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Distributed Tracing: From Theory to Practice

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I I we are in a really radio I I'm I'm sorry this is the very technical talk in a very sleepy talks slot cell and if you fall asleep in the middle I'll be super offended by am I will call you on it to hard this also 0 yeah I'm still
gotten it you don't know me I mean engineering parochial and today awareness on the web service interesting the lower started and birth couple housekeeping nodes all I to another link to my slides afterward so they'll be the me on the Internet and all these and code samples and some links so you'll be able to to check that out and if you want to get closer look
I'm and I also have a favor if you have seen these before I probably ask you this is where I am so really carrier the last thing anybody go and he had totally destroyed my voice on Sunday music some drinks a wire but otherwise idea really awkward and I don't like to do that so I am to fill the silences ask you to do something my friend militia lean came up with which is each time you take a drink of water to start clapping and cheering narrator out many this yeah I so that happens a lot in science and in some ways and so that the distributary
tracing I work on a tools team after review and we've been working on implementing distributed tracing for our internal sources that and only and unicycle Brady Bunch team thing how with the photos but I soon Knowledge Village a lot of the trial and error and the discovery that went into this topic I was really a team effort and across across my entire team the the so the basics of Eritrea
thank who knows the distributary singers think of people who have the pattern company rate the all ICU over a guy on so if you actually know what it is a warrior not really sure how you would implement that's you're in the right place at the right time for you and it's basically just the ability to trace the requests across distributed system boundaries and so you
might think fellow we're rails developers if not distributed systems Conference is is not scholar StrangeLoop if you go to those but they can really there's
this idea of a distributed system which is just a collection independent computers that appear to a user to act as a single coherent system and so the user loads your website and more than 1 service does some work to render that requires you actually have a distributed system and it technically because somebody will definitely will actually me that some you database and real that that's actually technically distributed system I will buy things I'm willing to talk more about is the application layer to the so simple use
for distributaries tracing I murine e-commerce site you want users see all of their recent orders monolithic
architecture you that 1 Web process or multiple web processes but they're all running same code i'm an area return information users orders users have many orders the orders have many items very simple rails the the authenticator user controller in grab all the orders all the items rendered on page not a big deal single those now enhancer more requirements in a mobile app at 2 so manie authentication so suddenly it's is a little more complicated and there's a team dedicated authentication so now you may have an authentication server and they don't care at all about orders so in a sense they don't need to know by your stuff you need about know that there's there's so could be a separate rail that on the same server are working and ever on difference or altogether the the the any keep getting more complicated now I wanna show recommendations based on past purchases so that scene in charge of his recommendations Monday assign the folks the only way Python bunch of machine learning so naturally the answer
my services obviously but I'm seriously it might be service is I 0 engineering team your products grow you don't have to have this micro-services bandwagon to find yourself supporting multiple services maybe 1 is rendering language the you might have its own in the structure means I'm like for example a recommendation engine the another 1 that's the teams for a larger I'm the services the maintain my began to look less and less like a very consistent garden and just more like a collection of different plants in different kinds of parts the and
so where this tributary seen fit into this big picture the so 1 day become a set become sappy website subtle and very very slowly and don't look in your application performance monitoring like new relic a skylight or user profiling tool you can see recommendation service it's taking a really long time to load but with the single process
monitoring tools all the services that you only in your system or that your company owns under look just like third-party API calls you're getting as much information about their latency as you would about straight or get however whoever you're calling out to them and so from the user's perspective you know there's 500 extra milliseconds to get the the recommendations we only know why without reaching out to the recommendations team checking out you know figuring out what kind of profiling tools they use for pipeline who knows and digging into the services and this is more and more complicated as your system is more complicated
the and even the day you cannot tell a coherent not her story by your application by monitoring these individual process is the did you ever done any
performance work people are very bad guesses an understanding bottlenecks the so what can we do to increase their visibility in the system and tell that macro-level story tracing back and help it's a way of come monetizing knowledge and cold is 1 of his it can maintainers he talks about how an increasingly complex systems you wanted everyone tools to understand this whole system as a whole without having to rely on these experts so cool Nomura convince
you you need that work Elise makes sense but what might actually be stopping you from implementing the surroundings you never and things that I make it tough to go from this theory to the practice with distributors and 1st and foremost is that
it's kind of outside the review wheelhouse it's not represented as really is not represented in the ecosystem at large and most people are working in go or job our pipeline you're not going to find a lot of sample apps or implementations that are written in Ruby there's also a lot of domain-specific vocabulary they goes interdistributary tracing so reading paradox can feel pretty slow and finally at the most difficult hurdle of all is that the ecosystem is extremely fractured is changing constantly because it's about tracing everything everywhere across frameworks across languages and it needs to support everything so in navigating the solutions that are out there and figuring out which 1 the right for you is not a trivial task so a work on how to get past some of these hurdles today when a server talking another theory which will help you get comfortable the fundamentals and then we'll cover checklist for evaluating add distributaries existence but yeah and that without momentary so 76 black box tracing the ideal a black box is that you do not know about and you can change anything inside your applications so um example black box tracing would be capturing and logging all of the traffic becomes in out at a lower level in your application like your TCP layer all that data it goes into a single large aggregated and then with the power of statistics you just can't get to magically understand the behavior of your system based on sometimes and but I'm not gonna talk a lot about black box tracing today because for us and review it was not a great that and sigh rate that for a lot of companies of a couple of reasons the
I 1 is the new YDD the accuracy based on statistical inference and because it uses statistical analysis by a comparison delays returning results but the biggest problem I is that an event driven systems so psychic or a multi-threaded system you can't guarantee causality the and when I mean exactly
so this is sort of an arbitrary code example but it also showed that if you have service kicks often a sink job and then immediately synchronously cause calls out service to there is no delay in year to your time sensitive certain correlate correctly service 1 isn't job OS and but if he's forgetting queuing delays and latency and then a timestamp might actually make it consistently look like your set your 2nd service is making a call so white
box tracing is a tool that people use to help get around the problem it seems that you have an understanding of the system you can actually changes the so how can we understand this packed ago their request make sources system we explicitly include information about where it came from using something called metadata propagation and is a type of white box traced in it's ysis fancy way of saying that we can change our real that's a anytime back to explicitly pass along information so that you have an explicit trail of how things got the and finally another benefit of white box tracing is real-time analysis and it so it can be almost the entire almost real time and to get results I
the very short history of metadata propagation I the so
the example that everyone talks about the talk I made a propagation is dapper any open source library that inspired calls so that the paper is published by Google in 2010 but is not actually the 1st distributed systems debugging tool to be built and so why is dapper so influential I'm what I'm this is because in contrast to all of these other systems that that came before it those papers were published pretty early in their development but Google publish this paper after it had been running in production and Google scale for many many years and so they're not only able to see that viable at a scale of like Google scale but also that it was valuable and so next that in and that's a project was started in Twitter during the reverse have weak and their goal was to implement kappa but and they open source in 2012 I'm and is currently maintained by measuring coal who is not actually twittering mark I'm the pedal and he spends most of his time working interdistributary synthesis the from here on out when I use the term distributary thing I'm is talk about dapper it like systems because white box metadata propagation distributed tracing systems is not quite as that is it being I think you will read more about things beyond his metadata propagation and there's a pretty cool paper that gives an overview about treat tracing distributed systems be honest time actually do
this the I will this through a few main
components that power most systems and that are of this caliber diverse is the tracer it's i the instrumentation you actually installing application itself there's the transport component which takes the data that they collect and sends it over to the distributed tracing collector and that's a separate act that runs it processes it stores the data and stories in storage compartments and finally there's a UI component that's typically running inside that I'm that allows you to view your tracing data so we'll talk 1st by and the level closest your application itself that's the trees I'm it's how you trace individual require and inside
replication in the real world it's installed as a gem I just like any other performance monitoring agent that would monitor a single process and tracer job is to record data from each system so that we can tell a whole story about your request you can think of the entire story of a single request life cycle as a tree this whole system can be
captured in a single tree NEC so that word span within a single trace are many stands at the top a chapter in that story the so in this case are e-commerce and calling out to the order servicing getting response back at the single step In fact any discrete piece of work can be captured by standard doesn't have to be network requests so we want certain mapping out the system what kind of information even surpassing along you could start by just doing a request ID so the you know that every single pacifistic this took through time you query logs see that's all 1 request you know the same is you have a black box tracing you can't guarantee cardinality just based on the times the need to explicitly create a relationship between each of these components and a really good way to do this is with a parent child relationship that the 1st request the system doesn't have a parent because somebody just click the button loading website so we know that at the top of the tree and then when you're out process starts the e-commerce process it and modify the request headers to pass along just a randomly generated ID as a parody here it's set to 1 but it could really be anything and keeps going on and on with each request so traces ultimately made up of many of these parent-child relationships and informs would call this a a directed acyclic graph and by tying all these things
together it's the were able actually not just understand this as an image but with a data structure and so actually talking a the minutes about how the tracer actually accomplishes that the the so without relationships if that's all we wanted to know we could stop there but that's really going to help us in the long term with the bugging ultimately wanna know more about that about timing information and we can use the annotations to make a more a rich ecosystem of information around these requests by explicitly annotating with time timestamps when each of these things recurring cycle we can begin to understand latency and hopefully you're not seeing a 2nd of latency between every event and is it would definitely not be like user-readable timestamps but this is an example those men drop process
and how it has become a the so in addition to pass along the tree side the parent and the child spans will also the request with the tag timestamp and by having our off that imitate that it's sending a request and R. e-commerce AP imitate that received the requests this will actually give you the network latency between the 2 so if you see a lot of requests queuing up you would see that time go up and on the other hand you can compare 2 timestamps between the server receiving and server sending back the information and you would be able to see if your app is getting very slow you'll the latency increase between those 2 things and finally you're able to close out that full cycle by by indicating that the client has received the final request but what about what happens that the each class again send
information via the transport layer to a separate application of an aggregated data and do a bunch of stuff to it have a process not add latency CCA Leen T. Leighton the tears so 1st it's only and propagate those ideas and beyond by adding information you're headers then it can gather the idea and is reported out of band to a collector and that's what actually does the processing and storing for example the pin has been used up sucker punch i to make a threaded asynch all out to this conserver and this is really similar to things that you would see in our metrics like vibrato any of your logging in metric system that use threads the data collected by the tracer transported via the transportation layer collected finally ready to be even in the UI the so this graph that reviewing here is a good way to understand how the request travels but is not actually good at helping us understand latency I'm or even understand the relationship between and calls with insistence so where youth gang churches from lean
fat so the open tracing I O documentation has a requester similar seminars and looking at it in this
format you'll actually be able to see each of the different services in the same way that we did before but now are able to better visualize how much time is spent in each sub request and how much time it takes relative to the other class you can also later mentioned earlier instrument and visualize internal traces they're happening inside a service not just service to service communications the here you can see billing service is being blocked by the authorization service you can also see that we have a threaded or parallel job execution inside the resource allocation service and if they're starting to be a widening gap between these 2 adjacent services it could mean that there's network requesting you you
you and result in all my sample at senior and you that some are a we know we want carrying so the minimum we wanna record information request and then and the request is up how do we do that grammatically revealing usually with the power of rack nowhere if you're running really at the odds are the were also running a at it the common interface between of for servers and applications to talk to each other so Naturales but use it it serves as a single entry and exit points for Kleiner require a request coming in the system the powerful thing about rack is that it's very easy had nowhere so that I can sit between your server in your application and allow you to customize his requests basic rapid back if you're not familiar with it I'm really object it's can respond coal takes 1 argument and the and returns status headers body that's that's the basic direct out and under the hood rails and Sinatra doing this and
middleware format is a very similar structure it's going to accept an could be your app itself or another set of middleware respond responded call musical Apple called the end so it keeps falling down the tree and the and return a response so if we wanted to do some tracing inside of a middleware what might that method method look like so like we talked about earlier when i wanna start and you stand on every request it's can record received the requests with a server received annotation like we talked about earlier than the yields are rock act to make sure that it executes in the next step in the chain is actually a run your code and then it returns back that the server has sent information back to the client this is just is is not actually a running tracer of is that can has really a really great implementation that you can check out online so then we get a seller application
user middleware to instrument our class and you're never gonna wanna simple every single request that comes in because that is crazy and overkill when you have a lot of traffic I'm so tracing solutions will tip typically ask you to configure a sample rate
with our questioning but in order to generate that big relationship tree that we saw earlier were also to need to continue work on information when request means such systems so these can be request actually PI Isaacs tried get have whatever but if you control that next service that it's talking to you can keep building up this chain media that with more middleware I in you use an HTTP client that supports middleware like eryday or X kind you can easily incorporate tracing into the client I'll use here is an example because it has a pretty similar pattern around so match the method signature it just like we did with I'm ends honestly theories is very similar racket you're using like Exxon it's gonna look a little bit different but this is an example the past the HTTP client app will do some tracing into calling on the train chain it's pretty pretty similar but the tracing itself is going to be a little bit different so are actually needed manipulate the headers to pass along some tracing information that way calling out an external service like a straight they're going to completely ignore these headers because they know what they are but if you're actually calling to another service that's in your europe earned you you'll be able to create that you'll be able to see further down the so each of these collars it's can represent an instrument application so 1 recording were starting client requests ensure that were receiving fire requests Adam
middleware just like we did with rock producing you need
Primerica lately automatically for all your request for some of your HTTP clients so that's the basics for how distributed tracing is implemented and let's talk about even shoes In this ecosystem what system is right for the 1st question is
how reading it was working I'm really a caveat that this ecosystem is ever-changing so this information could actually be incomplete right now I'm anchor the obsolete especially if you're watching this at home on the web I but let's
talk about what to by system yes if the math works out for you it's hard for me they're like really say whether you should do that and if you resourcing is limited and you will find a solution that works for you and it's not too expensive probably i'm unless you're running a super complex system I like said trace view examples of and offer really support I your APM provider might actually have it to you I as adopting an open source solution is another option for acetate solution of the PD solutions didn't work on so you have people on your team who were comfortable with the underlying framework and you have some capacity for managing infrastructure then this is really can work for you and for us for a small team for people for engineers and that that can happen running in a couple of months while also doing a million other things and but partially because wearable to leverage rope you to make the infrastructure components pretty easy and if you want to use a fully open source solution with reviews that can is pretty much a only option as far as I know so you may have heard of open tracing MIDI went up is open to rethink being that seems cool I'm a common misunderstanding is that open tracing is not actually interesting implementations if an API so it's job is just standardize the instrumentation like we can walk before so that all the tracing providers that conform to the API are interchangeable on your outside so if you wanna switch from open-source provided OK provider or price per so you don't need to re instrument each and every service that you maintain In theory and they're all being good citizens their conforming to this API that is all consistent the so where the open tracing that today I mean did publish Ruby API guidelines and back in January of but only light step which is a product in private beta has actually implemented a tracer that conforms API so the tracer an existing implementations lexicon other need to have a bridge between the tracing implementation they have today and I the open trees API and in is just not it's just not clear still is interoperability so example if you have a reaction tree of interesting API everything's great and you have is a provider doesn't support you can't necessarily used to providers that use open tracing and still send them to the same to the same collection system so it's really only at that level another thing to keep in mind is that I for both open-source and hosted solutions Ruby support means a old really wide range of things I'm at the minimum it means that you can start and end a tracing of the Act which is that on but you might have to so right all of your own Ragnar where your HTTP library middleware it's a a deal breaker we end up having to do that for x convers that can i'm but it may be an engineering time commitment that you are not prepared to and then unfortunately because this is racing everywhere you really need a rental API for every language that your company support so what you have to walk through all of these these thoughts in these guidelines and word go offered JavaScript or for any other language the so some companies find that like with the custom nature there and the structure there is a need to build out some or all of the elements in house the Google and they're running fully custom infrastructure and but other companies are actually and building custom components that are tapping and open source solutions so Pinterest I can trace just said is an open-source add add on to that again similar to yell so you're really curious about what other companies are doing large and small I Japanese at Brown
University published a snapshot of 26 companies and what they're doing i it is already out of date and like 1 of the things that are around and all that literally published a month ago and so fitting anything that gained 9 using custom internal solutions I but yeah most most people are actually using the so another component about this is way running in-house was your team rear art scene what they wanna run in-house INRIA any restrictions the there
is this dependency matrix of the tracer and the transport layer which need to be compatible with each 1 of your services and JavaScript go Ruby iron and so with the tracer in the transport layer need to be compatible across the board I'm so for example for us http and J. Sun is totally fine for a transport layer we just literally call out with web requests on 2 or is it can collector but if you knew you had time the NED something like that you might think that's cool and totally supported but if you look at documentation if is a Ruby and unit weight no abiding in 4 layers Stevens documentation it's only Geraghty so that they get total bachelor and so for each of these you really should is build a spreadsheet and because the it's pretty challenging to make sure your covering everything from the collection storage layers I doesn't have to be there's not really related to the services that you run but they might not be the kind that you use to running so for example the can is a job act which is totally different from the act rounds the the thing is to figure out is whether or
not you need to run as separate agent on the host machine itself as over some solutions and this is why we had to exclude a lot of them there were you actually need to install an agent on each host for each service the russian and because we want her grew on roku we can and we can't really do that because we can is given root level privileges to agent that's running out of the another thing to consider is up authentication I an authorization who can see and submit data tracing system of recitation was
missing both of those components it makes sense because it really needs to be everything for everybody and also adding on authentication authorization on top of that for every single company to use 1st library is not really I'm see running inside a VPN without authentication are the other option is using reverse proxy i which is what we ended up doing so we used to build packs and
act in the right packed and so we're able to get into next honor Heroquest lab which is like a bundle dependencies in and your code would act and its sister package manager for Linux so you can download and install a speech-specific Ferdinand next around as a reverse proxy run allows us to run are as it can application and engine Exelon alongside each other in the same house and we can we want anybody on the Internet it is the oldest send it is that can only give you this suddenly started sending data are as it can candidate instance that would be pretty weird so we want to make sure were only having parochial applications interacting with it and so we decided to use basic operation for that we use is the password to sets and Justine base credentials in a flat file as we only have about 25 different I'm different basic off configuration that we thought we and regions of looking
like this Roman architecture diagrams standpoint the client makes a request in annexes there were to intercept that checked against the basic valid off and basic often make sure it's valid and then it is to support along the that can otherwise a return air a and so adding
authentication on a client-side itself was is easy and going back to that rack nowhere file and updating of hosting with both the basic that was a really good solution for us Wilson not only in all the all the CER is it stuff on the internet I was right now views runs at an instance there is there is nothing to keep you from the antibodies and there's no authorization so I We use bitly I the has a loss to proxy which super awesome I'm it allows us to restrict access I to only people with broken up from e-mail addresses and so here on a browser and tried
access there's a since reject to the authorize otherwise is lot to proxy is the Hannibal full authentication so it's configurable with different had will bounces slash reverse proxies you know operator so it's it's actually really cool you need to run any kind of OR in front of the in front of a process but if you're
going hosted root and you don't need to handle any of this by infrastructure you're going to ask about how a learning get access to people who need it because you know what I be the team who said to manage this hand off of like sign on the inside and then 0 you need to e-mail this person you wanna manage all that it featured it's clear with your hosting provider and hiring manage manage access
the security use sensor data on your systems which a lot of people do you there 2 places specifically where we we were we had really keep an eye out for security issues 1 is
custom custom instrumentation
their example might seem odd seen add some custom internal tracing of our own services I using prepare and to trace all PostgreSQL and so when we look at it with nowhere earlier use were wrapping bad behavior with trees but the problem here is that if you're calling sequel . U S and that's sequel statement had any kind of private data and you wanna make sure that you're not just storing that blindly into your system I'm especially if you have PII any kind of like security compliance information that your storing the 2nd thing is you need a talk through before it happens what to do when your data leaks out for us running on system is a is a benefit because if we actually delete data where the data into our into a third-party provider it's easier for us to validate 3 years about personality will me on that data that we've white that data than having to coordinate with a 3rd party providers it is in you should use a third-party solution but you should ask them ahead of time what you doing data leaks what's the turn around how can we verify that you wanna do that when you're in the middle of a crisis the the last thing to consider is the people part is everybody on board for and the nature of the distributary tracing thing is that the work is distributed in your job is probably not going to and when you just get the service up and running you actually need to instrument act and there's a lot of cognitive load as you can see from that you know 13 30 minutes we talked about this in understanding has review tracing works so
set yourself up for success ahead of time by getting it on teams roadmaps if you can otherwise start opening PR as the other option I mean even in your polynomial to need to talk through like and what it is and why are adding but it's a lot easier when you can show them code and how tax a actually interacts with the system so here's the full full checklists
for evaluation and will cover 1 last thing before I I lay on their own and if you're thinking like this is so much information like where y even go next year I might devices if you have some free time
to work with the 20 % time where have we start the training and the opposite can up and running I mean if you don't want to use it in all i includes a test version of Cassandra built in I. so you need to get the job back itself up and running in you don't have to worry about all of these different components right off the bat and if you're able in if you're distance remaining reacts than that in is compatible you need and the players on a parochial and so here want the material just get is deployed the UI loaded this instrument 1 single happy even if the only thing that does is make a third-party straight call and it'll help the trends in music really abstract concepts into a concrete concepts so I have today folks I'm giving
questions I'm actually heading straight to the heroic booth after this Indiana in the big X also SAP I'll be there for about an hour and you can see and asking questions there you talk about her view sticker stuff yeah and would their so
if the whole WSJ reported
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Metadaten

Formale Metadaten

Titel Distributed Tracing: From Theory to Practice
Serientitel RailsConf 2017
Teil 50
Anzahl der Teile 86
Autor Cotton, Stella
Lizenz CC-Namensnennung - Weitergabe unter gleichen Bedingungen 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/31243
Herausgeber Confreaks, LLC
Erscheinungsjahr 2017
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract Application performance monitoring is great for debugging inside a single app. However, as a system expands into multiple services, how can you understand the health of the system as a whole? Distributed tracing can help! You’ll learn the theory behind how distributed tracing works. But we’ll also dive into other practical considerations you won’t get from a README, like choosing libraries for Ruby apps and polyglot systems, infrastructure considerations, and security.

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