Templates

## Implemented Redis Caching Layer for API Performance

Built a distributed caching system using Redis to reduce database load 
and improve API response times. Implemented cache invalidation strategy 
with TTL-based expiration and event-driven updates.

Added monitoring and alerting for cache hit rates and memory usage. 
Designed fallback logic to gracefully handle cache failures without 
impacting user experience.

**Impact:** Reduced average API response time from 450ms to 85ms (81% 
improvement). Decreased database load by 60%, enabling support for 5x 
more concurrent users.

**Technologies:** Python, Redis, FastAPI, Prometheus

## Sprint 14 Summary (Jan 1-15, 2026)

### Added
- Redis caching layer with distributed invalidation
- Cache hit rate monitoring dashboard
- Automatic fallback to database on cache miss
- TTL-based cache expiration (configurable per endpoint)
- Health check endpoint for cache connectivity

### Fixed
- Memory leak in WebSocket connections (issue #342)
- Race condition in session creation during high load
- Incorrect cache key generation for parameterized queries

### Changed
- Migrated from in-memory cache to Redis cluster
- Updated API response format to include cache metadata
- Increased default cache TTL from 5 minutes to 15 minutes

**Stats:**
- 23 commits
- 15 files changed
- +1,200 / -450 lines

## How We Cut API Response Time by 81%

Our API was slow. 450ms average response time doesn't sound terrible, 
but when you're making 10+ API calls to render a single page, users 
notice. We were getting complaints.

The root cause was obvious once we looked: every request hit the 
database, even for data that rarely changed. User profiles, settings, 
feature flags—all fetched fresh every time. We were doing 1000 database 
queries per second at peak load.

We needed caching, but not just any cache. In-memory wouldn't work 
across multiple servers. We needed distributed caching with proper 
invalidation.

Enter Redis. We built a caching layer that sits between the API and 
the database. For cacheable endpoints, we check Redis first. Hit? 
Return immediately (5-10ms). Miss? Fetch from database, populate cache, 
return result.

The tricky part was invalidation. We couldn't just set TTLs and hope 
for the best—stale data would break things. We implemented event-driven 
invalidation: when data changes (user updates profile, admin changes 
settings), we publish an event that invalidates the relevant cache keys 
across all servers.

We also added fallback logic. If Redis goes down, the app degrades 
gracefully—it just hits the database directly, like before. No cache 
shouldn't mean no service.

The results were dramatic. Response times dropped from 450ms to 85ms. 
Database load fell by 60%. We handled Black Friday traffic (5x normal) 
without adding servers.

But more importantly, we learned: measure first, cache second. We spent 
a day instrumenting the app with detailed metrics before writing a 
single line of cache code. That data guided every decision.

**Technologies:** Python, Redis, FastAPI, Prometheus, OpenTelemetry
**Timeline:** 3 weeks (Dec 15 - Jan 5)

## Improved API Performance Under High Load

**Situation:** Our API response times were averaging 450ms, causing 
poor user experience especially during peak hours (9am-12pm EST). 
Multiple customers had complained, and our NPS score dropped from 42 
to 35. The issue was particularly acute during our seasonal traffic 
spikes, when we'd see 5-10x normal load.

**Task:** I needed to reduce API response times by at least 50% without 
increasing infrastructure costs, and do it before our Black Friday 
sale (6 weeks away). The solution had to scale horizontally and degrade 
gracefully if components failed.

**Action:** I led the implementation of a distributed Redis caching 
layer. First, I instrumented the API with detailed tracing to identify 
which queries were slow and which data was being fetched repeatedly. 
This analysis showed that 60% of queries were for data that changed 
less than once per day.

I designed a caching strategy with three tiers: hot data (5min TTL), 
warm data (1hr TTL), and cold data (24hr TTL). I implemented event-
driven cache invalidation so updates propagate immediately, preventing 
stale data. I also built comprehensive monitoring (cache hit rates, 
memory usage, latency) and automated alerting.

To ensure reliability, I added fallback logic that bypasses cache if 
Redis is unavailable, and circuit breakers that prevent cascade 
failures.

**Result:** API response times dropped from 450ms to 85ms (81% 
improvement). Database load decreased by 60%. We handled Black Friday 
at 5x normal traffic with zero infrastructure changes and zero 
performance degradation.

Our NPS score recovered to 44 (9-point improvement), and we got 
positive feedback specifically mentioning "the app feels much faster." 
The monitoring system I built caught two other performance issues in 
the following months, preventing outages before they happened.

**Technologies:** Python, Redis, FastAPI, Prometheus, OpenTelemetry  
**Timeline:** 4 weeks solo work, 2 weeks with team for production rollout  
**Team:** Worked independently on design/implementation, collaborated 
with DevOps for deployment

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repr generate --template resume --since "6 months ago" --local
repr profile export --format md > performance-review.md

repr generate --template interview --local
repr stories  # Review and pick your top 5-8 stories
repr profile export --format md > interview-prep.md

repr commits --days 14
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# Focus on specific aspects
repr generate --template interview --prompt "Emphasize leadership and cross-team collaboration"

# Change the voice
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