How Relational Complexity Crushes Real-Time Dashboards

In NanoHertz Solutions' blog post by Jake Hertz, the challenges of using relational databases for real-time analytics with PostgreSQL are explored, particularly focusing on the "join explosion" problem that arises with complex queries on large datasets. As dashboards need to refresh rapidly with data from multiple tables, the computational burden of frequent joins can degrade performance, making dashboards sluggish as databases struggle with intensive CPU cycles and I/O operations. The solution proposed involves transitioning from a normalized relational schema to a flattened data model, where data is pre-joined at the point of ingestion, reducing the need for costly joins during query execution. This approach shifts the computational cost to write-time, enabling faster query performance and higher concurrency by allowing single index scans instead of multi-way joins. The blog outlines steps to implement this shift, including creating flattened tables, migrating data in batches, and using triggers for real-time data synchronization, while also addressing potential trade-offs like data redundancy against improved speed and scalability.