This lecture is part of an online commutative algebra course and will be about the Bernstein-Sato-Polynomiel. So this lecture will be a bit different from all the others because it's using some slightly non-commutative algebra. The point is a lot of the theory of commutative algebra extends to rings that aren't quite commutative. So some examples of these might be exterior algebras if you take the exterior algebra of a vector space then this is the property that x times y is sometimes equal to minus y times x instead of being y times x. And you can have algebras that almost commute, for instance a ring of differential operators. Now the ring of differential operators with polynomial coefficients might be generated over the complex numbers say by x and also by differentiation with respect to x so this gives you a perfect good ring. However Leibniz's rule says that d by dx of x is equal to x times d by dx plus one. Here we're thinking of these as being d by dx as being an operator not as something acting on x. So here d by dx and x don't quite commute but they're not far off because the amount that they don't commute by is just one. You can also have things like clifford algebras you get in Leigh Group's course where you might have a quadratic form and a b plus b a might be given by say the inner product of a and b if you've got some sort of inner product on a vector space. So all of these have the property that a b is closely related to b a. Roughly speaking a b is equal to b a or minus b a plus something simpler. Whatever simpler means for instance here d by dx and x commute up to a constant and a constant is obviously simpler than x or d by dx. And if you've got any algebra which is sort of close to being commutative in this sense then you can quite often apply the techniques of commutative algebra to it. And I'm going to give an application of this where we're going to use the ring of differential operators in order to show the existence of something called the Bernstein or Bernstein-Sato polynomial. By the way if you look up Bernstein polynomial it actually turns out there are two totally unrelated things called the Bernstein polynomial. So the sort of Bernstein polynomial used a bit of numerical analysis and it's absolutely nothing to do with this Bernstein polynomial. They're named after different Bernsteins. This is Joe Bernstein. So let's explain what the Bernstein polynomial is by looking at the gamma function. So as everybody knows the gamma function gamma of s is the integral 0 to infinity of e to minus t t to the s minus 1 dt. So this converges for the real part of s greater than zero. And if the real part of s is equal to zero then this bit diverges near zero. However you can analytically continue the gamma function by integrating by parts. So what you do is you find that gamma of s is equal to 1 over s gamma s plus 1. And what you do is you differentiate this bit and you integrate this bit in your integration by parts. And if you do that you find this relation between gamma of s and gamma of s plus 1. And this can be used to extend gamma s to all complex s possibly with poles. Because in order to extend it to s less than one you see you have to divide it by s and that divides it to the real part of s being greater than minus 1 and then when you extend it to the real part of s being greater than minus 2 you might get another pole somewhere else. So you can extend it as a meromorphic function. And let's see what the key point why you can extend it is because this bit here has the following property. We notice that d by dt of t to the s plus 1 is equal to s plus 1 t to the s. And this means you can sort of integrate t to the s and convert it into a t to the s plus 1 when you're integrating by parts. So now we come across the Bernstein-Sato polynomial B of s. Here f is a polynomial in several variables x1 up to xn. And suppose we can find a differential operation such that p of s f of x to the s plus 1 is equal to B of s times f of x to the s. So this is some sort of differential operator in x1 up to xn d by dx1 up to d by dxn and s. And this is going to be a polynomial in s. And if you can do this then you can extend the integral over r to the n of phi of x times f of x to the s dx provided let's suppose f is always greater than or equal to naught and this might be some nice function. Say it might be smooth, maybe vanishing infinity compact support say. Now if you look back at this you'll notice that e to the minus t is not smooth because we were only integrating from naught so it's got a discontinuity at naught and it's not compact support either but that doesn't really matter because these conditions are actually rather strong than we need. If phi is smooth and of compact support then you can write down this integral but you can quite often do it even if phi is a little bit more complicated. Anyway if you do this this might converge for s greater than or equal to zero obviously because this is perfect nice for s greater than or equal to zero and then by integrating by parts using this formula here so you're going to integrate by parts in a rather complicated way using this differential operator you can extend to all complex values of s except as before you'll pick up poles and you'll pick up poles at the zeroes that have something to do with the zeroes of this Bernstein-Sato polynomial. So for example in this particular case if you just take f of s equal to t then we will find b of s might be equal to s plus 1 say so that's a very simple example. Let's look at a slightly more complicated example in several variables so let's take f of x1 up to xn to be x1 squared plus plus all the way up to xn squared and now you want to find some polynomial so you want to find some differential operator which takes x1 squared plus xn squared to the s plus 1 to be something times x1 squared plus xn squared to the s so you want to fill in the missing bit here and the missing bit here well that's not too difficult to do because you can take d by dx1 squared plus plus d by dxn squared so you can take the Laplace operator here and if you apply the Laplace operator to this then you find what we get here is 4s plus 1s plus n over 2 times that so here we find b of s is equal to s plus 1 and s plus n over 2 and p of s would be say a quarter of sum of d by dx i squared so you might want to normalise the Bernstein-Sato polynomial so the leading coefficient is 1 and you actually notice if you go back here the possible polynomials you can get to actually form an ideal so you actually define the Bernstein-Sato polynomial to be a generator of that ideal with leading coefficient 1 and that identifies it uniquely but I mean once you've shown that the problem is to prove that some polynomial like that exists once you've proved that some polynomial like that exists then that's generally all you need the Bernstein-Sato polynomial is actually rather difficult to work out in general for example let's take f of x y to be x squared plus y cubed so it's only slightly more complicated than x squared plus y squared well in this case it's really rather hard to calculate the Bernstein-Sato polynomial I'll give you the answer so the answer is it's Bernstein-Sato polynomial is s plus 1 times s plus 5 sixths times s plus 7 sixths so you see already that's getting fairly complicated if you try and work it out or even check it you find you're getting having to deal with mountains of linear algebra of course there are lots of computer algebra systems these days that will compute the Bernstein-Sato polynomial for you but it's really not at all trivial to compute even in quite easy examples like this so the main theorem proved by Bernstein-Sato is the following theorem every polynomial every non-zero complex polynomial f has a Bernstein-Sato polynomial so that just means that there's some differential operator satisfying this equation here with b of s not being identically zero well I'm going to explain how to prove this using ideas from commutative algebra in the next couple of lectures but what I'll do for this lecture is just show you that the Bernstein-Sato polynomial is really quite powerful by giving an application of it so here's an easy corollary of the existence of a Bernstein-Sato polynomial the corollary is the Margrange-Erenpreis theorem this says that every differential operator with constant coefficients has a fundamental solution so let's think what this means well a differential operator might be called d and what you need is a fundamental solution f so that d of f is a direct delta function of course the direct delta function is not really a function it's really a distribution so here d is a differential operator in several variables and if you can find a fundamental solution of the differential operator then you can solve the equation df equals g for any reasonable function g by sort of convoluting so informally you write g as a sort of linear combination of delta functions that's really a sort of infinite continuous linear combination so if you can find a fundamental solution then you can pretty much solve this differential equation for any function g here so for a long time it was a major open question in the theory of linear differential equations to show that any differential equation had a fundamental solution this was finally proved by Margrange and Erenpreis and at the time it was considered this really big difficult theorem well to show you how powerful the Bernstein-Sato polynomial is we will just show that this follows almost trivially from its existence so the problem we want to solve df is the delta function well what we're going to do is we're going to take Fourier transforms so if we take Fourier transforms in several variables the Fourier transform converts differentiation into multiplication by x so it will convert any differential operator with constant coefficients into a polynomial q and it will convert f into some Fourier transform of f and it will convert the Dirac delta function into the function 1 so what we're trying to solve is this equation here here this is a polynomial and f is some distribution and this is the constant one and now you look at this and you think well this is perfectly trivial to solve let's try solution, let's try f hat equals 1 over q what's wrong with that? looks like a completely trivial thing to solve well this is, if q is none zero everywhere then this gives a perfectly good solution because 1 over q is a perfectly good function the problem is what if q has zeros and the problem is if q has zeros so near q of x equals zero 1 over q is not locally integrable and if it's not locally integrable then it's not at all clear how to make it into a distribution in general now if the zeros of q are fairly simple for instance if q has a simple zero that's just a non-singular variety so it's very easy to make 1 over q into a distribution and you can even do this with more complicated things for instance if the zeros are several nice smooth varieties meeting transversely so we say the singularities have normal crossings then again it's very easy to make sense of this as a distribution the problem is the zeros of q might be some very complicated singularity for instance if we're thinking of a function of two variables we might have a singularity of y cubed equals x to the 4 or something and it's not immediately obvious how to turn this into a distribution and in high dimensions things get even more complicated well there's one way to solve this by using a really big sledge hammer which is you quote Hieronaka's theorem about resolution of singularities in high dimensions and what Hieronaka's theorem says roughly is if you've got any singularity in high dimensions you can do this magical blow-up operation and turn it into a singularity with normal crossings and then you can find a distribution 1 over q but I mean Hieronaka's theorem was one of the most hardest theorems in mathematics when it came out and although it's been simplified a lot it's still pretty hard going I mean I've seen claims that you can present it in the last couple of weeks of a graduate course and I'm sure you can present it in the last couple of weeks of a graduate course but how many of the graduates will actually follow your presentation is not at all clear to me anyway so one way of solving this problem is by using Hieronaka's theorem well we don't want to do that because that's really a lot of work it's much easier to use the Bernstein-Sato polynomial so we can assume q is greater than or equal to 0 everywhere and that's very easy because 1 over q is just equal to the complex conjugate of q over q times q bar and this bit here is greater than or equal to 0 so if we can invert polynomials that are always non-negative then we can do all of them and now we notice that q to the s is holomorphic for the real part of s greater than or equal to 0 absolutely no problem we think of this as being a holomorphic as a distribution now using the Bernstein-Sato polynomial we can continue qs as a meromorphic function of s to all s in c now when I say it's a meromorphic function you have to be a little bit careful because it's actually a function taking values not in complex numbers but in distributions so what this means is that if we've got any nice function say smooth compact support then if we take the integral of phi with q over dx to the n this is going to be meromorphic in s for any nice function phi so it's not a meromorphic complex valued function it's something a little bit more complicated but it's not really much more complicated you just bang it against a test function and then you get a meromorphic complex valued function so it has poles related to 0s of B of s plus n where s is the Bernstein-Sato polynomial so it might have complicated poles all over the place now what we want to do is we want to define q to the minus 1 well q to the s might have pole at s equals minus 1 if it didn't have a pole then we'd be done we could define q to the minus 1 nicely well let's expand it as a Laurent series so we might have q to the s minus 1 equals q minus m s to the minus m plus q to the 1 minus m s to the 1 minus m plus q naught s to the naught and so on so this is just a Laurent series and the coefficients q something rather are they're of course not complex numbers they're going to be distributions so they become complex numbers if you bang them against some sort of test function now let's multiply by q so we find q to the s is equal to q times q to the minus m s to the minus m and so on plus q times q naught s to the naught and so on on the other hand we know q to the s is holomorphic at s equals 0 in fact at s equals 0 it's just 1 so this is equal to 1 plus something or other and all these terms here are 0 so we find q q minus m equals 0 up to q q minus 1 equals 0 and q q 0 equals 1 so q 0 is an inverse of q and we can use q 0 to find a fundamental solution to our differential operator in fact we just take the this will be a distribution we just take its Fourier transform q 0 twiddle is going to be the fundamental solution well there's actually a you might think there's a slight problem here because we notice that q times q minus m equals 0 all the way up to q times q minus 1 equals 0 so there are many inverses of q because we can take q 0 plus a linear combination of q minus m up to q minus 1 in fact generally there'll be lots of other stuff as well and this seems to be a bit of a problem because an element of a ring has at most one inverse because if we've got a ring q and we've got two inverses so q a equals 1 and q b equals 1 then we can say b a so b q start again b q a is equal to a because b times q is equal to 1 and it's also equal to b so what on earth am I going on about claiming that I can find lots of inverses of this polynomial q well first answer to that is that the product of distributions is not always defined so distributions don't actually form a ring you can quite often multiply distributions in fact you can always multiply distributions by smooth functions for example and you think about it a bit and you say well the fact the product of distributions is not always well defined doesn't actually affect this because here q is a smooth function all we're ever doing in this equation is multiplying a smooth function by a distribution so here we take a smooth function times a distribution we get 1 and we then multiply that by another distribution so although the product of distributions is not always defined this isn't actually what the problem here is the second problem is that when the product is defined it need not be associative and this is the real reason why we don't get a contradiction and I want to emphasize that because everybody knows products of distributions aren't defined but not so many people are aware that the product isn't associative when it is defined let me give a really simple example of a product of distributions not being associative so let me take the distributions x and I'm going to take 1 over x I'm going to make this into a distribution and okay this is not locally integrable but it's such a nice function it's not very difficult to make it into a distribution you can sort of take a Cauchy principle value or something and make that into a distribution and then I'm going to put here I'm going to put the Dirac delta function and then we know that x times delta equals 0 until we're multiplying a smooth function by distributions there's no problem but 1 over x times what times x is equal to 1 so you see that if we take 1 over x times x times delta this way this is equal to delta whereas 1 over x times x times delta well here we've got x times delta which is 0 so this is just 0 so even for very easy distributions in one variable where you're just multiplying distributions by smooth functions the product is still not associative okay so what we'll be doing in the next couple of lectures is using commutative algebra in particular the Hilbert polynomial to prove the existence of Bernstein polynomials


Welcome to the Option Edge Condor Max Trading System, one of the most sophisticated systems to trade and manage iron condors. Iron condors are one of the most popular option strategies out there and for very good reasons which we'll get into.  The Condor Max System is designed to produce a return of anywhere from 5 to 10% consistently on pretty much every iron condor you ever put on. But it's not a trade that you can put on and go to the beach for 30 days. So we'll look into what situations the Condor Max System can get into trouble and what we can expect to do in those situations. Let's get into the details so I can explain this proprietary trading system in much more detail. My name is Hari Swaminathan and I am the founder of OptionTiger.com, a cutting edge option trading and education company based in the Washington DC area. I have a bachelor's degree in engineering from India and an MBA from Columbia Business School in New York City. What you're watching now is part one. Part two will be a live session on Sundays at 8 p.m. Eastern time. In this live webinar you'll get an opportunity to ask all your questions but please be sure to watch this video completely so you'll be fully prepared with your questions on Sunday. A brief background about me and OptionTiger, I've been an options trader for over seven years now and just like many of you, I've learned options from a few different programs but mostly through making tough mistakes. All 47 options courses on OptionTiger are filled with deep insights on pitfalls from options trading and where things can go wrong. It's a very practical approach but with no compromise on the fundamental options concepts. OptionTiger is the home of proprietary strategies that gives every options trader a serious edge in every trade. The income max spreads and straddles system also gives every trader a powerful edge and is ideally suited for smaller accounts of $25,000 or less. Having an edge on every trade is critical and this is particularly important in the options market and there's a specific reason for this. The options market is very different from the stock market. In the stock market traders buy and sell against each other for the most part and only in rare circumstances where a market maker may step in trading is generally done in a peer-to-peer environment. In the options market every trade is done with a market maker on the other side. This is no different from walking into a casino and playing any of the games in the casino. We play against the house and the casino is the counterparty to our games. When we enter a trade in the options market it's no different. We trade against the market maker and they are the counterparty to all our trades. Let's first examine the facts around their business models. In both cases the market makers and the casinos it's important to understand that they create the rules of the game. Casinos create the rules for all the games like blackjack and roulette and market makers set the bid ask prices and they have considerable flexibility in setting option prices. The other critical element of their business model is that both parties take risk in their transactions and because they take risk they are legally allowed to have an edge in their business. With their ability to control the bid ask spread and option prices market makers are able to get a pretty serious edge on every trade. Although we are unable to see the profits of the market makers directly we can all see how well the casinos do under this model. Statistically in the long run both their business models are bulletproof. We traders may have a blockbuster day in the options market or we may have a blockbuster day in the casinos but in the long run we give this edge away little by little to the market maker. Perhaps now it may be better appreciated why even expert option traders may find themselves winning four out of five trades but they lose on the fifth and when you lose on the fifth you tend to give up what you made on the other four. This is not magic but it's simply the market makers long run statistical edge finding its way into reality and this should also not be surprising because if they did not have this edge why would they be a market maker? Why would the casinos or the market makers take risk being the counterparty if the tables were not tilted towards their side by design? No one would do that. The other way to think about the market maker is to consider what we traders do and what they do on a daily basis. We analyze and carefully construct every trade before we execute them. On the other hand the market maker has a few seconds at most to accept or reject our orders. If we believe we are getting into well thought out winning trades then this means the counterparty is getting into a bad trade. So let's think about this fact some more. Market makers are taking crappy trades all day from millions of traders but they still win and how could this be? It's because their business model is so bullet proof that they can sit and take bad trades all day and still come out ahead. We don't need any more proof and we don't need any more explanations to realize they have a serious edge. And this is precisely why we options traders need an edge and we need a serious edge at that to overcome the strengths of the market makers. And this is not easy. We may go to casinos for entertainment value but we enter the options markets for serious business and as if the bid ask spreads and the ability to set option prices weren't enough we traders also have transaction costs which can have a significant impact on our performance. We also make mistakes. We sometimes panic and do the wrong things whereas market makers can afford to act like robots. Not only do they have deep pockets to withstand losses they have sophisticated risk management and auto management systems and of course they are all options experts themselves. With these setbacks the odds against traders really become terrible. So every trade is a bit like going to box with Mike Tyson and you can imagine the chances of winning in this kind of a setting. It's no wonder that they say that 90% of options traders lose over their lifetime and there's probably a lot of truth to this. The general daily trading approach for most traders goes something like this. We fish for good trades whether it's through technical analysis charting skills or any other approach. We take up a particular strategy for a trade and we try to manage this trade through decisions we make on the spot based on how the stock moves. Today we could be trading an iron condor on Google and tomorrow we could be trading a calendar on Apple and how do we approach adjustments on an ad hoc basis. There's no system or there's no systematic approach to our trading style. This kind of trading plays right into the market makers edge and because we don't follow a system we tend to make mistakes and our adjustments could go wrong and so on and so forth. This is the surest way to lose money. If you don't have a system or a method to bring this edge seriously on our side we're going to lose money in the medium and long term and this aspect goes to the core of all the intellectual property of the option edge products. With every option edge system whether it's the condor max, the income max or any of our other products you'll have a 75-25 edge in your favor from the time you construct the base trade and the balance 25% is handled through a set of systematic adjustments and techniques all of which are part of the system itself. The key to success in the options markets is to trade with the system and this is the only way to overcome the serious edge that market makers have. Without this approach you may win a few trades here and there but what you really have to think of is consistency in the long run. If you're not performing consistently even if you're a fairly advanced options trader then these are the reasons why. The rules of the game are not designed for us traders to win consistently. We need more than just good options trading skills to be a consistent winner in the markets. We need a system and we need a systematic approach that can put a 75-25 edge on our side on each and every trade. Let's talk about the condor max trading system itself. The trade construct is unique and it gives that 75% edge in our favor from the time we start the trade. This trading system will work on stocks, ETFs and indices but we must avoid the one month prior to earnings. It's best to trade an ETF or the indices at this time. The trade is obviously non-directional and will profit from a move in either direction. The next three features are the most important for an iron condor and deals with the most problematic issues with the iron condor strategy itself. First of all the trade is flash crash proof and it's also capable of handling big moves and lastly it will not create big drawdowns. So let's think of the basic iron condor. It does not like big moves and you may see a drawdown of 50 to 60% on the margin of the trade if there was a huge move against either one of the short strike prices. If you have a $10,000 iron condor trade with a credit of $2,000 then a large move can result in a paper loss of up to $6,000 or $7,000. We all fall into the hopeless trap of hoping the stock turns around and this is how we end up taking a big loss. This in my opinion is the weakest point of the iron condor strategy itself and it's also the weakest point of us traders. The condomax trading system protects this critical aspect which turns most iron condors into losing trades. This is the core of the intellectual property in the condomax trading system and allows pretty much every iron condor trade you put on to be successful. The techniques that are part of the system is designed to not have a drawdown of more than 10 to 15% at any time. These techniques are simple and when we know ahead of time what we have to do we don't make mistakes. We also start to operate like robots just like the market makers and this is the only way to beat them at their game. When we don't have to think about what we should do we have a system and we can follow it successfully. I want to discuss the trade parameters and what you can expect to do in a typical trading cycle. This is a monthly income trade just like any iron condor trade so we'd expect to hold it for about a month. The trade setup itself will take about an hour to properly set up the base trade and all the risk parameters and the conditional orders. Once this is done you can expect to tweak the trade about one to two maybe three times a week. Many or most of these tweaks are small adjustment techniques but are very powerful and these techniques can be highly automated with the use of conditional orders. In fast moving markets we may need to step in at times. Major adjustments to the base structure are done only if needed and we limit that to two major adjustments in any given cycle. No trading system can be hundred percent perfect and if we find ourselves in a bad month you'll definitely have the opportunity to come out of the trade with either a small gain of maybe two or three percent or in the worst case scenario we take a scratch trade but the system itself is designed to make every iron condor trade pay at five to ten percent returns. Out of ten iron condor trades you should generally expect to produce in the range of five to ten percent on about seven trades and you should be able to produce at ten percent on at least one of them and between zero and five percent on one or two trades. Risk management is critical to limit the bad months especially the months that we are going to be dealing between zero and five percent and the system comes with all the techniques to protect every iron condor trade for either a no loss or a very small gain and these risk management techniques must be applied at all times and this is the key to the consistent success of the condor max system. Even if you can't monitor the markets you'll know exactly what to do in terms of risk management and almost all of these techniques can be highly automated. Once again all these techniques are part of the system so you don't have to go about thinking what you need to do and there's only four or five techniques you need to master and these techniques don't change they are the same month after month after month so once you learn to manage these trades in a robotic manner you have the recipe to make every iron condor pay. Now that's what I mean by trading with an edge. If you have any questions you can always email me at info at optiontiger.com or you can even call me at<PHONE_NUMBER>. Please register for the free live webinar on Sundays simply fill out your name and email address on this page and I look forward to seeing you on Sunday. Thank you for watching this presentation and have a great day.


What's up everybody Jason here for jazby's casebricks.com 2018 UDA supreme hardcore random players 2 bucks break number 59 Just sold out on our website and number 60s are already more than halfway. So I'm sure we'll do another one of these tonight Again 27 total spots everybody gets around a little player from the list below from Alan Iverson all the way down to Zach Levine So here you go. Let's roll the dice and Randomize the customer names 10 times 5 and 5 good luck one two three four five six seven eight nine And ten there down a Matthew ten times here one two three four six seven eight nine Ten Dennis Rodman down a car on three towns Larry with Dennis Robbins was Jerry West came in love Ryan with long the morning as well as Foltz and the events Carter South of George Gerbin Kenny with Julia serving Ryan with Wiggins Russell with Drexler Eric with Alan Iverson Chris with Murray Bradley with new whiskey Jackie got LeBron James LeBron James Larry with Kim Lodge one Eric with Penny Hardaway Jack with Christophe Portzinga's and Mike freshwater Zach Levine Larry with Larry Bird Matthew Garner you got Michael Jordan Michael S with Seth Steph curd Ryan with Ben Simmons Larry with on maker Josh Prusa Algern Baylor Ryan with Magic Johnson DeMarcus Cousins and Matthew with Carnegie Towns I'll give you guys a quick minute to make any trades I'm just gonna go out and grab two boxes at random or sorry no these ones I numbered sorry and I Believe I numbered them one through ten and I want to say we did two of them and it was Yeah, we did nine in two so every other number still available So let's generate two of them really quick and two so we're generated right now once I close the trade window So the generate two numbers and we did two and nine Three I'll go to three three was the first number was generated and Nine no that we do that one so three Eight three and eight. All right, so three and eight got that here. Let's burn it So again, here's a projected brick schedule times There you go. Good luck Three and eight. Good luck. Let's go to the first one. Good luck guys Jamal Murray autograph here Jamal Murray Gonna go to Chris Lombard Next one and ooh LeBron James just a little bit of patches No autograph, but there you go LaBron James Cavaliers, of course, but it looks like it could be calves LeBron James good Jack someone right to Mikey. I think you're I think you're a little lagging Mikey if you want to refresh What up, man? How's it going? Kristoff for zingas another one for Jack There you go. This one should be nice. Hopefully Good luck, and it's dr. Jay. There you go Julia serving on Hard court autograph There you go. Dr. Jay Julia serving going to Kenny. All right you guys and there you go. This was a hard court number 59 I'm sure we'll do another one tonight before we head off Jassy's case breaks calm


Great. All right. Thanks for that great introduction So today I'm here to talk to you about distributed tracing and the struggle that some of you may be aware of and I guess all of you are going to experience at some point real quick Who remembers what they were doing in March of 2014? Most people don't okay one person. All right, doesn't matter I'm not gonna walk through the whole room, but in any case for me March of 2014 was when I started working on monitoring and observability I started working at a company that I'm sure a lot of you know that was working on commoditizing APM for the masses And since then I've had the fortune of working for a bunch of vendors that you love and some vendors you probably less love All focused on sort of that goal of providing a better developer experience particularly around incidents as well as monitoring and observability So, you know closing in on nine years later Here I am talking about distributed tracing which there is this great white hope of Cloud native observability in particular. So time for an awkward segue quite literally So for those of you who aren't aware The segue was revealed to the world around the time that a bunch of us were still worried about why 2k there's a lot of hype and fanfare and Really it was meant to change the world if you believed every bit of hype and media that you saw out there You know things like the way that we move the way that our Towns and cities were going to be built was all going to be affected by this brand new product and technology Lots of flashy features Steve Jobs himself even said that it was going to be just as important as the personal computer Which is a pretty high praise from from someone who had a pretty big impact on I'm sure all of us here But as you may have guessed from the tombstone the segue ended up Wrapping up this manufacturing in 2020 due to really a lack of adoption and a lack of impact particularly on all the things that was hyped up about and infamously The owner of segue unfortunately literally rode one of these off a cliff in 2010 to his unfortunate early demise So chances are if you're here, you've probably seen or heard some issues around the use of distributed tracing And much in sort of the same way that the segue is your was really definitely didn't live up to its hype There's definitely a potential for distributed tracing to not have the impact that we all hope that it's going to have particularly if we keep treating it the way that we have up until now as We've gone from monoliths and VMs to microservices and Containers our engineering teams have really struggled with the amount of data the complexity of the observability problems that they're dealing with and You know distributed tracing is sort of generally seen as this. Okay, cool for distributed environments We've got distributed tracing makes sense It's all in the name really just need to load in some some tracing instrumentation Load up a tracing back end and you know throw as many complex analytical features of the problem as possible But despite all of these huge investments both the open source community Bartek covered some of those earlier as well as on the vendor side distributed tracing really runs a risk of passing into a relevance and really Being used as a cautionary anecdote and some future talk by someone probably not me Much like I'm using the segue today in these particular slides So I want to talk a little bit about the kinds of problems that our engineering teams are facing In using distributed tracing, but importantly we really need to look at the organizational problems that we're seeing not just the technical problems Not just the hey, what can I build? You know a new crew engine? What can you build a new dashboarding widget to try and solve? Because that's really the the heart of what we need to be able to look at when we look at building and operating developer tools So The story that I've heard time and time again When talking to many organizations large and small about the use of distributed tracing Is that near any team who goes and implements this stuff really runs into a bunch of problems that really center around adoption? The adoption never really hits that critical mass and it starts to fall off rapidly after day one, right? It's the most valuable it ever gets the most Engaged it ever gets in day one It starts to fall off and a lot of it tends to focus on for key problems for key themes that I hear continually The tools are really only useful for power users You know the smartest the brightest the the most long-tenured engineers are the ones who can operate these tools They know the technology they know the data set they know the architecture and they can make use of it But oftentimes at the bottleneck for the rest of their organization And the part of the thing is that it's not easy to go into this oftentimes tracing tooling sort of slapped on on top Of everything else is happening. It's not purposeful in the way that we're using it I've heard continually that engineers really lack trust in what's going on with tracing Oftentimes because of really heavy sampling because of the cost because of the strain on the system because of storage and what and so on and ultimately because you're so Required to rely on what everyone else is doing with a tracing instrumentation There's no real collective or singular ownership around the instrumentation and data collection So you run into issues where you really need data from someone else and they haven't done their instrumentation and vice versa and this ends up with this sort of sorry state of affairs where Tracing for those who have invested heavily and have utilized it for a period of time really see it as a high-promise high effort But sort of low outcome low value Sort of story that they've they've spent this time and effort on so How do we move away from sort of that potential future of the lonely little segue in the desert lost And how do we think about really taking a step back and moving away from the sort of features and functions and all that sort of like Technical focus that we've really applied to distributed tracing up until now and really think about the outcomes and think about well How do we be purposeful about this? There's no real silver bullets. I'm not like up here to say hey You know pernisfree or anything else is about to announce some new magic feature There's really making sure that we start thinking about this properly particularly is a mass Number of organizations are going to start adopting distributed tracing So let's think about maybe the the number one thing that most people think about when they think about the use of tracing is sort of instance and responding to all that's so You know, there's gonna be lots of different variations of this diagram But in terms of what I hear continually from from organizations How are they using it today as I said a lot of the times distributed tracing sort of gets slapped on existing processes But particularly as you've moved into more complex architectures more microservices more containers more cloud native I've got all these alerts going off.  How many teams do we go get involved? Maybe you look at logging first. Maybe you look at your legacy APM first. Maybe you look at metrics dashboards first But almost no one looks at distributed tracing first No, I'm not necessarily saying that you should but oftentimes distributed tracing is used by that, you know One your principal engineer at the back of the room going wait. I have a theory. I bet I could get Yeager to work through this So and it leads to the outcomes you see at the bottom reduced velocity high frustration poor customer experiences All those kinds of bad things that ultimately make us feel bad and really just make us question. Why are we doing this? So if you take another step back and think less about the the instant workflow and more a little bit about well What should it actually do for us? I think about well, we had all of this sort of microservices complexity. We deliberately added this why we did this because engineers should be able to go and move And operate independently of each other sort of think about your individual team think about your your coordination Only one tier away from you But you've introduced all this additional complexity and now you really can't handle it so If we think about distributed tracing as the answer to like well, can we scale back that complexity? Can we really make it so that a human being can think about the where of what's going on and then start applying all the other tools that? We've used before it's not about distributed tracing replacing everything, but it's making The the haystack that we've got to dig into a little smaller Then we can have sort of a much more focused use of it and we can think about okay We can be purposeful as how we use it in those incidents and other use cases So if we think about a Slightly nicer diagram, you've got your alerts those alerts They can lead into dashboards let you know about things like your symptoms justify your investigation Your symptoms and your impact you can use distributed tracing to figure out where So you can go and narrow that scope the the size of that haystack You can better use your logging your APM your metrics whatever it is events exceptions all those wonderful things that we want to use But we also need to use and feed it back into things So for example everyone focuses on SLOs tracing is a great data set to be able to improve our SLOs It's a great data set to be able to help illustrate what symptoms we're having and the impact that we're having on our customers And so being able to sort of tie this together and make it a loop means that you don't have that Oh distributed tracing is only as valuable as it was on day one type situation So ultimately really quickly What we want to do is drive the adoption and realize the value of distributed tracing But to do so we need to be purposeful about what we're doing We can't just you know throw up a distributed tracing back end and have one engineer Who's really good at fiddling around with an advanced career language. We need to integrate in workflows We need to focus on showing where problems exist because that's Allowing us to negate the complexity we introduced purposefully into this situation and make sure it's useful for every engineer It was really quick But just wanted to highlight that chronosphere here is that booth g15 as well as out there in the corridor We've got one bunch of little enticements for you to come talk to us And obviously if you disagree with any of this feel free to come hit me up. Thanks You