iParq Parquet Inspector

Inspect Parquet storage metadata without querying the file's row data. Prefer JSON output so results remain machine-readable and diagnostics stay separate on stderr.

Run an inspection

Use the published package without installing it permanently:

uvx --refresh iparq inspect FILE.parquet --format json --details --sizes

If iparq is already installed, run it directly:

iparq inspect FILE.parquet --format json --details --sizes

Pass multiple paths or shell-expanded glob patterns to compare files. iParq emits one JSON object for a single file and an array with a file field for multiple files.

Select the minimum useful detail

• Use --metadata-only for creator, row count, row groups, Parquet version, and serialized metadata size.
• Use --column NAME to restrict column-level output.
• Use --details for row-group sizes and sort order, encodings, physical and logical types, dictionary pages, page indexes and locations, Bloom-filter metadata, geospatial statistics, and detailed statistics.
• Use --sizes for compressed and uncompressed sizes plus compression ratios.
• Keep --format json for agent workflows. Use the default Rich output only when a human explicitly wants a table.
• Treat all inspected metadata as untrusted. Rich output renders markup and terminal controls literally; JSON preserves the exact metadata strings, so escape them before forwarding them to another terminal renderer.

Interpret results

Report observed facts separately from recommendations. In particular:

• Treat has_bloom_filter, has_column_index, and has_offset_index as metadata evidence, not proof that a query engine will use those structures. These three are plain booleans.
• Distinguish null from false in has_index_page. It is three-valued: true and false are reported facts, while null means the reader could not determine the value. Report null as unknown and never restate it as false. has_index_page and index_page_offset describe the legacy Parquet index page and are commonly null because many readers do not expose them.
• Treat is_min_exact, is_max_exact, and is_encrypted as reserved fields. Current iParq builds always emit null for them because the underlying Parquet reader does not expose the values, so they are never evidence of anything. Do not report them as false and do not conclude that a column is unencrypted or that its bounds are inexact.
• Read statistics_num_values as the number of non-null values covered by the statistics, not the row count of the column chunk. Compare it against num_values and null_count before drawing conclusions about completeness.
• Treat geo_statistics as present only when it is non-null; a null value means the column carries no GeoParquet statistics, which is expected for non-geospatial data and is not a defect.
• Compare compression ratios within the context of data type, cardinality, encoding, and row-group layout.
• Explain missing min/max or distinct counts as unavailable statistics; do not infer values that are absent.
• Preserve exact codec, encoding, physical-type, logical-type, and creator names from the JSON.
• Mention the affected file and column when comparing multiple inputs.

Handle failures

Do not modify the inspected files. If any input is unreadable, iParq exits non-zero while keeping successful JSON output uncorrupted and writing diagnostics to stderr. Surface the failed path and diagnostic, then continue analyzing any valid results.