Data Types & Variables

ClickHouse supports numerous data types including Integers, Floats, Decimals, Bools, Strings, UUIDs, DateTime, Array, Tuples, AggregateFunctions, nested structures and more.

As ClickHouse is a strongly typed datastore, Vulkn attempts to map existing Python datatypes to ClickHouse datatypes in a similar manner to an ORM. Vulkn also allows for creating additional datatypes the make it easier to work with ClickHouse from the Python eco-system.

ClickHouse reference


Variables are declared and assigned using normal Python notation:

x = Int8(34)
s = String('Hello World!')

Once declared you can use most variables as you would Python types and then use them within queries:

s = String('Hello world  ')
s = s.strip()
l = s.len()'stripped string'), l.alias('length')).s

  row  stripped string      length
-----  -----------------  --------
    1  Hello world              13

(1 row)

Typed variables also allow datatypes to propagate through their respective method calls'stripped string'), l.typename().alias('length')).s

  row  stripped string    length
-----  -----------------  --------
    1  String             UInt64

(1 row)

You can view the generated SQL at any time either by printing the individual variable using the print statement or using the .show_sql() method of a DataTable:'stripped string'), l.typename().alias('length')).show_sql()
    toTypeName(replaceRegexpAll('Hello world  ', '^s*|s*$', '')) AS `stripped string`,
    toTypeName(length(replaceRegexpAll('Hello world  ', '^s*|s*$', ''))) AS length;"""

Existing variables and columns can also be declared using <Type>(name='') (or <Type>(n='')). This allows use of the object/datatype type system by name rather than by value.

For example:

database = String(n='database')
tablename = String(n='name')
v.table('system.tables').select(database, tablename).limit(3).s

  row  database           name
-----  -----------------  --------------
    1  cached             test
    2  cached             test1
    3  database_for_dict  table_for_dict

(3 rows)

Available Types

Family ClickHouse Data Type Range Vulkn Data Type Vulkn Shortcut Examples
Integer Int8 [-128, 127] Int8 vI8 x = Int8(34)
x = vI8(34)
Integer Int16 [-32768, 32767] Int16 vI16 x = Int16(3445)
Integer Int32 [-2147483648, 2147483647] Int32 vI32 x = Int32(545678)
Integer Int64 [-9223372036854775808, 9223372036854775807] Int64 vI64 x = vI64(3372036854775)
Integer UInt8 [0, 255] UInt8 vU8 x = UInt8(34)
Integer UInt16 [0, 65535] UInt16 vU16 x = UInt16(3445)
Integer UInt32 [0, 4294967295] UInt32 vU32 x = UInt32(545678)
Integer UInt64 [0, 18446744073709551615] UInt64 vU64 x = UInt64(3372036854775)
Float Float, Float32 [3.40282e+38, 3.40282e+38] Float32 vF32 x = Float32(567.34)
Float Float64 [-1.79769e+308, 1.79769e+308] Float64 vF64 x = Float64(567.34)
String String - String vS x = String('hello world')'hello world').cast(String)).r
String - - URL vU x = URL('')
DateTime Date ['0000-00-00', '2106-02-07'] Date vD x = Date('2019-01-01')
DateTime DateTime ['0000-00-00 00:00:00', '2106-02-07 17:28:15'] DateTime vDT, vDT32 x = DateTime('2106-02-07 17:28:15')
Array Array - Array vA x = Array([1,2,3])

Variables and DataTables

Most methods within a DataTable allow for any combination of typed variables, column names (strings), column expressions (strings) or column literals.

Column Names

Column names are simple string names.

v.table('system.tables').select('database', 'name').limit(3).s

  row  database           name
-----  -----------------  --------------
    1  cached             iot_car_inbound
    2  cached             iot_car_outbound
    3  dict_store         number_plate_lookup

(3 rows)

Column Expressions

Column expressions are simple strings that allow you to specify the entire column formula/function as you would within a standard SQL statement. This can be applied to any method.

v.table('system.tables').select('substring(database, 3)', 'upper(name)').where("database = 'system'").limit(3).s

  row  substring(database, 3)    upper(name)
-----  ------------------------  ------------------------------
    1  stem                      AGGREGATE_FUNCTION_COMBINATORS
    2  stem                      ASYNCHRONOUS_METRICS
    3  stem                      BUILD_OPTIONS

(3 rows)

Column Literals

Column literals enable users to mix and match a literal SQL string with the type system. Use the functions c or col to declare a column literal.

ss = 'substring(database, 3)'
l = c('upper(name)').cast(String).length().alias('ucase_name')
v.table('system.tables').select(ss, l).where("database = 'system'").limit(3).s

  row  substring(database, 3)      ucase_name
-----  ------------------------  ------------
    1  stem                                30
    2  stem                                20
    3  stem                                13

(3 rows)

Mixing Python variables

Most Python variables, except dictionaries, can be mixed with most Vulkn variables and will be converted to the correct SQL type or a literal constant at query time.

Arrays, tuples, strings, integers, floats and datetime types can all be use within Vulkn:

>>>,10)), ('a','b'), ['my','array']).s

  row  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]    tuple(\'a\', \'b\')    [\'my\', \'array\']
-----  --------------------------------  ---------------------  ---------------------
    1  [0,1,2,3,4,5,6,7,8,9]             ('a','b')              ['my','array']

(1 row)

Dictionaries will be supported in the future as variants of the JSON data type and the JoinStorage table type.