You need to use structured logging with AWS Lambda

This is a lesson that I wished I learnt when I first started using AWS Lambda in anger, it would have made my life simpler right from the start. 

But, we did get there before long, and it allowed us to track and include correlation IDs in our log messages (which are then pushed to an ELK stack)which would also include other useful information such as:

  • age of the function execution
  • whether invocation was a cold start
  • log level
  • etc.

Looking back, I fell into the common trap of forgetting the practices that had served us well in a different paradigm of developing software, at least until I figured out how to adopt these old practices for the new serverless paradigm.

In this case, we know structured logs are important and why we need them, and also how to do it well. But like many others, we started with console.log because it was simple and it worked (to a limited degree).

But, this approach has a really low ceiling:

  • you can’t add contextual information with the log message, at least not in a way that’s consistent (you may change the message you log, but the contextual information should always be there, e.g. user-id, request-id, etc.) and easy to extract for an automated process
  • as a result, it’s also hard to filter the log messages by specific attributes – e.g. “show me all the log messages related to this request ID”
  • it’s hard to control what level to log at (i.e. debug, info, warning, …) based on configuration – e.g. log at debug level for non-production environments, but log at info/warning level for production

Which is why, if you’re just starting your Serverless journey, then learn from my mistakes and write your logs as structured JSON from the start. Also, you should use whatever log client that you were using before – log4j, nlog, loggly, log4net, whatever it is – and configure the client to format log messages as JSON and attach as much contextual information as you can.

As I mentioned in the post on how to capture and forward collection IDs, it’s also a good idea to enable debug logging on the entire call chain for a small % of requests in production. It helps you catch pervasive bugs in your logic (that are easy to catch, but ONLY if you have the right info from the logs) that would otherwise require you to redeploy all the functions on the entire call chain to turn on debug logging…

So there, a simple and effective thing to do to massively upgrade your serverless architecture. Check out my mini-series on logging for AWS Lambda which covers log aggregationtracking correlation IDs, and some tips and tricks such as why and how to send custom metrics asynchronously.

I’m afraid you’re thinking about AWS Lambda cold starts all wrong

When I discuss AWS Lambda cold starts with folks in the context of API Gateway, I often get responses along the line of:

Meh, it’s only the first request right? So what if one request is slow, the next million requests would be fast.

Unfortunately that is an oversimplification of what happens.

Cold start happens once for each concurrent execution of your function.

API Gateway would reuse concurrent executions of your function that are already running if possible, and based on my observations, might even queue up requests for a short time in hope that one of the concurrent executions would finish and can be reused.

If, all the user requests to an API happen one after another, then sure, you will only experience one cold start in the process. You can simulate what happens with Charles proxy by capturing a request to an API Gateway endpoint and repeat it with a concurrency setting of 1.

As you can see in the timeline below, only the first request experienced a cold start and was therefore noticeably slower compared to the rest.

1 out of 100, that’s bearable, hell, it won’t even show up in my 99 percentile latency metric.

What if the user requests came in droves instead? After all, user behaviours are unpredictable and unlikely to follow the nice sequential pattern we see above. So let’s simulate what happens when we receive 100 requests with a concurrency of 10.

All of a sudden, things don’t look quite as rosy – the first 10 requests were all cold starts! This could spell trouble if your traffic pattern is highly bursty around specific times of the day or specific events, e.g.

  • food ordering services (e.g. JustEat, Deliveroo) have bursts of traffic around meal times
  • e-commence sites have highly concentrated bursts of traffic around popular shopping days of the year – cyber monday, black friday, etc.
  • betting services have bursts of traffic around sporting events
  • social networks have bursts of traffic around, well, just about any notable events happening around the world

For all of these services, the sudden bursts of traffic means API Gateway would have to add more concurrent executions of your Lambda function, and that equates to a bursts of cold starts, and that’s bad news for you. These are the most crucial periods for your business, and precisely when you want your service to be at its best behaviour.

If the spikes are predictable, as is the case for food ordering services, you can mitigate the effect of cold starts by pre-warming your APIs – i.e. if you know there will be a burst of traffic at noon, then you can schedule a cron job (aka, CloudWatch schedule + Lambda) for 11:58am that will hit the relevant APIs with a blast of concurrent requests, enough to cause API Gateway to spawn sufficient no. of concurrent executions of your function(s).

You can mark these requests with a special HTTP header or payload, so that the handling function can distinguish it from a normal user requests and can short-circuit without attempting to execute the normal code path.

That’s great that you mitigates the impact of cold starts during these predictable bursts of traffic, but does it not betray the ethos of serverless compute, that you shouldn’t have to worry about scaling?

Sure, but making users happy thumps everything else, and users are not happy if they have to wait for your function to cold start before they can order their food, and the cost of switching to a competitor is low so they might not even come back the next day.

Alternatively, you could consider reducing the impact of cold starts by reducing the length of cold starts:

  • by authoring your Lambda functions in a language that doesn’t incur a high cold start time – i.e. Node.js, Python, or Go
  • choose a higher memory setting for functions on the critical path of handling user requests (i.e. anything that the user would have to wait for a response from, including intermediate APIs)
  • optimizing your function’s dependencies, and package size
  • stay as far away from VPCs as you possibly can! VPC access requires Lambda to create ENIs (elastic network interface) to the target VPC and that easily adds 10s (yeah, you’re reading it right) to your cold start

There are also two other factors to consider:

Finally, what about those APIs that are seldom used? It’s actually quite likely that every time someone hits that API they will incur a cold start, so to your users, that API is always slow and they become even less likely to use it in the future, which is a vicious cycle.

For these APIs, you can have a cron job that runs every 5–10 mins and pings the API (with a special ping request), so that by the time the API is used by a real user it’ll hopefully be warm and the user would not be the one to have to endure the cold start time.

This method for pinging API endpoints to keep them warm is less effective for “busy” functions with lots of concurrent executions – your ping message would only reach one of the concurrent executions and if the level of concurrent user requests drop then some of the concurrent executions would be garbage collected after some period of idleness, and that is what you want (don’t pay for resources you don’t need).

Anyhow, this post is not intended to be your one-stop guide to AWS Lambda cold starts, but merely to illustrate that it’s a more nuanced discussion than “just the first request”.

As much as cold starts is a characteristic of the platform that we just have to accept, and we love the AWS Lambda platform and want to use it as it delivers on so many fronts. It’s important not to let our own preference blind us from what’s important, which is to keep our users happy and build a product that they would want to keep on using.

To do that, you do need to know the platform you’re building on top of, and with the cost of experimentation so low, there’s no good reason not to experiment with AWS Lambda yourself and try to learn more about how it behaves and how you can make the most of it.

AWS Lambda – use the invocation context to better handle slow HTTP responses

With API Gateway and Lambda, you’re forced to use relatively short timeouts on the server-side:

  • API Gateway have a 30s max timeout on all integration points
  • Serverless framework uses a default of 6s for AWS Lambda functions

However, as you have limited influence over a Lambda function’s cold start time and have no control over the amount of latency overhead API Gateway introduces, the actual client-facing latency you’d experience from a calling function is far less predictable.

To prevent slow HTTP responses from causing the calling function to timeout (and therefore impact the user experience we offer) we should make sure we stop waiting for a response before the calling function times out.

“the goal of the timeout strategy is to give HTTP requests the best chance to succeed, provided that doing so does not cause the calling function itself to err”

– me

Most of the time, I see folks use fixed (either hard coded or specified via config) timeout values, which is often tricky to decide:

  • too short and you won’t give the request the best chance to succeed, e.g. there’s 5s left in the invocation but we had set timeout to 3s
  • too long and you run the risk of letting the request timeout the calling function, e.g. there’s 5s left in the invocation but we had set timeout to 6s

This challenge of choosing the right timeout value is further complicated by the fact that we often perform more than one HTTP request during a function invocation – e.g. read from DynamoDB, talk to some internal API, then save changes to DynamoDB equals a total of 3 HTTP requests in one invocation.

Let’s look at two common approaches for picking timeout values and scenarios where they fall short of meeting our goal.

requests are not given the best chance to succeed

requests are allowed too much time to execute and caused the function to timeout.

Instead, we should set the request timeout based on the amount of invocation time left, whilst taking into account the time required to perform any recovery steps – e.g. return a meaningful error with application specific error code in the response body, or return a fallback result instead.

You can easily find out how much time is left in the current invocation through the context object your function is invoked with.

For example, if a function’s timeout is 6s, but by the time you make the HTTP request you’re already 1s into the invocation (perhaps you had to do some expensive computation first), and if we reserve 500ms for recovery, then that leaves us with 4.5s to wait for HTTP response.

With this approach, we get the best of both worlds:

  • allow requests the best chance to succeed based on the actual amount of invocation time we have left; and
  • prevent slow responses from timing out the function, which allows us a window of opportunity to perform recovery actions

    requests are given the best chance to succeed, without being restricted by an arbitrarily determined timeout.

slow responses are timed out before they cause the calling function to time out

But what are you going to do AFTER you time out these requests? Aren’t you still going to have to respond with a HTTP error since you couldn’t finish whatever operations you needed to perform?

At the minimum, the recovery actions should include:

  • log the timeout incident with as much context as possible (e.g. how much time the request had), including all the relevant correlation IDs
  • track custom metrics for serviceX.timedout so it can be monitored and the team can be alerted if the situation escalates
  • return an application error code in the response body (see example below), along with the request ID so the user-facing client app can display a user friendly message like “Oops, looks like this feature is currently unavailable, please try again later. If this is urgent, please contact us at and quote the request ID f19a7dca. Thank you for your cooperation :-)”
  "errorCode": 10021,
  "requestId": "f19a7dca",
  "message": "service X timed out"

In some cases, you can also recover even more gracefully using fallbacks.

Netflix’s Hystrix library, for instance, supports several flavours of fallbacks via the Command pattern it employs so heavily. In fact, if you haven’t read its wiki page already then I strongly recommend that you go and give it a thorough read, there are tons of useful information and ideas there.

At the very least, every command lets you specify a fallback action.

You can also chain the fallback together by chaining commands via their respective getFallback methods.

For example,

  1. execute a DynamoDB read inside CommandA
  2. in the getFallback method, execute CommandB which would return a previously cached response if available
  3. if there is no cached response then CommandB would fail, and trigger its own getFallback method
  4. execute CommandC, which returns a stubbed response

Anyway, check out Hystrix if you haven’t already, most of the patterns that are baked into Hystrix can be easily adopted in our serverless applications to help make them more resilient to failures – something that I’m actively exploring with a separate series on applying principles of chaos engineering to Serverless.

Applying principles of chaos engineering to AWS Lambda with latency injection

This is part 2 of a multipart series that explores ideas on how we could apply the principles of chaos engineering to serverless architectures built around Lambda functions.

The most common issue I have encountered in production are latency/performance related. They can be symptomatic of a host of underlying causes ranging from AWS network issues (which can also manifest itself in latency/error-rate spikes in any of the AWS services), overloaded servers, to simple GC pauses.

Latency spikes are inevitable – as much as you can improve the performance of your application, things will go wrong, eventually, and often they’re out of your control.

So you must design for them, and degrade the quality of your application gracefully to minimize the impact on your users.

In the case of API Gateway and Lambda, there are additional considerations:

  • API Gateway has a hard limit of 29s timeout for integration points, so even if your Lambda function can run for up to 5 mins, API Gateway will timeout way before that

If you use moderate timeout settings for your API functions (and you should!) then you need to consider the effects of cold starts when calling an intermediate service.

Where to inject latency

Suppose our client application communicates directly with 2 public facing APIs, whom in turn depends on an internal API.

In this setup, I can think of 3 places where we can inject latency and each would validate a different hypothesis.

Inject latency at HTTP clients

The first, and easiest place to inject latency is in the HTTP client library we use to communicate with the internal API.

This will test that our function has appropriate timeout on this HTTP communication and can degrade gracefully when this request time out.

We can inject latency to the HTTP client libraries for our internal APIs, hence validating that the caller function has configured appropriate timeout and error handling for timeouts.

Furthermore, this practice should also be applied to other 3rd party services we depend on, such as DynamoDB. We will discuss how we can inject latency to these 3rd party libraries later in the post.

We can also inject latency to 3rd party client libraries for other managed services we depend on.

This is a reasonably safe place to inject latency as the immediate blast radius is limited to this function.

However, you can (and arguably, should) consider applying this type of latency injection to intermediate services as well. Doing so does carry extra risk as it has a broader blast radius in failures case – ie. if the function under test does not degrade gracefully then it can cause unintended problems to outer services. In this case, the blast radius for these failure cases is the same as if you’re injecting latency to the intermediate functions directly.

Inject latency to intermediate functions

You can also inject latency directly to the functions themselves (we’ll look at how later on). This has the same effect as injecting latency to the HTTP client to each of its dependents, except it’ll affect all its dependents at once.

We can inject latency to a function’s invocation. If that function is behind an internal API that are used by multiple public-facing APIs then it can cause all its dependents to experience timeouts.

This might seem risky (it can be), but is an effective way to validate that every service that depends on this API endpoint is expecting, and handling timeouts gracefully.

It makes most sense when applied to intermediate APIs that are part of a bounded context (or, a microservice), maintained by the same team of developers. That way, you avoid unleashing chaos upon unsuspecting developers who might not be ready to deal with the chaos.

That said, I think there is a good counter-argument for doing just that.

We often fall into the pitfall of using the performance characteristics of dev environments as predictor for the production environment. Whilst we seldom experience load-related latency problems in the dev environments?—?because we don’t have enough load in those environments to begin with?—?production is quite another story. Which means, we’re not programmed to think about these failure modes during development.

So, a good way to hack the brains of your fellow developers and to programme them to expect timeouts is to expose them to these failure modes regularly in the dev environments, by injecting latency to our internal APIs in those environments.

We can figuratively hold up a sign and tell other developers to expect latency spikes and timeouts by literally exposing them to these scenarios in dev environments, regularly, so they know to expect it.

In fact, if we make our dev environments exhibit the most hostile and turbulent conditions that our systems should be expected to handle, then we know for sure that any system that makes its way to production are ready to face what awaits it in the wild.

Inject latency to public-facing functions

So far, we have focused on validating the handling of latency spikes and timeouts in our APIs. The same validation is needed for our client application.

We can apply all the same arguments mentioned above here. By injecting latency to our public-facing API functions (in both production as well as dev environments), we can:

  • validate the client application handles latency spikes and timeouts gracefully, and offers the best UX as possible in these situations
  • train our client developers to expect latency spikes and timeouts

When I was working on a MMORPG at Gamesys years ago, we uncovered a host of frailties in the game when we injected latency spikes and faults to our APIs. The game would crash during startup if any of the first handful of requests fails. In some cases, if the response time was longer than a few seconds then the game would also get into a weird state because of race conditions.

Turns out I was setting my colleagues up for failure in production because the dev environment was so forgiving and gave them a false sense of comfort.

With that, let’s talk about how we can apply the practice of latency injection.

But wait, can’t you inject latency in the client-side HTTP clients too?

Absolutely! And you should! However, for the purpose of this post we are going to look at how and where we can inject latency to our Lambda functions only, hence why I have willfully ignored this part of the equation.

How to inject latency

There are 2 aspects to actually injecting latencies:

  1. adding delays to operations
  2. configuring how often and how much delay to add

If you read my previous posts on capturing and forwarding correlation IDsand managing configurations with SSM Parameter Store, then you have already seen the basic building blocks we need to do both.

How to inject latency to HTTP client

Since you are unlikely to write a HTTP client from scratch, so I consider the problem for injecting latency to HTTP client and 3rd party clients (such as the AWS SDK) to be one and the same.

A couple of solutions jump to mind:

  • in static languages, you can consider using a static weaver such as AspectJ or PostSharp, this is the approach I took previously
  • in static languages, you can consider using dynamic proxies, which many IoC frameworks offer (another form of AOP)
  • you can create a wrapper for the client, either manually or with a factory function (bluebirdjs’s promisifyAll function is a good example)

Since I’m going to use Node.js as example, I’m going to focus on wrappers.

For the HTTP client, given the relatively small number of methods you will need, it’s feasible to craft the wrapper by hand, especially if you have a particular API design in mind.

Using the HTTP client I created for the correlation ID post as base, I modified it to accept a configuration object to control the latency injection behaviour.

  "isEnabled": true,
  "probability": 0.5,
  "minDelay": 100,
  "maxDelay": 5000

You can find this modified HTTP client here, below is a simplified version of this client (which uses superagent under the hood).

To configure the function and the latency injection behaviour, we can use the configClient I first created in the SSM Parameter Store post.

First, let’s create the configs in the SSM Parameter Store.

You can create and optionally encrypt parameter values in the SSM Parameter Store.

The configs contains the URL for the internal API, as well as a chaosConfigobject. For now, we just have a httpClientLatencyInjectionConfig property, which is used to control the HTTP client’s latency injection behaviour.

  "internalApi": "", 
  "chaosConfig": {
    "httpClientLatencyInjectionConfig": {
      "isEnabled": true,
      "probability": 0.5,
      "minDelay": 100,
      "maxDelay": 5000

Using the aforementioned configClient, we can fetch the JSON config from SSM Parameter Store at runtime.

const configKey = "public-api-a.config";
const configObj = configClient.loadConfigs([ configKey ]);

let config = JSON.parse(yield configObj["public-api-a.config"]);
let internalApiUrl = config.internalApi;
let chaosConfig = config.chaosConfig || {};
let injectionConfig = chaosConfig.httpClientLatencyInjectionConfig;

let reply = yield http({ 
  method : 'GET', 
  uri : internalApiUrl, 
  latencyInjectionConfig: injectionConfig 

The above configuration gives us a 50% chance of injecting a latency between 100ms and 3sec when we make the HTTP request to internal-api.

This is reflected in the following X-Ray traces.

How to inject latency to AWSSDK

With the AWS SDK, it’s not feasible to craft the wrapper by hand. Instead, we could do with a factory function like bluebird’s promisifyAll.

We can apply the same approach here, and I made a crude attempt at doing just that. I must add that, whilst I consider myself a competent Node.js programmer, I’m sure there’s a better way to implement this factory function.

My factory function will only work with promisified objects (told you it’s crude..), and replaces their xxxAsync functions with a wrapper that takes in one more argument of the shape:

  "isEnabled": true,
  "probability": 0.5,
  "minDelay": 100,
  "maxDelay": 3000

Again, it’s clumsy, but we can take the DocumentClient from the AWS SDK, promisify it with bluebird, then wrap the promisified object with our own wrapper factory. Then, we can call its async functions with an optional argument to control the latency injection behaviour.

You can see this in action in the handler function for public-api-b .

For some reason, the wrapped function is not able to record subsegments in X-Ray. I suspect it’s some nuance about Javascript or the X-Ray SDK that I do not fully understand.

Nonetheless, judging from the logs, I can confirm that the wrapped function does indeed inject latency to the getAsync call to DynamoDB.

If you know of a way to improve the factory function, or to get the X-Ray tracing work with the wrapped function, please let me know in the comments.

How to inject latency to function invocations

The apiHandler factory function I created in the correlation ID post is a good place to apply common implementation patterns that we want from our API functions, including:

  • log the event source as debug
  • log the response and/or error from the invocation (which, surprisingly, Lambda doesn’t capture by default)
  • initialize global context (eg. for tracking correlation IDs)
  • handle serialization for the response object
  • etc..
// this is how you use the apiHandler factory function to create a
// handler function for API Gateway event source
module.exports.handler = apiHandler(
  co.wrap(function* (event, context) {
    ... // do bunch of stuff
    // instead of invoking the callback directly, you return the
    // response you want to send, and the wrapped handler function
    // would handle the serialization and invoking callback for you
    // also, it takes care of other things for you, like logging
    // the event source, and logging unhandled exceptions, etc.
   return { message : "everything is awesome" };

In this case, it’s also a good place for us to inject latency to the API function.

However, to do that, we need to access the configuration for the function. Time to lift the responsibility for fetching configurations into the apiHandlerfactory then!

The full apiHandler factory function can be found here, below is a simplified version that illustrates the point.

Now, we can write our API function like the following.

Now that the apiHandler has access to the config for the function, it can access the chaosConfig object too.

Let’s extend the definition for the chaosConfig object to add a functionLatencyInjectionConfig property.

"chaosConfig": {
  "functionLatencyInjectionConfig": {
    "isEnabled": true,
    "probability": 0.5,
    "minDelay": 100,
    "maxDelay": 5000
  "httpClientLatencyInjectionConfig": {
    "isEnabled": true,
    "probability": 0.5,
    "minDelay": 100,
    "maxDelay": 5000

With this additional configuration, we can modify the apiHandler factory function to use it to inject latency to a function’s invocation much like what we did in the HTTP client.

Just like that, we can now inject latency to function invocations via configuration. This will work for any API function that is created using the apiHandler factory.

With this change and both kinds of latency injections enabled, I can observe all the expected scenarios through X-Ray:

  • no latency was injected

  • latency was injected to the function invocation only

  • latency was injected to the HTTP client only

  • latency was injected to both HTTP client and the function invocation, but the invocation did not timeout as a result

  • latency was injected to both HTTP client and the function invocation, and the invocation times out as a result

I can get further confirmation of the expected behaviour through logs, and the metadata recorded in the X-Ray traces.

Recap, and future works

In this post we discussed:

  • why you should consider applying the practice of latency injection to APIs created with API Gateway and Lambda
  • additional considerations specific to API Gateway and Lambda
  • where you can inject latencies, and why you should consider injecting latency at each of these places
  • how you can inject latency in HTTP clients, AWS SDK, as well as the function invocation

The approach we have discussed here is driven by configuration, and the configuration is refreshed every 3 mins by default.

We can go much further with this.

Fine grained configuration

The configurations can be more fine grained, and allow you to control latency injection to specific resources.

For example, instead of a blanket httpClientLatencyInjectionConfig for all HTTP requests (including those requests to AWS services), the configuration can be specific to an API, or a DynamoDB table.


The configurations can be changed by an automated process to:

  • run routine validations daily
  • stop all latency injections during off hours, and holidays
  • forcefully stop all latency injections, eg. during an actual outage
  • orchestrate complex scenarios that are difficult to manage by hand, eg. enable latency injection at several places at once

Again, we can look to Netflix for inspiration for such an automated platform.

Usually, you would want to enable one latency injection in a bounded context at a time. This helps contain the blast radius of unintended damages, and make sure your experiments are actually controlled. Also, when latency is injected at several places, it is harder to understand the causality we observe as there are multiple variables to consider.

Unless, of course, you’re validating against specific hypothesis such as:

The system can tolerate outage to both the primary store (DynamoDB) as well as the backup store (S3) for user preferences, and would return a hardcoded default value in that case.

Better communication

Another good thing to do, is to inform the caller of the fact that latency has been added to the invocation by design.

This might take the form of a HTTP header in the response to tell the caller how much latency was injected in total. If you’re using an automated process to generate these experiments, then you should also include the id/tag/name for the specific instance of the experiment as HTTP header as well.

What’s next?

As I mentioned in the previous post, you need to apply common sense when deciding when and where you apply chaos engineering practices.

Don’t attempt an exercises that you know is beyond your abilities.

Before you even consider applying latency injection to your APIs in production, you need to think about how you can deal with these latency spikes given the inherent constraints of API Gateway and Lambda.

Unfortunately, we have run out of time to talk about this in this post, but come back in 2 weeks and we will talk about several strategies you can employ in part 3.

The code for the demo in this post is available on github here. Feel free to play around with it and let me know if you have any suggestions for improvement!


How can we apply the principles of chaos engineering to AWS Lambda?

This is the first of a multipart series that explores ideas on how we could apply the principles of chaos engineering to serverless architectures built around Lambda functions.

  • part 1: how can we apply principles of chaos engineering to Lambda?
  • part 2: latency injection for APIs
  • part 3: fault injection for Lambda functions

All the way back in 2011, Simon Wardley had identified Chaos Engines as a practice that will be employed by the next generation of tech companies, along with continuous deployment, being data-driven, and organised around small, autonomous teams (think microservices & inverse-conway’s law).

There’s no question about it, Netflix has popularised the principles of chaos engineering. By open sourcing some of their tools – notably the Simian Army – they have also helped others build confidence in their system’s capability to withstand turbulent conditions in production.

There seems to be a renewed interest in chaos engineering recently. As Russ Miles noted in a recent post, perhaps many companies have finally come to understand that chaos engineering is not about “hurting production”, but to build better understanding of, and confidence in a system’s resilience through controlled experiments.

This trend has been helped by the valuable (and freely available) information that Netflix has published, such as the Chaos Engineering e-book, and

Tools such as chaos-lambda by Shoreditch Ops (the folks behind the Artillery load test tool) look to replicate Netflix’s Chaos Monkey, but execute from inside a Lambda function instead of an EC2 instance – ence bringing you the cost saving and convenience Lambda offers.

I want to ask a different question however: how can one apply the principles of chaos engineering and some of the current practices to a serverless architecture comprised of Lambda functions?

When your system runs on EC2 instances, then naturally, you build resilience by designing for the most likely failure mode – server crashes (due to both hardware and software issues). Hence, a controlled experiment to validate the resilience of your system would artificially recreate the failure scenario by terminating EC2 instances, and then AZs, and then entire regions.

AWS Lambda, however, is a higher-level abstraction and has different failure modes to its EC2 counterparts. Hypothesis that focus on “what if we lose these EC2 instances” no longer apply as the platform handles these failure modes for you out of the box.

We need to ask different questions in order to understand the weaknesses within our serverless architectures.

More inherent chaos, not less

We need to identify weaknesses before they manifest in system-wide, aberrant behaviors. Systemic weaknesses could take the form of: improper fallback settings when a service is unavailable; retry storms from improperly tuned timeouts; outages when a downstream dependency receives too much traffic; cascading failures when a single point of failure crashes; etc. We must address the most significant weaknesses proactively, before they affect our customers in production. We need a way to manage the chaos inherent in these systems, take advantage of increasing flexibility and velocity, and have confidence in our production deployments despite the complexity that they represent.

— Principles of Chaos Engineering

Having built and operated a non-trivial serverless architecture, I have some understanding of the dangers awaiting you in this new world.

If anything, there are a lot more inherent chaos and complexity in these systems built around Lambda functions.

  • modularity (unit of deployment) shifts from “services” to “functions”, and there are a lot more of them
  • it’s harder to harden around the boundaries, because you need to harden around each function as opposed to a service which encapsulates a set of related functionalities
  • there are more intermediary services (eg. Kinesis, SNS, API Gateway just to name a few), each with their own failure modes
  • there are more configurations overall (timeout, IAM permissions, etc.), and therefore more opportunities for misconfiguration

Also, since we have traded off more control of our infrastructure* it means we now face more unknown failure modes** and often there’s little we can do when an outage does occur***.

* For better scalability, availability, cost efficiency and more convenience, which I for one, think it’s a fair trade in most cases.

** Everything the platform does for you – scheduling containers, scaling, polling Kinesis, retry failed invocations, etc. – have their own failure modes. These are often not obvious to us since they’re implementation details that are typically undocumented and are prone to change without notice.

*** For example, if an outage happens and prevents Lambda functions from processing Kinesis events, then we have no meaningful alternative than to wait for AWS to fix the problem. Since the current position on the shards is abstracted away and unavailable to us, we can’t even replace the Lambda functions with KCL processors that run on EC2.

Applying chaos to AWS Lambda

A good exercise regime would continuously push you to your limits but never actually put you over the limit and cause injury. If there’s an exercise that is clearly beyond your current abilities then surely you would not attempt it as the only possible outcome is getting yourself hurt!

The same common sense should be applied when designing controlled experiments for your serverless architecture. In order to “know” what the experiments tell us about the resilience of our system we also need to decide what metrics to monitor – ideally using client-side metrics, since the most important metric is the quality of service our users experience.

There are plenty of failure modes that we know about and can design for, and we can run simple experiments to validate our design. For example, since a serverless architecture is (almost always) also a microservice architecture, many of its inherent failure modes still apply:

  • improperly tuned timeouts, especially for intermediate services, which can cause services at the edge to also timeout

Intermediate services should have more strict timeout settings compared to services at the edge.

  • missing error handling, which allows exceptions from downstream services to escape

  • missing fallbacks for when a downstream service is unavailable or experiences an outage

Over the next couple of posts, we will explore how we can apply the practices of latency and fault injection to Lambda functions in order to simulate these failure modes and validate our design.

Further readings: