5 lessons from Netflix on surviving traffic surges

Sure, Netflix has had its fair share of outages (we've all seen the meltdowns on X). But most of the time, even when traffic spikes unpredictably, the app stays rock solid.

So how does it survive massive traffic surges, regional failures, and cloud chaos—while other apps crumble the moment a new product launches?

The answer is battle-tested resilience strategies. Netflix doesn't scale blindly: it expects failure, and engineers around it. From smart load shedding to multi-region traffic shifting, Netflix's architecture is designed to absorb shocks and recover fast. And every developer can learn from it.

In today's newsletter, we’ll break down:

• Why load spikes happen (and why they’re not always predictable)

• How Netflix auto-scales smarter—beyond basic CPU-based scaling

• The secret behind prioritized load shedding (a survival tactic for high-scale services)

• How Netflix shifts traffic across AWS regions without causing a meltdown

• What developers can learn from Netflix’s engineering playbook—even if you’re not running a global streaming empire

Let’s pull back the curtain and see what really keeps your binge-watching experience smooth.

What causes load spikes?#

Load spikes are sudden and unpredictable surges in user traffic that can overwhelm infrastructure if they're not handled efficiently. A few cases can cause load spikes:

• Regional failover: Region failure is a rare issue but still inevitable. Once a region goes down, all the services interacting with it will go offline, too, ultimately affecting businesses. In Netflix's case, service disruption for millions of users is unacceptable when there is no SLA for the region’s availability again. Shifting the traffic to another region can cause a load spike.

• Long and short spikes: Short-load spikes are temporary traffic surges that last a few seconds to minutes. Retries or some device bug often cause these. Long-load spikes are periods of high traffic that last for hours or even days. They typically occur during major events, such as the launch of a new title or the downtime of another streaming site.

The diagram above shows how the long surges occur (expected and unexpected) in the Netflix system.

These spikes can cripple infrastructure if not handled properly. But Netflix has built its entire architecture to absorb these shocks—let’s see how.

How Netflix works#

The secret lies in its multi-region architecture, predictive scaling, and microservice resilience. Instead of reacting to failure, Netflix designs for it. Let’s take a look at how their system is built to handle chaos at scale.

Leveraging multi-region architecture for resilience#

Netflix mainly operates in four regions: us-east-1, us-east-2, eu-west-1, and us-west-2. Netflix uses an active-active architecture that allows any region to take control independently, which is used in region failure load spikes.

If a region goes down, instead of serving the requests from the closest region, Netflix distributes the traffic across the available regions. Unlike active-passive failover models, where a secondary region is only used when the primary fails, Netflix’s active-active approach ensures continuous replication and instant failover (rerouting the affected traffic to the healthy regions in 1-2 minutes) when needed.

The graph below shows Netflix’s internal metric starts per second (SPS). In the dotted box, the line of the us-east-1 went down, and the lines of other regions became a little higher.

Note: Netflix uses region failover as a normal practice to test how resilient their system is.

Thousands of microservices#

Since Netflix runs thousands of microservices, and when load spikes occur, interconnected microservices can show variable load spikes.

For instance, the microservice handling the authorization is called for every x number of users; once authorized, every user will access different titles. So, the microservice handling the titles (fetching the data from the CDN or database) may show a 1.5x or 2x spike.

These microservices must be resilient enough to handle each other’s loads efficiently. Let’s see how Netflix engineers make the microservices resilient.

Netflix uses the concept of buffers, and every service operates with two key buffers: “Success bufferbuffer capacity that can entertain load spikes to some extent without disrupting the service and requests” and “Failure bufferbuffer capacity that sheds the requests to save the system from collapse. The users get service disruption error in this buffer space..” Both of these buffers serve different purposes.

The system resources are divided into three parts (as shown in the diagram above):

• The first is the desired capacity or normal utilization zone of service

• The second is above the desired capacity zone, which is the success buffer

• Finally, there is the failure buffer zone

These buffers serve as a headroom for incoming requests.

The requests are supposed to remain in the first zone (below the starting point of the success buffer), but when a sudden spike occurs, and the requests exceed the first zone, the success buffer plays its role and handles the additional requests. The users don’t face any issues during the success buffer.

However, once the requests enter the failure buffer, the system starts throwing errors, and the requests are not being served. Failure buffer is a preventive measure to save the system from collapse.

There are a few points that must be kept in mind while designing the solutions:

• The recovery time must be a minimum.

• Region failover should not be used as a primary solution to get out of trouble.

• Services must be resilient to load spikes at any time.

Netflix's solution to load spikes#

Now, let’s talk about the solutions Netflix uses to handle spikes. There are three main components of creating an effective solution.

1. Predictive scaling: Scale up the fleet of resources ahead of the load spike.

2. React quickly: Reduce the time to recovery during the scale-up process.

3. Stay available: Try to keep the system available as much as possible during the TTR.

Netflix runs entirely on AWS, leveraging its global footprint to maintain high availability. But just using AWS isn't enough—it's how Netflix uses AWS that makes the difference.

Predictive scaling#

The first and simplest approach is to pre-scale the resources before the load spike is expected to occur.

This measure works for expected load spikes based on an event or a historic pattern. In this approach, autoscaling scales up the services ahead of time to handle the traffic surges, which means increasing the success buffer zone.

Even when a title restricted to a specific geography launches, Netflix distributes the traffic across all four regions instead of overloading the region nearest to that geography.

How Netflix uses predictive scaling on AWS#

Predictive scaling is a proactive approach to handle expected traffic spikes by scaling resources before the surge occurs. This method is useful for applications that experience predictable load patterns, such as scheduled events, product launches, or seasonal traffic spikes.

Here’s how Netflix achieves predictive scaling using AWS services:

AWS auto scaling#

• AWS analyzes past traffic trends and predicts future demand.

• The Auto Scaling Group (ASG) automatically provisions extra instances before the expected spike.

• This ensures that resources are ready in advance, preventing performance bottlenecks.

Monitoring traffic patterns with CloudWatch#

• CloudWatch tracks metrics like CPU usage, request rates, and network traffic.

• Alarms and thresholds are set to trigger scaling actions when patterns suggest an upcoming surge.

• This automates the scaling decision, ensuring a smooth response to increasing load.

Overcoming auto scaling challenges#

Next up, let's discuss some of the challenges with (and solutions for) autoscaling Netflix experiences (and that can impact any cloud system).

Issues with autoscaling during load spikes#

In this example, Netflix's autoscaling policies worked fine when traffic gradually increased and decreased with specific patterns.

However, when sudden spikes occurred, Netflix engineers noticed a significant delay in scaling out the fleet. In the following equations, $T_{d}$ is the time to detect the load increase, which is approximately 4 minutes and then 2 minutes. $T_b$ is the time to boot. This cycle will execute several times until the services are scaled to meet the load spike. Combined, the TTR (time to recovery) is approximately 20 minutes.

But Netflix managed to reduce that time—significantly.

How did Netflix reduce 20 minutes of TTR to 3 minutes?#

If we analyze the above times, the detection time appears twice. This is because the system was not scaled enough to handle the load spike the first time, so it had to undergo the detection again. So, the main challenge is reducing the load detection time as much as possible so the full fleet scales up simultaneously.

The basic and very important metric of monitoring load at Netflix is SPS (starts per second), which maps to RPS (requests per second). Netflix used the CPU target tracking policy that has always worked for a smooth increase in workloads, but it is not enough if there is a 10x spike in RPS.

The picture above shows that when there is 2x RPS, CPU utilization is 100%, and for 10x RPS, the CPU utilization is still 100%. So, the CPU can reach a maximum of 100%, but RPS doesn’t have an upper limit. After a 2x increase in RPS, it is unclear how much to scale the resources.

Detection—RPS hammer policy#

A step-scaling policy was introduced to overcome the shortcomings of the CPU target tracking policy. This policy scales the resources to the maximum in one go. The benefit is that we get enough computation resources; the drawback is that we might have provisioned extra resources.

Detection—Higher resolution metrics#

The default resolution of CloudWatch metrics is 5 minutes. Netflix took advantage of detailed monitoring and, equipped with their internal monitoring systems, started sending logs to CloudWatch every 5 seconds. This helped improve detection time by 3x. Further optimizing the application and system startup the overall time to recovery becomes only 3 minutes.

Ensuring availability#

This is the third and the last part of the solution where the team at Netflix wanted to handle, or more appropriately balance, the requests as much as possible during the 3 minutes of recovery time.

Engineers tagged the services according to their business criticalities, defining the priorities in execution. They introduced prioritized CPU shedding (prioritized load shedding) in the success buffer to ensure the availability of business-critical services and APIs. The idea is to prioritize the requests in the success buffer and take the required actions before the requests enter the failure buffer and the system starts dropping all requests.

The slides above show the same success and failure buffers but with prioritized shedding implemented. The requests are divided into four types and they will be entertained and dropped according to their priority.

Intelligent request redirection#

Instead of outright dropping requests when a backend service is overloaded, Netflix attempts to retry the request in another AWS region. However, repeatedly retrying requests could overwhelm the new region, leading to cascading failures.

Risk mitigation with priority downgrade#

To prevent overloading another region, Netflix downgrades the priority of a retried request. For example:

• If a request originally had a priority of 3, it may be reassigned to 99 when retried in another region.

• This signals the new region: “Only process this if you have extra capacity; otherwise, drop it.”

This technique, called cross-region shifting, has been highly effective. It has helped rescue over 90% of otherwise throttled requests during major load spikes, significantly improving user experience.

5 things developers can learn from Netflix#

Netflix’s resilience strategies aren’t just for billion-dollar streaming giants. Here’s how you can apply these lessons in your own systems:

1. Scale smart, not just big – Predictive scaling isn’t just for Netflix. Use historical data to pre-scale for known events, rather than waiting for traffic spikes to overwhelm your system.

2. Expect failure and design for it – Outages will happen. Build for graceful degradation, rerouting, and redundancy instead of assuming your services will always be available.

3. Not all requests are equal – Prioritized load shedding ensures that critical services stay online. Identify what’s essential vs. nice-to-have in your system and handle overload accordingly.

4. Reduce retry storms – When failures happen, automatic retries can flood your system. Implement exponential backoff and priority downgrades to avoid cascading failures.

5. Multi-region resilience isn’t just for giants – Even if you’re not running at Netflix scale, you can still implement cross-region failover for critical services or databases.

Netflix stays online by combining proactive (autoscaling, redundancy) and reactive (load shedding, failover) strategies. You can apply these same principles to build a more resilient system—no matter the scale.

Building resilient systems at any scale#

Netflix’s ability to handle unexpected load spikes and failures isn’t magic—it’s engineering discipline at scale. By intelligently shedding non-essential traffic, shifting requests across regions, and continuously stress-testing its infrastructure, Netflix ensures users keep streaming—even when the system is under immense pressure.

As systems grow more complex, any organization operating in the cloud can take inspiration from Netflix’s playbook. Prioritized load shedding, multi-region failover, and fast failure recovery are essential techniques for keeping services reliable, whether you’re running a SaaS startup or a large-scale enterprise platform.

Resilience isn’t about avoiding failure—it’s about designing for it.

Stay tuned for more deep dives into scaling distributed systems, fault tolerance, and cloud resilience engineering in our upcoming editions! 

Until then, if you're interested in exploring more of AWS and getting hands-on with its services, Educative offers plenty of Cloud Labs to explore. No setup needed—you can experience AWS right from your browser.