integer types.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null     = false
    type     = Int
    unsigned = true
  }
}

IPv4 and IPv6​

The IPv4 and IPv6 types allow creating columns for storing IPv4 and IPv6 addresses.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = IPv4
  }
  column "c2" {
    null = false
    type = IPv6
  }
}

Spatial​

Atlas supports the standard ClickHouse types for creating spatial columns.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = Point
  }
  column "c2" {
    null = false
    type = Polygon
  }
  column "c3" {
    null = false
    type = MultiPolygon
  }
}

Ring​

The Ring type allows creating columns for storing ring values.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = Ring
  }
}

String​

The String type allows creating columns for storing string values.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = String
  }
}

UUID​

The UUID type allows creating columns for storing Universally Unique Identifiers (UUID).

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = UUID
  }
}

Tuple​

Atlas supports defining ClickHouse tuple types using the sql function.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = sql("Tuple(Int32, String)")
  }
}

LowCardinality​

Atlas supports defining ClickHouse low cardinality types using the sql function.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = sql("LowCardinality(String)")
  }
}

Nullable​

Atlas supports defining ClickHouse nullable types using the sql function. Null attribute is needed to be set to true for nullable types.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = true
    type = sql("Nullable(String)")
  }
}

JSON​

The JSON type allows creating columns for storing JSON objects.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    type = JSON
  }
}

AggregateFunction​

Atlas supports defining ClickHouse aggregate data types by using the sql function.

table "t" {
  schema = schema.test
  engine = Memory
  column "c1" {
    null = false
    type = sql("AggregateFunction(uniq, UInt64)")
  }
  column "c2" {
    null = false
    type = sql("SimpleAggregateFunction(sum, Int32)")
  }
}
info

The AggregateFunction and SimpleAggregateFunction are complex data types. Therefore, we recommend using a Dev Database to normalize these types.

Redshift​

Boolean​

The boolean and bool types allow creating standard SQL boolean columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = boolean
  }
  column "c2" {
    # Alias to boolean.
    type = bool
  }
}

Binary​

The binary_varying, varbinary and varbyte types allow creating binary string columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = binary_varying(255)
  }
  column "c2" {
    # Alias to binary_varying
    type = varbinary(255)
  }
  column "c3" {
    # Alias to binary_varying
    type = varbyte(255)
  }
}

Date, Time and Interval​

Atlas supports the standard Redshift types for creating date, time and interval columns.

table "t" {
  schema = schema.test
  column "c1" {
    type = date
  }
  column "c2" {
    # Equals to "time without time zone".
    type = time
  }
  column "c3" {
    # Equals to "time with time zone".
    type = timetz
  }
  column "c4" {
    # Equals "timestamp without time zone".
    type = timestamp
  }
  column "c5" {
    # Equals "timestamp with time zone".
    type = timestamptz
  }
  column "c6" {
    type = sql("interval year to month")
  }
}

Fixed Point (Decimal)​

The decimal and numeric types are supported for storing exact numeric values. Note that in Redshift the two types are identical.

table "t" {
  schema = schema.test
  column "c1" {
    # Equals to numeric.
    type = decimal
  }
  column "c2" {
    # Equals to numeric(5).
    type = decimal(5)
  }
  column "c3" {
    # Equals to numeric(5,2).
    type = decimal(5,2)
  }
}

Floating Point (Float)​

The real and double_precision types are supported for storing approximate numeric values.

table "t" {
  schema = schema.test
  column "c1" {
    type = real
  }
  column "c2" {
    type = double_precision
  }
  column "c3" {
    type = float(10)
  }
  column "c4" {
    type = float(30)
  }
  column "c5" {
    # Alias to real.
    type = float4
  }
  column "c6" {
    # Alias to double_precision.
    type = float8
  }
}

Integer​

The smallint, integer / int, bigint types allow creating integer types.

table "t" {
  schema = schema.test
  column "c1" {
    type = smallint
  }
  column "c2" {
    type = integer
  }
  column "c3" {
    type = int
  }
  column "c4" {
    type = bigint
  }
  column "c5" {
    # Alias to smallint.
    type = int2
  }
  column "c6" {
    # Alias to integer.
    type = int4
  }
  column "c7" {
    # Alias to bigint.
    type = int8
  }
}

String​

The varchar, nvarchar, char, nchar, bpchar, character_varying, character and text types allow creating string columns.

table "t" {
  schema = schema.test
  column "c1" {
    # Equals character_varying(256).
    type = varchar
  }
  column "c2" {
    # Alias to character_varying(255).
    type = varchar(255)
  }
  column "c3" {
    # Equals to character_varying(255).
    type = nvarchar(255)
  }
  column "c4" {
    # Equals to char(1).
    type = char
  }
  column "c5" {
    # Equals to char(5).
    type = nchar(5)
  }
  column "c6" {
    # Alias to character(5).
    type = char(5)
  }
  column "c7" {
    # Alias to character(5).
    type = bpchar(5)
  }
  column "c8" {
    # Equals to character_varying(256).
    type = text
  }
}

Other Types​

The hllsketch, super, geometry and geography types are supported by Atlas.

table "t" {
  schema = schema.test
  column "c1" {
    type = hllsketch
  }
  column "c2" {
    type = super
  }
  column "c3" {
    type = geometry
  }
  column "c4" {
    type = geography
  }
}