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Optimizing Platform Performance with Redis & TTL-Based Caching

· 2 min read
samundrak
JavaScript Dev

Overview​

As election and political data expanded, performance bottlenecks became visible—especially on pages rendering historical trends and analytics. I adopted Redis-based caching with time-based expiry to ensure:

  • Faster load times
  • Reduced DB load
  • Controlled freshness of data

This post details my approach using NestJS, Redis, and TTL strategies.

Problem Areas​

Pages that triggered expensive queries:

  • Party trends over decades
  • Leader performance timelines
  • Comparative election analytics

Each required multiple joins, aggregations, and normalization logic.

Redis Caching Layer​

I introduced Redis with a helper service:

async getOrSetCache<T>(key: string, ttl: number, cb: () => Promise<T>): Promise<T> {
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached);
  const fresh = await cb();
  redis.setex(key, ttl, JSON.stringify(fresh));
  return fresh;
}

Key Strategy​

Keys are designed per entity, per variant:

  • party:25:trends
  • leader:102:performance
  • election:1991:comparisons

This ensures data is scoped tightly and easy to purge.

TTL Expiry​

Each dataset has a tailored TTL (time-to-live):

  • Static historical data: 24h
  • Popular charts: 6h
  • Admin dashboards: 2m

This avoids staleness while reducing query load.

Invalidation Strategy​

For dynamic data (e.g. poll responses, comments), I use:

redis.del('poll:approval:25')

Upon any write, specific keys are flushed so the next read triggers a fresh load.

NestJS Service Integration​

All public services inject the caching helper. Example:

@Get('/party/:id/trends')
getTrends(@Param('id') id: string) {
  return this.cacheService.getOrSetCache(`party:${id}:trends`, 86400, () => this.service.getPartyTrends(id));
}

Monitoring​

I used Redis CLI and a small logging middleware to monitor hit/miss ratio. Future plans include Prometheus integration.

Summary​

By caching computed analytics with Redis + TTL:

  • Pages load faster
  • Data stays fresh enough
  • Infrastructure handles scale better

Next, I’ll show how I built a flexible, tag-driven metadata system for mapping ministries, party types, cabinet roles, and election categories.