Principle:Pola rs Polars Multi Format Data Reading
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, ETL, File_Format_Parsing |
| Last Updated | 2026-02-09 10:00 GMT |
Overview
Reading structured data from various file formats and sources into DataFrames or LazyFrames, supporting both eager (full load) and lazy (deferred) reading strategies.
Description
Multi Format Data Reading in Polars provides a format-agnostic data ingestion layer that abstracts file parsing across CSV, Parquet, JSON, NDJSON, IPC/Arrow, Excel, and database sources. The library exposes two families of functions:
- Eager reads (read_*): These functions materialize all data into memory immediately, returning a DataFrame. They are suitable for small-to-medium datasets or when the full dataset is needed upfront.
- Lazy scans (scan_*): These functions create query plan nodes that return a LazyFrame without reading any data. The actual I/O is deferred until .collect() is called. This enables the query optimizer to apply predicate pushdown (filtering at the source) and projection pushdown (reading only required columns), significantly reducing I/O and memory usage for large datasets.
Polars supports reading from multiple source types:
- Local files: Direct file paths on the local filesystem
- URLs: HTTP/HTTPS endpoints serving data files
- Glob patterns: Wildcard patterns matching multiple files (e.g., "data/*.parquet")
- Cloud storage URIs: S3, Azure Blob, and GCS URIs (e.g., "s3://bucket/path")
- Hugging Face Hub: Direct access to datasets hosted on Hugging Face (e.g., "hf://datasets/org/repo/data.parquet")
- Database connections: SQL queries against relational databases via connection URIs
Usage
Use eager read_* functions for exploratory analysis, small datasets, or when immediate materialization is required. Use lazy scan_* functions for production pipelines, large datasets, or when query optimization is beneficial. The choice between eager and lazy reading is the most impactful performance decision in a Polars data pipeline.
Theoretical Basis
Multi Format Data Reading in Polars draws on established ETL (Extract-Transform-Load) patterns and file format specification compliance:
Eager vs. Lazy Evaluation:
The distinction between eager and lazy reading follows the broader principle of evaluation strategies in programming language theory. Eager evaluation (strict evaluation) computes results immediately, while lazy evaluation (non-strict evaluation) defers computation until the result is needed. In the context of data I/O:
- Eager:
read_csv("file.csv")parses and loads the entire file into a DataFrame immediately - Lazy:
scan_csv("file.csv").filter(...).select(...).collect()builds a query plan, optimizes it, then executes only the minimal I/O required
Predicate and Projection Pushdown:
When a lazy scan is followed by filter and select operations, the query optimizer can push these operations down to the I/O layer:
- Predicate pushdown: Only rows matching filter conditions are read from disk (especially effective with Parquet row groups)
- Projection pushdown: Only the columns referenced in the query are loaded (especially effective with columnar formats)
Format Abstraction:
Each file format has its own specification (RFC 4180 for CSV, Apache Parquet format spec, JSON RFC 8259, Apache Arrow IPC spec). Polars abstracts these behind a unified API surface, allowing the same downstream processing logic regardless of the source format.
Pseudo-code:
# Abstract reading pipeline
# Eager: immediate full materialization
df = read_format(source, options) -> DataFrame
# Lazy: deferred optimized execution
lf = scan_format(source, options) -> LazyFrame
lf = lf.filter(predicate) # pushdown candidate
lf = lf.select(columns) # pushdown candidate
df = lf.collect() # actual I/O happens here