Workflow:Apache Paimon Distributed Processing With Ray
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Distributed_Computing, Data_Engineering |
| Last Updated | 2026-02-07 23:00 GMT |
Overview
End-to-end process for reading Paimon tables as distributed Ray Datasets, enabling parallel data processing with predicate pushdown, column projection, and Ray's built-in operations (filter, map, groupby).
Description
This workflow integrates PyPaimon's scan-then-read pipeline with Ray Data's distributed execution engine. After scanning a Paimon table to produce splits, each split is converted into a Ray Dataset block that can be processed in parallel across a Ray cluster. The integration supports predicate pushdown at the Paimon level (eliminating data files before distribution), column projection to minimize data transfer, and all standard Ray Dataset operations including filtering, mapping, grouping, and aggregation.
Usage
Execute this workflow when you need to process large Paimon tables that exceed single-machine memory, require distributed transformations (filter, map, aggregate), or need to feed data into Ray-based ML pipelines. This is the recommended path for analytics workloads on Paimon tables at scale.
Execution Steps
Step 1: Ray Cluster Initialization
Initialize a Ray runtime environment specifying available resources (CPUs, GPUs, memory). For local development, a single-node cluster suffices. For production, connect to an existing Ray cluster or configure auto-scaling. The Ray context manages task scheduling and data distribution across workers.
Key considerations:
- Specify CPU count to control parallelism
- Memory settings affect how much data each worker can hold
- Ray must be initialized before creating any Ray Datasets
Step 2: Catalog and Table Setup
Create a Paimon catalog (filesystem or REST) and obtain a reference to the target table. This step is identical to the basic Table_Read_Write workflow. The table reference provides the read builder needed for scan planning.
Key considerations:
- REST catalog is preferred for multi-user environments
- Table must exist before reads can be performed
- Authentication credentials flow through to data file access
Step 3: Scan Planning with Predicates and Projections
Create a read builder from the table and optionally apply optimization hints. Use the predicate builder to construct filter expressions that eliminate data files during scan planning. Use column projection to select only the columns needed for downstream processing.
Key considerations:
- Predicate pushdown reduces the number of splits generated
- Supported predicates include equality, comparison, range, and null checks
- Column projection is applied at the file reader level for Parquet and Lance formats
- Both optimizations reduce data transferred to Ray workers
Step 4: Distributed Dataset Creation
Execute the scan to produce splits, then convert them to a Ray Dataset. The conversion maps each Paimon split to a Ray block, with the number of blocks controlled by the override_num_blocks parameter. Each block is read independently by a Ray worker, enabling parallel I/O.
Key considerations:
- The number of blocks determines read parallelism
- Each block corresponds to one or more Paimon splits
- Data is lazily read when Ray operations are executed
- Arrow format is used as the zero-copy interchange between Paimon and Ray
Step 5: Distributed Data Operations
Apply Ray Dataset operations to process data in parallel. Common operations include filtering rows by condition, mapping transformations across all rows, grouping by columns with aggregation functions (sum, mean, count, min, max), and taking samples. Results can be collected to the driver as Pandas DataFrames or Arrow Tables.
Key considerations:
- Filter operations run per-block in parallel
- Map operations can add, remove, or transform columns
- GroupBy aggregations shuffle data across workers
- Use take() for sampling and count() for row counting
- Collect results with to_pandas() or to_arrow_refs()
Step 6: Result Collection
Collect the processed Ray Dataset back to the driver node for final output. Convert to Pandas DataFrame for interactive analysis, write to another Paimon table, or export to external systems. For large results, stream data using Ray's iterator interface rather than materializing everything in memory.
Key considerations:
- Materializing large datasets on the driver may cause out-of-memory errors
- Use iterators for streaming large result sets
- Results maintain column types from the original Paimon schema