Why Parquet Is Not Suitable for Opening in a Text Editor

2025-12-21

When people first meet Parquet, it’s common to try opening a .parquet file in Notepad, VS Code, or other “text tools”. The result often looks like random symbols.

That doesn’t mean the file is corrupted. It simply means: Parquet is not designed to be human-readable text.

This article explains why Parquet is not suitable for opening directly in a text editor, and what you should do instead.

1. Parquet is binary, not “readable text”

Formats like CSV/JSON/YAML are sequences of characters. A text editor can decode bytes using an encoding (UTF-8, etc.) and display them.

Parquet, however, is a binary container format:

  • It stores structured binary blocks
  • Uses offsets + metadata to locate content
  • Many byte sequences are not valid text under any encoding

So “garbled text” is expected.

2. Columnar layout is not line-oriented

Text tools assume:

  • One line = one record
  • Newlines separate rows
  • Commas/tabs separate fields

Parquet uses columnar storage:

  • Values of the same column are stored close together
  • Values of a single row are not necessarily contiguous on disk

Even if you could display bytes as characters, it would not map cleanly to rows/columns.

3. Compression and encoding make it even less text-like

A big reason Parquet is popular is high compression + fast analytical reads. In practice Parquet often uses:

  • Compression (Snappy, Gzip, ZSTD, etc.)
  • Encodings (dictionary encoding, RLE, bit-packing, etc.)

That means:

  • You are often looking at compressed byte streams
  • Even string columns may be stored in encoded/compressed form

A text editor cannot decompress/decode and reconstruct the original values.

4. You need metadata to interpret the file correctly

A Parquet file typically includes:

  • Schema (names, types, nullability, nested structure)
  • Row groups
  • Column chunks
  • Pages
  • Optional statistics (min/max, null count, etc.)

These structures are described by metadata and must be interpreted by a Parquet reader.

Without a parser, it’s extremely hard to tell which bytes belong to which column or row group.

5. Nested types and binary columns are common

Real-world Parquet datasets often contain:

  • struct / list / map nested types
  • Binary columns (images, audio, embeddings, serialized blobs)

Even with a proper reader, these need specialized rendering (expand JSON-like structures, media previews, placeholders). Text editors are not built for that.

6. Large files: opening in a text editor is slow and may freeze

Parquet is frequently used for analytics at scale, so files can be large.

Most text editors will:

  • Load slowly
  • Consume lots of memory
  • Make searching painfully slow

Parquet is meant to be inspected selectively:

  • Read schema only
  • Preview first N rows
  • Read selected columns
  • Filter with predicates/SQL to reduce scanning

7. How to inspect Parquet the right way

Recommended options:

  • Parquet Viewer: view schema, preview rows, search, and filter with SQL
  • DuckDB: query Parquet with SQL locally (or in-browser with DuckDB-WASM)
  • Spark / Trino / Presto: production-grade engines for big data
  • Python (pandas + pyarrow): great for development/debugging and smaller datasets

8. Practical advice: CSV vs Parquet

  • If your goal is “human reading / manual editing / simple sharing”:
    • Prefer CSV / JSON
  • If your goal is “analytics / compression / column pruning / large-scale data”:
    • Prefer Parquet

They are complementary: many teams store data as Parquet and export CSV only when needed.

Summary

Parquet is not suitable for opening in text editors because it is:

  • A binary container
  • Columnar, not line-oriented
  • Compressed and encoded
  • Interpreted via metadata-driven structures

When you need to inspect or troubleshoot Parquet, use a Parquet Viewer or an SQL engine like DuckDB to read data the right way.