We conduct rigorous stress tests on our application in our performance lab and certify the release. Despite our sincere efforts, still performance problems surface in the production environment. In this post, we intend to discuss the primary reasons for that happening and how to address them.
Performance Lab Challenges
Here are the primary performance lab challenges we face today:
1. Production & Performance Environment Not Identical
In most organizations, the performance testing environment rarely mirrors the production setup. Differences in hardware configurations, JVM memory settings, thread pool configurations, connection limits, or even OS patches can introduce significant deviations in behavior. A test environment might have relaxed timeout values, fewer backend dependencies, or disabled security layers, making it an inaccurate stand-in for production.
These mismatches create blind spots. You may tune your application for one environment, only to see it behave unpredictably in another. Unless the environment is aligned closely with production, your test results will give you a false sense of confidence.
2. Synthetic Data
In the performance lab we use synthetic data, which is not representative of production data. I used to work for a very large bank. A small fraction of customers has an unusually large number of accounts (like 250+ accounts), while the average customer has only 2-3 accounts. In our performance labs, we didn’t have test data to model such unusual customers’ behavior. However, when such an unusual customer uses the application, bottlenecks such as insufficient threads, inadequate database connections get exposed.
Besides data modelling, it’s quite hard to mimic the production traffic’s bursts & low tide traffic volume in the performance lab. Traffic volume mix and pattern that comes to the application also plays a vital role in influencing application’s performance, which is hard to model in the performance lab.
3. Lack of Long Running Tests
Several performance problems build up over the period of time. Say if your application is suffering from acute memory leak (i.e. small objects get accumulated in the memory). It would take several days (sometimes even weeks) for this acute memory leak to result in ‘OutOfMemoryError‘. However, in the performance lab, we don’t execute such long running endurance tests. In several organizations performance tests are conducted for a span of 1 hour. In a few organizations endurance tests are conducted for 1 day. Such tests aren’t adequate enough to catch the problems that need time to build up.
4. Absence of Real-World Chaos
In the real world, unexpected chaos can happen in a production environment such as backend slow down, network hiccups, Load Balancer Misrouting, pushing wrong settings by DevOps Engineers, incorrect kernel patches applied to the servers… However in our performance labs, we don’t simulate such real-world chaos. We tend to test the happy path scenario, which is not the case in the production environment.
5. Disconnected Engineering Roles
Often, performance testing is siloed. The engineer writing the test doesn’t own the architecture, and the person debugging production doesn’t own the test. This disconnect causes blind spots between what’s simulated and what actually matters.
How to catch Performance Problems during Testing Phase?
Challenges mentioned in the above section are ‘real’ and hard to address. However with good team effort, proper discipline and effective strategies outlined below we can improve chances of detecting performance issues before they hit production.
1. Certifying the Release based on Micro-Metrics
Today we are certifying our release based on the Macro-Metrics such as Response time, CPU and Memory. These are wonderful metrics, which we should continue to monitor in the performance labs. However, we should also start certifying releases based on the Micro-Metrics such as Garbage Collection Behavior, Object Creation Rate, GC Throughput, GC Pause Time, Thread Patterns, …
You may find this blog useful: Micro-Metrics Every Performance Engineer should validate before Sign-off.
By validating these Micro-Metrics in the performance lab, you gain the ability to forecast issues before they become customer-facing problems. For example, if your application is suffering from acute memory leak, you will start to see the increase in the GC behavior pattern & drop in the GC Throughput even in a 1-hour test. Similarly, if there is a DB connection leak, you will start to see the spike in the TCP/IP connection count – these issues will not show up in your macro KPIs.
This strategy doesn’t require a replica of your production environment, either. Even with non-identical setups or synthetic test data, Micro-Metrics help expose how your application internally handles load, resource pressure, and traffic patterns.
2. Chaos-Engineering
Chaos engineering helps you expose weaknesses in your system by intentionally injecting failure scenarios such as:
- Killing backend nodes mid-transaction
- Introducing latency in DB or network calls
- Restarting load balancers
- Changing thread or memory limits at runtime
This doesn’t have to be a massive undertaking. You can start with simple chaos experiments in your lower environments using tools like Chaos Monkey, BuggyApp or your own controlled scripts.
By practicing controlled failure in your test environment, you increase your system’s ability to withstand uncontrolled failure in production. It also helps validate whether your app is failing gracefully or falling apart under stress.
3. Record & Replay Production Traffic
One of the most effective ways to simulate realistic loads is to record traffic from production and replay it in your test environment. This gives you:
- Accurate request mix
- Real session behavior
- True concurrency patterns
- Edge case data that might be hard to think of manually
Tools like traffic sniffers, API gateways, or proxy recorders can help you collect request payloads and replay them during tests. The closer you get to production-like traffic patterns, the higher your chances of catching real-world performance issues in advance.
Final Thoughts
Performance issues slipping through the cracks is not a result of negligence, it’s often a reflection of real-world complexity. The gaps between production and lab environments, unrealistic test data, short test durations, lack of chaos simulation, and disconnected engineering workflows all contribute to why “green in testing” turns into “red in production.”
It could be hard to implement all three strategies at once. Chaos engineering and traffic replay may need time, tooling, and management buy-in. But starting with Micro-Metrics Certification is simple and highly impactful. It brings deep visibility with minimal effort and helps you catch the silent degradations. It doesn’t require new infrastructure or major buy-in. You can do it today with the logs and test runs you already have.
Share your Thoughts!