Serverless Functions OR Microservices

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A trick question in the cloud era, where we have both options available at our feet. What should we use? What are the tradeoffs? Why I should use and not the other one? If you join this session you might find answers to these questions.

Technology

The key thing to note is that legacy systems are only legacy because they’ve been successful enough to last this long.

legacy systems
successful
enough to last this long

Dreamer, Technology Enthusiast, Speaker, Trainer, People 1st, Microsoft Azure MVP, Crafter, Endava, Dreamer

Loosely
coupled
Deployable
independently
High
maintainable
and testable
Organized on
top of
business
needs and
capabilities

Event triggered
Ephemeral
No infrastructure to
manage
Pays per
execution

Event triggered
Ephemeral
No infrastructure to
manage
Pays per
execution
Writing a function that incorporates a business logic and deploy it

Execution Time
Technology Stack
Environment Scaling

Microservices
• Short or long
requests
• Few seconds
• Long
execution
time
Serverless
• Short life
• Few seconds
• Under 300s

Microservices
• Short or long
requests
• Few
secconds
• Long
execution
time
Serverless
• Short life
• Few seconds
• Under 300s

Microservices Serverless
Different
stacks
Limited tech
stacks
External
dependencies
Multiple
stacks
Legacy code
Legacy
dependencies

Microservices
• Cluster
• Nodes type
• Container OS
• OS Updates
• Stack patches
Serverless
• No
infrastructure
configuration
• Easy
configuration
• Focus on code
• Write and
deploy

Microservices
• Same
environment
• Isolated from
infrastructure
Serverless
• Environment
agnostic
• No
dependencies

Microservices
• Node spin-up latency
• Warm-up
Serverless
• Scale on the fly
• Fast warm-up
• Dynamic scaling
• No boundaries

Understand how to connect multiple microservices or
functions to fulfil a business requirement
Each service or collection of functions shall be seen as
individual projects with their own team, pipeline and lifetime
Middleware and internal communication

Business Perspective
• Operation costs
• Agility
• Pay per run
• Maintenance costs
Developer
Perspective
• No system administration
• Monitoring
• No backend infrastructure
• Scalable by design
User Perspective
• Faster updates
• Pluggable
• Cheaper
• Flexible

Business Perspective
• Vendor lock
• Cost not predictable
• Risks from 3rd parties
providers
Developer
Perspective
• Unclear best practices
• Execution duration
• Operational tools
User Perspective
• Wrong architecture can
increase latency

Operating
system
Maintenance and
support
(e.g. operation
system updates,
security patches)
Monitoring of
the OS
Deployment
mechanism
App
Deployment
&
Configuration
Infrastructure
management

Need to use external
services to be able
to deliver the same
functionality
Overcome the
limitations of disk
space, RAM and
execution duration
Legacy
dependencies to
different stacks or
systems

Hybrid App
Microservices Serverless
Proxy
Emit Request

Microservices
Personalization
Cart
Payment
Serverless
Authentication
Authorization
Image resize

Microservices
Personalization
Cart
Payment
Serverless
Authentication
Authorization
Image resize
Kubeless

Bare-metal
• Hardware as unit of scale
• Physical hosting env is abstract
IAAS
• OS as unit of scale
• Hardware is abstract
PAAS
• Application as unit of scale
• OS is abstract
Serverless
• Functions as unit of scale
• Language runtime is abstract

divide
partitioning
replicate/local redundancy
sync data
consistency
Cost control
tools
Code sharing
versioning
Process teams
Select interface,
middleware and channel

Thank you!
@RaduVunvulea
vunvulearadu.blogspot.com
https://www.linkedin.com/in/raduvunvulea/
vunvulear@outlook.com

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Dandelion Hashtable: beyond billion requests per second on a commodity server

Antonios Katsarakis

This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

Fueling AI with Great Data with Airbyte Webinar

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Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

Precisely

Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market. Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.

Apps Break Data

Ivo Velitchkov

How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?

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Serverless Functions OR Microservices

1. The key thing to note is that legacy systems are only legacy because they’ve been successful enough to last this long.

3. legacy systems successful enough to last this long

4. Dreamer, Technology Enthusiast, Speaker, Trainer, People 1st, Microsoft Azure MVP, Crafter, Endava, Dreamer

6. Avoid Prepare ManageSurvive

10. Loosely coupled Deployable independently High maintainable and testable Organized on top of business needs and capabilities

11. Loosely coupled Deployable independently High maintainable and testable Organized on top of business needs and capabilities

12.

13.

14.

15. Event triggered Ephemeral No infrastructure to manage Pays per execution

16. Event triggered Ephemeral No infrastructure to manage Pays per execution Writing a function that incorporates a business logic and deploy it

17.

18. Execution Time Technology Stack Environment Scaling

19. Microservices Serverless

20. Microservices • Short or long requests • Few seconds • Long execution time Serverless • Short life • Few seconds • Under 300s

21. Microservices • Short or long requests • Few secconds • Long execution time Serverless • Short life • Few seconds • Under 300s

22. Microservices • Short or long requests • Few secconds • Long execution time Serverless • Short life • Few seconds • Under 300s

23.

24. Microservices Serverless Different stacks Limited tech stacks External dependencies Multiple stacks Legacy code Legacy dependencies

25. Microservices • Cluster • Nodes type • Container OS • OS Updates • Stack patches Serverless • No infrastructure configuration • Easy configuration • Focus on code • Write and deploy

26. Microservices • Same environment • Isolated from infrastructure Serverless • Environment agnostic • No dependencies

27. Microservices • Node spin-up latency • Warm-up Serverless • Scale on the fly • Fast warm-up • Dynamic scaling • No boundaries

28.

29. Understand how to connect multiple microservices or functions to fulfil a business requirement Each service or collection of functions shall be seen as individual projects with their own team, pipeline and lifetime Middleware and internal communication

30. Understand how to connect multiple microservices or functions to fulfil a business requirement Each service or collection of functions shall be seen as individual projects with their own team, pipeline and lifetime Middleware and internal communication

31. Understand how to connect multiple microservices or functions to fulfil a business requirement Each service or collection of functions shall be seen as individual projects with their own team, pipeline and lifetime Middleware and internal communication

32.

33. K8 Cluster

34. K8 Cluster

35.

36. Business Perspective • Operation costs • Agility • Pay per run • Maintenance costs Developer Perspective • No system administration • Monitoring • No backend infrastructure • Scalable by design User Perspective • Faster updates • Pluggable • Cheaper • Flexible

37. Business Perspective • Vendor lock • Cost not predictable • Risks from 3rd parties providers Developer Perspective • Unclear best practices • Execution duration • Operational tools User Perspective • Wrong architecture can increase latency

38. Operating system Maintenance and support (e.g. operation system updates, security patches) Monitoring of the OS Deployment mechanism App Deployment & Configuration Infrastructure management

39. Need to use external services to be able to deliver the same functionality Overcome the limitations of disk space, RAM and execution duration Legacy dependencies to different stacks or systems

40. Need to use external services to be able to deliver the same functionality Overcome the limitations of disk space, RAM and execution duration Legacy dependencies to different stacks or systems Operating system Maintenance and support Monitoring of the OS Deployment mechanism App Deployment & Configuration Infrastructure management

41.

42.

43. Hybrid App Microservices Serverless Proxy Emit Request

44. Hybrid App Microservices Serverless Proxy Emit Request

45. Microservices Personalization Cart Payment Serverless Authentication Authorization Image resize

46. Microservices Personalization Cart Payment Serverless Authentication Authorization Image resize Kubeless

47. To sum up

48. Bare-metal • Hardware as unit of scale • Physical hosting env is abstract IAAS • OS as unit of scale • Hardware is abstract PAAS • Application as unit of scale • OS is abstract Serverless • Functions as unit of scale • Language runtime is abstract

49. divide partitioning replicate/local redundancy sync data consistency Cost control tools Code sharing versioning Process teams Select interface, middleware and channel

50.

51. Thank you! @RaduVunvulea vunvulearadu.blogspot.com https://www.linkedin.com/in/raduvunvulea/ vunvulear@outlook.com

Serverless Functions OR Microservices

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