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Comment by huzaif | Hacker News Reader

We can now achieve pretty high scalability from day 1 with a tiny bit of "engineering cost" up front. Serverless on AWS is pretty cheap and can scale quickly.

App operations: |App| <-> |Api Gateway| <-> |Lambda| <-> |Dynamo DB|

Add in Route53 for DNS, ACM to manage certs, Secrets Manager to store secrets, SES for Email and Cognito for users.

All this will not cost a whole lot until you grow. At that point, you can make additional engineering decisions to manage costs.

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Great, but this reads like a particularly blunt Amazon ad. Is there a way to achieve "high scalability" without selling my soul to Amazon?

I think if you use something like serverless, you can abstract the cloud layer. I've never used it for anything more than a toy project though.

https://serverless.com/

Yes, it does read like that.

In the context of a start-up, cost is a big factor and then perhaps (hopefully) handling growth. You could start small and refactor apps/infrastructure as you grow but I am unsure how one could afford to do that efficiently while also managing a growing startup.

On the selling soul to cloud provider, I don't see it like that. I have a start-up to bootstrap and I want to see it grow before making altruistic decisions that would sustain the business model.

Once you are past the initial growth stage, there are many options for serverless, gateway, caches, proxies that can be orchestrated in K8 on commodity VMs in the datacenter. Though this is where you would need some decent financial backing.

(I am not associated with Amazon, Google or Azure. I do run my start-up on Azure.)

I'm down a similar route, but I must point out that beyond a certain number of users / scale, Serverless becomes cost-prohibitive. For instance, per back-of-the-napkin calculation, the Serverless load I run right now, though very cost-effective for the smaller userbase I've got, would quickly spiral out of control once I cross a threshold (which is at 40k users). At 5M users, I'd be paying an astonishing 100x the cost than if I hosted the services on a VPS. That said, Serverless does reduce DevOps to an extent but introduces different but fewer other complications.

As patio11 would like to remind us all, we've got a revenue problem, not a cost problem. [0]

[0] https://news.ycombinator.com/item?id=22202301

Yes, sell your soul to Google.

The big clouds have similar enough products, just the names are changed, so at a high level, GP's list of AWS products can be swapped with eg, Azure's product names. https://www.wintellect.com/the-rosetta-stone-of-cloud-servic...

Sadly, anything more in-depth than that, you'll need to sign an NDA with AWS to learn anything about the performance limits of their services (eg Redshift), and you won't get that unless you're already a big customer there. Azure's not going to be falling over themselves to let you know where they fall short, either. This is vendor lock-in, and is why there are so many free cloud credits to be had to startups.

This is also a reason I believe SaaS companies will find it is harder than they realized to arbitrage between clouds, and business models based on that may not be able to get that right.

High scalability always revolves around the storage layer. There are plenty of options in the free software world; MongoDB, Redis, variants of MySQL/Postgres replication, Cassandra, FoundationDB, and many more.

Using one of those is where you'll spend most of your operational time if you really need that level of scalability. Most people don't, but the options are there if you really need them.

If you are happy with your storage layer, which most people are, the rest scales horizontally pretty easily. And there are plenty of free things you can use to get what a cloud provider gives you.

The CDN is always going to be tough to replicate on your own. In the end, latency is bounded by the speed of light, so you can only bring your files closer to your users. I wouldn't expect you to build one of these yourself; just buy one until you're the size of Google.

> App operations: |App| <-> |Api Gateway| <-> |Lambda| <-> |Dynamo DB|

Your app should be designed to scale horizontally; don't keep any state in your app, delegate it to your storage layer so you can scale a CPU-intensive app up across multiple servers.

There are quite a few API gateways around; Ambassador comes to mind but there are a million. I personally use raw Envoy for everything. I was load-testing my website the other day and pushed 5000qps through it from my cable connection before I decided "it's probably fine". (I started dropping frames on the Twitch stream I was watching, though ;)

There are plenty of "serverless" frameworks that emulate what Lambda does. knative comes to mind. I have not experimented with them in depth, but am intrigued by the idea. (I am more intrigued by turning config files into webassembly-compiled programs, to make existing apps more configurable at runtime. This is like serverless, but less general.)

> Add in Route53 for DNS, ACM to manage certs, Secrets Manager to store secrets, SES for Email and Cognito for users.

CoreDNS scales nicely and has an API. cert-manager is an open source way of obtaining certificates (though it's tightly coupled to Kubernetes); either ACME (letsencrypt) or your own root CA. There are a bunch of free software secret managers; Vault, bitnami-labs/sealed-secrets, etc. I personally use git-crypt ;)

Email deliverability is always going to be an issue. Like the CDNs, you might want to delegate it while you're small. Use anything except Mandrill.

I bet that DNC Iowa primaries app was serverless. Problem solved! [dusts off hands].

I hated DynamoDB. What good is there about it other than convenience?

If you don't mind watching a video: https://www.youtube.com/watch?v=6yqfmXiZTlM

I've found that KV stores like DynamoDB make for a good control-plane configuration repository. For instance, say, you need to know if a client, X, is allowed to access a resource, Y. And, say, you've clients in order of millions and resources in order of 100s, and you've got very specific queries to execute on such denormalized data and need consistently low latency and high throughput across key-combinations.

Another good use-case is to store checkpointing information. Say, you've processed some task and would like to check-in the result. Either the information fits the 400KB DynamoDB limit or you use DynamoDB as a index to a S3 file.

You could do those things with managed or self-hosted RDBMS, but DynamoDB takes away the need to manage the hardware, the backups, the scale-ups, and the scale-outs, reduces ceremony whilst dealing with locks, schemas, misbehaving clients, and myraid other configuration knobs whilst also fitting your queries patterns to a tee.

KV stores typically give you consistent performance on reads and writes, if you avoid cascading relationships between two or more keys, and make just the right amount of trade-offs in terms of both cross-cluster data-consistency and cross-table data-consistency.

Besides, in terms of features, one can add a write-through cache in front of a DynamoDB table, can point-in-time-restore data up to a minute granularity, can create on-demand tables that scale with load (not worry about provisioned capacity anymore), can auto-stream updates to Elasticsearch for materialised views or consume the updates in real-time themselves, can replicate tables world-wide with lax consistency guarantees and so on...with very little fuss, if any.

Running databases is hard. I pretty much exclusively favour a managed solution over self-hosted one, at this point. And for denormalized data, a managed KV store makes for a viable solution, imo.

All good points, but one thing people should look at very closely before choosing DynamoDB as a primary db is the transaction limits. Most apps are going to have some operations that should be atomic and involve more than 25 items. With DynamoDB, your only option currently is to break these up into multiple transactions and hope none of them fail. But as you scale, eventually some will fail, while others in the same request succeed, leaving your data in an inconsistent state.

While this could be ok for some apps, I think for most use cases it's really bad and ends up being more trouble than what you save on ops in the long run, especially considering options like Aurora that, while not as hands-off as Dynamo, are still pretty low-maintenance and don't limit transactions at all.

It will cost an arm and a leg when you eventually grow though.

AWS seems like it would be expensive long term.

Between my issues with AWS currently and the exterior look of Amazon, I'm skeptical AWS is a good solution.

Like most providers, it does depend. Some products are priced very competitively while others seem over-the-top. For smaller companies, the cloud is a cheaper starting point for many systems but even for larger organisations, there are savings to be made by outsourcing your servers. Do you know how much it costs to install and maintain a decent air-con system for your server room?

One of the other major advantages of cloud is that you can save a lot in support staff. Compare the wages of even 1 decent sysadmin looking after your own hardware compared to several thousand dollars of AWS and it's still loads cheaper. Hardware upgrades, OS updates etc. are often automatic or hidden.

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