Caching Strategies for Astrology APIs

What to Cache (and What Not To)

Birth charts are deterministic — same input always produces same output. Cache aggressively with TTL of 30 days. Daily horoscopes change daily — cache for 24 hours. Transit data changes continuously — cache for 1-4 hours. AI chat responses should never be cached (personalized, conversational).

Caching Architecture

// Redis caching layer for Vedika API calls
const Redis = require('ioredis');
const crypto = require('crypto');
const redis = new Redis(process.env.REDIS_URL);

async function cachedApiCall(endpoint, body, ttlSeconds) {
  // Generate cache key from endpoint + body hash
  const hash = crypto.createHash('sha256')
    .update(endpoint + JSON.stringify(body))
    .digest('hex');
  const cacheKey = `vedika:${hash}`;

  // Check cache
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);

  // Call API
  const result = await callVedikaApi(endpoint, body);

  // Store with TTL
  await redis.setex(cacheKey, ttlSeconds, JSON.stringify(result));
  return result;
}

// Usage with appropriate TTLs
const birthChart = await cachedApiCall(
  '/v2/astrology/birth-chart', birthDetails, 86400 * 30 // 30 days
);
const horoscope = await cachedApiCall(
  '/v2/western/horoscope/aries', {}, 86400 // 24 hours
);

With proper caching, you can reduce Vedika API calls by 70-90% for typical astrology apps. A birth chart is generated once and reused for all subsequent analysis. Daily horoscopes are shared across all users of the same sign.

Cost Impact

Without caching: 10,000 MAU × 5 API calls/user/day = 50,000 calls/day = Business plan needed. With caching: 10,000 MAU × 0.5 unique API calls/user/day = 5,000 calls/day = Starter plan sufficient. Annual savings: ~$1,296.