Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Lies, Service Level Agreements, Trust and Failure Mores

DZone's Guide to

Lies, Service Level Agreements, Trust and Failure Mores

Free Resource

The State of API Integration 2018: Get Cloud Elements’ report for the most comprehensive breakdown of the API integration industry’s past, present, and future.

I had a very interesting discussion with Kelly Sommers in twitter. But 140 characters isn’t really enough to explain things. Also, it is interesting topic in general.

Kelly disagreed with this post: http://www.shopify.ca/technology/14909841-kafka-producer-pipeline-for-ruby-on-rails

image

You can read the full discussion here.

The basic premise is, there is a highly reliable distributed queue that is used to process messages, but because they didn’t have operational experience with this, they used a local queue to store the messages sending them over the network. Kelly seems to think that this is decreasing reliability. I disagree.

The underlying premise is simple, when do you consider it acceptable to lose a message. If returning an error to the client is fine, sure, go ahead and do that if you can’t reach the cluster. But if you are currently processing a 10 million dollar order, that is going to kinda suck, and anything that you can do to reduce the chance of that happening is good. Note that key part in this statement, we can only reduce the chance of this happening, we can’t ensure it.

One way to try that is to get a guaranteed SLA from the distributed queue. Once we have that, we can rely that it works. This seems to be what Kelly is aiming at:

image

And that is true, if you could rely on SLAs. Just this week we had a multi hour, multi region Azure outage. In fact, outages, include outages that violate SLAs are unfortunately common.

In fact, if we look at recent history, we have:

There are actually more of them, but I think that 5 outages in 2 years is enough to show a pattern.

And note specifically that I’m talking about global outages like the ones above. Most people don’t realize that complex systems operate in a constant mode of partial failure. If you ever read an accident investigative report, you’ll know that there is almost never just a single cause of failure. For example, the road was slippery and the driver was speeding and the ABS system failed and the road barrier foundation rotted since being installed. Even a single change in one of those would mitigate the accident from a fatal crash to didn’t happen to a “honey, we need a new car”.

You can try to rely on the distribute queue in this case, because it has an SLA. And Toyota also promises that your car won’t suddenly accelerate into a wall, but if you had a Toyota Camry in 2010… well, you know…

From my point of view, saving the data locally before sending over the network makes a lot of sense. In general, the local state of the machine is much more stable than than the network. And if there is an internal failure in the machine, it is usually too hosed to do anything about anyway. I might try to write to disk, and write to the network even if I can’t do that ,because I want to do my utmost to not lose the message.

Now, let us consider the possible failure scenarios. I’m starting all of them with the notion that I just got a message for a 10 million dollars order, and I need to send it to the backend for processing.

  1. We can’t communicate with the distributed queue. That can be because it is down, hopefully that isn’t the case, but from our point of view, if our node became split from the network, this has the same effect. We are writing this down to disk, so when we become available again, we’ll be able to forward the stored message to the distributed queue.
  2. We can’t communicate with the disk, maybe it is full, or there is an error, or something like that .We can still talk to the network, so we place it in the distributed queue, and we go on with our lives.
  3. We can’t communicate with the disk, we can’t communicate with the network. We can’t keep it in memory (we might overflow the memory), and anyway, if we are out of disk and network, we are probably going to be rebooted soon anyway. SOL, there is nothing else we can do at this point.

Note that the first case assumes that we actually do come back up. If the admin just blew this node away, then the data on that node isn’t coming back, obviously. But since the admin knows that we are storing things locally, s/he will at least try to restore the data from that machine.

We are safer (not safe, but more safe than without it). The question is whatever this is worth it? If your messages aren’t actually carrying financial information, you can probably just drop a few messages as long as you let the end user know about that, so they can retry. If you really care about each individual message, if it is important enough to go the extra few miles for it, then the store and forward model gives you a measurable level of extra safety.

Your API is not enough. Learn why (and how) leading SaaS providers are turning their products into platforms with API integration in the ebook, Build Platforms, Not Products from Cloud Elements.

Topics:

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}