pipeline-context

To optimize a multi-stage processing pipeline, pre-compute expensive operations at the entry point and share them via a context object, preventing redundant calculations across subsequent stages.

The best way to share data across middleware chains is by defining a shared context object that stores pre-computed values, ensuring each stage accesses the same data without redundant processing.

You maintain context integrity by enforcing immutability discipline, making the shared context object read-only for pipeline stages, and defining strict stage contracts for dependency validation.

Yes, the pipeline context approach works for request lifecycles and build systems. It manages context creation, propagation, and disposal to ensure efficient data flow across various processing pipelines.

You should use a pre-computation strategy when multiple pipeline stages need the same expensive operation, such as tokenizing a query in an NLP pipeline, allowing you to compute once and share everywhere.

Using a shared context object requires careful context lifecycle management and strict stage contracts. Limitations include the overhead of designing immutable contexts and ensuring stages validate dependencies correctly.