DuckDB for Data Science
DuckDB is an open-source relational database management system (DBMS) tool that supports structural query language (SQL).
In the industry, there is a wide range of database management systems (DBMS). But all have different trade-offs for specific use-cases, there is no one-size-fits-all database system. DuckDB is no different. It is an in-process SQL-based OLAP database management system that provides a simple interface for in-process databases.
Why DuckDB?
Fast Analytical Operation
DuckDB is designed to support analytical query workloads (a.k.a. OLAP). The queries are complex, long-running, and take significant portions of the stored dataset. For example, aggregation over entire tables or join operation on several large tables.
To efficiently operate these operations, duckdb contains a columnar-vectorized query execution engine where queries are interpreted, but instead of performing the row-level operation, it processes a large batch of values (a vector) in one operation.
Simple Operation
DuckDB adopts the ideas of simplicity and embedded operation from the world’s most widely deployed DBMS (SQLite).
- DuckDB is a serverless database with no external dependencies.
- Embedded completely within a host process.
- High-speed data transfer to and from the database.
- It can process datasets without copying.
Free and Open-source
DuckDB’s development started while the main developers were public servants in The Netherlands. DuckDB is free to use and the entire code is available on GitHub. It comes with an MIT license which means you can use it for commercial purposes.
Feature Rich
- It provides serious data management features. Provide extensive support for complex SQL queries with a large function library etc.
- It provides transactional guarantees (ACID properties) through MVCC and bulk optimization.
- It supports secondary indexes for efficient query execution.
- It is deeply integrated into Python and R for efficient data analysis. It also provides APIs for JAVA, C, C++, and others.
Implementation
System Setup
As DuckDB provides deep integrations with Python and R language. So it is pretty easy to set up in the local system.
In Python, you can use the pip package for DuckDB installation
pip install duckdbSimilarly, you can use different languages for installation.
Data Import
In Python, DuckDB supports multiple ways to connect to datasets in the process like
Connect to the in-memory temporary dataset
Creating a temporary database instance is straightforward with DuckDB
Connect to dataset files
With DuckDB, it is really easy to connect to datasets in files.
It provides support file formats like CSV files and Parquet Files. You can use advanced parameters to support different versions of CSV and Parquet files.
Output for importing dataset directly to Python environment
Connect to Pandas Dataframe
Importing data through Dataframe
Python Like Function
For all operations, DuckDB provides python equivalent functions like
Aggregation Operation
DuckDB provides a wide range of aggregation operations. In the notebook, sharing a few. You can find the exhaustive list of the aggregation operations here
Other Operation
DuckDB provides an extensive list of functions for OLAP operations on datasets. Operations like
Conclusion
DuckDB is an open-source, in-process database management system that does not require any external dependencies, neither for compilation nor for execution.
- It follows the same SQL structure as PostgreSQL.
- It provides support to run complex SQL queries on a temporary dataset without copying the dataset.
- It is deeply integrated with Python and R language and also provides APIs for JAVA, C++, and C.
- Instead of performing row-level operations, DuckDB leverages a columnar-vectorized execution engine to process a large batch at a time.
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