This is why you should let data tell you whether or not cold starts are a problem for you, and where (as in, which functions). Also, even if the overall traffic confirms to a bell curve it doesn’t mean that there are no unpredictable spikes at the individual function’s level where cold starts occur. However, user behaviours are difficult to predict and traffic patterns can change over time. Or maybe their traffic pattern is so uniform and stable that there are seldom spikes that cause a flurry of cold starts. When should you care about cold starts?įor many, cold starts are a non-issue because their primary workload is data processing, so spikes in latency don’t negatively impact user experience. As we’ll see later in the post, several factors can affect this initialization time and therefore the total roundtrip time for processing a request during a cold start. When optimizing cold start durations, this is where you should focus on. 12 months ago, this used to average around 150–200ms.Īs such, module initialization (step 3) usually accounts for the bulk of cold start durations in the wild. Over the years, this part of the cold start has improved significantly across all language runtimes. It’s also an area where AWS has optimized aggressively. How long step 1 and step 2 takes are mostly outside of your control. For example, in the X-Ray trace below, we can infer that step 1 and step 2 took a total of ~80ms, while step 3 took 116ms.
We could, however, infer their duration from the available information. You can see these steps in an X-Ray trace for a cold start, although step 1 and step 2 are not explicitly captured in the trace. If you want to learn more about this process, then check out this talk from re:invent 2019 (around the 5:46 mark). find a space in its EC2 fleet to allocate the workerīefore it can pass the request to your handler function.This request takes longer to process because the Lambda service needs to: As mentioned above, a “cold start” is the 1st request that a new Lambda worker handles.
Aws lambda slow startup code#
Lambda automatically scales the number of workers that are running your code based on traffic. In this post, I will share with you everything I have learned about cold starts in the last few years and back it up with some data. Depending on who you talk to, you will likely get different advice on how best to reduce cold starts. It’s easily one of the most talked-about and yet, misunderstood topics when it comes to Lambda. So much has been written about Lambda cold starts. In this post, let’s look at three major ways you can improve the performance of your Lambda functions, starting with… 1. But cold starts still impact the tail latency for many applications and is a particular concern for user-facing APIs where slow response time can negatively impact user experience and can directly affect your bottom line! Once warmed, Lambda’s performance is on-par with serverful applications (whether they’re running in containers or on EC2 instances). That is, the first request for a newly created Lambda execution environment takes longer because there are additional steps involved, such as initializing the runtime and your application dependencies. How can we quickly identify the root cause for performance issues in our serverless applications and identify these slow dependencies?Īnother common challenge when it comes to optimizing the performance of Lambda is around cold starts. Sadly, having more CPU doesn’t help here as the additional CPU resource would simply go to waste as the function waits for a response from, say, a DynamoDB query. Often, when performance issues arise in these serverless applications, the root cause can be traced back to the response time from these IO operations. However, most serverless applications also need to perform IO operations such as calling other AWS or SaaS services. The more memory, the more CPU resource and network bandwidth the function receives but also the higher cost per millisecond of execution time.įor CPU-intensive workloads, raising the memory allocation setting is a great way to give the function more power to complete the workload quicker. When it comes to performance, AWS Lambda gives you a single configuration to control the performance and cost of your Lambda functions – by adjusting the memory allocation setting.
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