How Query Engines Work
How Query Engines Work
An Introductory Guide
About the Book
Andy Grove has worked on numerous projects that required custom query engines or integrations with existing query engines and this book provides an approachable introduction to the topic.
The book provides an introduction to the high-level concepts behind query engines and walks through every step of building a SQL query engine in Kotlin with full source code available in a companion github repository. Most of the book is programming language agnostic and Kotlin was chosen for the code examples due to its conciseness and readability. The concepts should be easily translatable to other programming languages.
Andy is a PMC member of Apache Arrow where he donated the initial Rust implementation and later donated the DataFusion query engine.
Please note that this is a short introductory book (around 100 pages). Around 4% of readers ask for a refund because they were expecting something far more comprehensive.
About the Author
Table of Contents
- Acknowledgments
-
Introduction
- Feedback
-
1 What Is a Query Engine?
- 1.1 Why Are Query Engines Popular?
- 1.2 What This Book Covers
- 1.3 Source Code
- 1.4 Why Kotlin?
-
2 Apache Arrow
- 2.1 Arrow Memory Model
- 2.2 Inter-Process Communication (IPC)
- 2.3 Compute Kernels
- 2.4 Arrow Flight Protocol
- 2.5 Arrow Flight SQL
- 2.6 Query Engines
-
3 Choosing a Type System
- 3.1 Row-Based or Columnar?
- 3.2 Interoperability
- 3.3 Type System
-
4 Data Sources
- 4.1 Data Source Interface
- 4.2 Data Source Examples
-
5 Logical Plans & Expressions
- 5.1 Printing Logical Plans
- 5.2 Serialization
- 5.3 Logical Expressions
- 5.4 Column Expressions
- 5.5 Literal Expressions
- 5.6 Binary Expressions
- 5.7 Comparison Expressions
- 5.8 Boolean Expressions
- 5.9 Math Expressions
- 5.10 Aggregate Expressions
- 5.11 Logical Plans
- 5.12 Scan
- 5.13 Projection
- 5.14 Selection (also known as Filter)
- 5.15 Aggregate
-
6 Building Logical Plans
- 6.1 Building Logical Plans The Hard Way
- 6.2 Building Logical Plans using DataFrames
-
7 Physical Plans & Expressions
- 7.1 Physical Expressions
- 7.2 Column Expressions
- 7.3 Literal Expressions
- 7.4 Binary Expressions
- 7.5 Comparison Expressions
- 7.6 Math Expressions
- 7.7 Aggregate Expressions
- 7.8 Physical Plans
- 7.9 Scan
- 7.10 Projection
- 7.11 Selection (also known as Filter)
- 7.12 Hash Aggregate
- 7.13 Joins
- 7.14 Subqueries
- 7.15 Creating Physical Plans
-
8 Query Planner
- 8.1 Translating Logical Expressions
- 8.2 Column Expressions
- 8.3 Literal Expressions
- 8.4 Binary Expressions
- 8.5 Translating Logical Plans
- 8.6 Scan
- 8.7 Projection
- 8.8 Selection (also known as Filter)
- 8.9 Aggregate
-
9 Query Optimizations
- 9.1 Rule-Based Optimizations
- 9.2 Cost-Based Optimizations
-
10 Query Execution
- 10.1 Apache Spark Example
- 10.2 KQuery Examples
- 10.3 Removing The Query Optimizer
-
11 SQL Support
- 11.1 Tokenizer
- 11.2 Pratt Parser
- 11.3 Parsing SQL Expressions
- 11.4 Parsing a SELECT statement
- 11.5 SQL Query Planner
- 11.6 Translating SQL Expressions
- 11.7 Planning SELECT
- 11.8 Planning for Aggregate Queries
-
12 Parallel Query Execution
- 12.1 Combining Results
- 12.2 Smarter Partitioning
- 12.3 Partition Keys
- 12.4 Parallel Joins
-
13 Distributed Query Execution
- 13.1 Embarrassingly Parallel Operators
- 13.2 Distributed Aggregates
- 13.3 Distributed Joins
- 13.4 Distributed Query Scheduling
- 13.5 Producing a Distributed Query Plan
- 13.6 Serializing a Query Plan
- 13.7 Serializing Data
- 13.8 Choosing a Protocol
- 13.9 Streaming
- 13.10 Custom Code
- 13.11 Distributed Query Optimizations
-
14 Testing
- 14.1 Unit Testing
- 14.2 Integration Testing
- 14.3 Fuzzing
-
15 Benchmarks
- 15.1 Measuring Performance
- 15.2 Measuring Scalability
- 15.3 Concurrency
- 15.4 Automation
- 15.5 Comparing Benchmarks
- 15.6 Publishing Benchmark Results
- 15.7 Transaction Processing Council (TPC) Benchmarks
-
Further Resources
- Open-Source Projects
- YouTube
- Sample Data
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