Standing on the shoulders of giants: Colm on fixed work

Now, unlike making coffee by hand, one of the great things about computers is that everything is very repeatable, and you don’t have to trade away quality for scale. Teach a computer how to perform something once, and it can do it again and again. Each time is exactly the same. There’s still craft and a human touch, but the quality goes into how you teach computers to do things. If you skillfully teach it all of the parameters it needs to make a great cup of coffee, a computer will do it millions of times over.

Still, doing something millions of times takes more time than doing something thousands or hundreds of times. Ask a computer to add two plus two a million times. It’ll get four every time, but it will take longer than if you only asked it to do it once. When we’re operating highly reliable systems, variability is our biggest challenge. This is never truer than when we handle increases in load, state changes like reconfigurations, or when we respond to failures, like a power or network outage. Times of high stress on a system, with a lot of changes, are the worst times for things to get slower. Getting slower means queues get longer, just like they do in a barista-powered café. However, unlike a queue in a café, these system queues can set off a spiral of doom. As the system gets slower, clients retry, which makes the system slower still. This feeds itself.

It’s hard to think of a more critical function than health checks. If an instance, server, or Availability Zone loses power or networking, health checks notice and ensure that requests and traffic are directed elsewhere. Health checks are integrated into the Amazon Route 53 DNS service, into Elastic Load Balancing load balancers, and other services. Here we cover how the Route 53 health checks work. They’re the most critical of all. If DNS isn’t sending traffic to healthy endpoints, there’s no other opportunity to recover.

From a customer’s perspective, Route 53 health checks work by associating a DNS name with two or more answers (like the IP addresses for a service’s endpoints). The answers might be weighted, or they might be in a primary and secondary configuration, where one answer takes precedence as long as it’s healthy. The health of an endpoint is determined by associating each potential answer with a health check. Health checks are created by configuring a target, usually the same IP address that’s in the answer, such as a port, a protocol, timeouts, and so on. If you use Elastic Load Balancing, Amazon Relational Database Service, or any number of other AWS services that use Route 53 for high availability and failover, those services configure all of this in Route 53 on your behalf.

Route 53 has a fleet of health checkers, broadly distributed across many AWS Regions. There’s a lot of redundancy. Every few seconds, tens of health checkers send requests to their targets and check the results. These health-check results are then sent to a smaller fleet of aggregators. It’s at this point that some smart logic about health-check sensitivity is applied. Just because one of the ten in the latest round of health checks failed doesn’t mean the target is unhealthy. Health checks can be subject to noise. The aggregators apply some conditioning. For example, we might only consider a target unhealthy if at least three individual health checks have failed. Customers can configure these options too, so the aggregators apply whatever logic a customer has configured for each of their targets.

So far, everything we’ve described lends itself to constant work. It doesn’t matter if the targets are healthy or unhealthy, the health checkers and aggregators do the same work every time. Of course, customers might configure new health checks, against new targets, and each one adds slightly to the work that the health checkers and aggregators are doing. But we don’t need to worry about that as much.

One reason why we don’t worry about these new customer configurations is that our health checkers and aggregators use a cellular design. We’ve tested how many health checks each cell can sustain, and we always know where each health checking cell is relative to that limit. If the system starts approaching those limits, we add another health checking cell or aggregator cell, whichever is needed.

The next reason not to worry might be the best trick in this whole article. Even when there are only a few health checks active, the health checkers send a set of results to the aggregators that is sized to the maximum. For example, if only 10 health checks are configured on a particular health checker, it’s still constantly sending out a set of (for example) 10,000 results, if that’s how many health checks it could ultimately support. The other 9,990 entries are dummies. However, this ensures that the network load, as well as the work the aggregators are doing, won’t increase as customers configure more health checks. That’s a significant source of variance… gone.

What’s most important is that even if a very large number of targets start failing their health checks all at once—say, for example, as the result of an Availability Zone losing power—it won’t make any difference to the health checkers or aggregators. They do what they were already doing. In fact, the overall system might do a little less work. That’s because some of the redundant health checkers might themselves be in the impacted Availability Zone.

So far so good. Route 53 can check the health of targets and aggregate those health check results using a constant work pattern. But that’s not very useful on its own. We need to do something with those health check results. This is where things get interesting. It would be very natural to take our health check results and to turn them into DNS changes. We could compare the latest health check status to the previous one. If a status turns unhealthy, we’d create an API request to remove any associated answers from DNS. If a status turns healthy, we’d add it back. Or to avoid adding and removing records, we could support some kind of “is active” flag that could be set or unset on demand.

If you think of Route 53 as a sort of database, this appears to make sense, but that would be a mistake. First, a single health check might be associated with many DNS answers. The same IP address might appear many times for different DNS names. When a health check fails, making a change might mean updating one record, or hundreds. Next, in the unlikely event that an Availability Zone loses power, tens of thousands of health checks might start failing, all at the same time. There could be millions of DNS changes to make. That would take a while, and it’s not a good way to respond to an event like a loss of power.

One other utility that calls for excessive reliability is the configuration of foundational elements from AWS, reminiscent of Community Load Balancers. When a buyer makes a change to their Community Load Balancer, reminiscent of including a brand new occasion or container as a goal, it’s typically essential and pressing. The client could be experiencing a flash crowd and wishes so as to add capability rapidly. Beneath the hood, Community Load Balancers run on AWS Hyperplane, an inner service that’s embedded within the Amazon Elastic Compute Cloud (EC2) community. AWS Hyperplane might deal with configuration adjustments by utilizing a workflow. So, every time a buyer makes a change, the change is changed into an occasion and inserted right into a workflow that pushes that change out to the entire AWS Hyperplane nodes that want it. They will then ingest the change.

The issue with this method is that when there are numerous adjustments all of sudden, the system will very seemingly decelerate. Extra adjustments imply extra work. When methods decelerate, clients naturally resort to making an attempt once more, which slows the system down even additional. That isn’t what we wish.

The answer is surprisingly easy. Slightly than generate occasions, AWS Hyperplane integrates buyer adjustments right into a configuration file that’s saved in Amazon S3. This occurs proper when the shopper makes the change. Then, relatively than reply to a workflow, AWS Hyperplane nodes fetch this configuration from Amazon S3 each few seconds. The AWS Hyperplane nodes then course of and cargo this configuration file. This occurs even when nothing has modified. Even when the configuration is totally similar to what it was the final time, the nodes course of and cargo the most recent copy anyway. Successfully, the system is at all times processing and loading the utmost variety of configuration adjustments. Whether or not one load balancer modified or lots of, it behaves the identical.

You may in all probability see this coming now, however the configuration can be sized to its most measurement proper from the start. Even after we activate a brand new Area and there are solely a handful of Community Load Balancers lively, the configuration file continues to be as large as it’s going to ever be. There are dummy configuration “slots” ready to be stuffed with buyer configuration. Nevertheless, as far the workings of AWS Hyperplane are involved, the configuration slots there nonetheless.

As a result of AWS Hyperplane is a extremely redundant system, there’s anti-fragility on this design. If AWS Hyperplane nodes are misplaced, the quantity of labor within the system goes down, not up. There are fewer requests to Amazon S3, as an alternative of extra makes an attempt in a workflow.

In addition to being easy and sturdy, this method could be very price efficient. Storing a file in Amazon S3 and fetching it again and again in a loop, even from lots of of machines, prices far lower than the engineering time and alternative price spent constructing one thing extra advanced.

There’s one other attention-grabbing property of those constant-work designs that I haven’t talked about but. The designs are usually naturally self-healing and can mechanically right for quite a lot of issues with out intervention. For instance, let’s say a configuration file was in some way corrupted whereas being utilized. Maybe it was mistakenly truncated by a community drawback. This drawback shall be corrected by the following move. Or say a DNS server missed an replace totally. It would get the following replace, with out build up any type of backlog. Since a continuing work system is consistently ranging from a clear slate, it’s at all times working in “restore the whole lot” mode.

In distinction, a workflow kind system is normally edge-triggered, which implies that adjustments in configuration or state are what kick off the incidence of workflow actions. These adjustments first must be detected, after which actions typically must happen in an ideal sequence to work. The system wants advanced logic to deal with circumstances the place some actions don’t succeed or have to be repaired due to transient corruption. The system can be liable to the build-up of backlogs. In different phrases, workflows aren’t naturally self-healing, it’s a must to make them self-healing.

I wrote about big-O notation earlier, and the way fixed work methods are normally notated as O(1). One thing vital to recollect is that O(1) doesn’t imply {that a} course of or algorithm solely makes use of one operation. It implies that it makes use of a continuing variety of operations whatever the measurement of the enter. The notation ought to actually be O(C). Each our Community Load Balancer configuration system, and our Route 53 well being examine system are literally doing many hundreds of operations for each “tick” or “cycle” that they iterate. However these operations don’t change as a result of the well being examine statuses did, or due to buyer configurations. That’s the purpose. They’re like espresso urns, which maintain lots of of cups of espresso at a time regardless of what number of clients are searching for a cup.

Within the bodily world, fixed work patterns normally come at the price of waste. If you happen to brew a complete espresso urn however solely get a handful of espresso drinkers, you’re going to be pouring espresso down the drain. You lose the power it took to warmth the espresso urn, the power it took to sanitize and transport the water, and the espresso grounds. Now for espresso, these prices become small and really acceptable for a café or a caterer. There could even be extra waste brewing one cup at a time as a result of some economies of scale are misplaced.

For many configuration methods, or a propagation system like our well being checks, this concern doesn’t come up. The distinction in power price between propagating one well being examine outcome and propagating 10,000 well being examine outcomes is negligible. As a result of a continuing work sample doesn’t want separate retries and state machines, it could possibly even save power compared to a design that makes use of a workflow.

On the identical time, there are circumstances the place the fixed work sample doesn’t match fairly as nicely. If you happen to’re operating a big web site that requires 100 internet servers at peak, you can select to at all times run 100 internet servers. This definitely reduces a supply of variance within the system, and is within the spirit of the fixed work design sample, but it surely’s additionally wasteful. For internet servers, scaling elastically could be a higher match as a result of the financial savings are giant. It’s common to require half as many internet servers off peak time as throughout the peak. As a result of that scaling occurs day in and time out, the general system can nonetheless expertise the dynamism often sufficient to shake out issues. The financial savings will be loved by the shopper and the planet.

I’ve used the phrase “easy” a number of occasions on this article. The designs I’ve lined, together with espresso urns, don’t have numerous shifting elements. That’s a type of simplicity, but it surely’s not what I imply. Counting shifting elements will be misleading. A unicycle has fewer shifting elements than a bicycle, but it surely’s a lot more durable to journey. That’s not less complicated. design has to deal with many stresses and faults, and over sufficient time “survival of the fittest” tends to remove designs which have too many or too few shifting elements or aren’t sensible.

After I say a easy design, I imply a design that’s straightforward to know, use, and function. If a design is sensible to a workforce that had nothing to do with its inception, that’s a great signal. At AWS, we’ve re-used the fixed work design sample many occasions. You could be shocked what number of configuration methods will be so simple as “apply a full configuration every time in a loop.”