Principle:Huggingface Datatrove JSONL Data Writing
| Sources | Domains | Last Updated |
|---|---|---|
| Huggingface Datatrove | Data_Output, Serialization | 2026-02-14 |
Overview
Serializing processed documents to JSON Lines format for storage and downstream consumption.
Description
JSONL writing serializes Document objects into line-delimited JSON using orjson for high-performance serialization. Each document becomes one JSON line containing text, id, media, and metadata fields. The writer supports transparent compression (gzip by default), filename templates with ${rank} and ${tag} placeholders for parallel-safe output, and optional metadata expansion where each metadata key becomes a top-level field instead of a nested dictionary.
The JSONL format is widely used for large-scale text datasets because it supports streaming reads, is easily splittable for distributed processing, and is human-readable. Each line is independently parseable, so corrupted lines do not affect the rest of the file.
Usage
As the output stage of processing pipelines to persist filtered or transformed documents. Typically placed as the final step in a datatrove pipeline after readers, filters, and deduplication steps. The resulting JSONL files can be consumed by downstream training frameworks or further pipeline stages.
Theoretical Basis
JSON Lines (JSONL) is a newline-delimited JSON format where each line is a valid JSON object. This format inherits JSON's schema flexibility while adding line-based streaming and splitting properties. Combined with gzip compression, it provides a practical balance between file size, read performance, and interoperability across tools.