Welcome back. So, we will continue with the mean field theory of the Ising model and as I said repeatedly in the mean field theory you have the dimension enters to the coordination number gamma and which is by and large generally is a good, good, good approximation there is no, no, no, no problem with that. So, the thing that we have already done is that the decomposition which is this construction of this table that this construction of this thing that you already read the up spins is a plus and whenever I draw a line from one up spins only from the up spins to all the nearest neighbors. So, in a two up spins next to each other then there are two lines and that becomes two n plus plus and those kind of things and for example, this is the, this is the condition that we derived that gamma n plus equal to 2 n plus plus plus n plus minus and these are the kind of relations that we just did at length. So, these are the things again. So, this is the book from Carson Huang at which is the best this from Carson Huang and this is the best description of Ising model of the Klein mean field theory Bragg Williams approximation that I am talking here is done this in the, in this, in this book of Systeria Mechanics. So, then one important thing in this process going through that. So, this was the kind of things that we have been discussing all this time that the writing down the Hamiltonian in terms of this what exactly I was telling before I do the mean field approximation. And this is the partition function this is fairly universal notation we find every word in the same every book we have this notation. So, that is a equivalence that means this we feel the Ising model and mean field theories are fairly well documented and well articulated because this is the kind of so important that we have to be more or less in unison. Now I just told you that this before I go to do the next step there is to show the importance of Ising model that this is applied to so many different things one of them is the lattice gas. In the lattice gas you say okay I have a lattice where I have a if there is a gas molecule then there is not if there is. So, I want in a gas where a lot of empty spaces and I want to model that by saying okay if there is a particle then there is a black and there is no particle that is empty. So, that simple thing essentially is again n plus when there is a n plus goes over to your occupied n minus is gas particles but that is nothing there and the two next to each other is n plus minus if two occupied next to each other n plus plus. So, you can now map the Ising model into lattice gas and you can do wonderful stuff. So, then again that as I said here total number of lattice sites total number of lattice sites then these total number of atoms are occupied. So, in this lattice when they are occupied they are occupied that is n plus plus when they are empty that is n minus and total number of mirrors never pairs is n plus plus. So, exactly that thing when you map that you can now get the and so if you say when they are next to each other there is an attraction and that attraction is epsilon then that gives you that. So, it is like two parallel spins of ferromagnetic interaction and then I can write the partition function I can write the partition function like that and then I can now get the exactly decompose into this only difference is that the beta H term here is given by this term. But again I have to evaluate the same thing the n plus n plus plus and e to the power beta H n plus n plus plus that is this thing. So, I am done. So, one then number of ways of distributing atoms n plus atoms n plus atoms into the n n number of lattice sites now something extremely important comes out of that then if my number of atoms occupied atoms my black dots or my here white dots if that is equal to n plus then I know n plus and minus n minus gives me the magnetization and where does n plus gives me here n plus gives me density. So, then I immediately have a wonderful isomorphism which is goes a long way that magnetization is equivalent to density that exactly turned out to be that is the way we can transform one equation state of magnetic system into equation state for the gas system this is a far reaching consequence. So, now we now just discuss that how lattice gas model can be mapped into Ising model we will next discuss one more interesting thing and that is these are all from Carson Huang now the binary alloy, binary alloy lead brass beta brass and that is zinc and copper and then I am now occupied my lattice sites are occupied either by zinc atom I can now consider order disorder transition that takes place at 742 Kelvin in this case that you can immediately see that my up spin could be copper my down spins are zinc or other way around and I can again write down a Hamiltonian. So, sure enough I write down a Hamiltonian exactly same way I now say okay my copper copper is energy interaction energy epsilon 1 sorry epsilon 1 my copper and zinc zinc this is thing and if I want copper zinc then like that. So, this kind of binary, binary mixture we are talking binary alloy that like copper and copper like each other and zinc and zinc like each other low temperature of course here there is a more long interactions because of the metallic city and all those things but at a level high temperature when you are talking order disorder transition those things but you have to say copper and copper like each other and zinc and zinc like each other very much like a binary mixture that we do and we will talk little bit about that later. So, now I exactly have the same conservation rules that means I start with the 5 here these 3 numbers N11 N22 and N12 and then I have the conditions just I have done before exactly same that up spin up spin now copper and copper down spin down spin zinc and zinc and copper and zinc N12 and copper plus zinc is total number of lattice sites and then I eliminate and I now get my energy or Hamiltonian I get energy Hamiltonian in terms of there N plus plus N plus minus I get N1 N1 that means N1 is number of lattice sites occupied by copper or zinc whatever and N11 when copper and copper and zinc are the same as neighbors. So, these things these things are exactly same what I have reduced to exactly same that my isent model. So, now I showed two cases gas liquid transition and binary alloy which are completely isomorphic and that in one shot explained beautiful many things for example if I do binary mixture I give you just one example before I pass on against density then this is what gas liquid transition and these the critical temperature you see. Now, if I now do that with a mole fraction x of one species is exactly same graph these exactly same when I plot magnetization against temperature because I already told you density is like magnetization. So, in one shot in one formulation you are getting three very fastly different phenomena which is the magnetization gas liquid transition and this order transition and phase separation in binary alloys this is just amazing that is why one takes the isent model so seriously both in statics equilibrium properties and our language of this transition is completely dependent on isent model this is the most important system of statistical in the statistical mechanics ok we can let us continue and these I was saying this is the magnetic transition magnetic transition happening magnetization against temperature this is this is same as this is same as I can instead of that I can plot it rho versus t and exactly same thing. So, this is the classic critical point when a magnetic system. So, order this order transition mole fraction all these things are the same this is one of the most huge huge success of of the isent model. So, now we go ahead and we complete our task of the of the mean field theory and that will be now little bit in. So, the way we do now is this is order this order transition that done because I do not need to do that, but what instead I will do what I was continuing with ok. So, now the mean field theory is same the way I am the the level I am doing is is bag Williams approximation and and this is thing and I need to make it little bit better. Now, so these are introduced long range order parameter and short range order parameter l sigma n plus 1 and these are the same thing and now I make the approximation that short range order parameter is is is given by long range order parameter. So, the approximation is contained in the statement that there is no long short range order apart from the long range order that means approximation states that statement that no short range order other than long range order and once I do that I do this then I get n when I make n plus by half gamma n equal to n plus squares that translates into sigma because this is nothing but the l plus you know n plus by n equal to half l plus 1 square remember that half l plus 1 sorry and when I use that into here I get sigma this condition that sigma when I use this into I in this into that then I get this condition sigma l plus 1. Now, that actually essentially means that I have the Hamiltonian now completely in Karshan Wang h is my b. So, my Hamiltonian is then 1 over n what is energy 1 over h is half minus half gamma j l square minus b l. So, just make a note that his h is my b and his gamma my gamma but his epsilon is my j this notation I am using a little bit more modern like we use j for rising model not epsilon as Karshan Wang has used and we do we do like to use Hamiltonian h but not e as I use Hamiltonian for h that deprives me of h for magnetic field. So, I use b but b equal to h mu. So, this there is no room for any confusion here once I do that then I write down now I am in a completely free domain now I know how to go I know the how many ways I can get n plus up spins in n and that of course is n factorial by n plus factorial n minus n factorial. So, that is I have written here and n plus is half n 1 plus l the other is n all minus l. So, I can write n factorial by n plus factorial n minus factorial then that is the way or factorial 2 n plus. So, that is the way I have written here that is I have written here this is the number of ways to distribute and for a given configuration with l because I started with n plus and n plus plus that was the exact then I I made the approximation in field approximation and as a result of mean field approximation I have on the n plus then I go back I already have l and sigma and when I eliminate n plus plus I eliminate sigma I have only l but it is a consistent way in a consistent way. So, I have the Hamiltonian and then I know how to so I put the Hamiltonian here and lo and behold I have fully now I can evaluate that. So, the in the large particle limit n going to infinity I take the logarithm to get the free energy and then I make the approximation I make the starlings approximation thus to you what we done in Mayer's theory we have done in the canonical partition function again and again we have picked up the maximum term. So, of the sum I pick up the maximum term. So, I this remove that one I take the log whatever even and then I maximize with respect to l and maximizing with respect to l is same as maximizing with respect to n plus. So, remember if you know already you have not noticed note that l is nothing but the essentially the magnetization and so things are well and good. So, then we can go and once we do that then the thing that happens is that I get an equation which is the following that I get an equation by doing that which is let me know because it is not very visible here l equal to tan hyperbolic. So, that is the so this is called a implicit solution or a transcendental equation where I have a l on the left hand side I have a l on the right hand side and given a b I have to solve that numerically. Now if b is 0 the external magnetic field is 0 I have this beautiful solution which is this one that means l equal to tan hyperbolic in many places dimension k is introduced which is j by k B T then I l equal to tan hyperbolic a gamma l low and behold this has a beautiful phase transition scenario that when we so and this is given here this phase transition scenario is described beautifully here that when this has only solution of this equation is only solution of this equation is only solution is that is at a temperature is high. So, gamma j by k B T is less than one then all even less than one only solution is l average which is the maximum term actually I prefer it l star but that ok l average that only solution is the magnetization 0. So, this is a random system number of plus spins up spins is same as number of down spins. However, a solution appears when you get a solution appears when you go to lower temperature. So, when you go to lower temperature that means gamma j by k B T is greater than 1 temperature is small. So, this becomes greater than 1 then a solution appears again 2 solution appears one of them up for up spins and that is for down spins. And you get the one can show that this root that 0 must be it corresponds to a minimum not to a maximum. So, you can neglect reject that term. So, then the solution reduces to the following things that l these 2 solutions to T greater than T C you have no magnetization disorder system below T C it can be either plus or minus it has to be 2 you cannot in the absence of magnetic field they are both are equally likely there is no way to choose one of the other the way the solution is made by doing a graphical method which is made which is shown here you the graphical solution is done in the following way that you plot l and tan hyperbolic l and then you when they become equal to 1 then that is the solution and the graph goes like this it crosses here at 1 and crosses here at 1. So, these are 2 solutions that you get and it is a let me see if I can get it a little bit up there should be something here a cursor. So, this is the this line is the going selecting you when that become equal to tan hyperbolic and l. So, this is the l versus l this line l line this the straight line and this is the tan hyperbolic you know the tan hyperbolic very well because tan hyperbolic are like this and these are the solutions when they meet that is the solution of that and that is l naught plus and minus solutions. So, this is part of these things works out beautifully. So, next go to the next page. So, one beautiful thing of that is the following of all these calculations is the following that in general can be it can be obtained numerically and one finds numerically some very interesting thing comes out that near the critical temperature when you are close to Tc close to Tc is a subject of great great interest because what is called the critical phenomena and we have done Landau theory of the critical phenomena somewhat. Then you find that numerically that these approximation then l naught behaves is like that when you are away from the critical point but when you are very in a region close to critical point then the order parameter or magnetization behaves as magnetization behaves as Tc minus T to the power half this is that means the magnetization varies sharply as a fractional exponent and this exponent is called in a critical phenomena magnetic is called exponent beta one is sorry to use the same beta many different ways this is the equation of state also this is the same way rho varies as again Tc minus T also the notation beta in mean field theory Landau theory Bragg Williams theory all these cases this exponent beta is half which experiment will be found to be wrong experiment you want found yes there is a fractional exponent and the basic thing is correct that this is very singular behavior it shows near the critical phenomena but this exponent is more like one third. So, this is called equation of state exponent and as I told you magnetization same as density in order disorder transition again the fact probability of one being occupied by copper orientation or you can say composition you know one minus other density of a minus density of the copper minus density of the zinc is exactly all now it need not be a strictly one third it varies from 0.36 to 0.31 and all these things but essentially very very similar. So, this is the essence of the critical phenomena and the critical exponents and the critical experiments in many different forms but this magnetization and density is specifically it also has a critical behavior that we will be discussing it will be here. So, now here the thermodynamic functions are summarized for this. So, this is the magnetization and so this is the free energy and then you get the as I said magnetization this is the internal energy and this is the specific heat that goes like that and the df's in the following fashion it in this case it does not. So, specific in the Bragg Williams approximation that does not diverge but it increases like that. So, this gamma j by increase the I increase the this is my gamma j as I increase the as I increase the temperature KBT and it goes and saturates to a value like that it increases like that. And then similarly one can go to that is gas this property comes from the this solution comes from this solution here this thing you can figure it out. So, that we just described in previous page similar behavior you get in the lattice gas very similar equations that table of correspondence is given and this is just exactly the same one model maps into the other model predictions are exactly the same will be binary alloy. So, now this is what it is this beautiful thing given here where I have been referring to all this time that this is the gas liquid all of you know van der Waals that pressure versus volume pressure versus volume all you know this isotherm and this is the critical point and this is the inverse inverse parabola that I was drawing as density versus density versus temperature in 5 plot against temperature I get that the top is the critical point and the lattice gas equation of state in lattice gas and same in the this is the mean field theory or simplest type of main field theory that we have approximation not too simple as we have seen. But it captures some aspects of the critical phenomenon but does not aspect the full aspect of critical phenomenon which requires far more work and the same thing you get from van der Waals the same thing you get from Landau theory and you need to do lot more work to go beyond this level of approximation. So, next one level one does better with considerable more work is the is the Bethe approximation but we are not going to do that. So, we will we will stop here as per mean field theory goes. So, what did we achieve we got a beautiful equation in average or in the in the we also got a equation similar equation in the presence of the magnetic field it is a little bit more complex not too much and both has to be solved numerically but in some cases as you showed that one can be after doing the numerical work there are some radical work one can do and one get the mitigation as a function of temperature in a critical exponent beta equal to half which is we call mean field exponent. And and that does not do a good job but what is the beauty of the whole thing is that it does describe the phase transition it does capture many many aspects of phase transition of three systems which is magnetic gas liquid then binary alloy order disorder transition in binary alloy all the three. So, it shows that these three systems to certain level is isomorphic and this was further in a beautiful paper that I recommend people to study by young and the 1952 in physical review he further post this analogy between icing and Latin gas and binary alloy actually some of these things follows what we have done follows from this classic paper young and the then you know these two gentlemen got the Nobel Prize for work their work on parity but in 1952 they wrote two physical review papers one after another where they mapped the Mayer's theories. So, they connected the icing model with Mayer's theory and they showed that the cluster integrals that we do in Mayer's theory are can be expressed in terms of zeros of the again canonical partition function and huge number of results were done together and all these views of icing model binary alloy all these things were put together in an epop making paper of in these two papers and you know what level mean field theory working what level not working. So, that along with Mayer's theory and icing model youngly to those two papers played essentially the starting point or say launching point at at sometimes obtain told to our study of the phase transitions and critical point. So, we might come return later to the critical phenomena and a little bit more we already have done the Landau theory and we have done the concept of order parameter and you see here already that in the free energy then ultimately described in terms of a L square term and L is the magnetization and Landau theory order parameter is the magnetization. So, Landau theories expansion of free energy in terms of L square L4 is fully consistent with the Bragg Williams or other way around though Bragg Williams has a microscopic basis it starts with a Hamiltonian but Landau's just writes down the free energy expansion. So, it is quite satisfying to see that the Landau theory is recovered in a more consistent theoretical framework and so the concept of order parameter that we introduced in Landau theory which is same as a long range order parameter L which is same as the magnetization which is same as the density which is same as the mole fraction. So, Bragg Williams approximation gives us a beautiful understanding of the free energy that is happening the flattening of the free energy surface all these things comes out of the Bragg Williams. What is phenomenologically assumed largely on symmetry arguments and the basic physics by Landau. So, we will stop here now we will probably get to use Leon Young again and we get to use Ising model again in future course. We stop here now thank you.


So Jack, welcome to Bratford City, what do you know about the club and how pleased are you to now be a member of Bratford City? Yeah, thank you. I know a fair bit, obviously I know it's a massive club, played here a couple of times we've seen how good the fan base is and how nice the stadium is so yeah, I know a fair bit so I'm pleased to join. You must be pleased to come in when there's still a couple of weeks before the start of the season so you can bed in with the lads and really be ready for that first game against Shrewsbury? Yeah, exactly, yeah it's important to get in as early as possible, get used to the lads, get used to the way everyone plays and it's good for me to come in at this point and get a good couple of weeks in before the first game of the season. So how's it going for you personally so far this pre-season? I mean how long ago did you know that you'd be going out on loan and what have you been doing to get yourself ready? Yeah well I knew pretty much straight away to be honest that I wouldn't be staying at Huddersfield so it was just about where was the right fit so yeah two or a couple of weeks to get it sorted but I'm glad to be here now but yeah in terms of my training I've been training every day, I feel fully fit and I'm ready to go so excited to get started. You've had a couple of loan spells at this level before, what do you know about the league? Yeah I've played a fair few times in this league so I know what to expect, I know what the stand is like and I know I can be successful in the league so yeah I'm excited. And your last appearance here was in December by the parade, what was it like playing on there, our old pitch in that time of the year? Yeah the pitch wasn't the best to be honest but the main thing that stuck out to me was the atmosphere, the fans were really good but obviously I've had a look at the pitch and it looks lovely and it looks like it's going to stay that way so now I'm excited. I think that'll probably suit your game, how would you describe yourself as a player for those who might not have seen you before? Yeah well I'm attacking midfielder, I like to get on the board as much as possible, I like to create, I like to score but yeah I work my hardest as well which is obviously most important, I work hard for the team but yeah I call myself a creative player. You've been a member of promotion winning squads before, very successful teams, what do you think it takes to get over the line in the league? Well I think it's important for the squads to have a lot of unity, to have a good bond, everyone gets on with each other and play a good type of football and just a winning mentality but looking at the squad we have there's no reason why that won't happen. So first preseason family for you, will you be tomorrow at HFC of the United East, are you fit and ready to go? Yeah I'm fit and ready to go, it's a good one to get myself involved with straight away so yeah I look forward to that, it should be a good occasion. In terms of longer term goals, do you have set yourself any expectations for the season ahead? I don't like to set too far ahead, I like to take it game by game but obviously the obvious target is to be involved as much as possible, to be involved with goals, assist and just to enjoy my football and hopefully the team is up there and hopefully just be successful for myself and the team.


What's going on, everyone? Sam here, late sports cards, ripping 22, 23, putting in the contenders, basketball, hobby 12 box, full case break number four, tiered random team style. There's a look again at your random results. As always, you can find these on the website under the results tab, as well as the link in chat. Good luck, guys. Let's have some fun with more contenders. Last contenders break of the night. Thank you guys for getting all of them filled up, filled up four of these full cases, tiered random team style. So thank you guys very much to appreciate it. We've got limited football coming up after this. First, half case random team is sold out. We have a couple more half case random teams to go. Jeez, whole bottom rows off. I think they're packed in tight in here. Come on. There we go. Two, three, and four. There you go. Again, you can see on the overhead, cases empty. Knock these out. I love contenders this year, man. Not a lot of base. Very easy to open. It's been fantastic. Yeah. See if we find a Palo or a key. My goal is Palo, Keegan Murray, or Jabari Smith in this case. One of the big three we have not seen yet. Seen most of the other big ones. There's a LeBron to 99. Nice one there for the Lakers. Stardomire, we got a premium auto of Jaden Ivy. There we go, Pistons. Let's go Detroit. That is going to Andrew L. Congrats, Andrew. Nice one there, buddy. We'll get that one magged for you, my friend. Very nice. Jaden Ivy starting us off here. Levine and Tobias Harris. Cracked ice Palo. Come out of here real quick. Josh Minot. Rookie ticket variation. Base rookie. Shade on sharp. Lottery ticket. Osemein Zheng. There's a premium, Demata Sabonis. Nice one there for the Kings. Another All-Star this year. Yeah, I can't believe the Kings finished as well as they did. That's kind of insane to me. Kings winning the Pacific this year. Got a Desmond Bain to 249 for the Grizzlies. Third year Bain. Another weird thing to say. Steph Curry. Base, base, base, and base. Yeah, guys, limited football coming up a little bit later, still with spots to go. Jabari Smith. Got spots in random team, tiered random team, and random division style. Got a Pat Connaughton for the Buc70 out of 99. Cool one there. Pat Connaughton. Oh, absolutely. If Mike Brown doesn't win it, it's a sham if he doesn't win Coach of the Year. That's insanity if he doesn't win it, Vaughn. I agree. Coach of the Two. The Kings. Yeah. They like weren't even, I don't think they were predicted to be a playoff team this year, and they're in third in the West. Jalen Green. Nice one there. Jabari Smith rookie. Got a MVP contenders. The Joker. Seven out of 15. Going to Greg. Let's go, Greg. Congrats, dude. Seven of 15, Nicola Jokic. That will work. That'll play right there, everybody. Let's go. Big time Joker MVP contenders. Very nice. Congrats, Greg. We'll get that magged. Great first two boxes. Two boxes, two mags. Nice. Yeah, true. Very true. There's a Kristaps Porzingis 299. Jaden Ivey Rookie. Ray Jon Ronda. I don't think so, Philly. I honestly couldn't tell you the last time I saw an NBA Auto in products. Rookie, that's severely damaged. I don't know what happened. There's like a burn mark on that. Look at that, dude. When I give this to you, take a look at that. Looks like someone burned it. We'll send an empty box and wrap her with the Hawks. Oh, wow, yeah. Looks like someone took a lighter to it. Yeah, it does. Jimmy the Intern got a little bored on his lunch break. Matrix. Sweet shots. There's age, Barry Walker. Nice one there for the Blazers. Basin. I feel like he has not signed since like before 2020. I don't know if that timeline sounds right to anybody else, but that's what it feels like. Chris Middleton, 275. Luca. Buddy Heald. Another two came in. Three came in for number eight. Thank you guys on that one. We're down to 16 to go on number eight. We're getting there, everybody. 16 to go. Again, don't have a ton of limited in the back either, so it's not something we're going to be breaking for super long. So it's a product you do enjoy. I would recommend getting in it sooner rather than later to ensure you have a group break spot. Jalen Brown, 2199. Jalen Williams. There's J-Dub. Base, base. 2018 was the last year he signed. Good god, man. It's been that long. It's a Yanis variation. Nice one there. Ron. I might miss an auto in this one as well. Jalen Brown, 249. Griffin. Base, base, base. Yeah, so we're going to miss an auto in this one. I apologize again, everybody. Dyson Daniels, Zion Superstars. Base, there is a Tyrese Martin for the Hawks on card rookie ticket auto. Fortunately, it is on average, so that will happen every time. We've got a ticket stub coming up here. That's going to be a ticket stub auto. Yeah, I think it's going to be. Paolo. Nice one there on the rookie ticket variation. Ticket stub auto. Coming up is going to be to 24. That is Walker Kessler. Boom. Six of 24 for the Jazz. There we go, Utah. Kay Rinn. There you go, buddy. Six of 24 ticket stub, Walker Kessler. Oh, and those throwback Jazz jerseys too. That is an awesome card. Variation as well. Got that magged up. Got Donovan Mitchell to 249. Kessler is, he is a stud. Jalen Duren. He, like defensively, he is so good. So good. There are Superstars, Anthony Edwards. Base, Trevor Kiehl's premium rookie ticket auto. Nice one there for the Knicks. Thank you, Greg, for taking the division and limited later. Thank you, Greg. Appreciate you, buddy. That is Terry Rozier to 199. Jabari Smith. Okay, so far, dude. This is awesome. Let's keep it going. That Kessler, dude, that ticket stub is awesome. With that, those Jazz jerseys, that's so freaking cool. Oh, yeah. Those jerseys.  There's a Miles Turner to 49. Jaden Ivy. Ray John Rhonda. Base, base, base. Wendell Moore Jr. to the Timberwolves. Auto coming up. It's going to be to 99. That is Keegan Murray. Oh my God. What a case. Go into the Kings. Sacramento. Brandon L. Congrats, man, to 99 on the variation. Holy cow, man. Let's go. What a case we're rocking with here. Good God. Four mags in five boxes right now. I'm not real. Davion to 199. It's going to be Anthony Edwards. Kevin Durant. Told you that was the goal, was to get one of those three guys. We got one of them. Now let's get the other two. Bari Smith rookie. Got Adam Morrison. What year are we in? Going to the Hornets. Oh my God, Vaughn. 25 of 25. That might be the varniest Vaughn card ever.  Adam Morrison. Yeah, I'm with Valley.  I'm at like a loss for words. Actually technically, yeah, that would be Pelicans. That's what I'm saying, Pythora. Let me, I'm going to text just really quick where that one would go. Give me one second, everybody. Well, so what happened basically was the Bobcats relocated to New Orleans and became the New Orleans Hornets. And then no, no, no, no, no, no, no, no. Oh, that works out well. Never mind. No, they just changed their name to the Hornets, right? Yeah, Bobcats were after the move, right? They didn't move to New Orleans. Never mind. Yeah, so I mean, it doesn't matter because Jory has the Hornets and the Pelicans. So it doesn't matter anyway. So yeah, that'll go Pelicans. So yeah, no, that'll go Hornets, I mean. So the Pelicans renamed themselves to the New Orleans Pelicans and the Bobcats switched their name as well. Never mind. Back to the Hornets. It goes to the Hornets, Hornets. Okay. Go to Charlotte. Yeah. Yeah, because the Pelicans moved, the Pelicans relocated, so the Hornets relocated to New Orleans, became the New Orleans Hornets. And then the Bobcats were founded, the New Orleans Hornets then switched their name to the New Orleans Pelicans and then the Charlotte Bobcats rebranded to be the Charlotte Hornets again. So it's confusing, but that's how that one works. I can't believe we just had all that over freaking Adam Morrison. What the, dude, why is Adam Morrison in there? So funny, bro. Oh my God. Bro, he was like, so he played it in Zaga in college. He was supposed to be like the next super, super, super star.  And he just stunk in the NBA. Stunk. Sorry, Rodney. Got a Musa Diabase going to the Clippers. Nice one there. Congrats on that. Yeah, that one's a confusing one with that old situation. The team's changing their name like five times. There's a Lando Ball to 199. I was going to say, because New Orleans used to be the Hornets, right? Yes, for a very short period of time, the New Orleans Hornets. So there was a Charlotte Hornets. So there was a period of time when the Charlotte Hornets did not exist. Right. So they used to be Charlotte, then they relocated to New Orleans. Charlotte did not have a team. The Bobcats were an expansion team. And then the Hornets in New Orleans renamed themselves to the Pelicans. They like rebranded in 2013. Right. And then the Hornets then rebranded to the Charlotte Hornets again to be like back to what they usually were. Yeah, that's all. So yeah, that's a confusing one. That's a confusing one. Yeah, and all that for Adam Morrison. Keegan Murray. Nice one there. Dyson Daniels. We've got Zion. It's going to be premium Walker Kessler for the Jazz. Wow. Dude, what a case. Oh my gosh.  Hell of a case. We got the ticket stub and the premium. Yeah, ticket stub and the premium. Mixing a water. Or we could not just keep drinking whatever this is. Whatever this concoction we found is. There's Jaren Jackson Jr. Jalen Williams. Yeah, similar to the Raven, kind of similar to the Raven Brown situation. Gotcha. Like the Browns then became a team and like, oh yeah, we opened a ton of it. Blitz. We opened a lot of series one. All right, 15 to go. Now on number eight for Limited, 15 to go. Breaking soon again, the first one is sold out. That breaks after this. Clay Thompson to 75. Yeah, we hit a bunch of good stuff out of there.  Dyson Daniels. That's wild. Another premium coming up to 249 is Jimmy Butler. LeBron, Jalen Williams. That's going to be premium of Nicolay Jovic. Nice one there. Heat. Congrats on that. Five mags. That's wild. Kyrie and Bam Adubile. Kevin Durant. It's for the Nets. Malachi Branham. So the horn, it's the... Nice. Oseman Zhang permit to dominate. Super short print. We've got A. Christian Brown to 49, the finals ticket. Nice one there for the Nuggets. Grats again, Denver. There you go, Greg. God, oh, I'm going to keep on his toes. Call you Matt for the day. Friday. Friday, Shulman. I will be Matt for the day. One of one. Clay Thompson. Champ ticket. Going to the Warriors. There we go, Golden State. Anthony. Congrats, man. What's going on?  Sixth mag of the case right now. One of one. Wow. Nana's case. Isaiah Mobley for the Cavaliers. Jane Nivey Rookie. It's going to be A. Terry Eason for the Rockets. Nice one there, Rockets. And A. It's five seconds too late. Again, guys, 15 to go. I can delete that now. 15 to go on number eight still. Am I tweeting that when you get a shot? There's Zach Levine to 249. Superstar's Tatum. Keegan Murray, Rookie of the Year contenders. There's Oseman Zhang. Ryan Rollins, 87 of 99 for the Warriors. Nice one there. Playoff ticket on. On card. That's back-to-back autos. Jake Laravia, 8 of 10 on the acetate. That's a mistake right there. That makes up for one right there, yeah. Average back. 8 of 10, Jake. All right. Dude, I'll never forget. Last year, I was top loading for Brad during one of these, and there was one of those mistakes as well. Two cards back-to-back. Cade Cunningham, cracked ice, and Scotty Barnes, printing plate auto. That's like crazy, dude. We were freaking out about that. Whoa, chill on the Ryan Rollins slander, illegal. It's nuts, dude. That's Colin Sexton at 249. Ochaik Bhaji. Dallin's Harry. Nice one there for the bulls on the variation on card rookie ticket. Five of seven! Brandon Ingram. God, what is this called? This is sick, though. Forget what this is called, but five of seven. Is that similar to the shimmer? Kind of, yeah. Yeah, Jake was showing me one of those earlier. They pulled. Five of seven. That's sweet. Shade on sharp. You can throw that in the recap. It's Carter. Hold on. We're connecting to your channels. Thanks for your stream. Pretty good, no? I was gonna log me out. I was like, yeah. Brody Russell Westbrook to our rookie ticket, not rookie, season ticket variation. Base, base. Got a redemption that is going to be Caleb Houston. Rookie variation season ticket premium edition going to the Magic, Lakeland Magic legend, Caleb Houston, according to Kyle. Another spot came in in number eight. We're down to 14, everybody. There's a Steph Curry of 75. Nice one again, Anthony. Shet. Sweet shots. 14 left in number eight for the limited as well. Coming up a bit later.  After this case, man. That is two, two 49, Anthony Simons. Got superstars, LeBron. That's going to be a Max Christie premium for the Lakers. Nice one there, LA. Max Christie, AD, Zion. It's been brutal. Bradley Beale for the Wizards. Then like, Matheryn. It's only been for basketball too, for the most part. It's been like Panini basketball. It's been the only one's lacking checklist. It's strange. Permit to dominate, or license to dominate Steph Curry. There you go, Anthony on the super short print. Congrats, man. That's awesome. Unreal, dude. You can just recap that one. We've got redemption, Christian Brown. Variation playoff ticket to 99 for the Nuggets. Nice one, Denver, Christian Brown. Lori Markenon, two, one, 99. Jalen Duren. Another premium. Good lord, man. Russell Westbrook. Keegan Murray lottery ticket. Sweet shots, Jason Tate. I'm premium, Peyton Watson for Denver. Nice one there again, Greg. Picking up Peyton Watson this time on the premium rookie ticket auto. We need a bounty for the one-of-one Adam Morrison. Us as a community, one-of-one Adam Morrison has to be owned by somebody we know. Anthony Simons to 75. I do not care. Dyson Daniels. I swear to God, I will start buying the Hornets and PYTs just for that. It's the only thing I care about. That is going to be a Musa Diabata, the Clippers. Superstar, Steph Curry. Redemption, Caleb Houston, rookie season ticket going to the magic again. Nice one. Orlando, Caleb Houston. There's a Donovan Mitchell, two, one, 99. Jalen Williams. Dude, look at this case so far. Oh boy. I see a man on a mags over there. Dude. One second, everybody. That seems like a lot. Play one-on-one. Okay. Premium Walker Castler. Okay. Keegan Murray to 99. Ticket stop, Castler. Oh my goodness. Yoakich. Okay. And a Jaden Ivy premium. On one case, dude. Nuts. Nuts. Oh my God. I like it a lot. See, Walker Castler, rookie of the year contenders. Superstar Luca. Chris Paul. That's A. Wendell Moore Jr. Variation, rookie ticket auto for the Timberwolves. Nice one there, Andrew. If you hit something big, you can bring it in here to show people. True. I'll let you know. Yeah, for sure. That is Keldon Johnson. Christian Coloco. Space. We got Marjon Bochamp for the bucks. Nice one there. Christian Wood to 75. Big Shot Billups. Base, base, base. My hair is on fire. Oh man. That's not good. Do a quick recap, everybody. That is insanity. Look at that stack of mags. Okay. We got licensed to dominate Curry. Brandon Ingram to seven. Permit to dominate is Oseman Zhang, Marjon Bochamp, Wendell Moore, Caleb Houston, Peyton Watson, Christian Brown, Max Christie, Caleb Houston, Dallin Terry, Jake LaRavia, Ryan Rollins, Terry Eason, Christian Brown, Nicolay Jovic, Musa Diabate, that just unreal that this is in here. Adam Morrison. I can't believe I'm saying that. Trevor Keels, Tyrese Martin, Jabari Walker, and Pat Connaughton. I'm just in shock. I really am. Premium, Jaden Ivy. For the Pistons, we had MVP contenders of Jokic to 75 auto. We had a Walker Kessler ticket stub, six to 24 auto. It makes it all worth it. Keegan Murray to 99. Auto for the Kings. We had a Walker Kessler premium as well, and then a one-of-one Clay Thompson on the base. That'll do it for the break, guys. Thanks again for the fill. Next up, we've got football.


So folks, just to introduce myself, I head the engineering team at Gabri Technologies. So we provide a cloud-based SaaS products to large retailers and other consumer-facing businesses to engage better with their end customers. So we are presenting about 30 countries at this point and working at a fairly decent scale. We touch almost 650 million consumers across these 30 countries and processing close to 15 billion line items every year, close to 3 to 4 billion transactions every year. So we are working at a fairly decent scale based in Bangalore, the small dissenter in Shanghai, China as well. And thanks to Abha for that insightful talk. I think what caught my attention was that $18 per day that you are spending with someone who is always looking to optimize my engineering expenditure. I think that definitely caught my attention and I will be reaching out to you to understand slightly more in detail. All right, thanks folks for joining. So the agenda for my talk is going to be fairly straightforward, it's more of a one-on-one country talk on observability. Just to, you know, I'll probably take an initial five minutes to set a context of how this whole, you know, the whole drive and the whole, the concept of observability came into being in the recent five to six years, right? How the trend has progressed and what are the three core pillars of observability and how they tie up into the other related areas of monitoring, alerting as well. And also I think in the towards the end, we spend about a couple of minutes on the recent emerging trends in observability space, like beyond the basic your metrics, monitoring and alerting, right? And after that, we can open up to questions and like discussions with the audience. Yeah, let's just dive in. Right, so I think as many of you folks know, right, release to production is as the beginning of any cycle of any software system of any product, right? So there have been very many, many studies in the past and I think from anecdotal evidence as well, like 40 to 90% of the total cost of software actually incurred after launch, right? So the maintenance, the keeping the uptime, keeping the performance healthy, pushing in bug fixes, patching, right? So it's a significant amount of cost that goes beyond just the production deployment or the first lease of the system, right? And as systems are becoming more and more complex, more and more distributed, more and as as you know, rightly said by a very famous VC that software is leading the world, right? So software is kind of patching more and more parts of our lives. The consequence of that is as systems fail, the impact of that on our day to day lives is also becoming more and more prominent, right? So and as systems become complex, they will fail, right? There's no, I mean, there's no doubt about it, right? No matter how hard you try to build a scalable uptime system, they will eventually fail, right? You will be up towards the tail end of your availability numbers, right? If the system will fail eventually, right? And as one of our goals, Amazon's YouTube keeps saying that everything fails all the time, right? Number of failure points in a distributed system will increase with every new component you add to it. The more number of parts, the more number of requests to components, interactions are increasing, right? The more the more chances of failure you are introducing in your system, right? So people say that, you know, if I don't, we can have a very strong monitoring framework, very strong monitoring on our systems to kind of alert us when some things are going wrong and we can just go and fix it, right? But the question that comes to mind is what do you monitor, right? I think that's what we probably just do a very simple case study, again, something which most of you folks would be very familiar with, right? Let's just take an example of a very simple RESTful API server. You're serving REST APIs over HTTP probably using JSON as your request response structure, right? What can be, what can be the potential points on which you would like to monitor, could be your API latencies, 9 to the percentile average, 99 percentile. If you are running on containers or even on your VM, your CPU usage, your load average, your memory stack, if you meet the database application, your heap usage, your HTTP error codes, exception rate, failure rate, if you are dependent on external systems and whatever latency the external APIs, which could be impacting your application latency as well, and many, many more, right? So, I mean, these are just a few high level sample points that we could potentially monitor on this API application, right? And you may want to multiply by that, by the number of servers you are running because for redundancy purpose, it's obvious, daily, obviously you would want to have more and more servers running with the same API server code base. And if you have into disaster recovery and you want to have a backup code or you are running in a hot cold kind of a setup, you also have the multiple error or the number of deployments or number of classes that you potentially have, right? So, just a simple back of the envelope calculation will tell you that, you know, you are looking at close to 400 to 500 or maybe even 1000 separate data points that you may want to monitor essentially, right? And this is just for one REST service that you could have at the top of your stack. There could be multiple such services in your overall software deployment that you have, right? So, it's really obvious, it does something that does not look like it, right? I mean, you can't have a monitoring system which is observing like thousands of metrics of across so many servers and applications running, right?  You will have to deploy a much, much larger team as to monitor and watch these metrics essentially, right? So, let's just step back and understand what monitoring is, right? So, in theoretical terms, what monitoring means is, you know, you're just capturing the state of the systems who try to determine what is the health of the system, right? So, a couple of methods that are commonly used is, you know, you have health checks running just to see whether the service is actually up and running and secondly, can you send work to it, right? So, in the container ecosystem, you have the liveness source to check whether your service is running or not. You have the readiness source to check whether it's application ready to accept, move, work or not, right? And you could have metrics, system, application and function, we probably do a deeper dive into this in the subsequent slides. And once you have understood these health checks and metrics, you can define anomalies on it. Like, for instance, if your, your, your litencies are going beyond a certain threshold, which you have said as per the acceptable number for your consumer, you can define an anomalous behavior on top, right? And alerts are usually set up on known failures. Basically the things that you already know can go wrong and you could find a threshold on top of it and you define alerts on top of it, right? It's a fairly knowledge-based process. It's a reactive post-outage process. You have an outage, you realize that something was going wrong and your system was not alerting on it. So, you end up adding an alert on top of it so that in future you can catch these, you can catch these anomalous behaviors proactively, right? But what about the unknown failures, right? I mean, you can always, you can always apply alerts on things that you already know could potentially go wrong, right? But as the complexity of the system is increasing, you're becoming more and more distributed, you're adding more and more integrations, external dependencies. The number of unknown failures are also increasing, right? So, I think that's the time, you know, observability kind of comes into the picture very nicely, right? It helps you to kind of keep a tab on these unknown failures, essentially, right? So, I started my career way back in 2007 and 2008, started working at Yahoo. So, at Yahoo, the dev team had zero access to production systems and the message that you see on the slide is something which I have lived through for almost one or two, three years. Once my code has gone to production, I did not even write and I lived whether it's running or not, just hand it over to the dev ops team, let them manage the systems, even if it comes under file, you know, it's a crash and burn kind of system, it's not our problem, where devs don't have access to production systems, right? But in the last decade or so, the way the industry has progressed, the PSI, which the developers are building software, this barrier between dev ops and dev is getting thinner and thinner, right? And this is one of the, I would say, you know, one of the very popular and remarkable posts by copy transfer, I mean, a big fan of the writing, right? So, he posted this article in 2017, it's a highly recommended read, right? Observability is, you know, because devs don't like to do monitoring, you need to package it in a new nomenclature to make it palatable and trendy, right? So, it's more of a lighter, it's a lighter take on what observability is, but yeah, in some form, you're essentially taking monitoring, keeping the systems up and running and then the operational aspect of your infra closer to their developers in some form, right? Baron Schott's one of the well-known names in relational databases, one of the chief architects of Percona, and he also, you see your vivid cortex, I'm not sure if you folks have used it again, a very, very nice tool to monitor and keep your databases up and running. So, he mentions it in a very, very clear and elusive way that monitoring tells you whether a system is working. Observability will help you understand why it is not working, right? That's why the whole, as the system ecosystem is becoming more and more common, observability is picking up at a similar phase, essentially. So, slightly theoretical definition, what observability is, it's a measure of how well the internal states of system can be inferred from the knowledge of its external outputs. Again, might seem too fancy or too verbose, but the key items over here is, you are trying to measure the internal states and by looking at its external outputs, and you're trying to infer what's going on inside your application, right? So, I kind of consider it to be fairly analogous to medical diagnosis, right? A human body to diagnose what's going wrong inside a human body, doctors just don't go and keep start opening up your body by doing surgery and all that. They try to infer what's going on inside your body by looking at the external output. It could be your blood pressure, it could be your heartbeat rate, it could be your temperature, it could be your pulse rate, and all those external signals that your body is sending out, right? So, essentially, observability of software systems is just an analog of that, essentially. You're trying to observe what's going on inside the software system by looking at the outputs that you're emitting from your applications, essentially, right? So, what could be the potential internal states of an application? Now, they're very, very context-specific. For instance, if I'm talking about a web server, the internal states could be characterized as the availability and the uptime that is the server actually responding to my requests, could be the incoming request rates, could be my response latency, that should be failure rates. Application services, you know, it could be my functional success rate, failure rates, for message queue, it could be my queue length, number of active producer consumers at any point in time. Okay, so these are fairly context-specific states that you may want to define depending on your application architecture. And the key point here is that to measure the internal states, you will have to instrument your code while you're writing it, right? It cannot be an afterthought. So, that's something I've seen very new engineers doing when they're writing code, right? They finished the whole application development when it's ready to go to UA or to move on to the next stage in the development process. They will start to actively instrumenting their code, right? I kind of strongly discourage that, right? While writing code, think of that, is this a point where I want to measure the internal state of my application? If yes, instrument it, right? External outputs, that's where I've come the three pillars of observability, right? Metrics, logging and tracing. So, health checks is also a form of observability, essentially, but I kind of club it under metrics, but again, it's fairly hazy, so feel free to club it wherever you want. But yeah, for the sake of discussion, I consider that part of my metrics chapter. Let's go, let's spend a bit of time in each of these three pillars, right? What are metrics, right? Metrics are essentially nothing but the external state which you're measuring at a broad scope. Now, when I say a broad scope, it's nothing but a time dimension because when you say a metric, you're looking at, you'll always look at metrics with the time as one of the access. For instance, what was the performance of my APIs between 8 a.m. to 8 a.m. to 9 a.m. ISP, right? Or what was the incoming request rate at particular slice or a packet of time, essentially, right? So you will always have a time dimension or time access available to you when you're talking about metrics, right? Could be system metrics, system metrics are nothing but your seat utilization, your memory swap, other network system parameters. Could be your application metrics, your success rate, failure rates, you can see the records. Again, these are all very applications specific and subjective things which will be dependent on your application architecture and your design. And lastly, your business and functional metrics, these are likely more relevant towards your business and your product managers, right? You're looking at your order rates. In case of payments, it could be a lot of successful power payments, reverse payments, reconciliation requests, in case of a opening system, it could be a lot of coupons you're issuing, number of coupons you're getting the team successfully. It's, again, business and functional metrics. So it's good to classify the metrics into these three pockets. They help you to kind of design this metrics better and also to create your task codes as aligned to your consumers and stakeholders of these metrics essentially, right? Metrics should, what should be the ideal of deciding what to emit, what not to emit? I follow a very blanket rule of thumb, be as generous as you want to be when editing your metrics. Some, if you want to get, if you want to read some government guidelines around how do you design your metrics, I'd highly recommend this article on metrics that matter, it came on ACM. It's a very, very nice framework, how you decide whether this is a metric that you want to track on, essentially, right? So be generous on metrics, however, be judicious on alerts because, again, you don't want to alert pretty. Going back to the point we are discussing in that simple case study that you know, you don't want to alert on 1000 different data points essentially, right?  Another common mistake I have seen people doing is that they end up stuffing a lot of dimensions or data points on their metrics, right? So it's a very, it's a highly advised that your metrics should have low cardinality on the metadata that you're attaching to them, right? So avoid attaching user specific IDs, order IDs, or entity IDs inside a metric, right? So metrics will always be providing you system summaries in some form and they will help you answer questions like how many transactions failed, how many logins succeeded, how many orders are being processed, how many payments are being processed. So they will help you answer how kind of questions, essentially, right?  Common tools available to capture metrics, Prometheus, again, we're both touched upon it in a deep dive, Influx DB, again, fairly, popular tool, timescale DB, one of the recent editions in the last two to three years. Graphite is, I think, one of the old trend IDs of your timescale databases. Although I'm not a personal kind of graphite, because the data model is very flat-ish and not very intuitive to people who come from a SQL-ish kind of a background. Open TSGB, again, fairly old timescales, science-scale database, they'll run on top of HPACE. SCUBA is, again, a very nice architecture propagated by Facebook, which is a very nice paper, highly recommended, if you want to go deep into the algorithms that are optimizing the each and right parts of a metric store. Apache, Druid, people are using it for metric storing, but it's largely an event storage, event analytics, and graphana for visualization, right? And one common pattern and common algorithm as data sector, you will see in this metric store that they all use log structure and mergers, because metric databases are, metric stores are, by nature, supposed to be extremely, extremely scalable on writes as compared to reads, because you are writing at a much, much faster rate than you're reading it. So I think Vivo also touched upon, they're touching close to 900,000 metrics per second, right? So obviously, the read could be of a much smaller scale than what their write scales are, write-to-write throughputs are, essentially, right? Coming to health checks, right? As I said, it's my service running in container world, liunas probes, can I send work to it? It's my application ready to respond to work, the liunas probes. What are the different ways to collect health checks, right? So in a lot of P2P distributed databases, you would have heard the term gossip protocols, very popular in Cassandra and React. Whenever a node enter the cluster, it broadcasts its availability and health checks in a gossip protocol, right? Service raise free, service comes up, it's available to serve traffic, it goes and serves itself successfully. When the service is going through a downtime, it will end up be resting it from the service, it's serving some service raise free and that's a way it's kind of propagating its health to the rest of the services in the system. And the, I mean, the older mechanisms and a lot of reliable mechanisms as well are doing health checks on via ERVs, HAProxies, NG9s. Again, they're very common and have been in use for many, many years, essentially, right? Coming to the second pillar, logging. So the way to understand logging is that, you know, logging helps you deep dive at a much smaller scope and not on the time dimension, but rather on an entity dimension, it could be a request or a customer or a transaction. Logging will help you answer another question, essentially, why couldn't a customer place an order? Why did a transaction fail? Why did a checkout on an e-commerce application fail? Why couldn't I add a product to a cart? The scope is much, much smaller. You're not trying to aggregate things across a large number of requests. You're trying to look at or do a dissection of a much smaller request, essentially, right? Logging has to be centralized. Here comes the log collection and aggregation technologies, Fluendee, Logstatt and tons of agents are available out there. Fluendee is a lot popular in the cloud and native ecosystem. Logs have to be searchable. It comes with your indexing technologies, elastic search and whatnot. In fact, index-free logging is also becoming quite popular these days. So, low-key is the last tool that you see is becoming a fairly, it has come into the Grafana ecosystem and they're doing some really, really kick-ass shop in promoting the index-free logging ecosystem. And logs, essentially, have to be correlatable by a common key, even being, in a fairly large distributed system, your request is touching multiple services. You have to tie the things together and kind of arrive at a common trace of the request. So, your request ID is a very commonly used parameter. A lot of commonly available load balancers allow, you know, you can inject UID that request ID as soon as they enter your input. So, standard tools these days, ELK-START, ESK-PAID technologies like Splunk and Sumo Logic and low-key, as I mentioned, a new trend in index-free logging is picking up, essentially. Right, anatomy of the log, very simple. Time-stamped levels, service commit IDs, build times, build version number, region, customer ID tracing and all that. So, having a well-structured logs helps you from day one to kind of get the maximum value out of your logging system. So, many popular libraries allow you to define your appenders and your logging format. So, it's a very, very common practice across multiple languages and libraries. Third pillar, tracing. So, tracing helps you to, if you're working at a system, you're working in a software style where multiple services, each service is responsible only for a very small component of your request processing. You may want to visualize it and you may want to trace what happened in which part of your request processing. That's where the whole tracing comes into the observability space, right? And it actually became popular after Google published its dapper paper, I think almost 10 to 11 years ago. Twitter also came up with an open tracing system called Zipkin. Then, the community of developers got together, they published a standard called Open Yeager, which we, again, been very popular. And with a lot of your application to how much monitoring tools like Neuralik, DataDog, or AppDynamics, tracing comes out of the box, essentially. So, it's a very, very useful tool for you to zoom in into which particular component of your request processing is slowing down, how much time is it taking, and the whole concept of spans and traces comes into the picture, right? If you look at the observability spectrum, right? So, you will have health checks and some form of metrics which are allowing you to catch known unknowns. And then comes your debugging and explorations space where the other part of metrics help you to query to understand what went wrong. You're trying to discover the unknown unknowns, right? The tracing and logging kind of help you in the debugging and exploration part. Your health checks and some form of your basic health metrics help you for monitoring and resiliency, essentially, right? We have discovered, maybe we have discussed metrics, we have discovered logs, we have discovered tracing, right? But coming back to the problem we started with, what do you want to alert on, right? Come service level objectives, one of the terms which has been popularized by Google's SRE teams a lot, essentially, right? Just folks bear with me, I might sound too much fun, at least if the audience is fairly mature, it might seem repetitive, but yeah, for the newer folks, SLO is a very nice concept. I think it's something which I promote very, very heavily in my teams, right? SLOs are nothing but a very simple, quantifiable and measurable goal for a service. And that goal should be linked to the user experience for a delight factor, right? So as engineers, we take a lot of pride into building scalable systems, but at the end of the day, we have to think that there's a user who's trying to derive value on a system that you have built, right? So SLOs help you tie up the technical health and the technical factor of your systems to the user experience and the delight factor, essentially, right? And SLOs should be something which you are defining before you start writing code, right? You should work backwards, define an SLO first and start then start writing code of your service, right? And have as few SLOs as possible and which are representative of your system behavior. You can probably get a more deeper insight into SLOs by reading through this article or looking at this talk on YouTube. Let's do a simple exercise on defining what could be the SLOs or let's assume that I'm writing a cart service, which I'll probably open it up for comments from the audience at this point, right? Let's assume that you are responsible for writing a service which is managing the cart on an e-commerce portal, right? What could be the potential SLOs that you would define for such kind of a service? Maybe you can pour in your comments and then pour in your answer in the comments that works. Or shall we open it up? I mean, if somebody wants to speak out, they can unmute. What do you want to do it? Yeah, so as usual, as I said earlier in the previous talk also, folks watching, we have around 10 people watching on YouTube live stream. We have more people joining the second talk who are all here. Thank you, Piyush, for such an insightful again talk. While this was more of a 101, but sometimes, as you correctly said, right? You need to start from scratch and there are always people coming into the practice new. They have to learn first principles, right? Which is something we always talk about, right? Just doing fancy tools probably is always not great. You need to understand how to use that data more often in the right way to arrive at your decisions, right? So, yeah, with that in mind. If you want me to finish, I'll probably just take a few minutes. Maybe just start upon the... Oh, sorry, I'm so sorry. I thought you were opening up for questions. I'm so sorry. I'll probably make a collaborative thing, you know, rather than just going to a monologue, right? Maybe just let the audience come in. Awesome, sure, sure. Sure, we can do that format also. So, yeah, folks, you know, since Piyush once said that way, feel free to, you know, have a conversation with him and interact with him. You can unmute yourself as he wants to and you can interact as he would like to direct this. Please go ahead, Piyush. Sorry, I thought wrong. Apologies. Yeah, no worries. So folks, again, let's assume that, you know, you are writing a service or managing a service which does one of these three functionalities, right? It could be a cart service. It could be a authentication authorization service, right? Again, we have built a service like Octa or Auth0 or maybe you have built a gateway communication gateway, sending out SMSes, emails, push notifications, right? So what could be the potential SLOs you may want to design for these kind of applications? So open for thought folks, I mean, feel free to send us comments or you can speak, feel free. So let's say I will pitch in here, you know, since folks are still deciding on stuff. Like I'll go for the second one, right? Auth0 and Auth0. Like one important SLO here would be that out of let's say a number of authentication requests, how many are getting successful, right? And what is the latency auto, right? So those are sort of, you know, base metrics that we need to and how many auth failures are happening. You know, they could happen due to wrong logic. They could happen due to unavailability of service. They could happen due to an edge case in the authorization logic because more often authentication is a simple. Authorization is where it becomes, you know, much more complicated for people to actually plan out the whole workflow and execute, right? So those test cases, those age cases, how we are navigating that should be, you know, our SLOs for those two services, right? Right, right. No, absolutely. I think that makes a lot of sense. And the idea is that, you know, SLOs are essentially representative of what your service represents, right? So what your service does essentially, right? So now, right? I mean, just going back to the SEP example, right? My, you know, my CPU spike can actually cause a high latency which can cause a failure in my authentication operation, right? So there could be thousands of reasons which can actually cause this behavior to kind of deviate from the expected norm, right? So, but having a tight watch on the expected behavior helps you capture multiple failures into a single metric essentially, right? So having alerts on the SLO and the SLO objectives makes a lot more sense on a much common, on a very complex application, right? So having alerts around SLOs makes the job a lot easier, right? You can always use the deeper level metrics for analyzing and troubleshooting when the SLO is not getting made. But thinking of an SLO first approach helps a lot, you know, in terms of designing your architecture, designing your routing and monitoring in a much more easy and communicable way, essentially, right? Yeah. So, yeah, please go on. Yeah, I mean, it should be like business metric driven, right? Like SLOs are mostly business metrics. And then you drill down to deeper system level stuff, more always level stuff, right? Right, right, right. Right, similarly for the card service, it would be, you know, the number of products are getting added to the card, number of quantities are able to change on a card, number of successful checkouts are able to do, right? So those are the SLOs on which I should be designing some of my metrics and essentially my alerts as well. And similarly on a convenient system could be the number of successful SMSs I'm sending out, number of incoming requests, right? Number of successful messages submitted to FCM or maybe send grade or some of those things, right? Rather than worrying about my lower level nuances of the email payload sizes or anything of that sort, essentially, right? Yeah. And lastly, why should we spend time on observability, right? Obviously through some of the really, you know, technical and operational aspects that you can build scalable systems, you can do better capacity and load planning. It leads to a lower mean time to repair. But besides that, you know, I've been running large teams for a very, very long time. One thing I realized is that, you know, having observability as first class citizen in the team, you know, provides a lot more data driven culture, right? All the conversations get driven around what metrics have you added? Do you have sufficient afterwards? Should we have sufficient alerts on the SLOs? So the whole culture kind of becomes a lot more data driven. Your teams are talking in terms of data rather than subjective terms like, is your service healthy? Can your service handle my scale? And things become a lot more data driven, right? Your outcomes become a lot measurable and remove subjectivity. In a sense that I have a bunch of architects on my group, right? I give them quarterly goals that here is a service which is operating at a 700 millisecond latency on 10,000 RPM. Your objective for the next quarter is to, you know, ensure that 10,000 RPM latency comes out of 500 MS. They have a clear measurable goal, no subjectivity, right? Someone cannot come back to me and say that, you know, I changed the whole data structure, I changed the whole algorithm, I optimized the code. If the API has not moved, essentially something has gone wrong, right? Either the hypothesis goes wrong or maybe the implementation goes wrong, right? It brings a lot more accountability across second-party teams. I have had so many product managers coming to me saying our experiments are not working because the implementation is unstable, it is buggy, right? The way I solve it is that, you know, when the tech team rolls out, I ask the product managers to give me three KPIs which define that implementation is successful. If those KPIs are made, the next analysis is completely on the hypothesis of the product, right? Was the hypothesis correct? Was the data analyzed correctly? Has the tech team delivered the features in a stable way? The product team can then go and conduct their experiments successfully. It brings a lot more accountability because you're talking data, I have some activity, there's no emotions they want to input, right? Do data-driven conversations, essentially, right? Also, it's, I think, very given how deep in the stack DevOps and observability is, it's very easy to use back of, you know, that you are actually doing all of this for a very different product, right? It's not, it's not about a single server, right? It's about what impact it's having. So having SLOs help us sort of prioritize the product thinking, right? I think for me, you know, as an engineering leader and manager, for me, you know, the accountability aspect is something which is helping a lot by bringing observability, right? So the engineering groups kind of commit to agreeing on SLA, SLO numbers to each other, right? Actually, I'm a consumer of the authentication of the service. I can clearly go and tell them that, you know, I foresee a traffic of at least 5,000 RPM coming in the next week, in the next coming quarter. Can you guarantee a 95% SLA of 50 milliseconds to me so that I can commit a 100 millisecond SLA to my consumer of the services, right? So this level of accountability becomes much easier when you have observability as part of your culture and part of your implementation, right? Talking about standards, again, given the plethora of, you know, metric tools, metrics, collection technologies coming into the picture, the developer community came together, started defining standards which can be followed and technology is abide by. So Open Metrics, one of the standards which came about three to four years ago, standardizing the structures of metrics, how do you emit it so that you can replace the metrics back in seamlessly. And Open Telemetry is one of the recent standards which is getting a lot more adoption. It's basically a combination of the open sensors and the open tracing standards, right? So they have defined very clear APIs and SDKs to cover all the pillars that we discussed, right? Metrics, tracing, context is nothing but adding more logging and annotations. So context is a generic broad term on top, right? And abiding by Open Telemetry standards makes you, Open Telemetry APIs and SDKs are essentially open source, they're vendor neutral. So tomorrow actually you want to replace your metrics back in from Promise ES to something else. So maybe you want to go away from proprietary vendors like Neuralink or DataDoc to open source to like Promise ES. Having an Open Telemetry implementation will allow you to do that seamlessly, right? And it also avoids a lot more logging. If you're not logged into a vendor and you don't have to commit to a long-term usage of a particular vendor essentially, right? And one of the recent trends in observability beyond just application metrics monitoring and all is the data observability. This is something which I have started looking into in the last few months, right? With the advent of large data systems and companies like Snowflake coming into the picture who are actually making data analysis and analytics at a much much large scale, very seamless and commodity. The data observability has also become a recent trend. The idea is since systems are becoming more and more integrated, you're collecting a lot more data from multiple sources. The quality of data, the correctness of the data, the availability of the data, the freshness of the data has become a lot more important because your data teams and business teams need reliable data, right? They need to be, they have to be sure that the numbers that they are looking at, they are actually on accurate data, right? So a lot of startups have recently sprung in this space in the last 10, 12, 15 months, right? Axel data is an Indian company, Montecarlo Soda, right? They're actually building some really, really nice, good platforms for observing your data very, very closely, right? So the five pillars of data observability are freshness, volume, schema, distribution and lineage. There are some reference articles you may want to read up, right? So freshness simply means is my data up to, the date is my data, two days old, three days old, like can I get the data that I'm looking at, the inferences and analysis I'm looking at, are they recent or are they slightly stable, right? Volume could be, I expect to ingest at least 500 degree of data on a daily basis. Suddenly we see a dip by 200 gigs, right? So what happened? Is my volume reduction organic? Is my volume reduction could be further helpful, right? Schema essentially means since I'm connecting to multiple systems, these days, a slight change in a schema or a JSON format in one endpoint could cause, you know, my whole data pipeline to crash essentially, right? Ensuring the integrity of the schema across different integration touch points, right? Distribution means again, how you're tracking the data pipeline. You have data coming in from multiple sources who are relaying and exchanging data to on different APIs and to different steps in the pipeline. Ensuring that whole exchange along the pipeline is healthy. Lineage again, something very, very critical especially for enterprise systems like what CapBerry has, right? User data is changing over time, user is updating their profiles over time, how do you draw the lineage graph of that data? How do you derive at a current state of a particular entity essentially, right? So these kind of questions can be answered by data observability platform, but again, it's a fairly new trend. I would say not more than a couple of years old, so still a lot more innovation to happen in this area, but I feel with the increase of data systems globally, I think this is gonna be a very, very popular or very closely watched trend in the coming years, right? That's all folks, happy observing. And some of the references strongly recommend folks reading it even if you are a matured observability practitioner running through these articles will definitely help. I kind of circle back to these articles every six to eight months, to be honest, right? Yeah, I'll open it up for Q&A and if you want to reach out to me, that's my Twitter handle, you'll be able to drop me a message or anything.