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From YouTube: How to Include Latency in SLO-based Alerting - Björn Rabenstein, Grafana Labs

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How to Include Latency in SLO-based Alerting - Björn Rabenstein, Grafana Labs

Chapter 5 of “The Site Reliability Workbook” is an excellent study of how to create meaningful alerts based on SLOs by measuring the rate at which the error budget is burned over different time windows. This rather complex approach is blissfully straight-forward to implement in Prometheus, as demonstrated in the chapter itself. However, all of it is based on error rates, leaving latency concerns out of scope. Björn “Beorn” Rabenstein will explore various options of applying the same ideas to latency-based SLOs. The foundation is a precise and meaningful definition of the SLO. From there, Beorn will explore various techniques to translate the SLO into an error budget and how to measure its burn rate with Prometheus. Once that is done, creating error-budget-based alerts is relatively simple. There are, however, pitfalls and trade-offs along the way, which Beorn will help cope with.

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A

Than you, I shall mention: I started to work for karana half a year ago before that I was at SoundCloud just mentioned that, because it makes a bit of sense. You understand some of the slides here. Also I'm kind of this Prometheus person and Prometheus had its childhood and SoundCloud, so that is all connected more or less. No, it's progress.

A

No okay! We have three acts in this talk. The first act is about the things. This talk is not about what I don't want to talk about, which is a bit weird, but I have to set the speech of it here. Slo is a word that does not simply put into an SL: no is it no hello. Does it work yeah, there's also as a lie and SLA, and all these acronyms have a lot of meaning and I could talk for an hour just about that I kind of assume.

A

We have a consensus, whatever working consensus on that and I don't have to go into the detail. If you want to read up there's a lot of literature, the blue as a Reebok that Google published like three years ago.

A

By now it's one of my favorite tech books and like I, guess you like it too yeah so whatever like you, should read it and it has a whole chapter, chapter 4, which is to vote LoZ and also you see just network SLA SLO as why it's all there, even in the beginning of the chapter, so we you can get on the same page if you read that also I'm, not in Cervantes, about s, allows that can go wrong. Slo! It's like a favorite word of a sorry course.

A

It's so great form design decisions to set the right goals to set alerting in the bed way, but is everything that is good totally overdo.

A

This talk, which I saw it on earlier this year on and off right? Perhaps it's.

A

Cause I did something wrong here. Let's put it here. First, that helps perhaps it works know. I might have squeezed the cable.

B

A

I would not be so I'll try if this fails or hold switch to this one. No problem you'll see. Okay, thank you, as I recall earlier this year in Dublin, a mere, not America version for America as well. They were brother of super-interesting, two oxen. Now they aren't talked about how s l's can actually go wrong, and this is Kai put this here as they like enjoy sls responsibly slide. This is really nice to hear about what can go wrong.

A

So if I talk about it, I won't download erase the false impression that this is always just working. So you have to think. As always, you have to keep thinking. Another target I saw recon Amir was latency, SLI was done right and I thought. Oh great. This is probably exactly my talk and I have already had already submitted the talk for puke on and a third purpose I'm just telling the same thing again as heinrich hoffmann back then it's an awesome talk. Unfortunately, it's totally different from my talk.

A

This is way more about, like the science behind measuring latency SLO s, which is extremely important. This talk is fairly hands-on. How you could do it and I think it's all mathematically correct, but in your situation you probably have to do things slightly different and once you do this once you deviate from the recipe, you should actually know the science behind it and then you should watch this talk. It's all video recorded I have provided the links so nice nice to watch.

A

Okay, then there's another hot topic. This is this touches with all metrics versus logs thing. That is discussed more and more viciously in our community I'm talking about alerting on as no breaches or like. Let's say you get close to breaching your SLO and you want to get an alert in real time. That's matrix price in the example I'm showing here, namely I'm using Prometheus. You could do the same thing with any other metric space monitoring system.

A

The other thing is: if you actually have a breach of an SLA, you know I'm in some ways changing the letters here like you have a customer. You have an agreement and you have not fulfilled your SLA. Then your customer gets some reimbursement, I guess and that's no exact science. You want to give them exactly the exact number of currency units as reimbursement, and then you don't go to your metric system. You go to your logs and you actually do an exact calculation. How much you have to return.

A

So that's a different thing, because that doesn't have to be a real-time. It has to be exact, you alerts, they should not be noisy, but they don't have to be like rocket science precise now you could totally do the alerting based on your logs processing if your Knox processing is reliable enough and fast enough and everything some vendors offer that these days I think the principles I will tell you here. They still apply because in the end, you're just creating metrics on the fly from your logs.

A

So all this old Rob is not supposed to tell you that you should use Prometheus for that. It's just kind of the easiest way to understand the ideas behind it, but it applies to all other metrics based system and arguably do Knox processing as well. Okay, you can actually do LOX processing allows you to divide a customer alerting or something even if you have millions of customers, not sure if that's a good idea, but that belongs into this whole cardinality thing, but I don't want to get deeper.

A

This is anyway, all this stuff that I don't talk about. Okay, alerting the other part in the title. Slo was the first like important thing. Now we get into alerting how is alerting supposed to work, there's also interesting stuff in the blue s or ebook Chapter six. This is actually the evolution of this classical viral google document that was titled. My alerting philosophy you might have heard about it by the same person who wrote the chapter. So that's where this whole kool-aid happened with symptom based, alerting and alerting on SO.

A

It's actually a really nice example of symptom based, alerting it's really good, alerting and to understand the whole philosophy behind it and also how to not overdo it. You can read that chapter. Also, the blue esso ebook has a chapter chapter, number, 10, practical learning from time series data. That's also nicknamed the boron chapter because explains how Google is traditionally doing. Metrics based alerting at monitoring. Bodmin is the system that was the spiritual grandfather of Prometheus, so that also helps you a lot to understand like basic concepts but you're, not talking about here.

A

Okay, this is the most important prerequisite. Reading I mentioned in the abstract. I hope you all did your homework and read it if you haven't read it before writing on soo. So this is not the asari book. This is the site, reliability workbook, which is the what they call it like the sequel or something the it's kind of a second sre book, with more practical, hands-on recipe, style instructions and stuff. So this is super hands-on and it's really nicely done. This is one of my favorite chapters.

A

It goes like iteratively through different approaches of alerting on SL O's, and in that way you really understand it like this, the first obvious way of doing it and then what is going wrong and then you understand why you have this really like more refined way of doing it, like the number six iteration is the one I want to go with here. So ideally, you have read this all. If you have read it, perhaps you have read this as well, so this is.

A

This is where I mention I was itself up before this is a blog post. I've wrote not too long ago, like in summer, I think when this came out, I was already a co-founder, but anyway I was a friendly transition, so I still wrote blog post, so this is even more hands-on.

A

This explains how we applied this chapter, 5 of the asari workbook at SoundCloud, on alerting or Nessarose, and it gives you like all the contexts and details and everything so might also be a nice read if you're lazy, I gave a talk version of that blog post at the Prometheus meetup in Berlin, which got recorded I only discovered that just before I made those lights so another link. This is not my own talk, so if you haven't read all of this, you can just be lazy and watch this talk.

A

This is this prequel for the sequel, whatever part 1 of the movie, this is property of the movie.

A

This like, if you haven't read this I, think you will still get something from this talk. Some parts might be a bit weird when we look at like real prom, cue expressions or stuff like that. But later you can read it up and then all the pieces will fall into place in your head. I'm sure also I mean I'm not going to talk about this. This is the of the part I'm not going to talk about, because this is all just about errors.

A

This is only about error budget if requests and in an error, and not yet latency we'll come to that later, but to set the stage. Even if you read everything, this is the super quick recap. This SLO base alerting from Chapter five of the workbook the whole trick here. Is you alert over different time windows? So you have in this table. You have different so-called long windows.

A

You calculate average error rates over those windows and then you alert on them be the easiest to understand is the one here, so you have four three day fairly normal window and if your average error rate is exactly factor one, the error rate you could sustain, while not blowing your error budget. Let's say you won 99.9 percent availability. Your error budget is 0.1 percent error over a billing period, usually a month. So if you have exactly this open, one percent error rate factor one over three days.

A

On average, that means you're exactly burning your error budget as fast as you are allowed to, which is kind of not too bad. But if anything happens in the rest of the month, you are screwed right. So this is why you would burn ten percent. Three days is ten percent of a month. You should do something about it, so you get a ticket because you don't have to wake somebody up. This is a long-term, slow error, but your burn doing work hours.

A

You can find out what's wrong in your system so and then it goes up and up it gives is faster, faster and the fastest one. It's the one hour window and you have a pretty generous factor of 14.4. So that means with open or percent error rate. If you right is 1.44 on average over one hour, you get a page and you have burned the moment. You get the page. You have burned two percent of your monthly higher budget, which is only happening in one hour. That's way too fast.

A

This is where somebody has to wake up and fix it. So Inc, you ssin, tells you that one hour average is right too long for a page. If you have four serious out, if you want to know within a minute, but the intuition is wrong- and this is more of this is the second most important graph from the chapter. If you actually have like a one point, four four percent error rate.

A

It's all logarithmic that's about here, then it actually takes an hour to detect that if you have a hundred percent outage like in a one-hour window, if everything is fine, zero percent error and then you have a very short amount of 100% errors. The average is one point four, four percent within minutes. So that's the interesting thing within less than a minute. This alert will page you, even if it's an average over one hour, so this alert has actually no problem with being too slow. If you have a full outage, it's pretty quick.

A

Only if you hit this threshold here, one point four: four percent: it ceases to fire, but then this other six hour window takes over and then the ticketing thing takes over read up the details. If you don't get this now, this is just a recap, but the interesting thing is you can like you, look at pretty long averages and still get meaningful alerts on slow and fast arrow burn, and this is super noisy. It only alerts you if you actually are in danger of blowing your budget. The problem with this alert is actually different.

A

One and I have to repeat this here as well, because this is the most important graph from the chapter 5. It's my laser pointer here. So the blue curve is the actual error rate, as it happens, so at t plus ten minutes, you have an outage. Whatever a few instances of you, micro servers go bad and you have like whatever fifteen percent error rate. Sorry, fifteen percent error rate and what happens the red line? Is this one hour average?

A

So it takes five minutes for this red line to go above the one point, four: four percent error threshold. So after five minutes, even with this partial outage, you get a page is presumably the engineer. On-Call wakes up fixes the problem within five minutes. Good engineer error. It goes down to zero engineer goes back to bed right. This is how it works.

A

The problem is this red light will now stay above the threshold for an hour because for an hour you have this arrow peak in your one hour average and the alert is firing all the time. So what do you do? If you have this? You snooze it on pager duty or your silence, the alert on Prometheus, something like that, all good! You go back to red, actually, not good, because if something happens here like let's imagine, the arrow strike is happening again. For some reason, nobody will notice because you have silenced the alert.

A

So what you want to go back to bed and be confident that you get woken up if the outlet returns you want the alert to reset. This is how they call it pretty quickly and now this is the trick, and it's like very simple, but ingenious. You take the five-minute average, and this is where we have the short window here right so for the long run or one or you take another window of five minutes, and you also calculate the average error rate you couldn't alert on this alone.

A

It would be way too noisy, like a short aerospike, would page you. This is what you don't want. That's why you have error budgets. You are allowed to have a few arrow spikes, but if you alert on both curves being above the threshold, then you get the best of both worlds like this page is here, because both green and red are both the phone and once you have fixed the outage, the green curve will go down really quickly. So from this moment on, you alert ceases to fire and that's pretty good right.

A

The red area there is when the alert is actually firing. That's exactly what you want. Ok, so this is the ingenious takeaway from this book. Ok, now how to configure all those alerts, this is a bit tedious in the as a river book. They give you all the instruction how to do it in Prometheus. You can transfer this to other systems, it's really elegant in prometheus, even if it looks a bit complicated, it's something with manual work and I recommend you use some kind of conflict management at Crafar.

A

Naboo, your JSON, advanced and I just had to put this here. I've also seen a few other talks that judge JSON it here. If you write a JSON a table here, this is precisely almost precisely the same table right, so you just type this table into JSON it, and then you have a bit of JSON that blue code that creates all the recording and alerting rules you need in Prometheus, pretty good, that's not yet open source I hope we open sourced this quickly. We usually open source. All this config.

A

We have pretty soon it's still kind of because we played with it. It's not yet done not yet pulled up into the open source products you so glad we did the same thing but we hand coded it. So this is the alerting one of the alerting rules in its whole glory from the blog post. This is the alert name, and then this is a recording role. You also have to write. You can look this up in the blog post, but you see here I mean it's very complicated.

A

You don't have to pass this now if you haven't seen it before, but here you see up sorry I always hit this button. You see see forum Boyd force the factor from the table. Here we have the one hour give with the five minute. Here's the logical and you see all those parts. He is the critical severity. It's all in there right. You can kind of guess how that works.

A

If you don't want to wait for your father to open-source there as the raw stuff materials Noble, who just had a talk a couple of hours ago, he is also a fan of JSON that he created a SLO lips on it and so that it's not too complicated for people who don't like JSON it. He created a web front-end for it which he put on his own website. So you know if rule generation as a service, so this is how it looks like you enter you or like availability target and the metric.

A

You want to apply this on and then hit create and you get all the Prometheus llamó files to feed to your permeate server. So very neat, okay, but this is all what I don't want to talk about. Mostly point is you of it talk we're all ready, so we should go to act 2.

A

This was all just about error, like request that and in a well-defined error like a 500 or something, but most of us, we actually care about latency, as our track host has told us already. So, let's think about making CSL all right now you will see in the real world that very few companies give you a latency, SL ace. Then right especially ISPs. They love to tell you something a shoe. We have 99.9% uptime. What does that mean?

A

I kind of hate this formulation, because it implies it's time based especially my internet connection- I, don't use it all the time like all the night, I'm asleep I'm, not using my internet connection and even if there's a down time, I won't notice that Mary iced tea will tell me we were up all the time sure right so like not using it properly means free up time for the service provider, which is kind of not nice for the customers, then I have a 10 minute.

A

Super important video conference, job interview whatever and my internet connection goes down. That's for me like a full outage and they say yeah 10 minutes per month. That's three nines! It's fine right, yeah! So to be nice, you should always do this per request. That's also in heinrich hoffmann to talk about as a latency as alright.

A

So this makes where we're saying during each month will serve 99 percent of requests successfully great right pro requests. If I have a lot of requests in a certain important time and there's an outage, I can actually tell the service provider that was bad. I want to get reimbursed. Okay, but there is no latency in there, which is kind of weird because, if, like let's say, I have a request: I want to get a response from my service provider. My browser times out eventually and I- tell my service provider. Okay, it's so time out.

A

This request didn't succeed and my service writer tells me no. No, it would have succeeded. It would just have taken like five minutes and your browser time out. Is you for five minutes whatever?

A

Even if you do this internally now we're like at a lower level again, you often have this among micro-services independent teams. They should promise each other. How well the micro servers work then, like one minute response time for something it's even it could create problems because you might queue of requests and then your instances like run out of RAM or something so you kind of want a latency in there and it's implicit like everybody would agree that like if it's five-minute it's it's like a failure.

A

So what to do to put latency in there and I went through this many iterations and, like often people come up with something like sure we will have like 99.9% availability serve 99% of request successfully and then like give your long tail, percentile, latency 99% of latency, that's pretty good 500 milliseconds right right, but thinking about it like an error, the open, 1% arrows they could actually be pretty fast like fail. Fast, is a good design principle. So you kind of get to 5, 4, 3 and 10 milliseconds. That is counting against this error budget.

A

But it's not counting at this one because it won't serve quickly right and then the 1% that are slow. They could be arbitrarily slow and still fulfill. This SLO and slow requests are kind of useless, so this would still allow one point, one percent of requests to be useless without breaching this not happening all the time, of course, but it's still kind of weird and it's also like now, we can't go to the service provider side.

A

I want to alert on error budget, but I have to error, watches, I've, open, don't present errors and I have 1% slow Cleary's, do alert if I breach, 1 error budget and not the other, or can I compensate 1 with E, it's like all complicated, so what I actually want- and this is not always applicable, but I would always recommend to try and I ended. Always I always ended up with this thought. Slow requests are just as bad as arrows right, so just try to make them the same. Tell your customer.

A

During each month we all serve 99% of requests successfully within 500 milliseconds. So that means, if an error comes in fast, it's still an error. Obviously, if a request comes back, but it's slow, it counts as now, and it's fair like it's like customers can easily reason with that. I can easily reason with I have a clearly defined SLO and I. Even think I mean this is pretty harsh. You could like lose some of the threshold like 99.9% within a second I think this is still better than something where you don't know.

A

If 1% of the Curie's will ever return in like finite time, so if you can do that right, there is a problem. If you have like a mixed query load- and this is especially true for a set karana- we have applied these ideas to our cortex, offering which is hosted. Prometheus people ingest all the Prometheus data into our cortex cluster, and then they can run prompt your queries against it and, of course, like every beginner can just type.

A

This super expensive fury of death in Prometheus, and we can never serve it in like a second or something but like law of large numbers or whatever it's called like. How often does that happen? Percy can still commit to this thing. You can do more complicated things and again, as I recon, my favorite, my second favorite conference after this one. There was another talk about stuff at Boca comm. Were you had exactly this problem? They actually created like pockets. They had pockets of expensive and not so expensive. Curious had different SFA's for them.

A

You can totally do that, but if you can avoid it just avoid it which brings us to act. 3 I want to implement the easy solution for you to like see how that works. Of course, you can iterate from there and make it more complex. But if you can just do this simple one, this is code X. We don't want to explain. Cortex here, they're called X, deep dive interest everything here at the conference.

A

This is the interesting thing for those SLO based alerts. You always found some kind of almost external entity which method measures things. So you can see what the system is responding, how fast it is responding from the outside perspective. Essentially so, at this soundcloud blog post, we use our like edge load balancer for that and in cortex you have something called cortex gateway, and then there is the ingestion path. I can't even see which color the engine is it's the right one.

A

So this is when people send us their Prometheus metrics, we have to ingest them and then there's a right path or people. A repast sorry were like, for example, a co-founder dashboard, aqueous cortex to draw a dashboard or like some human, it actively runs queries or some machine learning, whatever. Whoever wants promises metrics from colleagues. So what do we do here? In practice?

A

We created a histogram promises, histogram on this gateway, and this had pockets had money pockets, but we had one at one second and 2.5 seconds, because these are our SL O's. We will see them in a second and we partition this by status. Code method and route route is like read or write path. Now you might, if you're Prometheus fan, you might have seen Prometheus people like me, discouraging you from partitioning histograms, because histograms are really expensive in Prometheus and I more money to use cases where I want to partition the histogram.

A

We just did it like we, we have big enough primitive servers to do this shameless block. I gave the talk prom grande very recently, where I topped told people about my research of making histograms cheaper. So this will become better in the not too far future, but for now you can just do it if it's, if it's not too bad, because it's really useful so OS hello we wanted to go with- is complete 99.9% of write successful in less than one second and respond to 99.5% of reads in less than 2.5 seconds.

A

So you might say this is pretty lame, but it's also like prompt you like it's. It could be really expensive and also like a human hitting enter waiting 2.5 seconds for complicated, clear, it's probably acceptable. Most of them are faster right and even a dashboard. If you have to wait for a 2.5. Second, that's yeah I mean most of request should be faster, but that's kind of a reasonable threshold. To be clear, this is an SLO.

A

We are not yet we are still as lame as everybody else are not yet committing to our customers to the reimburse them if it's slower, but we want you right. This is our intention. I think it's a good pathway to set an SLO to inform your designs for this SLO and then, if you can make it and you can transform this into an SLA if your competitive advantage, because your competitors weren't there to come into that. Perhaps who knows so? Interestingly, there is a talk. There was a talk yesterday, I think blazing fast pump.

A

You all I, don't know if you've seen it by my boss from Wilkie. He talked about a caching layer for conics which works for Prometheus in general by now, and that was informed by this as alone. So you had this idea. Okay, how can we even do this? Like people run expensive, furious all the time, but then okay, if it's just this one dashboard or this expensive query, is run again and again, why not cash it? So we had an engineering decision or new like design or something it's by up our ass a lot.

A

That's that's the way it's supposed to be right, and now we have fast queries. Okay, so go to do this! Oh sorry, so you create recording rules and those recording worlds kind of give you an error ratio or like slow request, count like errors. So this you don't have to pass at all. You can look it up later, but you see this is the right rule like right, SLO errors per request, and there is the the orange pods are coming from the JSON it like. We have different intervals.

A

This is just for one hour, so you have the one hour here. So that means we have a range or one hour. This is the histogram. We picked the pocket with one second, because this is our right, hello, our target to do everything fast on 99% fast enough in one second, it's the push route, because it's the right path. We take from the Instagram all the five hundreds and divided by the total number, but this is very flexible.

A

You could change it to, for example, exclude for hundreds, because the storm or force will be responded to really quickly and that shouldn't be like free up time or something whatever like. This is how we do it. You can mix a match here and the read thing again over one hour. We take 2.5 seconds here and we take the query path, which is the read path against a dress code. Five hundred divided by all okay I mean you don't have to pass an hour completely, but can look it up later.

A

Now we at this table in the original approach, which was just arrow based this table, actually does change at all. We use exactly the same principles once we have defined those recording rules. We are back to square one. Essentially, we have now a recording rule that looks like error rate, but it's actually a rate of arrows and slope figures and from there on it's exactly the same thing as what we had before.

A

So this is an alert that almost looks precisely like the one from SoundCloud. So this is the right alert. Here's our other threshold, the open one, is the 99.9% inverted factor of 14.4, because that's the page over one hour, there's the one hour right and five minute window again same threshold, and you do this for every line in this table. Json I will generate this for you. You can also generate it manually if you want to type of it.

A

This is the read thing so open, 1, 5, 99.5%, inverted 14.4, there's the recording roll over 1 hour and 5 minute. At the end. It's all there. Ok, it's kind of quite easy. Once you're settled to this simple conclusion, and ideally you do that if it gets more complicated, you can iterate on that. But, like I, think at some point you will come back to that conclusions. So latency is actually very meaningful in an SLA and we should. We should give this service to our customers as the low base.

A

Eroding is really a great idea with caveats, of course, but yeah, it's still a great idea, concluding from both of them. He should have latency in your SLO based alerting, because you want to have both of them right so which could very well evolve into a real SLA. That's the other idea. You said as a long bit ambitious you let it inform your design and then at some point you could be really nice to your customers.

A

Keep it as simple as possible, like don't start with, like difficult to reason, with definitions, try to keep it really simple and now I mean this is kind of lame, but it's true as easy as possible, but not simpler. Thank you.

C

As if you relieve there is a there is a rating app, you can just rate the talk and any questions. We have some time writing questions.

B

Oh, can you can you put all that stuff into the alerting inside of Gravano? What son of tips would.

A

You use you mean the girl found out alerts, yeah, okay,.

B

Let's make it easy enough that I could recommend this to customers, not just use grifone. What I have to write like.

A

My personal relationship to profound own words is difficult. I think this is great for like interactively doing this, but I think it's not really great for production-ready usage. What I want like as a Prometheus person I want to make alert creation way more interactive in that way, but I think these specific alerts are really. They are so like derived from first principles that I, don't even think there's a good way of interactively like look at the dashboard and think this is a good alerting threshold, because this is actually informed by your SLI or SLO right.

A

So this should be better than just looking at dashboard everything. Okay! Can we wing it like? How good is it and it's all like very deductive Lee created it's it's kind of a counter example to interactively creating a learning rules, position which could make sense than other scenario. So what you use the recording.

B

A

So to generate new.

B

Metrics, sorry, that was what you use the recording rules to generate new metrics. Maybe that would help use recording rules to.

A

Make make that the microphone is going off and then I don't hear anything.

B

What you could use the recording rules inside to make new metrics and then maybe then you could use those to generate alerts on SLA. Is you.

A

Mean to generate grinnell births, yeah.

B

A

Just like I mean there there's there's the research going on with ingre know that I would just be very hesitant to use profound alerts and things.

B

I just want to know if any tips now could help.

D

Hey thank you for your. You know. My question is regarding: if you're working on distributed assistance, we have a lot of downstream services right, and maybe some of your assistants are not don't- doesn't have, for example, II kind of distributed tracing right, and what approach did you use on Soundcloud or graph on to measure the latest on the edge? Because first, for example- maybe you are alerting something in that? But the problem is the Layton's on your back-end system.

D

You know so in the end you're going to have, for example, a lot of alerts in a channel alerts, because you have all your dumb screens breach in there. Oh.

A

Yeah, that's a very good question. I mean the these alerts are just to let you know that something is wrong. They are not there to. Let you understand what is wrong. I mean you could use all your observability rules, which might include some like information alert as they call it from your metric system that might include booking prices their various race.

A

That's like a completely different topic, but you're also just another one whom to page right when something is bad deep down in your system, but get this the the symptom based alerting just tells you very generally something that's wrong. You don't exactly know what is it saga we actually had on the front end level we had the the low balance in you, which back-end would cost like the low parents had like five different backends, and it would page the team that is in charge of the backend that is currently causing the latency.

A

Now that only saves the one level right, you can go deeper and deeper I mean there was another interesting talk at s or economía, it's very like advertisement for the conference there were, there was Louis.

A

What's his last ever I already really which, like from the Londo, they actually created, they'd wired it the tracing into their paging in there they're, not routing, so they were check out boy of the tracing which service is actually bad right now and would page that team super interesting I just like for me: I always get a bit dizzy if I have a very complex system in my alerting chain, I won't alert him to be rock-solid and easy, and if it's really that bad and I page, like three teams except of one, it's perhaps acceptable- I mean it's the bigger your organization, the worse it becomes right.

A

I heard that amazon has this principle. We just pay to everybody in a very large organization and I wouldn't like that either. But it's it's a whole problem. Do we have more time for questions otherwise, like I could put this in here? If you want to ask for fun of people, especially co-found alerts, there are people are confounding who know them better than I. Do there's a group on our booth and there's also a previous project with here in this project?

A

Pavilion and I'll be on either one at any different time, so you can always ask questions there, but if you have more time, no, we don't give any much. Thank.

C

A