- Installation
- Documentation
- Getting Started
- Connect
- Data Import
- Overview
- Data Sources
- CSV Files
- JSON Files
- Overview
- Creating JSON
- Loading JSON
- Writing JSON
- JSON Type
- JSON Functions
- Format Settings
- Installing and Loading
- SQL to / from JSON
- Caveats
- Multiple Files
- Parquet Files
- Partitioning
- Appender
- INSERT Statements
- Client APIs
- Overview
- C
- Overview
- Startup
- Configuration
- Query
- Data Chunks
- Vectors
- Values
- Types
- Prepared Statements
- Appender
- Table Functions
- Replacement Scans
- API Reference
- C++
- CLI
- Go
- Java
- Julia
- Node.js
- Python
- Overview
- Data Ingestion
- Conversion between DuckDB and Python
- DB API
- Relational API
- Function API
- Types API
- Expression API
- Spark API
- API Reference
- Known Python Issues
- R
- Rust
- Swift
- Wasm
- ADBC
- ODBC
- SQL
- Introduction
- Statements
- Overview
- ANALYZE
- ALTER TABLE
- ALTER VIEW
- ATTACH / DETACH
- CALL
- CHECKPOINT
- COMMENT ON
- COPY
- CREATE INDEX
- CREATE MACRO
- CREATE SCHEMA
- CREATE SECRET
- CREATE SEQUENCE
- CREATE TABLE
- CREATE VIEW
- CREATE TYPE
- DELETE
- DESCRIBE
- DROP
- EXPORT / IMPORT DATABASE
- INSERT
- PIVOT
- Profiling
- SELECT
- SET / RESET
- SET VARIABLE
- SUMMARIZE
- Transaction Management
- UNPIVOT
- UPDATE
- USE
- VACUUM
- LOAD / INSTALL
- Query Syntax
- SELECT
- FROM & JOIN
- WHERE
- GROUP BY
- GROUPING SETS
- HAVING
- ORDER BY
- LIMIT and OFFSET
- SAMPLE
- Unnesting
- WITH
- WINDOW
- QUALIFY
- VALUES
- FILTER
- Set Operations
- Prepared Statements
- Data Types
- Overview
- Array
- Bitstring
- Blob
- Boolean
- Date
- Enum
- Interval
- List
- Literal Types
- Map
- NULL Values
- Numeric
- Struct
- Text
- Time
- Timestamp
- Time Zones
- Union
- Typecasting
- Expressions
- Overview
- CASE Statement
- Casting
- Collations
- Comparisons
- IN Operator
- Logical Operators
- Star Expression
- Subqueries
- Functions
- Overview
- Aggregate Functions
- Array Functions
- Bitstring Functions
- Blob Functions
- Date Format Functions
- Date Functions
- Date Part Functions
- Enum Functions
- Interval Functions
- Lambda Functions
- List Functions
- Map Functions
- Nested Functions
- Numeric Functions
- Pattern Matching
- Regular Expressions
- Struct Functions
- Text Functions
- Time Functions
- Timestamp Functions
- Timestamp with Time Zone Functions
- Union Functions
- Utility Functions
- Window Functions
- Constraints
- Indexes
- Meta Queries
- DuckDB's SQL Dialect
- Samples
- Configuration
- Extensions
- Overview
- Core Extensions
- Community Extensions
- Working with Extensions
- Versioning of Extensions
- Arrow
- AutoComplete
- AWS
- Azure
- Delta
- Excel
- Full Text Search
- httpfs (HTTP and S3)
- Iceberg
- ICU
- inet
- jemalloc
- MySQL
- PostgreSQL
- Spatial
- SQLite
- Substrait
- TPC-DS
- TPC-H
- VSS
- Guides
- Overview
- Data Viewers
- Database Integration
- File Formats
- Overview
- CSV Import
- CSV Export
- Directly Reading Files
- Excel Import
- Excel Export
- JSON Import
- JSON Export
- Parquet Import
- Parquet Export
- Querying Parquet Files
- Network & Cloud Storage
- Overview
- HTTP Parquet Import
- S3 Parquet Import
- S3 Parquet Export
- S3 Iceberg Import
- S3 Express One
- GCS Import
- Cloudflare R2 Import
- DuckDB over HTTPS / S3
- Meta Queries
- Describe Table
- EXPLAIN: Inspect Query Plans
- EXPLAIN ANALYZE: Profile Queries
- List Tables
- Summarize
- DuckDB Environment
- ODBC
- Performance
- Overview
- Environment
- Import
- Schema
- Indexing
- Join Operations
- File Formats
- How to Tune Workloads
- My Workload Is Slow
- Benchmarks
- Python
- Installation
- Executing SQL
- Jupyter Notebooks
- SQL on Pandas
- Import from Pandas
- Export to Pandas
- Import from Numpy
- Export to Numpy
- SQL on Arrow
- Import from Arrow
- Export to Arrow
- Relational API on Pandas
- Multiple Python Threads
- Integration with Ibis
- Integration with Polars
- Using fsspec Filesystems
- SQL Editors
- SQL Features
- Snippets
- Glossary of Terms
- Browse Offline
- Operations Manual
- Overview
- Limits
- Non-Deterministic Behavior
- Embedding DuckDB
- DuckDB's Footprint
- Securing DuckDB
- Development
- DuckDB Repositories
- Testing
- Overview
- sqllogictest Introduction
- Writing Tests
- Debugging
- Result Verification
- Persistent Testing
- Loops
- Multiple Connections
- Catch
- Profiling
- Release Calendar
- Building
- Benchmark Suite
- Internals
- Sitemap
- Why DuckDB
- Media
- FAQ
- Code of Conduct
- Live Demo
DuckDB can read multiple files of different types (CSV, Parquet, JSON files) at the same time using either the glob syntax, or by providing a list of files to read. See the combining schemas page for tips on reading files with different schemas.
CSV
Read all files with a name ending in .csv
in the folder dir
:
SELECT *
FROM 'dir/*.csv';
Read all files with a name ending in .csv
, two directories deep:
SELECT *
FROM '*/*/*.csv';
Read all files with a name ending in .csv
, at any depth in the folder dir
:
SELECT *
FROM 'dir/**/*.csv';
Read the CSV files flights1.csv
and flights2.csv
:
SELECT *
FROM read_csv(['flights1.csv', 'flights2.csv']);
Read the CSV files flights1.csv
and flights2.csv
, unifying schemas by name and outputting a filename
column:
SELECT *
FROM read_csv(['flights1.csv', 'flights2.csv'], union_by_name = true, filename = true);
Parquet
Read all files that match the glob pattern:
SELECT *
FROM 'test/*.parquet';
Read three Parquet files and treat them as a single table:
SELECT *
FROM read_parquet(['file1.parquet', 'file2.parquet', 'file3.parquet']);
Read all Parquet files from two specific folders:
SELECT *
FROM read_parquet(['folder1/*.parquet', 'folder2/*.parquet']);
Read all Parquet files that match the glob pattern at any depth:
SELECT *
FROM read_parquet('dir/**/*.parquet');
Multi-File Reads and Globs
DuckDB can also read a series of Parquet files and treat them as if they were a single table. Note that this only works if the Parquet files have the same schema. You can specify which Parquet files you want to read using a list parameter, glob pattern matching syntax, or a combination of both.
List Parameter
The read_parquet
function can accept a list of filenames as the input parameter.
Read three Parquet files and treat them as a single table:
SELECT *
FROM read_parquet(['file1.parquet', 'file2.parquet', 'file3.parquet']);
Glob Syntax
Any file name input to the read_parquet
function can either be an exact filename, or use a glob syntax to read multiple files that match a pattern.
Wildcard | Description |
---|---|
* |
matches any number of any characters (including none) |
** |
matches any number of subdirectories (including none) |
? |
matches any single character |
[abc] |
matches one character given in the bracket |
[a-z] |
matches one character from the range given in the bracket |
Note that the ?
wildcard in globs is not supported for reads over S3 due to HTTP encoding issues.
Here is an example that reads all the files that end with .parquet
located in the test
folder:
Read all files that match the glob pattern:
SELECT *
FROM read_parquet('test/*.parquet');
List of Globs
The glob syntax and the list input parameter can be combined to scan files that meet one of multiple patterns.
Read all Parquet files from 2 specific folders.
SELECT *
FROM read_parquet(['folder1/*.parquet', 'folder2/*.parquet']);
DuckDB can read multiple CSV files at the same time using either the glob syntax, or by providing a list of files to read.
Filename
The filename
argument can be used to add an extra filename
column to the result that indicates which row came from which file. For example:
SELECT *
FROM read_csv(['flights1.csv', 'flights2.csv'], union_by_name = true, filename = true);
FlightDate | OriginCityName | DestCityName | UniqueCarrier | filename |
---|---|---|---|---|
1988-01-01 | New York, NY | Los Angeles, CA | NULL | flights1.csv |
1988-01-02 | New York, NY | Los Angeles, CA | NULL | flights1.csv |
1988-01-03 | New York, NY | Los Angeles, CA | AA | flights2.csv |
Glob Function to Find Filenames
The glob pattern matching syntax can also be used to search for filenames using the glob
table function.
It accepts one parameter: the path to search (which may include glob patterns).
Search the current directory for all files.
SELECT *
FROM glob('*');
file |
---|
test.csv |
test.json |
test.parquet |
test2.csv |
test2.parquet |
todos.json |