Principle:Apache Paimon Lance Distributed Analytics
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Columnar_Storage |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for performing distributed analytical operations on Lance-format Paimon tables using Ray Data.
Description
Combining Lance format with Ray Data enables distributed analytical processing with efficient I/O. Lance's columnar format provides fast reads with predicate pushdown and column projection, while Ray distributes the processing across multiple workers. The to_ray() method converts Lance table splits into a Ray Dataset that can be processed with groupby, aggregation, and other distributed operations. This combination is optimal for large-scale analytics on Lance-stored data.
The distributed analytics pipeline operates as follows:
- Scan planning: Paimon's scan produces a list of splits representing data files
- Split distribution: The to_ray() method creates a RayDatasource that distributes splits across Ray workers
- Parallel reading: Each Ray worker reads its assigned splits using FormatLanceReader, applying column projection and predicate pushdown locally
- Distributed processing: The resulting Ray Dataset supports distributed operations such as groupby(), map_batches(), filter(), and aggregate()
- Result collection: Results can be collected to the driver via to_pandas() or show(), or written to another storage system
Usage
Use when performing distributed analytics (aggregations, joins, ML preprocessing) on large Lance-format Paimon tables. This approach is most beneficial when:
- Data exceeds single-machine memory: Ray distributes the data across multiple workers
- CPU-intensive analytics: Parallel execution across workers speeds up computation
- ML preprocessing pipelines: Ray's native ML integration enables seamless data-to-training workflows
- Multi-stage analytics: Complex pipelines with multiple transformation steps benefit from Ray's lazy execution
Theoretical Basis
Combines the advantages of columnar storage (efficient scans, compression) with distributed processing (parallel execution, scalability). Each Ray worker reads its assigned Lance splits independently, enabling linear scalability.
The architecture follows a shared-nothing parallel processing model:
- Data partitioning: Paimon splits naturally partition the data across files
- Independent processing: Each worker reads and processes its splits without coordination
- Aggregation: Results are combined using Ray's distributed shuffle for operations like groupby()
The key theoretical advantage is that Lance's I/O optimizations (column projection, predicate pushdown) apply independently at each worker. This means the total I/O reduction from these optimizations scales linearly with the number of workers, making the combination of Lance and Ray particularly efficient for analytical queries on large datasets.
The parallelism is controlled by the override_num_blocks parameter, which determines how many Ray read tasks are created. Setting this to match the number of available CPU cores typically provides optimal throughput.