This project has a hard contract: every run is exactly one hour. That constraint is what keeps results comparable across configurations and makes exported visualisation consistent.

After I fixed shutdown reliability, the next issue wasn't infrastructure. It was methodology.

The discovery: the "happy path" trap

Reviewing telemetry from a standard run, I noticed that my default load generation (single ECS task) was not reliably pushing Redis into the state I actually care about.

Memory usage was climbing, but in many cases the run could finish without reaching maxmemory. That means the test is still useful as a smoke check, but it's not a strong performance validation: it mostly measures a cache that's not under pressure.

AWS ElastiCache Lab project has a hard rule: a test run is defined as one hour. That only stays true if the lab reliably shuts down on schedule. If it doesn't, I lose cost control and-more importantly for benchmarking-I risk starting the next run from a non-clean baseline.

I hit exactly that problem on an evening run.

What I observed

The run finished, but the environment was still up. Nothing looked "broken" in the usual sense: services were alive and responsive. In this lab, though, "still works" past the run boundary is a defect, because it means the lifecycle automation failed silently.

Docs Confidence is that warm, fuzzy feeling you get after reading AWS whitepapers - right before your cache hits 99% memory, your p99 latency grows a tail, and your assumptions start to melt. It's not incompetence, it's the gap between documented behavior and observed behavior under your specific workload.

I built this repeatable ElastiCache benchmarking platform to close that gap with receipts: timestamped telemetry and exportable artifacts that stand up in a design review. This lab isn't just about Redis (or Valkey) as software, it's about the architectural decisions that land in production: which engine, which instance class, and which topology delivers the best outcome for the budget.

Up to to day PhpFilesAdapter stores compiled PHP files that OPcache reads fast. No services. Atomic writes. Good default for single-host pages and API fragments.

Install:

composer require symfony/cache

Usage Example:

<?php
use Symfony\Component\Cache\Adapter\PhpFilesAdapter;
use Symfony\Contracts\Cache\ItemInterface;
 
require __DIR__ . '/vendor/autoload.php';
 
$cacheDir = __DIR__ . '/../var/cache'; // place outside web root
$cache = new PhpFilesAdapter('myapp', 0, $cacheDir);
 
$key = 'page:home:v1';
 
$html = $cache->get($key, function (ItemInterface $item) {
  $item->expiresAfter(900); // 15 minutes
 
  return render_home();
});
 
echo $html;
 

Resume:

- Files only, very fast with OPcache.
- Atomic writes via callback; simple stampede control.
- Per-host cache. Clear by versioning keys or deleting files.

Picture your favorite coffee shop, where the barista knows your order and hands it to you right away. Caching in finance works similarly, ensuring systems run efficiently and fast in both B2C and B2B scenarios. In this post, we'll demystify caching through relatable examples and explore mechanisms like CDN cache, application-level cache, and built-in database caches. Discover how caching powers seamless financial experiences, all while keeping things simple for non-technical readers.