Airfold uses ClickHouse data types and table engines as its foundation. This page covers the most commonly used types and engines.
Data Types
Basic Types
| Type | Description |
|---|
Int | Integer numbers (Int8, Int16, Int32, Int64) |
Float | Floating-point numbers (Float32, Float64) |
Decimal | Fixed-point numbers with configurable precision |
String | Variable-length strings |
JSON | Semi-structured JSON data with efficient storage |
Date and Time
| Type | Description |
|---|
Date | Date values (YYYY-MM-DD) |
DateTime | Date and time with timezone support |
Time | Time values without date |
Special Types
| Type | Description |
|---|
Enum | Enumerated values with named constants |
UUID | Universally unique identifier |
Composite Types
| Type | Description |
|---|
Array(T) | Array of elements of type T |
LowCardinality(T) | Memory-optimized wrapper for low-cardinality data |
Nullable(T) | Allows NULL values for type T |
Aggregate Function Types
| Type | Description |
|---|
AggregateFunction | Stores intermediate state of aggregate functions |
SimpleAggregateFunction | Simplified version for basic aggregations |
Key Concepts
PARTITION BY
Partitioning
- Divides data into logical parts based on partition expression
- Enables efficient data management and query performance
- Common patterns: partition by date, month, or other logical groupings
- Allows for efficient data pruning and deletion
CREATE TABLE events (
event_date Date,
user_id UInt64,
event_type String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_date, user_id)
Benefits of Partitioning:
- Faster queries when filtering by partition key
- Efficient data lifecycle management
- Parallel processing of partitions
- Simplified data maintenance operations
Reference
For detailed documentation on data types and table engines, refer to the official ClickHouse documentation:
