cwida/duckdb
Duckdb
DuckDB is an embedded, in-process analytical SQL database and OLAP database management system. It functions as a data engine for Parquet and CSV files, allowing users to execute complex SQL queries on large datasets without requiring a separate server process.
The system is designed for local analytical processing and embedded data science workflows. It enables the direct querying and analysis of Parquet and CSV files from disk, bypassing the need to load data into a permanent database.
The engine provides high-performance analytical SQL execution, including support for window functions and nested subqueries. It incorporates a columnar storage layout and vectorized query execution to handle large-scale data manipulation and exploration.
The database is accessible via a standalone command line interface and language-specific bindings for Python, R, Java, and Wasm.
Features
- In-Process Analytics - Runs the analytical database engine directly within the host application process to eliminate network latency.
- Analytical Workload Engines - Optimized for heavy local analytical processing and OLAP workloads via an embedded engine.
- Columnar Databases - Implements a storage engine optimized for analytical queries using columnar data structures.
- Embedded Databases - Implements a relational storage engine that runs within the application process without a separate server.
- File-Based Data Engines - Serves as a data engine that imports and queries large Parquet and CSV files directly using SQL.
- File-Based Data Import - Enables direct loading and querying of CSV and Parquet files by referencing them within SQL queries.
- Parquet Scanners - Optimizes Parquet integration by mapping file structures directly to internal formats for fast scanning and predicate pushdown.
- Parquet Data Processing - Enables direct querying and analysis of Parquet and CSV files from disk without requiring a separate load step.
- SQL Query Execution Engines - Provides a high-performance SQL execution engine supporting advanced window functions and nested subqueries.
- Vectorized Execution Engines - Employs a vectorized execution engine that processes batches of data to maximize CPU cache efficiency.
- Embedded Data Science Workflows - Integrates SQL database capabilities directly into Python, R, and Java environments for fast data exploration.
- Morsel-Driven Parallelism - Utilizes morsel-driven parallelism to distribute data chunks across CPU cores for high concurrency.
- Large Dataset Optimizations - Optimizes the processing of massive structured datasets using high-performance SQL queries and window functions.
- Push-Based Query Pipelines - Implements a push-based execution pipeline to improve CPU cache locality and reduce function call overhead.
- Data Management - Embeddable SQL database management system for analytics.
Star history
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