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The Evolution of the SQL Query Optimizer
How Cost-Based Optimization Shaped Modern Data Systems

Book Details
About this book:

The Evolution of the SQL Query Optimizer Every SQL query you run triggers a hidden miracle: in milliseconds, a declarative request becomes an optimal physical plan spanning indexes, joins, and distributed data shuffles—or it doesn't, and your cluster stalls for hours. *The Evolution of the SQL Query Optimizer* tells the fifty-year story of how that miracle was engineered, from the pointer-chasing nightmares of IMS and CODASYL through the breakthrough of System R's cost-based architecture, the rule-driven extensibility of Volcano and Cascades, and into today's world of learned cardinalities, adaptive re-planning, and autonomous cloud optimizers. You'll see why join enumeration is NP-hard, how histograms evolved into HyperLogLog sketches, and why vectorized execution and hardware-aware cost models are rewriting the rules again.

Written for systems engineers, database architects, and curious practitioners who refuse to treat the optimizer as a black box, this book connects the mathematical foundations—relational algebra, dynamic programming, statistical estimation—to the engineering trade-offs that decide whether your analytical workload finishes in seconds or days. Chapters trace the lineage from Selinger's seminal cost formulas through star-schema OLAP, SQL-on-Hadoop, schemaless document optimization, and the emerging frontier of ML-driven plan selection. Each chapter builds the mental model you need to read an EXPLAIN plan with confidence, diagnose cardinality-estimation disasters, and understand why the optimizer chooses a hash join over a merge join—or when to override it.

Whether you're tuning a latency-critical OLTP query, designing a petabyte-scale lakehouse, or building the next generation of data-intensive systems, *The Evolution of the SQL Query Optimizer* gives you the deep, practical history that turns opaque magic into transparent, controllable engineering.

Table of Contents:
  • Introduction
  • Chapter 1 The Declarative Promise: Pre-Relational Data Access and the Need for Optimization
  • Chapter 2 System R and the Genesis of Cost-Based Optimization
  • Chapter 3 The Architecture of the Selinger Optimizer
  • Chapter 4 Relational Algebra as the Foundation of Query Transformation
  • Chapter 5 Heuristics vs. Costs: Rule-Based Approaches and Early Commercial Engines
  • Chapter 6 The Search Space Explosion: Join Enumeration and Dynamic Programming
  • Chapter 7 Statistics and Selectivity Estimation: The Early Statistical Models
  • Chapter 8 Beyond Uniformity: Equi-Depth, Equi-Width, and Multi-Dimensional Histograms
  • Chapter 9 The Volcano Model: Extensibility and Dynamic Equivalence
  • Chapter 10 The Cascades Framework: Object-Oriented, Rule-Driven Optimization
  • Chapter 11 Subquery Flattening, Unnesting, and View Inlining
  • Chapter 12 Outer Join Simplification and Reordering Strategies
  • Chapter 13 Physical Plan Selection: Index Scans, Hash Joins, and Merge Joins
  • Chapter 14 Parallel Query Optimization for Shared-Memory and Shared-Nothing Systems
  • Chapter 15 Distributed Query Optimization and Data Shipping Paradigms
  • Chapter 16 Modern Cardinality Estimation: Sketches, Wavelets, and HyperLogLog
  • Chapter 17 Adaptive and Dynamic Query Optimization: Re-planning at Runtime
  • Chapter 18 Parametric and Robust Query Optimization: Defending Against Estimation Errors
  • Chapter 19 Vectorized and Code-Generating Engines: How Modern Hardware Impacts Cost Models
  • Chapter 20 Optimizing Analytical Workloads: Star Schema, Columnar Storage, and OLAP
  • Chapter 21 The SQL-on-Hadoop Era: Optimizing Queries in Massively Distributed Big Data Engines
  • Chapter 22 Bridging Relational and NoSQL: Schemaless, Semi-Structured, and Document Optimization
  • Chapter 23 Machine Learning for Query Optimization: Learned Cardinalities and Plan Selection
  • Chapter 24 Autonomous and Self-Tuning Optimizers in the Cloud
  • Chapter 25 The Future of Cost-Based Optimization: Open Challenges and Next Frontiers
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Author:

Christopher K. Simpson

Published By:

MixCache.com Extended Catalog

Type:

Nonfiction

Date Published:

September 16, 2026

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