What is vaex.mdx?

vaex.mdx is a file format created by the Vaex library for storing large tabular datasets. It saves data in a column‑oriented, memory‑mapped way so that you can work with tables that are much bigger than your computer’s RAM without loading everything into memory at once.

Let's break it down

  • Vaex: a Python library that lets you explore and analyse big data quickly.
  • .mdx: the file extension used by Vaex for its own on‑disk storage format.
  • Column‑oriented: each column is stored separately, which makes reading only the columns you need very fast.
  • Memory‑mapped: the operating system loads pieces of the file into RAM only when they are accessed, so the whole file never has to be read into memory.

Why does it matter?

Because many real‑world datasets (e.g., sensor logs, financial tick data, scientific measurements) are too large to fit in RAM. vaex.mdx lets you:

  • Open and query these datasets instantly.
  • Perform operations like filtering, grouping, and aggregating without waiting for the whole file to load.
  • Keep your computer responsive while working with millions or billions of rows.

Where is it used?

  • Data science and analytics projects that need fast, interactive exploration of big CSV or Parquet files.
  • Finance for analysing high‑frequency trading data.
  • Astronomy and physics for handling large observational catalogs.
  • Any Python workflow that uses Vaex to speed up data‑intensive tasks.

Good things about it

  • Speed: column‑wise access and memory‑mapping make reads and calculations very fast.
  • Scalability: works with datasets far larger than available RAM.
  • Zero‑copy: Vaex can operate directly on the file without creating extra copies in memory.
  • Simple API: you load a .mdx file with the same Vaex commands you use for a DataFrame, so learning curve is low.
  • Portable: the file can be moved between machines and opened with any Vaex installation.

Not-so-good things

  • Vaex‑specific: other tools (Pandas, Spark) cannot read .mdx directly, so you may need to convert the file if you switch libraries.
  • Write‑only: creating a .mdx file requires an initial conversion step; you cannot edit it in place like a CSV.
  • Limited compression: the format focuses on speed rather than maximum compression, so files can be larger than compressed Parquet or Feather files.
  • Less community support: because it is a niche format, documentation and examples are fewer compared to more common formats.