The nightmare of large distributed systems

Please find the updated version of this post here: https://piotr.westfalewicz.com/blog/2017/07/the-nightmare-of-large-distributed-systems/

There are certain classes of exciting problems which are surfaced only in a massively distributed systems. This post will be about one of them. It's rare, it's real and if it happens, it will take your system down. The root cause, however, is easy to overlook.

Assumptions

For this post sake, let's make some assumptions:

• Critical service is a service which is crucial from your business perspective, if it goes down, you don't make money, and your customers are furious.
• Your business is a veterinary clinic - or rather - immense, world-wide, distributed, most popular, top-notch veterinary clinic network. You make the money when the clients sign up online for visits. A critical service would be any service, which prevents the client from signing up. Let's call the path of signing up a critical path.
• The massive distributed system is a system consisting of, for example, 333 critical services, each one of them has it's own fleet of 1222 hosts (so in total, there are 406926 hosts running critical services in your system).
• Each critical service is managed by a two pizza team.

All in all, I'm thinking about something like this:

Large distributed system - exponential failure problem

We have a customer, who want's to sign up, the load balancer takes the request, there are 332 healthy services involved, and there is this one red-star service, which is asked 4 times on the critical path. If it goes down, the customer can't sign up. I would compare it to kind of SELECT N+1 problem.

In such a big system, it may happen that a particular, red service will be called 10+ times on a single sign up. The sign up itself may have many steps, and each step may call many services, including our red service, many times. Architecture smell... design smell, you may tell... Yes and no. Think about the scale. 333 critical services, times two pizza teams = around 1333 people involved in just the critical path. Many more involved for non-critical services. I think this situation is inevitable at scale.

Problems

Availability

When the red service is down - that's an obvious situation, easy to spot. What happens if our service has some kind of issues that leads to 90% availability? What is the customer impact, assuming 10 calls made to the red service during sign up? Unfortunately, only 35% of customers will be able to go through the critical path. Why? The first call has 90% chances of success, the second call has also 90% chances of success, but both calls combined have only 0.9*0.9 chances of success. For 10 calls, that will be 0.9^10= ~35%. Unfortunately, in case of "system is down" event, while having enormous amount of alarms going off, tens of services impacted, loosing many customers each second, 10% drop in availability in one service is very, very easy to miss.

Timeouts

The second nasty problem is with the timeouts. The origin of the timeouts can span network problems, hosts problems, code bugs and more. It may happen that random 10% of requests will time out. Let's assume the red service has normally 20ms latency (on average). It's being called 10 times, so total wait time on a single sign up is 200ms. However, with those 10% timeouts, let's say 1.5 seconds each, changes the average latency to 20ms * 0.9 + 1500ms * 0.1 = 168ms. Now the total wait time for the red service is 1680ms. Probably the customer is getting timeouts at this point. It's nasty, because the 10% timeouts issue can be well hidden. For example, it might be a core router which is misbehaving, but still works and our monitoring doesn't discover the packet loss.

TL;DR;

Even tiny issues - like 10% timeouts or 10% availability drop - of only one service out of hundreds on a critical path in large distributed system can take it down.

Please note: the views I express are mine alone and they do not necessarily reflect the views of Amazon.com.

What does it mean to design a highly scalable system?

Please find the updated version of this post here: https://piotr.westfalewicz.com/blog/2017/06/what-does-it-mean-to-design-a-highly-scalable-system/

Please note: the views I express are mine alone and they do not necessarily reflect the views of Amazon.com.

It's surprising how the volume of data is changing around the world, in the Internet. Who would have thought 10 years ago, that in future a physical experiment will generate 25 petabytes (26 214 400 GB) of data, yearly? Yes, I'm looking at you, LHC. Times are changing, companies are changing. Everyone is designing for scale a tad different. And that's good, it's important to design for the right scale.

Over scaling

Let's assume you are a startup building a brand new, unique CMS (unlike the 2000 other ones). Is it worth thinking about NoSQL/Cassandra/DynamoDB/Azure Blob Storage? Probably not. It's safe to assume that most of data will fit into one small or medium SQL database. When performance problems will appear, that's good. It means your startup is working (or you just don't know SQL...). That means you have clients, paying clients. Also, at that point you will have probably completely different idea about the system - you gone from "no clients and your imagination only" state to - "working product with customers and proven solutions" state. You can reiterate over your architecture now. Hopefully you have founds now. What I've heard multiple times is failing a startup because someone created complicated, scalable system for 321 000 clients. All the money is spent on IT, none on business. Total failure.

No need for scaling

Now, some systems don't have to scale, or the requirements with scale progress slower than the progress of the new hardware (so effectively they fall into the first category). Probably some ERP systems for most medium sized companies are good example. Large SQL database, maybe an Azure Blob Storage/DynamoDB and all scalability problems are solved.

Some scaling needed

As I mentioned before, sometimes throwing an NoSQL database into the "ecosystem" solves the problem. Unfortunately for us, geeks, that's usually the case.

Scaling definitively needed

There are times when people say "Azure Blob is highly scalable". Well, that statement is a joke. Azure Storage isn't scalable at all. Theirs 20 000 Requests Per Second limit sometimes might be a only a tiny part what you need. Furthermore, there are other, hard limits: Azure Storage Scalability and Performance Targets. To be fair, DynamoDB has limits too. However, you can contact support and request as much throughput as you need. There is one more catch too - pricing. In Azure you pay for requests amount (not throughput) + storage, in DynamoDB for provisioned throughput + storage. Depending on your use case, one might be much cheaper than the other.

Unique scale

Finally, there are times when you need to build a new solution and even have a dedicated team for your challenge. Let's imagine you work at a big company, your company has hundreds of thousands of services, each service is called many thousands of times per second, and each call is generating logs. You want a solution to store the logs for months and search within them. The scale is unusual, and it's expected the number of calls will grow 30% year over year, having 4x more traffic on some days. This time, you can probably start thinking about your own, new storage engine, strictly coupled to your needs.


TL;DR: covered some scalability levels, turns out everyone should scale different.