API Caching & Processing Optimization
Improving API performance by reusing previously generated results when fresh processing is not required.
What I delivered
- → Implemented caching for expensive processing results in a commercial backend system
- → Added logic to determine when an existing result could be safely reused
- → Prevented unnecessary repeated processing when meaningful input had not changed
- → Preserved fresh processing whenever cached data was unavailable or no longer valid
- → Kept caching concerns separated from core business logic
- → Built a separate public .NET project demonstrating related multi-layer caching principles
This work combines real commercial experience with a separate public implementation.
In the commercial system, I implemented caching around an API workflow that produced reusable results through a more expensive processing path. The goal was to avoid repeating the same work when an existing result was still valid.
To demonstrate related backend principles publicly without exposing private code or business logic, I later built a standalone .NET API using memory cache, Redis, and PostgreSQL.
The problem
Some API operations depended on a multi-step processing workflow.
Running the complete workflow for every request created unnecessary work when the underlying data affecting the result had not meaningfully changed.
At the same time, the system could not simply reuse every previous response. It needed to distinguish between cases where an existing result remained valid and cases where fresh processing was required.
Solution
I implemented input-aware result caching for the commercial API workflow.
Previously generated results could be reused when the data affecting the outcome remained unchanged. When meaningful input changed, when no reusable result was available, or when the cached value could no longer be considered valid, the system continued through the normal processing flow.
The caching logic was kept separate from the core business logic so that the workflow remained maintainable and the processing path stayed clear.
A separate public project demonstrates the broader caching approach through a layered read flow:
How it fits together
The commercial implementation introduced a cache decision before the more expensive processing stage. The public demonstration extends the idea into a layered hierarchy.
The public project demonstrates related architectural principles but does not reproduce the commercial system or its proprietary workflow.
Key features
Technical challenges
The main challenge was defining when a previously generated result was still valid.
A cache hit could not be based only on the presence of an existing value. The system also needed to account for whether the data affecting the result had meaningfully changed.
The implementation had to avoid two opposite problems:
- ◆repeating expensive processing when the previous result was still valid;
- ◆returning stale data when fresh processing was required.
Another important consideration was keeping the caching logic isolated so that it did not spread through the business workflow or make the application tightly dependent on a specific cache implementation.
Outcome
The commercial implementation reduced unnecessary repeated processing and created a faster path for requests that could safely reuse an existing result.
It also preserved the original processing flow for cases where a new result was required.
The accompanying public project provides inspectable code and a reproducible environment demonstrating related caching, data-access, and separation-of-concerns principles without exposing commercial code, internal services, or proprietary business logic.
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