I once designed a service to handle hundreds of thousands of requests per second with tight latency requirements. Customers would notice any problems in the service. How would I know if the service would work as designed at peak traffic? I never built something that would actually need to handle that kind of scale.

We simulated the projected traffic to the service to check if it could meet the requirements, a process known as load testing. We built the first version of the service, prepared a comprehensive load test, alerted everyone that we were going to run it, and started the test.

We dialed up the load testing traffic. 5% of projected traffic… no issue. 10%… 15%… still good. At 20%, however, response times skyrocketed. Errors increased. The service fell over. We pulled the plug on the test and evaluated what to do next.

This can sound like a nightmare scenario. We only hit 1/5 of the required traffic before it died? We built and ran the load test to tell us just this kind of information! If the service can’t sustain its required load, I’d rather find out under a controlled circumstance than when customers go to use it and can’t.

There’s a line I think about from The Zen of Python:

In the face of ambiguity, refuse the temptation to guess.

Load testing helps us reduce ambiguity and avoid guessing if the service will be able to meet ours and our customers’ expectations. I’d like to share what we learned along the way while load testing and optimizing that service.

Working Backwards from the Customer

One lesson I’ve taken Amazon is to work backwards from the customer. In this case, we run load tests because we want to make sure the service is ready for customer traffic at scale. So what level of traffic do we need to support?

Perhaps we want to ensure our service is ready for a big initial launch, a high-profile event such as a big event or holiday sales spike, or large-scale data backfill. In any case, we can’t know that our service will work without testing it.

We can consider what customer access patterns might look like for the service. We can then make some assumptions about the high traffic event and model traffic on a per-service and even per-API basis.

A Sample Application

For this post, we’ll consider load testing a new service we’re adding to a pre-existing food ordering website. This service, which we’ll call MenuService, provides menus to customers and allows them to browse items they can order from each restaurant.

Let’s assume we have the following APIs for our MenuService:

  1. GetMenusForRestaurant: Returns menus for the restaurant. This could include breakfast, lunch, etc, and time ranges for the menus.
  2. GetMenuSummary: Returns all the items on the menu, their descriptions, and prices.
  3. GetMenuItem: Returns a single menu item and any pictures associated with it.

We’ll revisit these APIs in the next few sections.

Preparing for Launch

On a typical day, let’s say we receive 1 million orders. These orders likely aren’t evenly distributed across the day. Perhaps our US-based service receives 80% of its traffic between 3 PM Pacific (6 PM Eastern) and 9 PM Pacific. That means our typical order volume is 80% * 1 million / 6 hours, or 133K orders per hour. Our new service will need to handle traffic to support about 40 orders per second on a typical day.

We’re launching MenuService in August and the business is nervous about launching before Halloween. They’d like assurance that the service is ready for the traffic spike.

In the US, Halloween is one of the biggest online ordering days, with volume significantly higher than usual. Let’s assume Halloween order volumes are 5x the average day, which gets us to about 200 orders per second. How do we provide assurance to the business that the service can easily handle that order volume?

Translating Projections into API Traffic

We have an assumption about how much orders the business will receive. How can we translate from order volume to service traffic?

We can make some more assumptions and / or base it on pre-existing metrics. We can capture our assumptions and confirm them with others. These values will be important for evaluating success, and making sure the service is successful when we need it.

If we say that a customer will typically place an order 40% of the time they browse, and the average customer to conversion ratio is something like 20:1, we can use that for modeling API traffic.

In our example, let’s consider the typical call flows for a customer browsing the ordering site. Possible customer scenarios to model:

  1. Customer chooses a restaurant they like and loads the menu. They don’t see anything they like and close the site.
  2. Customer chooses a restaurant, adds a few items to cart, and again changes their mind and stops browsing.
  3. Customer tries to check out and their credit card is rejected.
  4. Customer adds a few items and successfully places their order.

Traffic Projections

Given our three APIs defined above (GetMenusForRestaurant, GetMenuSummary, GetMenuItem), we can mimic traffic for these scenarios. This can include capturing different access patterns such as a popular restaurant, where we distribute a higher percentage of requests to that restaurant during the load test.

Let’s make a few more assumptions about traffic. From our numbers before, we projected there will be 200 orders per second at projected peak. If we assume that there’s a 20:1 conversion, that means there’s approximately 4000 customers active on the site at any time. From these assumptions, we can derive traffic projections on a per-API basis.

Example Projections

Prospective hungry diners hit the GetMenusForRestaurant API at least once per restaurant they browse. If we have 4000 customers on the site at any given time, and we assume a customer will load a menu once per minute, we need to support ~70 requests per second (4000 / 60) request for this API.

Note that the requests per second chosen in this toy example may be able to run on a single instance with enough tuning or a simple access pattern. The exercises that follow work best when applied to a large system which requires significant infrastructure. Please feel free to substitute a service which has higher throughput if you like.

We can go through this exercise for all APIs and provide traffic projections. These traffic projections will help inform how we build our load tests as well. If we go through these projections, we may end up with numbers like this at 100% traffic:

  • GetMenusForRestaurant: 70 TPS
  • GetMenuSummary: 100 TPS
  • GetMenuItem: 500 TPS

Defining Success

Once we have our forecast for traffic during the load test, and a plan for how to distribute the traffic, we can think about what a successful load test looks like.

Defining SLAs

An SLA is a Service-Level Agreement. It’s a contract to the business and your customers for how your service will behave, including uptime, response times, and so forth. To check whether our test is successful, we need to define the SLAs and see if the service meets them under load.

Questions we can ask:

  • How many errors are acceptable? Is there a percentage, such as 99.99% of requests are successful? An absolute threshold such as no more than 25 errors in a 5 minute period?
  • What response time is acceptable for your service and at what percentiles? Is a 99th percentile request at 100 ms response time acceptable for your service?
  • Under what criteria should we halt the test’s execution?

Instrumenting Code

How can we check if the service meets our SLAs? We’ll need metrics to help us find out. Does the service emit metrics on each API indicating how long it took and whether it was successful? If not, it’s the perfect time to add profiling and metrics to your code.

Profiling will help us dig more into the performance details. For example, we should be able to tell what’s slow for each primary point in the request. If something is slow, why is it slow? Is it because of network calls? A slow database query? Repeated or inefficient algorithms? Profiling can help us answer these questions.

Something that has happened to me before is running a load test, failing it, and not being able to find out what went wrong. At least when that happens, it’s an opportunity to improve monitoring!

Ensuring Alarm Coverage

We want to know if our service is unable to meet its SLAs. That means ensuring we have alarms which alarms cover the SLAs and let us or the on-call know if there’s an issue.

A good start here would be to look at your SLAs and consider how quickly you’d like to know if you’re in violation. Is a 5 minute delay before someone starts to look at the issue ok? 15 minutes? Longer?

Something I like to do is build in layers of alarms. We should likely get paged if we’re well above SLA. If our service is almost out of SLA, or inconsistently above SLA, we may want our system to alert us during business hours to investigate. Having different alarm severities at different levels allows us to detect issues before things get too bad.

Load Testing

Configuring the Load Test

We can finally start building out the load tests! Here’s a few tips:

  • Gradually increase traffic to desired levels. For example, maybe orders on Halloween are not immediately at 100% traffic. Perhaps they reach that over an hour as more people decide they want to order. Our earlier assumptions can be a good starting point for modelling how long the ramp-up should take.

  • Go where your customers are. Your load testing run should try to emulate your customers as much as possible, including (if possible) being outside of your code or infrastructure to capture network latency. We’re trying to model how slow the experience will be from their perspective. Cheating by putting your load test source in or near your service infrastructure leads to an inaccurate picture of the latency. What if the public facing load balance or API Gateway is causing latency which we don’t see until we start to test with actual customers?

  • Set up the world. Your test will likely run a lot of requests against a pre-defined set of identities. For example, in our earlier assumptions for MenuService, we mentioned that there will be a popular restaurant that gets more requests than others. Perhaps we need to pre-configure or ensure a test restaurant exists. Or, we could create a list of restaurant IDs to access, and store those in a configuration file on Amazon S3 or similar that the tests pull.

  • Run outside of production if you can. To reduce risk, it’s best to run in a non-production environment such as a staging environment.

  • Coordinate Game Days. When running a load test for a high traffic event, the service under test will not be the only one experiencing higher than usual traffic volumes. Consider running your load tests in coordination with your dependencies to see how the overall customer experience will be. Perhaps your service can handle its traffic in isolation. When your service and your downstream dependency each have to handle increased request volume, something may fall over.

  • Minimize Variables. When testing, try to isolate to a single variable under test. For example, perhaps we’d like to validate how instance size (e.g. memory or CPU) affects throughput. If a medium-sized instance can handle 100 TPS per instance, can a large handle 200? It’s tempting to try to test multiple things at once! If we test multiple changes at one time, it can be hard to tell which change yielded the improvement (or regression), and will wind up taking longer overall.

Before Running the Test

As we prepare for the load test, consider what can go wrong. Here’s what I’ve done:

  • Make a checklist. Build the steps you’re going to follow for running the test.
  • Start small. Do a small scale test up to 5-10% to test that everything works as you expect.
  • Communicate: To build trust, let your team and any interested parties (including potentially your cloud vendor!) know:
    • The timelines for your test run
    • The conditions under which you’ll halt the test
    • Any other precautions you’ll be taking during the time

Running the Load Test

Before you run the test, check that the service is in a healthy state before starting, and there’s no issues in your downstream or upstream services. If there’s already a problem, it may be a good idea to delay until a later time when the surrounding environment is more stable.

Let others know when you start the test and communicate as you run through your checklist above. I suggest running the service at each threshold and making sure it’s healthy before progressing to the next one. For example, let the service stabilize at 15% before going to 25%.

Keep an eye on your dashboards and metrics. If you start to see errors beyond your SLAs, or issues in your downstream dependency, stop the run. It’s easier to stop a test and resume another time than to take down your site or service during the load test.


When the test concludes, we can review the results and improve the service with what we’ve learned.

Capturing Results

After your test run, capture a snapshot of all the graphs during your test if you can. It’ll make analysis later easier. For example, if you can point to all the graphs the moment you dialed up and started seeing exceptions, it’ll be easier to pinpoint what started the service to fall over. Maybe you had a database issue or a cache which didn’t behave as expected.

Another tip would be to make it so you can easily grab a graph of your load testing. This may include making a specific load testing dashboard, which you can then change the time bounds. That way, the next time you run these tests, you have little extra work to capture all the graphs and dig through them.

Digging into Your Results

Do a small write-up of what you found and what you’d like to do more investigation on.

Some questions to ask:

  • Looking at the graphs and performance, did you meet your goals? If not, how far were you from meeting them?
  • Are the servers you’re using optimal for your usage? Could they use more RAM, CPU, or disk? What was the bottleneck for scaling?
  • How long did it take to add capacity? If you’re using autoscaling of some kind, how long did it take new capacity to come online and start serving traffic? For example, I found that using larger servers would (sometimes) take longer to bring online (as long as 45 minutes), while we could provision a larger number of small server quickly.
  • How did your database or caching layer do during the exercise?
  • What leading indicators did you have that there was a problem? Did one API start to experience problems before the others?
  • Did the hosts receive an approximately even distribution of traffic?

Making Improvements

You may find many things to change! I recommend making these changes iteratively.

We’ve established a baseline with the initial tests and can measure against it as we improve the service. It’s helpful to get specific numbers for the optimizations. For example, “We optimized the queueing behavior of server workers and took latency at p99 from 55 ms to 35 ms”.

If you make several improvements at once before re-running or re-evaluating your load test, it can be hard to see how effective one change was. We may also optimize something only to find it introduces a new problem! Keeping the changes small between re-tests limits that risk too. Run a small test - it doesn’t necessarily need to be full scale.

Preventing Regressions

A good way to ensure your service stays fast and healthy is to build in load testing as part of your deployment process. It’ll help pinpoint if you make a code change which introduces a slowdown, and help prevent customers from seeing a performance regression.

In addition, after completing load testing and ensuring the service can handle its projected traffic, we can test the service’s resilience with chaos engineering. For example, we could inject latency into dependency calls, increase memory usage, or take out an availability zone. Chaos engineering is out of the scope of this post. One reference for further reading: Chaos Engineering: the history, principles, and practice.

Load Testing Takes Time

It takes time to understand your customers, build out tests that simulate their behavior (or at least approximate it), run these tests, and optimize so that they have a good experience.

Going back to my story from this post’s start, we did (eventually) optimize enough of the application and its infrastructure to meet the required projected traffic levels. It took investigation, persistence, and support from many talented software engineers.

Regardless of the size of service you’re looking to launch, I hope you’ll find at least a few of these practices helpful for ensuring that your service can meet its requirements and delight your customers.

Thanks to David Iola and Richard Cousart for comments/corrections/discussion.