Kotlin Microservices with Micronaut, Spring Cloud and JPA

Micronaut Framework provides support for Kotlin built upon Kapt compiler plugin. It also implements the most popular cloud-native patterns like distributed configuration, service discovery and client-side load balancing. These features allows to include your application built on top of Micronaut into the existing microservices-based system. The most popular example of such approach may be an integration with Spring Cloud ecosystem. If you have already used Spring Cloud, it is very likely you built your microservices-based architecture using Eureka discovery server and Spring Cloud Config as a configuration server. Beginning from version 1.1 Micronaut supports both these popular tools being a part of Spring Cloud project. That’s a good news, because in version 1.0 the only supported distributed solution was Consul, and there were no possibility to use Eureka discovery together with Consul property source (running them together ends with exception).

In this article you will learn how to:

  • Configure Micronaut Maven support for Kotlin using Kapt compiler
  • Implement microservices with Micronaut and Kotlin
  • Integrate Micronaut with Spring Cloud Eureka discovery server
  • Integrate Micronaut with Spring Cloud Config server
  • Configure JPA/Hibernate support for application built on top Micronaut
  • For simplification we run a single instance of PostgreSQL shared between all sample microservices

Our architecture is pretty similar to the architecture described in my previous article about Micronaut Quick Guide to Microservice with Micronaut Framework. We also have three microservice that communicate to each other. We use Spring Cloud Eureka and Spring Cloud Config for discovery and distributed configuration instead of Consul. Every service has backend store – PostgreSQL database. This architecture has been visualized on the following picture.

micronaut-2-arch (1).png

After that short introduction we may proceed to the development. Let’s begin from configuring Kotlin support for Micronaut.

1. Kotlin with Micronaut – configuration

Support for Kotlin with Kapt compiler plugin is described well on Micronaut docs site (https://docs.micronaut.io/1.1.0.M1/guide/index.html#kotlin). However, I decided to use Maven instead of Gradle, so our configuration will be slightly different than instructions for Gradle. We configure Kapt inside Maven plugin for Kotlin kotlin-maven-plugin. Thanks to that Kapt will create Java “stub” classes for each of your Kotlin classes, which can then be processed by Micronaut’s Java annotation processor. The Micronaut annotation processors are declared inside tag annotationProcessorPaths in the configuration section. Here’s the full Maven configuration to provide support for Kotlin. Besides core library micronaut-inject-java, we also use annotations from tracing, openapi and JPA libraries.

<plugin>
	<groupId>org.jetbrains.kotlin</groupId>
	<artifactId>kotlin-maven-plugin</artifactId>
	<dependencies>
		<dependency>
			<groupId>org.jetbrains.kotlin</groupId>
			<artifactId>kotlin-maven-allopen</artifactId>
			<version>${kotlin.version}</version>
		</dependency>
	</dependencies>
	<configuration>
		<jvmTarget>1.8</jvmTarget>
	</configuration>
	<executions>
		<execution>
			<id>compile</id>
			<phase>compile</phase>
			<goals>
				<goal>compile</goal>
			</goals>
		</execution>
		<execution>
			<id>test-compile</id>
			<phase>test-compile</phase>
			<goals>
				<goal>test-compile</goal>
			</goals>
		</execution>
		<execution>
			<id>kapt</id>
			<goals>
				<goal>kapt</goal>
			</goals>
			<configuration>
				<sourceDirs>
					<sourceDir>src/main/kotlin</sourceDir>
				</sourceDirs>
				<annotationProcessorPaths>
					<annotationProcessorPath>
						<groupId>io.micronaut</groupId>
						<artifactId>micronaut-inject-java</artifactId>
						<version>${micronaut.version}</version>
					</annotationProcessorPath>
					<annotationProcessorPath>
						<groupId>io.micronaut.configuration</groupId>
						<artifactId>micronaut-openapi</artifactId>
						<version>${micronaut.version}</version>
					</annotationProcessorPath>
					<annotationProcessorPath>
						<groupId>io.micronaut</groupId>
						<artifactId>micronaut-tracing</artifactId>
						<version>${micronaut.version}</version>
					</annotationProcessorPath>
					<annotationProcessorPath>
						<groupId>javax.persistence</groupId>
						<artifactId>javax.persistence-api</artifactId>
						<version>2.2</version>
					</annotationProcessorPath>
					<annotationProcessorPath>
						<groupId>io.micronaut.configuration</groupId>
						<artifactId>micronaut-hibernate-jpa</artifactId>
						<version>1.1.0.RC2</version>
					</annotationProcessorPath>
				</annotationProcessorPaths>
			</configuration>
		</execution>
	</executions>
</plugin>

We also should not run maven-compiler-plugin during compilation phase. Kapt compiler generates Java classes, so we don’t need to run any other compilator during the build.

<plugin>
	<groupId>org.apache.maven.plugins</groupId>
	<artifactId>maven-compiler-plugin</artifactId>
	<configuration>
		<proc>none</proc>
		<source>1.8</source>
		<target>1.8</target>
	</configuration>
	<executions>
		<execution>
			<id>default-compile</id>
			<phase>none</phase>
		</execution>
		<execution>
			<id>default-testCompile</id>
			<phase>none</phase>
		</execution>
		<execution>
			<id>java-compile</id>
			<phase>compile</phase>
			<goals>
				<goal>compile</goal>
			</goals>
		</execution>
		<execution>
			<id>java-test-compile</id>
			<phase>test-compile</phase>
			<goals>
				<goal>testCompile</goal>
			</goals>
		</execution>
	</executions>
</plugin>

Finally, we will add Kotlin core library and Jackson module for JSON serialization.

<dependency>
	<groupId>com.fasterxml.jackson.module</groupId>
	<artifactId>jackson-module-kotlin</artifactId>
</dependency>
<dependency>
	<groupId>org.jetbrains.kotlin</groupId>
	<artifactId>kotlin-stdlib-jdk8</artifactId>
	<version>${kotlin.version}</version>
</dependency>

If you are running the application with Intellij you should first enable annotation processing. To do that go to Build, Execution, Deployment -> Compiler -> Annotation Processors as shown below.

micronaut-2-1

2. Running Postgres

Before proceeding to the development we have to start instance of PostgreSQL database. It will be started as a Docker container. For me, PostgreSQL is now available under address 192.168.99.100:5432, because I’m using Docker Toolbox.

$ docker run -d --name postgres -e POSTGRES_USER=micronaut -e POSTGRES_PASSWORD=123456 -e POSTGRES_DB=micronaut -p 5432:5432 postgres

3. Enabling Hibernate for Micronaut

Hibernate configuration is a little harder for Micronaut than for Spring Boot. We don’t have any projects like Spring Data JPA, where almost all is auto-configured. Besides specific JDBC driver for integration with database, we have to include the following dependencies. We may choose between three available libraries providing datasource implementation: Tomcat, Hikari or DBCP.

<dependency>
	<groupId>org.postgresql</groupId>
	<artifactId>postgresql</artifactId>
	<version>42.2.5</version>
</dependency>
<dependency>
	<groupId>io.micronaut.configuration</groupId>
	<artifactId>micronaut-jdbc-hikari</artifactId>
</dependency>
<dependency>
	<groupId>io.micronaut.configuration</groupId>
	<artifactId>micronaut-hibernate-jpa</artifactId>
</dependency>
<dependency>
	<groupId>io.micronaut.configuration</groupId>
	<artifactId>micronaut-hibernate-validator</artifactId>
</dependency>

The next step is to provide some configuration settings. All the properties will be stored on the configuration server. We have to set database connection settings and credentials. The JPA configuration settings are provided under jpa.* key. We force Hibernate to update database on application startup and print all the SQL logs (only for tests).

datasources:
  default:
    url: jdbc:postgresql://192.168.99.100:5432/micronaut?ssl=false
    username: micronaut
    password: 123456
    driverClassName: org.postgresql.Driver
jpa:
  default:
    packages-to-scan:
      - 'pl.piomin.services.department.model'
    properties:
      hibernate:
        hbm2ddl:
          auto: update
        show_sql: true

Here’s our sample domain object.

@Entity
data class Department(@Id @GeneratedValue(strategy = GenerationType.SEQUENCE, generator = "department_id_seq") @SequenceGenerator(name = "department_id_seq", sequenceName = "department_id_seq") var id: Long,
                      var organizationId: Long, var name: String) {

    @Transient
    var employees: MutableList<Employee> = mutableListOf()

}

The repository bean needs to inject EntityManager using @PersistentContext and @CurrentSession annotations. All functions needs to be annotated with @Transactional, what requires the methods not to be final (open modifier in Kotlin).

@Singleton
open class DepartmentRepository(@param:CurrentSession @field:PersistenceContext val entityManager: EntityManager) {

    @Transactional
    open fun add(department: Department): Department {
        entityManager.persist(department)
        return department
    }

    @Transactional(readOnly = true)
    open fun findById(id: Long): Department = entityManager.find(Department::class.java, id)

    @Transactional(readOnly = true)
    open fun findAll(): List<Department> = entityManager.createQuery("SELECT d FROM Department d").resultList as List<Department>

    @Transactional(readOnly = true)
    open fun findByOrganization(organizationId: Long) = entityManager.createQuery("SELECT d FROM Department d WHERE d.organizationId = :orgId")
            .setParameter("orgId", organizationId)
            .resultList as List<Department>

}

4. Running Spring Cloud Config Server

Running Spring Cloud Config server is very simple. I have already described that in some of my previous articles. All those were prepared for Java, while today we start it as Kotlin application. Here’s our main class. It should be annotated with @EnableConfigServer.

@SpringBootApplication
@EnableConfigServer
class ConfigApplication

fun main(args: Array<String>) {
    runApplication<ConfigApplication>(*args)
}

Besides Kotlin core dependency we need to include artifact spring-cloud-config-server.

<dependency>
	<groupId>org.springframework.cloud</groupId>
	<artifactId>spring-cloud-config-server</artifactId>
</dependency>
<dependency>
	<groupId>org.jetbrains.kotlin</groupId>
	<artifactId>kotlin-stdlib-jdk8</artifactId>
	<version>${kotlin.version}</version>
</dependency>

By default, config server tries to use Git as properties source backend. We prefer using classpath resources, what’s much simpler for our tests. To do that, we have to enable native profile. We will also set server port to 8888.

spring:
  application:
    name: config-service
  profiles:
    active: native
server:
  port: 8888

If you place all configuration under directory /src/main/resources/config they will be automatically load after start.

micronaut-2-2

Here’s configuration file for department-service.

micronaut:
  server:
    port: -1
  router:
    static-resources:
      swagger:
        paths: classpath:META-INF/swagger
        mapping: /swagger/**
datasources:
  default:
    url: jdbc:postgresql://192.168.99.100:5432/micronaut?ssl=false
    username: micronaut
    password: 123456
    driverClassName: org.postgresql.Driver
jpa:
  default:
    packages-to-scan:
      - 'pl.piomin.services.department.model'
    properties:
      hibernate:
        hbm2ddl:
          auto: update
        show_sql: true
endpoints:
  info:
    enabled: true
    sensitive: false
eureka:
  client:
    registration:
      enabled: true
    defaultZone: "localhost:8761"

5. Running Eureka Server

Eureka server will also be run as Spring Boot application written in Kotlin.

@SpringBootApplication
@EnableEurekaServer
class DiscoveryApplication

fun main(args: Array<String>) {
    runApplication<DiscoveryApplication>(*args)
}

We also needs to include a single dependency spring-cloud-starter-netflix-eureka-server besides kotlin-stdlib-jdk8.

<dependency>
	<groupId>org.springframework.cloud</groupId>
	<artifactId>spring-cloud-starter-netflix-eureka-server</artifactId>
</dependency>
<dependency>
	<groupId>org.jetbrains.kotlin</groupId>
	<artifactId>kotlin-stdlib-jdk8</artifactId>
	<version>${kotlin.version}</version>
</dependency>

We run standalone instance of Eureka on port 8761.

spring:
  application:
    name: discovery-service
server:
  port: 8761
eureka:
  instance:
    hostname: localhost
  client:
    registerWithEureka: false
    fetchRegistry: false
    serviceUrl:
      defaultZone: http://${eureka.instance.hostname}:${server.port}/eureka/

6. Integrating Micronaut with Spring Cloud

The implementation of distributed configuration client is automatically included to Micronaut core. We only need to include module for service discovery.

<dependency>
	<groupId>io.micronaut</groupId>
	<artifactId>micronaut-discovery-client</artifactId>
</dependency>

We don’t have to place anything in the source code. All the features can be enabled via configuration settings. First, we need to enable config client by setting property micronaut.config-client.enabled to true. The next step is to enable specific implementation of config client – in that case Spring Cloud Config, and then set target url.

micronaut:
  application:
    name: department-service
  config-client:
    enabled: true
spring:
  cloud:
    config:
      enabled: true
      uri: http://localhost:8888/

Each application fetches properties from configuration server. The part of configuration responsible for enabling discovery based on Eureka server is visible below.

eureka:
  client:
    registration:
      enabled: true
    defaultZone: "localhost:8761"

7. Running applications

Kapt needs to be able to compile Kotlin code to Java succesfully. That’s why we place method inside class declaration, and annotate it with @JvmStatic. The main class visible below is also annotated with @OpenAPIDefinition in order to generate Swagger definition for API methods.

@OpenAPIDefinition(
        info = Info(
                title = "Departments Management",
                version = "1.0",
                description = "Department API",
                contact = Contact(url = "https://piotrminkowski.wordpress.com", name = "Piotr Mińkowski", email = "piotr.minkowski@gmail.com")
        )
)
open class DepartmentApplication {

    companion object {
        @JvmStatic
        fun main(args: Array<String>) {
            Micronaut.run(DepartmentApplication::class.java)
        }
    }
	
}

Here’s the controller class from department-service. It injects repository bean for database integration and EmployeeClient for HTTP communication with employee-service.

@Controller("/departments")
open class DepartmentController(private val logger: Logger = LoggerFactory.getLogger(DepartmentController::class.java)) {

    @Inject
    lateinit var repository: DepartmentRepository
    @Inject
    lateinit var employeeClient: EmployeeClient

    @Post
    fun add(@Body department: Department): Department {
        logger.info("Department add: {}", department)
        return repository.add(department)
    }

    @Get("/{id}")
    fun findById(id: Long): Department? {
        logger.info("Department find: id={}", id)
        return repository.findById(id)
    }

    @Get
    fun findAll(): List<Department> {
        logger.info("Department find")
        return repository.findAll()
    }

    @Get("/organization/{organizationId}")
    @ContinueSpan
    open fun findByOrganization(@SpanTag("organizationId") organizationId: Long): List<Department> {
        logger.info("Department find: organizationId={}", organizationId)
        return repository.findByOrganization(organizationId)
    }

    @Get("/organization/{organizationId}/with-employees")
    @ContinueSpan
    open fun findByOrganizationWithEmployees(@SpanTag("organizationId") organizationId: Long): List<Department> {
        logger.info("Department find: organizationId={}", organizationId)
        val departments = repository.findByOrganization(organizationId)
        departments.forEach { it.employees = employeeClient.findByDepartment(it.id) }
        return departments
    }

}

It is worth to take a look on HTTP client implementation. It has been discussed in the details in my last article about Micronaut Quick Guide to Microservice with Micronaut Framework.

@Client(id = "employee-service", path = "/employees")
interface EmployeeClient {

	@Get("/department/{departmentId}")
	fun findByDepartment(departmentId: Long): MutableList<Employee>
	
}

You can run all the microservice using IntelliJ. You may also build the whole project with Maven using mvn clean install command, and then run them using java -jar command. Thanks to maven-shade-plugin applications will be generated as uber jars. Then run them in the following order: config-service, discovery-service and microservices.

$ java -jar config-service/target/config-service-1.0-SNAPSHOT.jar
$ java -jar discovery-service/target/discovery-service-1.0-SNAPSHOT.jar
$ java -jar employee-service/target/employee-service-1.0-SNAPSHOT.jar
$ java -jar department-service/target/department-service-1.0-SNAPSHOT.jar
$ java -jar organization-service/target/organization-service-1.0-SNAPSHOT.jar

After you may take a look on Eureka dashboard available under address http://localhost:8761 to see the list of running services. You may also perform some tests by running HTTP API methods.

micronaut-2-3

Summary

The sample applications source code is available on GitHub in the repository sample-micronaut-microservices in the branch kotlin. You can refer to that repository for more implementation details that has not been included in the article.

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Up-to-date cache with EclipseLink and Oracle

One of the most useful feature provided by ORM libraries is a second-level cache, usually called L2. L2 object cache reduces database access for entities and their relationships. It is enabled by default in the most popular JPA implementations like Hibernate or EclipseLink. That won’t be a problem, unless a table inside a database is not modified directly by third-party applications, or by the other instance of the same application in a clustered environment. One of the available solutions to this problem is in-memory data grid, which stores all data in a memory, and is distributed across many nodes inside a cluster. Such a tools like Hazelcast or Apache Ignite has been described several times in my blog. If you are interested in one of that tools I recommend you read one of my previous article bout it: Hazelcast Hot Cache with Striim.

However, we won’t discuss about it in this article. Today, I would like to talk about Continuous Query Notification feature provided by Oracle Database. It solves a problem with updating or invalidating a cache when the data changes in the database. Oracle JDBC drivers provide support for it since 11g Release 1. This functionality is based on receiving invalidation events from the JDBC drivers. Fortunately, EclipseLink extends that feature in their solution called EclipseLink Database Change Notification. In this article I’m going to show you how to implement it using Spring Data JPA together with EclipseLink library.

How it works

The most useful functionality provided by the Oracle Database Continuous Query Notification is an ability to raise database events when rows in a table were modified. It enables client applications to register queries with the database and receive notifications in response to DML or DDL changes on the objects associated with the queries. To detect modifications, EclipseLink DCN uses Oracle ROWID to intercept changes in the table. ROWID is included to all queries for a DCN-enabled class. EclipseLink also retrieves ROWID of saved entity after an insert operation, and maintains a cache index on that ROWID. It also selects the database transaction ID once for each transaction to avoid invalidating the cache during the processing of transaction.

When a database sends a notification it usually contains the followoing information:

  • Names of the modifying objects, for example a name of changed table
  • Type of change. The possible values are INSERT, UPDATE, DELETE, ALTER TABLE, or DROP TABLE
  • Oracle’s ROWID of changed record

Running Oracle database locally

Before starting working on our sample application we need to have Oracle database installed. Fortunately, there are some Docker images with Oracle Standard Edition 12c. The command visible below starts Oracle XE version and exposes it on default 1521 port. It is also possible to use web console available under port 9080.

$ docker run -d --name oracle -p 9080:8080 -p 1521:1521 sath89/oracle-12c

We need to have sysdba role in order to be able to grant privilege CHANGE NOTIFICATION to our database. The default password for user system is oracle.

GRANT CHANGE NOTIFICATION TO PIOMIN;

You may use any Oracle client like Oracle SQL Developer to connect with database or just login to a web console. Since I run Docker on Windows it is available on my laptop under address http://192.168.99.100:9080/em. Of course it is Oracle, so you need to settle in for a long haul, and wait until it starts. You can observer a progress of an installation by running command docker logs -f oracle. When you finally see a “100% complete” log entry you may grant the required privileges to the existing user or create a new one with a set of needed privileges, and proceed to the next step.

Sample application

The sample application source code is available on GitHub under address https://github.com/piomin/sample-eclipselink-jpa.git. It is Spring Boot application that uses Spring Data JPA as a data access layer implementation. Because the default JPA provider used in that project is EclipseLink, we should remember about excluding Hibernate libraries from starters spring-boot-starter-data-jpa and spring-boot-starter-web. Besides a standard EclipseLink library for JPA, we also have to include EclipseLink implementation for Oracle database (org.eclipse.persistence.oracle) and Oracle JDBC driver.

<dependency>
	<groupId>org.eclipse.persistence</groupId>
	<artifactId>org.eclipse.persistence.jpa</artifactId>
	<version>2.7.1</version>
</dependency>
<dependency>
	<groupId>org.eclipse.persistence</groupId>
	<artifactId>org.eclipse.persistence.oracle</artifactId>
	<version>2.7.1</version>
</dependency>
<dependency>
	<groupId>com.oracle</groupId>
	<artifactId>ojdbc7</artifactId>
	<version>12.1.0.1</version>
</dependency>

The next step is to provide connection settings to Oracle database launched as a Docker container. Do not try to do it through application.yml properties, because Spring Boot by default uses HikariCP for connection pooling. This in turn causes a conflict with Oracle datasource during application bootstrap. The following datasource declaration would work succesfully.

@Bean
public DataSource dataSource() {
	final DriverManagerDataSource dataSource = new DriverManagerDataSource();
	dataSource.setDriverClassName("oracle.jdbc.driver.OracleDriver");
	dataSource.setUrl("jdbc:oracle:thin:@192.168.99.100:1521:xe");
	dataSource.setUsername("piomin");
	dataSource.setPassword("Piot_123");
	return dataSource;
}

EclipseLink with Database Change Notification

EclipseLink needs some specific configuration settings to succesfully work with Spring Boot and Spring Data JPA. These settings may be provided inside @Configuration class that extends JpaBaseConfiguration class. First, we should set EclipseLinkJpaVendorAdapter as default JPA vendor adapter. Then, we may configure some additional JPA settings like detailed logging level or automatic creation of database objects during application startup. However, the most important thing for us in the fragment of source code visible below is Oracle Continuous Query Notification settings.
EclipseLink CQN support is enabled by the OracleChangeNotificationListener listener which integrates with Oracle JDBC in order to received database change notifications. The full class name of the listener should be passed as a value of eclipselink.cache.database-event-listener property. EclipseLink by default enabled L2 cache for all entities, and respectively all tables in the persistence unit are registered for a change notification. You may exclude some of them by using the databaseChangeNotificationType attribute of the @Cache annotation on the selected entity.

@Configuration
@EnableAutoConfiguration
public class JpaConfiguration extends JpaBaseConfiguration {

	protected JpaConfiguration(DataSource dataSource, JpaProperties properties, ObjectProvider jtaTransactionManager, ObjectProvider transactionManagerCustomizers) {
		super(dataSource, properties, jtaTransactionManager, transactionManagerCustomizers);
	}

	@Override
	protected AbstractJpaVendorAdapter createJpaVendorAdapter() {
		return new EclipseLinkJpaVendorAdapter();
	}

	@Override
	protected Map getVendorProperties() {
	    HashMap map = new HashMap();
	    map.put(PersistenceUnitProperties.WEAVING, InstrumentationLoadTimeWeaver.isInstrumentationAvailable() ? "true" : "static");
	    map.put(PersistenceUnitProperties.DDL_GENERATION, "create-or-extend-tables");
	    map.put(PersistenceUnitProperties.LOGGING_LEVEL, SessionLog.FINEST_LABEL);
	    map.put(PersistenceUnitProperties.DATABASE_EVENT_LISTENER, "org.eclipse.persistence.platform.database.oracle.dcn.OracleChangeNotificationListener");
	    return map;
	}

}

What is worth mentioning EclipseLink’s CQN integration has some important limitations:

  • Changes to an object’s secondary tables will not trigger it to be invalidate unless a version is used and updated in the primary table
  • Changes to an object’s OneToMany, ManyToMany, and ElementCollection relationships will not trigger it to be invalidate unless a version is used and updated in the primary table

The conclusion from these limitations is obvious. We should enable optimistic locking by including an @Version in our entities. The column with @Version in the primary table will always be updated, and the object will always be invalidated. There are three entities implemented. Entity Order is in many-to-one relationship with Product and Customer entities. All these classes has @Version feature enabled.

@Entity
@Table(name = "JPA_ORDER")
public class Order {

	@Id
	@SequenceGenerator(sequenceName = "SEQ_ORDER", allocationSize = 1, initialValue = 1, name = "orderSequence")
	@GeneratedValue(generator = "orderSequence", strategy = GenerationType.SEQUENCE)
	private Long id;
	@ManyToOne
	private Customer customer;
	@ManyToOne
	private Product product;
	@Enumerated
	private OrderStatus status;
	private int count;

	@Version
	private long version;

	public Long getId() {
		return id;
	}

	public void setId(Long id) {
		this.id = id;
	}

	public Customer getCustomer() {
		return customer;
	}

	public void setCustomer(Customer customer) {
		this.customer = customer;
	}

	public Product getProduct() {
		return product;
	}

	public void setProduct(Product product) {
		this.product = product;
	}

	public OrderStatus getStatus() {
		return status;
	}

	public void setStatus(OrderStatus status) {
		this.status = status;
	}

	public int getCount() {
		return count;
	}

	public void setCount(int count) {
		this.count = count;
	}

	public long getVersion() {
		return version;
	}

	public void setVersion(long version) {
		this.version = version;
	}

	@Override
	public String toString() {
		return "Order [id=" + id + ", product=" + product + ", status=" + status + ", count=" + count + "]";
	}

}

Testing

After launching your application you see the following logs generated with Finest level.

[EL Finest]: connection: 2018-03-23 15:45:50.591--ServerSession(465621833)--Thread(Thread[main,5,main])--Registering table [JPA_PRODUCT] for database change event notification.
[EL Finest]: connection: 2018-03-23 15:45:50.608--ServerSession(465621833)--Thread(Thread[main,5,main])--Registering table [JPA_CUSTOMER] for database change event notification.
[EL Finest]: connection: 2018-03-23 15:45:50.616--ServerSession(465621833)--Thread(Thread[main,5,main])--Registering table [JPA_ORDER] for database change event notification.

The registration are stored in table user_change_notification_regs, which is available for your application’s user (PIOMIN).

$ SELECT regid, table_name FROM user_change_notification_regs;
     REGID TABLE_NAME
---------- ---------------------------------------------------------------
       326 PIOMIN.JPA_PRODUCT
       326 PIOMIN.JPA_CUSTOMER
       326 PIOMIN.JPA_ORDER

Our sample application exposes Swagger documentation of API, which may be accessed under address http://localhost:8090/swagger-ui.html. You can create or find some entities using it. If try to find the same entity several times you would see that the only first invoke generates SQL query in logs, while all others are taken from a cache. Now, try to change that record using any Oracle’s client like Oracle SQL Developer, and verify if cache has been succesfully refreshed.

eclipse-link-1

Summary

When I first heard about Oracle Database Change Notification supported by EclipseLink JPA vendor, my expectations were really high. It is very interesting solution, which guarantees automatic cache refresh after changes performed on database tables by third-party application avoiding your cache. However, I had some problems with that solution during tests. In some cases it just doesn’t work, and the detection of errors was really troublesome. It would be fine if such a solution could be also available for other databases than Oracle and JPA vendors like Hibernate.

JPA caching with Hazelcast, Hibernate and Spring Boot

Preface

In-Memory Data Grid is an in-memory distributed key-value store that enables caching data using distributed clusters. Do not confuse this solution with in-memory or nosql database. In most cases it is used for performance reasons – all data is stored in RAM not in the disk like in traditional databases. For the first time I had a touch with in-memory data grid while we considering moving to Oracle Coherence in one of organizations I had been working before. The solution really made me curious. Oracle Coherence is obviously a paid solution, but there are also some open source solutions among which the most interesting seem to be Apache Ignite and Hazelcast. Today I’m going to show you how to use Hazelcast for caching data stored in MySQL database accessed by Spring Data DAO objects. Here’s the figure illustrating architecture of presented solution.

hazelcast-1

Implementation

  • Starting Docker containers

We use three Docker containers. First with MySQL database, second with Hazelcast instance and third for Hazelcast Management Center – UI dashboard for monitoring Hazelcast cluster instances.

docker run -d --name mysql -p 33306:3306 mysql
docker run -d --name hazelcast -p 5701:5701 hazelcast/hazelcast
docker run -d --name hazelcast-mgmt -p 38080:8080 hazelcast/management-center:latest

If we would like to connect with Hazelcast Management Center from Hazelcast instance we need to place custom hazelcast.xml in /opt/hazelcast catalog inside Docker container. This can be done in two ways, by extending hazelcast base image or just by copying file to existing hazelcast container and restarting it.

docker run -d --name hazelcast -p 5701:5701 hazelcast/hazelcast
docker stop hazelcast
docker start hazelcast

Here’s the most important Hazelcast’s configuration file fragment.

<hazelcast xmlns="http://www.hazelcast.com/schema/config" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.hazelcast.com/schema/config http://www.hazelcast.com/schema/config/hazelcast-config-3.8.xsd">
     <group>
          <name>dev</name>
          <password>dev-pass</password>
     </group>
     <management-center enabled="true" update-interval="3">http://192.168.99.100:38080/mancenter</management-center>
...
</hazelcast>

Hazelcast Dashboard is available under http://192.168.99.100:38080/mancenter address. We can monitor there all running cluster members, maps and some other parameters.

hazelcast-mgmt-1

  • Maven configuration

Project is based on Spring Boot 1.5.3.RELEASE. We also need to add Spring Web and MySQL Java connector dependencies. Here’s root project pom.xml.


	<parent>
		<groupId>org.springframework.boot</groupId>
		<artifactId>spring-boot-starter-parent</artifactId>
		<version>1.5.3.RELEASE</version>
	</parent>
	...
	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-web</artifactId>
		</dependency>
		<dependency>
			<groupId>mysql</groupId>
			<artifactId>mysql-connector-java</artifactId>
			<scope>runtime</scope>
		</dependency>
	...
	</dependencies>

Inside person-service module we declared some other dependencies to Hazelcast artifacts and Spring Data JPA. I had to override managed hibernate-core version for Spring Boot 1.5.3.RELEASE, because Hazelcast didn’t worked properly with 5.0.12.Final. Hazelcast needs hibernate-core in 5.0.9.Final version. Otherwise, an exception occurs when starting application.

	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-data-jpa</artifactId>
		</dependency>
		<dependency>
			<groupId>com.hazelcast</groupId>
			<artifactId>hazelcast</artifactId>
		</dependency>
		<dependency>
			<groupId>com.hazelcast</groupId>
			<artifactId>hazelcast-client</artifactId>
		</dependency>
		<dependency>
			<groupId>com.hazelcast</groupId>
			<artifactId>hazelcast-hibernate5</artifactId>
		</dependency>
		<dependency>
			<groupId>org.hibernate</groupId>
			<artifactId>hibernate-core</artifactId>
			<version>5.0.9.Final</version>
		</dependency>
	</dependencies>
  • Hibernate Cache configuration

Probably you can configure it in several different ways, but for me the most suitable solution was inside application.yml. Here’s YAML configurarion file fragment. I enabled L2 Hibernate cache, set Hazelcast native client address, credentials and cache factory class HazelcastCacheRegionFactory. We can also set HazelcastLocalCacheRegionFactory. The differences between them are in performance – local factory is faster since its operations are handled as distributed calls. While if you use HazelcastCacheRegionFactory, you can see your maps on Management Center.

spring:
  application:
    name: person-service
  datasource:
    url: jdbc:mysql://192.168.99.100:33306/datagrid?useSSL=false
    username: datagrid
    password: datagrid
  jpa:
    properties:
      hibernate:
        show_sql: true
        cache:
          use_query_cache: true
          use_second_level_cache: true
          hazelcast:
            use_native_client: true
            native_client_address: 192.168.99.100:5701
            native_client_group: dev
            native_client_password: dev-pass
          region:
            factory_class: com.hazelcast.hibernate.HazelcastCacheRegionFactory
  • Application code

First, we need to enable caching for Person @Entity.

@Cache(usage = CacheConcurrencyStrategy.READ_WRITE)
@Entity
public class Person implements Serializable {

	private static final long serialVersionUID = 3214253910554454648L;

	@Id
	@GeneratedValue
	private Integer id;
	private String firstName;
	private String lastName;
	private String pesel;
	private int age;

	public Integer getId() {
		return id;
	}

	public void setId(Integer id) {
		this.id = id;
	}

	public String getFirstName() {
		return firstName;
	}

	public void setFirstName(String firstName) {
		this.firstName = firstName;
	}

	public String getLastName() {
		return lastName;
	}

	public void setLastName(String lastName) {
		this.lastName = lastName;
	}

	public String getPesel() {
		return pesel;
	}

	public void setPesel(String pesel) {
		this.pesel = pesel;
	}

	public int getAge() {
		return age;
	}

	public void setAge(int age) {
		this.age = age;
	}

	@Override
	public String toString() {
		return "Person [id=" + id + ", firstName=" + firstName + ", lastName=" + lastName + ", pesel=" + pesel + "]";
	}

}

DAO is implemented using Spring Data CrudRepository. Sample application source code is available on GitHub.

public interface PersonRepository extends CrudRepository<Person, Integer> {
	public List<Person> findByPesel(String pesel);
}

Testing

Let’s insert a little more data to the table. You can use my AddPersonRepositoryTest for that. It will insert 1M rows into the person table. Finally, we can call enpoint http://localhost:2222/persons/{id} twice with the same id. For me, it looks like below: 22ms for first call, 3ms for next call which is read from L2 cache. Entity can be cached only by primary key. If you call http://localhost:2222/persons/pesel/{pesel} entity will always be searched bypassing the L2 cache.

2017-05-05 17:07:27.360 DEBUG 9164 --- [nio-2222-exec-9] org.hibernate.SQL                        : select person0_.id as id1_0_0_, person0_.age as age2_0_0_, person0_.first_name as first_na3_0_0_, person0_.last_name as last_nam4_0_0_, person0_.pesel as pesel5_0_0_ from person person0_ where person0_.id=?
Hibernate: select person0_.id as id1_0_0_, person0_.age as age2_0_0_, person0_.first_name as first_na3_0_0_, person0_.last_name as last_nam4_0_0_, person0_.pesel as pesel5_0_0_ from person person0_ where person0_.id=?
2017-05-05 17:07:27.362 DEBUG 9164 --- [nio-2222-exec-9] o.h.l.p.e.p.i.ResultSetProcessorImpl     : Starting ResultSet row #0
2017-05-05 17:07:27.362 DEBUG 9164 --- [nio-2222-exec-9] l.p.e.p.i.EntityReferenceInitializerImpl : On call to EntityIdentifierReaderImpl#resolve, EntityKey was already known; should only happen on root returns with an optional identifier specified
2017-05-05 17:07:27.363 DEBUG 9164 --- [nio-2222-exec-9] o.h.engine.internal.TwoPhaseLoad         : Resolving associations for [pl.piomin.services.datagrid.person.model.Person#444]
2017-05-05 17:07:27.364 DEBUG 9164 --- [nio-2222-exec-9] o.h.engine.internal.TwoPhaseLoad         : Adding entity to second-level cache: [pl.piomin.services.datagrid.person.model.Person#444]
2017-05-05 17:07:27.373 DEBUG 9164 --- [nio-2222-exec-9] o.h.engine.internal.TwoPhaseLoad         : Done materializing entity [pl.piomin.services.datagrid.person.model.Person#444]
2017-05-05 17:07:27.373 DEBUG 9164 --- [nio-2222-exec-9] o.h.r.j.i.ResourceRegistryStandardImpl   : HHH000387: ResultSet's statement was not registered
2017-05-05 17:07:27.374 DEBUG 9164 --- [nio-2222-exec-9] .l.e.p.AbstractLoadPlanBasedEntityLoader : Done entity load : pl.piomin.services.datagrid.person.model.Person#444
2017-05-05 17:07:27.374 DEBUG 9164 --- [nio-2222-exec-9] o.h.e.t.internal.TransactionImpl         : committing
2017-05-05 17:07:30.168 DEBUG 9164 --- [nio-2222-exec-6] o.h.e.t.internal.TransactionImpl         : begin
2017-05-05 17:07:30.171 DEBUG 9164 --- [nio-2222-exec-6] o.h.e.t.internal.TransactionImpl         : committing

Query Cache

We can enable JPA query caching by marking repository method with @Cacheable annotation and adding @EnableCaching to main class definition.

public interface PersonRepository extends CrudRepository<Person, Integer> {

	@Cacheable("findByPesel")
	public List<Person> findByPesel(String pesel);

}

In addition to the @EnableCaching annotation we should declare HazelcastIntance and CacheManager beans. As a cache manager HazelcastCacheManager from hazelcast-spring library is used.

@SpringBootApplication
@EnableCaching
public class PersonApplication {

	public static void main(String[] args) {
		SpringApplication.run(PersonApplication.class, args);
	}

	@Bean
	HazelcastInstance hazelcastInstance() {
		ClientConfig config = new ClientConfig();
		config.getGroupConfig().setName("dev").setPassword("dev-pass");
		config.getNetworkConfig().addAddress("192.168.99.100");
		config.setInstanceName("cache-1");
		HazelcastInstance instance = HazelcastClient.newHazelcastClient(config);
		return instance;
	}

	@Bean
	CacheManager cacheManager() {
		return new HazelcastCacheManager(hazelcastInstance());
	}

}

Now, we should try find person by PESEL number by calling endpoint http://localhost:2222/persons/pesel/{pesel}. Cached query is stored as a map as you see in the picture below.

hazelcast-3

Clustering

Before final words let me say a little about clustering, what is the key functionality of Hazelcast in memory data grid. In the previous chapters we based on single Hazelcast instance. Let’s begin from running second container with Hazelcast exposed on different port.

docker run -d --name hazelcast2 -p 5702:5701 hazelcast/hazelcast

Now we should perform one change in hazelcast.xml configuration file. Because data grid is ran inside docker container the public address has to be set. For the first container it is 192.168.99.100:5701, and for second 192.168.99.100:5702, because it is exposed on 5702 port.

     <network>
        ...
	<public-address>192.168.99.100:5701</public-address>
        ...
     </network>

When starting person-service application you should see in the logs similar to visible below – connection with two cluster members.

Members [2] {
Member [192.168.99.100]:5702 - 04f790bc-6c2d-4c21-ba8f-7761a4a7422c
Member [192.168.99.100]:5701 - 2ca6e30d-a8a7-46f7-b1fa-37921aaa0e6b
}

All Hazelcast running instances are visible in Management Center.

hazelcast-2

Conclusion

Caching and clustering with Hazelcast are simple and fast. We can cache JPA entities and queries. Monitoring is realized via Hazelcast Management Center dashboard. One problem for me is that I’m able to cache entities only by primary key. If I would like to find entity by other index like PESEL number I had to cache findByPesel query. Even if entity was cached before by id query will not find it in the cache but perform SQL on database. Only next query call is cached. I’ll show you smart solution for that problem in my next article about that subject In memory data grid with Hazelcast.