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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTo preview a Parquet file without Python, pandas, or a running service, use DuckDB’s SQL interface to query the file directly. Start with a schema check, then select a small number of rows; DuckDB also offers SQL functions for inspecting Parquet metadata and schema.
Inspect the schema before previewing rows
Open a DuckDB SQL or CLI session using the installation appropriate for your operating system. The commands below assume the file is accessible by path from that session. DuckDB’s Parquet documentation demonstrates reading a Parquet path directly in SQL.
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Check the column names and types:
DESCRIBE SELECT * FROM 'path/to/file.parquet'; -
Show a compact sample of rows:
SELECT * FROM 'path/to/file.parquet' LIMIT 20;
The LIMIT 20 clause caps the number of rows returned by the query. It does not guarantee that every file will open quickly or use little memory; performance can depend on file size, compression, row-group size, and the number of files.
Inspect Parquet metadata and structure
If you need file details rather than a row preview, DuckDB documents these functions:
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parquet_metadata('path/to/file.parquet')returns row-group and column metadata.parquet_file_metadata('path/to/file.parquet')reports file-level metadata.parquet_kv_metadata('path/to/file.parquet')reads key-value metadata.parquet_schema('path/to/file.parquet')describes the Parquet schema.
These queries are useful when you need to understand how a file is organized without first converting it into another format.
Read several Parquet files together
DuckDB can query multiple files by passing a list of paths or a glob pattern. For example:
SELECT * FROM ['data/january.parquet', 'data/february.parquet'] LIMIT 20;A glob can select matching files:
SELECT * FROM 'data/*.parquet' LIMIT 20;A directory path can also be used to read Parquet files recursively. If files have different schemas, DuckDB’s union_by_name option can combine columns by name rather than by position. Consult the DuckDB documentation for the applicable syntax and details.
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If you prefer a table interface, the Parquet Viz listing in the Visual Studio Marketplace describes a VS Code editor with a paginated table, column filtering, an SQL panel, and charts. The listing says processing happens locally in the webview using DuckDB-Wasm and Apache Arrow, and that file data is not sent to external services by default. Those are the extension listing’s claims, not independently verified guarantees for every version or configuration.
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This option requires VS Code and the extension. DuckDB’s SQL approach is a direct fit for checking a schema or a few rows; the extension adds visual navigation and filtering.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What affects preview performance
A row limit is a useful way to keep the displayed result small, but it is not a universal performance guarantee. DuckDB identifies the number of files, individual file size, compression algorithm, and row-group size as factors that can affect performance. Its Parquet support also includes projection and filter pushdown, which can help avoid reading data that a query does not need. Actual results depend on the file and system.
For a straightforward inspection, run DESCRIBE first, then select only the columns and rows you want to inspect. You can query the Parquet file directly without importing it into pandas or starting a separate database service.
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