Jcon2020 keynote-high-performance-java-cloud-native

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Java has been the leading programming language for more than 20 years. However, many people still believe that Java is complex, heavyweight, and memory-hungry. Today, other languages and especially modern serverless approaches seem to overtake Java more and more. What are the plans for Java, is Java also suited for the cloud, what is coming next and, why should I continue to use Java in the future? Good news: The holy grail is already found! With GraalVM and a new generation of microservices frameworks such as Micronaut, a completely new Java technology stack is emerging that will replace Java as we know it so far and bring Java to a completely new level and establish it in the cloud. Developing native applications with Java, Java as fast as C, app start in milliseconds, minimal memory footprint, and database access up to 1000 times faster. This is not a vision - it is already possible today. In this session, you will learn everything about the Java technology stack of the future and how you can use it to develop lightweight and at the same time ultra-fast in-memory cloud-native applications and microservices in Java. While watching our incredible performance demo, you better fasten your seatbelt.

High-Performance Java-Cloud-Native
Apps & Microservices
JCON2020#
www.jcon.one
Markus Kett
CEO MicroStream
Our Partners 2020:

Software systems of the future will
produces tons of data, must be able
to process tons of data in realtime,
must run in the cloud as well as on
devices and machines. Java is 25
years old! Is Java really suited and
ready for the future? Are today‘s
database still fast enough?

#1 69% #151B 30B
Programming
Language
Developers
Run Java
Active JVMs Active
Cloud JVMs
Developer Choice
in the Cloud
Source: Oracle

RDBMS
Cache
OS
JVM
In-Memory Database
In-Memory Data Grid
Distributed Cache
ORM
App Server
App

RDBMS
Cache
JVM
In-Memory Database
In-Memory Data Grid
Distributed Cache
App
Container Platform
Container
ORM
App Server

RDBMS
Cache
JVM
In-Memory Database
In-Memory Data Grid
Distributed Cache
ORM
Microframework
App
Container Platform
Container

RDBMS
In-Memory Database
In-Memory Data Grid
Distributed Cache
Container Platform
NoSQL

From Full Stack to
Microservice
Frameworks

Polyglot Programming Native Images Startup Time Memory Footprint

RDBMS
In-Memory Database
In-Memory Data Grid
Distributed Cache
Container Platform

RDBMS
Java VM
Tables
Java Object Graph
Queries
Impedance Mismatch !

RDBMS
Java VM
Tables
Java Object Graph
OR-Mapping

Java Object Graph
Key-Value / Document /
Graph / Column-Store
NoSQL
Impedance Mismatch !
Java VM

HDD
~ 9 ms
~ 9,000,000 ns
CPU Cache
< 1 ns
DRAM
~ 65 ns
~ 100,000x faster than HDD
~ 4,500x faster than SSD
SSD HDD
~ 250 µs
~ 250,000 ns

RDBMS
Java Objects
NoSQL
Java VM
Local Cache
In-Memory Database
In-Memory Data Grid
Distributed Cache
Data Streaming Platform
OR-Mapping
Custom Mapping (NoSQL)

Java Object Graph
Key-Value / Document /
Graph / Column-Store
NoSQL
Java VM
Mapping
RDBMS
Distributed Cache / IMDB / IMDG
Local Cache
Tables
Key-Value
Impedance Mismatch !

Today’s Database Application Infrastructures
are highly inefficient, complex, and expensive
Database Cluster
Distributed Application
Distributed Cache
In-Memory DB / Data-Grid
$

•
•
•
•
•
•
•
•
•
•
High Effort of Development:

RDBMS
Cache
GraalVM / Quarkus
In-Memory Database
In-Memory Data Grid
Distributed Cache
ORM
Microservice Framework
App
Container Platform
Container

RDBMS
MicroStream
GraalVM / Quarkus
In-Memory Database
In-Memory Data Grid
Distributed Cache
Data Streaming Platform
Microservice
Microservice Framework
Container Platform
Container
NoSQL
Cloud Object Store

RDBMS
NoSQL
Native Images:
Microservice Frameworks:
Persistence:

Performance Enables Revolutionary
New Innovations, Features and Products
Today – AI, ML, IoT, Automotive
1960s – The Main Frame
1976 – The Personal Computer
1983 – The Graphical User Interace
2007 – The Smartphone

Revolutionizing Cloud Infrastructure
- 87.5 %
40 vCPUs
$18,800
Per year
Only 1 Machine !
320 vCPUs
$134,400
Per year
Conventional Cluster for Running a globally App
Today with MicroStream:
Costs of Infrastructure
annually

Simplifies your Development Process
▪
▪
▪
▪
▪
▪
▪
▪
▪

Producer
JVM
Consumer
JVM
Consumer
JVM
Consumer
JVM
Consumer
JVM
Consumer
JVM
Object-Stream-Mapping

HACKATHON
www.microstream.one
$10,000 prize
Deadline: November 30
SHOW EXCITING USE-CASES AND HOW
MICROSTREAM INTEGRATES WITH ANY FRAMEWORK OR LIBRARY.
TICKET CODE: MICROSTREAM-WORKSHOP – OCT. 30 09:00 or 18:00 CET
SHOW EXCITING USE-CASES AND HOW
MICROSTREAM INTEGRATES WITH ANY FRAMEWORK OR LIBRARY.
MicroStream goes Open Source !

Java has been the leading programming language for more than 20 years. However, many people still believe that Java is complex, heavyweight and memory-hungry. Today, other languages and especially modern serverless approaches seem to overtake Java more and more. What are the plans for Java, is Java also suited for the cloud, what is comming next and, why should I continue to use Java in the future? Good news: The holy grail is already found! With GraalVM and a new generation of microservices frameworks such as Micronaut, a completely new Java technology stack is emerging that will replace Java as we know it so far and bring Java to a completely new level and establish it in the cloud. Developing native applications with Java, Java as fast as C, app start in milliseconds, minimal memory footprint, and database access up to 1000 times faster. This is not a vision - it is already possible today. In this session, you will learn everything about the Java technology stack of the future and how you can use it to develop lightweight and at the same time ultra-fast in-memory cloud-native applications and microservices in Java. While watching our incredible performance demo, you better fasten your seatbelt.

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Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production. Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process? In this session we will cover: - The Art of Effective Code Reviews - Streamlining the Review Process - Elevating Reviews with Automated Tools By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces

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JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.

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Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.

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Jcon2020 keynote-high-performance-java-cloud-native

1. High-Performance Java-Cloud-Native Apps & Microservices JCON2020# www.jcon.one Markus Kett CEO MicroStream Our Partners 2020:

2. ▪ ▪ ▪ ▪ ▪ ▪

3. Software systems of the future will produces tons of data, must be able to process tons of data in realtime, must run in the cloud as well as on devices and machines. Java is 25 years old! Is Java really suited and ready for the future? Are today‘s database still fast enough?

4. #1 69% #151B 30B Programming Language Developers Run Java Active JVMs Active Cloud JVMs Developer Choice in the Cloud Source: Oracle

5. RDBMS Cache OS JVM In-Memory Database In-Memory Data Grid Distributed Cache ORM App Server App

6. RDBMS Cache OS JVM In-Memory Database In-Memory Data Grid Distributed Cache ORM App Server App

7. RDBMS Cache JVM In-Memory Database In-Memory Data Grid Distributed Cache App Container Platform Container ORM App Server

8. RDBMS Cache JVM In-Memory Database In-Memory Data Grid Distributed Cache ORM Microframework App Container Platform Container

9. RDBMS In-Memory Database In-Memory Data Grid Distributed Cache Container Platform NoSQL

10. From Full Stack to Microservice Frameworks

11. Polyglot Programming Native Images Startup Time Memory Footprint

12. RDBMS In-Memory Database In-Memory Data Grid Distributed Cache Container Platform

13. RDBMS Java VM Tables Java Object Graph Queries Impedance Mismatch !

14. RDBMS Java VM Tables Java Object Graph OR-Mapping

15. Java Object Graph Key-Value / Document / Graph / Column-Store NoSQL Impedance Mismatch ! Java VM

16. HDD ~ 9 ms ~ 9,000,000 ns CPU Cache < 1 ns DRAM ~ 65 ns ~ 100,000x faster than HDD ~ 4,500x faster than SSD SSD HDD ~ 250 µs ~ 250,000 ns

17. RDBMS Java Objects NoSQL Java VM Local Cache In-Memory Database In-Memory Data Grid Distributed Cache Data Streaming Platform OR-Mapping Custom Mapping (NoSQL)

18. Java Object Graph Key-Value / Document / Graph / Column-Store NoSQL Java VM Mapping RDBMS Distributed Cache / IMDB / IMDG Local Cache Tables Key-Value Impedance Mismatch !

19. „

20.

21. Today’s Database Application Infrastructures are highly inefficient, complex, and expensive Database Cluster Distributed Application Distributed Cache In-Memory DB / Data-Grid $

22. • • • • • • • • • • High Effort of Development:

23.

24.

25. Object Graph

26. Object Graph

27. RAM Storage

28. RAM Storage

29.

30.

31. RDBMS Cache GraalVM / Quarkus In-Memory Database In-Memory Data Grid Distributed Cache ORM Microservice Framework App Container Platform Container

32. RDBMS MicroStream GraalVM / Quarkus In-Memory Database In-Memory Data Grid Distributed Cache Data Streaming Platform Microservice Microservice Framework Container Platform Container NoSQL Cloud Object Store

33. RDBMS NoSQL Native Images: Microservice Frameworks: Persistence:

34. RDBMS Binaries MicroStream

35. Performance Enables Revolutionary New Innovations, Features and Products Today – AI, ML, IoT, Automotive 1960s – The Main Frame 1976 – The Personal Computer 1983 – The Graphical User Interace 2007 – The Smartphone

36. Revolutionizing Cloud Infrastructure - 87.5 % 40 vCPUs $18,800 Per year Only 1 Machine ! 320 vCPUs $134,400 Per year Conventional Cluster for Running a globally App Today with MicroStream: Costs of Infrastructure annually

37. Simplifies your Development Process ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪

38.

39.

40.

41. Producer JVM Consumer JVM Consumer JVM Consumer JVM Consumer JVM Consumer JVM Object-Stream-Mapping

42.

43. ▪ ▪ ▪ ▪ ▪ ▪ ▪

44. Note ▪ ▪

45. HACKATHON www.microstream.one $10,000 prize Deadline: November 30 SHOW EXCITING USE-CASES AND HOW MICROSTREAM INTEGRATES WITH ANY FRAMEWORK OR LIBRARY. TICKET CODE: MICROSTREAM-WORKSHOP – OCT. 30 09:00 or 18:00 CET SHOW EXCITING USE-CASES AND HOW MICROSTREAM INTEGRATES WITH ANY FRAMEWORK OR LIBRARY. MicroStream goes Open Source !

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