Workflow:Apache Paimon Lance Format Analytics
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Analytics, Columnar_Storage |
| Last Updated | 2026-02-07 23:00 GMT |
Overview
End-to-end process for creating Paimon tables backed by the Lance columnar format, enabling optimized analytical queries with efficient column projection, predicate pushdown, and Ray Data integration.
Description
This workflow leverages the Lance columnar file format as the storage backend for Paimon tables. Lance provides efficient random access reads, fast column projection, and optimized scan performance for analytical workloads. The workflow covers table creation with Lance format configuration, multi-batch data writing with primary key support, and reading via multiple output formats (Pandas, Arrow, Ray Dataset). It combines Paimon's transactional guarantees (snapshots, atomic commits) with Lance's columnar performance.
Usage
Execute this workflow when analytical query performance is a priority and your workload involves frequent column-selective reads, range scans, or integration with ML pipelines that benefit from Lance's efficient random access. Lance format is particularly useful for tables with many columns where queries typically access only a subset.
Execution Steps
Step 1: Catalog and Lance Table Creation
Create a Paimon catalog and define a table with the Lance file format option. Set the file format to Lance in the schema options, configure primary keys for upsert support, and set the bucket count for write distribution. The table schema uses standard Paimon data types, which are automatically mapped to Lance's type system.
Key considerations:
- Set file format to Lance via table options
- Primary keys enable merge-on-read semantics with Lance files
- Bucket count affects the number of Lance files per partition
- Lance supports all Paimon primitive types and nested structures
Step 2: Multi-batch Data Writing
Write data to the Lance table in multiple batches within a single write session. Each batch can be a Pandas DataFrame or PyArrow Table. The writer accumulates data files across batches, and a single commit at the end atomically publishes all written data.
Key considerations:
- Multiple batches can be written before committing
- Each batch produces one or more Lance data files
- The commit is atomic across all batches
- Primary key tables track keys for merge-on-read
Step 3: Multi-format Reading
Read the table using different output formats depending on the use case. Convert results to Pandas DataFrames for interactive analysis, PyArrow Tables for zero-copy processing, or Ray Datasets for distributed computation. Lance's columnar layout enables efficient reads for all formats.
Key considerations:
- Pandas output materializes all data in memory
- Arrow output provides zero-copy access to columnar data
- Ray output distributes reads across workers
- All formats benefit from Lance's columnar storage
Step 4: Predicate Pushdown on Lance Files
Apply filter predicates at the read builder level to push comparisons down to the Lance file reader. Supported predicates include equality, range comparisons, null checks, and compound expressions. The Lance reader evaluates predicates at the column chunk level, skipping irrelevant data pages.
Key considerations:
- Predicates are evaluated during scan planning and file reading
- Range predicates benefit from Lance's column statistics
- Compound predicates (AND, OR) are supported
- Pushdown reduces both I/O and memory usage
Step 5: Column Projection with Lance
Select specific columns at read time using the projection API. Lance's columnar layout means only the requested columns are read from storage, providing significant I/O savings for wide tables. This optimization is applied at the file reader level.
Key considerations:
- Only projected columns are read from Lance files
- Projection works with both predicate pushdown and Ray integration
- Column order in the output matches the projection specification
- Nested column projection is supported for struct types
Step 6: Distributed Analytics with Ray
Convert the Lance-backed table to a Ray Dataset for distributed analytical operations. Apply Ray's filter, map, groupby, and aggregation functions to process data in parallel. The combination of Lance's efficient column reads and Ray's distributed execution provides high-performance analytics.
Key considerations:
- Ray blocks map to Paimon splits over Lance files
- GroupBy operations trigger data shuffles across workers
- Aggregation functions include sum, mean, min, max, and count
- Results can be collected or streamed back to the driver