- Installation
- What's New in the Docs for 2.0
- Documentation
- Getting Started
- Connect
- Data Import and Export
- Overview
- Data Sources
- CSV Files
- IO
- 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
- Lakehouse Formats
- Client APIs
- Overview
- ADBC
- C
- Overview
- Startup
- Configuration
- Query
- Data Chunks
- Vectors
- Values
- Types
- Prepared Statements
- Appender
- Table Functions
- Replacement Scans
- API Reference
- C++
- CLI
- Overview
- Arguments
- Dot Commands
- Output Formats
- Agent Mode
- Editing
- Friendly CLI
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- Autocomplete
- Syntax Highlighting
- Known Issues
- Go
- Overview
- Connect
- Import Data
- Run Queries
- Handle Results
- Write User Defined Functions
- Profile and Monitor
- Troubleshoot
- Java (JDBC)
- Overview
- Connect
- Import Data
- Run Queries
- Handle Results
- Write User Defined Functions
- Profile and Monitor
- Deploy as Native Image
- Troubleshoot
- Node.js (Neo)
- ODBC
- Python
- Overview
- Data Ingestion
- Appender
- Conversion between DuckDB and Python
- DB API
- Relational API
- Function API
- Types API
- Expression API
- Spark API
- API Reference
- Known Python Issues
- Migrating to 2.0
- R
- Rust
- Overview
- Connect
- Import Data
- Run Queries
- Handle Results
- Write User Defined Functions
- Profile and Monitor
- Troubleshoot
- Wasm
- Tertiary Clients
- SQL
- Introduction
- Statements
- Overview
- ANALYZE
- ALTER DATABASE
- ALTER TABLE
- ALTER VIEW
- ATTACH and DETACH
- CALL
- CHECKPOINT
- COMMENT ON
- CONNECT and DISCONNECT
- COPY
- CREATE INDEX
- CREATE MACRO
- CREATE SCHEMA
- CREATE SECRET
- CREATE SEQUENCE
- CREATE TABLE
- CREATE VIEW
- CREATE TRIGGER
- CREATE TYPE
- DELETE
- DESCRIBE
- DROP
- EXPORT and IMPORT DATABASE
- INSERT
- LOAD / INSTALL
- MERGE INTO
- PIVOT
- Profiling
- PREPARE, EXECUTE, and DEALLOCATE
- SELECT
- SET / RESET
- SET VARIABLE
- SHOW and SHOW DATABASES
- SUMMARIZE
- Transaction Management
- UNPIVOT
- UPDATE
- UPDATE EXTENSIONS
- USE
- VACUUM
- Query Syntax
- SELECT
- FROM and 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
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- Date
- Enum
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- List
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- NULL Values
- Numeric
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- Time Zones
- Union
- Typecasting
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- Expressions
- Overview
- CASE Expression
- Casting
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- Comparisons
- IN Operator
- Logical Operators
- Star Expression
- Subqueries
- TRY
- Functions
- Overview
- Aggregate Functions
- Array Functions
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- Date Format Functions
- Date Functions
- Date Part Functions
- Enum Functions
- Geometry Functions
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- 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
- Variant Functions
- Window Functions
- Constraints
- Indexes
- Meta Queries
- DuckDB's SQL Dialect
- Overview
- Indexing
- Friendly SQL
- Keywords and Identifiers
- Order Preservation
- PostgreSQL Compatibility
- SQL Quirks
- PEG Parser
- Samples
- Configuration
- Extensions
- Overview
- Installing Extensions
- Advanced Installation Methods
- Distributing Extensions
- Extension Repositories
- Community Extensions
- Versioning of Extensions
- Troubleshooting of Extensions
- Core Extensions
- Overview
- AutoComplete
- Avro
- AWS
- Azure
- Delta
- DuckLake
- Encodings
- Excel
- Full Text Search
- httpfs (HTTP and S3)
- Overview
- HTTP(S) Support
- Hugging Face
- S3 API Support
- S3 and AWS Authentication
- Legacy Authentication Scheme for S3 API
- Iceberg
- ICU
- inet
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- Legacy Parser
- MotherDuck
- MySQL
- ODBC
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- Excel Import
- Excel Export
- JSON Import
- JSON Export
- Parquet Import
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- Querying Parquet Files
- File Access with the file: Protocol
- Meta Queries
- Describe Table
- EXPLAIN: Inspect Query Plans
- EXPLAIN ANALYZE: Profile Queries
- List Tables
- Summarize
- DuckDB Environment
- Network and Cloud Storage
- Overview
- HTTP Parquet Import
- HTTP CSV Import
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- S3 Iceberg Import
- S3 Express One
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- DuckDB over HTTPS / S3
- Share a Views-Only Database
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- ODBC
- Performance
- Overview
- Environment
- Import
- Schema
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- File Formats
- How to Tune Workloads
- My Workload Is Slow
- Out-of-Memory Issues
- Benchmarks
- Working with Huge Databases
- Python
- Installation
- Executing SQL
- Jupyter Notebooks
- marimo 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
- Integration with PyTorch
- Using fsspec Filesystems
- SQL Editors
- Beekeeper Studio SQL Editor
- DBeaver SQL IDE
- DBine SQL Editor
- LibreDB Studio SQL Editor
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- SQL Features
- AsOf Join
- Full-Text Search
- Graph Queries
- query and query_table Functions
- Merge Statement for SCD Type 2
- Timestamp Issues
- Snippets
- Creating Synthetic Data
- Dutch Railway Datasets
- Sharing Macros
- Analyzing a Git Repository
- Importing Duckbox Tables
- Copying an In-Memory Database to a File
- Calculating a Database Checksum
- Troubleshooting
- Glossary of Terms
- Browsing Offline
- Operations Manual
- Overview
- Migrating to DuckDB v2.0
- Backup Extension Repositories
- DuckDB's Footprint
- Installing DuckDB
- Logging
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- Securing DuckDB
- Non-Deterministic Behavior
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- Development
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- Building DuckDB
- Overview
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- Building Extensions
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- Android
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- Troubleshooting
- Unofficial and Unsupported Platforms
- Benchmark Suite
- Testing
- Overview
- sqllogictest Introduction
- Writing Tests
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- Loops
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- Catch
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- Internals
- Sitemap
- Live Demo
The latest stable version of the DuckDB Dart client is .
DuckDB.Dart is the native Dart API for DuckDB.
Installation
DuckDB.Dart can be installed from pub.dev. Please see the API Reference for details.
Use This Package as a Library
Depend on It
Add the dependency with Flutter:
flutter pub add dart_duckdb
This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):
dependencies:
dart_duckdb: ^
Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.
Import It
Now in your Dart code, you can import it:
import 'package:dart_duckdb/dart_duckdb.dart';
Usage Examples
See the example projects in the duckdb-dart repository:
cli: command-line applicationduckdbexplorer: GUI application which builds for desktop operating systems as well as Android and iOS.
Here are some common code snippets for DuckDB.Dart:
Querying an In-Memory Database
import 'package:dart_duckdb/dart_duckdb.dart';
void main() async {
final db = await duckdb.open(":memory:");
final connection = await duckdb.connect(db);
await connection.execute('''
CREATE TABLE users (id INTEGER, name VARCHAR, age INTEGER);
INSERT INTO users VALUES (1, 'Alice', 30), (2, 'Bob', 25);
''');
final result = (await connection.query("SELECT * FROM users WHERE age > 28")).fetchAll();
for (final row in result) {
print(row);
}
connection.dispose();
db.dispose();
}
Using Multiple Connections
DuckDB.Dart automatically manages dedicated background isolates per connection, enabling efficient non-blocking I/O for concurrent queries. Each connection handles its own isolate internally, so you can simply create multiple connections for parallel operations:
import 'package:dart_duckdb/dart_duckdb.dart';
void main() async {
final db = await duckdb.open(":memory:");
// Create a table
final con1 = await duckdb.connect(db);
await con1.execute('''
CREATE TABLE users (id INTEGER, name VARCHAR);
INSERT INTO users VALUES (1, 'Alice'), (2, 'Bob');
''');
// Query from multiple connections concurrently
final con2 = await duckdb.connect(db);
final con3 = await duckdb.connect(db);
final future1 = con2.query("SELECT * FROM users WHERE id = 1");
final future2 = con3.query("SELECT * FROM users WHERE id = 2");
final result1 = (await future1).fetchAll();
final result2 = (await future2).fetchAll();
print(result1);
print(result2);
con1.dispose();
con2.dispose();
con3.dispose();
db.dispose();
}
Web Support
DuckDB.Dart supports web platforms through DuckDB WASM. For Flutter web builds, you need to configure the necessary JavaScript dependencies.
Setup for Flutter Web
Add the following to web/index.html inside the <head> section to load DuckDB WASM and Apache Arrow:
<script type="importmap">
{
"imports": {
"apache-arrow": "https://cdn.jsdelivr.net/npm/[email protected]/+esm"
}
}
</script>
<script type="module">
import * as duckdb from "https://cdn.jsdelivr.net/npm/@duckdb/[email protected]/+esm";
import * as arrow from "apache-arrow";
window.duckdbWasmReady = new Promise((resolve) => {
window.duckdbduckdbWasm = duckdb;
window.ArrowTable = arrow.Table;
resolve();
});
</script>
Usage in Flutter Web
Once configured, you can use DuckDB the same way as on other platforms:
import 'package:dart_duckdb/dart_duckdb.dart';
void main() async {
final db = await duckdb.open(":memory:");
final connection = await duckdb.connect(db);
await connection.execute('''
CREATE TABLE data (id INTEGER, value VARCHAR);
INSERT INTO data VALUES (1, 'hello'), (2, 'world');
''');
final result = (await connection.query("SELECT * FROM data")).fetchAll();
for (final row in result) {
print(row);
}
connection.dispose();
db.dispose();
}
For more platform-specific details, see the Building Instructions.