Cost-Based Optimization: When Statistics Drive the Plan

The choices physical planning makes are only as good as the size estimates behind them, and cost-based optimization, CBO, is the part of Catalyst that tries to make those estimates accurate using real statistics about your tables. Without statistics, Catalyst falls back to crude heuristics, guessing sizes from file bytes and rule-of-thumb selectivity. With statistics, it can estimate the size of each intermediate result and choose join orders and strategies that minimise the total work. Statistics come from analyzing a table: row counts, the number of distinct values in a column, min and max, null counts, sometimes histograms of the value distribution. With these, the optimizer can estimate, for example, that filtering on a particular value will keep roughly one percent of the rows, and th

About This Interactive Section

This section is part of the Inside Catalyst: The Four Phases lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.