Principle:Apache Paimon Distributed Result Collection
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Distributed_Computing |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for collecting distributed processing results back to the driver node for final consumption.
Description
After distributed data operations, results must be collected from Ray workers back to the driver node. This materialization step converts the distributed Ray Dataset into a local pandas DataFrame or other consumable format. The collection operation triggers execution of the lazy computation DAG and transfers data from workers to the driver. Care must be taken with large result sets as they must fit in driver memory.
Key considerations:
- Memory constraints - the entire result must fit in driver memory
- Lazy evaluation - collection triggers execution of the full computation DAG
- Serialization - data is serialized from Arrow format to pandas format during collection
- Ordering - result order may not match input order due to parallel execution
Usage
Use this principle as the final step in a distributed processing pipeline when the results need to be consumed locally (display, save to file, return from API).
Theoretical Basis
Result collection is the gather phase in scatter-gather distributed processing. It materializes the distributed computation graph and coalesces results. The operation has O(n) data transfer where n is the result size, so it should be applied after all possible reductions (filters, aggregations) have been completed.
The collection process involves:
- DAG execution - Ray materializes all pending lazy transformations
- Block collection - distributed data blocks are transferred to the driver
- Concatenation - individual blocks are concatenated into a single DataFrame
- Format conversion - Arrow RecordBatches are converted to pandas format