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Architecture Principles

OOCANA is OOMOL's core workflow engine, providing the foundational runtime support for the entire OOMOL Studio.

OOCANA Overview​

OOCANA is an orchestration tool designed for scheduling and executing sequences of task units. The architecture consists of:

  • Scheduling logic stored in flow.oo.yaml files (referred to as flow)
  • Task execution content stored in block.oo.yaml files (referred to as block)
  • block.oo.yaml contains references to execution code, which we call block functions

Block functions can be written in either Nodejs or Python, with a mandatory function name main. This function accepts two parameters:

  1. Input data
  2. Auxiliary functions provided by the block runtime

The function should return a dictionary object (or in Nodejs, a Promise that resolves to a dictionary), which becomes the block's output data.

The flow file defines the triggering sequence and data transfer relationships between blocks.

Block Orchestration and Execution​

The execution sequence of blocks is determined by connections defined in the flow, following a data-flow driven model — when required data is ready, the corresponding block is automatically triggered for execution. This differs from event-driven step triggering in GitHub Actions (where the next step is triggered after the previous one completes), which is primarily used in CI/CD scenarios like compilation and packaging.

Currently, blocks support Nodejs and Python runtime environments. Blocks of the same language run in the same environment. Data between different language environments is transferred via JSON, while blocks in the same language environment can transfer data through JSON or directly via variables. Note that variables follow pass-by-reference principles, so modifying a variable value in one block affects the same variable in other blocks.

Block Data Transfer Specifications​

Data formats are defined separately in block and flow. Currently, OOCANA runtime does not validate these data formats (some GUI components handle this validation). During execution, the actual data format returned by a block is not yet validated. Therefore, developers must ensure that returned data formats conform to the specifications defined by both flow and block.

Block Debugging and Acceleration Strategies​

When a flow contains numerous blocks with time-consuming execution, debugging can be accelerated by specifying block execution strategies. Two acceleration methods are supported:

  1. Termination Point Strategy: Execution ends after reaching a specified block
  2. Cache Strategy: Utilizing previously executed data cache (JSON data only) to skip blocks that don't need re-execution, quickly reaching the specified block