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Serverless Presentation from Devoxx 2017 Casablanca (AWS Lambda / FaaS / Fn Project)
- 3. • Founded munz & more in 2007 • > 15 years Middleware, Cloud, and Distributed Computing • Consulting and High-End Training • Wrote two WebLogic and a Cloud book Dr. Frank Munz @frankmunz
- 4. Content 1. Clouds 2. FaaS (AWS Lambda) 3. Serverless 4. Frameworks (Fn Project) 5. Conclusion #serverless @frankmunz
- 7. Elasticity
- 8. Pay Per Use • 1 US cent/h (micro), geo-distribution included • You own a massively parallel, distributed and highly available supercomputer with linear costs: 1 instance for 100 hours $= 100 instances for 1h @frankmunz
- 9. Cloud Computing API Elasticity Pay per use Fully Programmable Data Center
- 10. PaaS IaaS Amazon EC2 Oracle CI AWS Beanstalk Oracle ACCS FaaS AWS Lambda Oracle TBD Automated elasticity. True pay per use. Container Service AWS ECS OCCS API? Elasticity? Pay per use?
- 12. FaaS: AWS Lambda • AWS announced Lambda in 2014 • Lambda is Function as a Service (FaaS) @frankmunz
- 13. AWS Lambda AWS SAM Local: test and locally run Lambda (Public Beta) event: event passed to function context: runtime context callback: optional return (or null) @frankmunz
- 15. Test Code 1 2 34
- 17. Example: Image Recognition Flow munz & more #19 Extract meta data AI based recognition Create thumbnail How to model flow?
- 18. Pricing Every started request is charged: • First 1 million req/month are free, forever + 400,000 GB seconds of compute time • Measured in increments of 100 msec • $0.20 per 1 million req + $0.00001667 per GB second Tip: Use AWS Cloud Watch alarms for billing! @frankmunz
- 19. Cost Savings Expedia(2016): – 2.3 billions calls – 200k hours / month – $ 550 a month Postlight – API GW, Lambda, Serverless – Costs dropped 2 orders of magnitude down to $ 370 https://www.youtube.com/watch?v=gT9x9LnU_rE https://trackchanges.postlight.com/serving-39-million-requests-for-370-month-or-how-we-reduced-our-hosting-costs-by-two-orders-of-edc30a9a88cd http://serverlesscalc.com/ @frankmunz
- 21. Function as a Service • Automated elasticity • True pay per invocation • Ephemeral compute Definition Characteristics • Event based • Reduced control plane (mem + CPU + I/O) • Uses container tech
- 22. AMIGO: Public Cloud Services Amazon Microsoft IBM Google Oracle Name Lambda Azure Functions Open Whisk Cloud Functions tbd Year 2014 2016 2016 2016 2016 Status prod prod prod beta announced Language Support NodeJS 4.3.2, Java8,Python 2.7, .Net C#, native Linux JS, PHP, C#, F#, Python, bash, PowerShell NodeJS, Java, Phython, (Docker) NodeJS (not Java) Docker based ? Remarks Bundle functions, OneDrive and Bot integration Apache Incubator Deploy from github or BitBucket, CLI Based on open source Fn Project? Link https://aws.amazon. com/lambda/ https://azure.microsoft.co m/en- us/services/functions/ https://openwhisk.apa che.org/ https://azure.microsoft.co m/en- us/services/functions/ https://cloud.oracle.com/home
- 23. Security “Try to attack something that only lives for few milli-seconds” (AWS) @frankmunz
- 24. Youtube: Gone in 60ms https://www.youtube.com/watch?v=YZ058hmLuv0 @frankmunz
- 25. Microservices Developing a single application as • a suite of small services • each running in its own process / owns it‘s data • communicating with lightweight mechanisms (M. Fowler / J. Lewis) https://martinfowler.com/articles/microservices.html @frankmunz
- 26. FaaS = Microservice? • FaaS fulfills M. Fowler’s microservices definition • A real micro service consists of multiple functions • FaaS vs. Docker / Kubernetes? ✅ Benefits of FaaS - Serverless - Zero config auto scaling - True pay per use -> higher abstraction Limits of FaaS - Language choice - Resource limits - Vendor lock-in -> reduced flexibility @frankmunz
- 29. Serverless Mark Cavage, Java One 2017: “Reduce notion of all infrastructure” Serverless Example: AWS SQS@frankmunz Wasn’t that PaaS ? Yes it‘s PaaS, but serverless requires… • Never pay for idle • Automated elasticity • Reduced config settings
- 31. Serverless Frameworks dealing with … @frankmunz AWS Chalice PortabilityComplexity Standards
- 32. FaaS OS Frameworks Overlap in functionality -> expect consolidation Survival is about: • Integration: K8s, Traefik, Zipkin, Prometheus, Kafka etc. • Adoption, adoption, adoption • FaaS as PaaS implementation? https://github.com/faas-lane/ FaaS-Lane/tree/master/candidates@frankmunz
- 33. Fn Project • Apache 2, Open Source Java One 2017 • Fn Server (micro API GW), LB, Flow • Function / Container duality • Docker is only dependency -> polyglot @frankmunz
- 34. Fn Project • Jackson for JSON marshalling in Java • JAX-RS demo • Hot functions (30 seconds) • Fn PaaS service likely? @frankmunz
- 35. Fn Flow Java 8 CompletableFutures API
- 36. Fancy a demo?
- 37. Outlook • Oracle’s Cloud Native Container strategy – Kubernetes – Wercker – Pay per invocation (?) • Fn Project as PaaS and on premises • Azure Container Instance: pay per invoke (today!) @frankmunz
- 39. TL;DR #serverless & #FaaS in all major clouds / could compose μ out of 𝛌 / PaaS ⊇ Serverless ⊇ FaaS / FaaS = stateless / true pay per use / automated scalability / event driven / integrates with other CS / architect against vendor lock in / cloud agnostic FaaS frameworks emerge / FaaS frameworks consolidation ahead / 𝛌 only technically the new μ (AWS). @frankmunz
- 40. 3 Membership Tiers • Oracle ACE Director • Oracle ACE • Oracle ACE Associate bit.ly/OracleACEProgram 500+ Technical Experts Helping Peers Globally Connect: Nominate yourself or someone you know: acenomination.oracle.com @oracleace Facebook.com/oracleaces oracle-ace_ww@oracle.com
Editor's Notes
- Global one man show Intersection of Open Source / Enterprise SW Background HPC / Brain Research
- REST API with JSON Structures that go across wire
- Autoscaling rules 5 people, many rules
- Free lunch! Actually lunch included in $0.01
- Happy days…
- Well almost. So far, theory. What we write when we write a book. Look at reality… Reality i rocky. Monte Cervino, Matterhorn, 4478m Most photographed mountain in the alps DIVING story. IaaS: Launch Configuration, +Autoscaling Groups +Scaling Events, pay /h PaaS: Min/Max, trigger, IaaS pricing CS: Easy Ccontainer scaling IaaS pay per use pricing
- SAM = Serverless Application Model, based on Cloud Formation
- NO EVENTBUS! Stateless, with 5 min max exec time Start threads and processes Events based, but no event bus visible Retries + dead letter Q You can use environment variables Invocation times differ per language and per call No config for scaling You pay for GB *s, metered in 100 ms One event can trigger several funcs Function executes at least once (-> idempotent f) AWS Step Function: visual designer No SLAs
- Not Http Reuse of container is undpredictable Higher startup times on first hit Higher startup times for Java
- Speachlet is technically a servlet Create a skill Define Intents = function Utterances = example user input, you give it several. “similar” is AI.
- True pay per use Duration is calculated from the time your code begins executing until it returns or otherwise terminates, rounded up to the nearest 100ms. Like your mobile …
- Continuous, automated scaling
- Also the size of the attack vector is historically small ! Container lifetime = milli-seconds No open ports Not listening to an IP Lambda triggered by event
- Scaling - deal with hundreds of services Modular structure - modularity vs. overhead Independend change cycles - welcome to having 112 versions My EAR story
- Serverless is full of servers.
- If your PaaS can start efficiently instances in 20 msec that run for half a second, then call it serverless AWS Dynamo: normal mode, you provision r/w throughput and pay. Even if not used.
- Alexis ellis
- Hot functions keeps container running for 30 secs CF : about 50 different methods for composing, combining, executing asynchronous computation steps and handling errors.
- Hot functions keeps container running for 30 secs CF : about 50 different methods for composing, combining, executing asynchronous computation steps and handling errors.
- Currently JSON - .thenCompose : add work when first func is done, uses Lambda type safety across severall fn calls. Looks like single programm, but every stepp is exec single JVM, massive paralleziation. Next, the thenCompose call adds some work to be done once the flight booking function completes. This is in the form of a lambda that takes the result of the flight booking call flightRes and returns another future with more work to do. In this case another invokeFunction call but this time to the hotel booking function. Note that because we’ve specified some type information in the invokeFunction call (BookingRes.class) we have type safety across multiple serverless function invocations. And the compiler was able to infer the type of the thenCompose lambda for us. Very cool. Finally there is the whenComplete call which triggers when all of the preceding stages have returned. This takes a lambda with two parameters, a result and an error. One of these will always be null and we can use this to check for and handle errors.
- most changes ever within 1 major product version number aka 12.1