You’re running performance tests between Redis and Memcached, but the results aren’t making sense or your cache is behaving unexpectedly. This is a common headache when choosing between these two popular caching solutions, and getting accurate real-world performance data is crucial for making the right decision for your application.

Step-by-Step Fixes

Step 1: Reset Your Testing Environment

Start fresh by clearing both caches completely. For Redis, run:

“`bash

redis-cli FLUSHALL

“`

For Memcached, use:

“`bash

echo ‘flush_all’ | nc localhost 11211

“`

Wait at least 30 seconds before starting new tests. This ensures you’re not seeing skewed results from leftover data or memory fragmentation.

Step 2: Check Your Connection Pooling

Both Redis and Memcached perform differently based on how you handle connections. If you’re using a single connection for all operations, you’re bottlenecking your tests.

For Redis with Python, ensure you’re using connection pooling:

“`python

import redis

pool = redis.ConnectionPool(host=’localhost’, port=6379, db=0)

r = redis.Redis(connection_pool=pool)

“`

For Memcached with Python:

“`python

import pymemcache

from pymemcache.client.base import Client

client = Client((‘localhost’, 11211))

“`

Step 3: Verify Memory Allocation Settings

Redis and Memcached handle memory differently, which directly impacts performance. Check Redis memory with:

“`bash

redis-cli INFO memory

“`

Look for the “used_memory_human” value. For Memcached, connect via telnet:

“`bash

telnet localhost 11211

stats

“`

Check the “limit_maxbytes” value. If either cache is hitting memory limits during tests, your results will be wildly inaccurate.

Step 4: Run Isolated Benchmark Tests

Use dedicated benchmarking tools instead of custom scripts for initial testing. For Redis:

“`bash

redis-benchmark -t set,get -n 100000 -q

“`

For Memcached, install memtier_benchmark:

“`bash

memtier_benchmark -s localhost -p 11211 -P memcache_text –test-time=30

“`

These tools eliminate variables from your application code and give you baseline performance numbers.

Step 5: Monitor System Resources During Tests

Open a separate terminal and run:

“`bash

htop

“`

Watch CPU and memory usage during your tests. If either cache is maxing out CPU cores or if your system is swapping memory, your results won’t reflect real performance capabilities.

Step 6: Test with Realistic Data Sizes

Many performance comparisons fail because they use tiny test values. Create test data that matches your actual use case:

“`python

For 1KB values (typical for user sessions)

test_data = ‘x’ 1024

For 100KB values (typical for cached API responses)

large_data = ‘x’ 102400

“`

Test with various sizes since Redis and Memcached have different sweet spots for different data sizes.

Likely Causes

Cause #1: Network Latency Masking True Performance

If you’re testing Redis and Memcached on different servers or in containers with different network configurations, network latency can completely overshadow actual cache performance differences.

To check: Run both caches on the same machine as your test script. Use localhost connections only. If you must test over a network, use ping to ensure latency is identical:

“`bash

ping -c 100 redis-server-ip

ping -c 100 memcached-server-ip

“`

Look for consistent average times. Even a 1ms difference will significantly impact your results when running thousands of operations.

Cause #2: Persistence Settings Affecting Redis Performance

Redis offers persistence options that Memcached doesn’t have. If Redis is configured with aggressive persistence settings, it will appear much slower than Memcached in write-heavy tests.

Check your Redis persistence configuration:

“`bash

redis-cli CONFIG GET save

redis-cli CONFIG GET appendonly

“`

For fair performance comparison, disable persistence temporarily:

“`bash

redis-cli CONFIG SET save “”

redis-cli CONFIG SET appendonly no

“`

Remember to re-enable these settings after testing if you need persistence in production.

Cause #3: Serialization Overhead Differences

Your client libraries might be serializing data differently for Redis versus Memcached, creating an unfair comparison. Some Redis clients automatically serialize complex objects while Memcached clients might not.

Test with raw strings first:

“`python

Use simple strings for both

simple_test = “test_value_12345”

“`

Then test with pre-serialized data:

“`python

import json

complex_data = json.dumps({“user”: “test”, “data”: [1,2,3,4,5]})

“`

This ensures both caches are doing the same amount of work.

When to Call a Technician

Consider getting professional help if you’re seeing performance differences greater than 50% between Redis and Memcached for similar operations, especially if your application requires sub-millisecond response times. A DevOps specialist can help identify infrastructure-specific bottlenecks like kernel parameter tuning, NUMA node configuration, or network card settings that significantly impact cache performance.

Also seek help if you need to test more complex scenarios like Redis Cluster versus Memcached with consistent hashing, or if you’re planning to cache more than 100GB of data. These setups require expertise to configure and test properly.

Copy-Paste Prompt for AI Help

“I’m comparing Redis and Memcached performance for my web application in 2025. My test shows [Redis/Memcached] is [X]% faster for [operation type]. My setup: [your OS], [RAM amount], testing with [data size] objects, [number] operations per second. Redis version: [version], Memcached version: [version]. Using [programming language] with [client library]. Are these results realistic? What might I be missing in my benchmark methodology?”