First Past the Post: Evaluating Query Optimization in MongoDB

TL;DR

This paper evaluates MongoDB's 'first past the post' query optimization approach, revealing its preference bias and suboptimal plan choices, which can significantly impact query performance.

Contribution

It provides an analysis of MongoDB's FPTP optimizer, visualizes its effectiveness, and identifies causes of its suboptimal plan selections.

Findings

FPTP often prefers index scans over collection scans, even when slower.

The optimizer can select plans with over twice the runtime of the optimal plan.

Identifies bias causes leading to suboptimal query plan choices.

Abstract

Query optimization is crucial for every database management system (DBMS) to enable fast execution of declarative queries. Most DBMS designs include cost-based query optimization. However, MongoDB implements a different approach to choose an execution plan that we call "first past the post" (FPTP) query optimization. FPTP does not estimate costs for each execution plan, but rather partially executes the alternative plans in a round-robin race and observes the work done by each relative to the number of records returned. In this paper, we analyze the effectiveness of MongoDB's FPTP query optimizer. We see whether the optimizer chooses the best execution plan among the alternatives and measure how the chosen plan compares to the optimal plan. We also show how to visualize the effectiveness and identify situations where the MongoDB 7.0.1 query optimizer chooses suboptimal query plans. Through experiments, we conclude that FPTP has a preference bias, choosing index scans even in many cases where collection scans would run faster. We identify the reasons for the preference bias, which can lead MongoDB to choose a plan with more than twice the runtime compared to the optimal plan for the query.