How to Debug Cache Misses in Production

Your cache hit rate dropped from 95% to 60%. Users are complaining about slow pages. You're staring at Redis logs wondering what went wrong. Sound familiar?

Cache misses are inevitable, but unexpected cache misses are a problem. This guide walks you through a systematic approach to identifying and fixing cache miss issues in production.

Step 1: Measure What's Actually Happening

Before debugging, establish baseline metrics. You need to know:

• Hit rate by key pattern: Are all keys affected or just specific patterns?
• Miss rate over time: Did it spike suddenly or gradually increase?
• Eviction count: Is data being pushed out before it's accessed?
• Memory usage: Are you hitting memory limits?

# Redis: Check hit/miss stats
redis-cli INFO stats | grep keyspace

# Sample output:
# keyspace_hits:4521890
# keyspace_misses:892341
# Hit rate: 4521890 / (4521890 + 892341) = 83.5%

Step 2: Identify the Culprit Keys

Not all cache misses are created equal. Find which keys are missing most frequently:

// Add cache miss logging
async function cacheGet(key) {
    const value = await redis.get(key);
    if (!value) {
        metrics.increment('cache.miss', { key_pattern: extractPattern(key) });
        console.log(`Cache miss: ${key}`);
    }
    return value;
}

function extractPattern(key) {
    // user:12345 -> user:*
    return key.replace(/:\d+/g, ':*');
}

Common Cache Miss Causes

1. TTL Too Short

Data expires before it's accessed again. Check if access frequency exceeds TTL:

// If average time between accesses is 5 minutes,
// but TTL is 3 minutes, you'll miss every time
await cache.set(key, value, { ttl: 180 }); // 3 min - too short!
await cache.set(key, value, { ttl: 600 }); // 10 min - better

2. Memory Pressure / Evictions

When cache fills up, older items get evicted. Check eviction stats:

# Redis eviction check
redis-cli INFO stats | grep evicted_keys

# If evicted_keys is high, you need more memory
# or smarter eviction policies

3. Key Mismatch

The cache key used for writes differs from reads. This is surprisingly common:

// BUG: Different key formats
// Write:
await cache.set(`user:${user.id}`, user);

// Read (different format!):
await cache.get(`users:${userId}`);  // user vs users

4. Cache Warming Missing

After deploys or restarts, cache is cold. Popular data hasn't been loaded yet:

// Add cache warming on startup
async function warmCache() {
    const popularItems = await db.query(
        'SELECT * FROM products ORDER BY view_count DESC LIMIT 100'
    );

    for (const item of popularItems) {
        await cache.set(`product:${item.id}`, item, { ttl: 3600 });
    }
    console.log('Cache warmed with 100 popular products');
}

5. Serialization Errors

Data fails to serialize, so nothing is stored:

// This silently fails - circular reference
const user = { name: 'Alice' };
user.self = user;  // Circular!
await cache.set('user:1', user);  // Fails silently

// Add error handling
try {
    await cache.set(key, JSON.stringify(value));
} catch (e) {
    console.error(`Failed to cache ${key}:`, e.message);
}

Debugging Checklist

1. Check memory usage: Are you at max capacity?
2. Check eviction policy: Is LRU evicting hot data?
3. Verify key consistency: Are read/write keys identical?
4. Review TTL settings: Are they appropriate for access patterns?
5. Check for errors: Are SET operations failing silently?
6. Monitor after deploys: Does hit rate drop after releases?

Prevention Strategies

Stop cache misses before they happen:

• Implement cache warming: Pre-load popular data on startup
• Use predictive caching: ML models can predict what data will be needed
• Set up alerting: Get notified when hit rate drops below threshold
• Test cache behavior: Include cache scenarios in integration tests