What Is Object Caching? 7 Proven Server Performance Metrics

Object caching saves data that your app would otherwise have to fetch or calculate again. It can reduce repeated database work and help pages respond faster. Measure the change before buying a larger server.

What is object caching?

An object cache keeps reusable data in fast memory, or RAM. This often includes database query results. Your app looks for a saved result before asking the database again.

• A cache hit means the saved data is available.
• A cache miss means the app must fetch or rebuild the data.
• A time to live (TTL) sets how long a cached item may remain valid.

A cache is not the main source of your data. Refresh or remove saved items when the underlying data changes.

How the cache works

1. Your app needs a value, such as a product record.
2. It checks the cache using a key that identifies the value.
3. On a hit, it reads the saved value.
4. On a miss, it queries the database and may save the result for later.

App requests data → Check the cache

Cache hit

Read the saved value from memory.

Cache miss

Query the database, then cache the result.

Keys and values

A key names a saved item. The value is its data. Memory access is fast, but total response time also depends on your app, network, and database.

WordPress and transients

WordPress normally stores transients in the database. A persistent object cache can store them outside the database instead. See the WordPress Transients guide for the details.

Redis or Memcached?

Both tools can keep reusable data in memory. Choose the one your app supports and your team can manage.

• Memcached is a memory cache with a simple key-value model. A restart clears its cached data, so the app must rebuild it.
• Redis supports data types such as lists, sets, and hashes. It also offers optional disk persistence. Saved data can help after a restart, but it does not guarantee uninterrupted service.

Set up the cache carefully

Connection and memory limits

Connect your app to the cache using a supported integration. A local Unix socket can be an option when both run on the same host. Test it against TCP in your own setup.

Set a memory limit that leaves room for the operating system and other services. Keep your cache private and allow access only to the apps that need it.

Decide what to remove when memory fills up

This is called an eviction policy. Redis offers several choices:

• allkeys-lru removes keys based on how recently they were used. It can suit a cache-only instance.
• volatile-lru considers only keys with an expiry time. It cannot free space this way if no such keys exist.

Choose a policy that fits your data. Do not apply a cache-only policy blindly to an instance that also holds sessions or other important data. Read the Redis eviction guide.

Seven metrics to measure

Compare the same workload before and after enabling the cache. Use similar traffic, test duration, and server resources. A cache that is still filling up can give different results from a warm cache.

1. Time to first byte (TTFB). How long a visitor waits for the first response byte. Compare typical results and slower requests.
2. Database queries per request. How many queries the app runs. Fewer repeated reads can show that the cache is helping.
3. Database query time. How long those queries take. Check whether costly reads are happening less often.
4. Database CPU use. How much processing the database needs under the same load. Track changes rather than assuming a fixed saving.
5. Cache hit rate. The share of lookups that find a saved value. Calculate hits divided by hits plus misses, then multiply by 100.
6. Cache memory use. How much memory cached data needs. Compare it with your memory limit and the host's available RAM.
7. Evictions and expired keys. How often entries leave the cache. Frequent removals may point to memory pressure or a TTL that is too short.

There is no single hit-rate target that fits every app. Read the counters over a useful time window and compare them with your workload.

For Redis, these read-only commands provide useful counters:

redis-cli INFO stats
redis-cli INFO memory

MONITOR streams commands and can add significant overhead. Use it only for a brief, controlled investigation. The Redis MONITOR documentation explains its cost.

Plan for restarts and growth

Failover with Redis Sentinel

Sentinel can monitor Redis servers and promote a replica if the primary fails. Your app needs a client that supports Sentinel. Failover can still cause a brief interruption, and replication does not remove all data-loss risks.

Split larger data sets across nodes

Redis Cluster distributes keys across nodes using hash slots. It can help when a single server is too small. Learn more about scaling the data layer. Measure latency as you grow; adding nodes does not guarantee a fixed response time.

Common questions

Does object caching reduce CPU use?

It can reduce database CPU use by avoiding repeated work. The benefit depends on what your app caches and how often it reuses that data.

Can I use it with shared hosting?

Check whether your plan includes a supported object-cache integration. Some shared plans restrict custom server software. A VPS gives you more control over your cache setup.

How do I clear a Redis cache?

Prefer your app's cache-clear tool when available. redis-cli FLUSHALL deletes every key in every database on that Redis instance. It can remove sessions or other data too. Use it only when you know the instance holds disposable cache data.

Your next steps

• Find the repeated database work first.
• Choose a cache your app supports.
• Set sensible memory, expiry, and refresh rules.
• Compare the seven metrics under the same workload.
• Plan how the app will behave if the cache is unavailable.

Start with measured results. Improve the cache setup before assuming that more server resources will fix a slow app.