Dataflow and Reactive Programming Systems
Dataflow and Reactive Programming Systems
A Practical Guide to Developing Dataflow and Reactive Programming Systems
About the Book
Dataflow concepts are the heart of Reactive Programming, Flow-Based Programming (e.g. NoFlo), Unix pipes, Actors and message passing in general.
Dataflow-based systems are easy to design once you understand the large number of implementation details that could drastically change how the system operates. Understanding these vectors of change is important so you don’t waste your time developing the wrong system.
Embedded dataflow-like languages are used in a wide range of applications. Video games, web pages, circuit simulation and music production are just a few of the domains that have been using dataflow for years. Every one of those has a specialized dataflow engine designed for the task at hand. This book will help you understand the whole dataflow universe before starting your own system.
By the end of the book you will understand…
- All possible design choices with dataflow-like systems
- How their effects interplay
- How to develop your own dataflow-like system
Public domain source code of an example dataflow system will be available in many common languages once completed (ftp://DataflowBook.com).
- DSP Robotics
- Clean Code Developer School
- Synthetic Spheres
- ANKHOR Software GmbH
- Code Examples
- 1.1 Overview of the Book
- 1.2 Reactive Programming is Dataflow
- 1.3 Von Neumann Architecture
- 1.4 Benefits of Dataflow
- 1.5 History
- 1.6 The Purpose of this Book
2 Dataflow Explained
- 2.1 Pipeline Dataflow
- 2.2 Nodes
- 2.3 Data
- 2.4 Arcs
- 2.5 Dataflow Graphs
- 2.6 Executing a Graph
2.7 Features of Dataflow Systems
- 2.7.1 Push or Pull Data
- 2.7.2 Mutable or Immutable Data
2.7.3 Static or Dynamic
- 220.127.116.11 Dynamic
- 18.104.22.168 Static
- 2.7.4 Functional or Stateful Nodes
2.7.5 Synchronous or Asynchronous Activation
- 22.214.171.124 Asynchronous
- 126.96.36.199 Synchronous
- 188.8.131.52 Hybrid
2.7.6 Multiple Inputs and/or Outputs
- 184.108.40.206 Multiple Inputs
- 220.127.116.11 Multiple Outputs
- 2.7.7 Fire Patterns
- 2.7.8 Cycles and Feedback
- 18.104.22.168 Implementation of Recursive Nodes
2.7.10 Compound Nodes
- 22.214.171.124 Execution of Compound Nodes
- 126.96.36.199 Design of Compound Nodes
- 2.7.11 Arc Capacity > 1
- 2.7.12 Arc Joins and/or Splits
- 2.7.13 Multi-Rate Token Production and Consumption
2.8 Common Dataflow Nodes
- 2.8.1 Switch Node/ Choice Node
- 2.8.2 Merge Node/ Correlate Node/ Join Node
- 2.8.3 Distribute Node/ Splitter Node
- 2.8.4 Gate Node
- 2.8.5 Terminal Node
- 2.8.6 Source Node
- 2.8.7 Sink Node
2.9 Miscellaneous Topics
- 2.9.1 Granularity
- 2.9.2 When is it Done?
3 Actor Model
- 3.1 Summary of the Actor Model
- 3.2 Comparison to Object Oriented Programming
- 3.3 Relation to Dataflow
- 3.4 Dataflow Features
- 3.5 Where is the Actor Model Used?
- 3.6 Where is it Not Used?
4 Flow-Based Programming
- 4.1 Summary of Flow-Based Programming
- 4.2 Dataflow Features
- 4.3 Benefits of Flow-Based Programming
5 Communicating Sequential Processes
- 5.1 Summary of CSP
- 5.2 Message Passing Channels
- 5.3 Channels as a Concurrency Primitive
- 5.4 Channel Implementations
6 Implicit Dataflow
- 6.1 Unix Pipes
- 6.2 Sockets
- 6.3 Function
- 6.4 Manager Controlled Communication
- 6.5 Message Passing Channels
- 6.6 Feature Creep
7 Asynchronous Dataflow Implementation
- 7.1 Architecture Overview
- 7.2 Implementation Walk-Through
7.3 Main Data Types
- 7.3.1 Port Address
- 7.3.2 Data Token
- 7.3.3 Execute Token
- 7.3.4 Node
- 7.3.5 Node Definition
- 7.3.6 Arc
- 7.3.7 Fire Pattern
- 7.3.8 Token Store
- 7.3.9 Node Store
- 7.3.10 Arc Store
- 7.3.11 Dataflow Program
7.4 Implementation Components
- 7.4.1 IO Unit
- 7.4.2 Transmit Unit
- 7.4.3 Enable Unit
- 7.4.4 Execute Unit
- 7.5 Program Execution Example
- 7.6 Preparing a Program for Execution
- 7.7 Multiple Dataflow Engines
8 Synchronous Dataflow Implementation
- 8.1 Compilation
8.2 How to Build a Schedule
- 8.2.1 Label Nodes/Arcs and Token Rates
- 8.2.2 Create a Topology Matrix
- 8.2.3 Does a Schedule Exist?
- 8.2.4 Determine Initial Arc Capacities
- 8.2.5 Execution Simulation
- 8.2.6 Simulation Process Overview
8.2.7 Simulation Process in Detail
- 188.8.131.52 Step 1: Create a new activation matrix:
- 184.108.40.206 Step 2: Create an activation vector
- 220.127.116.11 Step 3: Create new Token and Fire Count Vectors
- 18.104.22.168 Step 4: Stop or Repeat
- 8.2.8 Analyze for Errors
- 8.2.9 Search for a Schedule
- 8.2.10 Test Schedule
- 8.3 Parallel Schedules
9 Dynamic Dataflow Implementation
- 9.1 Introduction
- 9.2 Overall Design
- 9.3 Features of this Design
- 9.4 Notation Convention
- 9.5 General Types
- 9.6.1 Pipeline Node
- 9.6.2 PipelineNodeObject Methods
- 9.6.3 Developer Accessible Nodes
- 9.6.4 Primitive Node
- 9.6.5 PrimitiveNodeObject Methods:
- 9.6.6 Operation of a PimitiveNodeObject
- 9.6.7 Compound Nodes
- 9.6.8 CompoundNodeObject Methods
- 9.6.9 NodeClass and NodeObject
- 9.6.10 NodeObject Methods
- 9.7 Limitations:
- 9.8 Implementation Language Requirements:
- Special Thanks
- Important Books and Papers
- Synchronous Dataflow
- Communicating Sequential Processes
- Actor Model
- Programming Languages
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