How DuckDB and Java Can Fix Slow Analytics

A team fixed slow MongoDB analytics by using DuckDB's Java Table Functions to query live data without a complex data pipeline.

A development team recently shared a detailed case study on the DuckDB blog, outlining how they solved a persistent and frustrating performance problem with their analytics dashboard. Their application, which stores large volumes of client vulnerability data as documents in MongoDB, initially performed well. However, as their dataset grew, the analytics screen became progressively slower, impacting user experience. The team reported spending many months on conventional fixes, including intensive query optimization, data restructuring, and adding numerous indexes to their MongoDB collections. While these efforts provided temporary relief, they couldn't keep pace with the data growth. This performance bottleneck led them to explore a different architectural approach, ultimately using the in-process analytical database DuckDB to run fast queries directly against their existing application data, bypassing the limitations of their transactional database for analytical workloads.

The technical linchpin of their solution is a feature known as Java Table Functions. DuckDB is an in-process database specifically designed for fast analytical processing (OLAP), which involves complex aggregations over large datasets—a task fundamentally different from the quick, simple lookups of transactional processing (OLTP) that databases like MongoDB excel at. Instead of building a traditional data pipeline to copy data from MongoDB into DuckDB, a process known as ETL (Extract, Transform, Load), the team wrote a custom table function. This function acts as a dynamic bridge, teaching DuckDB how to read and interpret data directly from their Java application's objects, which in turn held the data from MongoDB. When a user requested an analytics report, the application invoked DuckDB, which used the custom function to pull only the necessary data from the live MongoDB source and perform the complex calculations in its own high-performance, in-memory engine. This clever integration provided the speed of a dedicated analytical database without data duplication or synchronization delays.

This approach exemplifies a significant industry trend toward more flexible and embedded data analytics, challenging the traditional, monolithic data warehouse model. For decades, the standard solution for business intelligence was to centralize all data into a single warehouse, which required complex, brittle, and often slow ETL jobs to move information from various operational systems. The emergence of powerful in-process databases like DuckDB, along with data interoperability formats like Apache Arrow, is enabling a more federated and lightweight model. In this paradigm, computation is brought directly to the data, wherever it may reside. This is especially transformative for operational analytics, where business users and application features require real-time insights from the live data that powers day-to-day operations. It effectively bypasses the need for a separate, expensive data stack for many common use cases, radically simplifying system architecture and shortening the time from data generation to actionable insight.

For developers, CTOs, and architects facing similar challenges, this case study offers a practical and powerful blueprint. When an application's primary transactional database—be it MongoDB, PostgreSQL, or MySQL—starts to buckle under the strain of analytical queries, the default response is often to begin planning a large-scale data warehousing or data lake project. This example demonstrates a much simpler, more direct path is often available. By embedding a specialized analytical engine like DuckDB directly within the application's backend service, teams can surgically offload heavy analytical work while keeping their overall architecture lean and manageable. This strategy can significantly cut down on development time, reduce infrastructure costs, and lower long-term operational complexity. The key takeaway is to evaluate in-process analytics as a first-line solution before committing to a traditional, heavyweight data pipeline, especially when speed, simplicity, and direct access to live operational data are the primary requirements.

This case study shows a practical alternative to building complex ETL pipelines for operational analytics. For developers, it demonstrates how an in-process database like DuckDB can directly query application data sources, simplifying architecture and reducing latency for real-time dashboards.

Companies can deliver faster analytics and improve user experience without investing in costly data warehousing projects. This approach reduces infrastructure complexity and engineering overhead, allowing teams to solve performance bottlenecks quickly and enable more responsive, data-driven features.