Principle:Eventual Inc Daft Data Materialization
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Query_Optimization |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Technique for triggering execution of a lazy query plan and materializing results into memory.
Description
Daft uses lazy evaluation where operations such as select, filter, and join build a logical plan rather than executing immediately. Materialization triggers the optimized execution of the entire plan, producing concrete results in memory. Before execution, the query optimizer applies transformations such as predicate pushdown, projection pruning, and partition pruning to minimize the amount of data read and processed. Once materialized, subsequent operations on the DataFrame can access results directly without re-execution.
Usage
Use materialization when you need to trigger execution of a lazy DataFrame and get concrete results. This is necessary before inspecting data values, writing results to external storage, or passing data to non-Daft libraries that require materialized arrays.
Theoretical Basis
Lazy evaluation with query plan optimization follows this pipeline:
Logical Plan Construction:
df = read(...).filter(...).select(...) # builds plan, no execution
Query Optimization:
optimized_plan = optimize(logical_plan)
- Predicate pushdown: push filters closer to data source
- Projection pruning: read only required columns
- Partition pruning: skip irrelevant data partitions
Physical Execution:
materialized = execute(optimized_plan)
- Partition-parallel execution
- Memory management and spilling
- Result caching for repeated access
This deferred execution model enables global optimization of the entire query plan before any data is read or processed.