7 Infrastructure Caching and Performance Improvements

Abstract geometric gradient illustration

1. CDN Caching for Suitable Public Content

Cache only responses appropriate for shared caching, with keys and bypass rules that reflect the request context.

2. Reverse Proxy Microcaching

This technique absorbs repeated traffic patterns during peak times, without affecting freshness.

3. Review Internal Traffic Paths

Evaluate private paths where appropriate, including their network and security implications.

4. Database Read Routing and Query Caching

Aurora performs well but can become a bottleneck under heavy read traffic, risking potential downtime. By implementing ProxySQL, we:

Together, these moves offloaded Aurora's writer node and reduced replica count.

5. Redis as a Shared Cache

Redis is often used for sessions or queues, but we turned it into a smart shared cache layer. Without touching the app, we cached:

These were pre-warmed and managed via background workers, not app logic.

6. MySQL Index Review

We did a targeted audit and restructured a few tables for high-frequency queries.

7. Observe and Tune

Armed with cache hit ratios and traffic patterns, we adjusted TTLs, fine-tuned rules, and scaled with precision.

Measure the Combined Effect

7 Infrastructure Caching and Performance Improvements

1. CDN Caching for Suitable Public Content

Cache only responses appropriate for shared caching, with keys and bypass rules that reflect the request context.

2. Reverse Proxy Microcaching

This technique absorbs repeated traffic patterns during peak times, without affecting freshness.

3. Review Internal Traffic Paths

Evaluate private paths where appropriate, including their network and security implications.

4. Database Read Routing and Query Caching

Aurora performs well but can become a bottleneck under heavy read traffic, risking potential downtime. By implementing ProxySQL, we:

Together, these moves offloaded Aurora's writer node and reduced replica count.

5. Redis as a Shared Cache

Redis is often used for sessions or queues, but we turned it into a smart shared cache layer. Without touching the app, we cached:

These were pre-warmed and managed via background workers, not app logic.

6. MySQL Index Review

We did a targeted audit and restructured a few tables for high-frequency queries.

7. Observe and Tune

Armed with cache hit ratios and traffic patterns, we adjusted TTLs, fine-tuned rules, and scaled with precision.

Measure the Combined Effect

Key takeaways

Evaluate caching and performance changes against actual traffic and data freshness requirements.
Never serve personalized responses from a shared cache without correct isolation.
Measure latency, load, invalidation behavior and cost before and after each change.

FAQ

Can every response be cached at the edge?

No. Private or personalized responses require careful cache rules and isolation, and some should bypass shared caches entirely.

Are read replicas appropriate for every query?

No. Replica lag and read-after-write requirements mean some reads should remain on the writer.

Do infrastructure changes always require no application changes?

No. Some cache layers can be configured independently, but others require application integration or changes to data access patterns.

7 Infrastructure Caching and Performance Improvements

7 Infrastructure Caching and Performance Improvements

This technique absorbs repeated traffic patterns during peak times, without affecting freshness.

Aurora performs well but can become a bottleneck under heavy read traffic, risking potential downtime. By implementing ProxySQL, we:

Together, these moves offloaded Aurora's writer node and reduced replica count.

Redis is often used for sessions or queues, but we turned it into a smart shared cache layer. Without touching the app, we cached:

These were pre-warmed and managed via background workers, not app logic.

We did a targeted audit and restructured a few tables for high-frequency queries.

Armed with cache hit ratios and traffic patterns, we adjusted TTLs, fine-tuned rules, and scaled with precision.

This technique absorbs repeated traffic patterns during peak times, without affecting freshness.

Aurora performs well but can become a bottleneck under heavy read traffic, risking potential downtime. By implementing ProxySQL, we:

Together, these moves offloaded Aurora's writer node and reduced replica count.

Redis is often used for sessions or queues, but we turned it into a smart shared cache layer. Without touching the app, we cached:

These were pre-warmed and managed via background workers, not app logic.

We did a targeted audit and restructured a few tables for high-frequency queries.

Armed with cache hit ratios and traffic patterns, we adjusted TTLs, fine-tuned rules, and scaled with precision.

Further reading

Key takeaways

FAQ

Further reading

Key takeaways

FAQ