DuckDB now integrated with Clojure for out-of-memory data processing

Addressing Large Data Challenges in Clojure

The tech.ml.dataset (TMD) platform in Clojure is designed for in-memory column-major data processing. However, when datasets become too large to fit into memory, users typically resort to sampling, filtering, or using persistence methods like nippy, arrow, or parquet. For very large relational datasets, such as 100GB CSV files, existing tools can become difficult to manage, often leading to considerations of distributed systems like Spark.

DuckDB Integration for Out-of-Memory Processing

Relational databases are well-suited for out-of-memory storage and fast relational queries. The integration of DuckDB with Clojure's TMD provides a way to leverage these capabilities while maintaining the advantages of functional programming. This avoids the inefficiencies of converting data through JDBC's row-to-column API for large datasets.

Improvements in DuckDB's C Interface

Initial integrations of DuckDB with Clojure in 2021 had limitations, such as returning all query results at once and lacking a high-performance append or insert system. Over the past two years, DuckDB's C interface has significantly improved, now offering a batched system for both inserts and queries. These enhancements enable efficient processing of very large joins and faster data loading, making it a more viable solution for large-scale data manipulation within Clojure.

Practical Application

The improved integration allows Clojure users to utilize DuckDB's vectorized SQL execution engine for large datasets. An example cited involves loading a 50-gigabyte CSV file containing 400,000,000 rows of transaction data into DuckDB, demonstrating its capability to handle substantial data volumes efficiently.