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[1205.80 --> 1209.82] I think that's like the first requirement for observability. Are you curious? Do you care? |
[1210.38 --> 1216.08] And if you care, great. So what are you going to do to understand your production or your system? |
[1216.14 --> 1219.48] It doesn't have to be production, but it typically is because that's where the most interesting |
[1219.48 --> 1226.42] things happen. So how do you do that? How do you take all those metrics, logs and traces or events, |
[1226.66 --> 1230.10] whatever you call them, it doesn't really matter, to understand how the system behaves? |
[1230.50 --> 1234.74] It's an interesting kind of way of phrasing it, right? Because what I think, what we really |
[1234.74 --> 1241.36] internalize at Grafana Labs is kind of avoiding a one size fits all solution, right? So I know there |
[1241.36 --> 1245.76] are some incredibly powerful solutions out there that are incredibly flexible, but at the end of the |
[1245.76 --> 1250.38] day, we internally call it this kind of big tent philosophy, right? Where we try and embrace multiple |
[1250.38 --> 1255.06] different solutions and multiple different combinations of solutions and really kind of focus |
[1255.06 --> 1260.72] on helping users get the best out of a wide variety of techniques. Because really, you go into any |
[1260.72 --> 1266.04] sufficiently large organization, it doesn't even have to be thousands of people, like even just hundreds |
[1266.04 --> 1271.36] of people. And there's going to be one team over there that uses one monitoring solution and a team over |
[1271.36 --> 1276.48] there that uses a different logging solution. And they're all going to be stuck in their own little silos, and they're |
[1276.48 --> 1281.80] all going to have their own, you know, tools to use to analyze their data. And really, what we're trying to do at |
[1281.80 --> 1286.82] Grafana is bring them all together into a single place and give them all the same experience. The way I've always |
[1286.82 --> 1291.46] thought about it is, you know, when you get paged in the middle of the night, I don't want a system to |
[1291.46 --> 1295.08] tell me necessarily what's wrong, because the reality is, if the system can tell me what's wrong, |
[1295.34 --> 1298.66] it should probably be able to fix it for me. And I probably should have thought of it ahead of time, |
[1298.78 --> 1302.64] and it probably should never have paged me. I only ever really want to get paged for things that I |
[1302.64 --> 1307.42] wasn't expecting, right? And therefore, you know, I want to engage that kind of creative part of my brain. |
[1308.06 --> 1314.18] And I want to come up with hypotheses as to why it's broken, right? And I'm going to, and then I want tools |
[1314.18 --> 1320.44] that help me test those hypotheses and develop new hypotheses. So really, I'm not looking for a tool |
[1320.44 --> 1326.06] that claims to automate kind of root cause analysis, or, or tell me exactly what's broken, |
[1326.06 --> 1329.88] because, you know, if it can do that, it probably shouldn't have broken in that, |
[1330.06 --> 1335.24] in that particular way. I'm looking for a tool that helps me test theories that I've got. Oh, |
[1335.72 --> 1339.46] is it broken because of this? Oh, I can, I can correlate some metrics and some logs, |
[1339.46 --> 1346.28] and I can see if that's the case. Is it broken because there's a tiny little service running on a |
[1346.28 --> 1350.14] computer under someone's desk that's gone down? Oh, I can go and look at a distributed trace and |
[1350.14 --> 1354.72] it will tell me if that's the case. Like I want a tool that helps me access data and test hypotheses. |
[1355.22 --> 1360.52] And the nice thing I think about that as a guiding principle is it doesn't say, well, |
[1360.62 --> 1364.54] the best way of doing that is with logs. It doesn't say the best way of doing that is with events. |
[1364.54 --> 1369.78] And it doesn't say the best way of doing it is with metrics. It says the best way of doing it is |
[1369.78 --> 1373.54] situational and depends on the problem and depends on the tools you've got available. |
[1373.92 --> 1374.68] That's great. |
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[1430.32 --> 1443.76] I really liked your last answer. And I think now is a great time to start looking at the Grafana |
[1443.76 --> 1451.04] ecosystem, the Grafana Labs Cloud, just because Grafana means many things. How would you solve |
[1451.04 --> 1457.30] specific problems with the tools that you have available in Grafana? So let's take a specific |
[1457.30 --> 1465.30] example. Let's imagine that every now and then, my website, some of the requests are slow. What |
[1465.30 --> 1470.74] would I do to understand why certain requests are slow? Let's imagine this is a monolithic application, |
[1470.74 --> 1476.42] changelog.com. I'm winking right now. It's a Phoenix app. So what would I do? |
[1476.42 --> 1478.18] Actually, I don't know what Phoenix is. |
[1478.74 --> 1483.46] It's a framework similar to Ruby on Rails, but it's based on Elixir, which is |
[1484.18 --> 1488.02] syntax is similar to Ruby, but it's really all running on the Erlang VM. |
[1488.74 --> 1489.22] Oh, wow. |
[1489.22 --> 1490.66] So it's like Ruby on Rails. |
[1491.22 --> 1496.02] Is that a particularly large user base? It seems very nice. I've not heard of that before. Cool. |
[1496.02 --> 1500.82] Right. So not necessarily. I mean, depending on what you mean by large, |
[1500.82 --> 1503.06] but it scales really well because it's the Erlang VM. |
[1503.06 --> 1504.26] Yeah, because it's Erlang. Yeah. |
[1504.74 --> 1506.10] Everything is message passing. |
[1506.10 --> 1506.58] Sweet. |
[1506.58 --> 1512.34] You can have a cluster. It clusters natively. It forms a cluster. It starts sending messages. |
[1512.34 --> 1518.74] I think one of the more popular apps that uses Erlang is WhatsApp. Everybody knows. Everybody uses. |
[1519.30 --> 1524.02] And RabbitMQ is another messaging queue that also uses the same Erlang VM. |
[1524.02 --> 1531.30] And I think the last one is React. It was like the database. I think it still exists. And it was by |
[1531.30 --> 1531.78] Basho. |
[1531.78 --> 1532.34] By Basho. |
[1532.34 --> 1535.78] I remember it was like in the same quadrant, right? Where Acuna Analytics was there. |
[1535.78 --> 1541.70] Manu was there. I think he was their managing director for the EU team. And he was at Acuna a |
[1541.70 --> 1542.34] long time ago. Yeah. |
[1542.34 --> 1544.58] There you go. So it's a small world, isn't it? |
[1544.58 --> 1548.74] I think he's now at one of the cryptocurrency companies, but yeah, sorry, unrelated. |
[1548.74 --> 1552.58] So coming back to this like Phoenix app. So the reason why I mentioned that it's a monolithic |
[1552.58 --> 1556.82] app. It's important because it's not microservices, right? You don't have HTTP calls or |
[1556.82 --> 1561.86] GRPCs. There's no such thing. It's a single app. It's a monolithic app. It talks to a database. It |
[1561.86 --> 1566.98] has an Ingress Nginx actually in front. There's like a load balancer. And then in front of that, |
[1566.98 --> 1571.62] you have a CDN. So the request comes, and this is like very specific, and maybe this will help. |
[1571.62 --> 1577.94] The request goes through a CDN fastly. It hits a load balancer, which is a managed one, |
[1577.94 --> 1584.42] like your ELB, whatever, the equivalent of that. Then it goes to Ingress Nginx. And then from Ingress |
[1584.42 --> 1590.26] Nginx, it gets proxy to the right pod. Well, service pods, I don't have to start decomposing |
[1590.26 --> 1594.74] this. And eventually it hits the database and then it comes back in again. At any one point, |
[1594.74 --> 1601.22] it could be cached. Sometimes requests are slow. Why? How would we find out with the tools that exist |
[1601.22 --> 1606.58] in the Grafana ecosystem world? No, it's a great question. So you already know that requests are slow. |
[1606.58 --> 1610.90] So that's kind of interesting. I'm going to guess, or for the sake of this discussion, |
[1610.90 --> 1615.38] that you've been told by your users that your requests are slow. So I would actually say, |
[1615.38 --> 1620.34] first things first, let's kind of confirm that. We want to instrument the system. We want to get as |
[1620.34 --> 1627.22] many useful metrics as we can out of it. You mentioned in ELB there, for instance, we put the |
[1627.94 --> 1632.18] CloudWatch exporter on there and get the ELB metrics out into Prometheus. Now you can do that with the |
[1632.18 --> 1638.26] open source exporter. We're also working on a service in Grafana Cloud where effectively we run |
[1638.26 --> 1642.74] and manage that exporter for you just to reduce the number of things you need to run. This will give |
[1642.74 --> 1647.46] you access to some rudimentary metrics, but generally I don't find CloudWatch metrics to be super useful. |
[1647.46 --> 1652.26] I'm sorry, that was a bad example. So I gave an analogy. It's actually a Linode node balancer. I'm |
[1652.26 --> 1655.70] pretty sure you don't think to agree with that, but it's like a managed HA proxy. |
[1655.70 --> 1661.54] I wouldn't underestimate the Prometheus ecosystem. There's probably an exporter for Linode metrics |
[1661.54 --> 1666.26] that import them into. And if there isn't, there will be by the time you finish this recording, |
[1666.26 --> 1667.14] I imagine. I hope so. |
[1667.14 --> 1670.58] Yeah. So I get metrics on the load balancer because it's always good to start at the very edge. |
[1670.58 --> 1672.18] The CDN is first. What about the CDN? |
[1672.18 --> 1677.46] Yeah. I don't know enough about Fastly, I'm afraid to really comment, but I'm sure there's some way of |
[1677.46 --> 1683.22] getting logs or metrics from that. Okay. So we've hit something which I wasn't expecting to hit, |
[1683.22 --> 1689.06] but let's just go with it. Okay. I looked at integrating Fastly logs with Grafana Cloud. |
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