serverless - when to faas?
TRANSCRIPT
The Rise of Serverless - When to FaaS?
Photo by Shane PerryIcon by Serverless
◉ Cloud Architecture & Development Consulting◉ Serverless Enthusiast◉ Ex-
Find me at @benikbauer
I am Benny Bauer
Shalom!
EvolutionGenesis Custom Built Product (+ rental) Commodity (+ utility)
Cloud evolution in 60 sec
Data center Hosting
Virtualization IaaS
Icons made by freepik from www.flaticon.com & unlimicon from Noun Project
On Premise
Compute
Platform PaaS Serverless
What is Serverless
FaaS BaaS B2D SaaS
What is FaaS - Function as a Service
Fully-managed compute
Provisioning, patching, scaling, monitoring, logging are provided out-of-the- box
Deploy your code
Just package and upload the code
Pay for actual usage
Getting charged only upon code execution, per 100ms
100%
UTILIZATION
LESS OPS
How it works
Deploy your code
Define triggers
Code execution
(ephemeral instance)
EVENT-DRIVEN
AUTO SCALING
+
AVAILABILITY
Event-driven on steroids
◉ HTTP requests
◉ Storage (e.g. file upload)
◉ DB (e.g. row insert)
◉ Events stream
◉ Queue/Messaging
◉ Scheduled job
◉ Monitoring
◉ Infrastructure events
◉ Code build events
◉ Many more...
Distributed architecture on steroids
Monolith Microservices Serverless
Source: http://ryanjbaxter.com/2015/07/15/using-microservices-to-build-cloud-native-applications-part-1/
Bad
◉ Monitoring
◉ Debugging
◉ Deployment
◉ Security
Distributed architecture on steroids
Good
◉ Scaling
◉ Fault tolerance
◉ Ownership
◉ Cost (better utilization)
Let’s combine these building blocks to something useful
Use Cases
Use case: Web backend
Use case: Web backend
Source: https://www.linkedin.com/pulse/serverless-architecture-cloud-guru-ryan-kroonenburg
Use case: Mobile backend
Use case: Mobile backend
Async request
Use case: File processing
Use case: File processing
Use case: Bot
Use case: Distributed compute
import pywren
def my_function(x):
return x + 7
wrenexec = pywren.default_executor()
futures = wrenexec.map(my_function, range(10))
pywren.get_all_results(futures)
pywren
Use case: Stream processing
◉ Log processing
◉ Analytics
◉ etc.
Use case: Scheduled tasks
◉ Health checks
◉ Monitoring
◉ Periodical digest
◉ etc.
Use case: Operations
Policy enforcement on AWS resources.
Examples:
● Unmanaged instances handling● Low utilized instances resizing
Triggered by:
● CloudWatch events● Scheduled
Use case: CI
Source: https://github.com/lambci/lambci
LambCI(Travis alternative)
Use case: IoT backend
Source: https://www.hackster.io/jose-troche/alexa-drone-pilot-7f2deb
Use case: IoT backend
Source: https://medium.com/serverless-stories/building-a-serverless-garden-monitoring-system-with-lambda-c72c54113df2
Garden Monitoring
System
Use case: CDN
Source: https://www.slideshare.net/AmazonWebServices/new-launch-bringing-aws-lambda-to-the-edge
◉ Purpose:
○ Better UX
○ Reduce load from
backend
◉ Use cases:
○ Request validation
○ A/B testing
○ Content customizations
according to user-agent
Use case: CDN
Source: https://www.slideshare.net/AmazonWebServices/new-launch-bringing-aws-lambda-to-the-edge
Source: https://www.slideshare.net/AmazonWebServices/aws-welcome-to-reinvent-recap-20161214
Use case: @Device
Use case: @Device - example
Source: https://www.slideshare.net/AmazonWebServices/aws-welcome-to-reinvent-recap-20161214
Use case: More
But isn’t it limitationless??
Mind The Limitations
Limitations: Stateless
◉ Instances are ephemeral (memory, storage)but not necessarily disposable
◉ No sticky sessions◉ Workarounds:
○ Websockets through IoT (MQTT)
○ Manage state yourself:
■ Pass on request
■ Store in cache/db
Execution time:5 min
Memory:1.5GB
Disk:500MB
Package (zip):50MB
Limitations: Provider limits
◉ Ephemeral => cold start on first or infrequent calls
◉ Inconsistent
Limitations: Latency
Source: http://www.yusp.com/blog/cold-start-problem-recommender-systems/
Limitations: Latency
Source: https://blog.newrelic.com/2017/01/11/aws-lambda-cold-start-optimization/
Frequency:
1 minute
10 minutes
30 minutes
60 minutes
Limitations: Latency
Source: http://ericjonas.com/pywren.html
Limitations: Costs
AWS Lambda EC2
Duration Per 100 msPer 1 hourReduced cost for RI
Cost of ownership
Low High
Additional costs RequestsRedundancyStorage
Cost effective For low or changing load For high utilization
Limitations: Costs
Cost
Usage
EC2
AWS Lambda
Limitations: Vendor lock-in
◉ The real lock-in is the integrated services
◉ A tradeoff: control vs. convenienceSource: https://www.slideshare.net/AmazonWebServices/reinvent-recap-session-1-whats-new-with-aws-lambda
Limitations: Vendor lock-in(alternatives)
Kubeless
funktion
Limitations: Vendor lock-in(alternatives)
Know the challenges
◉ Development○ Local development
○ Integration tests
○ Debugging
◉ Granularity○ Deployment
○ Monitoring
○ Logging
○ Security
○ Service discovery
Frameworks
Chalise
Tools
The FaaS effectNew emerging practices
Photo by TimOve (https://flic.kr/p/4aTMMx)
Business model
◉ Worth Based Development (“FinDev”)
https://hackernoon.com/why-the-fuss-about-serverless-4370b1596da0#.yct5knd7h by Simon Wardley
Business model
◉ API Marketplace
Architecture
◉ More distributed and pluggable ◉ More event-driven◉ Frameworks are changing
Operations
DevOps is changing
◉ Less about provisioning, capacity planning◉ More about distributed systems:
○ Monitoring & visibility ○ Debuggability○ Graceful degradation & Resiliency○ CI/CD○ Costs optimisations
SummaryWhat was it all about?
Predicting the future
◉ Lower costs◉ Less limitations◉ More use cases◉ More tooling
Takeaways
◉ Deliver faster with lower costs for many use cases
◉ Consider limitations◉ Get ready for new emerging practices
Any questions?
Thanks!
Slides template by SlidesCarnival
You can find me at
◉ @benikbauer◉ [email protected]
This work is licensed under a CC Attribution 4.0 International License.