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[700.90 --> 705.52] the Cortex co-authors, right? You've started Cortex. I don't know who the other author is. |
[705.52 --> 711.30] It was Julius actually from the chap who was one of the original founders of the Prometheus project. |
[711.68 --> 712.30] Julius Valtz? |
[712.64 --> 713.24] Julius Valtz. |
[713.56 --> 721.94] Right. Okay. So you and Julius, you started Cortex, which went to grow. And I think it's part of the |
[721.94 --> 727.22] very important component of Grafana Cloud as an engine, an inspiration for Loki, which I think |
[727.22 --> 731.24] you also had something to do with, right? Like when you started the code base. So how does that work? |
[731.24 --> 736.68] How can you be VP of product and code go at a very advanced level? How does it work? |
[737.10 --> 742.92] Titles in the abstract, pretty meaningless, right? So yes, my title is VP of product. And I do have a |
[742.92 --> 747.74] lot of kind of product management responsibilities in the company, but my background is a software |
[747.74 --> 753.84] engineer. I've been a software engineer now for 15, 16 years. I've always worked on open source code |
[753.84 --> 758.52] bases, you know, straight out of university. I was kind of tangentially involved in the Zen |
[758.52 --> 763.38] hypervisor project. And so I worked a little bit on the kind of control tools there. I started a |
[763.38 --> 768.76] company that got involved in the Cassandra distributed database. And then, you know, then |
[768.76 --> 774.54] worked on Prometheus and Cortex. I've just always been a software engineer. I took a brief stint as |
[774.54 --> 779.42] doing some engineering management at Google, also some site reliability engineering, where I kind of |
[779.42 --> 784.34] learned a lot about the whole monitoring side of things. But yeah, at the end of the day, I've always |
[784.34 --> 789.76] been a software engineer. I've always been passionate about this kind of thing. And it's just, you know, |
[789.76 --> 795.58] I don't get to do as much software engineering now as it perhaps seems. You know, I have a large team |
[795.58 --> 800.08] of software engineers who do that and really should take a lot more of the credit than perhaps I do. |
[800.52 --> 805.48] But yeah, I still, you know, I was doing, I did a few PRs yesterday. That was mostly on some kind |
[805.48 --> 810.72] of continuous deployment for some internal SLO dashboards. But I still, you know, I still try and |
[810.72 --> 815.26] write bit of code. We had a hackathon recently internally where everyone in the company took a |
[815.26 --> 820.70] week to kind of just code on whatever their imagination had been, you know, noodling over |
[820.70 --> 825.66] for the past few months. And I took part. That was like, that was pretty cool. I managed to get a |
[825.66 --> 829.04] couple of days of solid coding in. I'm not going to tell you what the project was though, because |
[829.04 --> 834.68] that might become a future product. Who knows? Interesting. I was just going to ask that if any of |
[834.68 --> 840.30] those projects are public, but I'm sure the good ones will be, right? Oh yeah. No, no. Some of them are, |
[840.30 --> 846.14] right. So Bjorn and Dieter and Ganesh were working on one of their hackathon projects was |
[846.14 --> 850.38] high definition histograms in Prometheus. And Ganesh has already tweeted about that and will |
[850.38 --> 854.60] be putting out more information and the codes out there in public. I've seen that. There's a few of |
[854.60 --> 859.62] them that are public and a lot of them are going to form future projects and potentially even future |
[859.62 --> 864.92] products. I can give you a bit of a hint what the project I was working on was. So not a lot of |
[864.92 --> 870.60] people know Grafana Labs, actually its first kind of time series database that it built for Grafana |
[870.60 --> 875.78] Cloud. It's called Metric Tank. Metric Tank is a graphite oriented, still written in Go, |
[876.30 --> 880.44] still using a lot of the same techniques from modern time series databases like the guerrilla |
[880.44 --> 886.48] encoding and so on, but mainly focused on building a kind of scalable multi-tenant cloud version of |
[886.48 --> 891.44] graphite. And that's what kind of bootstrapped Grafana Cloud before I joined the company. |
[891.44 --> 896.86] And then I joined and brought Cortex in with me. And since then, of course, the architecture has now |
[896.86 --> 901.58] kind of moved towards a Cortex style architecture. The Metric Tank team within Grafana Labs for the |
[901.58 --> 908.04] past year or so have actually been working on putting a graphite query engine on top of Cortex. |
[908.72 --> 912.12] And we've actually, I think the launch of that, you know, it'll be seamless launch. Customers |
[912.12 --> 917.34] shouldn't notice, right, that being moved off of Metric Tank and onto Graphite V5. That's actually |
[917.34 --> 922.12] happening very soon. And that's kind of to give you a bit of a hint in the direction we're going. Now, |
[922.56 --> 926.56] Grafana Enterprise Metrics and Grafana Cloud is a single time series database that you can query |
[926.56 --> 931.56] through multiple different query languages. That's fascinating. And now you reminded me |
[931.56 --> 938.84] the link between Acuna Analytics, the company that you were part of at some point, and the startup that |
[938.84 --> 942.92] I was working for at the time, which was GoSquared, which was like real-time visitor analytics. |
[942.92 --> 948.90] So GoSquared, we were using, I think, MongoDB heavily, and we were starting to look into |
[948.90 --> 952.92] Cassandra. There was a Cassandra conference, and I thought you were presenting the analytic |
[952.92 --> 960.56] side of things. And at the time, I was heavily invested in Graphite. Ganglia was there as well. |
[960.72 --> 960.90] Yeah. |
[961.02 --> 965.88] And I thought like, wow, this Graphite and scaling, those like fun days, challenging days. |
[966.54 --> 970.72] And I looked at Acuna, I thought, wow, this is interesting. So they're using Cassandra |
[970.72 --> 974.52] for the metrics, and it works really well. I remember even the demo that you gave. |
[974.88 --> 978.48] I forget the conference name. This was 2012, 2013. |
[979.04 --> 980.28] Yeah, I don't remember that then. |
[980.28 --> 985.90] A long time ago, something like that. Yes. And so Graphite, right, was a great system, |
[986.04 --> 990.76] but it didn't really scale. It was very problematic. And then Grafana came along, |
[990.86 --> 995.32] but Grafana on top of Prometheus. So Prometheus had something new with it. But Prometheus in its |
[995.32 --> 1001.32] incipient phase was, again, like single process, single instance. How do you scale that? Well, |
[1001.40 --> 1008.56] it's not as easy. And Cortex, as far as I know, scales the way anyone would expect, right? You can |
[1008.56 --> 1013.32] shard those metrics, you can replicate them, you have different backends for them. That was really, |
[1013.48 --> 1019.54] really nice. So I can see history in a way repeating itself with Prometheus and Graphite. |
[1019.54 --> 1024.62] And now I can see the link, right, where it's actually part of Cortex, or it will be part of |
[1024.62 --> 1028.24] Cortex. That's really fascinating. Well, so it's interesting you mentioned that, right? Because |
[1028.24 --> 1031.58] one of the things Acunu did, one of its contributions to the Cassandra project |
[1031.58 --> 1036.42] was a technique called virtual nodes, right? Which is where in the earlier versions of Cassandra, |
[1036.60 --> 1040.78] each node basically owned a single range in its distributed hash ring. I remember that. |
[1040.94 --> 1044.36] The technique that Acunu added, and has been in Cassandra for ages now, |
[1044.66 --> 1048.32] was the ability for a node to own multiple ranges, right? And the whole principle there being, |
[1048.32 --> 1053.00] once you can own multiple ranges, like hundreds, like you then just pick them at random, |
[1053.36 --> 1057.88] and you achieve a very good statistical kind of load balancing. What's maybe particularly |
[1057.88 --> 1063.42] interesting is exactly the same techniques in Cortex, in Loki, in Tempo. And that's the ring I was |
[1063.42 --> 1068.70] referring to earlier. This is like, it's basically just an almost identical copy, just in Go, |
[1069.20 --> 1070.36] of the Cassandra hash ring. |
[1070.98 --> 1074.86] This makes me think of the old GoSquare team, because I remember Cassandra and how they were like, |
[1074.92 --> 1078.30] so excited about this. And this was mentioned, like, wow, this is amazing. |
[1078.32 --> 1085.36] Like MongoDB, I think rather Cassandra. I remember that. And it wasn't even like version one at the |
[1085.36 --> 1091.06] time. I know that Netflix were big on it as well. And Adrian Cockroft had like a great talk about it. |
[1091.20 --> 1096.92] And like in that context, the AWS cloud came in. So many threads connecting in my head right now. |
[1097.32 --> 1104.08] Wow. Okay. So let's take a step back from all these, I want to say rabbit holes, but like reminiscing |
[1104.08 --> 1110.40] specific things, which are a thing of the past. And let's come back into the present with a question, |
[1110.40 --> 1115.74] which I know very many people are, I'm not sure what they're struggling with, but they are, you know, |
[1116.30 --> 1122.82] there are two sides to them. What is observability? Some say that it is not the three pillars, which is |
[1122.82 --> 1128.10] metrics, logs, and traces. Some say that's not what observability is. What do you think? What is |
[1128.10 --> 1133.08] observability to you, Tom? I mean, it's definitely a bit of an industry buzzword right now. The three |
[1133.08 --> 1137.92] pillars definition is not that useful as a definition, right? It doesn't really describe |
[1137.92 --> 1142.18] what you're trying to do or what the problem you're trying to solve. It more describes maybe |
[1142.18 --> 1147.48] how you're solving some other problem, right? So whilst I don't necessarily think it's wrong, |
[1147.74 --> 1153.36] like in a lot of places, in a lot of situations, observability does revolve around metrics and logs |
[1153.36 --> 1159.10] and traces. It's not an answer to the question, what is observability? I've always really liked |
[1159.10 --> 1166.12] the definition of observability is, you know, the name for the movement that is like helping |
[1166.12 --> 1172.18] engineers understand the behavior of their applications and their infrastructure. It's about |
[1172.18 --> 1178.50] any tool, any source of data, any technique that helps you understand how a large and complicated |
[1178.50 --> 1185.80] distributed system is behaving and helps you analyze that. That's really my preference. I don't |
[1185.80 --> 1189.28] necessarily think I speak for many people though when I say that. I've been thinking about this for |
[1189.28 --> 1193.68] a couple of years. I had a couple of interesting discussions. Even the episode before this, that's |
[1193.68 --> 1198.14] a really interesting one. If this is the first one that you're listening to, check that out, see, |
[1198.28 --> 1205.80] you know, how the two compare for you. But I also agree that being curious about how things behave, |
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