Principle:Apache Paimon Lance Column Projection
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Columnar_Storage |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for selecting specific columns during Lance file reading to reduce I/O and memory usage.
Description
Column projection restricts which columns are read from Lance-format files. By specifying a projection list via ReadBuilder.with_projection(), only the requested columns are loaded from storage. The FormatLanceReader passes the read_fields parameter to lance.file.LanceFileReader(columns=read_fields), leveraging Lance's columnar storage for efficient partial reads. This is especially beneficial for wide tables where only a few columns are needed.
The column projection pipeline works as follows:
- Projection specification: The user provides a list of column names via ReadBuilder.with_projection()
- Schema resolution: The read_type() method resolves the projection against the table schema to produce a list of DataField objects for the selected columns
- Reader configuration: The FormatLanceReader receives the projected column list as read_fields
- Selective I/O: The Lance file reader reads only the data for the specified columns, skipping all other column data on disk
Usage
Use when only a subset of columns is needed from a wide Lance table to reduce I/O and memory footprint. Column projection is particularly beneficial for:
- Wide tables: Tables with dozens or hundreds of columns where only a few are needed
- Aggregation queries: When computing sums, counts, or averages on specific columns
- Feature selection: ML pipelines that need specific feature columns from a large feature store
- Dashboard queries: Visualization queries that only display a few dimensions and metrics
Theoretical Basis
Column projection is a fundamental optimization in columnar storage systems. By only reading the columns that are needed, I/O is reduced proportionally to the fraction of columns selected.
In a columnar format like Lance, each column's data is stored contiguously on disk. This layout enables the reader to seek directly to the columns of interest and read only their data, skipping over all other columns entirely. The I/O savings are:
I/O reduction = 1 - (selected columns / total columns)
For example, reading 3 columns from a 30-column table reduces I/O by approximately 90%. This optimization compounds with predicate pushdown: first, column projection eliminates unnecessary columns, then predicate pushdown eliminates unnecessary rows within the selected columns.
Additionally, column projection reduces memory usage since only the selected columns need to be materialized in memory. This enables processing of tables that would otherwise exceed available memory if all columns were loaded.