Cache in front of a slow database ?

 

Should You Front a Slow Database with a Cache?

Most of us have been there: a slow database query is dragging down response times, dashboards are red, and someone says, “Let’s put Redis in front of it.”

I have done it myself for an advertising system that needed response times of less than 30 ms. It worked very well.

It’s a tried-and-true trick. Caching can take a query that costs hundreds of milliseconds and make it return in single-digit milliseconds. It reduces load on your database and makes your system feel “snappy.” But caching isn’t free — it introduces its own problems that engineers need to be very deliberate about.

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Good Use Cases for Caching

• Read-heavy workloads
  When the same data is read far more often than it’s written. For example, product catalogs, user profiles, or static metadata.

• Expensive computations
  Search queries, aggregated analytics, or personalized recommendations where computing results on the fly is costly.

• Burst traffic
  Handling sudden spikes (sales events, sports highlights, viral posts) where the database alone cannot keep up.

• Low latency requirements
  Some systems have low latency requirements. Clients need a response is say less than 50 ms or client aborts.

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The Catch: Cache Consistency

The hardest part of caching isn’t adding Redis or Memcached — it’s keeping the cache in sync with the database.

Here are the main consistency issues you’ll face:

1. Stale Data
   If the cache isn’t updated when the database changes, users may see outdated results.
   Example: A user updates their shipping address, but the checkout flow still shows the old one because it’s cached.

2. Cache Invalidation
   The classic hard problem: When do you expire cache entries? Too soon → database load spikes. Too late → users see stale values.

3. Race Conditions
   Writes may hit the database while another process is still serving old cache data. Without careful ordering, you risk “losing” updates.

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Common Strategies

• Cache Aside (Lazy Loading)
  Application checks cache → if miss, fetch from DB → populate cache.
  ✅ Simple, common.
  ❌ Risk of stale data unless you also invalidate on updates.

• Write-Through
  Writes always go through the cache → cache updates DB.
  ✅ Consistency is better.
  ❌ Higher write latency, more complexity.

• Write-Behind
  Writes update the cache, and DB updates happen asynchronously.
  ✅ Fast writes.
  ❌ Risk of data loss if cache fails before DB is updated.

• Time-to-Live (TTL)
  Expire cache entries after a set period.
  ✅ Easy safety net.
  ❌ Not precise; stale reads possible until expiry.

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So, Is It Worth It?

If your workload is read-heavy, latency-sensitive, and relatively tolerant of eventual consistency, caching is usually a big win.

But if your workload is write-heavy or requires strict consistency (think payments, inventory, or medical records), caching can create more problems than it solves.

The lesson: don’t add Redis or Memcached just because they’re shiny tools. Add them because you’ve carefully measured your system, know where the bottleneck is, and can live with the consistency trade-offs.

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Takeaway:
Caching is like nitrous oxide for your system — it can make things blazing fast, but you need to handle it with care or you’ll blow the engine.