Back in August 2017 I wrote a post about: Who has the serverless advantage? AWS Lambda vs GCP Cloud Functions vs Azure Functions. We were three years into commercial serverless offerings and there were still a lot of limitations. My main observation was:
What really matters is the availability and consumption of other services within the cloud provider ecosystem.
Being able to execute functions in response to events is only as useful as what you can actually do within the execution pipeline. This is where the services differ — their ability to to pass data to backend services, perform calculations, transform data, store results, and quickly retrieve data.
AWS benefits from being the leader in cloud by the sheer size of its product portfolio. The core services of compute, storage and networking are commodities — the differentiation is what’s built on top of them.
At the time, Azure Functions and AWS Lambda were similar in functionality and significantly ahead of Google Cloud Functions, which was still in Beta. So how have things progressed for each provider since then, and what does this say about their approach to serverless?
It is an interesting coincidence that both Alexa and Lambda were released in Nov 2014, which suggests that the requirements of one product may have influenced the other. Alexa, a deployed on the Echo devices and in other hardware, in an inherently event-driven product. It is only doing things when you trigger it. It fits the Lambda execution model exactly.
If we assume AWS created Lambda to service Alexa as the first customer, this is important because it means the product development is driven by internal stakeholders who provide the initial demand and use case validation well in advance of input from the general market, rather than copying competitors.
Given how well formed Lambda was on release, I think this is a reasonable assumption. The continued development of the product since 2014 means it has a significant headstart, and the internal use case meant there was always an incentive to invest even in advance of adoption by external customers.
This means that Lambda essentially had no competition for 2 years until Azure Functions and Google Cloud Functions came out in 2016, and Google wasn’t really in the game until March 2017 due to the gap between the Feb 2016 Alpha and March 2017 Beta.
Today, Lambda supports runtimes in Java, Go, PowerShell, Node.js, C#, Python, and Ruby and has added complex functionality such as Step Functions and integration into other AWS products. Many other AWS products generate events which can be processed in Lambda which is proving how you can tie together the full ecosystem to deal with logging, metrics and security in particular. The original Alexa use case is still valid, but many of these improvements are clearly driven by external customers.
Lambda@Edge is also interesting because it allows you to execute your functions in CloudFront CDN locations, allowing for low latency applications close to the user. However, it does have some major limitations (such as 1MB response limits and a maximum of 25 deployed functions) so is clearly still an immature product. But what we know about AWS suggests this will progress rapidly. As nicely visualised by CloudZero, serverless is truly a spectrum:
Azure offers very similar functionality when compared with Lambda, with more language support in experimental runtimes e.g. Bash and PHP.
A big difference is the availability of on-premise functions, where you can run the workers wherever you wish (so long as you have a Windows server). Until the recent release of AWS Outposts, this was a major difference in product philosophy between AWS and Azure. The former was focused on pure cloud, with functionality to help you move there whereas the latter adopted a hybrid approach from the beginning. This makes sense for Azure given Microsoft’s history of on-premise software. Now we see AWS expanding to try and include the relatively small but still significant share of workloads which will always run on-premise.
But it’s not just functions being used – as I mentioned in the original article, the rest of the Azure product portfolio is just as important. HIBP makes extensive use of Azure Table Storage, which you could call another “serverless” product because it is a database as a service – no need to deal with running SQL Server. This is where AWS has often had a lead in the past because of the range of products they have. That’s less relevant today because both Azure and Google have products in all the key areas: storage, compute and databases.
Google Cloud Functions
GCF has only been Generally Available since July 2018 which makes it the youngest platform of the major three. However, it has actually been available in public since 2016. This is something I’ve called App Engine Syndrome in the past:
Cloud Functions seems to be suffering from App Engine syndrome — big announcements of Alpha/Beta features, followed by silence/minimal progress until the next big announcement the following year. The focus of Google’s serverless ambitions seems to be Firebase, not Cloud Functions.
The release notes show regular updates and new functionality during the beta but there was nothing between July 2018 and November 2018.
This is somewhat frustrating because at Server Density we made extensive use of Google Cloud Platform and it is my preferred vendor of the big three – they have the best console UX, documentation and APIs, and I’ve found all their GA products to be well designed and robust.
Indeed, GCF is a good product that is easy to work with through the command line and console. It has good integration into the rest of GCP whether as direct triggers, through pub/sub, storage, monitoring and most recently scheduled functions. The lack of development on the core product is somewhat misleading because of how it continues to be built into the rest of the platform, but it is concerning that the development velocity of what is a growing technology in the industry is seemingly being neglected. At least publicly, and in comparison to the rapid progress of AWS Lambda.
My criticism of Google Cloud has long been that not enough of Google’s own products use it. Of course, they use the underlying technologies and the physical infrastructure, but unlike Amazon.com using AWS for their critical retail operations, it is unclear how much of Google is using the same platform that GCP customers are using. If Google had a use case similar to AWS and Alexa, that might provide incentives to increase the velocity of development in addition to any GCP customers. Maybe they do. But we still see Google falling further and further behind.
AWS, Azure and Google have the position of being the main three cloud providers. Indeed, they are the only ones that matter for general use cases. Their advantage is the vast resources each company can invest in infrastructure and product development. However, that doesn’t mean they are the only players in serverless. There are other, specialist providers who have particular use cases in mind.
I mentioned Cloudflare in the context of Have I Been Pwned above. Whilst all of the main processing for HIBP happens on Azure, over 99% of traffic is actually being served by Cloudflare’s CDN and their Workers product.
Moving caching and request response serving to the edge reduced HIBP’s cost from $9 per day to $0.02 per day. This was not just by being able to serve many requests within Cloudflare’s free tier but also by eliminating almost 500GB of Azure network traffic:
As HIBP shows, avoiding network traffic is a second order benefit of using serverless. Colocating other services on the same platform eliminates a large amount of traffic so all you need to deal with is internal networking. Serverless certainly means not having to deal with scaling server infrastructure and so spending only based on what you use. But it also means that where you use other products like CDN, you can avoid costly traffic back to the origin.
This is a big part of the use case for why we launched EdgeEngine at StackPath – we have huge volumes of CDN traffic where we serve as the edge delivery provider, but requests still have go to back to the origin for dynamic processing. One of the major selling points of StackPath CDN is the diversity of PoP locations around the world. Your origin might be in AWS US East but if you are serving traffic to Spain, you will still have a significant volume of requests needing to go back to the centralised cloud provider.
Network costs at cloud providers are one of the biggest hidden taxes on using public cloud, especially if you use third party services like a CDN. So if you can eliminate those requests entirely you can not only provide better application latency but also save on your bill. Now we have our EdgeEngine product, you can do things like API token validation without it ever having to hit that central infrastructure.
AWS Lambda vs Azure Functions vs Google Cloud Functions
Lambda created the market for serverless and continues to innovate and lead on functionality. It benefits from the vast AWS product portfolio so is often a default choice for those already on AWS.
Azure Functions are just as mature as Lambda. It’s the Lambda equivalent for Microsoft fans, so is an easy default if you use Azure. .NET languages are well supported, as you would expect.
Google Cloud Functions still has the same problems of slow product development but the overall platform portfolio has improved significantly. It’s likely to the first choice for developers starting from scratch, just because the overall experience of working with Google Cloud is better than AWS or Azure. Google is also innovating in other areas, such as with the Cloud Spanner database product. GCF may benefit from those who want something in the platform they chose for other reasons.
Ultimately, serverless is not just about functions. If you want more than just simple request manipulation or one time processing then you need to be able to connect to a datastore and other services like logging and monitoring. It’s been possible to build full applications using only serverless, like HIBP, for a long time. How sophisticated those applications get will now depend on what other services appear to support serverless functions, and what the role for low latency edge deployment plays in adding to the use cases. To quote Troy Hunt of HIBP:
So, in summary, the highlights here are:
1. Choose the right storage construct to optimise query patterns, performance and cost. It may well be one you don’t expect (as blob storage was for me).
2. Run serverless on the origin to keep cost down and performance up. Getting away from the constraints of logical infrastructure boundaries means you can do amazing things.
3. The fastest, most cost-effective way to serve requests is to respond immediately from the edge. Don’t hit the origin server unless you absolutely have to!Troy Hunt
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