Elastic and Cloud-ready Applications with Payara Micro
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This document discusses how to build elastic and cloud-ready applications using Payara Micro. It covers several key aspects: 1) Payara Micro provides a scalable runtime by allowing applications to run as a lightweight executable JAR file that can dynamically form clusters of multiple instances for scalability. 2) Features like JCache, CDI events, and JCA connectors allow applications to support requirements for cloud deployments like pluggable persistence, loose coupling, and failure recovery. 3) Microprofile Config allows applications to access configuration from external sources in a standardized way, and Microprofile metrics and health provide monitoring capabilities. 4) Payara Micro integrates these technologies to provide a complete solution for building resilient
Elastic and Cloud-ready Applications with Payara Micro
- 1. Elastic and Cloud-ready Applications with Payara Micro Ondro Mihályi | Payara Engineer | @omihalyi | @Payara_Fish
- 2. What is cloud ready? ● Java EE or Spring ● Amazon AWS or Openshift ● SQL or NoSQL ● REST or EJB
- 3. Who am I? • Payara Engineer • Java EE developer and lecturer • Czech JUG leader • Member of JCP and Eclipse foundation • @omihalyi
- 4. Is it really about the technology?
- 5. Cloud ready requirements ● Pluggable persistence ● Scalable according to the load ● Low coupling ● External configuration ● Monitoring ● Failure recovery ● Security There are more according to the 12 factor applications manifesto
- 6. Solution? ● Simple API abstractions ● Flexible implementations ● Application logic first, against a solid foundation ● Choose the technology later to meet the needs
- 8. 1. JCache
- 9. JCache
- 10. JCache ● Temporary cache → optimization of frequent reads ● Temporary key-value persistence, extensible to permanent with a read/write- through policy ● More than 10 implementations, supported also by Payara Micro and Spring ● Distributed implementations allow scalable persistence ● Can cache results of data-retrieval method calls
- 11. JCache API @CacheResult User getUserForName(String name) { /*do if not cached*/ } @Inject Cache<String, User> users; users.put(user.getName(), user); User user = users.get(name); StreamSupport.stream(users.spliterator(), false) .filter( e -> e.getValue().getAge() > 50) .count()
- 12. JCache in Payara Micro ● Powered by Hazelcast In-Memory Data Grid ● Integration with Java EE using CDI injection and interceptors ● Distributed, auto-discovery of nodes by default via multicast – Data replication and even distribution – Lite nodes without data in heap ● Other Hazelcast features available via injected HazelcastInstance ● Hazelcast Jet library for fast stream processing
- 14. JCache
- 15. What is Payara Micro? • Executable JAR (~60MB disk size, ~30 MB RAM) • Runs WAR and EAR from command line • Fully embeddable (if you want…) • Forms dynamically scalable cluster • Web Profile “plus” • On Maven Central
- 16. Scale dynamically ● Run multiple instances with the same command java -jar payara-micro.jar --deploy application.war ● Package as a single executable JAR java -jar payara-micro.jar --deploy application.war –outputUberJar application.jar ● Run embedded PayaraMicro.getInstance().addDeployment("application.war").bootStrap() ● Run using Maven plugin
- 17. 3. RESTful
- 18. JCache RESTAPI
- 19. REST services in Java EE ● JAX-RS endpoint @GET @Path("/{id}") @Produces(MediaType.APPLICATION_JSON) public User getUser(@PathParam("id") Integer id) { return userById(id); } ● JAX-RS client User user = client.target(url).path("all").request().get(User.class)
- 20. REST services in Java EE ● JSON binding @JsonbNillable public class User implements Serializable { private String name; @JsonbTransient private String description; @JsonbProperty("userId") private long id; } - new in Java EE 8 - Payara Micro 5-Alpha More about JSON-B: http://json-b.net
- 21. 4. Messaging
- 22. CDI events, really? ● Part of Java EE API already ● Easy to send and observe messages ● Is it enough? What about: – Sending events to other services – Message broker to decouple services – Transactions
- 23. CDI events, really? ● Part of Java EE API already ● Easy to send and observe messages ● Is it enough? What about: – Sending events to other services (nothing else is important initially) – Message broker to decouple services – Transactions
- 24. Events as an initial abstraction ● Transfer events to other services manually – Using distributed queues – Using any message broker ● Or use a solution provided by the cluster out of the box Temporary solutions often become final solutions (when sufficient)
- 25. Payara CDI event bus ● Out of the box in messaging in Payara Micro ● Uses already embedded Hazelcast ● No configuration needed, events reliably dispatched to all observers @Inject @Outbound Event<MyPayload> event; void onMessage( @Observes @Inbound MyPayload event)
- 27. One more option… JCA connector ● Message-driven beans, does it ring the bell? – Not only for JMS but for any messaging infrastructure ● Connetors exists for Amazon SQS, Azure Bus, Kafka, MQTT – https://github.com/payara/Cloud-Connectors @MessageDriven(activationConfig = { … }) public class KafkaMDB implements KafkaListener { @OnRecord( topics={"test"}) public void getMessageTest(ConsumerRecord record) { …
- 28. JCache RESTAPI CDI eventsJCA connector
- 29. JCache RESTAPI CDI eventsJCA connector
- 30. Or evolution to avoid refactoring event observer JCA connection observer MDB event
- 31. Evolutionary architecture „An evolutionary architecture supports continual and incremental change as a first principle along multiple dimensions.“ „Microservices meet this definition.“ Neal Ford, Rebecca Parsons http://evolutionaryarchitecture.com/
- 34. ● Standard config sources – Env. variables – System properties ● Supports pluggable sources – Database?, AWS key service? ● More sources in Payara Micro – Cluster-wide – Scoped (server, app, module) Microprofile Configuration @Inject @ConfigProperty(name = "myservice.url") URL myService; @Inject Config config; … myService = config .getValue("myservice.url", URL.class)
- 35. 6. Monitoring
- 36. Is there a free lunch? ● JVM and managed resources often monitored out of the box by runtimes – Thread & connection pools, HTTP and EJB stats, CPU and MEM usage ● Payara Micro – Metrics over JMX, REST – Notifiers route data from Request tracing & Health checks ● Microprofile – Metrics – monitoring data, statistics – Health – problem detection and autorecovery (green/red light)
- 37. JCache JAX-RS JCA connector Microprofile Configuration Microprofile Metrics, Health Microprofile JWT Future? Microprofile FaultTolerance
- 38. Cloud-ready with Payara Micro ● Payara Micro v4.1.2.173 – JCache – Dynamic clustering – JAR runtime, uber JAR, embedded – JAX-RS, JSON-Processing – CDI event bus, cloud JCA connectors – Microprofile Config – Monitoring (JMX, REST, Notifiers) ● Future Payara Micro v5 – JSON-Binding, Security API (Java EE 8) – Microprofile 1.2 ● JWT auth ● Metrics ● Health ● Fault Tolerance
- 39. Cloud-ready with Payara Micro Not by accident: Payara Micro is designed for running resilient Java EE applications in a modern containerized / virtualized infrastructure. Steve Millidge, Payara project lead https://www.payara.fish/
- 40. Questions?