1 00:00:00,000 --> 00:00:02,960 So I was thinking earlier today, I was just walking up here. 2 00:00:03,040 --> 00:00:05,960 I was trying to get my daughter out of my home office. 3 00:00:06,200 --> 00:00:09,720 She's about, well, she's going to be free next month. 4 00:00:09,960 --> 00:00:12,680 In a few weeks, we better get started planning her birthday. 5 00:00:13,080 --> 00:00:14,940 Just that's a pro tip right there. 6 00:00:15,200 --> 00:00:19,600 But I was thinking, you know, she's at that stage where almost everything she does, she announces. 7 00:00:19,600 --> 00:00:22,480 Like, I'm going to go get my book, watching Frozen. 8 00:00:23,040 --> 00:00:24,660 You know, like, I've got poop. 9 00:00:25,480 --> 00:00:29,180 And I was thinking, you know, there's a lot of computer services like that. 10 00:00:29,240 --> 00:00:32,360 I mean, that's basically how, like, UDP and multicast works, right? 11 00:00:32,420 --> 00:00:38,260 Is you've got all these things in your big distributed system, and they just tell you every time they have poop in their diaper and everything that's going on. 12 00:00:38,760 --> 00:00:42,380 And there must be some name for that, like, broadcast everything style. 13 00:00:42,600 --> 00:00:47,660 More interestingly, there must be a name for the, I guess it would be a pull mechanism. 14 00:00:47,660 --> 00:00:49,760 It only tells you something if you want to know. 15 00:00:49,860 --> 00:00:51,140 I guess that would be SNMP. 16 00:00:51,520 --> 00:00:55,060 You've got the traps in SNMP, which broadcast everything out. 17 00:00:55,220 --> 00:00:57,380 But you do have to explicitly subscribe to them. 18 00:00:57,420 --> 00:01:04,120 Whereas with my daughter, there was no message sent about subscribing to her diaper notification service. 19 00:01:04,260 --> 00:01:06,080 I guess that's implicit in being a parent. 20 00:01:06,220 --> 00:01:08,140 It's kind of defaults that are set in your configuration. 21 00:01:08,760 --> 00:01:13,540 But there must be some name for the situation where broadcasting is not done. 22 00:01:13,540 --> 00:01:17,640 The mum broadcast paradox or something. 23 00:01:18,300 --> 00:01:18,700 That's right. 24 00:01:18,760 --> 00:01:19,740 It's like big kid data. 25 00:01:20,000 --> 00:01:21,040 You're just a lot of volume. 26 00:01:21,360 --> 00:01:21,840 That's right. 27 00:01:22,060 --> 00:01:23,400 And a variety coming in there. 28 00:01:23,480 --> 00:01:27,760 I know there's no filtering out this, you know, we ask our systems now to tell us more and more. 29 00:01:27,860 --> 00:01:32,980 And unless you have systems on the other end reading it, you just get, you know, dumped this way too much data. 30 00:01:33,080 --> 00:01:34,040 It's like, not now, Slack. 31 00:01:34,140 --> 00:01:35,560 Stop telling me so many things. 32 00:01:35,680 --> 00:01:36,800 Just relax. 33 00:01:37,040 --> 00:01:39,840 So I know our systems are very chatty, just like our children. 34 00:01:40,260 --> 00:01:47,240 Slack has this one thing that drives me crazy, which is like, there's no way to turn off the at here, at channel thing. 35 00:01:47,240 --> 00:02:09,100 And like any system where it's – I think there's probably some rule that also has some principle law or theory behind it that if there is a mechanism in a group listening situation by which you can get everyone's attention, it will be abused. 36 00:02:09,480 --> 00:02:11,020 It's just like, that'll happen. 37 00:02:11,700 --> 00:02:19,120 So it almost seems like that channel in here thing is a – it's a misguided feature to have. 38 00:02:19,360 --> 00:02:20,540 It's the opposite of what you want. 39 00:02:21,100 --> 00:02:27,460 I saw you mentioning on Twitter this morning that if – boy, maybe you could just do one thing at a time versus 50, maybe you'd be more productive. 40 00:02:27,560 --> 00:02:31,560 But I think we just accept the fact that continuous partial attention is our reality. 41 00:02:31,800 --> 00:02:32,200 That's right. 42 00:02:32,400 --> 00:02:33,780 You've got to learn how to cope with it. 43 00:02:34,120 --> 00:02:34,520 That's right. 44 00:02:34,740 --> 00:02:37,860 You know, this reminds me of another highly related thing. 45 00:02:37,860 --> 00:02:39,680 I remember I was reading that – how do you say it? 46 00:02:39,700 --> 00:02:40,960 Nassim Taleb guy. 47 00:02:41,420 --> 00:02:44,520 And he said two paradoxical things, which I'm sure delighted to him. 48 00:02:44,680 --> 00:02:47,500 He likes to be in a quad paradox situation constantly. 49 00:02:48,040 --> 00:02:56,820 But he's essentially saying, you know, hey, I made a lot of money with arbitrage, so the following I am lucky enough to be able to experience. 50 00:02:57,080 --> 00:03:04,100 Just like, you know, you should just wander around and like eat really good meals and only like work about an hour a day, and that'll work out better. 51 00:03:04,180 --> 00:03:10,320 And yet, paradoxically, he said he reads like 10 or 15 books at a time because he gets bored with them and he's got to bounce between them. 52 00:03:10,920 --> 00:03:14,520 So there's silos of focus, I guess. 53 00:03:14,620 --> 00:03:16,360 You just got to figure out your situation. 54 00:03:16,560 --> 00:03:21,240 So, you know, I was thinking of all this distributed stuff, and we'll get to this after our brief news thing. 55 00:03:21,320 --> 00:03:32,360 But our topic today was another part in our Circle of Code exhibition, going over the Spring Cloud services, which are based on the Netflix OSS stuff. 56 00:03:32,480 --> 00:03:41,340 But it seems like what's becoming the industry-leading way to do microservices and all of the, as we would say, sort of day two operational stuff, or even day one. 57 00:03:41,500 --> 00:03:47,320 But all of the operation stuff around it as well, which we'll get to in the second half. 58 00:03:47,420 --> 00:03:51,900 At your suggestion, you're doing a good job driving our Circle of Code agenda here. 59 00:03:52,420 --> 00:03:53,580 I'm just behind the scenes. 60 00:03:53,840 --> 00:03:54,760 You know, you're the face of us. 61 00:03:56,340 --> 00:03:58,100 On the audio-only podcast. 62 00:03:58,900 --> 00:03:59,300 Yes. 63 00:04:01,860 --> 00:04:08,880 So with that, you know, I think the VM world is actually going on at the moment, like, as we speak. 64 00:04:09,020 --> 00:04:10,980 I think it's the first day as we're recording. 65 00:04:11,160 --> 00:04:12,720 I really haven't caught up on it. 66 00:04:12,800 --> 00:04:20,420 It's not, you know, some people think that we're all in this federation, and we have some sort of conspiratorial, like, Richard Ailes memo that's sent out every day. 67 00:04:20,860 --> 00:04:23,000 But it's not the case. 68 00:04:23,340 --> 00:04:26,460 So we'll have to, I'll have to catch up on the news that's coming out of there. 69 00:04:26,820 --> 00:04:30,480 But, you know, that's kind of interesting that it's happening. 70 00:04:30,680 --> 00:04:46,360 And then I was reminded of this because of another item we have in here is, I think it was last week, the second half of last week, or something, that Microsoft is getting PowerShell to run on Linux, which is a, speaking of circles, that's a fascinating full circle. 71 00:04:47,080 --> 00:04:47,220 Yeah. 72 00:04:47,720 --> 00:04:49,140 Yeah, it's wild stuff. 73 00:04:49,200 --> 00:04:49,560 It's good. 74 00:04:49,560 --> 00:04:56,660 And, you know, Amazon, I think, gave some PowerShell modules, kind of their plug-in components and, you know, donating that. 75 00:04:56,740 --> 00:04:59,800 And putting that in, so it's open sourcing, it's widely available. 76 00:04:59,800 --> 00:05:02,680 And you could ask, if I'm a Linux developer, why would I pick this up? 77 00:05:02,700 --> 00:05:03,860 And the answer may be, you won't. 78 00:05:04,120 --> 00:05:10,020 It may be, you know, live with your Bash shell, and, you know, you're scripting, and you're fine, and you wouldn't use PowerShell, if anything. 79 00:05:10,540 --> 00:05:21,400 I was reading something else where Microsoft got asked to change the name of a couple packages, because back when it was only Microsoft World, they used things like WGET and other terms that were Linux-based, but who cares? 80 00:05:21,480 --> 00:05:23,020 Because this was never going to overlap. 81 00:05:23,520 --> 00:05:27,940 Now it does, and now you have these packages with the same name that shouldn't be using that name. 82 00:05:28,100 --> 00:05:33,640 So it's just very funny now when you build systems and never expecting worlds to collide and probably joke about it. 83 00:05:33,900 --> 00:05:36,820 Now when they do, you actually have a little bit of a mess on your hands. 84 00:05:37,200 --> 00:05:39,200 So amusing, but it's a good trend. 85 00:05:39,380 --> 00:05:45,160 I forget what year it was that, I guess it was called Monad a long time ago that that came out. 86 00:05:45,160 --> 00:05:53,520 But I remember this was back when I was at RedMonk, and I think I even did a video here and there, which has probably, like, disappeared into the nothingness of the Internet. 87 00:05:54,180 --> 00:05:59,180 But it was Jeffrey Snover who was architecting that, and he had been working at Tivoli for a long time. 88 00:05:59,700 --> 00:06:11,000 And I remember the story that he had is he basically, you know, among other things, well, I think he's kind of like the head architect of their server business or something now, or some highfalutin position. 89 00:06:11,940 --> 00:06:20,180 But, you know, it was basically, like, part of what he was hired over is to, like, do this, sort of like, hey, we should make Windows more Linux and Unix-like. 90 00:06:20,380 --> 00:06:28,900 And I remember him telling me that the first thing that he spent a lot of time saying is that in the Linux world, people use the command line. 91 00:06:30,400 --> 00:06:31,120 There we go. 92 00:06:31,280 --> 00:06:38,260 Which, you know, speaking of, like, a Zen presentation, that would have been a good single-slide presentation for, like, a multi-day meeting, right? 93 00:06:38,340 --> 00:06:41,280 Like, you know, like, what does it mean to use the command line? 94 00:06:41,380 --> 00:06:49,920 And naturally, I think what, if you think about, at least the way that I think about PowerShell is sort of, like, the implementation of that in Windows land. 95 00:06:50,160 --> 00:07:04,000 And I remember some of the first things were, like, you know, you could basically do batch changes over multiple machines with PowerShell things and configure everything that you were sort of used to bucking around in a GUI for. 96 00:07:04,320 --> 00:07:15,340 And before that, like, right around that time, thankfully, I'd just been, well, I mean, for this story at least, and I guess also for myself, but I'd been working at BMC on their, like, some of their Windows monitoring stuff. 97 00:07:15,420 --> 00:07:26,840 So I had this weird passing understanding of, like, WMI and Windows internals, and it was basically exposing all those Windows internals to a command line interface, which is pretty interesting. 98 00:07:26,960 --> 00:07:33,340 And I think, I mean, you probably know better than I do at this point, being more in Microsoft land and stack stuff. 99 00:07:33,500 --> 00:07:41,160 But, like, yeah, I mean, it seems like PowerShell is, like, a full-on, like, shell and scripting language. 100 00:07:41,360 --> 00:07:52,720 Like, way beyond even what, I mean, if you were to think about what the command line in the shell in Unix and Linux are, and how, like, anything that's successful in decades old, they're kind of bound to the past. 101 00:07:53,220 --> 00:07:59,500 Like, PowerShell is probably one of the newer, like, complete reinventing of what that idea is and what you would want to do. 102 00:08:00,060 --> 00:08:02,840 Yeah, they've done a nice job with it, a super powerful platform. 103 00:08:02,960 --> 00:08:11,860 That's where, again, I think the benefit you'll see is if I'm a Windows admin who now has these sort of Linux boxes in my purview, I don't have to necessarily completely shift my thinking. 104 00:08:11,980 --> 00:08:16,420 I can actually start pointing this at Linux boxes and doing things in my entire environment. 105 00:08:16,420 --> 00:08:25,720 So, you know, PowerShell is a first-class citizen by Microsoft, but as you say, you're also dealing with an OS on a platform that traditionally has not been very CLI-centric. 106 00:08:25,920 --> 00:08:27,200 It's been more point-and-click. 107 00:08:27,480 --> 00:08:35,620 So, a new way of thinking, I guess I'll also promote the blog post that we put out last week around Bosch for Windows and treating Windows as immutable servers. 108 00:08:35,780 --> 00:08:46,520 This is novel stuff, treating a Windows box as something that you stand up, you tear down, and everything's cool, versus stand it up, apply all these group policies, do all these other things. 109 00:08:47,340 --> 00:08:48,720 That's a very new way of thinking. 110 00:08:48,820 --> 00:08:56,400 It was actually a really good blog post and goes into our thinking about what does it mean to do immutable Windows, which most people have thought was pretty tough to do. 111 00:08:56,900 --> 00:08:58,160 Hmm, immutable Windows. 112 00:08:58,520 --> 00:09:03,080 That sounds like those Windows we used to have in the co-op I lived in college that you could never open. 113 00:09:03,800 --> 00:09:08,020 And you might have to be careful that you paid the extra for the AC if you were in one of those rooms. 114 00:09:08,260 --> 00:09:15,680 Can you imagine, like in the 90s, you could actually rent a college room in Austin, Texas without AC? 115 00:09:16,080 --> 00:09:17,460 I was thinking about that the other day. 116 00:09:17,540 --> 00:09:18,400 That's just insanity. 117 00:09:18,900 --> 00:09:26,380 No, I try to think of how my parents had jobs with cars with no air conditioning and how did they possibly not get to work as a sopping mess. 118 00:09:26,600 --> 00:09:28,160 And it's just a different world back then. 119 00:09:28,460 --> 00:09:28,680 Yeah. 120 00:09:29,200 --> 00:09:35,100 Clearly, clearly, you know, aside from the last 30 years, humanity was suffering for the eons before that. 121 00:09:35,240 --> 00:09:36,740 I have no idea how it worked out. 122 00:09:36,860 --> 00:09:37,940 Just all dumb luck. 123 00:09:37,940 --> 00:09:42,000 It proves that UFOs must have exist because they had to come down and help us. 124 00:09:44,020 --> 00:09:47,260 So, you know, related nothing to that, I guess. 125 00:09:48,220 --> 00:09:52,620 What's this item about Salesforce having an AI called Einstein? 126 00:09:53,560 --> 00:09:57,500 Yeah, I mean, they're apparently going to announce something coming up at their upcoming conference. 127 00:09:57,700 --> 00:09:59,160 I think, you know, this jumped out at me. 128 00:09:59,240 --> 00:10:00,720 It's an AI product called Einstein. 129 00:10:01,140 --> 00:10:04,380 You can imagine, you know, it was light on details in terms of the stories. 130 00:10:04,480 --> 00:10:08,820 But you can think about it as you're doing complex CRM and sales managing campaigns, 131 00:10:08,920 --> 00:10:14,180 that there could be some value in doing machine learning or AI or bots or something as you're interacting with customers. 132 00:10:14,620 --> 00:10:21,660 But this just seems to be this constant drumbeat that AI is starting to hit the masses among tech companies. 133 00:10:21,660 --> 00:10:23,580 You're seeing some of these things come in real life. 134 00:10:23,660 --> 00:10:30,200 Microsoft just bought some natural language and AI-type company last week that they might try to mix into office-based stuff. 135 00:10:30,440 --> 00:10:35,680 And, again, if I'll plug something that I've enjoyed reading, Jack Clark, former journalist for Bloomberg and Register, 136 00:10:35,860 --> 00:10:38,280 has an AI newsletter, which is really good. 137 00:10:38,340 --> 00:10:40,100 It has some really good stories in there every week. 138 00:10:40,600 --> 00:10:45,140 And even if you're not in that space, I can understand if you're deep in the bowels of an insurance company, 139 00:10:45,140 --> 00:10:46,400 you might not think about AI. 140 00:10:47,340 --> 00:10:54,360 But these are areas where it's probably good to just know what things are going on because this might be one of these things that bum rushes us. 141 00:10:54,400 --> 00:11:00,260 And all of a sudden it's embedded into a lot of tech and a lot of libraries are available that all of a sudden you might want to know what's going on. 142 00:11:00,300 --> 00:11:01,660 So it's a fun thing to keep an eye on. 143 00:11:02,060 --> 00:11:03,640 Watch out for the cyber bums. 144 00:11:04,180 --> 00:11:04,700 That's what you got. 145 00:11:05,120 --> 00:11:05,360 Yeah. 146 00:11:05,460 --> 00:11:13,840 You know, I'm always, I don't know, I guess a skeptic on AI stuff, mostly just not that it's not possible, 147 00:11:14,060 --> 00:11:17,140 but I'm always just curious what the applications are. 148 00:11:17,240 --> 00:11:19,980 And it's like a lot of stuff that I'm skeptical on the tech world. 149 00:11:20,060 --> 00:11:25,700 It's like I'm more skeptical as a forcing function for like, why don't you talk about the practical things? 150 00:11:25,700 --> 00:11:27,640 And like, I can think of one thing. 151 00:11:27,700 --> 00:11:34,800 I remember there's, I think it's like AI.x and it's some service you can sign up for, for like scheduling meetings. 152 00:11:35,020 --> 00:11:37,720 And you just like CC them and they've got cutesy names. 153 00:11:37,900 --> 00:11:40,160 You can either get like Jane or John or something. 154 00:11:40,320 --> 00:11:43,020 And you can, if we were to have a meeting, right? 155 00:11:43,060 --> 00:11:44,840 Someone's like, hey, Coté, we should have lunch. 156 00:11:45,320 --> 00:11:51,140 And, and then I, I would, I would reply back to them and CC Jane or John and be like, Jane, can you schedule us lunch? 157 00:11:51,480 --> 00:11:58,000 And then it would like the AI thing or it would sort of figure out like emailing back and forth to figure out where to go and when and everything. 158 00:11:58,000 --> 00:11:59,580 And like that, that would be kind of nice. 159 00:11:59,580 --> 00:12:08,080 And so, you know, I tried it a little bit and it's, it's, uh, it might be my problem, but it's the equivalent of like, I never want to use like a travel agent to book my travel. 160 00:12:08,180 --> 00:12:08,540 Right. 161 00:12:08,560 --> 00:12:11,560 Like, it's just like, I have too many opinions and I want to tweak it too much. 162 00:12:11,560 --> 00:12:14,240 So maybe that's the issue, but just like, yeah. 163 00:12:14,240 --> 00:12:17,140 And we have one of those, uh, those Amazon echo things. 164 00:12:17,140 --> 00:12:20,500 So every now and then we're telling Alexa stuff, but Alexa is not too bright. 165 00:12:20,680 --> 00:12:22,260 That's more like speech recognition. 166 00:12:22,560 --> 00:12:23,100 No, I know. 167 00:12:23,120 --> 00:12:28,540 That's the thing is I don't think we all, I finished a good book recently was talking about the complexity of technology. 168 00:12:28,540 --> 00:12:35,080 And one of these things that you can either fall into multiple camps, either you are just in awe of technology, which is a dangerous place to be. 169 00:12:35,140 --> 00:12:37,440 You need to understand what things are, or you can fear it. 170 00:12:37,440 --> 00:12:39,120 Like you have to be Skynet and that's not good. 171 00:12:39,500 --> 00:12:42,340 You need to have this sort of like healthy respect for tech. 172 00:12:42,340 --> 00:12:45,660 And even with AI, I think a lot of people just don't trust algorithms yet. 173 00:12:45,740 --> 00:12:54,500 I mean, I drove to Vancouver, British Columbia this last weekend from Seattle and, you know, I used my mapping technology and there still be times where you take some ridiculous looking route. 174 00:12:54,760 --> 00:12:57,840 And it's just like a stupid algorithm, you know, you just yell at it. 175 00:12:58,160 --> 00:13:00,160 I just don't think we have a lot of faith yet. 176 00:13:00,800 --> 00:13:05,460 If we really had to put our entire trust in these things, that it's making all the right decisions. 177 00:13:05,460 --> 00:13:09,040 And maybe we're getting there when we have self-driving cars and planes with no pilots. 178 00:13:09,040 --> 00:13:14,400 But right now, you know, I still can't trust, you know, my hand going under the sink that it'll actually turn the water on. 179 00:13:14,600 --> 00:13:16,600 I'm not ready to have a driverless car. 180 00:13:16,820 --> 00:13:19,160 Yeah, maybe like mapping stuff. 181 00:13:19,260 --> 00:13:24,180 I'm sure some highly nerded out AI person would say that's not AI. 182 00:13:24,440 --> 00:13:25,700 But like that feels like AI. 183 00:13:25,880 --> 00:13:30,600 Like I was, to fill more anecdotes in here, I was using Waze the other day. 184 00:13:30,940 --> 00:13:34,700 And, you know, I was going from one highway to the other that I drive all the time. 185 00:13:35,160 --> 00:13:37,840 And it told me to go on the frontage road instead of the flyover. 186 00:13:38,920 --> 00:13:40,340 And I was like, well, that's stupid. 187 00:13:40,460 --> 00:13:41,320 So I went on the flyover. 188 00:13:41,380 --> 00:13:43,060 And sure enough, there was a wreck on the flyover. 189 00:13:43,180 --> 00:13:44,820 So I just sat there for a long time. 190 00:13:45,540 --> 00:13:45,860 Right? 191 00:13:45,960 --> 00:13:56,100 And it was, it's, again, like that's, that's not really like complicated AI that's going to go and like, you know, read about enlightenment and go sequester themselves off somewhere to talk with each other. 192 00:13:56,400 --> 00:13:59,280 But like, like it's, it's, it's, it's pretty nice. 193 00:14:00,580 --> 00:14:00,940 Right? 194 00:14:00,940 --> 00:14:11,100 Like that, even that level of, of, of AI, we could call it artificial compute conclusions, just like helping me make conclusions would be nice that. 195 00:14:11,440 --> 00:14:13,700 Yeah, we'll, we'll keep seeing more machine learning. 196 00:14:13,820 --> 00:14:20,660 I think if you're any enterprise, this just drives an importance on making sure you're collecting good data, you're having figuring out ways to ask questions of it. 197 00:14:20,700 --> 00:14:24,560 And at some point, maybe you can have things that make decisions based on it. 198 00:14:24,560 --> 00:14:27,460 But, you know, step one is if you don't have data, you can't train a model. 199 00:14:27,820 --> 00:14:28,980 AI is meaningless to you. 200 00:14:29,020 --> 00:14:30,720 Same with machine learning, those sorts of things. 201 00:14:30,940 --> 00:14:32,720 Yeah, you got to edumacate them. 202 00:14:32,780 --> 00:14:32,960 Right. 203 00:14:33,080 --> 00:14:33,900 Get them learned up. 204 00:14:34,260 --> 00:14:42,920 Well, also this week, to close out our new stuff a little bit, or this is last week, actually, the, there was finally an announcement that Rackspace was going to be acquired. 205 00:14:42,920 --> 00:14:44,520 That, which is to say, go private. 206 00:14:44,620 --> 00:14:48,820 I don't know much about the Apollo Global Management LLC. 207 00:14:49,680 --> 00:14:58,960 In some article I read, they said that they also had bought the security company ADT, which, you know, not, not, not to be all, you know, a scan side. 208 00:14:59,020 --> 00:15:05,940 But that sounds like the kind of thing that you write when you search in a database and you just plop that in a paragraph, like, of all the coverage. 209 00:15:06,020 --> 00:15:10,200 So I think it's like, was it 4.3 billion that they're being sold for? 210 00:15:10,240 --> 00:15:12,120 And of course, it's got to close and all that stuff. 211 00:15:12,240 --> 00:15:16,700 But, you know, that was the thing I was most curious about is like, who are these Apollo people? 212 00:15:16,800 --> 00:15:26,240 Like, I know they can play a liar really good, but other than that, and they get really upset if you try to, like, you know, try to compete against them, they might skin you and hang you upside down on a tree. 213 00:15:26,400 --> 00:15:27,480 You got to watch out for that. 214 00:15:27,880 --> 00:15:29,800 But other than that, what's their deal? 215 00:15:30,520 --> 00:15:30,720 Yeah. 216 00:15:30,980 --> 00:15:35,940 I don't know if it is a shadowy finance group or they've all just have tons of good finance stuff. 217 00:15:36,020 --> 00:15:39,460 I think it was, you know, 4.3 billion is not selling for spare parts. 218 00:15:39,740 --> 00:15:43,960 So you are clearly identifying this as a very good asset that you plan on doing something with. 219 00:15:44,220 --> 00:15:51,740 I think that's going to be the interesting part is when it's great they're private, they can probably be more focused on certain things now, but they've already shed some parts of the business. 220 00:15:52,000 --> 00:15:54,320 And the question is, how much of a leash do you have? 221 00:15:54,740 --> 00:15:56,920 You know, I think you and I have been through acquisitions. 222 00:15:57,600 --> 00:16:01,440 You know, many things sound great until certain things start to hit the fan. 223 00:16:01,800 --> 00:16:08,040 So hopefully things go fantastic for them as they double down on being a managed service provider across clouds and things like that. 224 00:16:08,880 --> 00:16:17,180 So from my experience, you don't typically get a long leash or a lot of time to show value when you have an impatient parent. 225 00:16:17,620 --> 00:16:17,740 Yeah. 226 00:16:17,920 --> 00:16:18,140 Yeah. 227 00:16:18,280 --> 00:16:24,480 Well, we on my other podcast, Software Defined Talk podcast, we talked about this at length last week. 228 00:16:24,720 --> 00:16:29,580 And I also wrote up some little highlights over on my blog that I'll put in the show notes over at Coté.io. 229 00:16:29,680 --> 00:16:33,860 But I think, you know, the only other notable thing I would say is, well, there's two things. 230 00:16:33,860 --> 00:16:44,320 One, you know, we talked about the IaaS magic quadrant a while ago, where Rackspace is in the, they're high up in it, but they're kind of in the dreaded lower left-hand quadrant. 231 00:16:44,520 --> 00:16:49,040 Now, that said, there's a managed cloud magic quadrant that they're the leader in. 232 00:16:49,220 --> 00:16:51,980 So I think that's the way they've been repositioning themselves. 233 00:16:52,220 --> 00:16:55,620 So it depends what glasses you look at them through. 234 00:16:56,240 --> 00:17:00,960 And also, I mean, it's, I think, I think pursuing that strategy seems to be interesting. 235 00:17:00,960 --> 00:17:09,800 And I think if you look at our, our customer base, like the idea of having a managed cloud seems to be very appealing, right? 236 00:17:09,820 --> 00:17:12,460 Like, like having someone take care of that for them. 237 00:17:12,460 --> 00:17:18,840 Like, I think most of the customers we have, or at least that I talk with, would be quite interested in that in the preceding years. 238 00:17:19,500 --> 00:17:26,620 Yeah, the hard part is, yeah, you like when it's managed, but then you don't like the lack of agility of, I just want to do stuff. 239 00:17:26,700 --> 00:17:28,600 I don't want to open a ticket with a managed provider. 240 00:17:28,760 --> 00:17:37,460 So there's always this weird place of, look, I'm trying to be more agile, but I'm still giving the keys to the kingdom, to someone who has great processes to operate lots of different customers. 241 00:17:38,040 --> 00:17:39,560 So, yeah, I don't know. 242 00:17:39,600 --> 00:17:40,360 The jury will be out there. 243 00:17:40,440 --> 00:17:43,000 Clearly, they make good money there, as do other managed providers. 244 00:17:43,000 --> 00:17:47,620 But is that the future in this sort of self-service, automation-driven world? 245 00:17:47,720 --> 00:17:48,500 I don't know. 246 00:17:48,860 --> 00:17:49,060 Yeah. 247 00:17:49,080 --> 00:17:49,960 It'll be interesting to see. 248 00:17:50,260 --> 00:17:50,460 Yeah. 249 00:17:50,580 --> 00:17:52,520 You've got to have a really fast ticket desk. 250 00:17:53,040 --> 00:17:54,640 That's the innovation that needs to happen. 251 00:17:55,020 --> 00:18:04,880 So just as a pointer, so I, last week I also point, I've been working on trying to, we get asked about return on investment a lot, as you can imagine, being an enterprise software vendor. 252 00:18:05,160 --> 00:18:09,660 And I took a first drafty kind of rambly pass at writing up something there. 253 00:18:09,800 --> 00:18:19,580 I'll put a link to it, but I have it over on my Medium blog, and hopefully sometime I'll write some, what you might call, professional versions of that, instead of me kind of ranting about it. 254 00:18:19,620 --> 00:18:22,260 But I would be curious for listeners for this. 255 00:18:22,340 --> 00:18:31,040 Like, when it comes to, and as I define it in that piece, ROI basically usually means a lot of different things, not just a straight-up return on investment. 256 00:18:31,040 --> 00:18:41,000 And it's usually when someone asks for ROI to do Agile or DevOps or Cloud Native or use a platform, what they're saying is, help me justify spending money on this. 257 00:18:41,780 --> 00:18:41,860 Right? 258 00:18:41,980 --> 00:18:49,840 Like, and one way of doing that is to show that if I spend this amount of money in time, I will get a return on that investment and we will make profit from it. 259 00:18:49,840 --> 00:18:54,480 Which, for something like Agile, is kind of weird to think about how you would do that. 260 00:18:54,720 --> 00:18:58,360 It's sort of like, you know, what's the ROI on brushing my teeth every day? 261 00:18:58,840 --> 00:19:03,640 It's sort of a long-term, there's no pivot table for that, that has a short time horizon. 262 00:19:04,540 --> 00:19:07,300 And that's, let me take a note, pivot table for brushing teeth. 263 00:19:08,180 --> 00:19:17,780 Anyways, I'm always curious to have people's realistic input on how they, in their own business justifications, have done ROI and things like that. 264 00:19:17,880 --> 00:19:31,820 Because despite the fact that people are always asking for a spreadsheet to do it, my theory is that every single time it's something slightly different for something like for Agile and higher-level stuff up the stack. 265 00:19:33,040 --> 00:19:33,720 Fair enough. 266 00:19:34,020 --> 00:19:44,620 So, with that, what's the topic we have this week, like I was saying at the beginning, is to go over, basically, Spring Cloud services and Netflix OSS and all those things. 267 00:19:44,700 --> 00:19:46,160 So, why don't you tee that up? 268 00:19:46,420 --> 00:19:48,600 Yeah, I mean, this is, to some extent, self-serving. 269 00:19:48,720 --> 00:19:56,660 Since I've signed up to write a white paper on Spring Cloud and Netflix OSS for Pivotal, I thought I'm going to road test some ideas with Coté and our illustrious audience. 270 00:19:56,660 --> 00:20:01,080 So, but it also fits, clearly, into our circle of code discussion. 271 00:20:01,200 --> 00:20:08,060 We talked, what, last week, two weeks ago, about, last week, about project management or, you know, using Pivotal Tracker. 272 00:20:08,180 --> 00:20:11,920 And this idea of how do you start to collect stories that drive your Agile work. 273 00:20:12,140 --> 00:20:18,380 And, you know, the next part of this circle, and if you get a chance to view Ansi, he's our VP of engineering. 274 00:20:18,500 --> 00:20:23,380 This video on YouTube from the Pivotal kickoff earlier this year or the one from SpringOne, which was great. 275 00:20:23,720 --> 00:20:27,100 This idea of saying software development happens in a rhythm. 276 00:20:27,360 --> 00:20:28,700 It's not a one-and-done exercise. 277 00:20:28,700 --> 00:20:35,720 It's often about the idea through development, through testing and deployment, to collecting feedback, to incorporating that feedback. 278 00:20:36,020 --> 00:20:39,680 And you're constantly iterating in, like, a circle, as we love circle shapes. 279 00:20:40,100 --> 00:20:46,400 So, one of the pieces of development is, you know, how am I accelerating what I'm building here? 280 00:20:46,400 --> 00:20:54,080 And so, the Netflix OSS group, if, you know, I don't know if anybody on this podcast does not know who Netflix is. 281 00:20:54,140 --> 00:21:06,360 But when you're talking about 80-plus million customers, many of those international, 30-plus million now, you know, at peak times, representing over 30% of network bandwidth in the country with people streaming. 282 00:21:06,720 --> 00:21:10,240 So, they solve problems at scale that probably few companies see. 283 00:21:10,780 --> 00:21:24,440 And so, when they've been an Amazon customer for years, but, you know, as the platform was still growing and as their use cases were so edge in terms of their volume and their processing, they've built a ton of services and tools to help them operate at scale. 284 00:21:24,840 --> 00:21:28,960 And so, they've open-sourced nearly all of it, obviously, the stuff they talk about. 285 00:21:29,380 --> 00:21:38,040 And so, they define Netflix OSS as this kind of layer for reliability, for processing, that helps them do the things they need to do at high availability. 286 00:21:38,280 --> 00:21:49,580 Anytime you read stories about, you know, Amazon US East 1 going down, Netflix is rarely ever impacted because they have simply built for a level of resilience that, frankly, I don't think many do. 287 00:21:50,120 --> 00:21:58,500 But, what's interesting about all of this is, and Adrian Cockroft mentioned this at his SpringOne keynote, is these are now solved problems. 288 00:21:58,980 --> 00:22:10,940 And so, you have these questions or these decisions of, do I reinvent the wheel from things that, you know, Netflix processes billions of requests per day through Zool and some of their edge stuff? 289 00:22:11,480 --> 00:22:12,680 Am I going to do better than that? 290 00:22:12,800 --> 00:22:13,620 Do I need to? 291 00:22:13,820 --> 00:22:18,040 And so, they have some really cool tech that says, look, this has now been done. 292 00:22:18,500 --> 00:22:19,420 How do I use it? 293 00:22:19,460 --> 00:22:25,360 And then, with Spring Cloud, we partnered with them, Pivotal, what, a year or so ago, and said, let's make this stuff more consumable. 294 00:22:25,480 --> 00:22:30,440 Because I can go to the open-source repo, download bits, and run Eureka myself. 295 00:22:30,600 --> 00:22:31,940 I absolutely can do those things. 296 00:22:31,980 --> 00:22:33,260 We'll talk through some of these services. 297 00:22:33,840 --> 00:22:34,900 But should I be doing that? 298 00:22:34,920 --> 00:22:37,320 Is that the best thing to be fiddling with this for a few days? 299 00:22:37,460 --> 00:22:45,340 Or would it be great if I just add some annotations in my Spring Boot app, and all of a sudden, I've got, you know, circuit breakers, or I've got things like that? 300 00:22:45,400 --> 00:22:55,420 So, the idea of how do I operationalize and productize these libraries to make them just brain-dead simple to consume, while still not losing any of the power, we've baked it into Spring Cloud. 301 00:22:55,420 --> 00:23:04,740 And then, with Spring Cloud services, those are the ones that run cleanly, natively on Pivotal Cloud Foundry, just with a button click, deploy out the config server, deploy out things like that. 302 00:23:05,260 --> 00:23:07,340 That's kind of the history of where some of this comes from. 303 00:23:07,460 --> 00:23:14,020 So, then, kind of starting from the top and then getting down to the components, there's basically, I don't know what top is. 304 00:23:14,100 --> 00:23:15,320 It's more like a chronology. 305 00:23:16,160 --> 00:23:27,520 But there's, so Netflix writes all this stuff to basically handle their big distributed microservices-based application that allows us to watch videos. 306 00:23:28,180 --> 00:23:29,280 All in the cloud, though. 307 00:23:29,440 --> 00:23:32,500 Yeah, and, you know, does a bunch of other stuff as well. 308 00:23:32,500 --> 00:23:38,780 And, you know, no one's going to get more upset than someone who just started to try to watch, like, what's that strange thing show? 309 00:23:38,880 --> 00:23:40,660 And episode six doesn't come up. 310 00:23:40,780 --> 00:23:42,200 So, they need some resiliency. 311 00:23:42,900 --> 00:23:45,460 Or people are going to get really pissed, as it were. 312 00:23:45,920 --> 00:23:52,540 And so, they write all this stuff to be able to be a resilient distributed system. 313 00:23:53,080 --> 00:23:55,840 And then, they decide they want to open source it, whenever that is. 314 00:23:56,100 --> 00:24:00,720 And then, along comes, so you've got Netflix OSS, and they have a bunch of their own separate stuff. 315 00:24:00,860 --> 00:24:06,280 And then, so we have Spring Cloud, which takes a lot of that stuff, and probably some other things as well, 316 00:24:06,680 --> 00:24:14,360 and bundles it in a Spring way that makes it, on its own, just easier to consume by Spring's opinion. 317 00:24:15,480 --> 00:24:22,480 Like, that's always, that seems to be a lot of what Spring Frameworks people's ethos, I guess, is always. 318 00:24:22,600 --> 00:24:28,780 It's like, let's focus on getting maximum usability for this API and layer it around there. 319 00:24:28,860 --> 00:24:34,600 And then, the next thing is that, if you have Pivotal Cloud Foundry, the use of Spring Cloud services, 320 00:24:34,800 --> 00:24:41,180 and therefore, much of Netflix OSS, is basically baked into and pre-configured and wired up in Pivotal Cloud Foundry, 321 00:24:41,280 --> 00:24:49,320 so that you don't really have to spend much time at all, especially as a developer, setting it up or configuring it to work with each other. 322 00:24:49,380 --> 00:24:51,640 It just sort of works out of the box, if you will. 323 00:24:51,880 --> 00:24:57,000 Yeah, I mean, if the job is to bootstrap devs so that they're writing the stuff that matters, right? 324 00:24:57,000 --> 00:25:06,120 Business logic, experience with customers, getting to do that faster, this sort of stuff, which is scaffolding, which is plumbing, super powerful plumbing, 325 00:25:06,460 --> 00:25:10,300 but it's plumbing nonetheless, you know, your customers should just see a resilient app. 326 00:25:10,400 --> 00:25:12,480 They're not going to ever see these services. 327 00:25:12,660 --> 00:25:16,000 These are something that help you stay online at all times or be durable, whatever. 328 00:25:16,540 --> 00:25:22,880 So, I think it's that mix of saying, look, as a dev, if my job is to get into building awesome stuff first, 329 00:25:23,240 --> 00:25:29,860 then I don't want to necessarily have all this other bootstrapping of getting projects stood up, figuring out these libraries, standing them up somewhere. 330 00:25:30,100 --> 00:25:36,100 If I can just kind of have that baked into my framework, sometimes with opinions, so they're not as flexible maybe as just doing it raw, 331 00:25:36,460 --> 00:25:38,500 I'm just getting to more meaningful work faster. 332 00:25:39,080 --> 00:25:40,240 And there's something to that. 333 00:25:42,400 --> 00:25:47,540 So, then let's walk through, like, the components of it, like, what makes up this thing? 334 00:25:48,540 --> 00:25:50,060 Yeah, they've got a handful of categories. 335 00:25:50,180 --> 00:25:55,080 If you go to the netflix.github.io, it's kind of their landing page for all sorts of projects. 336 00:25:55,300 --> 00:26:00,880 And they've got tons of categories, big data, runtime services, where we're going to spend our time, content encoding. 337 00:26:01,120 --> 00:26:06,080 You can imagine they've just got petabytes of content, data persistence, reliability, security. 338 00:26:06,400 --> 00:26:14,840 But when we look at the Netflix OSS services we're thinking about, and again, for the layperson, the one you may have heard of is things like Chaos Monkey. 339 00:26:15,280 --> 00:26:24,100 Now, Chaos Monkey is kind of the famous one from Netflix that just kind of turns off things within a particular area of the cloud for them. 340 00:26:24,180 --> 00:26:25,180 They just turn boxes off. 341 00:26:25,320 --> 00:26:28,320 And the premise is, their system should keep working. 342 00:26:28,500 --> 00:26:34,240 So, this is almost a test in production sort of service that says, let me just go in there and randomly turn things off. 343 00:26:34,340 --> 00:26:37,200 And if I've built my system correctly, that shouldn't impact anything. 344 00:26:37,700 --> 00:26:38,840 Things should be self-healing. 345 00:26:39,000 --> 00:26:40,060 Things should be discoverable. 346 00:26:40,500 --> 00:26:46,600 Now, I contend if you unleashed Chaos Monkey within most enterprise data centers, you'd probably shut the business down in four minutes. 347 00:26:47,000 --> 00:26:49,580 And that's just, you know, the nature of how they've built apps. 348 00:26:49,620 --> 00:26:51,360 It's not necessarily, it just is. 349 00:26:51,400 --> 00:26:56,280 So, they've also built some other chaos things in their family that turn off a whole region and things like that. 350 00:26:56,320 --> 00:27:04,960 And the idea is, to really test resiliency, you've got to be able to lose components all over the place in ways you might not have thought of and prove that things work. 351 00:27:05,040 --> 00:27:06,780 Now, there's no Chaos Monkey in Spring Cloud. 352 00:27:06,940 --> 00:27:08,480 It doesn't really have that same concept. 353 00:27:08,880 --> 00:27:11,220 But that's really the most famous of the Netflix services. 354 00:27:11,320 --> 00:27:18,600 When you get into the ones that are in Spring Cloud Netflix, it's a more famous one. 355 00:27:18,640 --> 00:27:20,620 So, you have Eureka, if you've come across that. 356 00:27:20,620 --> 00:27:28,980 That is, service discovery in this microservices-y world where, you know, instead of two monolithic apps, I have 400 microservices. 357 00:27:29,560 --> 00:27:33,340 The question is, how do I find stuff for load balancing purposes or what have you? 358 00:27:33,600 --> 00:27:36,520 How do I know that the order service is here so I can call it? 359 00:27:36,580 --> 00:27:41,720 Now, there's also a whole school of thought that says, well, service discovery is, I don't like that concept anyway. 360 00:27:41,800 --> 00:27:45,940 I wish I just had a message bus that could pass messages and say, hey, here's a new order. 361 00:27:46,120 --> 00:27:47,140 You do something with it. 362 00:27:47,180 --> 00:27:48,480 I don't want to look up an order service. 363 00:27:48,480 --> 00:27:51,020 I just want to have all these things hanging off a bus. 364 00:27:51,240 --> 00:27:53,060 And that could be cool and that could work. 365 00:27:53,420 --> 00:27:57,860 But there may be synchronous examples where I need, like, give me Coté's customer record. 366 00:27:58,400 --> 00:27:59,640 Maybe that doesn't go through a bus. 367 00:27:59,760 --> 00:28:01,540 Like, there's reasons to know that's the service. 368 00:28:01,740 --> 00:28:06,820 And even in that case, you need some sort of directory that tells you how to get to the bus, right? 369 00:28:06,860 --> 00:28:09,200 Like, no one's born at the bus stop. 370 00:28:09,780 --> 00:28:12,740 Like, you have to know, like, how to locate yourself to it. 371 00:28:13,020 --> 00:28:23,260 And it is, I mean, that's, you know, going back to, that's the, it's sort of the my daughter versus people who don't tell you what they're doing model of broadcasting versus looking things up. 372 00:28:23,420 --> 00:28:27,800 And so it seems to me, I mean, Eureka is basically a directory, right? 373 00:28:27,900 --> 00:28:35,940 Like, as we used to call it in the JEE world, I don't know what they call it in .NET land, but, you know, there used to be like JNDI and all that. 374 00:28:36,000 --> 00:28:44,600 And, like, it's sort of like the first step to any application that uses the network, get a hold of the thing that tells you about other things. 375 00:28:45,420 --> 00:28:48,960 And there's all sorts of, like, gymnastics you always go through. 376 00:28:49,060 --> 00:28:56,120 It's almost like it's the prime mover thing of, like, you know, who tells the network thing where the network is. 377 00:28:56,300 --> 00:28:58,660 Like, eventually, someone has to give you some information. 378 00:28:59,000 --> 00:29:11,500 But at least in general, in a larger system, you just go to something like Eureka or the directory to say, like, where's my thing that does, I don't know, that looks up what movie to stream or authorizes this customer or do this thing. 379 00:29:11,940 --> 00:29:13,340 And that's kind of basic. 380 00:29:13,420 --> 00:29:25,760 Now, I'm no expert on all this stuff, but it seems like something that Eureka does that's slightly different than traditional directories is it has a fair amount of, like, health checks and monitoring built into it. 381 00:29:25,900 --> 00:29:31,360 So, I guess maybe it's also a source of, like, is this thing healthy or are there healthier ones that you could use? 382 00:29:31,980 --> 00:29:38,420 Yeah, there's a lot of good integration with some of their other libraries that do things around, you know, other load balancing and circuit breakers. 383 00:29:38,520 --> 00:29:45,620 So, Eureka is unique from just traditional, you know, Amazon Elastic Load Balancer or their DNS service because it also does local stuff. 384 00:29:45,760 --> 00:29:47,880 So, there's a client and a server, if you will. 385 00:29:47,880 --> 00:29:53,360 So, my web app actually pulls things into memory about where my different services are in the directory. 386 00:29:53,600 --> 00:29:56,760 And then it can make local decisions about, well, let me route to here or to here. 387 00:29:56,940 --> 00:30:01,320 And so, there's some smarts that say, let's not just have a single point of failure on my registry either. 388 00:30:01,780 --> 00:30:03,840 Let's actually push some of that data to each client. 389 00:30:03,940 --> 00:30:06,660 They can make local decisions and then just refresh regularly. 390 00:30:07,280 --> 00:30:08,980 So, a neat model. 391 00:30:09,060 --> 00:30:13,140 And again, Netflix builds these things because they don't see it in the core service. 392 00:30:13,140 --> 00:30:17,720 And then we all reap the benefit of a more intelligent sort of ecosystem here. 393 00:30:18,300 --> 00:30:24,000 And this is one of those that's like, ultimately, you can configure it with like a Git repo or something, right? 394 00:30:24,780 --> 00:30:26,360 Yeah, I mean, pretty simple stuff. 395 00:30:26,400 --> 00:30:28,640 And this is one that's also baked into Spring Cloud services. 396 00:30:28,800 --> 00:30:33,660 So, if you're a PCF customer today, this is a button click to get your service discovery set up. 397 00:30:33,920 --> 00:30:39,260 So, it's a little different than config server, which does, you know, use a Git backend and things like that for just config values. 398 00:30:39,660 --> 00:30:44,160 But when I do need to look up my stuff, I don't remember all the backing stores for Eureka. 399 00:30:44,300 --> 00:30:46,180 But it's nice to have, I think you have some choices there. 400 00:30:46,680 --> 00:30:52,620 And then, you know, last item, it sets the stage that there's going to be a lot of silly names for things. 401 00:30:53,200 --> 00:30:56,020 Nothing is named like some serious adult stuff. 402 00:30:56,460 --> 00:30:57,920 It's all fun stuff. 403 00:30:58,280 --> 00:30:59,540 Not with that many microservices. 404 00:30:59,800 --> 00:31:01,700 It's one thing when you get two services, right? 405 00:31:01,720 --> 00:31:03,280 You can go with some Lord of the Rings characters. 406 00:31:03,400 --> 00:31:07,980 But now you're going like B-side when you get the 400 microservices to name these things uniquely. 407 00:31:08,200 --> 00:31:10,100 You'd have to use the Simpsons naming scheme. 408 00:31:10,100 --> 00:31:13,140 I think that's the broadest universe available. 409 00:31:14,440 --> 00:31:16,360 The extended Marvel universe something. 410 00:31:16,540 --> 00:31:19,920 You're clearly hitting the long tail of names here. 411 00:31:21,180 --> 00:31:29,400 Yeah, so the second one, which it starts to get more advanced, but Hystrix, which, I don't know, sounds like something my doctor gives me a cream for. 412 00:31:29,720 --> 00:31:32,100 But it's meant for a circuit breaker. 413 00:31:32,280 --> 00:31:40,700 So, this idea of, you know, as I have more and more services, you know, Coté, you're probably familiar with this idea of, you know, you look at nines. 414 00:31:40,840 --> 00:31:42,740 Everyone wants five nines, three nines, whatever. 415 00:31:43,320 --> 00:31:46,180 If I have, you know, they have the example on their web page. 416 00:31:46,180 --> 00:31:48,220 I think it's 4D services with three nines. 417 00:31:48,800 --> 00:31:52,820 You know, on the, you know, even four nines, you can look at that and go, that's pretty good. 418 00:31:53,180 --> 00:31:59,660 But all of a sudden, when you multiply 40 times 999, all of a sudden, I'm still taking a couple hours of downtime a month. 419 00:31:59,660 --> 00:32:04,060 Because you have to do the aggregate of all of those services if my app uses them all. 420 00:32:04,300 --> 00:32:06,900 Like, individually, it's great if you're online all the time. 421 00:32:07,620 --> 00:32:12,180 Collectively, that's not very good when I'm consuming all these services that individually still have downtime. 422 00:32:12,320 --> 00:32:15,860 So, how do I prevent cascading failures is the term. 423 00:32:15,940 --> 00:32:22,000 How do I make sure that if my customer record service is offline, everything doesn't crash to a halt? 424 00:32:22,120 --> 00:32:30,060 And so, the idea of hysterics or a circuit breaker is I'm going to make sure that when I detect an unhealthy service, I kind of open the circuit. 425 00:32:30,180 --> 00:32:30,860 I always do that wrong. 426 00:32:30,960 --> 00:32:31,960 I think I open the circuit. 427 00:32:32,440 --> 00:32:34,420 And, therefore, traffic might not go there anymore. 428 00:32:34,760 --> 00:32:37,920 And I have a fallback service or I might just return some cash data. 429 00:32:38,320 --> 00:32:43,000 And then, over time, it slowly closes the circuit, I think, and lets some data come through. 430 00:32:43,100 --> 00:32:45,460 And then, if it's healthy, it opens it all back up again or closes it. 431 00:32:45,480 --> 00:32:46,840 I don't have electronics, well. 432 00:32:47,280 --> 00:32:49,040 But it follows that model. 433 00:32:49,040 --> 00:32:56,800 But the idea is how do I make sure that a bad service gets quickly shut off so that my other things that might depend on it don't come crashing to a halt? 434 00:32:57,240 --> 00:32:57,320 Yeah. 435 00:32:57,500 --> 00:33:02,180 You know, I think it took me a long time to figure out, like, why a circuit breaker is good. 436 00:33:02,420 --> 00:33:04,560 I mean, the full picture of it. 437 00:33:04,560 --> 00:33:11,660 Because it seems like, obviously, you know, if it's going to spew a bunch of stuff that's going to bring down your system, you should turn that off. 438 00:33:11,660 --> 00:33:15,880 But then, my thinking was always like, but then it doesn't work. 439 00:33:16,660 --> 00:33:17,060 Right. 440 00:33:17,340 --> 00:33:17,700 Right? 441 00:33:17,820 --> 00:33:17,960 Right. 442 00:33:18,220 --> 00:33:30,520 Like, you know, again, using the Netflix example, if, to greatly simplify, I'm sure, which is a much more complicated system, if I go request, you know, episode six of Strange Things and it's broken, 443 00:33:30,920 --> 00:33:34,240 the circuit breaker, I guess, opens the circuit and I don't get my video. 444 00:33:34,560 --> 00:33:36,000 So, it's broken. 445 00:33:36,720 --> 00:33:49,600 But, you know, on the other hand, I think the goal is that, as you're saying, like, and, you know, a lot of the problems, especially performance problems you encounter in applications are due to, like, cascading problems. 446 00:33:49,600 --> 00:33:52,100 And this thing happened over here that caused this to break. 447 00:33:52,220 --> 00:33:58,460 And there's a reason we have a whole, like, cult of root cause analysis, because you're always having to dig down to see what actually caused the problem. 448 00:33:58,560 --> 00:34:07,920 So, the idea is, if you, to mix the metaphors and make it confusing, if you close off that service, instead of, as you say, open it up. 449 00:34:07,920 --> 00:34:17,320 But if you close off that service, you hopefully can reduce the amount of, like, things further up the stack or upscreen, you know, issues that happen. 450 00:34:18,180 --> 00:34:20,320 But you bring up a great point there. 451 00:34:20,320 --> 00:34:34,220 I think for, like, very complicated distributed applications that are composing together multiple services, this is probably a pattern that you want to use a lot more than in, not so much non-complicated, but sort of, like, vertically integrated things. 452 00:34:34,220 --> 00:34:40,560 Where, you know, there's single calls going down instead of multiple things that you're waiting on that could blow everything up for you. 453 00:34:41,160 --> 00:34:41,240 Right. 454 00:34:41,440 --> 00:34:45,060 No, you made a good point there, specifically among many good points, of course. 455 00:34:45,180 --> 00:34:53,320 But, you know, there's a certain level of responsibility you take on now as a service consumer, because I can't just blindly assume your service is up all the time. 456 00:34:53,400 --> 00:34:59,260 I might need to have, you know, be able to handle cached results, or I need to be able to, you know, do something else. 457 00:34:59,340 --> 00:35:06,440 Hystrix takes care of returning back data from a backup channel or things, but you can't just assume your endpoints are all, your dependencies are always online. 458 00:35:06,900 --> 00:35:09,960 And so even as a service developer, you do have to think a little bit differently. 459 00:35:10,220 --> 00:35:17,680 Yeah, and I guess, I mean, I don't know if this actually fits in the best way of doing it, but I guess you could also have, like, service degradation as sort of a pattern, right? 460 00:35:17,680 --> 00:35:24,440 Like, I can't serve up the HD video, but I can serve up the 480 video or whatever. 461 00:35:24,840 --> 00:35:26,640 Yeah, as a consumer, that's probably better, right? 462 00:35:26,720 --> 00:35:28,240 I mean, there's a fine line. 463 00:35:28,340 --> 00:35:31,780 I think we all know that I would rather have no internet than slow internet sometimes. 464 00:35:31,900 --> 00:35:35,720 I mean, there are certain cases where a degraded experience is actually worse than nothing. 465 00:35:35,980 --> 00:35:38,400 But if you do it right, then sure. 466 00:35:38,600 --> 00:35:42,820 You know, maybe I don't get the weird subtitles at the bottom or the options, but my video playback still works. 467 00:35:42,980 --> 00:35:44,100 I can tolerate that. 468 00:35:44,100 --> 00:35:48,360 Or, again, I'm a banking customer online or I'm, you know, a storefront. 469 00:35:48,760 --> 00:35:56,300 Maybe I can't show you recommended products because that service is offline, but I shouldn't fail to draw the page because I can't show recommended products. 470 00:35:56,740 --> 00:35:59,080 So I think it's just thinking of these components differently. 471 00:35:59,340 --> 00:36:10,280 And then once again, as with all of these things, or most all of them, the other part that makes it valuable, especially when integrated into a full platform, is you have the actual monitoring and management stuff on it, right? 472 00:36:10,280 --> 00:36:22,300 So whether it's like a developer or, I'm putting this in air quotes, a DevOps or an operator or whoever, they can actually see these things happening so that they can try to go fix them. 473 00:36:22,540 --> 00:36:28,220 And you can get a lot of information, especially if you throw in something like Zipkin or Spring Sleuth. 474 00:36:28,320 --> 00:36:33,620 You can start to, like, figure out, like, if something's going wrong and, let's see if I get this right, a circuit opens. 475 00:36:34,140 --> 00:36:36,700 Anyways, you shut down a problem. 476 00:36:37,160 --> 00:36:41,880 You can start going in there and tracing out, like, what problems you might have and things like that. 477 00:36:41,940 --> 00:36:57,040 So the other thing built into most of these tools is once something goes wrong, here's a whole bunch of information you would need to try to fix it in production, not just, you know, dump out a stack trace and go file a ticket and have someone fix it one day. 478 00:36:57,700 --> 00:36:58,060 Right. 479 00:36:58,780 --> 00:36:59,240 No, it's good. 480 00:36:59,240 --> 00:37:02,400 The show notes for this will be Coté and Richard talk electronics. 481 00:37:02,760 --> 00:37:06,240 And, you know, when the zombie apocalypse hits, I will not sign up for electrician. 482 00:37:07,080 --> 00:37:07,320 That's right. 483 00:37:07,480 --> 00:37:10,640 Two software marketers walk into a radio shack. 484 00:37:11,360 --> 00:37:13,160 You can imagine what happens next. 485 00:37:13,800 --> 00:37:14,520 Good times. 486 00:37:15,200 --> 00:37:32,120 So, yeah, I mean, hopefully also the gist you're getting as we talk through these is that, you know, the sort of old client server model of I just build up a web app and I deploy it, you know, that is very different in this world of a lot of distributed components that might span on-prem and cloud or cross-cloud and I've got different availability. 487 00:37:32,120 --> 00:37:35,880 And, you know, everything has gotten more complex so that you can be more agile. 488 00:37:36,280 --> 00:37:42,980 It's just weird, probably sometimes it seems counterintuitive, but, you know, agility is hard to create. 489 00:37:43,500 --> 00:37:49,340 And to do it, you end up using services like this, which give you a lot of awesome controls, but complexity is going up. 490 00:37:51,000 --> 00:37:57,780 The next one is Ribbon, which you'll find out in Spring Cloud, it's not in Spring Cloud Services, but part of Spring Cloud. 491 00:37:57,840 --> 00:37:59,160 This is client-side load balancing. 492 00:37:59,160 --> 00:38:07,320 So, this is the idea of, again, integrating with the registry and others so that it can actually do client-based load balancing within your web app or whatever you've got. 493 00:38:07,720 --> 00:38:10,280 So, it's actually making some of those decisions about where to go. 494 00:38:10,460 --> 00:38:15,680 So, it's just an interesting model versus just having a pure server-based load balance. 495 00:38:15,740 --> 00:38:18,380 It gives you some additional flexibility for how to target workloads. 496 00:38:18,380 --> 00:38:19,580 Right. 497 00:38:19,580 --> 00:38:28,240 And my assumption is, like, this is the kind of thing where having the health information from something like Eureka is helpful. 498 00:38:28,440 --> 00:38:36,180 Because then, speaking of AI or, like, very simple AI, you can start to make some analysis of who you might call reliably and stuff like that. 499 00:38:36,260 --> 00:38:42,180 I mean, I assume that in most distributed systems like this, it's just sort of, like, that's nice. 500 00:38:42,340 --> 00:38:43,800 Let's just do round robin. 501 00:38:44,320 --> 00:38:45,360 Like that. 502 00:38:45,440 --> 00:38:45,920 Exactly. 503 00:38:46,120 --> 00:38:46,820 That seems to be what happens. 504 00:38:46,820 --> 00:38:53,180 And sometimes that is fine, but it is nice to know you've got that local flexibility and not relying on the single server for all of it. 505 00:38:55,540 --> 00:39:04,240 The next one to talk about, Zool, referencing, of course, Ghostbusters, is it's a nice kind of reverse proxy or an edge server. 506 00:39:04,560 --> 00:39:09,920 So, this is something where, again, they send billions and billions of requests through this on a given day. 507 00:39:10,200 --> 00:39:14,500 But the idea of just giving you some more flexibility for routing and things at the edge. 508 00:39:14,640 --> 00:39:16,480 So, I haven't used this as much myself yet. 509 00:39:16,540 --> 00:39:17,740 This is one I haven't stood up. 510 00:39:17,740 --> 00:39:27,800 But the idea of giving you a way to, I think, not really expose a lot of your back-end services, even to the microservices thing, and then have this sort of front-door edge router that sends things. 511 00:39:27,860 --> 00:39:31,820 I don't think it's quite a load balancer, but the idea is it's a smarter routing engine. 512 00:39:33,100 --> 00:39:33,980 That makes sense. 513 00:39:34,480 --> 00:39:42,280 The one that I'm kind of excited about, and we talked about this at SpringOne, we should include some links from SpringOne, but Spinnaker. 514 00:39:42,800 --> 00:39:44,920 So, this is what Netflix built for. 515 00:39:45,120 --> 00:39:45,860 It's open source. 516 00:39:45,960 --> 00:39:47,000 Of course, all this is open source. 517 00:39:47,080 --> 00:39:48,920 But multi-cloud deployment. 518 00:39:49,300 --> 00:39:56,940 So, the idea of I've got my package and I want to go deploy this app to Amazon or I want to deploy it to Cloud Foundry or I want to deploy it to Azure. 519 00:39:57,220 --> 00:39:58,640 All of that through the same tool. 520 00:39:59,420 --> 00:40:00,600 Pretty exciting stuff. 521 00:40:00,600 --> 00:40:02,800 So, there were some good demos at SpringOne. 522 00:40:03,460 --> 00:40:08,240 This just got baked into Spring Cloud, I think, last week as its first milestone. 523 00:40:08,540 --> 00:40:11,760 So, you can just add some annotations and get some Spinnaker apps stood up. 524 00:40:12,260 --> 00:40:13,560 So, pretty cool stuff. 525 00:40:13,640 --> 00:40:16,200 We're actually going to have this deployable on Cloud Foundry itself. 526 00:40:16,300 --> 00:40:18,500 So, the Spinnaker service would run on PCF. 527 00:40:18,640 --> 00:40:21,380 So, you're kind of using Cloud Foundry to deploy to Cloud Foundry. 528 00:40:21,640 --> 00:40:23,180 Don't let your head hurt on that one. 529 00:40:23,480 --> 00:40:29,160 But it's a pretty kind of exciting way to have these multi-cloud deployment tools that is pretty good stuff. 530 00:40:29,220 --> 00:40:33,500 So, that's one you should definitely look at if you're looking at using more than one cloud environment. 531 00:40:33,860 --> 00:40:34,020 Yeah. 532 00:40:34,120 --> 00:40:35,860 You know, this reminds me of something. 533 00:40:36,060 --> 00:40:38,900 I need to go watch the videos from SpringOne platform on this. 534 00:40:38,900 --> 00:40:46,160 But I began to understand that multi-cloud means not only multiple infrastructure clouds you're using. 535 00:40:46,600 --> 00:40:51,240 But within a large organization, you have multiple of your own clouds that you want to deploy to. 536 00:40:51,440 --> 00:40:55,680 You know, whether they're, you know, development and staging and all these different environments are geographic. 537 00:40:55,860 --> 00:41:04,880 And I think more and more when you look at the Pivotal Cloud Foundry customer base, they're in this situation where they have multiple clouds that they own. 538 00:41:05,240 --> 00:41:13,140 And so, they need to have consistent ways of deploying and using them, which is a whole other angle on multi-cloud that I think most people don't think about at first. 539 00:41:14,360 --> 00:41:15,140 Yeah, no, agreed. 540 00:41:15,340 --> 00:41:17,520 I think that's a good use case as well. 541 00:41:18,580 --> 00:41:22,540 So, the last one I wanted to point out to you, Mr. Coté, is Atlas. 542 00:41:22,540 --> 00:41:24,240 So, this is for time series data. 543 00:41:24,460 --> 00:41:27,920 As you might imagine, Netflix collects a lot of data. 544 00:41:28,400 --> 00:41:30,080 And so, how do I use this for operational purposes? 545 00:41:30,220 --> 00:41:31,540 How am I using this to trace? 546 00:41:31,920 --> 00:41:37,920 You know, this is something, if you've seen us talk about Zipkin and Spring Cloud Sleuth, this isn't using Atlas at this point. 547 00:41:37,980 --> 00:41:47,500 But the idea of doing distributed tracing, saying, how am I uncovering where I have latency, following the call path, as you said, with these vertical apps as well, so that I can see this call stack. 548 00:41:47,520 --> 00:41:53,080 And where am I hitting latency, maybe I uncover that a service is being called all the time and it's not supposed to be. 549 00:41:53,140 --> 00:42:03,520 Or, you know, it's a great tool to have, to be able to uncover these usage patterns because, frankly, I can't predict how my services are going to be used anymore when I've got this microservices world. 550 00:42:03,940 --> 00:42:05,840 I'm having a lot of composable apps. 551 00:42:05,940 --> 00:42:07,760 I have a lot of different interaction patterns. 552 00:42:08,260 --> 00:42:10,680 And so, being able to do real distributed tracing is huge. 553 00:42:11,120 --> 00:42:13,460 So, Atlas is kind of what Netflix OSS offers. 554 00:42:13,560 --> 00:42:15,120 And, again, you can download and run that. 555 00:42:15,480 --> 00:42:21,260 When you're using kind of a Spring Cloud Foundry world, we like Spring Cloud Sleuth based on Zipkin, which came out of Twitter. 556 00:42:23,260 --> 00:42:26,060 So, that's the stack of everything in there, huh? 557 00:42:26,660 --> 00:42:28,840 Yeah, there's another probably dozen and a half of them. 558 00:42:28,840 --> 00:42:36,020 And I encourage you to read the Netflix tech blog, which is always fantastic, techblog.netflix.com, which has some great stuff. 559 00:42:36,180 --> 00:42:38,940 And now, one question could be, well, sheesh, I'm a Node developer. 560 00:42:39,180 --> 00:42:40,280 You know, I'm a .NET developer. 561 00:42:40,560 --> 00:42:42,560 Why do Spring Cloud people have all the fun? 562 00:42:42,660 --> 00:42:47,000 Well, you know, hey, Netflix uses a lot of Java because that's what big boy companies use. 563 00:42:47,260 --> 00:42:54,320 But at the same time, you know, if I'm a .NET developer, we've actually been incubating the Steel Toe stuff, which you may have heard us mention. 564 00:42:54,320 --> 00:43:00,720 We talked about a little bit at SpringOne as well, which these are kind of connectors for .NET apps into Spring Cloud. 565 00:43:00,980 --> 00:43:06,340 So, whether I'm doing Eureka or Hystrix or the config server, I can do that from .NET apps. 566 00:43:06,420 --> 00:43:11,500 Now, I can be doing circuit breakers for my .NET apps, or I could be looking up the registry from .NET apps. 567 00:43:11,620 --> 00:43:15,420 So, kind of cool to make .NET also a first-class citizen in there. 568 00:43:15,420 --> 00:43:23,580 And then when I was trolling through the JavaScript NPM repository, which is their Node Package Manager repository, saw some ones. 569 00:43:23,600 --> 00:43:28,720 I don't think they're from Netflix themselves, but people had built some JavaScript libraries to talk to these endpoints as well. 570 00:43:28,760 --> 00:43:36,180 Which is cool because many of these are just HTTP endpoints, of which we've wrapped up some good Java goodness to make these look like local calls. 571 00:43:36,360 --> 00:43:41,500 But nothing prevents me from having these other apps that call these endpoints and cache data locally and what have you. 572 00:43:41,580 --> 00:43:43,280 So, it's a pretty open framework. 573 00:43:43,840 --> 00:43:45,620 Obviously, all the love has gone into Java. 574 00:43:45,620 --> 00:43:50,760 But if I'm using JavaScript or Node.js or .NET, you know, you can party on this data. 575 00:43:51,460 --> 00:44:03,780 Yeah, I mean, I think that's a good signal that you're reaching some wide adoption and thus long-term stability for a system like this is that you have more and more language support. 576 00:44:03,840 --> 00:44:07,600 Especially if you jump into .NET land and JavaScript. 577 00:44:07,600 --> 00:44:14,940 You know, I think ensuring that'll be a good test for people to pay attention to, seeing that those are well done. 578 00:44:15,220 --> 00:44:22,840 Then having, you know, I don't know, three different language pools supported is always healthy and then actually used. 579 00:44:23,640 --> 00:44:26,520 Well, as always, this has been Pivotal Conversations. 580 00:44:26,760 --> 00:44:33,960 You can find the freshest, most latest episodes over in SoundCloud if you go to SoundCloud.com slash Pivotal Conversations. 581 00:44:34,420 --> 00:44:35,940 All one long word. 582 00:44:36,220 --> 00:44:37,960 You can practice how to spell conversations. 583 00:44:38,620 --> 00:44:44,560 Also, we put the show notes on our blog, which you can get to at blog.pivotal.io. 584 00:44:45,180 --> 00:44:54,400 And I would encourage you, and I'm sure Richard would as well, if you like this, it's always nice to share it with your friends, whether it's in computer land or in person. 585 00:44:54,880 --> 00:44:59,860 And if you want to take the time to go into iTunes and leave us a review or a star rating, that's always also helpful. 586 00:45:00,420 --> 00:45:05,260 And if you have any feedback on any of this stuff, feel free to email us at podcast at pivotal.io. 587 00:45:05,520 --> 00:45:07,200 Or you can reach out to us on Twitter. 588 00:45:07,440 --> 00:45:13,660 I'm Coté, C-O-T-E, and Richard is R-Siroder, which is spelled pretty much how it sounds. 589 00:45:15,000 --> 00:45:15,620 There you go. 590 00:45:15,740 --> 00:45:17,660 And with that, we'll see everyone next time. 591 00:45:18,240 --> 00:45:18,780 Thanks, all.