Okay two beers coming up. Thanks Mike. Mud in your eye. That's 20 cents paid before your hoist in Trusting guy Yeah, oh Mike. Yeah. Here's a tip for you a little something extra. It's a good gag, huh? But you know, I don't think Mike appreciated it So big Mike is dead Murdered by two unfriendly strangers It seems from the listening to be an open and shut case You simply find the two men who went into the bar at that particular time Finding them shouldn't be difficult Since they each had a drink Left their glasses on the bar Oh creeps This is T4Y Opening the doors to the mystery plan tonight. We bring you another story by your old favorite William Irish It's called leg man You've already heard the crime being committed Now we plan to lead you slowly through the maze of clues and evidence See if you're as smart as you think you are Now then The rest of leg man by William Irish the scene Mike's tavern on the lower west side of Manhattan The place is jammed with policeman detectives photographers sprawled behind the bar is the body of Mike two bullets in it All right boys put those two glasses on the bar and let these reporters get some pictures Spectre house chances of some info. My name is Burgess from the globe.  No, we've got the culprit solid He's cooked wash and bother see those glasses. We just put back there in the bar. Yeah, I see him Whiskey jigger and a beer glass. Yeah Well the whiskey jigger was covered with fingerprints who's a guy named Hastings Chuck Hastings motive Plain as the nose on your face That's some kind of a row with Mike and threatened to get even with him in front of about a hundred people any alibi none at all Says he was alone in his room all morning. Who's Prince on the beer glass? None Wipe clean. That's funny, isn't it? If the beer glass was wiped clean. Why not the whiskey jigger, too? Sounds fishy to me. Hey you looking for trouble One of the two guys had more presence of mind than the other one remembered to wipe the glass one forgot The one who forgot was too busy. He was firing the shot. That's all the case is closed It's a very simple case a grudge a killing and some fingerprints plastered all over a whiskey glass And the man who made them locked up everybody was satisfied except Chuck Hastings the man They locked up and a pencil happy leg man called Burgess who was crazy enough to start messing around in an open and shot murder case All right Burgess speaking No, wait a minute boss. I don't have the whole story Well as a matter of fact the arrest has been made already a guy called Hastings He had a grudge against the owner and his prints are on the death glass Listen boss. I noticed something. I think the police have missed and it makes me think this whole thing may be a frame Boss give me a break Hello, give me two fingers coming up You're the newspaper guy was in here this morning asking questions about the killing ain't you yeah Don't I'd seen you before Hey, yeah, that's 40 cents. What's the matter? Don't you trust me till I finished it? Sure, just have it I guess The owner Mike he never trusted anyone He always collected for drinks right as he put him down instead of waiting until after picked it up from him I guess sounds like a tight guy Mike you said I'm mouthful Guy like that must have plenty of enemies sure like who for instance I don't know there's a safe bet. I'd pick this fella Chuck Hastings the police pulled in What's this grudge they say had against Mike? Well, I don't know as I could say I was just wondering For the paper, you know, I thought I could court you Yeah Well, of course, it's no secret Hastings and Mike had a row in here one night This Hastings hands a boss a ten dollar bill to pay him off And the boss just gives him change for five Things start to get rough and Mike jumps over the counter and throws this Hastings out. I wouldn't say that was too much of a Mike throws him out Hastings picks himself off the street and hollers in the door. I'm gonna get you for this if it's the last thing I do I guess he did all right Funny thing though Looks like Mike still didn't trust him made him pay up this morning when he got his drink like always When I came in the register was rung up at 20 cents at 20 cents. Are you sure of that? I got eyes ain't I? You know it It strikes me that a man as tight-fisted as Mike sounds would have a lot of people who'd like to bump him off Well, he did knock up against a few tough customers like Just for the heck of it Minds you I ain't pointing a finger at no one in particular about this shoot. No, no, of course not well There's a fella named Edge Joe Edge in the wholesale liquor racket He and Mike have had it in for each other for a couple of years now. How come? Well Edge had the old squeeze play pretty much on He soaked Mike the limit for the liquor he sold him and then was plenty sore when Mike got tired of it and switched To another out sore enough to kill him now look I didn't say I forget it forget it say by the way was it a Glass like this they found on the bar this morning. Yeah a whiskey jigger and a beer glass front of the other seat Do you keep an account of how many jiggers like this you're having stock not usually but it just happens We got in three dozen yesterday Lost any since then no, I'm pretty careful And you ought to have 35 not counting the one the police took right well 35 and 18 we were down to before we reordered that makes it a 53 look I'm not one of those betting guys, but I've got nothing else to do Here's a dollar bill says you haven't got 53 in stock right now Fade me. Well That's an easy way to double my money Yeah, see this stick I'll hit the glasses so they sing out and you can keep count with me. Okay, right? one two three four five six 46 47 48 49 50 51 52 53 54 well, I'll be darned. I must have figured wrong No, you didn't needed an eye. Oh, don't you see you have one extra shot glass? Find out where it came from and you've got the guy who killed Mike Yeah, buddy. Listen was a whiskey jigger like this taken from your bar within the last few nights No, I'll take a thing like that. Don't bother me. Okay. Thanks. Anyway Did anyone take a shot glass like this from your bar? Are you kidding mister? Okay? Thanks No jigger taken thanks Nope, thanks Listen was a jigger like this taken from your bar the last couple of nights. Yeah, okay. Thanks. Hey wait, mister Was that you asked me? I said was one of these shot glasses swiping your bar within the last couple of nights Yeah, how did you know that? Who told you you got any idea who took it? Yeah, a fellow named Hastings He comes in here a lot Hastings Hastings did you see him take it well? No, but it was the one he was drinking from so he must have when I went over to clean up There was no glass there. I couldn't figure it. Just beginning to see the light myself Quite a bit of light. I think I'll try and get a few more gleams from Hastings himself You'd think this was old home week in the detention cells Hastings is being held for murder and you walk into whole hands or something. I just swing it the power of the press officer This is so yeah Happy here. What's the fan here? Five minutes beat it up Hastings. I had a hard time getting in here. I didn't listen. I've got to talk fast Answer me straight with you. It's the only way I can help What do you want to know? Who'd you shake hands with last night? What are you a little punchy or something listen? I mean it Who'd you shake hands with in a bar the night before you arrested for the killing? I don't I Didn't mean anyone. I knew I was by myself the whole time You were in a place called Sullivan's on Union Avenue Sullivan's Wait a minute. I Didn't shake hands with anyone, but somebody did grab my method and pump it Remember that now It was one of those drunks next to me look like a mechanic wearing overalls First he apologized for slapping over against me then he said No hard feelings and meets down and pop my hand about 16 times Now what you wanted that's exactly what I wanted. Did you notice your hands when you got home last night? Was there anything on them? What are you mind reader? It was grease on them axle grease or something Hastings. I'm on the right track Was he anyone you ever saw before? No, I never saw before my life He was just some stray barf. He wasn't strain. He was no barfly, but never mind about that now Hastings who hates you pretty much the whole world away. It looks tonight Look, I'm trying to help Who hates you enough to frame you for a murder? Sometimes you don't know it when they do. Oh, you usually have a pretty fair idea Well, this Harlem Maybe Tom Strickland Al Vogel ever since that mix up in Jersey City Joe Edge Joe Edge. What's he got against you? He's a liquor distributor now, but he used to operate a speakeasy during prohibition. I worked for him One night we were rated and federally tried a Lot of inside dope turned up during the trial as though someone had ratted He always thought it was me. I got off pretty light and he got a stiff term prohibition ended in 1933 I mean he'd wait 12 years to get even he's always had it in for me. I wouldn't be surprised I wouldn't be surprised either Hastings. I Wouldn't be at all surprised Is this the Joseph Edge who's in the liquor distributing business? Nothing, I figured maybe there was something I could do for you. I'm in a phone booth at Mike's tavern That's where you were this morning at about nine nine fifteen, weren't you? That's where they shot Mike I'll explain I Was the first customer at Mike's tavern this morning. I went in a minute or two after the place was opened up. I Went into the washroom. I Was in there when you came in you Didn't think to look in there Well, tell that to the cops. The only drawback is they don't pay I've been saving this for a rainy day and it's pouring cats and dogs right now You don't believe I was there to I'll draw you a picture There was another guy with you You stood up against the bar on the right-hand side. He stood on the left After the shooting one glass was wiped clean and the other Now is it? Clouding up a little bit. You're in About how large an umbrella would it take to keep you dry? $500 one This is a pretty heavy rainstorm Look, there's no harm in getting together and talking this over Meet me tomorrow morning at nine o'clock In front of a barbershop called the empire. You can't miss it. It's on central Avenue Got it. Got it Okay Bye Who you kidding what time do you close up here? Well, the boys usually kept it open until about four But I thought I'd close up in about ten minutes Want a nightcap before I cork up with my yeah, give me a short one. Don't change Hey, just take it over to the register with you. Good night. Good night. I'll be He said keep the change And it's nothing but a piece of paper all scribbled up with some writing on it Keep the change Say something buddy What do you want me to say just that that would do very nicely Here's your matches back buddy, that's not matches that's a gun you've got there right you are It says get in and make yourself comfortable. Oh wait a minute Okay, step on it have I been in a warehouse where they store liquor. It's very instructive Frighten my time for us. No, this guy doesn't frighten so quick You don't have to be afraid buddy.  I see How are we gonna do that? Well, there's four of us here You say you saw two guys come in the Mike's tavern and shoot him yesterday All right Pick out the two you say you saw Give me a minute's time. I want to be sure of what I'm doing Yeah, you want to be plenty sure Well, there's You Number one granite face there number two Eyes a little shifty. Come on. Either you know you don't I don't know I Want to figure out what it's best for me to say Especially with that very efficient looking gun pointing at my stomach So you know who it was I Suppose you know why it was to I do It was a case of two birds with one stone or with one whiskey jigger a Guy had a long-standing grudge against tastings and a hotter one against Mike Oliver for refusing to be hijacked by his liquor distributing So he figured one murder should be enough to take care of both men Right Go on. We're listening. Oh, it's quite simple Joe Edge had one of his men with grease in his hand put on a south side Follow Hastings into a bar Shackens with him Hastings printed his own tips all over the glass he was using without even knowing it Then the would be south swiped it and brought it to edge Edge took at the Mike's with him Shot Mike switch glasses left at behind there to frame Hastings shall I go on? There's not much more except That the two guys I saw come into Mike's tavern and shoot Mike Oliver were you Joe Edge in Front of me with a gun You over there The fourth guy with a shifty eyes That was the wrong answer You're two were the ones that were there so what's wrong about it sure We were there That's why it's the wrong answer If you picked the wrong to you probably would have walked out of here alive But you picked the right So you'll have to take your 500 in capsule form now Hey The rest here keep not the places covered you all right Burgess. Yeah, I guess I am inspector lions Well, you sure got here just in time that look pretty close. We tagged the car and followed it You gambled heavily on that bartender son. I know I was afraid to talk to him for all I know someone of the gang was watching or listening. I I scribble a message for him to call you He did better than that. He saw you get in the car and phone the license in but look How did you ever figure out that whiskey jigger was a plant? Well edge got careless He he didn't think it mattered what he ordered at the bar from Mikey was taking his glass out No one would see it so he ordered beer and the character with him acting on the suggestion ordered beer, too but Mike Oliver was a peculiar cuss He insisted on being paid when the drinks were ordered and he rang up the money before they shot him When they found Mike's body the cash register said 20 cents. What what does that prove? Spectre you can't break 20 cents down into a whiskey and a beer any day of the week And there was a whiskey and a beer glass side-by-side on the bar. Yes, but well Even if the beer glass was just a chaser for the whiskey the price for the cheapest whiskey Mike carried was 25 cents 20 cents I'll buy two beers For the sake of simple arithmetic the whiskey glass had to be planted well, I'll be Son if you ever want a job on the police force. Thanks, sir. I'm plenty satisfied with a job. I have now Wait a minute. What's wrong? I just remembered. I haven't got a job. I was fired. Excuse me. I have to call my boss. Hey wait That was the story called legman written by William Irish An old friend to you mystery playhouse fans. There's a moral to the story too Moral of the story is Don't ever pay for your drinks So after you've dropped them Getting early again It's almost not night anymore This is T4 why closing the doors of the mystery playhouse and saying good night Sleep tight This is the armed forces radio service


t films. I don't yes I don't think oh, they have I've seen several bigger ones, but there was uh Back a camera which one there was one that got a lot of public was shown in theaters and they had it on youtube And and nintendo's lawyers got them while they were pretty close I mean, maybe if it's in theaters, which was was part of the problem But I mean if it's on youtube, I don't I don't know. I just yeah, they literally it's not on youtube anymore It's completely shut down But you know what's you know, it's kind of messed up about that y'all remember the big fake out of Who was the ign that did the major's mask the april fool's joke. Yeah, yeah First of all, no, no, no, let's do just two different things. There's a there's a Zelda movie trailer that was the ign april fool's thing and then there's also the cgi major's mass fan made project It's just the most incredible thing known to mankind I think was the ign one but because that one looked incredible As well and that was I know what when you're talking about it is pretty good looking out almost incredible thing known to mankind Hyperbole is a word that exists for a reason sometimes But it is really good. It is if you guys haven't seen the major's mask. It's it's very very good And I mean I I that's like around when social media was a little bit was a little less What it is now because now it's it's insanely too much I want to tell them pull the master sword about that Take whatever context from that you want some verdict. Oh Actually talk on it until you turn it into something weird What it doesn't have to be weird.   His mind is he is I don't know where he's going. I'm talking all sword and a stone incident here And I don't know what he's talking about. I don't think he's talking about pizza first of all, but anyway What he got going on in his house is his business That's a grown man. Yeah, I'm so sorry to anyone else who calls it this This is going off the rails. I love it We went off the rails once stream yard did so we're we y'all Nate and I when we talked about this behind the scenes We I finally just said, you know what Nate? We just gonna pray and wing it This is Don't test anything ever. Yeah, never test anything. Don't I mean, but I got off work late So, um, oh wait, I'm still good. I'm still getting requests. Y'all are awesome. Like we seriously We seriously have like the best community like don't Speak into that. Yeah shot. I got a super myself. Hold on. Don't argue with me. Are you super chatting yourself again? Well, I want to do a shot of the rules of the rules No, just ask your your twin brother who owns the island and everything just ask him Yeah, I did. I think he's sleeping now. Oh, okay I'm gonna hold it for like a month. I hope he didn't get like eaten up by a storm or something Well chat if of course, you know, um, Nate likes his shots So if you all have any other questions if you don't want to call in please feel free to super chat We love to talk about zelda That's really the only reason andre is over here. He just likes to talk about zelda Any yeah, I forgot this was happening He totally did I reminded him yesterday and he's like, oh Right, okay. Well, that's because my plans We're making it work. I made it work. You didn't make it work when I was like I know I feel kind of bad like I probably should have reminded you a few days ago When we talked about it I kind of assumed that this was gonna be in place for that week and then you're like, oh Tomorrow it is definitely not a podcast Yeah, now you see why technical difficulties go lower But this was also kind of a test run. We wanted to see how our community would Our community Nate's community. Um, I'm just a part of it. Uh, see how you guys would enjoy better around the second time around That's for sure. It it will be because now we know Kind of what to expect and andre is slowly eating his pizza for you all's delight. So really Oh, if I know what this is just all for you guys Hey, by the way, when the cameras are set up the other way, it's less weird It really is Nate. We look so much better I look I look absolutely frightening Or we'll just look at okay. Everybody just watch andre's eat his pizza This is your pg-13 Stream I can zoom you in even more if I need to It's don't See You see, I know that people think this is so much better. I'm gonna explain why for me It is not when you're watching on the phone. We're midgets Yes We shouldn't be midgets. No, we should be Easily viewable. You gotta think of the viewing experience for the but I have more room to sit to the side And this way I have to sit this way and it hurts my back. I'm old Nate Thank you.  Well, this would be over quick All this mad thing complaining And then I'll smack the smacking. Oh, yeah You know, it's funny. I'd mute your microphone to be That started happening Don't a real quick shout out to natzoo. I wore plaid just for him and I expect my flowers and praise Expeditiously, yeah You should be saying thank you. I'm just kidding Or plaid today, I just Was the shirt that was on top of my pile Andres eating pizza. We're in plaid.  I don't want to ever hear that audience is not giving what they want Seriously though seriously Okay, I have a favor to ask you. What's that? Is it money? I'm pissed.  Because I never got to hear about your bingo predictions. I could just show it As long as I get to see it Yeah, I don't want to go over it all but I could put it on Maybe for a minute I guess the reason why you might be upset because it's not all zelda. I assume. No, I'm not All right, so zelda remaster there's your zelda duck hunt 40 is that like a NSO that was paul's idea Okay Diddy Kong racing returns Love the name new controller gimmick. Yeah, man, probably new pokemon legends. I hope so New mario kart is it coming out? Or is it? Yes In 2024 you dog mother three on nso, but I believe it new ip Probably, I mean maybe something small. I don't know I don't know. I don't think there's so much things that can't come out this year that I can see it happening next year instead But yeah, maybe you start bro Bro, I mean, I guess we're trying to get five of these right in a row. So yeah, I feel you. Yeah Top-down zelda themes menu music zelda movie cast reveal three mar released new curbie Is he gonna be chronicles x? I don't know with the back the bottom two left of bottom left left is metric prime four shown What's wrong with you? That was me three pumps a switch to Why would you only so shown? Why wouldn't you what what you're going? You're doing all these wild predictions But metric prime four you're like the conservative one. Oh, well, what made me see it Smash starts with dk. Okay. I like it. All right. Thank you for sharing with me I got a question guys Not only Not only I appreciate that buddy And guys the bingo card is for all of 2024 right now. So any of that could come out at any point in the year Okay, so let's see A metric prime four only being shown.  It's your prediction, but it's also that the prediction for boulders It's like it's filled with crazy stuff But that's the one you're like, you know, why don't be really careful here It's because I want to get the that it's because no matter what I want to get the metric prime four point I don't care If it doesn't come out this year as long as they show it. I just want the point Fine. I mean you're right regardless as long as it gets shown. Yeah, it's it's a safety I mean, look, I could have did that with with someone like, uh Uh, like the smash brawl ball one that's not actually like smash coming this year That's a the smash bros is announced for switch to this year and not coming this year Oh, I thought you're gonna just be or I thought you were just pulling the smash something so it could be no No, a new smash was being announced but not coming this year. Okay They're giving us that pre announcement like two years early kind of thing Okay Now Nate's Nate's heard me say this before so Andrea would like your thoughts and I think um Unless anybody has anything else after this, I think you can kind of call it a night, but I want to know your thoughts so Uh, Andre, I don't know if you've ever heard my famous, uh, zelda is not a damsel in distress ramp um but Yeah, but she's not because we won feminism. I don't know how many years ago far was born really um, so here's the thing I'm not into Zelda changing who she is For the main game games when it comes to warriors games. Okay fine do something different But for the main games, how would we feel? about us getting a constant to Legend of zelda or and a legend of link hear me out So we get our traditional legend of zelda links going through with the sword and everything but then we also get the other side of the story which would be zelda and Maybe she's maybe we don't fight the same way. Maybe we don't do things the same way It's a completely different thing with her ability with the abilities that she's known for Actually, I think zelda might even make a good assassin, but not necessarily swords and things of that nature. She's not That kind of hero. She's a different kind of hero. Yeah, maybe like kunai and senbon Kunai and senbon Who that is A kunai is a ninja weapon as is the center.  I know what you're talking about. I mean, she I think she uses senbon in smash Yeah, I haven't played enough smash. So I can't tell you Well, but I liked her with her pretty sword in hyrule in the first hyrule warriors The rapier sword or whatever it was called that is cool That was cool But because but I appreciated it because it's not canon so they could do something completely different At least the first hyrule warriors wasn't you know, so how to so if it's a Ruby's like give me some pizza But i'm saying like if we were and so we have the story and we have lynx point of view like we always have but then we get Uh same story but it's all from zelda's point of view so we can kind of see the opposite side Of everything and we get to do things the way she would do things and then it kind of brings the whole story together I always thought that was interesting Paul of course agrees with me because he's like like me. He'll take anything zelda. We're just happy about zelda And I I know manate and I've talked about this but i'd be interested to hear what you have to say I mean, I I definitely would love to see You know zelda have A game in some way. I don't think it would be like another open world adventure, right? Like I don't think that's a realistic option um I think that's always going to be like lynx thing, but you know, maybe either a offer Pockets a gameplay within a mainline game where she's playable or Maybe spin-offs, right? Like I mean retro studios at one point was planning a You know kind of like a a sheikah kind of game where someone likes she You know something like that and so, you know if they were to make a spin-off in the future Maybe it's based off sheik but sheik is zelda and so That could be a thing like I could see that I don't I don't see what I can't happen if Princess Peach Showtime can happen a zelda game of some kind can totally happen We're going to add that to the petition And we're going to put that in the Wii U tech demo graphics. Good night, everybody. Oh my god I mean if it's smaller scale is Very it's very doable even with a smaller team Yeah, and I don't because think about I think they could it could work on the same map It's just that we would have to see a very different side of it So the so I think the challenge for the dev team would be how do we use this same? It doesn't have to be Breath of the Wild or anything we see before they could do this with the next set of zelda games How do we tell the story from these two different points of view and show two different uh sides of Hyrule You know maybe because she's uh undercover or something. Nobody realizes. She's the princess She can kind of get into the underbelly of Hyrule or whatever because it even though people are kind of Flippant about link there's not a whole lot of crazy just disrespect unless they just somebody just doesn't doesn't like it for some reason Like there's certain instances where the Zora don't like uh don't like Hyrule Twilight princess the goons don't like you for the first part of the game that sort of thing So I I've always wanted to so that I would like like to play with I just don't it just bugs me as a woman Yes, as a woman it bugs me when people look at zelda like she's this damsel in distress and nobody appreciates who she is And appreciates a friendship She does a lot but no and then people don't appreciate the French at least the friendship or camaraderie that she has With link and people want to make it seem like well, she could do it herself No, if I'm in trouble, I don't really care who or what you are come save my ass.  Right. So, you know the awkward time remake theory well You know a lot of like Nintendo remakes and remasters have like some sort of side adventure or epilogue story Right. Well, what if one they do for awkward time remake if this happens is Sheik is perspective Sheik's perspective We don't see what she does behind, you know off-screen, but she's doing a whole bunch of stuff And so my money That's doable, right and like like she also watches over uh, Cockerico village like maybe there's some sort of political thing going on there Like, you know, you can maybe touch on some of the the zelda, you know, like Um chess game this political chess game. She has to play somewhat there, too Like there's you could you could do a little bit of that that wouldn't be its own thing It would be like a side thing, right? But you know, so wouldn't be all explored at nauseam But you know, maybe a little bit that would be I can honestly I can see it, but I'm a crazy man Well, we're both crazy then because I'm the one who proposed the question Ari says make a zelda game where link travels to the future. We got it. We got a few of them, don't Link travels because awkward time he travels to the future time, I suppose. Yeah, he goes to the past.  No, it didn't kick me out I bumped out so I had the thing open and my mouse back button bumped the side of my desk Oh, it was like, oh, we're out of here Oh, Ari clarified their question. Um, she's saying no, I mean a future where a future like 20 years into the future I don't see why not because I don't see why not I just would if the story if it made sense Look I I got no problem with it. We always like talk about the past or go to the past I mean do think I'll think of like the memories for zeldin tears They can how far in the past that was like why not Have this thing where we jump to the future So I got an idea for you guys ready ready All right, so you know how I've told you guys about this idea of a zelda game Right where you have like the ancient part where it's so ancient the plant life The animals are like ginormous.  And it's it's very it feels very dangerous and wild more wild than rest a lot by A lot, right? But then you also have these cities that are encased in giant walls very attack of the titanesque But it's it's not old time. It's futuristic style like cyberpunk style almost and so you have This ancient world filled a giant plant life and animals and some ruins scattered throughout, right? So you still can get a lot of that ancient zelda stuff, but these also have the cyberpunk stuff But let me kind of add another little angle to this There is time travel going back to the future and the past that this is where they flipped it At on you when you go back to the past, this is where you find the future cities And when you go to the future, this is where you find the giant plant life and ancient stuff So it's kind of like subverting your expectations because you would think when you go to the past you would see the old stuff When you go to the future, you think you would see the new stuff, but they flip it on its head because Zelda has such a rich history such a long history that there are there have been advanced civilizations in the past And there have been very archaic times in the future Hmm Did you guys follow that or was that too scatterbrained? I follow I have to marinate on that I like the idea because i'm always about making zelda fresh and new so i'm i'd be down for it. I guess In my own mind, I would love all the all the crazy ideas. I guess I just don't see how that would work for the entire Fan base and gaming as a whole because I could just see nintendo laughing at both of us But on the same note, I still think it's a good idea I I think my idea works, but but the time travel part is just like a funny little twist that that's probably where That's the part that we need to be more fleshed out But like just the idea of having two different styles of environment one being futuristic one being very ancient And then being kind of separated but within one giant world you can explore Like I think that's something we could actually see right Introducing time travel where you're going back and forth That's cool But I guess how is that actually implemented into gameplay and that's something they would have to be fleshed out a lot more I mean one interesting thing that I feel like people would have wanted is like Basically a portable timeshift stone right In an entire open world map which would require some extreme extreme Like ambition with designing stuff because any other design puzzles across the entire map to like basically have to make you have to make two maps and then overlay them Right and then you know if you would have to do it in a way where the puzzles make sense as well Right like it you could if you want to just make do it for looks That's one thing, but it's more than just looks it's about thinking about how why why you can bother Kind of going back to the future in the past all this all the time You know we kind of got a taste of that and um Skyward sword with the desert or a single dungeon and I was totally like And see in my mind when I was thinking with with tears of the kingdom I was thinking it would loop back around And that's what where we that's where the desert would end up being and things like that I thought we would eventually kind of loop back around and I mean they still could um But that's but I wanted to see more of what that ancient tech because I actually felt bad for those little robots Like they had no idea that All that time it passed and they weren't Where they were where they thought they were you know anymore. I actually felt bad for them and they were so cute Yeah, it's after 11 uh, y'all old lady joys to get that All right cereal We got a member Oh, it's michael Phenomenous michael michael lorenz the second How about if they remake a queen of time have a side quest for chic And how she gets to link every time she meets up with him in the game. I would so love that. I want to know that That's like why is you finally good? Yeah Yeah, why why is you following me? Why do you always know where I am? stalker Seriously, she's she is not only zelda. She's also a stalker straight up stalker Really big like how does she always know clearly she's creeping somewhere They I always wondered where zelda and impa went in our Korean time I always don't it and it's just like You don't know they're just gone That's the truth right they're just gone But like where where where did they run off to you know, where did they hide hide away for seven straight years? And that's a really great question because kakariko is actually a little too small even the little hiding Places and everything for the princess to hang out at you know what I mean for seven years I mean if I had to guess Somewhere on kakariko it makes sense just from a story perspective right like because you know Impa is the sage of that area right and then Sheik right which is zelda right we do see in the the lead up to the spirit temple or the Shao temple I mean Um, she does pay more attention to the kakariko village right like she's dealing with the well and stuff like that So she may have in some sense because impa likely taught or raised her right in that seven-year span She probably feels some type of way about kakariko village more so than other areas So, I mean, I I think there's something to the idea of that's where they were probably were But also like the town seems so small you see no actual evidence evidence of it in your in your experience of the game And also it's right next to hyrule castle like and then you know The other thing I just say One of the reasons I really like sheik Especially after you find out the whole story and everything. Um, it's just that I'm like sheik you brought up a good point. Sheik probably had to raise her at that point Because she or excuse me impa. Yeah Tell you I'm tired. Um And because zelda I feel like that was where she was able to hone her powers because she was able to At the at the well she was able to block link from the shadow coming out and basically trying to eat them Yeah, I'm tired because I'm not I'm not I'm not she gets ragdoll um Yeah, I'll be cool. It'll be cool. I mean, maybe the remake they could flush things out a little bit, but We'll see that would be good or what if we got the ocarina of time remake and then we get sheik story Like it's an epilogue thing in it or like separate like separate release separate epilogue. I don't care I still think um that there's room even now To go back and answer some of those questions because there's so many holes in the timeline um Or in the In the whole series that we can go back to a lot of things even today It I don't think any because here's the thing the first the very first zelda that came out in 1986 Is supposed to be later in the timeline After ocarina time right I think it's after it's a long time line It's supposed to be story wise after ocarina time. So obviously graphics don't matter So I think even now they can give us the legend of zelda sheik uh the story of sheik You know And if we could still do our whole what ocarina time look like and everything but it's all from sheik's point of view Y'all don't understand how ham I would go out as soon as they announced it. I'm not pre-ordering it I'm just sitting outside gangsta for six months two years. Okay. Yeah two years It's realistic actually, but yeah I'll just put out this Next game the legend of zelda holes of the timeline Here we go And they're just a bunch of different stories Yeah, and but but what's really cool is that you know how like ultra hands like the big new physics mechanic ability in tears of the kingdom In holes of the timeline the big uh new uh tool or ability is like an ethereal shovel holes of the timeline All right, uh, I give us. Yeah, we so sleepy. I think I think that's gonna do it I'll just form graze re-energizing right now. Anyways, um Uh, I don't know when we'll do this again, but whenever we do do it again We'll uh, I wish to be much we learned a lot tonight on how to do it, right? So we'll make sure it's much smoother next time You guys are awesome And uh, this is my last stream of the week. So I will catch you guys on monday. Otherwise just check out the videos I don't know what on andres. You got a stream coming up at all over the weekend or not? Unlike and I do yay for uh one Trying to organize this try people and then two I think you have something you want to say at the end I do for any of you who uh, want to see me, uh, you know how I wake out I was the beginning of the stream if you want to see me awake I am going to be hanging out with mike odyssey tomorrow on the intent on min topics Podcast paul gale will be hanging out with us as well and we'll be talking more nintendo goodness So come see me over there tomorrow night All right guys. I'm going to be it later everyone. Good night guys. Thanks so much for being with us


Alright, thank you everyone for coming on a Saturday morning, nice and early, at the end of Singapore, so I live on the other side, so it's a bit of a journey coming over here. So my name is Blaire Layton, I work at Amazon, at AWS, and I've been working there now for about three years, and I think it's ten months, which in Amazon years is a very very long time. So if you go to Amazon and you talk to people and ask how long they've been there, a lot of people are less than a year, so I'm like a part of the furniture of your life. What I do is I focus on our database services from a business development perspective. So that means that yes, I do selling, but I also help architect solutions, I work back with headquarters in terms of talking about what have we seen in the APAC market across Korea, ASEAN, India and Australia and New Zealand, and then say well these are the priorities of the roadmap that we're getting from our customers, we need these features in the services and products or new features that we don't have, sorry new services we don't have. But what I want to talk about today is obviously Postgres related because of the Postgres events that we're attending, and we're going to be talking about Amazon Aurora and the new edition that we're introducing that is now in preview. I'm going to go into quite a bit of technical detail about how that works and how it helps a Postgres run very well. So why would we go about building this anyway? If you look at traditional relational databases, you've got this wonderful stack over here with SQL at the top, transactions, a caching tier, some logging and then of course storage at the bottom, and this is a monolithic stack. Now if you were looking at this and trying to scale it, traditionally most people just get a bigger box, right?  Then this charting approach, so this is typically what you see in a startup environment where traditionally Facebook and so on would then take a database and charge multiple copies and split the data across that. Then you need to put all of the application logic there to know where the data is across these different databases. The next one is looking at shared nothing, where you're going to take the data and put that into different nodes of the database, but the application is going to be able to know at the SQL layer that it's going to be able to execute a query and the SQL node where to get the data. So the first one is the application, the next one is the SQL and then here you've got what's called shared storage. Does anyone know of a vendor that does this approach? There's two really. So one is Oracle Rack and the other one is DB2 on the mainframe. This definitely does this as well. Yeah, so DB2 and Oracle are basically the two that do this approach. This is what's right? But I think the way it works best is on the mainframe traditionally. Yeah, that's where the histories come from. If you look at over here, there's many different vendors doing this one and this is really up to you. You can do this with any database. Now, if you were going to do a relational database today, would you take those same approaches? And the answer is clearly not really. So what we would go and do is look at a service orientated architecture for a database. And essentially that's what we are building with the Aurora platform. So if you look at the logging and storage here, this is the main focus of what we're going to be talking about today, where we've actually created a storage service for databases. And that's a very different approach to how databases work today. And then we're backing that up into Amazon S3, a simple storage service. And then over here, we're leveraging a number of Amazon services to build a control plane, which manages it all. So we'll go into more detail about some of these different approaches. Now, we did, as some of you might know, in 2014, we launched Amazon Aurora MySQL Compatible Edition. And then what we're doing now is we're adding Postgres compatibility to this engine. So now you can choose whether you want to do this on-prem, whether you want to use our EC2 service, install it yourself for Postgres, or you want to use RDS and just run exactly the normal Postgres code that you would expect. Or here, you can use a different platform with Postgres compatibility to give you superior performance and reliability. So what does this Postgres compatibility mean? Well, we're starting with 96 code base, and then we're going to add Amazon Aurora Cloud Optimize Storage. So it's Postgres working with a different storage engine, fundamentally. Now, the performance we're seeing is up to two times better, which is quite compelling on the same hardware. Fail over time of less than 30 seconds, and we hope to get that down even lower. And then durability, six copies across three availability zones. So that's six copies of your data in case something goes wrong. And then we also have read replicas that are going to be operating off the same storage tier. So very, very low latency in terms of propagation of data to those read replicas. So single digit, milliseconds often. So cloud native security, because security obviously for Amazon is the first job. That's the thing that we concentrate on. Otherwise, we're going to have issues with our customers not wanting to come back to us if they have security problems. We want to make sure that it's easy to manage for RDS as a platform built in what you use today as part of Aurora as well. So easy to launch, easy to modify, and also backups taken care of, being able to do patching and those types of things all done for you. Easy to load and unload. Remember, this is Postgres compatible. If you're not happy with Aurora, you simply unload that and put it back into Postgres either on RDS on EC2 or even on-prem. We're not stopping you once you move into this platform from taking that out and putting it back to where you would like. Or you could even run multiple environments, some on Aurora, some on RDS, EC2, and all that. It's all up to you and exchange that data across there. Now, this is the most important one. Fully compatible with Postgres for now and the foreseeable future. So we have taken the code base, as I said, and it's literally just the storage engine underneath that we concentrated the effort on. So durability and availability, this is the storage architecture. If you're looking at the storage across three availability zones here, then we're replicating that and putting that across those three availability zones. We also have a primary database node at the top here and a read replica or secondary node on multiple availability zones as well. So what happens if something goes wrong over here? We're going to be able to fail over to one of these read replicas, which remember is up to date within milliseconds. And the storage is also consistent. So you're going to be able to get very quick recovery times here. Now, the other thing as well is that, notice there's no slave here, which is doing nothing. Traditionally, there would be a slave that's sitting there waiting for a failover in RDS. So this is a different thing as well, and we'll go into a bit more detail about that. So this is a very different approach. And a key point, so I should say, it's log structured storage. We'll go into exactly what that means and how that works in terms of the architecture later on. So in terms of the storage engine overview, as I mentioned, six times across three availability zones being backed up to S3. And then continuous monitoring of those nodes and making sure that, for example, there's no hotspots that we've got a good even workload across that. If this goes wrong, then we're able to repair that and make sure that we're constantly monitoring that storage tier. It works in 10 gig segments, so you no longer have to worry about database storage. Basically, it can go from your starting point up to 64 terabytes at the moment, and you don't have to worry about provisioning that. If you start using the database and it needs more space, we're just going to add 10 gig segments and keep going. Also, as quorum for read and write, and we'll go into what that means in terms of the failure modes, it doesn't store writes if there's not enough people, there's been some failures. So we'll talk about how many we need in the next one. And then storage volume, as I said, can grow up to 64 terabytes. So here's the failure mode. If we get segment failures on the disks, then obviously we've got multiple copies of that across multiple availability zones. If we get a node failure on a particular storage, then we've got all copies of that even in the same availability zone, but also across others. If we get an easy failure because of network or whatever's happened in that particular availability zone, by the way, for everyone who's not familiar, an availability zone, you can think of it logically as a data center. Sometimes they are groupings of data centers, but you can think of them logically as a data center. Then if we come across here, we can see that four out of six for a right quorum. So we could actually lose two and we can still write to the database. So it's very, very durable and three out of six for read. So you can still do reads when there's a massive problem with the storage service or a large outage, which of course we hope will never happen. So read replicas, you can think of these as dual purpose. They are read replicas in the sense that you know and love them now where you can read off them, but they are also fail over targets. And you can actually prioritize which fail over target you want. So if you've got a series of them here, as we do, you can say, right, well, one of those is going to be my analytics workload where the buffer cache is going to have completely different data to what the application would typically use. I don't want to fail over to that. So I put that at the lowest priority. So you can determine what will happen in the event of disasters. Now, in terms of the continuous backup, the storage engine itself has got these different segments. And then there'll be backups of these segments constantly happening and being put out to S3. So this is happening in the background and not having performance impact on the database at all. Now it allows us to also, because of the way that we're storing these segments and at their log records, to accelerate recovery from these backups, which we'll talk about right now. Here you can see on traditional database that you're looking at a check pointed data, and then your log or well, and in this case for Postgres. And then what you have to do is you have to recover and read that in sequence. On Aurora, we know at the time when the system had a failure, and we can recover to that in parallel, asynchronously, and very, very quickly. So this changes the whole recovery process for databases that we're running on here, MySQL and Postgres. So now this is kind of like the failure modes that you would typically have. The top one here is on RDS with standard Postgres. And what you've got is database failure. There's a failure detection period, right? We don't want a false positive. So there's some period of time where we have to detect what's going on. Then you've got DNS propagation to make sure that we're getting the DNS entry into updated into the DNS for the new server that's now the master. And then database recovery happens until that application is available. Now database recovery in a sequential mode could be very quick if nothing much was happening on the database, but if you were doing a massive batch job that wasn't committed and there's been no checkpoint, then there's going to be a lot of data that needs to be recovered. Over here, you've got a failure detection DNS propagation recovery is very, very quick. And you've got to know if you've got an application that's aware of replicas, you'll be able to recover ideally in three to 10 seconds. But traditionally, you can say 30 seconds. So we've basically halved this down to a maximum of 30 seconds. And that's kind of what we're going with with GA for the Postgres edition. So this is pretty compelling. When we get down to our target of 10 seconds, then we're getting into Oracle Rack territory, right? Where we'll be able to go and talk to customers and say, Hey, this Postgres database is pretty cool. And you can also get the recovery times that you're traditionally looking for a very short recovery time. So performance, if you're looking on the left hand side here, we've got a Postgres database with traditional storage running on AWS. So these are EBS volumes with 45,000 IOPS. And then we've got a M416 large database. And then up the top, we've got a C48X large. Now, if you're wondering, well, what does that mean? 16 large is a beast. All right. It's got, I think, 256 gigs of RAM, at least, if not more, and plenty of cores. And then on the C3, sorry, C48X large, these are some of our biggest boxes in these particular families. Then if you come across here, we're running exactly the same clients and database instance, but with the Aurora storage and platform. Okay. Now, here are some of the results for the performance improvements. Now, keep in mind, this is also on the preview release that was done, these benchmarks were done, I think late last year. So hopefully we'll even get better as we move along in terms of performance and scale. So if you're looking at the blue line, that is Postgres. And if you can't see these little numbers here, this is 512 connections, 768, 1024, 1280, 1536. And then the throughput is on the left-hand side there. So you're looking at a very, very good improvement of at least two times from standard Postgres. Now, if we come across here and look at this particular benchmark where we're looking at write-only throughput, you can see that Postgres peaks at this point here. And then Aurora peaks further along when we got a higher number of connections. And you can see that Postgres is starting to trail off here as we get more connections through. So we're getting three times at the highest throughput and two times from the Postgres peak at the same point. Sustained suspense throughput, about 120K,000 as 120,000 writes through that database per second. And then this is really impressive, right? The start-up time where we're copying the data in and then doing vacuum and index build on Postgres versus the vacuum and index build that happens on Aurora. So we're getting huge improvements in terms of this massive IO operation, right, to do all this, that the Aurora storage engine is really accelerating this activity before we kick in the benchmark here. And this one shows you consistency as well. So we're looking at response time here under heavy load. And you can see that Aurora response time is essentially flat. But with Postgres, we've got this kind of pattern happening. Can anyone think of what that pattern is? Yeah, down back. Exactly. So what's going on here is that you're having to do a massive amount of IO for a checkpoint, which is then stalling a lot of activity. And then it's starting to catch up again. And then it does another checkpoint. So you're getting this consistent pattern of checkpointing across Postgres. But that is not happening with Aurora, essentially because we're doing different types of storage. And the way that you're doing with traditional Postgres is you have to get the blocks and you have to write those out to disk. Here, the storage engine itself is just taking the database changes and building the blocks at the storage tier itself. So the database no longer has to worry about that. They can just keep on going. Also, when you're looking at consistent throughput, you can see here Aurora at the top there with higher throughput and much more consistent versus the range that we're getting on Postgres itself. This is also good information for the community on where to help and try and focus and improve things. Actually, there is a lot of work which has been done in 905 and 906. Simon is here just now. In controlling when the checkpointed purpose are flushed to the disk. So a lot of that has already come in 906. There's a slide on that too. Okay. So we'll get to that. But I think even if you look at the performance difference, the consistency is for enterprise applications is very important, right? So that's something that we need to take into account. So looking at as the database grows, so this one is a 10 gig test and this one is the 100 gig test and you're seeing the difference from Aurora scalability here that as the database gets larger, we're going from 1.5 times to 3 times in terms of performance gain. Now in terms of recovery and this relates to the checkpointing sizes, the configurable sizes here, right? So we don't have checkpoints. So that makes things much, much quicker on the Aurora side of things. The first one here is 2.1 gigs. The next one is 8.3 and then 12.5 gigs of data before a checkpoint keeps in. Now you can see that the rights dramatically increase when you're doing the checkpoints as you would expect and therefore the recovery time also takes a lot longer as you've got more data that needs to be saved as part of that checkpoint process. So really in terms of recovery, you're going to be able to get up and running much quicker. You're going to be able to have more consistent throughput. So just a quick summary by the numbers. If we look at PG bench about 2 times faster. If we look at Sysbench up to 3 times, data loading up to 3 times, a response time about 2 times faster, throughput jitter 3 times more consistent and then 3 ported scale up to 3 times faster and then recovery speed up to 85 times faster. So looking at the performance architecture, now how do we do this, right? It's quite a major improvement to take an existing database and then accelerate that by a factor of 2 to 3 times. So primarily doing less work, you know, anyone who likes to be the lazy mathematician, I certainly was one when I was at school, if you do less work, then you'll be able to get through and do things quicker because you're getting the same results, right? Do fewer IOs, minimize network packets and offload the database engine. Those are the core principles of what we're doing. Also try and do things asynchronously instead of having to rely on doing things synchronously, reduce the latency path and then unlock, sorry, use lock-free data structures and put batch operations together. Now, if we look about the IO, then that's fundamentally what databases or especially relational databases are all about where you want consistency on disk. So that's where we focus all of these efforts. So this is a slide describing what happens in a normal RDS database running Postgres with what we call multi-AZ. So this is with a primary in one data center or AZ and then a slave or secondary in the other. Now, what is happening here is that IBS itself, our storage has replication internally and then we are replicating also from the primary to the secondary or standby another set of those operations. So there's a lot of IO that is going on when we're committing data to disk in a standard RDS environment. When you change this to Aurora, it's very, very different. So here you've got the primary database node, which is sending only the changes that the database is actually made, not the physical blocks, just the little changes, the log entries, and then sending that to the storage engine, which is then updating that across all of those availability zones and then building the blocks as required for when the database is needing to read that back. Now, those read replicas are operating off that same storage. So they don't need to receive the data and then do the writes again. So that's a huge improvement from having the standbys there as well, not needing them to do all the writes again. It also means your read replicas are not having to apply and do any writes from the well. They are essentially 100 percent there for reads, except for some minor data, which is basically cache consistency information about what's going on. So you're really getting massive improvements. Now, in terms of how does this work at the lowest level? So for people who are not so technical, this is about as technical as we're going to get today. So if you look at what's actually happening here, on the left-hand side, you've got the primary database at the top. It's sending a log record of changes in the first step that's going into an update queue. As soon as that is persisted in the update queue, then the database gets acknowledged that write is done. So we're basically having a very quick write and acknowledge back to the database. Then what happens on the third is we're starting to do some sorting on the data here and then doing pair-to-pair communication across the storage nodes. We're also then going into coalescing on the data blocks to build those data blocks and doing some garbage collection, some scrubbing on that, and also bringing that into S3 for backups. So you can see that all of this stuff after point two happens in the background and the database is merrily going along. So it's a huge performance increase in terms of what's required for the database to care about and also by making sure that the storage engine is just having to deal with logs instead of dealing with blocks. So the IO traffic for read replicas here. So you can see again on the traditional Postgres, so this is what I was talking about where you still have a 70% write workload on your database. The read replica still is going to have to do 70% exactly the same of those writes compared to the reads that it's going to have to do because you have to get that data across there somehow. Whereas now what we're sending across in Aurora is that green line up here, the page cache update. So saying, okay, what's changed in the cache? Send that across and update your cache. That gives you a server which is now doing 100% reads and very consistent with low latency of that storage tier below it. So this changes the effectiveness of read replicas quite dramatically. Now this one is really cool. If you have a database server and something goes wrong with it, if you have to shut down and restart your database, then when that database comes up, where's the data? Is it in memory? It's on the disk, right? So as the application starts coming in and the users start connecting, all the data has to be read again off the disk into the buffers. What happens here with Aurora is that we've taken the cache and we put it in a separate process. So if we need to restart the database for whatever reason, whether it's planned maintenance or unplanned maintenance, or there's a failure of some kind and we need to move across to a read replica, then that cache is going to be warm on the read replica or even if it's just a master, that cache is also going to be warm if we're just restarting the database. So you're not going to get the brownouts that traditionally happen where the performance gradually gets better and better as the data starts coming into the database. So this is a very, very good thing for the platform. Now there's something that I've had feedback from customers from the corporate world who are looking at Postgres, and this was mentioned yesterday as well, that the database itself is pretty good. The features in terms of when you look at some of the enhancements that have been made over the years, whether it's now JSON support, you've also got pretty good Postgres support there for spatial data. People look at this and they say, hey, this is coming along pretty well, and then we've got parallel query, seeing good progress here. Where they're not really happy is with the tooling, and there was a comment yesterday about GUIs are not really the core strength of Postgres at the moment. So what we're looking to do here, and this is what we're starting with Postgres, but we're going to be doing this across all of the engines on RDS, is enabling more detailed monitoring and giving you a nice GUI with that as well. So our first step was what we call enhanced monitoring. So traditionally, if you're managing the database and you're looking at it yourself, whether it's on EC2 or on-premise, you can go in and run IO stat, VM stat, you can do top and all that kind of stuff on Linux. Now if you want access to that data on RDS before, it was like a black box, you couldn't really see that. So then we enabled metrics down to one second and plenty of OS data, including process lists and those types of things that you can now see on RDS. So that was step one. And then what we decided, okay, well, that's the operating system level. Now we need to focus on the database tuning side of things. So RDS, of course, is all about managed database. Essentially what we want to be able to do is get to be the best DBA you could ever have. So the customers want easy tools to be able to manage their databases. They may not have deep knowledge about how to do tuning and diagnostics as well. So if we can put that into the product, give it a single pane of glass, then that's what we're aiming to do. So this is an example of a database load from performance insights.  Now this is something that is going to be, as I said, made available on Postgres and the Aurora preview that's available now. And it is actually available in the preview. So if you sign up, you will get access to this today. But it will be coming across the other engines. So here you can look at specific statements and see what statements are doing inside the engine as well. And you can see what contention is happening based on those statements that you're concerned about and drilling down into that as well. So beyond the database load, we're going to be looking at lock detection, execution plans, API access. It's going to be, as I said, included with RDS. You'll get 35 days of data retention. And then by the end of this year, fingers crossed, we should have that across all the engines that we support in RDS. So that's MariaDB, MySQL, Postgres, SQL Server, Oracle, and I'm probably forgetting one. But that's all I can think of right now. So Amazon Aurora, looking at the roadmap. This, you probably can't read that tiny writing at the back. But fundamentally, these are the features that we're looking to launch at GA later this year. Now, the key things if we want to look at security, encryption at rest is supported SSL connections. So in transit, we're securing data and then VPC. So inside Amazon, the virtual private cloud, which means that when you create a database there, it's not accessible publicly. You can only access it through either a VPN or direct connection. On the performance side, we're aiming for two times performance, up to 64 terabytes of data. Over here, we want to have all the Postgres SQL features as per 9.6, all the RDS Postgres SQL extensions, and then all DMS support. So DMS is a database migration service to help you get data into Aurora Postgres Compatible Edition as well as out. Yeah. And you can take that out from AWS back to on-prem, or you can take data on-prem, or you can take it from the clouds into AWS as well. So lots of flexibility there. We want to be able to read replicas, about 15 read replica targets, and then also have instant crash recovery as part of that. So these are kind of the highlighted features that we aim to get at GA. So customer quotes. Here is a company in Australia that I've been working with quite closely, Technology One. So they're the largest ISV for enterprise software in Australia, and they have software across governments, health, and all sorts of different sectors, highly regulated in terms of some of the environments that they operate in, and they've been looking at getting away from commercial databases, moving to an open-source platform. Now, you can see the quote from Ian here, and you can read through all of that, but the key point here is the one I bolded. In our test, we're able to use it with zero changes to our software oil schema, and it's so fast we'll be able to operate at a level of at least 10 times faster than SQL Server Enterprise Edition, and the next bit is the key one, and without 50 pages of license agreements. So this is probably a very unique use case where they're able to get very good performance over and above SQL Server Enterprise Edition, but it shows you what is possible, and these guys are very committed at moving to Postgres and looking at moving their development environments, switching them from SQL Server to Postgres very, very soon. Looking at a case study. So going through a customer, sorry, let's put that in the wrong place. Sorry, the Aurora database family is all the core features around the actual Aurora platform. So secure, high performance and scale, compatible with Postgres or MySQL, convenient because of the managed services that we offer around it, the core RDS features plus the ability to move data in and out with CDC support with the database migration servers, three replicas, automatic failover for availability and durability as I mentioned. Those are like, if you want to take away what we kind of covered, this is the slide that covers off all the core features about Aurora. So this is where the case study was supposed to start. So UberFusion is a partner of ours, and they did a migration of a single sign on system that was on SQL Server to Postgres for a media company. So I can't tell you who the company is because I didn't get the PR clearance, unfortunately, but you can possibly guess. So it's an ASEAN media company. They've got 4.7 million residential customers and roughly about 66% penetration of the TV households. And they've got quite a few channels that they offer on the platform as well. So the number of applications that were integrated was around 37 when they started this migration. So you can imagine the dependencies on the system very high, it's very important, business critical. The current server was that they had before they migrated was on SQL Server 2008 and it was enterprise edition. It wasn't a big system, so about 102 tables, 55 gigs of data, and the server was 128 gigs of RAM. But here I think the last point is the key one, 24 cores. So you can imagine licensing SQL Server enterprise edition on 24 cores and what kind of price that would be. So 2 million or approximately registered single sign-on users at the point when they were doing the migration. And the ideal state is they wanted to be in AWS and operating there to manage the scale that they were looking for specifically around some of the events that were coming up. So what you can see across here is limited scalability. So the on-premise machine was getting limited in terms of the hardware. The costs of the hardware was expensive and of course the software as well. And then the shared infrastructure, the on-premise database infrastructure was shared across a number of different applications. So when there was peaks and troughs and things it was a bit difficult to manage. So they had a new mandate for the single sign-on system and they had urgency because of Euro 2016 and the Olympics which they were delivering that service across their to their TV subscribers and then enabling them to sign in to the single sign-on service to get the entitlements that they're allowed to see because of course you sometimes sign up for some channels, right, not others, and some events, not others. So the migration to the cloud, the decision was made to go with Postgres running on RDS, so the relational database service on Amazon. And this was worked out to be around 11.5 times cheaper than using SQL Server for this platform. So challenges, you can see very small writing over here, but they really had to worry about zero data loss. It had to be transparent to users and then they had a very short migration window to get this across. Lots of legacy data that had to be looked at in cleanse. So these are some of the issues that they faced doing this particular migration. So the solution was with about one hour of time that they could afford for downtime, that they were going to re-architect some of the application to include things like Redis as well, and then the data size they wanted to reduce it to about 1.5 gigabytes. So it gives you an idea of how much data was actually in there in the system that they could have got rid of because of all the legacy processes. So there's a hint for application writers in the room. Make sure you know what you're doing with your data and how to get rid of legacy data to make sure that databases can be smaller. So they used tools to get the data out, and they leveraged things like Kibana with Elasticsearch so that they could take some of the logging and move that out of the system into more modern tools. And then they were leveraging their .NET application, and then they were doing that with Redis as well as Postgres to accelerate the outcome in terms of performance. So the steps. So on SQL Server, first of all, they disabled the SQL sign-on service. They ran a script to export the data and flat files according to a new schema because not only were they doing a migration of the existing application, they were also making some changes to the schema. So they had to make sure that they tested this very thoroughly so there was no data loss as they were changing the data formats. Then remove the headers from the exported files, input that into Postgres, and test and verify the integrity of the data, and then go live on the Postgres system. Now, obviously they tested that many times to make sure that once it was doing the live migration, it was all good. So benefits. And this is really quite compelling. So I have to call these out because you can't read them. Page load times improved 50% in terms of, you know, looking at the page loads and what was happening. They've had 500 sessions, 500,000 sessions in June, and the transactions per second increased four times over what they were doing before. Now, that was probably attributed to using caching in front of it rather than relying on the database itself to carry out the load. So that was a huge and important thing that they got out of that. And not only that, I think the biggest one is obviously the cost that they were able to get the massive cost reductions. And then on the infrastructure, they're now able to quickly provision environments once they go to AWS for development or copying their production environments, doing testing and so forth, taking from what they had to do previously, which is more than five days on premise to less than one day on AWS. So that's a business agility thing that they got by moving to AWS as well. So question times. Just before we get to that, the timeline, the preview is active now. You can sign up for it at that particular link. And it now has the read replicas and performance insights available. And that's just within the last few days. So it shows you that we're making progress towards general availability. So if you want to get on there and start testing this, you can. And then there's some facts in terms of how all of the questions and things that you would like to get answered are available on our webpage. Going to be from Michael.


It's the kick of destiny. You're kicking a field goal, my friend, live during the Super Bowl somehow with $10 million in fan-to-ball free bets on the line. Now, I flagged this like a month ago because TMZ caught you practicing kicks and I'm like, what's going on here? So when you're training, what would you say your make percentages? First off, I'm kind of a toe kicker. I actually made a field goal in high school and it was with the toe. I was the guy that kicked off at my high school as well but I always use my toe. But I don't want to use my toe. I want to learn the correct way. It's I feel like when you do it the soccer style, when you use side of your foot, your accuracy percentage just goes way up. My percentage sideways is probably about like 20% right now but I got a big practice day tomorrow and I got another big practice day over the weekend. I want to hit this kick for America because I'm for America and I'm going to do it for America. $10 million of free fats. Let's go.


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Alpha 61 Tango Echo. Alpha 61 Tango Echo, HB9 in the Alpha Juliet calling. HB9 in the Alpha Juliet calling. This is 4U1 ITU calling operated by the ITUSG. My name is Doreen KD2JTX and it is with immense pleasure today that I make the first QSO on QO-100 from the amateur radio station of ITU. 4U1 ITU. This is Alpha 71 Alpha. Good day. I copy you very well. HB9.


OK. Thank you very much for attending this session.OK. My name is Shohei Matsura.I am a senior software engineer at L.Y. Corporation.The company was known as Yafu Japan Corporation before until this September.But the company has changed its name due to M&A with Align Corporation,a popular messaging service company in East Asia.Today I'm going to talk about database parameter or tuning through CI-CD pipelines.But before doing so, please allow me to introduce myself a little bit more.Again, my name is Shohei.And I also have another name Dominic.That sounds like French.This is because I was given this name by the Canadian Immigration OfficeAnd I was studying abroad in Canada in Montreal where people speak French.So I do have a name that sounds like French.It's unique, right?So you can call me Shohei or Dominic either way works.Going back to the talk.I'm at L.Y. Corporation.I'm in charge of R&D in database system.Where I develop in-house storage engine for my SQL.And I do research and implementation of new tech trends for RDBs.And prior to joining L.Y. and Yafu Japan Corporation,I was a developer of commercial RDBMS.And where I develop like query optimizer and concurrency controland disaster recovery feature and etc.So I do love database technology a lot.Now let me illustrate what we are going to talk about today.Data-based parameter tuning is very hard, right?Because we need to consider many factors at the same time.For example, we need to consider application workloadand hardware resources we have.And we also need to decide what parameter to tuneto a fat value to meet the SLA.So to make the things easier, we use machine learning.We integrate machine learning models into a CICD systemto automate the tuning process.This is when application developers put their codeinto a CICD system, we let the CICD systemto analyze application workloadand infer optimal parameter settingwith machine learning modelsand apply the inference resultto the target database automatically.That's the topic we are talking about today.So here is the agenda of the talk.First, let me illustrate the data infrastructureat L.Y. Corporationand explain why we are motivated by this research.Second, let me further explainwhy we need to combine database parameter tuningwith machine learning and CICD.Third, let me demonstrate our system CICD tunethat executes the automatic tuning with machine learningin the CICD pipeline.At last, I would like to conclude the sessionby sharing with you the future direction of our research.Now let's begin.Now let me explain L.Y. Corporationand its data infrastructure.L.Y. Corporation has a visionto create an amazing live platform to our usersand the company has more than 28,000 employees in total.One of the unique characteristics of the companyis that it has diverse service portfolioto fulfill people's everyday needs.So we deliver more than 100 servicesranging from media service to commerce,entertainment, lifestyle, and online services.And to deliver our diverse internet servicesto our users, we build and operateour own data centersand they are located multiple regionsacross Japan.To make sure they are always keep runningeven in the case of natural disasteror a power outage of a certain region.We also, in addition,virtualize our infrastructureby using open source software stacksuch as open stack and Kubernetesto use our resources efficiently.And here is a brief diagramover data infrastructureand it is made redundantand reliable with OSS software stack.And in our data infrastructuremy SQL plays an important roleand their nodes are spreadacross multiple regionsand availability zonesto make sure one node is always runningeven in the case of natural disaster.And data are always synchronizedacross region and availability zoneby data replication.Another characteristicof our data infrastructureis that it is providedas a form of service or a device.And it is made easyfor application developer to useand the application developercan launch a MySQL clusterby specifying our flavorfrom our web UIin a self-service manner.Now let me explainwhy we need to combinedb parameter tuningwith machine learningfirst before combining itwith CI CD.First, as I said beforedb parameter tuning is hardbecause we need to consider many factorsat the same time.But it is even harder in D-Bus.This is because in D-BusDb Instancerlaunched by the application developeron their zone on their ownwithout having the involvementof the DBAwho maintain the D-Bus environment.So for the DBAthere is no chance to knowwhat application is doingand they have no ideawhat to tunedb parameterbecause they don't knowthe application workload.In contrastin old daysbefore D-Bus appearsDBA wereoften asked to helpapplication developerin schema designand query designand hardware capacity planningso they knewwhat the application was doingso they hadthe database tuningwas a little bit easierthan D-Bus.So to facilitatedb parameter tuningeasier in D-Busdb researchercame up with the ideato autonomously tunedb parametersaccording toapplication workloadwith the aid ofmachine learning.And there are many academicresearchesthat are going onand many outputspublished.Here is a brief descriptionof theD-Bus parameter tuningwith machine learningand its flow.In D-Bus parameter tuningwith machine learningwe deploy agentin D-Bus serverin the production environmentto collect metricsfrom there.And these collected metricsare then sent tothe old tuning systemthat is separate fromthe database servers.And the collectedin the old tuning systemthere are two machinelearning models running.The first model is calledfeature extractionand this model is usedto extractworkload featuresfrom the collected inputmetric.And these extractedworkload featuresare then fed intothe second machine learning modelcalled automatic tuningmachine learning modelto inferoptimal databaseparameter settingwith respectto the applicationworkload features.And automatic tuning systemthen automaticallydeploy the inference resultto the production environment.And this whole processkeeps running foreverand to catch anyworkload changein the database serverin the production environment.And this processautomated the databaseparameter tuningbut there is an issuewith this approach.That is the accuracyis not 100%.Inference is justinferenceand sothe inference resultcould be wrong.So if the inference resultis wrongit could causeand if itdeployed to the productionenvironmentit could causeexpected performancedegradationin the production environment.On the right sideof this slidewe evaluatedmachine learningbased automatictuning modelagainstour e-commerceworkloadup to y.The x-axisis just time.The y-axisis query latencyin millisecond.The dotted lineis a query latencywith the defaultparameter setting.And the solidblackundotted linea query latencywith autotunedparameter applied.And if you lookat this graphyou can seethe query latencyhas worsenedmore than 10 timesdue towrongautotuning resultapplied there.This is a seriousissue, right?Because we haveperformance degradationin the productionenvironment.So onto copethis issuewe have came upwith the ideato autunedb parameterswith machine learningbutwe makeits risk assessmentin the CICD pipelinebefore deploying themto the production environment.We integratemachine learning modelsinto CICD systemand correct metricsfrom the db serverin the developmentwhile testsand application buildare being runin a CICD pipeline.And at the end of theCICDapplication developerscan checkwhether SLAor performance requirementis metwithout tuningresultor not.And they can nowdecidewhether to deploythe inference resultto the production environment.With this approachwe can avoidunexpectedperformance degradationcaused bywrong automatictuning resultapplied tothe production environment.Nowlet medemonstrate ourproposal systemCICD tuneby whichexecuteML-based automaticdata-basedparameter tuningin CICD pipeline.It's very busy slideand it's probablyhard to see.But here isthe overall architectureof the system.As the CICD systemwe useopen-sourceCICD systemfor the screw driverand it is connectedtomachine learning model repositorywhich hoststwo machine learning modelsfeature extractionand automatic tuning models.And these two machine learning modelsaretrained offlinein a separate environmentfrom CICDby usingdata-basedparametersand metricscollected frommanydata-based servers.When application developerpushdata-based application codeto CICD systemor screw driverand the screw driverpulls to machine learning modelfrom MLmachine learning modelrepoitryand start learningapplication buildand test.While executingapplication buildand testmetrics arecollectedfrom thedata-based serverin thedevelopmentand these arefed intomachine learning modelsin CICD systemto inferoptimaldata-basedparameterssettingwith respectto theapplication.finallydb-o-tunedparameterssettingis outputand shownto thereturnto theapplicationdeveloperand nowthey canmakethe determinationwhetherto deploythe automatictuningresultto thecorrespondingdbserverin theproductionenvironment.And thisis theoverallarchitectureof ourproposedsystem.Nowlet's seehowactuallyCICDtuneworks.I hopethevideoworkswell.In thedemonstrationwe tryto buildasimplewebapplicationtoexecutesysbenchworkloadagainstmyseql.And wemakethedefinitionofaCICDjobtobuildthiswebapplicationand wealsospecspecifywhichAPIto testnearCICDpipelineandapplicationdeveloperpushthisdefinitiontogetherwithapplicationcodetokicktheCICdjobandyou canseethewholeprocessthatnowlearning.And ifyou clickthenodedbparameterinferenceyou canseeactuallyhowdatabaseortuninginferenceisbeingdonebyusingmachinecalsotheo-tunedparametersettingin acsvformatandifyouclickthenodedbriskassessmentnodeifyougotothebottomyoucanseethequerylatencieswitho-tunedparametersandthedefaultsettingandinthiscasesincethequerylatencywitho-tunedparameterislowerthanthedefaultsettingand the application developer can rely on this message to deploy the O-tuned result to the production environment.Let me just briefly describe two machine learning models used in the demo.The automatic tuning consists of two layers.The model number one is called Workload Embeddlers.This model is used to extract workload features.And the second model and the output of the first model is used as an input of the second model.And the second model is called TPS Estimator.And these models infer db parameters to either maximize TPS or minimize query latency for the given workload.At last, I'd like to share with you the future direction of the risk of our research.One direction is to do more POC with more diverse workload.In the demo, we have just tried to O-tune a very simple web application workload, namely sysbench.But at LY, we have more than 100 services.And we would like to validate the usefulness of our proposal system against this diverse workload.This challenge is more fundamentalIn our proposal system, that is to absorb the differences between the environment and the product environment.In the CI CD, we usually use the environment, right?However, there are always differences between the environment and the product environment,such as hardware specs on the side of data and many others.So, we need some way to transfer db parameter inference result obtained in the environment to the product environmentin deploying them to the production environment.Because production environment has larger data or larger machine.To tackle this problem, we are now collaborating with academia to predict db performance of a production environmentwith a given parameter setting from db performance in the development environment.And by using this prediction model, we can determine if an O-tuning result can meet the performance requirement in the product environment.And the research is going on right now.And before closing my presentation, I'd like to express my gratitude to the following people.First, Mr. Nakamori and Dr. Kawashima from Keio University for their promotion collaboration of this research.And I'd like to also express my gratitude Mr. Hoshino from Cyborg Lab for his support and comment on this research.Also, I'd like to express my special thanks to Mr. Kino Shita from Keio University for his effort on building machine learning models to O2 database parameters.At last, I'd like to express my gratitude to all our colleagues in L.Y. Corporation for their support on this research without their support and this research cannot be done.So, thank you very much.I'd like to finish my presentation.And I think a few minutes is left, so I can take a few questions.And thank you very much for attending this session.But as you know, I'm not fluent in English.So, please speak slowly or you can ask me questions in French.Okay, I don't speak French, so we'll have to stick to English.How well do the results from the development environment translate to a production environment?Well, yeah, I know some, but I cannot tell you right now because I am going to publish the result in academia soon.Okay, so we'll wait for that.Thank you.I have one more question.Hello, Miguel Martinez.My understanding is that the scope of this research is database engine parameters, right?Have you considered a structural schema impact against those parameters on your research?Yeah, that's a very good question.And I have yet thought of that kind of questions.Thank you.We need to catch the schema change as well.So, the same thing, like when you have a multi-tenant shed environment, are there different results?When you run the same models and how do you translate them to specific tenants?Have you thought about that?Yeah, in the database environment, we have usually a multi-tenancy, right?So, that's a very important question, but I don't know the answer to your question, so I'm sorry.Similar to other people, have you considered moving this to things such as consuming query logs to provide app developers with suggestions on how to improve their queries or giving them feedback on common deadlocks in ways that they could improve the application on that side?Yes.So, similarly, we have tested like consuming query logs and like slow query logs and other operating system logs to improve the machine learning models, but we are still working on that, so yeah, sorry.Yeah, I will keep you updated probably in the next opportunity.Thank you very much.


Today I'm going to discuss the topic called correlation of signals. Let us start with the learning outcomes first. At the end of this session, students will be able to identify similar activity between two signals. Find the auto correlation and cross correlation of the signals. These are the some of the contents. Start with the definition of correlation and its application types, implementation of auto and cross correlation and references. Let us start with the definition. Correlation is defined as a similarity between a pair of signals. Basically, it is used to compare two signals. The application of correlation is in radar system where the location of the target is measured by comparing the transmitted and reflected signals. You transmit a signal from the transmitter. It reaches to receiver and it hits back the signal and those two will be compared. Then you can identify the distance between the transmitter and receiver. This is one of the important application of this correlation. There are two types of correlation. One is auto correlation and second one is cross correlation. Let us discuss auto correlation first. Auto correlation of a sequence is correlation with itself. The auto correlation of sequence x of n is given by rho xx of l is equal to summation n equal to minus infinity infinity x of n x of n minus l. In this auto correlation, you are going to get only one sequence and you need to take the time reversal of that one. That means, you need to take the flipped version of the sequence x of n that is called the x of n minus l. Let us take an example to identify or find the correlation of signal x of n is equal to 1 2 3 4. We need to write the x of n as well as a x of n minus l in the diagram shown. So, x of n will be written in the first row and x of n minus will be written in the column in time reversal format. Then first element in the column will be multiplied with first element of the row and that is 4 into 1 is 4. Similarly, first element in the column will be multiplied with second element in the row and 4 into 2 is 8. Similarly, first element in the column will be multiplied with third element in the row 4 into 3 is 12. Similarly, 4 into 4 will be 16. This will be continued for element present in the column that is second element 3 will be multiplied with all the elements in the present in the row. Similarly, third element in the column will be multiplied with all the elements in the row we are going to write 2 4 6 8. Similarly, 1 that is last element in the column will be multiplied with all elements in the row that is 1 2 3 4. Then combine those things and sum it up as shown in the diagram. The first still single element we are going to get that is 4 second 8 and 3 will be combined 8 plus 3 is 11. Similarly, third group will be 12 plus 6 18 plus 2 that is 20 and next 30 and 20 11 and 4. So, this will continue. Once if you see the result R excess of L then 30 will be the central element or middle element. Then at 30 we are going to get the symmetrical sequence or symmetrical to the center of element of this sequence. Then now let us move with the cross correlation. The cross correlation of a signal and its impulse response is given by rho x y is equal to summation n equal to minus infinity to infinity x of n y of n minus l where l is equal to 0 comma plus or minus 1 comma plus or minus 2 etcetera. The computation of correlation takes the following steps or we need to follow these different steps to find the cross correlation of the sequence. First one is obtain the sequence y of n minus l that is nothing but the second sequence and its time reversal by shifting the sequence right by the time lag l. Multiply the second will be multiply the shifted sequence y of n minus l by x n and sum all the values to obtain rho x y of l. Third one repeats steps one and two for all values of the lag one. Let us see by taking one example how we are going to follow and find the cross correlation of the sequence. The question arises whether the procedure for finding the cross correlation is same as auto correlation? Fast for a moment and think whether the procedure which is used in the auto correlation is same as the cross correlation. The answer is yes. Let us consider an example to find the cross correlation of the sequence. Given sequences are x of n is equal to 1 2 1 1 and y of n is equal to 1 1 2 1. The shifted sequence y of n is y of n minus l that is 1 2 1 1 that is 1 1 2 1 will be time reversal we can get 1 2 1 1. Same procedure as that of auto correlation write the x of n in the first row and y of n minus 1 second sequence and its time reversal will be written in the column. Then multiply the first element in the column with all the elements present in the row and write as 1 2 1 1. Similarly second element in the column will be multiplied with the all the elements in the row that is 2 4 2 2. The third element in the column will be multiplied with all the elements present in the row at 1 2 1 1. Similarly last element in the column will be multiplied with all the elements in the row 1 2 1 1. Again group as shown in the diagram and submit we are going to get the cross correlation of the sequence. First element is 1 second element is 2 plus 2 4 third element will be 4 plus 2 6 fourth element is again 6 fifth element will be 1 plus on 2 and last will be 1. The cross correlation of the sequence is 1 4 6 6 5 2 1. This is not symmetrical to the element present in the center of this sequence. Let us take one more example to find the cross correlation of the sequence. But interesting thing is when I take the same sequence of x of n and y of n then you are going to get the result as same as the auto correlation sequence. Let us see here x of n is equal to 1 2 3 4 and y of n is equal to 1 2 3 4. The shifted sequence of y of n is y of n minus l which is 4 3 2 1. Then write the first sequence in the first row and time shifted sequence y of n minus will be written the first column. Then multiply the first element present in the column will be with all the elements in the row you write 4 8 12 16 second element will be multiplied with the all the elements present in the row and write 3 6 9 12 and third element will be multiplied with all the elements present in the row so on. Then group those elements and sum it up first will be 4 and second will be 8 plus 3 11 12 plus 6 18 plus 2 20 third will be 30 fourth will be 12 plus 6 18 plus 2 20 fifth will be 11 and last one will be 4. When you look for this answer for cross correlation of two sequences then interesting thing is it is symmetrical to the central element present in the output sequence that is 30 with respect to 30 you can see that is this is a symmetrical sequence. These are the some of the references I used thank you.