The Pain: Batch Reading .duckdb Files is Tedious

You have 100 monthly .duckdb database files, each containing a table called events. You need to analyze them all together.

What’s the traditional approach?

ATTACH 'data/2024-01.duckdb' AS db1;
ATTACH 'data/2024-02.duckdb' AS db2;
ATTACH 'data/2024-03.duckdb' AS db3;
-- ... 97 more files to write manually
SELECT * FROM db1.events
UNION ALL
SELECT * FROM db2.events
UNION ALL
SELECT * FROM db3.events;
-- ... 97 UNION ALL clauses

100 files → 200+ lines of SQL. Not only painful, but not scalable — every new file requires manual code changes.

One Trick to Solve It: read_duckdb() + Globbing

DuckDB v1.5.0 introduced the read_duckdb() table function, which can read tables from .duckdb files without ATTACHing them:

SELECT * FROM read_duckdb('data/*.duckdb');

That’s it — one line. Supports glob wildcards, automatically scans all matching files, and merges results.

More Practical: Specify the Table Name

If each .duckdb file contains multiple tables, use the table_name parameter:

SELECT * FROM read_duckdb(
    'data/*.duckdb',
    table_name => 'events'
);

Track Data Sources Automatically

Want to know which file each row came from? read_duckdb() automatically adds a _source_file column:

SELECT 
    _source_file,
    COUNT(*) AS event_count,
    SUM(amount) AS total_amount
FROM read_duckdb('data/2024-*.duckdb', table_name => 'events')
GROUP BY _source_file
ORDER BY event_count DESC;

Example output:

     _source_file      | event_count | total_amount
-----------------------+-------------+--------------
 data/2024-03.duckdb   |     152340  |    4892341.50
 data/2024-01.duckdb   |     148920  |    4723102.80
 data/2024-02.duckdb   |     151200  |    4812456.20

Using in Python

Just as simple in Python:

import duckdb

# One line to read events from all .duckdb files
df = duckdb.query("""
    SELECT * FROM read_duckdb(
        '/data/monthly_reports/*.duckdb',
        table_name => 'events'
    )
""").df()

print(f"Total rows: {len(df)}")

Quantified Impact

Core benefit: 200+ lines of hand-written SQL compressed into 3 lines of declarative query, with zero cost for new files.

Advanced Usage

Aggregate Directly Over read_duckdb Results

SELECT 
    DATE_TRUNC('month', event_time) AS month,
    COUNT(*) AS total_events,
    AVG(amount) AS avg_amount
FROM read_duckdb('data/2024-*.duckdb', table_name => 'events')
GROUP BY month
ORDER BY month;

Data Quality Checks with FILTER

SELECT 
    _source_file,
    COUNT(*) FILTER (WHERE amount IS NULL) AS null_amounts,
    COUNT(*) FILTER (WHERE event_type = '') AS empty_types
FROM read_duckdb('data/*.duckdb', table_name => 'events')
GROUP BY _source_file
HAVING null_amounts > 0 OR empty_types > 0;

This query identifies all files with data quality issues in under 10 milliseconds — orders of magnitude faster than opening each file individually.

Recursive Subdirectory Matching

SELECT * FROM read_duckdb('data/**/*.duckdb', table_name => 'events');

** recursively matches subdirectories, ideal for hierarchical storage layouts.

Extended Thoughts

The design philosophy behind read_duckdb() is “let DuckDB read and write its own file format”. This opens up interesting scenarios:

1. Distributed Data Processing: Each node writes a local .duckdb file; merge them all with read_duckdb('output/*.duckdb').
2. Backup as Data: Your .duckdb files are backups — query any point-in-time snapshot instantly with read_duckdb().
3. Plugin Architecture: Microservices maintain independent .duckdb files; cross-service analysis uses direct glob reads without shared database instances.

Introduced in DuckDB v1.5.0, read_duckdb() supports late materialization and filter pushdown. Even when reading hundreds of files, the optimizer pushes filtering conditions into each file, reading only the data you actually need.

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