part 1 : new challenges to observability part 2 : 1st party observability tools from AWS [this post] part 3 : 3rd party observability tools part 4: the future of Serverless observability In part 1 we talked about the challenges serverless brings to the table. In this post, let’s look at 1st party tools from AWS Out of the box we get a bunch of tools pro­vid­ed by AWS itself: Cloud­Watch for mon­i­tor­ing, alert­ing and visu­al­iza­tion Cloud­Watch Logs for logs X-Ray for dis­trib­uted trac­ing Ama­zon Elas­tic­Search for log aggre­ga­tion CloudWatch Logs When­ev­er you write to , those out­puts are cap­tured by the Lamb­da service and sent to Cloud­Watch Logs as logs. This is , as it’s pro­vid­ed by the plat­form. std­out one of the few background processing you get All the log mes­sages (tech­ni­cal­ly they’re referred to as ) for a giv­en function would appear in Cloud­Watch Logs under a sin­gle . events Log Group As part of a , you have many . Each con­tains the logs from one con­cur­rent exe­cu­tion (or ) of your func­tion, so there’s a one-to-one map­ping. Log Group Log Streams con­tain­er So that’s all well and good, but it’s . There’s cur­rent­ly . Whilst AWS has been improv­ing the ser­vice, it still pales in com­par­i­son to oth­er alter­na­tives on the mar­ket. not easy to search for log mes­sages in Cloud­Watch Logs no way to search the logs for mul­ti­ple func­tions at once It might suf­fice as you start out, but you’ll prob­a­bly find your­self in need of some­thing more soon after. For­tu­nate­ly, it’s straight­for­ward to get your logs out of Cloud­Watch Logs. You can stream them to Amazon’s host­ed Elas­tic­search ser­vice. But don’t expect it to be a like-for-like expe­ri­ence with your self-host­ed ELK stack though. wrote a detailed on some of the prob­lems they ran into when using Ama­zon Elas­tic­search at scale. Please give that a read if you’re think­ing about adopt­ing Ama­zon Elas­tic­search. Liz Ben­nett post Alter­na­tive­ly, you can stream the logs to a Lamb­da func­tion, and ship them to a log aggre­ga­tion ser­vice of your choice. I won’t go into detail here as I have writ­ten about it at length pre­vi­ous­ly, just go and read instead. this post You can stream logs from CloudWatch Logs to just about any log aggregation service, via Lambda. CloudWatch Metrics With Cloud­Watch, you get some basic met­rics out of the box. Invo­ca­tion count, error count, invo­ca­tion dura­tion, etc. All the basic teleme­try about the health of a func­tion. But Cloud­Watch is miss­ing some valu­able data points, such as: esti­mat­ed costs : Cloud­Watch only report this for func­tions with reserved con­cur­ren­cy con­cur­rent exe­cu­tions cold starts : Lamb­da reports this in Cloud­Watch Logs, at the end of every invo­ca­tion. Because Lamb­da invo­ca­tions are billed in 100ms blocks, a 102ms invo­ca­tion would be billed for 200ms. It will be a use­ful met­ric to see along­side to iden­ti­fy cost opti­miza­tions) billed dura­tion Invo­ca­tion Dura­tion : Lamb­da reports this in Cloud­Watch Logs too, but it’s not record­ed in Cloud­Watch mem­o­ry usage You get 6 basic metrics about the health of a function. There are ways to record and track these met­rics your­self, see on how to do that. Oth­er providers like IOPipe (more on them in the next post) would also report these data points out of the box. this post You can set up Alarms in Cloud­Watch against any of these met­rics, here are some good can­di­dates: throt­tled invo­ca­tions : set thresh­old based on % of your cur­rent region­al lim­it region­al con­cur­rent exe­cu­tions tail (95 or 99 per­centile) laten­cy against some accept­able thresh­old 4xx and 5xx errors on API Gate­way And you can set up basic dash­board in Cloud­Watch too, at $3 per month per dash­board (first 3 are free). X-Ray For dis­trib­uted trac­ing, you have X-Ray. To make the most of trac­ing, you should instru­ment your code to gain even bet­ter vis­i­bil­i­ty. Like Cloud­Watch Logs, . It’s a back­ground pro­cess­ing that the plat­form provides for you. col­lect­ing traces do not add addi­tion­al time to your function’s invo­ca­tion From the trac­ing data, X-Ray can also show you a ser­vice map like this one. X-Ray gives you a lot of insight into the run­time per­for­mance of a func­tion. How­ev­er, , the dis­trib­uted aspect is severe­ly under­cooked. As it stands, . its focus is nar­row­ly on one func­tion X-Ray cur­rent­ly doesn’t trace over API Gate­way, or asyn­chro­nous invo­ca­tions such as SNS or Kine­sis It’s good for hom­ing in on per­for­mance issues for a par­tic­u­lar func­tion. But it offers lit­tle to help you build intu­ition about how your sys­tem oper­ates . For that, I need to step away from what hap­pens inside one func­tion, and be able to look at the . as a whole entire call chain After all, when the engi­neers at Twit­ter were talk­ing about the need for , it wasn’t so much to help them debug per­for­mance issues of any sin­gle end­point, but to help them make sense of the behav­iour and of their sys­tem. A sys­tem that is essen­tial­ly one big, com­plex and high­ly con­nect­ed graph of ser­vices. observ­abil­i­ty performance With Lamb­da, this graph is going to become a lot more com­plex, more sparse and more con­nect­ed because: instead of one ser­vice with 5 end­points, you now have 5 func­tions func­tions are con­nect­ed through a greater vari­ety of mediums — SNS, Kine­sis, API Gate­way, IoT, you name it event-dri­ven archi­tec­ture has become the norm Our trac­ing tools need to help us make sense of this graph. They need to help us between our func­tions. And they need to help us fol­low data as it enters our sys­tem as a user request, and reach­es out to far cor­ners of this graph through both syn­chro­nous and asyn­chro­nous events. visu­al­ize the con­nec­tions And of course, X-Ray do not span over non-AWS ser­vices such as Auth0, or Google Big­Query, or Azure func­tions. But those of us deep in the server­less mind­set see the world through SaaS-tint­ed glass­es. We want to use the ser­vice that best address­es our needs, and them togeth­er with Lamb­da. glue At Yubl, we . Auth0, Google Big­Query, GrapheneDB, Mon­go­Lab, and Twillio to name a few. And it was great, we don’t have to be bound by what AWS offers. used a num­ber of non-AWS ser­vices from Lamb­da My good friend Raj also did a good talk at NDC on how he uses ser­vices from both AWS and Azure in his wine start­up. You can watch his talk . here And final­ly, I think of our sys­tem like a brain. Like a brain, our sys­tem is made up of: (func­tions) neu­rons (con­nec­tions between func­tions) synaps­es and (data) that flow through them elec­tri­cal sig­nals Like a brain, our sys­tem is alive, it’s con­stant­ly chang­ing and evolv­ing and it’s con­stant­ly work­ing! And yet, when I look at my dash­boards and my X-Ray traces, that’s not what I see. Instead, I see a tab­u­lat­ed list that does not reflect the and . It doesn’t help me build up any intu­itive under­stand­ing of what’s going on in my sys­tem. move­ment of data areas of activ­i­ty A brain sur­geon wouldn’t accept this as the pri­ma­ry source of infor­ma­tion. How can they pos­si­bly use it to build a men­tal pic­ture of the brain they need to cut open and oper­ate on? I should add that this is not a crit­i­cism of X-Ray, it is built the same way most observ­abil­i­ty tools are built. But maybe our tools need to evolve beyond human com­put­er inter­faces (HCI) that wouldn’t look out of place on a clip­board (the phys­i­cal kind, if you’re old enough to have seen one!). And it actu­al­ly reminds me of one of Bret Victor’s sem­i­nal talks, . stop draw­ing dead fish Net­flix made great strides towards this idea of a live dash­board with . Which they have also kind­ly . Vizcer­al open sourced Conclusions AWS pro­vides us with some decent tools out of the box. Whilst they each have their short­com­ings, they’re to get start­ed with. good enough As 1st par­ty tools, they also enjoy home field advan­tages over 3rd par­ty tools. For exam­ple, Lamb­da col­lects logs and traces with­out adding to your func­tion invo­ca­tion time. Since we can’t access the serv­er any­more, 3rd par­ty tools can­not per­form any back­ground pro­cess­ing. Instead they have to resort to workarounds or are forced to col­lect data syn­chro­nous­ly. How­ev­er, as our server­less appli­ca­tions become more com­plex, these tools need to either evolve with us or they will need to be replaced in our stack. Cloud­Watch Logs for instance, can­not search across mul­ti­ple func­tions. It’s often the first piece that need to be replaced once you have more than a dozen func­tions. In the next post, we will look at some 3rd par­ty tools such as , and . We will dis­cuss their val­ue-add propo­si­tion as well as their short­com­ings. IOPipe Dash­bird Thun­dra Like what you’re reading but want more help? I’m happy to offer my services as an and help you with your serverless project — architecture reviews, code reviews, building proof-of-concepts, or offer advice on leading practices and tools. independent consultant I’m based in and currently the only UK-based . I have nearly of with running production workloads in AWS at scale. I operate predominantly in the UK but I’m open to travelling for engagements that are longer than a week. To see how we might be able to work together, tell me more about the problems you are trying to solve . London, UK AWS Serverless Hero 10 years experience here I can also run an to help you get with your serverless architecture. You can find out more about the two-day workshop , which takes you from the basics of AWS Lambda all the way through to common operational patterns for log aggregation, distribution tracing and security best practices. in-house workshops production-ready here If you prefer to study at your own pace, then you can also find all the same content of the workshop as a I have produced for Manning. We will cover topics including: video course authentication authorization with API Gateway Cognito & & testing running functions locally & CI/CD log aggregation monitoring best practices distributed tracing with X-Ray tracking correlation IDs performance cost optimization & error handling config management canary deployment VPC security leading practices for Lambda, Kinesis, and API Gateway You can also get the face price with the code . Hur­ry though, this dis­count is only avail­able while we’re in Manning’s Ear­ly Access Pro­gram (MEAP). 40% off ytcui

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Serverless observability, what can you use out of the box?

Serverless observability, what can you use out of the box?

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