Faster, greener, and happier- why Quarkus should be your next tech stack

Holly Cummins Senior Principal Software Engineer, Quarkus Code Europe June
10, 2024 @[email protected] faster greener happier why quarkus should be your next tech stack

@holly_cummins #RedHat

@holly_cummins #RedHat software development cycle

@holly_cummins #RedHat the problem:

@holly_cummins #RedHat tea: slow feedback loop→developers drink a lot of
tea the problem:

@holly_cummins #RedHat 2010s-era java web frameworks are verbose there is
a lot of typing

tea the problems:

tea the problems: typing: verbose programming model → repetitive boilerplate

tea the problems: typing: verbose programming model → repetitive boilerplate tedium

@holly_cummins #RedHat “I can’t bring up the microservices in my
Java dev stack

@holly_cummins #RedHat “I can’t bring up the microservices in my
Java dev stack … on my brand new Apple laptop with a M1 chip and 64GB of RAM.” - fintech CTO

@holly_cummins #RedHat in production, it’s worse

@holly_cummins #RedHat in production, it’s worse example microservices maths:

@holly_cummins #RedHat in production, it’s worse example microservices maths: airline
maintenance scheduling system

maintenance scheduling system single service: ½ core + 1 GB RAM

maintenance scheduling system single service: ½ core + 1 GB RAM HA → 3x instances

maintenance scheduling system single service: ½ core + 1 GB RAM HA → 3x instances ~100 microservices

maintenance scheduling system single service: ½ core + 1 GB RAM HA → 3x instances ~100 microservices = 150 cores + 300 GB RAM

@holly_cummins #RedHat all this resource usage is expensive

tea the problems: typing: verbose programming model → repetitive boilerplate

tea the problems: typing: verbose programming model → repetitive boilerplate tolls: high resource usage → paying cloud vendors lots of money

@holly_cummins #RedHat software development cycle c’mon, what about the rest
of the world?

@holly_cummins #RedHat c’mon, what about the rest of the world?
coffee, not tea

@holly_cummins #RedHat the problems: c’mon, what about the rest of
the world? internationalised version coffee, not tea

@holly_cummins #RedHat coffee: slow feedback loop→developers drink too much coffee
the problems: c’mon, what about the rest of the world? internationalised version coffee, not tea

the problems: copious code: verbose programming model → repetitive boilerplate c’mon, what about the rest of the world? internationalised version coffee, not tea

the problems: copious code: verbose programming model → repetitive boilerplate cost: high resource usage → paying cloud vendors lots of money c’mon, what about the rest of the world? internationalised version coffee, not tea

@holly_cummins #RedHat why is this happening? mismatch between what we
need and what the platform is optimised for

@holly_cummins #RedHat java was not designed for the cloud

@holly_cummins #RedHat old Java frameworks were optimised for …

@holly_cummins #RedHat long-lived processes old Java frameworks were optimised for
…

@holly_cummins #RedHat long-lived processes annual (!) deployments old Java frameworks
were optimised for …

@holly_cummins #RedHat long-lived processes annual (!) deployments late-binding old Java
frameworks were optimised for …

@holly_cummins #RedHat long-lived processes annual (!) deployments late-binding re-configurable without
restart old Java frameworks were optimised for …

@holly_cummins #RedHat Container platform machine go go go go go
go go go go go go go go go

@holly_cummins #RedHat Container platform machine node.js node.js node.js node.js node.js
node.js node.js machine go go go go go go go go go go go go go go

@holly_cummins #RedHat Container platform machine HotSpot Heap HotSpot Heap HotSpot
Heap HotSpot Heap machine node.js node.js node.js node.js node.js node.js node.js machine go go go go go go go go go go go go go go

can we do better than that?

enter … quarkus a Java framework that gets you going
faster, faster.

@holly_cummins #RedHat traditional cloud-native java stack traditional cloud-native java stack
traditional cloud-native java stack traditional cloud-native java stack node.js node.js node.js node.js node.js node.js node.js go go machine go go go go go go go go go go go go go go go go go go go quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus quarkus spoiler: we made stuff better :) container orchestration machine machine machine https:/ /developers.redhat.com/blog/2017/03/14/java-inside-docker/

@holly_cummins #RedHat machine quarkus quarkus quarkus quarkus quarkus quarkus quarkus
quarkus quarkus quarkus quarkus quarkus quarkus quarkus container orchestration machine traditional cloud-native java stack traditional cloud-native java stack traditional cloud-native java stack traditional cloud-native java stack … a lot better quarkus native

@holly_cummins #RedHat machine quarkus quarkus quarkus quarkus quarkus quarkus quarkus
quarkus quarkus quarkus quarkus quarkus quarkus quarkus container orchestration machine traditional cloud-native java stack traditional cloud-native java stack traditional cloud-native java stack traditional cloud-native java stack … a lot better quarkus native (but quarkus on jvm is also way smaller than traditional java)

quarkus applications start fast quarkus + graalvm 0.014 Seconds rest
application quarkus + open jdk 0.75 Seconds traditional cloud-native stack 4.3 Seconds https://quarkus.io/blog/runtime-performance/

@holly_cummins to the code! demo!

@holly_cummins #RedHat mvn quarkus:build -Pnative target/code-with-quarkus-1.0.0-SNAPSHOT-runner

faster than a lightbulb

ok but does startup time matter?

ok but does startup time matter? fast boot time means
auto-scaling works better more resilience to load spikes

quarkus + GraalVM 13 MB quarkus + OpenJDK 74 MB
Traditional Cloud-Native Stack 140 MB rest application https://quarkus.io/blog/runtime-performance/ quarkus improves memory utilization

#Quarkus @holly_cummins

#Quarkus @holly_cummins hey, wanna see quarkus? hey, wanna see quarkus?
hey, wanna see quarkus? hey, wanna see quarkus? hey, wanna see quarkus?

hey, wanna see quarkus? hey, wanna see quarkus? hey, wanna see quarkus? uhh … are you supposed to shut down applications after using them?

hey, wanna see quarkus? hey, wanna see quarkus? hey, wanna see quarkus? uhh … are you supposed to shut down applications after using them? 120 instances (!)

ok but does memory footprint matter?

ok but does memory footprint matter? in the cloud, memory
footprint is money

@holly_cummins #RedHat remember the airline scheduling application?

@holly_cummins #RedHat remember the airline scheduling application? “[With Quarkus], we
can run 3 times denser deployments without sacrificing availability and response times of services. ” – Thorsten Pohl

let’s talk about throughput. https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper 48 concurrent connections Traditional cloud
native stack 3555 req/s

let’s talk about throughput. quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper 48
concurrent connections Traditional cloud native stack 3555 req/s

let’s talk about throughput. quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper a
trade-off of throughput against footprint 48 concurrent connections Traditional cloud native stack 3555 req/s

@holly_cummins Native compilation trade-offs throughput startup time + footprint

@holly_cummins this is a classic tradeoff

but … traditional cloud native stack 3555 req/s quarkus native
3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper a trade-off of throughput against footprint 48 concurrent connections

but … traditional cloud native stack 3555 req/s quarkus on
jvm 6389 req/s quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper a trade-off of throughput against footprint 48 concurrent connections

but … traditional cloud native stack 3555 req/s quarkus on
jvm 6389 req/s quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper no trade-off, just better :) a trade-off of throughput against footprint 48 concurrent connections

@holly_cummins we beat the trade-off. throughput startup time + footprint

@holly_cummins we beat the trade-off. throughput startup time + footprint
it’s a double-win.

@holly_cummins #RedHat https://medium.com/arconsis/spring-boot-vs-quarkus-part-2-jvm-runtime-performance-af45d0db116e

@holly_cummins #RedHat https://medium.com/arconsis/spring-boot-vs-quarkus-part-2-jvm-runtime-performance-af45d0db116e spring boot quarkus response time 1901 ms
294 ms throughput 523 req/s 3374 req/s

#Quarkus @holly_cummins users care (a lot) about response time

better startup better footprint better throughput wait, isn’t that something
for nothing?

@holly_cummins #RedHat the two ways of improving performance •trade off
one thing against another •eliminate waste

traditional cloud native stack 3555 req/s quarkus on jvm 6389
req/s quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper

req/s quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper a trade-off of throughput against footprint

req/s quarkus native 3212 req/s https://www.redhat.com/en/resources/mi-quarkus-lab-validation-idc-analyst-paper no trade-off, just better :) a trade-off of throughput against footprint

@holly_cummins #RedHat where the win comes from

@holly_cummins #RedHat application frameworks were optimised for dynamism

@holly_cummins #RedHat application frameworks optimised for dynamism dynamism has a
cost

@holly_cummins #RedHat paying a dynamism tax … even though the
app is not dynamic

@holly_cummins #RedHat cloud apps are immutable now

@holly_cummins #RedHat a highly dynamic runtime in a container is
pointless

pointless loading classes that aren’t needed

pointless loading classes that aren’t needed expensive, slow, reflection

pointless loading classes that aren’t needed expensive, slow, reflection the same initialisation on every startup

@holly_cummins #RedHat how does a java framework start?

@holly_cummins #RedHat how does a java framework start? build time

runtime

@holly_cummins #RedHat how does a java framework start? packaging (maven,
gradle…) build time runtime

runtime

@holly_cummins #RedHat how does a java framework start? </> build
time runtime load and parse • config files • properties • yaml • xml • etc.

@holly_cummins #RedHat how does a java framework start? </> build
time runtime

@holly_cummins #RedHat how does a java framework start? @ @
</> build time runtime • classpath scanning and annotation discovery • attempt to load class to enable/disable features

</> build time runtime

</> build time runtime build a metamodel of the world

</> build time runtime

</> build time runtime start • thread pools • I/O • etc.

</> build time runtime ready to do work!

@holly_cummins #RedHat what if we start the application more than
once? @ @ </>

once? @ @ </> @ @ </>

once? @ @ </> @ @ </> @ @ </>

once? @ @ </> @ @ </> @ @ </> @ @ </>

once? @ @ </> @ @ </> @ @ </> @ @ </> so much work gets redone every time

@holly_cummins Hibernate speed example: JTA auto-wiring

@holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”);

@holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”);

@holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”);

@holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); …

@holly_cummins Hibernate speed example: JTA auto-wiring Class.forName(“LikelyJTAImplementation”); Class.forName(“APossibleJTAImplementation”); Class.forName(“AnotherJTAImplementation”); Class.forName(“NicheJTAImplementation”);
…

Class.forName(“VeryNicheJTAImplementation”); …

Class.forName(“VeryNicheJTAImplementation”); … ~129 auto-wiring attempts

Class.forName(“VeryNicheJTAImplementation”); … ~129 auto-wiring attempts every single start.

@holly_cummins it’s not just JTA this happens for lots of
internal service bindings

@holly_cummins JVM footprint example: Hibernate

@holly_cummins JVM spends time loading classes for specific databases JVM
class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database footprint example: Hibernate

class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database class for unused database turns out they’re never used footprint example: Hibernate

turns out they’re never used JIT spends time unloading classes footprint example: Hibernate

@holly_cummins Hibernate example: ~500 classes which are only useful if
you're running an Oracle database loaded and then unloaded

@holly_cummins Hibernate example: ~500 classes which are only useful if
you're running an Oracle database loaded and then unloaded every single start.

@holly_cummins the true cost of loaded classes isn’t just memory
+ start time

+ start time method dispatching:

@holly_cummins interface the true cost of loaded classes isn’t just
memory + start time method dispatching:

@holly_cummins unused implementation the one we want interface unused implementation
unused implementation the true cost of loaded classes isn’t just memory + start time method dispatching:

@holly_cummins unused implementation the one we want interface megamorphic call
slow dispatching unused implementation unused implementation the true cost of loaded classes isn’t just memory + start time method dispatching:

+ start time the one we want interface

+ start time the one we want monomorphic call fast dispatching interface

@holly_cummins how do we fix all this?

@holly_cummins #RedHat @ @ </> build time runtime what if
we initialize at build time?

@holly_cummins #RedHat @ @ </> build time runtime start •
thread pools • I/O • etc. what if we initialize at build time?

@holly_cummins #RedHat @ @ </> build time runtime ready to
do work! start • thread pools • I/O • etc. what if we initialize at build time?

@holly_cummins #RedHat @ @ </> repeated starts are now efficient

@holly_cummins #RedHat @ @ </> repeated starts are now efficient
less wasted work

@holly_cummins #RedHat the Quarkus way enables native compilation native (graalvm)
@ @ </> jvm build time

ok but we don’t need native

ok but we don’t need native quarkus is faster and
smaller than legacy frameworks, even running on the jvm

@holly_cummins

@holly_cummins doing more up-front

@holly_cummins doing more up-front - speeds up start

@holly_cummins doing more up-front - speeds up start - shrinks
memory footprint

@holly_cummins doing more up-front - speeds up start - shrinks
memory footprint - improves throughput (!)

ok but is performance all there is?

ok but is performance all there is? developer joy

@holly_cummins #RedHat with quarkus, developers have to type less boilerplate
this is good, because we’re about to do a live demo with a broken finger

to the code! @holly_cummins #RedHat

@holly_cummins #RedHat mvn quarkus:dev zero-config live coding

@holly_cummins #RedHat tests are run on every code change “reverse
code coverage” means only relevant tests run mvn quarkus:dev continuous testing

@holly_cummins #RedHat developer UI

@holly_cummins #RedHat zero-config testcontainers

@holly_cummins #RedHat testcontainers integration … without quarkus @TestConfiguration(proxyBeanMethods = false)
public class ContainersConfig { @Bean @ServiceConnection public PostgreSQLContainer<?> postgres() { return new PostgreSQLContainer<>(DockerImageName.parse("postgres:14")); } } public class TestApplication { public static void main(String[] args) { SpringApplication .from(MySpringDataApplication::main) .with(ContainersConfig.class) .run(args); } } @Import(ContainersConfig.class)

@holly_cummins #RedHat testcontainers integration … without quarkus

@holly_cummins #RedHat zero-config testcontainers integration the only thing you need
to do to make testcontainers work is not configure anything # configure your datasource quarkus.datasource.db-kind = postgresql quarkus.datasource.username = sarah quarkus.datasource.password = connor quarkus.datasource.jdbc.url = jdbc:postgresql://localhost:5432/mydatabase # drop and create the database at startup quarkus.hibernate-orm.database.generation = drop-and-create

to do to make testcontainers work is not configure anything # drop and create the database at startup quarkus.hibernate-orm.database.generation = drop-and-create

to do to make testcontainers work is not configure anything

to do to make testcontainers work is not configure anything quarkus also auto-invokes flyway and liquibase

@holly_cummins #RedHat zero-config testcontainers integration realistically, use profiles so things
work in production :) # configure your datasource %prod.quarkus.datasource.db-kind = postgresql %prod.quarkus.datasource.username = sarah %prod.quarkus.datasource.password = connor %prod.quarkus.datasource.jdbc.url = jdbc:postgresql://localhost:5432/mydatabase # on real databases, defaults to ‘none’, but let’s validate %prod.quarkus.hibernate-orm.database.generation = validate

@holly_cummins #RedHat • databases • redis • keycloak • kafka
• elasticsearch • kubernetes • … or add your own auto-provision services “dev services” using testcontainers under the hood

@holly_cummins #RedHat more opinions, less boilerplate

@holly_cummins #RedHat package com.example; import org.jboss.logging.Logger; public class MyService {
private static final Logger log = Logger.getLogger(MyService.class); public void doSomething() { log.info("It works!"); } } example: logging

@holly_cummins #RedHat package com.example; import org.jboss.logging.Logger; public class MyService {
private static final Logger log = Logger.getLogger(MyService.class); public void doSomething() { log.info("It works!"); } } example: logging import io.quarkus.logging.Log; Log

@holly_cummins “but isn’t that dynamism expensive?”

@holly_cummins no. “but isn’t that dynamism expensive?”

@holly_cummins no. it’s done at build time “but isn’t that
dynamism expensive?”

@holly_cummins logging: compiled version public class MyService { // injected
private static final Logger $logger = Logger.getLogger(Thing.class) public void doSomething() { $logger.info(“It works!”); } }

@holly_cummins #RedHat example: query parameters public String hello(@QueryParam("name") String name)
{

@holly_cummins #RedHat example: query parameters public String hello(@RestQuery String name)
{

@holly_cummins #RedHat package org.acme; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public
class SpringDemo { public static void main(String[] args) { SpringApplication.run(SpringDemo.class, args); } } example: declaring an application

@holly_cummins #RedHat example: declaring an application

@holly_cummins what if… you could inherit boilerplate Hibernate queries from
a superclass, instead of having to write them all? example: hibernate

@holly_cummins #RedHat @ApplicationScoped public class GreetingRepository { public Entity findByName(int
name) { return find("name", name).firstResult(); } void persist(Entity entity) {} void delete(Entity entity) {} Entity findById(Id id) {} List<Entity> list(String query, Sort sort, Object... params) { return null; } Stream<Entity> stream(String query, Object... params) { return null; } long count() { return 0; } long count(String query, Object... params) { return 0; } } example: panache + hibernate

@holly_cummins #RedHat example: panache + hibernate @ApplicationScoped public class GreetingRepository
implements PanacheRepository<Greeting> { public Entity findByName(int name) { return find("name", name).firstResult(); } }

@holly_cummins #RedHat DAO example: panache + hibernate @ApplicationScoped public class
GreetingRepository implements PanacheRepository<Greeting> { public Entity findByName(int name) { return find("name", name).firstResult(); } } repository pattern

@holly_cummins #RedHat example: panache + hibernate

@holly_cummins #RedHat example: panache + hibernate active record pattern @Entity
public class Greeting extends PanacheEntity { public String name; public LocalDate issued; @Version public int version; public static List<Greeting> getTodaysGreetings() { return list("date", LocalDate.now()); } }

@holly_cummins #RedHat int port = 8081; ClientInterceptor[] interceptors = new
ClientInterceptor[3]; interceptors[0] = new EventLoopBlockingCheckInterceptor(); interceptors[1] = new IOThreadClientInterceptor(); interceptors[2] = new StorkMeasuringGrpcInterceptor(); // etc Channel channel = ManagedChannelBuilder .forAddress(“localhost", port) .usePlaintext() .build(); GreeterGrpc.GreeterStub greeter = GreeterGrpc .newStub(channel) .withInterceptors(interceptors); example: gRPC

@holly_cummins #RedHat example: gRPC @GrpcClient Greeter greeter;

@holly_cummins #RedHat ok but i don’t like magic

@holly_cummins #RedHat ok but i don’t like magic enrichment happens
at build time

@holly_cummins #RedHat ok but i don’t like magic enrichment happens
at build time no performance drag

doing more up-front enables better devex runtime build time @
@ </>

doing more up-front enables better devex runtime build time we
can do cool code introspections here that would be too expensive and annoying to do at runtime @ @ </>

@holly_cummins #RedHat ok but i still don’t like magic

@holly_cummins #RedHat the old ways all still work ok but
i still don’t like magic

@holly_cummins #RedHat the old ways all still work but you
don’t have to type all the stuff unless you want to ok but i still don’t like magic

@holly_cummins #RedHat ok but we use spring for existing applications,
there is a trade-off there.

@holly_cummins #RedHat <dependency> <groupId>io.quarkus</groupId> <artifactId>quarkus-spring-web</artifactId> </dependency> <dependency> <groupId>io.quarkus</groupId> <artifactId>quarkus-spring-data-jpa</artifactId> </dependency>
ok but we use spring option 1: compatibility libraries option 2: migration tooling • migration toolkit for applications (mta) • windup • open rewrite

@holly_cummins #RedHat “After a week of development with quarkus, I
was able to regain the same level of productivity as when I was developing with Spring Boot.” – Fawaz Paraïso, Decathlon

@holly_cummins #RedHat one last thing …

@holly_cummins #RedHat does being faster and lighter mean quarkus is
greener?

digression: how do you measure carbon?

@holly_cummins #RedHat step 1: measure power usage

@holly_cummins #RedHat step 1: measure power usage wall power measurement

@holly_cummins #RedHat step 1: measure power usage wall power measurement
RAPL

@holly_cummins #RedHat load

@holly_cummins #RedHat Source: Teads EC2 instances carbon dataset

@holly_cummins #RedHat density Source: Clement Escoffier experiment 1: cloud Setup:
• 800 requests/second, over 20 days • SLA > 99% • AWS instances Assumptions: • Costs are for us-east-1 data centre

@holly_cummins #RedHat Setup: • 800 requests/second, over 20 days •
SLA > 99% Assumptions: Source: Clement Escoffier x Teads cloud carbon impact of framework choice interpolated carbon metrics

@holly_cummins #RedHat Setup: • 800 requests/second, over 20 days •
SLA > 99% Assumptions: Source: Clement Escoffier x Teads cloud carbon impact of framework choice the carbon is lower because the cost is lower interpolated carbon metrics

@holly_cummins #RedHat Setup: • REST + CRUD • large heap
• RAPL energy measurement • multiple instances to support high load  Assumptions: • US energy mix Source: John O’Hara experiment 2: RAPL measurements

@holly_cummins #RedHat Setup: • REST + CRUD • large heap
• RAPL energy measurement • multiple instances to support high load  Assumptions: • US energy mix Source: John O’Hara experiment 2: RAPL measurements quarkus on JVM has the lowest carbon … because it has the highest throughput

• quarkus cuts carbon by ~2-3x* • native consumes more
carbon than JVM carbon measurements: conclusions

@holly_cummins #RedHat another last thing …

@holly_cummins #RedHat open source community of contributors

@holly_cummins #RedHat Emiliia Nesterovych Emmanuel Bernard Emre Kaplan Enrique gonzález
Martínez Enrique Mingorance Cano Eoin Gallinagh Eric Deandrea Eric Wittmann Erik Åsén Erik Mattheis Erin Schnabel Eugene Berman Evan Shortiss Fabricio Gregorio faculbsz Falko Modler Fedor Dudinskiy Felipe Carvalho dos Anjos Formentin Felipe Henrique Gross Windmoller Fernando Comunello Fernando Henrique fhavel Fikru Mengesha Filippe Spolti Florian Beutel Florian Bütler Florian Heubeck Florin Botis Foivos Zakkak Foobartender Fouad Almalki Francesco Nigro Francisco Javier Tirado Sarti Francois Steyn Frank Eichfelder franz1981 freakse-sa Fred Bricon Frédérc Blanc Freeman Fang Fu Cheng Gabriele Cardosi Galder Zamarreño galiacheng Gavin King Gavin Ray Geert Schuring Geoffrey De Smet Geoffrey GREBERT Georg Leber George Gastaldi manofthepeace Manyanda Chitimbo Marat Gubaidullin Marc Nuri Marc Schlegel Marc Wrobel Marcel Hanser Marcel Lohmann Marcell Cruz Marcelo Pereira Marcin Czeczko Marcin Kłopotek Marco Bungart Marco Schaub Marco Yeung Marco Zanghì Marcus Paulo Marek goldmann Marek Skacelik Marián Macik Mario Fusco MarioHNogueira Mark Lambert Mark Little Mark McLaughlin Mark Sailes marko-bekhta Markus Heberling Markus Himmel Markus Schwer Martin C. Richards Martin Grammelspacher Martin Kouba Martin Muzikar Martin Panzer Martin Weiler martin-kofoed-jyskebank-dk MartinWitt Marvin B. Lillehaug masini Matej Novotny Matej Vasek Matheus Cruz Mathias Holzer Matteo Mortari Matthias Andreas Benkard Matthias Cullmann mauroal Max Andersen Max Gabrielsson Max Rydahl Andersen Victor Hugo de Oliveira Molinar Vincent Sevel Vincent van Dam Vinícius Ferraz Campos Florentino Viswa Teja Nariboina Vladimir Konkov Vojtech Juranek Vratislav Hais w.glanzer Walter Medvedeo Wayne Ellis Werner Glanzer Willem Jan Glerum William Antônio Siqueira Wim goeman Wippermueller, Frank wojciech.stryjewski Xavier Xieshen xstefank Y. Luis Yann-Thomas LE MOIGNE Yannick Reifschneider YassinHajaj Yelzhas Suleimenov yesunch9 Yoann Rodière Yoshikazu Nojima Youngmin Koo Yubao Liu yugoccp Yukihiro Okada Zaheed Beita zanmagerl zedbeit Zheng Feng Žiga Deisinger Zineb Bendhiba zohar Zoran Regvart Шумов Игорь Юрьевич 出门三不惹 open source community of contributors

@holly_cummins #RedHat tl;dpa (too long didn’t pay attention) deployment density
lower cloud bill frictionless development experience Medium Nano auto-provision services zero-config live coding continuous testing developer UI greener

@holly_cummins #RedHat tl;sdpa (too long still didn’t pay attention) what
problems is quarkus solving? tea typing tedium tolls taxes

slides Holly Cummins Red Hat https://hollycummins.com/faster-greener-code-europe/

Faster, greener, and happier- why Quarkus shoul...

Faster, greener, and happier- why Quarkus should be your next tech stack

More Decks by Holly Cummins

Other Decks in Programming

Featured

Transcript