Data Types

Moose supports a comprehensive set of ClickHouse column types across both TypeScript and Python. This section covers all supported types, their syntax, and how they map to ClickHouse.

Type Categories

Basic Types

Strings — String, LowCardinality(String), UUID
Integers — Int8 to Int64, UInt8 to UInt64
Floats — Float32, Float64
Decimals — Fixed-point Decimal(P,S)
Booleans — Boolean
Date & Time — Date, DateTime, DateTime64
Network — IPv4, IPv6

Complex Types

Arrays — Array(T)
Maps — Map(K, V)
Nested — Embedded objects
Tuples — Named tuples
Enums — Enumerated values
Geometry — Point, Ring, Polygon, etc.

Special Types

JSON — Dynamic and typed JSON columns
Nullable — Optional fields
Aggregates — SimpleAggregateFunction for pre-aggregation

Schema Features

Column Comments — Add descriptions to columns that appear in ClickHouse DDL

Quick Reference

Category	TypeScript	Python
String	`string`	`str`
Integer	`Int32`, `UInt64`, etc.	`Annotated[int, "int32"]`
Float	`Float32`, `Float64`, `number`	`float`
Boolean	`boolean`	`bool`
Date	`Date`, `DateTime`	`date`, `datetime`
Array	`T[]`	`List[T]`
Map	`Record<K, V>`	`Dict[K, V]`
Optional	`field?: T`	`Optional[T]`

Best Practices

Use specific integer types when you know the value ranges to save storage
Prefer Float64 for most floating-point calculations unless storage is critical
Use LowCardinality for string columns with repeated values (< 10,000 unique)
Choose appropriate DateTime precision based on your accuracy needs
Order columns by cardinality (low to high) for better compression
Add column comments to document field purposes in your schema

Data Types

Moose supports a comprehensive set of ClickHouse column types across both TypeScript and Python. This section covers all supported types, their syntax, and how they map to ClickHouse.

Type Categories

Basic Types

Strings — String, LowCardinality(String), UUID
Integers — Int8 to Int64, UInt8 to UInt64
Floats — Float32, Float64
Decimals — Fixed-point Decimal(P,S)
Booleans — Boolean
Date & Time — Date, DateTime, DateTime64
Network — IPv4, IPv6

Complex Types

Arrays — Array(T)
Maps — Map(K, V)
Nested — Embedded objects
Tuples — Named tuples
Enums — Enumerated values
Geometry — Point, Ring, Polygon, etc.

Special Types

JSON — Dynamic and typed JSON columns
Nullable — Optional fields
Aggregates — SimpleAggregateFunction for pre-aggregation

Schema Features

Column Comments — Add descriptions to columns that appear in ClickHouse DDL

Quick Reference

Category	TypeScript	Python
String	`string`	`str`
Integer	`Int32`, `UInt64`, etc.	`Annotated[int, "int32"]`
Float	`Float32`, `Float64`, `number`	`float`
Boolean	`boolean`	`bool`
Date	`Date`, `DateTime`	`date`, `datetime`
Array	`T[]`	`List[T]`
Map	`Record<K, V>`	`Dict[K, V]`
Optional	`field?: T`	`Optional[T]`

Best Practices

Use specific integer types when you know the value ranges to save storage
Prefer Float64 for most floating-point calculations unless storage is critical
Use LowCardinality for string columns with repeated values (< 10,000 unique)
Choose appropriate DateTime precision based on your accuracy needs
Order columns by cardinality (low to high) for better compression
Add column comments to document field purposes in your schema