Examples

Read a single Parquet file:

SELECT * FROM 'test.parquet';

Figure out which columns/types are in a Parquet file:

DESCRIBE SELECT * FROM 'test.parquet';

Create a table from a Parquet file:

CREATE TABLE test AS
    SELECT * FROM 'test.parquet';

If the file does not end in .parquet, use the read_parquet function:

SELECT *
FROM read_parquet('test.parq');

Use list parameter to read three Parquet files and treat them as a single table:

SELECT *
FROM read_parquet(['file1.parquet', 'file2.parquet', 'file3.parquet']);

Read all files that match the glob pattern:

SELECT *
FROM 'test/*.parquet';

Read all files that match the glob pattern, and include the filename virtual column that specifies which file each row came from (this column is available by default without a configuration options since DuckDB v1.3.0):

SELECT *, filename
FROM read_parquet('test/*.parquet');

Use a list of globs to read all Parquet files from two specific folders:

SELECT *
FROM read_parquet(['folder1/*.parquet', 'folder2/*.parquet']);

Read over HTTPS:

SELECT *
FROM read_parquet('https://some.url/some_file.parquet');

Query the metadata of a Parquet file:

SELECT *
FROM parquet_metadata('test.parquet');

Query the file metadata of a Parquet file:

SELECT *
FROM parquet_file_metadata('test.parquet');

Query the key-value metadata of a Parquet file:

SELECT *
FROM parquet_kv_metadata('test.parquet');

Query the schema of a Parquet file:

SELECT *
FROM parquet_schema('test.parquet');

Write the results of a query to a Parquet file using the default compression (Snappy):

COPY
    (SELECT * FROM tbl)
    TO 'result-snappy.parquet'
    (FORMAT parquet);

Write the results from a query to a Parquet file with specific compression and row group size:

COPY
    (FROM generate_series(100_000))
    TO 'test.parquet'
    (FORMAT parquet, COMPRESSION zstd, ROW_GROUP_SIZE 100_000);

Export the table contents of the entire database as parquet:

EXPORT DATABASE 'target_directory' (FORMAT parquet);

Parquet Files

Parquet files are compressed columnar files that are efficient to load and process. DuckDB provides support for both reading and writing Parquet files in an efficient manner, as well as support for pushing filters and projections into the Parquet file scans.

Parquet datasets differ based on the number of files, the size of individual files, the compression algorithm used, row group size, etc. These have a significant effect on performance. Please consult the Performance Guide for details.

read_parquet Function

If your file ends in .parquet, the function syntax is optional. The system will automatically infer that you are reading a Parquet file:

SELECT * FROM 'test.parquet';

Multiple files can be read at once by providing a glob or a list of files. Refer to the multiple files section for more information.

Parameters

There are a number of options exposed that can be passed to the read_parquet function or the COPY statement.

Partial Reading

DuckDB supports projection pushdown into the Parquet file itself. That is to say, when querying a Parquet file, only the columns required for the query are read. This allows you to read only the part of the Parquet file that you are interested in. This will be done automatically by DuckDB.

DuckDB also supports filter pushdown into the Parquet reader. When you apply a filter to a column that is scanned from a Parquet file, the filter will be pushed down into the scan, and can even be used to skip parts of the file using the built-in zonemaps. Note that this will depend on whether or not your Parquet file contains zonemaps.

Filter and projection pushdown provide significant performance benefits. See our blog post “Querying Parquet with Precision Using DuckDB” for more information.

Inserts and Views

You can also insert the data into a table or create a table from the Parquet file directly. This will load the data from the Parquet file and insert it into the database:

Insert the data from the Parquet file in the table:

INSERT INTO people
    SELECT * FROM read_parquet('test.parquet');

Create a table directly from a Parquet file:

CREATE TABLE people AS
    SELECT * FROM read_parquet('test.parquet');

If you wish to keep the data stored inside the Parquet file, but want to query the Parquet file directly, you can create a view over the read_parquet function. You can then query the Parquet file as if it were a built-in table:

Create a view over the Parquet file:

CREATE VIEW people AS
    SELECT * FROM read_parquet('test.parquet');

Query the Parquet file:

SELECT * FROM people;

Writing to Parquet Files

DuckDB also has support for writing to Parquet files using the COPY statement syntax. See the COPY Statement page for details, including all possible parameters for the COPY statement.

Write a query to a Snappy-compressed Parquet file:

COPY
    (SELECT * FROM tbl)
    TO 'result-snappy.parquet'
    (FORMAT parquet);

Write tbl to a zstd-compressed Parquet file:

COPY tbl
    TO 'result-zstd.parquet'
    (FORMAT parquet, COMPRESSION zstd);

Write tbl to a zstd-compressed Parquet file with the lowest compression level yielding the fastest compression:

COPY tbl
    TO 'result-zstd.parquet'
    (FORMAT parquet, COMPRESSION zstd, COMPRESSION_LEVEL 1);

Write to Parquet file with key-value metadata:

COPY (
    SELECT
        42 AS number,
        true AS is_even
) TO 'kv_metadata.parquet' (
    FORMAT parquet,
    KV_METADATA {
        number: 'Answer to life, universe, and everything',
        is_even: 'not ''odd''' -- single quotes in values must be escaped
    }
);

Write a CSV file to an uncompressed Parquet file:

COPY
    'test.csv'
    TO 'result-uncompressed.parquet'
    (FORMAT parquet, COMPRESSION uncompressed);

Write a query to a Parquet file with zstd-compression and row group size:

COPY
    (FROM generate_series(100_000))
    TO 'row-groups-zstd.parquet'
    (FORMAT parquet, COMPRESSION zstd, ROW_GROUP_SIZE 100_000);

Write data to an LZ4-compressed Parquet file:

COPY
    (FROM generate_series(100_000))
    TO 'result-lz4.parquet'
    (FORMAT parquet, COMPRESSION lz4);

Or, equivalently:

COPY
    (FROM generate_series(100_000))
    TO 'result-lz4.parquet'
    (FORMAT parquet, COMPRESSION lz4_raw);

Write data to a Brotli-compressed Parquet file:

COPY
    (FROM generate_series(100_000))
    TO 'result-brotli.parquet'
    (FORMAT parquet, COMPRESSION brotli);

To configure the page size of Parquet file's dictionary pages, use the STRING_DICTIONARY_PAGE_SIZE_LIMIT option (default: 1 MB):

COPY
    lineitem
    TO 'lineitem-with-custom-dictionary-size.parquet'
    (FORMAT parquet, STRING_DICTIONARY_PAGE_SIZE_LIMIT 100_000);

DuckDB's EXPORT command can be used to export an entire database to a series of Parquet files. See the “EXPORT statement” page for more details:

Export the table contents of the entire database as Parquet:

EXPORT DATABASE 'target_directory' (FORMAT parquet);

Encryption

DuckDB supports reading and writing encrypted Parquet files.

Supported Features

The list of supported Parquet features is available in the Parquet documentation's “Implementation status” page.

Installing and Loading the Parquet Extension

The support for Parquet files is enabled via extension. The parquet extension is bundled with almost all clients. However, if your client does not bundle the parquet extension, the extension must be installed separately:

INSTALL parquet;