Goal: Breakdown of the key performance differences and trade-offs between stateful and stateless Logic Apps:
Stateful Logic Apps:
High Resiliency: Designed to withstand outages. If an outage occurs, stateful logic apps can be reconstructed from their saved state and rerun to completion once services are restored.
Long-Running Processes: They can run for extended periods, making them suitable for workflows that require persistent state and the ability to track progress over time.
Detailed Run History: Stateful logic apps store input, output, and workflow states in external storage. This allows for comprehensive review and debugging after each run.
Performance Considerations: The reliance on external storage for state persistence can introduce performance overhead compared to stateless logic apps.
Stateless Logic Apps:
Enhanced Performance: By not persisting state to external storage, stateless logic apps offer faster execution speeds, reduced latency, and higher throughput. This makes them well-suited for scenarios that prioritize performance.
Short-Lived Executions: Designed for shorter tasks with a maximum execution time of just a few minutes. Their in-memory operation makes them unsuitable for long-running processes.
Limited Run History: Input, output, and workflow states are not saved to external storage. While temporary debugging is possible, it impacts performance. This makes post-run analysis and debugging more challenging.
Message Size Limits: To prevent memory issues, stateless logic apps enforce payload size limit on messages. Larger files necessitate the use of stateful executions.
Synchronous Execution Only: They only support synchronous operations and are not suitable for asynchronous patterns requiring callbacks.
Choosing the Right Type:
Stateless Execution: Ideal for scenarios demanding low latency and high throughput, where run history isn't critical. Examples include read-heavy operations or integrations with systems like Dynamics 365 or Salesforce.
Stateful Execution: More suitable when reliability, durability, and comprehensive run history are essential, even at the cost of some performance overhead. This is preferable for long-running processes or tasks involving large data volumes.
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