1 00:00:00,000 --> 00:00:02,280 All right, well, here we are at, where are we, Josh? 2 00:00:02,680 --> 00:00:04,020 Huh, Singapore. 3 00:00:04,340 --> 00:00:04,940 Can you believe it? 4 00:00:05,280 --> 00:00:05,940 Singapore, yeah. 5 00:00:05,960 --> 00:00:07,080 How many times have you been here? 6 00:00:07,420 --> 00:00:07,780 Just here? 7 00:00:08,200 --> 00:00:08,920 Just in general. 8 00:00:09,180 --> 00:00:10,860 Just more than a handful? 9 00:00:11,040 --> 00:00:11,320 Oh, yeah. 10 00:00:11,320 --> 00:00:11,840 Or a bucket? 11 00:00:12,200 --> 00:00:16,660 So when you come here, what would you recommend as a good thing to eat? 12 00:00:16,880 --> 00:00:17,240 Laksa. 13 00:00:17,760 --> 00:00:18,360 What's that? 14 00:00:19,220 --> 00:00:20,880 That's not related to lox, is it? 15 00:00:21,060 --> 00:00:28,400 No, it's a soup with spice and prawns and shellfish. 16 00:00:28,560 --> 00:00:28,760 And prawns? 17 00:00:28,760 --> 00:00:29,380 I don't do... 18 00:00:29,380 --> 00:00:31,340 Now, how do you distinguish between a prawn and a shrimp? 19 00:00:31,480 --> 00:00:32,240 I think it's the same thing. 20 00:00:32,440 --> 00:00:36,920 It's a shrimp and fish of some sort and spicy noodles. 21 00:00:37,120 --> 00:00:39,580 My feeling is that a prawn is bigger than a shrimp. 22 00:00:39,820 --> 00:00:42,600 Like, have you ever seen a prawn that's the size of an American shrimp? 23 00:00:42,980 --> 00:00:43,460 I don't know. 24 00:00:43,620 --> 00:00:44,280 No, I don't... 25 00:00:44,280 --> 00:00:44,460 No. 26 00:00:44,720 --> 00:00:46,460 So I think prawns are always big. 27 00:00:46,480 --> 00:00:47,480 Is that why they call them shrimps? 28 00:00:47,540 --> 00:00:48,340 Because they're shrimp-y? 29 00:00:48,680 --> 00:00:49,960 Or it may be the other way around. 30 00:00:50,020 --> 00:00:52,560 Maybe they were called shrimp originally and someone... 31 00:00:52,560 --> 00:00:54,760 We use that as a slang for a diminutive person. 32 00:00:54,880 --> 00:00:55,400 I guess. 33 00:00:55,500 --> 00:00:56,020 I don't know. 34 00:00:56,480 --> 00:00:56,800 Yeah. 35 00:00:57,300 --> 00:00:58,760 Okay, so... 36 00:00:58,760 --> 00:00:59,380 What was it again? 37 00:01:00,060 --> 00:01:00,340 Laksa. 38 00:01:00,440 --> 00:01:01,000 L-A-K-S-A. 39 00:01:01,000 --> 00:01:01,960 So you got prawns? 40 00:01:02,000 --> 00:01:02,220 Yeah. 41 00:01:02,500 --> 00:01:02,780 Broth. 42 00:01:03,300 --> 00:01:03,660 Spice. 43 00:01:03,860 --> 00:01:04,080 Uh-huh. 44 00:01:04,320 --> 00:01:05,320 Now, how do you eat that? 45 00:01:06,280 --> 00:01:07,080 Just with a spoon? 46 00:01:07,500 --> 00:01:09,620 There's a spoon and there's a lot of slurping involved. 47 00:01:09,840 --> 00:01:10,000 It's not... 48 00:01:10,000 --> 00:01:13,120 So here's a challenge I have with a lot of soups. 49 00:01:13,220 --> 00:01:13,360 Sure. 50 00:01:13,360 --> 00:01:14,380 It's like, it's really hot. 51 00:01:14,920 --> 00:01:15,300 Yeah, and this is... 52 00:01:15,300 --> 00:01:16,300 So how do you deal with it? 53 00:01:16,300 --> 00:01:17,100 Not spicy hot. 54 00:01:17,220 --> 00:01:17,400 Right. 55 00:01:17,400 --> 00:01:20,720 You have that problem with this as well. 56 00:01:20,720 --> 00:01:24,740 And so is it expected that there's sort of like a five-minute literal cooling-off period? 57 00:01:24,940 --> 00:01:25,020 Yeah, yeah. 58 00:01:25,020 --> 00:01:25,140 That's fine. 59 00:01:25,140 --> 00:01:26,560 So you just kind of hang out? 60 00:01:26,640 --> 00:01:26,720 Totally. 61 00:01:26,720 --> 00:01:27,740 You have the soup in front of you? 62 00:01:27,820 --> 00:01:27,960 Yeah. 63 00:01:28,060 --> 00:01:30,060 I mean, it is going to be the longest five minutes you've ever had. 64 00:01:30,180 --> 00:01:31,520 It'll sit there salivating. 65 00:01:31,700 --> 00:01:35,040 The other thing is, here in Singapore in particular, they have chicken rice. 66 00:01:35,140 --> 00:01:35,740 Hainan chicken rice. 67 00:01:35,840 --> 00:01:37,680 So you are the third person to mention this now. 68 00:01:37,720 --> 00:01:38,520 What's the deal with chicken rice? 69 00:01:38,520 --> 00:01:38,860 Well, okay. 70 00:01:38,960 --> 00:01:40,320 So it's not actually a Singaporean thing. 71 00:01:41,380 --> 00:01:41,660 It's... 72 00:01:41,660 --> 00:01:42,660 Well, you can get in Hong Kong. 73 00:01:42,780 --> 00:01:43,240 You can get in Malaysia. 74 00:01:43,660 --> 00:01:44,580 It's everywhere but here. 75 00:01:44,720 --> 00:01:46,480 But they have here the... 76 00:01:46,480 --> 00:01:47,920 And I did this like four trips ago. 77 00:01:48,220 --> 00:01:54,780 They have the first Michelin star, Michelin guide, one star, hawker stall. 78 00:01:54,880 --> 00:01:55,580 So they have hawker stalls. 79 00:01:55,820 --> 00:01:56,560 Oh, yeah. 80 00:01:56,680 --> 00:01:57,840 Someone was telling me that's here. 81 00:01:57,920 --> 00:01:58,360 Right, right, right. 82 00:01:58,480 --> 00:01:59,200 They have hawker stalls. 83 00:01:59,300 --> 00:02:00,680 Hawker stalls are... 84 00:02:00,680 --> 00:02:01,360 Where you eat hawk. 85 00:02:01,860 --> 00:02:04,480 Well, they're little sheds, basically. 86 00:02:04,560 --> 00:02:05,260 They're little booths. 87 00:02:05,260 --> 00:02:05,800 Right, right, right. 88 00:02:05,820 --> 00:02:06,400 And they're open. 89 00:02:06,940 --> 00:02:08,540 You can go to these stall centers. 90 00:02:08,680 --> 00:02:10,360 You know, they have like 50 of them. 91 00:02:10,600 --> 00:02:11,000 Right, right. 92 00:02:11,000 --> 00:02:11,420 All lined up in a queue. 93 00:02:11,420 --> 00:02:12,960 Street food is what we would call it back home. 94 00:02:13,000 --> 00:02:14,900 But you don't have to wander the streets. 95 00:02:15,220 --> 00:02:16,700 It's like a mall but it's outside. 96 00:02:16,820 --> 00:02:17,160 It's just a fanciful term. 97 00:02:17,380 --> 00:02:18,320 Yeah, it's an outside mall. 98 00:02:18,580 --> 00:02:20,400 And every vendor is a food vendor. 99 00:02:20,560 --> 00:02:22,860 So in this vendor, are they the chicken rice people? 100 00:02:22,880 --> 00:02:24,360 Yeah, Hainan chicken rice, specifically. 101 00:02:24,560 --> 00:02:29,700 And it's just rice, some like vinegar and soy and whatever. 102 00:02:29,900 --> 00:02:34,540 You know, there's like a few sauces and rice and then chicken strips. 103 00:02:34,540 --> 00:02:35,340 It's just chicken. 104 00:02:35,640 --> 00:02:37,520 Now, is the chicken flavored or is the rice flavored? 105 00:02:37,700 --> 00:02:38,340 What's the deal? 106 00:02:38,560 --> 00:02:39,100 I don't know. 107 00:02:39,460 --> 00:02:40,020 It just tastes good. 108 00:02:40,380 --> 00:02:42,900 You know, if you taste chicken rice... 109 00:02:42,900 --> 00:02:44,200 And how would you describe the flavor? 110 00:02:44,320 --> 00:02:44,960 It's not sweet. 111 00:02:45,100 --> 00:02:46,800 No, it's a little bit salty sometimes. 112 00:02:46,920 --> 00:02:47,260 It's nice. 113 00:02:47,600 --> 00:02:49,100 It's like soy sauce and vinegar. 114 00:02:49,320 --> 00:02:50,960 Do they include the skin with the chicken and the bones? 115 00:02:50,980 --> 00:02:53,520 And that's what makes it so, oh, the fat. 116 00:02:53,720 --> 00:02:54,520 It's just perfect. 117 00:02:54,720 --> 00:02:58,180 And you dip it in, you dip into the soup. 118 00:02:58,480 --> 00:03:00,400 You got the chicken, you got the rice, you got the sauces. 119 00:03:00,760 --> 00:03:01,160 Life is good. 120 00:03:01,360 --> 00:03:02,160 Dip into the soup. 121 00:03:02,360 --> 00:03:02,620 Okay. 122 00:03:02,840 --> 00:03:04,380 Well, maybe I'll get some of that. 123 00:03:04,620 --> 00:03:08,980 I've gone towards more of the, I don't know what people call it, but the dumpling things. 124 00:03:09,100 --> 00:03:09,220 Yes. 125 00:03:09,220 --> 00:03:10,460 I always like that. 126 00:03:10,480 --> 00:03:11,400 Sha Long Bao, that kind of thing? 127 00:03:11,560 --> 00:03:11,800 Sure. 128 00:03:11,960 --> 00:03:12,940 I don't know what that is, but it sounds good. 129 00:03:12,940 --> 00:03:16,520 So they have a, in China, they have this thing, in Shanghai, in that region, they have this 130 00:03:16,520 --> 00:03:19,340 thing called Sha Long Bao, which is hot soup dumplings. 131 00:03:20,160 --> 00:03:21,980 And it's the same thing. 132 00:03:22,040 --> 00:03:25,460 You better wait until, better wait a few minutes before you eat it because otherwise you'll... 133 00:03:25,460 --> 00:03:26,140 You'll burn your mouth. 134 00:03:26,200 --> 00:03:26,860 You'll scald your mouth. 135 00:03:27,020 --> 00:03:28,780 And it'll hurt so good because it does taste good. 136 00:03:29,040 --> 00:03:35,800 But they have a, Sha Long Bao, there's a restaurant that's pretty, I think it's from Taiwan. 137 00:03:35,920 --> 00:03:36,360 I could be wrong. 138 00:03:36,420 --> 00:03:37,180 I could be wrong on this part. 139 00:03:37,400 --> 00:03:41,260 There's a restaurant called Din Tai Fung, and they have that, it's international, right? 140 00:03:41,300 --> 00:03:45,900 So they have it in Taiwan, they have it in Singapore, they have it in Malaysia, they have 141 00:03:45,900 --> 00:03:46,780 it in all over China. 142 00:03:47,060 --> 00:03:47,300 Yeah. 143 00:03:47,640 --> 00:03:49,020 And in Hong Kong. 144 00:03:49,540 --> 00:03:52,660 And they have it here in Singapore. 145 00:03:52,820 --> 00:03:56,700 So you go to the Din Tai Fung, and it's famous. 146 00:03:56,700 --> 00:03:59,960 It's like these locations, you know, every celebrity in Asia has been to these places. 147 00:04:00,120 --> 00:04:03,920 Is it like a steakhouse where they've got pictures of people on the wall who've been there? 148 00:04:03,920 --> 00:04:04,600 Or caricatures and stuff. 149 00:04:04,740 --> 00:04:05,380 Oh, even better. 150 00:04:05,480 --> 00:04:06,380 The one in... 151 00:04:06,380 --> 00:04:07,280 Small body, big head? 152 00:04:07,680 --> 00:04:08,240 Yeah, exactly. 153 00:04:08,320 --> 00:04:09,020 And signatures, too. 154 00:04:09,100 --> 00:04:09,900 They actually signed it, you know? 155 00:04:10,220 --> 00:04:18,340 So I lived in Shanghai for several months in 2012, and I just got fat on these Sha Long Bao, 156 00:04:18,440 --> 00:04:18,580 you know? 157 00:04:18,720 --> 00:04:19,780 So how do you eat those? 158 00:04:19,860 --> 00:04:21,100 How are they presented to you? 159 00:04:22,200 --> 00:04:26,600 You get a bucket, you get a wicker basket kind of with some paper on it. 160 00:04:26,600 --> 00:04:27,940 And there's a bunch of Sha Long Bao in there. 161 00:04:28,060 --> 00:04:28,260 Okay. 162 00:04:28,360 --> 00:04:32,540 And you use your top stick, and just carefully, when you bite into it, it's going to explode 163 00:04:32,540 --> 00:04:33,640 in soup, you know? 164 00:04:33,980 --> 00:04:37,380 And there's different flavors, like garlic and whatever, all sorts of different flavored 165 00:04:37,380 --> 00:04:38,660 Sha Long Bao soups. 166 00:04:38,760 --> 00:04:39,360 What's your favorite? 167 00:04:40,400 --> 00:04:41,020 Classic is great. 168 00:04:41,140 --> 00:04:41,700 Garlic is great. 169 00:04:42,320 --> 00:04:42,980 What's a classic? 170 00:04:43,120 --> 00:04:43,520 I don't know. 171 00:04:43,580 --> 00:04:45,140 It's just, it's a little bit of vinegary. 172 00:04:45,420 --> 00:04:46,940 It's a little sour. 173 00:04:47,040 --> 00:04:47,340 It's nice. 174 00:04:47,420 --> 00:04:47,960 It's just a soup. 175 00:04:48,160 --> 00:04:48,500 All right. 176 00:04:48,560 --> 00:04:48,760 All right. 177 00:04:48,760 --> 00:04:49,980 Well, now that we've got the important things covered. 178 00:04:49,980 --> 00:04:50,240 Yeah, yeah. 179 00:04:50,460 --> 00:04:53,600 The point is, Singapore is great, because you've got this nexus of all these different flavors. 180 00:04:53,620 --> 00:04:54,120 Yeah, yeah, yeah. 181 00:04:54,120 --> 00:04:54,320 Of course. 182 00:04:54,320 --> 00:04:57,020 Right, so you're right next to Malaysia, and they have their own, like... 183 00:04:57,020 --> 00:05:00,480 That's why I've been asking what people recommend, because there's so many things to choose 184 00:05:00,480 --> 00:05:00,720 from. 185 00:05:00,900 --> 00:05:01,880 And there's no real... 186 00:05:01,880 --> 00:05:06,100 There are some things that are Singaporean, but the best part about Singapore is the nexus 187 00:05:06,100 --> 00:05:11,340 of all these other things from Asia proper, like the rest of it, China, Thailand's north 188 00:05:11,340 --> 00:05:12,060 of here. 189 00:05:12,200 --> 00:05:13,180 You've got, you know... 190 00:05:13,180 --> 00:05:14,400 A veritable melage. 191 00:05:14,640 --> 00:05:16,260 Yeah, and then, oh, and the Indian food. 192 00:05:16,380 --> 00:05:16,720 Oh, God. 193 00:05:16,780 --> 00:05:17,400 And the Malay food. 194 00:05:17,640 --> 00:05:18,600 Just everything you can imagine. 195 00:05:19,000 --> 00:05:21,260 Now, what's the characteristic of Malay food? 196 00:05:21,820 --> 00:05:22,800 Malay food... 197 00:05:22,800 --> 00:05:23,640 What's its deal? 198 00:05:24,460 --> 00:05:27,500 Well, it's not too different, because they are just next door, right? 199 00:05:27,560 --> 00:05:28,120 Sure, sure. 200 00:05:28,280 --> 00:05:31,240 But when you say Malaysian, that encompasses the country... 201 00:05:31,240 --> 00:05:31,440 It's big. 202 00:05:31,700 --> 00:05:32,840 Well, yeah, but the country... 203 00:05:32,840 --> 00:05:35,320 And then there's different regions that have their own culture, like Penang. 204 00:05:35,580 --> 00:05:36,060 Right, right, right. 205 00:05:36,080 --> 00:05:38,940 It's own food, you know, thing. 206 00:05:39,320 --> 00:05:46,080 But Malaysia has, like, a 20% ethnic Chinese population. 207 00:05:46,840 --> 00:05:47,740 So you've got that going on. 208 00:05:47,760 --> 00:05:49,280 And then you've got the rest who are Malay. 209 00:05:49,540 --> 00:05:50,060 Right, right, right. 210 00:05:50,060 --> 00:05:53,380 Right, and so Malay doesn't describe everybody in the country. 211 00:05:53,480 --> 00:05:54,660 Malaysians describe everybody in the country. 212 00:05:54,660 --> 00:05:54,840 Sure, sure. 213 00:05:55,060 --> 00:06:03,220 So the Malay people predate, obviously, the Chinese, and they have a very different cuisine. 214 00:06:03,620 --> 00:06:04,700 And what's their cuisine like? 215 00:06:05,300 --> 00:06:14,660 Oh, um, as far as I, you know, it's in some, for some parts of that cuisine, for some part 216 00:06:14,660 --> 00:06:18,920 of the population, it's informed by religion, which in this case is Muslim, right? 217 00:06:19,020 --> 00:06:19,260 Sure. 218 00:06:19,260 --> 00:06:21,420 So there are some things that are not in that cuisine. 219 00:06:21,420 --> 00:06:21,740 For sure. 220 00:06:22,040 --> 00:06:27,000 But otherwise, it's, I think it's fairly, I don't know, it's, I don't know how to describe 221 00:06:27,000 --> 00:06:27,200 it, actually. 222 00:06:27,200 --> 00:06:28,960 Well, let me model for you, right? 223 00:06:29,740 --> 00:06:32,120 I would say, and this is not complete, but... 224 00:06:32,120 --> 00:06:32,280 Sure. 225 00:06:32,660 --> 00:06:35,020 Uh, if I were to describe American food, right? 226 00:06:35,020 --> 00:06:36,060 We have a, we don't have a... 227 00:06:36,060 --> 00:06:40,500 Hold on, if I were to describe American food, I would say we have, we have a lot of meat 228 00:06:40,500 --> 00:06:44,040 that we cook, and we'll have sides of vegetables with it. 229 00:06:44,100 --> 00:06:46,120 That's kind of like a template for generic American food. 230 00:06:46,120 --> 00:06:46,240 Right. 231 00:06:46,340 --> 00:06:48,580 As opposed to the rest of the world where it's vegetables and some salad. 232 00:06:48,580 --> 00:06:52,440 You might fry the chicken, or you might have slices of beef, you might have a steak, but 233 00:06:52,440 --> 00:06:54,940 you have separate, separate sides. 234 00:06:55,100 --> 00:06:58,860 Now, we have another style, which I would call anything between two slices of bread. 235 00:06:59,060 --> 00:06:59,280 Right. 236 00:06:59,420 --> 00:07:03,340 And you could have a hamburger, you could have a sandwich, you could have a Reuben, whatever 237 00:07:03,340 --> 00:07:05,640 it is, we're going to put it between two slices of bread. 238 00:07:05,800 --> 00:07:05,980 Yeah. 239 00:07:06,100 --> 00:07:10,640 And then I think, it's debatable which one is bigger, but I would say we have the Chinese 240 00:07:10,640 --> 00:07:11,640 slash Mexican. 241 00:07:12,060 --> 00:07:12,220 Yeah. 242 00:07:12,300 --> 00:07:12,460 Right? 243 00:07:12,520 --> 00:07:14,680 And then that's a, that's another subset of food. 244 00:07:14,860 --> 00:07:15,000 Right. 245 00:07:15,080 --> 00:07:17,460 And the American Chinese food is its own distinct thing. 246 00:07:17,520 --> 00:07:18,640 It's from, from, right, right. 247 00:07:18,660 --> 00:07:19,640 Sugar with some flavors, yeah. 248 00:07:19,640 --> 00:07:20,000 Right, right. 249 00:07:20,100 --> 00:07:23,940 And, and, and us cosmopolitan worldly people be like, oh my God, it's terrible. 250 00:07:24,120 --> 00:07:29,140 But then, in fact, American Chinese food is kind of like chicken and dumplings, it's comfort 251 00:07:29,140 --> 00:07:29,440 food. 252 00:07:29,820 --> 00:07:29,940 Yeah. 253 00:07:30,300 --> 00:07:34,740 So, and then, but then we also have Mexican food, which is like tacos, burritos, occasionally 254 00:07:34,740 --> 00:07:35,520 enchiladas, chips. 255 00:07:35,520 --> 00:07:36,040 American Mexican, yeah. 256 00:07:36,040 --> 00:07:36,200 Right. 257 00:07:36,300 --> 00:07:41,360 So like, that whole collection, if someone were to ask me, how, what's the deal with American 258 00:07:41,360 --> 00:07:42,780 food, that's what I would start with. 259 00:07:42,880 --> 00:07:44,380 Well, okay, so it's the opposite for the rest of the world. 260 00:07:44,840 --> 00:07:47,460 The rest of the world does vegetables first and a dash of meat. 261 00:07:47,740 --> 00:07:48,060 Right, right, right. 262 00:07:48,060 --> 00:07:53,580 So if you go to China, if you go to, even here, soup is mostly broth and vegetables and, you 263 00:07:53,580 --> 00:07:56,700 know, mushrooms and whatever, and then maybe for flavoring, meat. 264 00:07:57,200 --> 00:07:57,600 Right, right. 265 00:07:57,740 --> 00:07:57,960 Sure. 266 00:07:58,200 --> 00:08:01,980 And, so now, so now back, let me take you back to Malaysia. 267 00:08:01,980 --> 00:08:04,400 So Malaysia actually has a little bit, I think in the, in the meat to vegetable ratio, I 268 00:08:04,400 --> 00:08:07,580 think it's a little bit more even handed than, than it is, for example, China. 269 00:08:07,880 --> 00:08:08,080 Right. 270 00:08:08,220 --> 00:08:10,060 Where you have a lot of, a lot of vegetables. 271 00:08:10,360 --> 00:08:12,840 Oh, oh, and another characteristic of American food, not spicy. 272 00:08:13,100 --> 00:08:13,460 Oh, sorry. 273 00:08:13,760 --> 00:08:15,680 Malay food can be spicy and sometimes not. 274 00:08:15,820 --> 00:08:16,800 It can be a lot of fried food. 275 00:08:16,880 --> 00:08:19,260 There can be a lot of, you know, I don't know, it's just. 276 00:08:19,560 --> 00:08:22,100 Oh, now fried food, that's an important like trait of food. 277 00:08:22,240 --> 00:08:26,480 Like it's, it would be a slight misconception to think that American food is fried a lot. 278 00:08:26,680 --> 00:08:29,120 In fact, there are some special parts and not special. 279 00:08:29,120 --> 00:08:33,740 There's, there's some sections that there's a lot of fried food, but really you don't really 280 00:08:33,740 --> 00:08:37,660 see fried food as much as I think you do in other, other cultures in American food. 281 00:08:37,780 --> 00:08:38,960 So there's a lot of frying. 282 00:08:39,160 --> 00:08:39,280 Right. 283 00:08:39,640 --> 00:08:41,680 Well, not, yeah, at least it seems to me, but. 284 00:08:42,060 --> 00:08:43,020 A noticeable amount. 285 00:08:43,200 --> 00:08:46,960 Here's my, here's where I get into trouble is because I want, while I want to characterize 286 00:08:46,960 --> 00:08:51,740 certain things as being typically Malaysian, a lot, it's not, a lot of the food that's 287 00:08:51,740 --> 00:08:54,980 in Malaysia is actually also Indonesian. 288 00:08:55,360 --> 00:08:55,680 Right, right, right. 289 00:08:55,680 --> 00:08:56,960 So, and so you get into trouble. 290 00:08:56,960 --> 00:09:00,860 Well, I mean, the basis of American food is like, you know, Western Europeans and then, 291 00:09:00,860 --> 00:09:02,940 and then South Americans, right? 292 00:09:02,940 --> 00:09:03,760 South, yeah, yeah. 293 00:09:04,120 --> 00:09:04,200 So. 294 00:09:04,600 --> 00:09:05,160 Southwestern cuisine. 295 00:09:05,160 --> 00:09:05,520 Corn. 296 00:09:05,800 --> 00:09:06,180 Right, right. 297 00:09:06,200 --> 00:09:06,580 Maize. 298 00:09:07,040 --> 00:09:07,220 Yeah. 299 00:09:07,340 --> 00:09:07,440 Yeah. 300 00:09:07,620 --> 00:09:10,200 So our, our food isn't from us. 301 00:09:10,400 --> 00:09:11,260 Well, but I mean, yeah, exactly. 302 00:09:11,340 --> 00:09:16,560 So I don't, so one of my favorite dishes that I would, that I would eat all day for, I mean, 303 00:09:16,580 --> 00:09:21,040 I could eat it every meal for the rest of my life is Nazi goreng, which is a Malaysian, 304 00:09:22,340 --> 00:09:23,720 sorry, Indonesian dish. 305 00:09:24,080 --> 00:09:24,400 Right. 306 00:09:24,400 --> 00:09:25,460 But you can get it in Malaysia. 307 00:09:25,680 --> 00:09:26,460 So, so what is it? 308 00:09:26,760 --> 00:09:29,420 It's rice and meat and certain spices in the rice. 309 00:09:29,520 --> 00:09:30,900 And it is so good. 310 00:09:31,360 --> 00:09:35,560 It is, they have it actually in our hotel where you and I are staying at one of those 311 00:09:35,560 --> 00:09:37,600 Starwood properties here in Singapore, of which there are many. 312 00:09:38,180 --> 00:09:40,660 And that is not the real thing. 313 00:09:41,020 --> 00:09:41,520 No, no. 314 00:09:41,720 --> 00:09:43,200 That's like American Chinese food. 315 00:09:43,560 --> 00:09:44,740 You don't want to get that. 316 00:09:44,860 --> 00:09:48,360 It's, I mean, they do, normally our hotel does a pretty good job, but that's not, it's 317 00:09:48,360 --> 00:09:48,860 not the real thing. 318 00:09:49,860 --> 00:09:51,800 But yeah, yeah. 319 00:09:52,260 --> 00:09:52,500 Okay. 320 00:09:52,720 --> 00:09:53,020 All right. 321 00:09:53,420 --> 00:09:53,720 All right. 322 00:09:53,780 --> 00:09:57,560 Well, you've done a good job of dodging, describing Malaysian food. 323 00:09:57,840 --> 00:10:00,460 I'll have to go look it up in Wikipedia and I'll sort it out. 324 00:10:00,460 --> 00:10:00,900 Or just go try it. 325 00:10:00,920 --> 00:10:02,220 You can get it here in Singapore. 326 00:10:02,340 --> 00:10:02,500 They have it. 327 00:10:02,500 --> 00:10:06,980 You see, if you could identify where I would go to get it, then therefore it exists. 328 00:10:07,300 --> 00:10:07,720 Or that's Malaysia. 329 00:10:07,720 --> 00:10:08,860 And then you could say what it was. 330 00:10:08,980 --> 00:10:09,240 Yeah, yeah. 331 00:10:09,360 --> 00:10:10,660 I mean, there's, yeah. 332 00:10:11,200 --> 00:10:12,540 You know who would know even better? 333 00:10:13,000 --> 00:10:14,220 Our buddy Adrian Cole. 334 00:10:14,780 --> 00:10:15,620 Oh, yeah, yeah. 335 00:10:15,640 --> 00:10:16,440 He probably wouldn't know. 336 00:10:16,460 --> 00:10:17,060 He lives in Malaysia. 337 00:10:17,200 --> 00:10:17,760 He probably wouldn't know. 338 00:10:17,820 --> 00:10:18,620 Well, all right. 339 00:10:18,660 --> 00:10:20,060 For the second half of our conversation. 340 00:10:20,980 --> 00:10:22,580 So, so, you know. 341 00:10:22,800 --> 00:10:23,800 You're just going to let that pass. 342 00:10:23,860 --> 00:10:25,720 We don't even want to explain who Adrian is. 343 00:10:25,720 --> 00:10:26,360 No, it's fine. 344 00:10:26,460 --> 00:10:28,340 People that have the internet, they can look it up. 345 00:10:28,600 --> 00:10:29,460 Oh, look, I'll be like you. 346 00:10:29,860 --> 00:10:31,300 Does anyone have the internet? 347 00:10:31,580 --> 00:10:31,960 Yeah, yeah. 348 00:10:32,000 --> 00:10:32,360 The internet. 349 00:10:32,700 --> 00:10:33,000 Internet. 350 00:10:33,400 --> 00:10:33,700 Internet. 351 00:10:34,320 --> 00:10:34,800 Well, Adrian. 352 00:10:35,060 --> 00:10:37,400 Do you have three and a half floppy disks? 353 00:10:37,800 --> 00:10:38,100 Anyone? 354 00:10:38,420 --> 00:10:40,020 Three and a half floppy disks. 355 00:10:40,160 --> 00:10:40,360 Exactly. 356 00:10:41,160 --> 00:10:42,400 Well, you should Google him. 357 00:10:42,580 --> 00:10:46,020 He's a, he works on the Spring Cloud team, does distributed tracing. 358 00:10:46,480 --> 00:10:47,020 Yeah, yeah. 359 00:10:47,240 --> 00:10:47,600 Open tracing. 360 00:10:47,600 --> 00:10:51,860 So, so, so, so you, you have this book that you wrote a while ago, and I heard the journey 361 00:10:51,860 --> 00:10:53,700 of the bird who flies through the clouds and everything. 362 00:10:53,760 --> 00:10:55,400 Yeah, from Indonesia, no less. 363 00:10:55,420 --> 00:10:55,520 Yeah. 364 00:10:55,520 --> 00:10:57,540 I like, I always like the way you, you open up your talks. 365 00:10:57,600 --> 00:10:58,060 Very nice. 366 00:10:58,220 --> 00:11:02,100 It's very important to be, make sure they understand from the outside that this is, this is not, 367 00:11:02,420 --> 00:11:04,560 this is not the smartest talk you could be in. 368 00:11:04,660 --> 00:11:05,260 Sure, sure. 369 00:11:05,560 --> 00:11:07,180 I don't, because they're going to arrive at that conclusion one way or another. 370 00:11:07,320 --> 00:11:08,960 I want to make sure that I'm the one who breaks the music. 371 00:11:08,960 --> 00:11:09,020 That's right. 372 00:11:09,020 --> 00:11:10,360 I prefer that approach as well. 373 00:11:10,360 --> 00:11:10,680 Yeah, yeah. 374 00:11:10,920 --> 00:11:13,700 The, please do not expect very much. 375 00:11:14,240 --> 00:11:15,900 Under promise, over deliver. 376 00:11:16,080 --> 00:11:17,280 Well, or at least just under promise. 377 00:11:17,280 --> 00:11:17,940 Yeah, there you go. 378 00:11:17,940 --> 00:11:18,240 That first book. 379 00:11:18,540 --> 00:11:20,840 So, so then, so you wrote this big book with Kenny, right? 380 00:11:20,880 --> 00:11:21,080 Yes. 381 00:11:21,220 --> 00:11:22,240 Another great person. 382 00:11:22,480 --> 00:11:22,760 Amazing. 383 00:11:22,760 --> 00:11:27,340 And what, what is, maybe there's more than one, but what's one part of the book that 384 00:11:27,340 --> 00:11:30,180 you find you like never talk about that you wish you would? 385 00:11:30,680 --> 00:11:31,000 Operations. 386 00:11:31,420 --> 00:11:32,640 And so, so what's in that part? 387 00:11:32,920 --> 00:11:35,240 It's all about, it's all, it's all, it's all. 388 00:11:35,260 --> 00:11:39,300 It is everything involved after you've gotten the REST API up and running or the message. 389 00:11:39,520 --> 00:11:41,920 So all the stuff like observability, how do you monitor it? 390 00:11:41,940 --> 00:11:43,140 How do you, what about remediation? 391 00:11:43,260 --> 00:11:45,840 How do you, what happens when the service falls down? 392 00:11:46,180 --> 00:11:46,320 Right. 393 00:11:46,440 --> 00:11:49,840 And we use Spring and Cloud Foundry in the book. 394 00:11:49,840 --> 00:11:52,500 The book is all about how to build apps that survive and thrive in the cloud using Spring 395 00:11:52,500 --> 00:11:53,640 Boot, Spring Cloud, and Cloud Foundry, right? 396 00:11:53,720 --> 00:11:59,060 And so one thing that we really harp on in the book is that if there's a problem you want 397 00:11:59,060 --> 00:12:01,240 to solve with infrastructure, you can program that infrastructure. 398 00:12:01,460 --> 00:12:03,900 So the way to solve problems with code is just to write more code, right? 399 00:12:04,000 --> 00:12:07,760 So, and that's the nice thing about Cloud Foundry is you can just, you can program it. 400 00:12:07,880 --> 00:12:10,680 And there's a nice reactive Java client that people on the Spring team wrote. 401 00:12:10,760 --> 00:12:12,580 So it works nicely with that stack, you know? 402 00:12:12,800 --> 00:12:17,420 And we, so for example, if something goes wrong, you can detect, you can write sensors. 403 00:12:17,500 --> 00:12:20,880 You can write things that look at the output of logs using, you can use Java API to read 404 00:12:20,880 --> 00:12:21,880 things from LogoGator. 405 00:12:22,020 --> 00:12:23,540 You can start to analyze what's been going on. 406 00:12:23,540 --> 00:12:26,620 Yeah, and then use that to feed into event processing, basically. 407 00:12:26,800 --> 00:12:29,080 Say, okay, well, I've detected, or complex event processing, right? 408 00:12:29,080 --> 00:12:29,260 Mm-hmm. 409 00:12:29,260 --> 00:12:32,180 I've detected, I've detected the word error in the log two times in the last minute. 410 00:12:32,480 --> 00:12:33,600 Send in a pager or something, you know? 411 00:12:33,600 --> 00:12:34,200 Oh, yeah, yeah, yeah. 412 00:12:34,200 --> 00:12:37,540 And you can use, you can use Spring Cloud Data Flow to compose pipelines that respond to 413 00:12:37,540 --> 00:12:38,540 those errors. 414 00:12:38,680 --> 00:12:42,160 You can do, so, I mean, Cloud Foundry, this book predates ever so slightly some of the more 415 00:12:42,160 --> 00:12:45,360 complex options for load, for auto-scaling in Cloud Foundry. 416 00:12:45,360 --> 00:12:45,780 Oh, right, right. 417 00:12:46,000 --> 00:12:47,060 You put that in the updates. 418 00:12:47,420 --> 00:12:53,140 Yeah, well, definitely, the update is already, there's so much to do, but in the book, we 419 00:12:53,140 --> 00:12:57,240 demonstrate, okay, well, if there's an error condition detected by some sort of sensor, 420 00:12:57,580 --> 00:13:03,200 put that into a Spring Cloud Data Flow pipeline, have something on the other end, a sync, which 421 00:13:03,200 --> 00:13:07,680 is a component that takes inputs and then does something accordingly, have a sync that auto-scales 422 00:13:07,680 --> 00:13:09,880 based on whatever arbitrary condition. 423 00:13:09,880 --> 00:13:14,300 So you can take whatever input over here, turn that into a ratio between 1 and 10 or whatever, 424 00:13:14,900 --> 00:13:17,580 and if it exceeds that number, then you scale up. 425 00:13:17,620 --> 00:13:20,400 If it falls below that number, then you scale down, right? 426 00:13:20,520 --> 00:13:26,340 So where do you think the line, and just like defining Malaysian food, maybe it's very blurry, 427 00:13:26,780 --> 00:13:31,920 but like, where do you think the line is between the developers putting all that instrumentation 428 00:13:31,920 --> 00:13:35,900 and stuff in there and determining how it should behave, and then the people who are, like, 429 00:13:35,960 --> 00:13:37,060 operating the software? 430 00:13:37,540 --> 00:13:44,640 Like, who, how does the developer figure out, oh, I've done enough putting stuff in 431 00:13:44,640 --> 00:13:44,980 a sync. 432 00:13:45,180 --> 00:13:45,340 Right. 433 00:13:45,520 --> 00:13:50,360 Like, now I've done all I can, or all that I should, because I don't really know what I'm 434 00:13:50,360 --> 00:13:54,020 doing here, and someone else needs to, like, figure out what they would do with the stuff 435 00:13:54,020 --> 00:13:54,420 I'm throwing. 436 00:13:54,460 --> 00:14:01,940 So the nice thing about this is the platform has had, at the time we wrote this, three existing 437 00:14:01,940 --> 00:14:02,860 options for auto-scaling. 438 00:14:03,180 --> 00:14:06,820 And those are just knobs you could turn that were built into the platform. 439 00:14:07,340 --> 00:14:09,040 And so all we're doing in this... 440 00:14:09,040 --> 00:14:09,380 Well, that's true. 441 00:14:09,440 --> 00:14:11,880 You've got limited options, so you can kind of, like... 442 00:14:11,880 --> 00:14:12,760 Like RAM, CPU... 443 00:14:12,760 --> 00:14:13,560 Or, I shouldn't say limited. 444 00:14:13,720 --> 00:14:15,020 You've got the perfect amount of options. 445 00:14:15,020 --> 00:14:15,220 Right. 446 00:14:15,320 --> 00:14:17,220 Well, you've got the ones that are calm, 80% cases. 447 00:14:17,580 --> 00:14:20,340 So by definition, you're already set for the 80% case. 448 00:14:20,340 --> 00:14:24,420 What we wanted to show was that if you wanted to, if there's something outside of that 20%, 449 00:14:24,420 --> 00:14:29,720 outside that 80% in the 20% case, that you had the toolkit there to build that, right? 450 00:14:29,880 --> 00:14:30,040 Right. 451 00:14:30,180 --> 00:14:35,480 And so operators, this would be an operator solution first, and only then does it turn 452 00:14:35,480 --> 00:14:36,200 into a developer solution. 453 00:14:36,640 --> 00:14:37,680 And if it's... 454 00:14:37,680 --> 00:14:39,620 Hopefully, at that point, it doesn't go back to the operator. 455 00:14:39,720 --> 00:14:41,020 Hopefully, at that point, we've solved it, you know? 456 00:14:41,220 --> 00:14:41,600 Yeah, yeah. 457 00:14:41,980 --> 00:14:45,700 But if you do need to, again, you can drive alerting based on this. 458 00:14:45,800 --> 00:14:46,540 You could... 459 00:14:46,540 --> 00:14:47,100 Again, I showed... 460 00:14:47,100 --> 00:14:51,720 I mentioned having the output of those sensors feed into something that does auto-scaling, 461 00:14:51,820 --> 00:14:55,260 but the sync that does auto-scaling could just as easily be something that sends an email 462 00:14:55,260 --> 00:14:56,780 by using SendGrid or something, right? 463 00:14:56,900 --> 00:14:57,360 Yeah, yeah, yeah. 464 00:14:57,540 --> 00:14:58,760 Or PagerDuty or whatever. 465 00:14:59,000 --> 00:14:59,240 Right. 466 00:14:59,240 --> 00:15:01,960 And then, so, like, we... 467 00:15:01,960 --> 00:15:03,600 You know, you mentioned Adrian earlier, right? 468 00:15:03,760 --> 00:15:03,860 Yeah. 469 00:15:04,240 --> 00:15:09,060 I assume he still does this, but, you know, he's worked on distributed tracing and things 470 00:15:09,060 --> 00:15:09,400 like that. 471 00:15:09,560 --> 00:15:09,860 Open tracing, yeah. 472 00:15:09,860 --> 00:15:16,040 And it seems like a large part of open tracing, as you say, or whatever, is having the developers 473 00:15:16,040 --> 00:15:18,460 properly instrument their code somewhere, right? 474 00:15:18,540 --> 00:15:24,100 Like, because, you know, it's entertaining to see the execution path of something, right? 475 00:15:24,160 --> 00:15:27,300 Like, I guess what I'm getting at is there's two models of doing tracing. 476 00:15:27,420 --> 00:15:30,700 One of them is from the outside, and you're kind of looking at the opaqueness. 477 00:15:30,980 --> 00:15:31,080 Right. 478 00:15:31,080 --> 00:15:32,160 You can't really get into the code. 479 00:15:32,560 --> 00:15:35,260 And another one is, like, it's actually... 480 00:15:35,260 --> 00:15:36,140 How would it go? 481 00:15:36,300 --> 00:15:38,020 Outside, no, inside out. 482 00:15:38,140 --> 00:15:38,300 Right. 483 00:15:38,440 --> 00:15:38,600 Right? 484 00:15:38,980 --> 00:15:41,260 But that implies that developers... 485 00:15:41,260 --> 00:15:41,700 Need to be aware. 486 00:15:41,700 --> 00:15:44,440 Someone has come around and, like, stuck things in the code. 487 00:15:44,520 --> 00:15:46,120 Now, maybe they do it, like... 488 00:15:46,120 --> 00:15:49,820 I don't know what the Java kids call it anymore, but maybe they do it after it was compiled and 489 00:15:49,820 --> 00:15:51,320 they reflectively put it in or whatever. 490 00:15:51,440 --> 00:15:54,900 But there's, like, actual getting in there and messing around with stuff. 491 00:15:55,120 --> 00:15:55,280 Right. 492 00:15:55,480 --> 00:15:57,540 And so is that accurate, first of all? 493 00:15:57,620 --> 00:15:59,760 And then so are there... 494 00:15:59,760 --> 00:16:03,460 Like, what's emerging is, like, if you're going to do this tracing, here are the things 495 00:16:03,460 --> 00:16:04,240 you should trace. 496 00:16:04,380 --> 00:16:05,020 Or does it... 497 00:16:05,020 --> 00:16:06,440 There's no common pattern. 498 00:16:06,560 --> 00:16:07,360 It's just, like, whatever. 499 00:16:07,460 --> 00:16:08,860 How do you figure out what to put in there? 500 00:16:09,040 --> 00:16:09,400 Well, okay. 501 00:16:09,500 --> 00:16:10,260 So, yeah. 502 00:16:10,420 --> 00:16:12,400 So the platform will give you... 503 00:16:12,400 --> 00:16:18,700 The platform will give you the opportunity to... 504 00:16:18,700 --> 00:16:20,880 Like, for example, these days we have Istio, right? 505 00:16:20,960 --> 00:16:21,440 We can use Istio. 506 00:16:21,680 --> 00:16:28,240 Istio is an ideal fit for ingress coming into the application. 507 00:16:28,680 --> 00:16:35,420 But once you've passed that threshold from the cloud controller or whatever, then it's 508 00:16:35,420 --> 00:16:35,880 up to your code. 509 00:16:36,000 --> 00:16:40,300 So there's actually intra-process, not between processes, but inside one single process. 510 00:16:40,720 --> 00:16:43,360 There's actually stuff happening inside that opaque black box, right? 511 00:16:43,720 --> 00:16:45,620 So if you're using something like... 512 00:16:45,620 --> 00:16:50,240 If you're using a service mesh of some sort, or a route service in Cloud Foundry or something 513 00:16:50,240 --> 00:16:54,600 like that, right, you can easily add tracing in that layer. 514 00:16:54,720 --> 00:16:55,460 Right, on the outside. 515 00:16:55,540 --> 00:16:57,520 And that'll give you the network. 516 00:16:57,700 --> 00:16:59,620 That'll give you the, you know, one hop to another. 517 00:16:59,780 --> 00:17:05,300 But when you want to go a step further, you need something that you can use for component 518 00:17:05,300 --> 00:17:07,220 boundaries, if not network boundaries, right? 519 00:17:07,240 --> 00:17:07,660 Yep, yep. 520 00:17:07,660 --> 00:17:10,100 And that is where you need to have code in your application. 521 00:17:10,240 --> 00:17:11,780 So these days we have Spring Cloud. 522 00:17:11,780 --> 00:17:19,100 Spring Cloud has an SPI called Sleuth, S-L-E-U-T-H, which is one of the hard... 523 00:17:19,100 --> 00:17:20,260 I get so many questions about that. 524 00:17:20,680 --> 00:17:24,960 You wouldn't believe the proportion of the world for which that particular English word 525 00:17:24,960 --> 00:17:25,780 is a troublesome word. 526 00:17:25,980 --> 00:17:26,580 Oh, yeah. 527 00:17:26,700 --> 00:17:28,240 Just knowing what it means or saying it? 528 00:17:28,240 --> 00:17:29,800 Just pronouncing it and all of the above. 529 00:17:29,860 --> 00:17:29,960 Yeah. 530 00:17:30,000 --> 00:17:31,920 It's just not a word that is easy to... 531 00:17:31,920 --> 00:17:32,720 That is a weird word. 532 00:17:32,980 --> 00:17:34,380 I have to look up the etymology of that. 533 00:17:34,660 --> 00:17:37,100 That feels like a word that came from, like, the Normans. 534 00:17:37,380 --> 00:17:38,140 Not the Normans. 535 00:17:38,220 --> 00:17:39,660 That came from, like, Vikings or something. 536 00:17:39,660 --> 00:17:39,900 Right, yeah. 537 00:17:40,000 --> 00:17:42,600 Something that was designed to flummox people a thousand years later. 538 00:17:42,600 --> 00:17:43,200 Oh, flummox. 539 00:17:43,220 --> 00:17:44,420 See, that's in the same school there. 540 00:17:44,680 --> 00:17:45,200 Darn it again. 541 00:17:45,800 --> 00:17:46,180 Either way. 542 00:17:47,740 --> 00:17:52,040 Yeah, so we have this SPI called Sleuth, of which we have one implementation that supports 543 00:17:52,040 --> 00:17:55,840 Zipkin, the Twitter distributed tracing platform, on which, of course, Adrian works. 544 00:17:56,600 --> 00:18:00,420 And so Sleuth provides hooks. 545 00:18:00,860 --> 00:18:02,300 We implement the Sleuth API. 546 00:18:02,420 --> 00:18:04,160 We use it in Spring Cloud and in Spring. 547 00:18:04,160 --> 00:18:08,020 So everywhere you have ingress and egress, whether it's across the network or from one 548 00:18:08,020 --> 00:18:11,860 service to another or from one component to another, that's useful to have that, we do it. 549 00:18:12,240 --> 00:18:12,340 Right. 550 00:18:12,400 --> 00:18:17,620 And then if you add the Zipkin binding for Sleuth, then it automatically publishes those 551 00:18:17,620 --> 00:18:25,800 bindings out of band over either HTTP, which is not the best choice because it's synchronous. 552 00:18:26,180 --> 00:18:29,660 So your request arrives and then something sends an HTTP request. 553 00:18:29,860 --> 00:18:31,840 Well, I don't want to sit there and wait for an acknowledgement. 554 00:18:31,840 --> 00:18:33,700 It's for all those curl programmers out there. 555 00:18:33,840 --> 00:18:35,160 Yeah, don't do that. 556 00:18:35,520 --> 00:18:36,840 Or you can use a broker. 557 00:18:37,080 --> 00:18:38,280 So you can publish it, fire and forget. 558 00:18:39,320 --> 00:18:40,880 And this is automatically done for you. 559 00:18:41,480 --> 00:18:45,660 So you make a REST call in your Spring code, for example, or if you invoke another method 560 00:18:45,660 --> 00:18:50,780 or whatever, you can tell Spring, I want all these things to be sampled or some percentage 561 00:18:50,780 --> 00:18:51,260 thereof. 562 00:18:51,760 --> 00:18:53,680 And you can prescribe a sampler. 563 00:18:55,100 --> 00:19:01,380 And Sleuth will publish that to whatever, you can say to publish it over Kafka or RabbitMQ. 564 00:19:01,860 --> 00:19:06,480 And then those messages eventually arrive at Zipkin itself, which then assimilates all the trace 565 00:19:06,480 --> 00:19:07,720 information into a graph. 566 00:19:07,780 --> 00:19:09,300 So we've got this wonderful framework. 567 00:19:09,440 --> 00:19:09,580 Right. 568 00:19:09,640 --> 00:19:15,000 And then equally wonderful is that you have a standardized way of doing it. 569 00:19:15,080 --> 00:19:15,220 Right. 570 00:19:15,280 --> 00:19:19,080 Like, you might even call that a, what are they used to, I never got a computer science 571 00:19:19,080 --> 00:19:20,820 degree, but a data structure. 572 00:19:21,080 --> 00:19:21,260 Yeah. 573 00:19:21,380 --> 00:19:23,480 Like, it's like, this is the way that you format stuff. 574 00:19:23,560 --> 00:19:25,160 This is the expected interaction pattern. 575 00:19:25,600 --> 00:19:27,520 So you don't invent that every single time. 576 00:19:27,540 --> 00:19:29,640 And actually, you don't even deal with it. 577 00:19:29,700 --> 00:19:34,680 So if you're using Cloud Foundry and you're doing something smart with the cloud controller 578 00:19:34,680 --> 00:19:40,720 and the route service or using a service mesh, or if you're using Spring Cloud Sleuth, there 579 00:19:40,720 --> 00:19:44,040 is no, you have the library, you have the dependency on the cost path, that's it. 580 00:19:44,500 --> 00:19:45,040 Right, right. 581 00:19:45,160 --> 00:19:45,760 So you're not actually. 582 00:19:46,500 --> 00:19:51,100 So then, like, one of your, if not the most common demo is a reservation system. 583 00:19:51,140 --> 00:19:51,340 Yes. 584 00:19:51,460 --> 00:19:51,600 Right? 585 00:19:52,320 --> 00:19:55,360 And if you remember the term business logic. 586 00:19:55,540 --> 00:19:55,820 Yes. 587 00:19:55,980 --> 00:19:58,360 Some places then there is some business logic. 588 00:19:58,360 --> 00:19:58,620 Yeah. 589 00:19:58,920 --> 00:19:59,640 Not much. 590 00:19:59,800 --> 00:20:00,420 Yeah, not much. 591 00:20:00,480 --> 00:20:03,620 So maybe this is a hard question and it automatically gets done. 592 00:20:03,700 --> 00:20:05,680 But for those little parts where there's business logic. 593 00:20:05,880 --> 00:20:06,040 Yeah. 594 00:20:06,260 --> 00:20:08,620 And let's assume you had to put in the tracing stuff. 595 00:20:08,740 --> 00:20:08,840 Right. 596 00:20:08,840 --> 00:20:09,960 Like, what would you put in? 597 00:20:10,140 --> 00:20:13,140 Ah, so if you wanted to instrument particular paths yourself. 598 00:20:13,160 --> 00:20:13,460 Yeah, yeah. 599 00:20:13,500 --> 00:20:14,940 Like, we're really inside the black box. 600 00:20:14,940 --> 00:20:15,520 So you can inject. 601 00:20:15,520 --> 00:20:18,800 Not only the application, but the actual, like, business logic-y code. 602 00:20:19,000 --> 00:20:19,360 Well, okay. 603 00:20:19,400 --> 00:20:19,980 So you can do that. 604 00:20:20,060 --> 00:20:21,780 You can inject what's called a span accessor. 605 00:20:21,980 --> 00:20:22,840 I think that's what it's called. 606 00:20:22,940 --> 00:20:23,940 It may have changed, actually. 607 00:20:23,940 --> 00:20:31,540 But we just moved some of that API to somewhere else. 608 00:20:31,860 --> 00:20:32,560 No, it's a different. 609 00:20:32,760 --> 00:20:33,220 Slightly changed. 610 00:20:33,280 --> 00:20:35,280 So it used to be you can inject something called a span accessor. 611 00:20:35,500 --> 00:20:35,660 Uh-huh. 612 00:20:35,780 --> 00:20:38,800 And then you would, basically there's some way by which you can get access to what we call 613 00:20:38,800 --> 00:20:39,240 a span. 614 00:20:39,520 --> 00:20:39,780 Uh-huh. 615 00:20:39,920 --> 00:20:40,300 Yeah, yeah, yeah. 616 00:20:40,300 --> 00:20:40,620 That's right. 617 00:20:40,700 --> 00:20:43,500 Which is, like, a segment of a process that's running somewhere. 618 00:20:43,500 --> 00:20:43,860 It's a hop. 619 00:20:44,220 --> 00:20:44,800 Yeah, yeah, yeah. 620 00:20:44,820 --> 00:20:45,140 That's right. 621 00:20:45,200 --> 00:20:45,380 That's right. 622 00:20:45,380 --> 00:20:47,360 So you have the trace, which is the aggregate A to Z. 623 00:20:47,640 --> 00:20:47,820 Yeah. 624 00:20:47,880 --> 00:20:49,160 I forgot I read about that. 625 00:20:49,180 --> 00:20:49,320 Right. 626 00:20:49,440 --> 00:20:49,820 Yeah, yeah. 627 00:20:49,820 --> 00:20:50,440 That makes sense. 628 00:20:50,600 --> 00:20:52,280 And then E to hop in the journey is called a span. 629 00:20:52,380 --> 00:20:52,740 So you might have. 630 00:20:52,740 --> 00:20:53,580 I just impressed myself there. 631 00:20:53,640 --> 00:20:54,580 I've actually read up on this stuff. 632 00:20:54,580 --> 00:20:54,940 You're pretty awesome. 633 00:20:55,080 --> 00:20:55,280 You should. 634 00:20:55,440 --> 00:21:01,080 But, like, I mean, what I'm getting at is, like, what do you want to log in the business 635 00:21:01,080 --> 00:21:01,600 logic? 636 00:21:01,760 --> 00:21:01,940 Oh. 637 00:21:02,200 --> 00:21:02,320 Right? 638 00:21:02,560 --> 00:21:03,720 Like, and what do you put in there? 639 00:21:03,840 --> 00:21:05,760 If I have a five-line method call. 640 00:21:06,020 --> 00:21:06,220 Yeah. 641 00:21:06,380 --> 00:21:10,760 That's just writing data to the database, I might want to trace that in addition to what 642 00:21:10,760 --> 00:21:11,860 Springwater automatically do. 643 00:21:12,580 --> 00:21:17,900 So you might say, if I remember your reservation system, I want to look up a person. 644 00:21:18,180 --> 00:21:18,440 Sure. 645 00:21:18,560 --> 00:21:18,760 Right? 646 00:21:19,060 --> 00:21:23,600 And one of the things you might log is when I get this person's name, I want to say, here's 647 00:21:23,600 --> 00:21:27,820 the name that I got, and here's what I looked up, or something like that. 648 00:21:27,820 --> 00:21:32,760 Now, the issue is that a lot of that could automatically be done for you, but, like, I mean, so that's 649 00:21:32,760 --> 00:21:34,100 why the reservation system's hard. 650 00:21:34,180 --> 00:21:38,320 But at some point, you want to know that, like, someone searched for the Coté Reservation, 651 00:21:38,920 --> 00:21:40,440 and this happened or not happened. 652 00:21:40,620 --> 00:21:45,320 Like, here's something that I'm sure, I don't know if this happens to you, but this happens 653 00:21:45,320 --> 00:21:50,540 to me all the time, and it's infuriating, is, like, on my passport, my name is Michael 654 00:21:50,540 --> 00:21:51,300 Richard Coté. 655 00:21:51,500 --> 00:21:51,720 Okay. 656 00:21:51,880 --> 00:21:52,100 Right? 657 00:21:52,520 --> 00:21:56,180 And for whatever reason, in Concur, my name is Michael R. Coté. 658 00:21:56,860 --> 00:21:59,360 And so the computers get very confused. 659 00:21:59,440 --> 00:21:59,600 Yeah. 660 00:21:59,760 --> 00:22:00,000 Right? 661 00:22:00,060 --> 00:22:01,600 And it causes all sorts of problems. 662 00:22:01,700 --> 00:22:06,700 And so at some point, I feel like there should be some tracing that's like, Michael Richard 663 00:22:06,700 --> 00:22:10,780 Coté came in, but then we were looking for Michael R. Coté. 664 00:22:11,180 --> 00:22:11,740 Awkward. 665 00:22:11,840 --> 00:22:14,940 So what, and, like, how do I log that or whatever? 666 00:22:14,960 --> 00:22:17,560 Well, okay, so tracing isn't a great fit for that. 667 00:22:17,920 --> 00:22:18,240 Right, right. 668 00:22:18,260 --> 00:22:24,100 Because, so you can include context about the journey of a particular message, but the 669 00:22:24,100 --> 00:22:26,900 goal isn't so much to record the context, it's about to record the message. 670 00:22:26,900 --> 00:22:31,940 So basically all I'm doing in tracing is, whatever span I'm operating in, whatever sub-segment 671 00:22:31,940 --> 00:22:35,680 of the overall transaction, I'm just kind of throwing extra stuff in there. 672 00:22:35,720 --> 00:22:36,500 Meditate it if you want. 673 00:22:36,500 --> 00:22:37,840 That might be useful at some point. 674 00:22:38,100 --> 00:22:40,880 But you shouldn't use this as a way of finding information about that. 675 00:22:41,080 --> 00:22:45,160 So you'd have a whole separate system that was tracing, like, this name. 676 00:22:45,400 --> 00:22:48,000 Well, because, and the reason is because sampler, the reason is because your distributed 677 00:22:48,000 --> 00:22:52,320 tracing system isn't going to keep by default if you're doing something, unless you're doing 678 00:22:52,320 --> 00:22:55,520 something terribly wrong, which is, by the way, what I do on stage, and I tell people 679 00:22:55,520 --> 00:22:56,020 don't do it. 680 00:22:56,200 --> 00:22:59,500 Right, you're like, I'm the number one contributor, the number one contributor of, the number one 681 00:22:59,500 --> 00:23:00,760 contributor of bugs. 682 00:23:01,080 --> 00:23:07,320 But no, but I tell people don't, because in your demo, in your exam, in my demo, I record 683 00:23:07,320 --> 00:23:08,420 every single trace. 684 00:23:08,660 --> 00:23:09,160 Right, right, right. 685 00:23:09,160 --> 00:23:11,600 Every single request response anywhere in the system I record. 686 00:23:11,600 --> 00:23:12,880 So that's why my question is going to worry. 687 00:23:12,880 --> 00:23:13,720 But you're not going to do that for most people. 688 00:23:13,720 --> 00:23:17,440 You're actually recording, you're tracking the execution of the program. 689 00:23:17,440 --> 00:23:20,760 Right, but most people don't, you would overwhelm your tracing system if you did that. 690 00:23:21,040 --> 00:23:23,160 So by default, Spring Cloud Sleuth would capture 10%. 691 00:23:23,160 --> 00:23:26,840 Right, right, and then you do, like, I don't know, numbers, but you do the statistical thing 692 00:23:26,840 --> 00:23:27,640 where it's a sampling. 693 00:23:27,920 --> 00:23:28,380 Yeah, exactly. 694 00:23:28,500 --> 00:23:32,960 And you can provide a custom sampler, which Spring Cloud will consult to determine whether 695 00:23:32,960 --> 00:23:34,400 it should trace this request or this span. 696 00:23:35,040 --> 00:23:39,020 So my point is, that information is incidental. 697 00:23:39,100 --> 00:23:43,000 It's nice to know it was Michael, but we're more interested in the timing and the latency. 698 00:23:43,000 --> 00:23:47,220 And as I found out by the airline systems in the world, no one cares if that error happens. 699 00:23:47,380 --> 00:23:47,520 No. 700 00:23:47,900 --> 00:23:48,180 Yeah, no. 701 00:23:48,640 --> 00:23:53,220 They just return some red text on the web page and they say, must check in at desk. 702 00:23:53,560 --> 00:23:54,120 All the time. 703 00:23:54,200 --> 00:23:55,260 Oh, that's so annoying. 704 00:23:55,500 --> 00:23:55,580 Yeah. 705 00:23:55,900 --> 00:24:00,420 Like, you would think we would use some of our Bayesian big data machine learning to 706 00:24:00,420 --> 00:24:02,960 figure out that, like, this is the same person. 707 00:24:03,160 --> 00:24:03,700 It can't be done. 708 00:24:03,960 --> 00:24:04,100 Huh. 709 00:24:04,240 --> 00:24:08,040 All right, so is there another section that you feel like doesn't, that was operations? 710 00:24:08,520 --> 00:24:09,240 Yeah, and that's a big one. 711 00:24:09,260 --> 00:24:12,640 And then we burrowed into, like, tracing and sleuths and Zipkinites. 712 00:24:12,660 --> 00:24:14,440 No, that's covered in that chapter as well. 713 00:24:14,540 --> 00:24:14,980 Right, right. 714 00:24:15,140 --> 00:24:17,060 And also actuator, the observable. 715 00:24:17,360 --> 00:24:21,060 So sampling for traces is one thing, but also metrics. 716 00:24:21,540 --> 00:24:21,800 Oh, yes. 717 00:24:21,800 --> 00:24:23,780 And these are two things, these are two sides of the same corner. 718 00:24:23,780 --> 00:24:25,940 What does metrics mean in your world? 719 00:24:26,080 --> 00:24:31,660 Metrics are, I want to accumulate keys and values and calculate statistics based on those. 720 00:24:32,300 --> 00:24:33,200 Like, wait, what's a good example? 721 00:24:33,200 --> 00:24:33,820 So if you think about... 722 00:24:33,820 --> 00:24:34,180 Or a bad one. 723 00:24:34,440 --> 00:24:40,840 Well, okay, so imagine, so now, instead of asking about Richard or Josh's particular transaction, 724 00:24:41,160 --> 00:24:43,480 I want to know just how many transactions I've had, period. 725 00:24:43,580 --> 00:24:44,940 I want to count every single one of them. 726 00:24:45,120 --> 00:24:47,420 But I'm not going to store a heap of metadata about each one. 727 00:24:47,460 --> 00:24:49,020 I just want to increment a counter somewhere. 728 00:24:49,320 --> 00:24:49,520 Right. 729 00:24:49,600 --> 00:24:51,940 And then I want to be able to see a subset for that. 730 00:24:51,940 --> 00:24:54,580 I want to say, okay, these are all, for example, all the HTTP requests. 731 00:24:54,680 --> 00:24:56,320 I had one million of them, right? 732 00:24:56,620 --> 00:24:59,680 And then I could say, oh, well, how many of them were HTTP GET requests? 733 00:24:59,860 --> 00:25:01,420 Well, there's, you know, 80% of those. 734 00:25:01,500 --> 00:25:04,180 And then how many of them were HTTP GET that returned 200? 735 00:25:04,380 --> 00:25:05,880 Okay, so 99% of those. 736 00:25:06,000 --> 00:25:09,360 And so you keep different derivations of these numbers. 737 00:25:09,440 --> 00:25:10,200 But they're still just numbers. 738 00:25:10,740 --> 00:25:15,100 And you can keep those in your Java process, if you like. 739 00:25:15,460 --> 00:25:19,800 But that's pretty useless, because then you'd have to visit each node to see the stats about that process. 740 00:25:20,240 --> 00:25:24,420 Or you could publish them to a time series database or a monitoring tool. 741 00:25:24,740 --> 00:25:27,640 And so we have now this new framework in Spring Boot. 742 00:25:28,040 --> 00:25:29,780 Well, we have this framework that we Pivotal developed. 743 00:25:30,260 --> 00:25:36,240 We hired a guy named John Schneider from Netflix who, you know, they're not terrible at operations and monitoring, are they? 744 00:25:36,300 --> 00:25:38,020 So we thought he'd be a good fit. 745 00:25:38,060 --> 00:25:43,760 So he came in and he built this framework called Micrometer, which is you can use it independent of Spring. 746 00:25:44,120 --> 00:25:47,460 But why you would, I would, you know, it's not a thing. 747 00:25:47,520 --> 00:25:49,360 Well, developers like to do all sorts of crazy things. 748 00:25:49,380 --> 00:25:50,460 They're crazy, wild and crazy, yeah. 749 00:25:50,600 --> 00:25:54,540 So anyway, this Micrometer framework, it's a metrics facade. 750 00:25:55,060 --> 00:26:01,700 So you use one SPI, one SPI, and it'll publish based on whatever adapter you've contributed, you've configured. 751 00:26:02,080 --> 00:26:13,620 It'll publish those metrics to things like Prometheus, Graphite, OpenTSDB, Dynatrace, Atlas, you know, everything you can imagine. 752 00:26:13,820 --> 00:26:16,040 Datadog, SignalFX, InflexDB. 753 00:26:16,580 --> 00:26:20,000 And so these tools will then give you the ability to drill down, to draw pretty graphs. 754 00:26:20,240 --> 00:26:20,420 Right. 755 00:26:20,540 --> 00:26:25,720 So you pump your, from their perspective, raw metrics into this. 756 00:26:25,820 --> 00:26:29,920 And then they know how to analyze it and make sense of it, or make a sense of it. 757 00:26:30,120 --> 00:26:33,800 I don't want to be mono-judgmental and say there's only one sense. 758 00:26:34,280 --> 00:26:37,340 But like, you know, they make sense of all this stuff and do something with it. 759 00:26:37,340 --> 00:26:38,940 And you can query into that now. 760 00:26:39,280 --> 00:26:46,040 So again, Spring Boot and the platform will both give you automatically certain metrics that are interesting, like CPU usage and number of HTTP requests. 761 00:26:46,140 --> 00:26:51,920 But you can also say, okay, all these HTTP requests, you know, I don't want to know just the HTTP requests. 762 00:26:52,040 --> 00:26:55,460 I also want to know how many people have checked out the shopping cart. 763 00:26:55,900 --> 00:26:57,540 How many people purchased this product? 764 00:26:57,820 --> 00:26:59,420 How many people visited this page? 765 00:26:59,480 --> 00:27:01,520 How many people signed up for new accounts today? 766 00:27:01,560 --> 00:27:03,620 These are key... 767 00:27:03,620 --> 00:27:04,340 Right, right, right. 768 00:27:04,420 --> 00:27:04,620 What is that? 769 00:27:04,640 --> 00:27:05,400 Performance indicators? 770 00:27:05,420 --> 00:27:05,700 Yes. 771 00:27:06,320 --> 00:27:06,680 KPIs. 772 00:27:06,820 --> 00:27:07,420 My brain's a little... 773 00:27:07,420 --> 00:27:07,840 There you go. 774 00:27:08,000 --> 00:27:11,320 So you can have business metrics as well as technical metrics. 775 00:27:11,420 --> 00:27:14,060 And this informs both operations and the business, right? 776 00:27:14,400 --> 00:27:14,580 Right. 777 00:27:14,580 --> 00:27:15,000 And developers. 778 00:27:15,280 --> 00:27:18,360 So you get that ever-important single pane of glass, you know? 779 00:27:18,520 --> 00:27:19,060 Right, right, right. 780 00:27:19,080 --> 00:27:19,860 I have two more questions. 781 00:27:19,960 --> 00:27:20,560 Okay, I've got them. 782 00:27:20,680 --> 00:27:21,320 Are you ready? 783 00:27:21,420 --> 00:27:22,240 Then we'll wrap up one. 784 00:27:22,760 --> 00:27:24,540 So what's the deal with time series databases? 785 00:27:25,060 --> 00:27:29,020 Why do you just call it a time series database and not just a database? 786 00:27:29,700 --> 00:27:30,720 Well, yeah. 787 00:27:31,060 --> 00:27:36,820 It's like we have document databases, we have key value databases, we have graph databases, and we have time series databases. 788 00:27:36,820 --> 00:27:40,180 So a time series database is like a dictionary, like Redis. 789 00:27:40,820 --> 00:27:42,280 It stores the keys and values. 790 00:27:43,020 --> 00:27:45,860 But it also stores every iteration of that value over time. 791 00:27:46,600 --> 00:27:46,780 Right, right, right. 792 00:27:46,780 --> 00:27:48,040 So you can look at the history of that value. 793 00:27:48,560 --> 00:27:50,640 You know, what number was it a minute ago, a week ago, an hour ago? 794 00:27:50,820 --> 00:27:58,560 And then because you have that availability, you have that visibility, you have that data, you can then feed that into a graph and then plot trends. 795 00:27:59,020 --> 00:27:59,640 Right, right, right. 796 00:27:59,660 --> 00:28:01,060 And you can summarize it if you wanted to. 797 00:28:01,060 --> 00:28:06,660 And I guess more of my question is like, so do you call it a time series database? 798 00:28:06,820 --> 00:28:10,060 Is it because it's built around doing only that? 799 00:28:10,180 --> 00:28:10,260 Yeah. 800 00:28:10,260 --> 00:28:11,780 And so most performant doing it? 801 00:28:11,920 --> 00:28:14,820 Or because you could use that data structure anywhere, right? 802 00:28:14,840 --> 00:28:16,560 Like you could just store that wherever you wanted. 803 00:28:16,720 --> 00:28:18,620 But it may be a bad idea performance-wise. 804 00:28:18,620 --> 00:28:18,840 Right. 805 00:28:18,960 --> 00:28:23,120 But, you know, you could make a relational database that's stored time series stuff. 806 00:28:23,320 --> 00:28:25,220 But it may technically be a bad idea. 807 00:28:25,400 --> 00:28:25,800 Terribly, yeah. 808 00:28:25,820 --> 00:28:30,860 And so the reason you call it a time series database is because it's optimized for that way of storing things. 809 00:28:30,860 --> 00:28:33,680 And that's an important, this is actually a burgeoning community right now. 810 00:28:33,800 --> 00:28:37,860 Netflix Atlas is a fairly new technology that was open sourced in the last five years, I think. 811 00:28:38,580 --> 00:28:39,620 Prometheus is super new. 812 00:28:39,780 --> 00:28:41,500 That came out of, I think, SoundCloud.com. 813 00:28:42,320 --> 00:28:46,960 This is, and there's, there's a, the, you know, do you ever read the morning paper from Adrian Collier? 814 00:28:47,140 --> 00:28:47,460 Sometimes. 815 00:28:47,780 --> 00:28:47,980 Yeah. 816 00:28:48,200 --> 00:28:52,940 He's a, he did one that looked at a time series database a few months ago. 817 00:28:53,340 --> 00:28:55,220 And it was, it was something out of academia. 818 00:28:55,220 --> 00:29:00,660 And basically this was like, it was optimized for huge data sets, like, you know, trillions of records or whatever. 819 00:29:00,720 --> 00:29:07,440 So do you think there was a time when sticking everything into a relational database was a good idea? 820 00:29:07,700 --> 00:29:07,940 No. 821 00:29:08,180 --> 00:29:10,420 Because that seems to be what we had for like 20 years. 822 00:29:10,620 --> 00:29:18,380 It's like, I think the last thing before this whole no SQL, no one calls it, but whatever, was basically, remember object-oriented databases? 823 00:29:18,600 --> 00:29:18,860 Yeah, yeah. 824 00:29:19,020 --> 00:29:19,700 Like that existed. 825 00:29:19,860 --> 00:29:21,900 And then everyone was like, well, that's not cool. 826 00:29:22,420 --> 00:29:22,740 I don't know about that. 827 00:29:22,740 --> 00:29:25,200 And then we went back to RDBMSs forever. 828 00:29:25,680 --> 00:29:35,140 And so like, whereas now we have the opposite, like as we were just discussing, we're like, you want a database that fits the type, not even the type of stuff you're putting in there, 829 00:29:35,140 --> 00:29:40,400 but the way that you want to retrieve and conceptualize the stuff. 830 00:29:40,400 --> 00:29:41,480 Purpose built for certain spaces. 831 00:29:41,480 --> 00:29:41,780 Right, right. 832 00:29:41,860 --> 00:29:47,460 But what matters, what the database is doing is servicing pretty much you, the human, right? 833 00:29:47,500 --> 00:29:47,680 Right. 834 00:29:47,780 --> 00:29:49,520 You want this in a document format. 835 00:29:49,660 --> 00:29:51,020 You want it in a time series format. 836 00:29:51,300 --> 00:29:52,160 It's not you, the human. 837 00:29:52,280 --> 00:29:54,260 It's the representation of that data that you're serving. 838 00:29:54,780 --> 00:29:55,040 Oh, yeah. 839 00:29:55,100 --> 00:30:00,460 What I mean to say is that we humans are programming and so we have a preference for the way that we arrange things. 840 00:30:00,520 --> 00:30:00,640 Right. 841 00:30:00,640 --> 00:30:04,440 If you ask the computers if you could, they would probably be like, I don't know, make it numbers. 842 00:30:04,780 --> 00:30:04,860 Sure. 843 00:30:05,000 --> 00:30:05,140 Right. 844 00:30:05,240 --> 00:30:05,940 Like, zero and one. 845 00:30:06,040 --> 00:30:10,800 Like, whenever all these databases store something, it's probably not in a format that humans would find useful. 846 00:30:11,120 --> 00:30:11,260 Right. 847 00:30:11,360 --> 00:30:11,520 So. 848 00:30:11,820 --> 00:30:12,020 No. 849 00:30:12,220 --> 00:30:17,840 So, like, the whole point of all these different databases is, like, we want our data to be arranged in these different ways. 850 00:30:17,980 --> 00:30:19,740 Now, it's not purely our desire. 851 00:30:19,840 --> 00:30:23,620 It might be because we're doing this stuff, it has to be in this format to optimize it. 852 00:30:23,680 --> 00:30:24,760 But we make those choices. 853 00:30:24,960 --> 00:30:25,060 Right. 854 00:30:25,060 --> 00:30:29,500 So, in contrast, we used to be like, I'll deal with relational databases. 855 00:30:29,840 --> 00:30:30,040 Right. 856 00:30:30,040 --> 00:30:34,080 Like, it didn't even occur to us that we could have an opinion about, like, what things would look like. 857 00:30:34,080 --> 00:30:37,020 I think, so I think a lot of, yeah, okay, so fair enough. 858 00:30:37,080 --> 00:30:45,780 But I think it's weird because any programmer with her or his salt would know that you need a data structure suited to the data. 859 00:30:45,880 --> 00:30:46,220 Right, right. 860 00:30:46,280 --> 00:30:46,520 No, no. 861 00:30:46,640 --> 00:30:52,800 And that's a, you would have your ORM tools or whatever that would take stuff from the database and get it in a format that works for you. 862 00:30:52,800 --> 00:30:57,760 They would, the thing is, we would, we all know to use a data structure, but then you get to the database section. 863 00:30:57,840 --> 00:30:59,000 You're like, oh, I give up. 864 00:30:59,180 --> 00:31:00,480 I guess I just don't know how to do any of this. 865 00:31:00,480 --> 00:31:00,760 Exactly. 866 00:31:00,960 --> 00:31:02,020 I've forgotten how to do data structures. 867 00:31:02,020 --> 00:31:03,380 That's kind of the weird shift that happened. 868 00:31:03,480 --> 00:31:10,800 It's like the, to put it in this term, the taboo of making demands on the database was somehow removed. 869 00:31:11,520 --> 00:31:13,360 Like, we lived under this error for a long time. 870 00:31:13,840 --> 00:31:16,800 Whereas you characterize it, it was just like, no, you can't tell the database what to do. 871 00:31:16,980 --> 00:31:18,540 It does the one, it's relational, and that's it. 872 00:31:18,560 --> 00:31:19,740 And developers would forget. 873 00:31:19,740 --> 00:31:22,520 They knew all the, like, in the code themselves, they'll sit there and haggle. 874 00:31:22,800 --> 00:31:25,860 To, to no end about what data structure we should use. 875 00:31:25,860 --> 00:31:26,080 That's right. 876 00:31:26,080 --> 00:31:29,560 But then it's like, oh, I need to persist this longer than the session of my current running application. 877 00:31:30,260 --> 00:31:31,260 I guess we only have one choice. 878 00:31:31,380 --> 00:31:32,240 There's only one option here. 879 00:31:32,340 --> 00:31:35,500 Maybe, and we'll skip my last, but closing out with food, right? 880 00:31:35,580 --> 00:31:35,820 Okay. 881 00:31:35,920 --> 00:31:38,660 For a long time, it was like, a taco is the following. 882 00:31:39,200 --> 00:31:41,120 You either have a crispy shell or a soft shell. 883 00:31:41,200 --> 00:31:41,380 Yeah. 884 00:31:41,500 --> 00:31:42,180 Or it's not a shell. 885 00:31:42,600 --> 00:31:46,040 And you're going to put some spicy meat into it. 886 00:31:46,040 --> 00:31:46,320 Mm-hmm. 887 00:31:46,800 --> 00:31:49,400 Or, if you're a vegetarian, we'll figure that out for you. 888 00:31:49,440 --> 00:31:49,760 But whatever. 889 00:31:49,900 --> 00:31:55,680 You have some ground meat or some chicken or some skirt steak that's been marinated and made into a fajita. 890 00:31:56,060 --> 00:31:57,980 And then you're going to put some cheese on top of it. 891 00:31:57,980 --> 00:31:59,640 And then you're going to put lettuce on top of it. 892 00:31:59,640 --> 00:32:02,400 And maybe some tomatoes, and if you're lucky, some guacamole, right? 893 00:32:02,400 --> 00:32:03,400 You're making me hungry. 894 00:32:03,620 --> 00:32:06,880 That was like 30 to maybe 200 years of tacos, right? 895 00:32:07,160 --> 00:32:12,720 And then at some point in the last 15 years, someone was like, you know, we could change the taco. 896 00:32:13,400 --> 00:32:13,500 Right? 897 00:32:13,540 --> 00:32:14,900 We could make demands on the taco. 898 00:32:15,180 --> 00:32:15,360 It's not a taco. 899 00:32:15,360 --> 00:32:15,420 It's not a taco. 900 00:32:15,420 --> 00:32:17,320 And we could put, like, bulgaki in there. 901 00:32:17,600 --> 00:32:17,800 Oh. 902 00:32:17,800 --> 00:32:21,400 Or, like, we could put, like, what if I put, like, a sushi roll in the taco? 903 00:32:22,200 --> 00:32:25,100 And nowadays, you put whatever you want in a taco, right? 904 00:32:25,100 --> 00:32:25,580 Yeah, you're right. 905 00:32:25,580 --> 00:32:33,300 So, similarly, like, it used to be, like, you go to the relational database, and it was just, like, rows and columns, buddy, and inner and outer whatevers. 906 00:32:33,660 --> 00:32:34,580 Like, that's what you got. 907 00:32:34,860 --> 00:32:35,940 You got tacos a taco. 908 00:32:36,400 --> 00:32:39,800 But then someone decided, no, I'll do whatever I want. 909 00:32:39,920 --> 00:32:43,500 You know, I think, and I think they decided this because, well, there's two things. 910 00:32:43,560 --> 00:32:45,960 I think there's several factors that play into this. 911 00:32:46,120 --> 00:32:47,140 First of all, data got bigger. 912 00:32:47,520 --> 00:32:50,920 I mean, we all, like, physicists have been looking at this going, yeah, it's always been big. 913 00:32:50,920 --> 00:32:51,740 It's like Ant-Man. 914 00:32:52,540 --> 00:32:52,980 It's bigger. 915 00:32:53,340 --> 00:32:54,380 Yeah, well, now, yeah. 916 00:32:54,700 --> 00:32:57,740 So, and by the way, that new movie, Ant-Man and Wasp, looks amazing. 917 00:32:58,000 --> 00:32:58,240 Oh, yeah? 918 00:32:58,420 --> 00:33:02,480 Anyway, we had, data's gotten, okay, it's gotten bigger for Enterprise. 919 00:33:02,760 --> 00:33:02,980 Uh-huh. 920 00:33:03,060 --> 00:33:09,860 I should caveat that NoSQL has not been taboo, specialized data stores have not been taboo in, you know, science. 921 00:33:10,720 --> 00:33:11,300 Oh, sure, sure. 922 00:33:11,540 --> 00:33:13,760 Physicists generating billions of events. 923 00:33:13,780 --> 00:33:14,980 But they're not real programmers, are they? 924 00:33:14,980 --> 00:33:16,080 They're much more than real. 925 00:33:16,300 --> 00:33:18,160 They're, like, they're actually doing really hard stuff, right? 926 00:33:18,420 --> 00:33:21,840 There's no, nobody's ever bothered to invent Visual Basic for physicists, you know? 927 00:33:21,840 --> 00:33:23,140 No, no, they probably wouldn't use it. 928 00:33:23,140 --> 00:33:24,460 No, because it's just way too hard. 929 00:33:24,540 --> 00:33:25,300 Right, right, right. 930 00:33:25,500 --> 00:33:29,560 But, so that, they've always used specialized data stores. 931 00:33:29,640 --> 00:33:30,720 They've written their own databases, you know? 932 00:33:31,180 --> 00:33:41,160 And, and by the way, it's, it's for that reason that it's no small coincidence that Dr. Dave Pollack, sorry, Dr. Mug Pollack, on the Spring team, his previous life was in physicists. 933 00:33:41,600 --> 00:33:42,320 He was in physics. 934 00:33:42,740 --> 00:33:43,180 Right, right. 935 00:33:43,180 --> 00:33:49,820 And he was the first founder of Spring Data, which is our project for specialized data stores, you know, SQL, whatever. 936 00:33:49,960 --> 00:33:50,260 Right, right. 937 00:33:50,380 --> 00:33:52,380 Right, because that was, his background was doing that, you know? 938 00:33:52,760 --> 00:33:55,820 Anyway, so, first of all, it got more, it got bigger, right? 939 00:33:56,240 --> 00:34:00,320 And then we, we started needing to be able to query it. 940 00:34:01,380 --> 00:34:03,720 We needed to be able to query this data in particular ways. 941 00:34:03,880 --> 00:34:04,080 Right. 942 00:34:04,100 --> 00:34:05,060 And still be performing, right? 943 00:34:05,080 --> 00:34:07,440 We couldn't, we have, we move more and more things online. 944 00:34:07,960 --> 00:34:12,440 And so, if it's offline, then yeah, you can afford to take this database, which is in a representation. 945 00:34:12,440 --> 00:34:16,020 Get it at a warehouse and put it, put it in whatever format is handy for querying it. 946 00:34:16,320 --> 00:34:18,460 But, but yeah, but okay, there you go, that's a good point. 947 00:34:18,760 --> 00:34:25,800 The warehouse model, that, that offline, you know, non-online transaction, that, that's the exact sort of thing that we did before. 948 00:34:26,120 --> 00:34:29,760 But we just assumed that you would only access that warehouse ever so infrequently. 949 00:34:30,060 --> 00:34:36,060 So even there, you had it in your SQL database, and then you'd turn it into something that would be optimized for reporting. 950 00:34:36,260 --> 00:34:36,840 Yep, yep. 951 00:34:36,840 --> 00:34:39,940 But now, that reporting has to be up to date almost instantly. 952 00:34:39,940 --> 00:34:47,700 And so, and so my theory, it would be that there was, there was a, point is the wrong word. 953 00:34:47,780 --> 00:34:59,940 There was, there was a time series, points of time, so to speak, where maybe it was not ideal, but the most efficient, optimized way to do stuff was to have a relational database, right? 954 00:34:59,980 --> 00:35:02,540 Like, it was like the tool that worked the best. 955 00:35:02,800 --> 00:35:03,460 I don't know about that. 956 00:35:03,460 --> 00:35:06,620 And again, and, and, and, and, and again, we can invalidate this theory at some point. 957 00:35:06,700 --> 00:35:10,900 But then, as you're saying, the needs for data changed, right? 958 00:35:11,000 --> 00:35:11,120 Right. 959 00:35:11,320 --> 00:35:16,400 And then also, the technologies changed such that we could actually make something better than an RDBMS. 960 00:35:16,560 --> 00:35:21,420 Well, we, because, okay, so I'm, I see what you're saying, but I think, I would say that we've always had specialized databases. 961 00:35:21,800 --> 00:35:21,980 Right. 962 00:35:22,040 --> 00:35:27,560 But the performance wasn't so, so provably bad that we were forced to care about it. 963 00:35:27,560 --> 00:35:32,020 So, so this, this, the, the amount of data that we have and the speed at which we want to do something with it. 964 00:35:32,020 --> 00:35:32,320 Yeah. 965 00:35:32,460 --> 00:35:34,960 Sort of like, what did it do? 966 00:35:35,620 --> 00:35:36,740 It exacerbated the problem. 967 00:35:36,800 --> 00:35:39,440 Whatever a nice way of saying broke the backup is. 968 00:35:39,520 --> 00:35:39,580 Right. 969 00:35:39,580 --> 00:35:40,160 That sounds very violent. 970 00:35:40,280 --> 00:35:44,760 But, but it, but it, it's, it's superseded the, the functionality of the relational database. 971 00:35:44,860 --> 00:35:45,000 Yeah. 972 00:35:45,120 --> 00:35:58,380 And, and, and I think we all, and, you know, solve some, in doing so, in changing that, we've, we've fixed some problems and spawned a whole industry of vendors who want to, who want to create more problems. 973 00:35:58,560 --> 00:35:58,560 Right, right, right. 974 00:35:58,560 --> 00:36:00,920 Well, every, every solution today is tomorrow's problem. 975 00:36:01,160 --> 00:36:02,420 And, oh, golf game. 976 00:36:02,940 --> 00:36:03,580 Golf game. 977 00:36:03,800 --> 00:36:09,320 There's always a golf game behind some terrible solution deployed in some, some, you know, vendorware. 978 00:36:09,660 --> 00:36:10,000 That's right. 979 00:36:10,060 --> 00:36:10,240 All right. 980 00:36:10,280 --> 00:36:11,720 Well, well, I've got to give a talk. 981 00:36:12,140 --> 00:36:12,420 Oh, yeah. 982 00:36:12,660 --> 00:36:13,180 And then maybe. 983 00:36:13,360 --> 00:36:14,000 What's it going to be about? 984 00:36:14,220 --> 00:36:16,640 Oh, it's just, it's just me trying to figure out what Kubernetes is. 985 00:36:16,680 --> 00:36:19,320 And then maybe, maybe I'll get some chicken rice. 986 00:36:19,600 --> 00:36:20,060 Honey and chicken rice? 987 00:36:20,060 --> 00:36:20,440 Or something. 988 00:36:20,620 --> 00:36:21,040 I don't know. 989 00:36:21,080 --> 00:36:21,660 We should go together. 990 00:36:21,980 --> 00:36:22,200 Yeah. 991 00:36:22,360 --> 00:36:22,980 That would be great. 992 00:36:22,980 --> 00:36:23,260 All right. 993 00:36:23,340 --> 00:36:25,900 Well, as always, this has been Pivotal Conversations. 994 00:36:26,120 --> 00:36:27,960 Why don't you tell people where you are on the Twitter? 995 00:36:28,260 --> 00:36:32,480 Oh, find me at, at, at, at Starbucksman, S-T-A-R-B-U-X-M-A-N. 996 00:36:32,880 --> 00:36:34,320 And, uh, you know. 997 00:36:34,380 --> 00:36:35,540 And do you use email, Josh? 998 00:36:35,820 --> 00:36:36,140 Email? 999 00:36:36,460 --> 00:36:36,780 Yeah. 1000 00:36:37,020 --> 00:36:37,260 Email? 1001 00:36:37,360 --> 00:36:37,920 You can. 1002 00:36:38,100 --> 00:36:39,480 It's, it's a little async, you know. 1003 00:36:39,740 --> 00:36:40,060 Telephone. 1004 00:36:40,140 --> 00:36:41,140 Josh at joshlong.com. 1005 00:36:41,240 --> 00:36:41,880 You use voicemail. 1006 00:36:41,880 --> 00:36:48,520 But, but also, um, uh, check out the, uh, the Gitter. 1007 00:36:48,960 --> 00:36:49,780 You know, you know, Gitter? 1008 00:36:49,920 --> 00:36:50,840 Do people use Gitter anymore? 1009 00:36:51,160 --> 00:36:52,440 We have a G-I-T-T-E-R. 1010 00:36:52,460 --> 00:36:53,480 Is that like Finger or something? 1011 00:36:53,580 --> 00:36:54,560 No, the, uh, the chat room. 1012 00:36:54,620 --> 00:36:56,380 It's like a, it's like a open source. 1013 00:36:56,520 --> 00:36:58,000 It's not, it's, it's like an open Slack. 1014 00:36:58,280 --> 00:37:00,700 So it's G-I-T-T-E-R dot I-M for slash. 1015 00:37:00,860 --> 00:37:02,260 And then Spring Hyphen Projects. 1016 00:37:02,260 --> 00:37:04,640 And you'll find chat rooms for all the different Spring Projects. 1017 00:37:04,720 --> 00:37:05,280 Oh, I see. 1018 00:37:05,320 --> 00:37:08,620 And what's the, what's the gender neutral word for manned? 1019 00:37:09,180 --> 00:37:10,320 For manned? 1020 00:37:10,320 --> 00:37:11,340 Like manned the ship. 1021 00:37:11,540 --> 00:37:11,780 Staffed? 1022 00:37:12,040 --> 00:37:12,400 Staffed. 1023 00:37:12,480 --> 00:37:12,800 Thank you. 1024 00:37:13,420 --> 00:37:13,800 Thank you. 1025 00:37:14,160 --> 00:37:16,040 Staffed by the Spring Engineers. 1026 00:37:16,300 --> 00:37:16,600 Ah. 1027 00:37:16,600 --> 00:37:20,380 So if you have questions, comments, feedback, whatever, we are, as always, happy to engage. 1028 00:37:20,680 --> 00:37:21,360 That sounds good. 1029 00:37:21,460 --> 00:37:26,160 And also you can find all the old episodes at soundcloud.com slash pivotalconversations. 1030 00:37:26,260 --> 00:37:29,220 Now do you think there's a space in that URL or an underscore? 1031 00:37:29,360 --> 00:37:30,000 What do you think, Josh? 1032 00:37:31,060 --> 00:37:31,380 Underscore? 1033 00:37:31,840 --> 00:37:32,540 No, no underscore. 1034 00:37:32,940 --> 00:37:33,580 Trick question. 1035 00:37:34,020 --> 00:37:35,660 Just all, all one word, no spaces. 1036 00:37:35,840 --> 00:37:36,520 That's the worst. 1037 00:37:36,680 --> 00:37:36,880 Yeah. 1038 00:37:37,080 --> 00:37:38,600 No, no percent 20s in there. 1039 00:37:38,720 --> 00:37:40,380 See, my mind would want to forget that. 1040 00:37:40,540 --> 00:37:41,180 I would actually try to forget that. 1041 00:37:41,180 --> 00:37:41,200 I know. 1042 00:37:41,200 --> 00:37:44,760 And that's why I like to tell people, Pivotal Conversations, no spaces, hyphens, other special 1043 00:37:44,760 --> 00:37:45,180 characters. 1044 00:37:45,480 --> 00:37:47,140 So, you know the one thing you would never think to do? 1045 00:37:47,240 --> 00:37:47,600 Do that. 1046 00:37:48,280 --> 00:37:48,620 That's right. 1047 00:37:48,700 --> 00:37:49,020 That's right. 1048 00:37:49,120 --> 00:37:49,900 Anti-Larry Wall. 1049 00:37:50,320 --> 00:37:53,740 Isn't he the one who said software should, no, I made this mistake the other day. 1050 00:37:53,780 --> 00:37:56,900 It was one of the pragmatic programmers who was like, software should do what you expect 1051 00:37:56,900 --> 00:37:57,220 it to. 1052 00:37:57,540 --> 00:37:57,820 Oh. 1053 00:37:57,940 --> 00:38:00,380 Which, which I think that's like saying food should taste good. 1054 00:38:00,600 --> 00:38:00,780 Right. 1055 00:38:00,860 --> 00:38:03,140 So, you know, that's still good. 1056 00:38:03,140 --> 00:38:04,160 It's true, but kind of useless. 1057 00:38:04,400 --> 00:38:04,760 Yeah, yeah. 1058 00:38:04,820 --> 00:38:06,480 So, we'll see everyone next time. 1059 00:38:06,600 --> 00:38:07,100 Bye-bye. 1060 00:38:07,240 --> 00:38:07,440 Bye.