Principle:Datajuicer Data juicer Distributed Data Export
| Knowledge Sources | |
|---|---|
| Domains | Distributed_Computing, Data_Engineering |
| Last Updated | 2026-02-14 17:00 GMT |
Overview
A distributed serialization pattern that exports Ray datasets to multiple sharded files in various formats using Ray's parallel write capabilities.
Description
Distributed Data Export writes processed Ray datasets to disk using Ray's native parallel write methods. Unlike the single-machine Exporter, it leverages Ray's distributed I/O to write multiple shards concurrently across cluster nodes. It supports a wider range of formats (JSON, JSONL, Parquet, CSV, TFRecords, WebDataset, Lance) and can write to both local filesystems and cloud storage (S3).
Usage
Use this principle as the final step in distributed Ray pipelines to persist processed results. It replaces the standard Exporter when operating in Ray mode.
Theoretical Basis
# Abstract algorithm (NOT real implementation)
# Ray parallel write pattern
ray_dataset.write_{format}(
path=export_path,
num_rows_per_file=shard_size,
# Ray handles parallelism automatically
)
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