Small File Problem

Concepts covered: paSmallFiles

What They Want to Hear 'Too many small files kill read performance. Each file requires a separate metadata lookup, a separate file open, and a separate read request. Thousands of 1KB files are far slower to read than one 128MB file with the same data. The target file size is 128MB to 256MB. To fix small files, I use coalesce() to reduce the number of output partitions before writing, or run a compaction job that rewrites small files into larger ones.' That is the answer. Target size, the problem, and two fixes.

About This Interactive Section

This section is part of the Distributed Compute: Beginner lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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