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Modelling
Model InterfaceModel InitialisationAnnotationsModel ContextModel ConfigurationGlobal StateDeterminismActions & SequencingMessagingRandom Number Generation
Agents
CreationConnectionCommunication
Data Management
Loading DataOutputting DataTime Series VariablesOutput ChannelsAvro SchemaMultirun Output
Data Output
ParquetJSONCSVMySQLHive via ParquetH2 and JDBC
Run and Deploy
Multirun SetupCLI Model RunnerREST API UsageDeploying via Local ServerDeploying via DockerDeploying via KubernetesDeploying on Azure Web ServicesDeploying on AWS
Distributed Computation
Distributed RequirementsSpark SetupSpark Tuning & Model Debugging
Testing and Logging
Logging SetupLogging ExampleTestKit SetupUsing TestKitExtended Testkit ExampleAtomic Debug LoggingContinuous Integration Tools
System Dynamics
What is System Dynamics?Modelling System DynamicsSavings Model Example
Experimental
Akka Distributed GraphAuto-compilerModel ScaffoldingSystem MessagingSDK with Jupyter Notebook
Configuration
Example simudyneSDK.propertiesExample pom.xmlProperty Configuration

Modelling

Last updated on 16th July 2024

  • Model Interface: Explaining the core parts of what makes up your model
  • Model Initialisation: Initialisation of Agents and other model elements
  • Annotations: Annotations are how the console and output process your variables
  • Model Context: The model context provides various functionality related to your overall simulation
  • Model Configuration: How you can configure your simulation parameters and settings without needing to recompile
  • Global State: The Global state is the user defined state of the world your simulations variables exist in
  • Determinism: In order to verify results your model should be made repeatable and the SDK provides multiple methods to mitigate 'pure randomness'
  • Actions & Sequencing: A brief explanation of the power that creating various actions and placing them into a multi-threaded sequential flow
  • Messaging: A basic understanding of how messages work, and how to modify them as needed
  • Random Number Generation: Useful in generation of data and distributions while not incurring non-deterministic behavior.
Model Interface