book

Data-Oriented Programming

Name: Data-Oriented Programming
Author: Yehonathan Sharvit
ISBN: 9781617298578

by Yehonathan Sharvit

August 2022

Beginner to intermediate

424 pages

10h 19m

English

Manning Publications

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inside front cover
Data-Oriented Programming
Copyright
dedication
Brief contents
contents
front matter
forewordsprefaceacknowledgmentsabout this bookWho should read this book?How this book is organized: A road mapAbout the codeliveBook discussion forumabout the authorabout the cover illustrationdramatis personae
Part 1. Flexibility
1 Complexity of object-oriented programming
1.1 OOP design: Classic or classical?1.1.1 The design phase1.1.2 UML 1011.1.3 Explaining each piece of the class diagram1.1.4 The implementation phase1.2 Sources of complexity1.2.1 Many relations between classes1.2.2 Unpredictable code behavior1.2.3 Not trivial data serialization1.2.4 Complex class hierarchiesSummary
2 Separation between code and data
2.1 The two parts of a DOP system2.2 Data entities2.3 Code modules2.4 DOP systems are easy to understand2.5 DOP systems are flexibleSummary

3 Basic data manipulation
3.1 Designing a data model3.2 Representing records as maps3.3 Manipulating data with generic functions3.4 Calculating search results3.5 Handling records of different typesSummary
4 State management
4.1 Multiple versions of the system data4.2 Structural sharing4.3 Implementing structural sharing4.4 Data safety4.5 The commit phase of a mutation4.6 Ensuring system state integrity4.7 Restoring previous statesSummary
5 Basic concurrency control
5.1 Optimistic concurrency control5.2 Reconciliation between concurrent mutations5.3 Reducing collections5.4 Structural difference5.5 Implementing the reconciliation algorithmSummary
6 Unit tests
6.1 The simplicity of data-oriented test cases6.2 Unit tests for data manipulation code6.2.1 The tree of function calls6.2.2 Unit tests for functions down the tree6.2.3 Unit tests for nodes in the tree6.3 Unit tests for queries6.4 Unit tests for mutationsMoving forwardSummary
Part 2. Scalability
7 Basic data validation
7.1 Data validation in DOP7.2 JSON Schema in a nutshell7.3 Schema flexibility and strictness7.4 Schema composition7.5 Details about data validation failuresSummary
8 Advanced concurrency control
8.1 The complexity of locks8.2 Thread-safe counter with atoms8.3 Thread-safe cache with atoms8.4 State management with atomsSummary
9 Persistent data structures
9.1 The need for persistent data structures9.2 The efficiency of persistent data structures9.3 Persistent data structures libraries9.3.1 Persistent data structures in Java9.3.2 Persistent data structures in JavaScript9.4 Persistent data structures in action9.4.1 Writing queries with persistent data structures9.4.2 Writing mutations with persistent data structures9.4.3 Serialization and deserialization9.4.4 Structural diffSummary
10 Database operations
10.1 Fetching data from the database10.2 Storing data in the database10.3 Simple data manipulation10.4 Advanced data manipulationSummary
11 Web services
11.1 Another feature request11.2 Building the insides like the outsides11.3 Representing a client request as a map11.4 Representing a server response as a map11.5 Passing information forward11.6 Search result enrichment in actionDelivering on timeSummary
Part 3. Maintainability
12 Advanced data validation
12.1 Function arguments validation12.2 Return value validation12.3 Advanced data validation12.4 Automatic generation of data model diagrams12.5 Automatic generation of schema-based unit tests12.6 A new giftSummary
13 Polymorphism
13.1 The essence of polymorphism13.2 Multimethods with single dispatch13.3 Multimethods with multiple dispatch13.4 Multimethods with dynamic dispatch13.5 Integrating multimethods in a production systemSummary
14 Advanced data manipulation
14.1 Updating a value in a map with eloquence14.2 Manipulating nested data14.3 Using the best tool for the job14.4 Unwinding at easeSummary
15 Debugging
15.1 Determinism in programming15.2 Reproducibility with numbers and strings15.3 Reproducibility with any data15.4 Unit tests15.5 Dealing with external data sourcesFarewellSummary
Appendix A. Principles of data-oriented programming
A.1 Principle #1: Separate code from dataA.1.1 Illustration of Principle #1A.1.2 Benefits of Principle #1A.1.3 Cost for Principle #1A.1.4 Summary of Principle #1A.2 Principle #2: Represent data with generic data structuresA.2.1 Illustration of Principle #2A.2.2 Benefits of Principle #2A.2.3 Cost for Principle #2A.2.4 Summary of Principle #2A.3 Principle #3: Data is immutableA.3.1 Illustration of Principle #3A.3.2 Benefits of Principle #3A.3.3 Cost for Principle #3A.3.4 Summary of Principle #3A.4 Principle #4: Separate data schema from data representationA.4.1 Illustration of Principle #4A.4.2 Benefits of Principle #4A.4.3 Cost for Principle #4A.4.4 Summary of Principle #4Conclusion
Appendix B. Generic data access in statically-typed languages
B.1 Dynamic getters for string mapsB.1.1 Accessing non-nested map fields with dynamic gettersB.1.2 Accessing nested map fields with dynamic gettersB.2 Value getters for mapsB.2.1 Accessing non-nested map fields with value gettersB.2.2 Accessing nested map fields with value gettersB.3 Typed getters for mapsB.3.1 Accessing non-nested map fields with typed gettersB.3.2 Accessing nested map fields with typed gettersB.4 Generic access to class membersB.4.1 Generic access to non-nested class membersB.4.2 Generic access to nested class membersB.4.3 Automatic JSON serialization of objectsSummary
Appendix C. Data-oriented programming: A link in the chain of programming paradigms
C.1 Time lineC.1.1 1958: LispC.1.2 1981: Values and objectsC.1.3 2000: Ideal hash treesC.1.4 2006: Out of the Tar PitC.1.5 2007: ClojureC.1.6 2009: Immutability for allC.2 DOP principles as best practicesC.2.1 Principle #1: Separate code from dataC.2.2 Principle #2: Represent data with generic data structuresC.2.3 Principle #3: Data is immutableC.2.4 Principle #4: Separate data schema from data representationC.3 DOP and other data-related paradigmsC.3.1 Data-oriented designC.3.2 Data-driven programmingC.3.3 Data-oriented programming (DOP)Summary
Appendix D. Lodash reference
index
inside back cover

Content preview from Data-Oriented Programming

4 State management

Time travel

This chapter covers

A multi-version approach to state management
The calculation phase of a mutation
The commit phase of a mutation
Keeping a history of previous state versions

So far, we have seen how DOP handles queries via generic functions that access system data, which is represented as a hash map. In this chapter, we illustrate how DOP deals with mutations (requests that change the system state). Instead of updating the state in place, we maintain multiple versions of the system data. At a specific point in time, the system state refers to a specific version of the system data. This chapter is a deep dive in the third principle of DOP.

Principle #3 Data is immutable.

The maintenance of multiple versions ...

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Data-Oriented Programming

by Yehonathan Sharvit

4 State management

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