AWS ElastiCache

Amazon ElastiCache is a managed in-memory data store. It keeps frequently used data in fast memory so applications skip slow trips to a database. Web pages load quicker, and databases handle fewer requests.

Imagine a chef who keeps salt, oil, and spices on the counter instead of walking to the storeroom for every dish. The counter is the cache. The storeroom is the database. Ingredients used often stay within arm's reach.

Why Caching Matters

Databases read data from disks, and disks respond in milliseconds. Memory responds in microseconds. A product page viewed ten thousand times an hour does not need ten thousand database queries. One query fills the cache, and the cache serves the rest.

• Pages respond faster.
• Databases face less pressure.
• Applications handle sudden traffic spikes with fewer servers.
• Database costs drop because smaller instances cope with lighter loads.

Supported Engines

ElastiCache runs open-source engines that developers already know:

• Redis OSS and Valkey offer rich data types, persistence options, replication, and failover.
• Memcached offers a simple key-value cache with multi-threaded speed.

Redis and Memcached Compared

+------------------+--------------------+--------------------+
| Feature          | Redis / Valkey     | Memcached          |
+------------------+--------------------+--------------------+
| Data types       | Strings, lists,    | Strings only       |
|                  | sets, hashes       |                    |
| Replication      | Yes                | No                 |
| Automatic        | Yes                | No                 |
| failover         |                    |                    |
| Backup option    | Yes                | No                 |
| Best for         | Sessions, ranking  | Simple, throwaway  |
|                  | boards, queues     | cache              |
+------------------+--------------------+--------------------+

The Cache-Aside Pattern

Most applications use the cache-aside pattern. The app checks the cache first. A cache hit returns the answer immediately. A cache miss sends the app to the database, which returns the answer, and the app stores a copy in the cache for the next visitor.

User requests product page
          |
          v
   Application checks cache
      /            \
   HIT              MISS
    |                 |
Return data     Query database
 instantly            |
                Save result in cache
                      |
                Return data to user

A Shopping Site Example

A store lists its top ten bestselling items on the home page. Calculating the list needs a heavy database query. The application runs the query once, saves the result in ElastiCache for five minutes, and serves every visitor from memory. After five minutes the entry expires, and the next visitor triggers a fresh calculation.

Time to Live and Eviction

Time to Live (TTL) sets how long an item stays in the cache. A short TTL keeps data fresh. A long TTL saves more database work. Choose a value based on how often the underlying data changes.

Memory is finite. When the cache fills, the engine removes items according to an eviction policy. A common policy removes the least recently used items first.

Popular Use Cases

• Session storage for login states across several web servers
• Leaderboards in games that rank players in real time
• Caching results of expensive database queries
• Rate limiting, which counts requests per user per minute
• Short-lived tokens and temporary codes

Architecture Inside AWS

            Internet
               |
         Load Balancer
               |
      +--------+--------+
      |                 |
  Web Server        Web Server
      |                 |
      +--------+--------+
               |
     +---------+---------+
     |                   |
 ElastiCache          RDS Database
 (Private subnet)    (Private subnet)

ElastiCache nodes live inside your VPC. Place them in private subnets and allow access only from your application servers through security groups. Public access is not part of the standard design.

Cluster Basics

• A node is a single cache server.
• A cluster groups nodes together.
• A replica copies data from a primary node and takes over if the primary fails.
• Sharding splits data across several nodes so the cache grows beyond one machine.

Best Practices

• Cache data that is read often and changes rarely.
• Set a TTL on every key so stale data disappears.
• Encrypt data in transit and at rest.
• Watch the cache hit rate in CloudWatch. A low hit rate signals poor key design or a TTL that is too short.
• Treat the cache as disposable. The database remains the source of truth.

Common Mistakes

• Caching data that changes every second
• Storing the only copy of important data in the cache
• Opening the cache to the internet

A well-designed cache turns a slow application into a responsive one with surprisingly little code.