Architecture

We are running multiple instances of a single Spring Boot application on Kubernetes. Each application exposes port 8080 for HTTP API access and 5701 for Hazelcast cluster members discovery. Hazelcast instances are embedded into Spring Boot applications. We are creating two services on Kubernetes. The first of them is dedicated for HTTP API access, while the second is responsible for enabling discovery between Hazelcast instances. HTTP API will be used for making some tests requests that add data to the cluster and find data there. Let’s proceed to the implementation.

Example

The source code with sample application is as usual available on GitHub. It is available here https://github.com/piomin/sample-hazelcast-spring-datagrid.git. You should access module employee-kubernetes-service.

Dependencies

An integration between Spring and Hazelcast is provided by hazelcast-spring library. The version of Hazelcast libraries is related to Spring Boot via dependency management, so we just need to define the version of Spring Boot to the newest stable 2.2.4.RELEASE. The current version of Hazelcast related to this version of Spring Boot is 3.12.5. In order to enable Hazelcast members discovery on Kubernetes we also need to include hazelcast-kubernetes dependency. Its versioning is independable from core libraries. The newest version 2.0 is dedicated for Hazelcast 4. Since we are still using Hazelcast 3 we are declaring version 1.5.2 of hazelcast-kubernetes. We also include Spring Data Hazelcast and optionally Lombok for simplification.

<parent>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-parent</artifactId>
   <version>2.2.4.RELEASE</version>
</parent>
<dependencies>
   <dependency>
      <groupId>org.springframework.boot</groupId>
      <artifactId>spring-boot-starter-web</artifactId>
   </dependency>
   <dependency>
      <groupId>com.hazelcast</groupId>
      <artifactId>spring-data-hazelcast</artifactId>
      <version>2.2.2</version>
   </dependency>
   <dependency>
      <groupId>com.hazelcast</groupId>
      <artifactId>hazelcast-spring</artifactId>
   </dependency>
   <dependency>
      <groupId>com.hazelcast</groupId>
      <artifactId>hazelcast-client</artifactId>
   </dependency>
   <dependency>
      <groupId>com.hazelcast</groupId>
      <artifactId>hazelcast-kubernetes</artifactId>
      <version>1.5.2</version>
   </dependency>
   <dependency>
      <groupId>org.projectlombok</groupId>
      <artifactId>lombok</artifactId>
   </dependency>
</dependencies>

Enabling Kubernetes Discovery for Hazelcast

After including required dependencies Hazelcast has been enabled for our application. The only thing we need to do is to enable discovery through Kubernetes. The HazelcastInstance bean is already available in the context, so we may change its configuration by defining com.hazelcast.config.Config bean. We need to disable multicast discovery, which is enabled by default, and enable Kubernetes discovery in the network config as shown below. Kubernetes config requires setting a target namespace of Hazelcast deployment and its service name.

@Bean
Config config() {
   Config config = new Config();
   config.getNetworkConfig().getJoin().getTcpIpConfig().setEnabled(false);
   config.getNetworkConfig().getJoin().getMulticastConfig().setEnabled(false);
   config.getNetworkConfig().getJoin().getKubernetesConfig().setEnabled(true)
         .setProperty("namespace", "default")
         .setProperty("service-name", "hazelcast-service");
   return config;
}

We also have to define Kubernetes Service hazelcast-service on port 5701. It is referenced to employee-service deployment.

apiVersion: v1
kind: Service
metadata:
  name: hazelcast-service
spec:
  selector:
    app: employee-service
  ports:
    - name: hazelcast
      port: 5701
  type: LoadBalancer

Here’s Kubernetes Deployment and Service definition for our sample application. We are setting three replicas for our deployment. We are also exposing two ports outside containers.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: employee-service
  labels:
    app: employee-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: employee-service
  template:
    metadata:
      labels:
        app: employee-service
    spec:
      containers:
        - name: employee-service
          image: piomin/employee-service
          ports:
            - name: http
              containerPort: 8080
            - name: multicast
              containerPort: 5701
---
apiVersion: v1
kind: Service
metadata:
  name: employee-service
  labels:
    app: employee-service
spec:
  ports:
    - port: 8080
      protocol: TCP
  selector:
    app: employee-service
  type: NodePort

In fact, that’s all that needs to be done to succesfully run Hazelcast cluster on Kubernetes. Before proceeding to the deployment let’s take a look on the application implementation details.

Implementation

Our application is very simple. It defines a single model object, which is stored in Hazelcast cluster. Such a class needs to have id – a field annotated with Spring Data @Id, and should implement Seriazable interface.

@Getter
@Setter
@EqualsAndHashCode
@ToString
public class Employee implements Serializable {

   @Id
   private Long id;
   @EqualsAndHashCode.Exclude
   private Integer personId;
   @EqualsAndHashCode.Exclude
   private String company;
   @EqualsAndHashCode.Exclude
   private String position;
   @EqualsAndHashCode.Exclude
   private int salary;

}

With Spring Data Hazelcast we may define repositories without using any queries or Hazelcast specific API for queries. We are using a well-known method naming pattern defined by Spring Data to build find methods as shown below. Our repository interface should extend HazelcastRepository.

public interface EmployeeRepository extends HazelcastRepository<Employee, Long> {

   Employee findByPersonId(Integer personId);
   List<Employee> findByCompany(String company);
   List<Employee> findByCompanyAndPosition(String company, String position);
   List<Employee> findBySalaryGreaterThan(int salary);

}

To enable Spring Data Hazelcast Repositories we should annotate the main class or the configuration class with @EnableHazelcastRepositories.

@SpringBootApplication
@EnableHazelcastRepositories
public class EmployeeApplication {

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

Finally, here’s the Spring controller implementation. It allows us to invoke all the find methods defined in the repository, add new Employee object to Hazelcast and remove the existing one.

@RestController
@RequestMapping("/employees")
public class EmployeeController {

   private static final Logger logger = LoggerFactory.getLogger(EmployeeController.class);

   private EmployeeRepository repository;

   EmployeeController(EmployeeRepository repository) {
      this.repository = repository;
   }

   @GetMapping("/person/{id}")
   public Employee findByPersonId(@PathVariable("id") Integer personId) {
      logger.info("findByPersonId({})", personId);
      return repository.findByPersonId(personId);
   }
   
   @GetMapping("/company/{company}")
   public List<Employee> findByCompany(@PathVariable("company") String company) {
      logger.info(String.format("findByCompany({})", company));
      return repository.findByCompany(company);
   }

   @GetMapping("/company/{company}/position/{position}")
   public List<Employee> findByCompanyAndPosition(@PathVariable("company") String company, @PathVariable("position") String position) {
      logger.info(String.format("findByCompany({}, {})", company, position));
      return repository.findByCompanyAndPosition(company, position);
   }
   
   @GetMapping("/{id}")
   public Employee findById(@PathVariable("id") Long id) {
      logger.info("findById({})", id);
      return repository.findById(id).get();
   }

   @GetMapping("/salary/{salary}")
   public List<Employee> findBySalaryGreaterThan(@PathVariable("salary") int salary) {
      logger.info(String.format("findBySalaryGreaterThan({})", salary));
      return repository.findBySalaryGreaterThan(salary);
   }
   
   @PostMapping
   public Employee add(@RequestBody Employee emp) {
      logger.info("add({})", emp);
      return repository.save(emp);
   }

   @DeleteMapping("/{id}")
   public void delete(@PathVariable("id") Long id) {
      logger.info("delete({})", id);
      repository.deleteById(id);
   }

}

Running Hazelcast on Kubernetes via Minikube

We will test our sample application on Minikube.

$ minikube start --vm-driver=virtualbox

The application is configured to run with Skaffold and Jib Maven Plugin. I have already described both these tools in one of my previous articles. They simplify the build and deployment process on Minikube. Assuming we are in the root directory of our application we just need to run the following command. Skaffold automatically builds our application using Maven, creates a Docker image based on Maven settings, applies a deployment file from k8s directory, and finally runs the application on Kubernetes.

$ skaffold dev

Since, we have declared three instances of our application in the deployment.yaml three pods are started. If Hazelcast discovery is succesfully finished you should see the following fragment of pods logs printed out by Skaffold.

Let’s take a look at the running pods.

And the list of services. HTTP API is available outside Minikube under port 32090.

Now, we may send some test requests. We will start by calling POST /employees method to add some Employee objects into Hazelcast cluster. Then we will perform some find methods using GET /employees/{id}. Since all the methods have finished succesfully, we should take a look at the logs that clearly show the working of Hazelcast cluster.

$ curl -X POST http://192.168.99.100:32090/employees -d '{"id":1,"personId":1,"company":"Test1","position":"Developer","salary":2000}' -H "Content-Type: application/json"
{"id":1,"personId":1,"company":"Test1","position":"Developer","salary":2000}
$ curl -X POST http://192.168.99.100:32090/employees -d '{"id":2,"personId":2,"company":"Test2","position":"Developer","salary":5000}' -H "Content-Type: application/json"
{"id":2,"personId":2,"company":"Test2","position":"Developer","salary":5000}
$ curl -X POST http://192.168.99.100:32090/employees -d '{"id":3,"personId":3,"company":"Test2","position":"Developer","salary":5000}' -H "Content-Type: application/json"
{"id":3,"personId":3,"company":"Test2","position":"Developer","salary":5000}
$ curl -X POST http://192.168.99.100:32090/employees -d '{"id":4,"personId":4,"company":"Test3","position":"Developer","salary":9000}' -H "Content-Type: application/json"
{"id":4,"personId":4,"company":"Test3","position":"Developer","salary":9000}
$ curl http://192.168.99.100:32090/employees/1
{"id":1,"personId":1,"company":"Test1","position":"Developer","salary":2000}
$ curl http://192.168.99.100:32090/employees/2
{"id":2,"personId":2,"company":"Test2","position":"Developer","salary":5000}
$ curl http://192.168.99.100:32090/employees/3
{"id":3,"personId":3,"company":"Test2","position":"Developer","salary":5000}

Here’s the screen with logs from pods printed out by Skaffold. Skaffold prints pod id for every single log line. Let’s take a closer look on the logs. The request for adding Employee with id=1 is processed by the application running on pod 5b758cc977-s6ptd. When we call find method using id=1 it is processed by the application on pod 5b758cc977-2fj2h. It proves that the Hazelcast cluster works properly. The same behaviour may be observed for other test requests.

We may also call some other find methods.

$ curl http://192.168.99.100:32090/employees/company/Test2/position/Developer
[{"id":2,"personId":2,"company":"Test2","position":"Developer","salary":5000},{"id":3,"personId":3,"company":"Test2","position":"Developer","salary":5000}]
$ curl http://192.168.99.100:32090/employees/salary/3000
[{"id":2,"personId":2,"company":"Test2","position":"Developer","salary":5000},{"id":4,"personId":4,"company":"Test3","position":"Developer","salary":9000},{"id":3,"personId":3,"company":"Test2","position":"Developer","salary":5000}]

Let’s test another scenario. We will remove one pod from the cluster as shown below.

Then we send some test requests to GET /employees/{id}. No matter which instance of the application is processing the request the object is being returned.

The post Hazelcast with Spring Boot on Kubernetes appeared first on Piotr's TechBlog.

Let’s return to the topic of outdated cache. That’s why we need Striim, a real-time data integration and streaming analytics software platform. The architecture of presented solution is visible on the figure below. We have two applications. The first one employee-service uses Hazelcast as a cache, the second one employee-app performs changes directly to the database. Without such a solution like Striim data changed by employee-app is not visible for employee-service. Striim enables real-time data integration without modifying or slowing down data source. It uses CDC (Change Data Capture) mechanisms for detecting changes performed on data source, by analizing binary logs. It has a support for the most popular transactional databases like Oracle, Microsoft SQL Server and MySQL. Striim has many interesting features, but also one serious drawback – it is not an open source. An alternative for the presented solution, especially when using Oracle database, can be Oracle In-Memory Data Grid with Golden Gate Hot Cache.

I prepared sample application for that article purpose, which is as usual available on GitHub under striim branch. The application employee-service is based on Spring Boot and has embedded Hazelcast client which connects to the cluster and Hazelcast Management Center. If data is not available in the cache the application connects to MySQL database.

1. Starting MySQL and enabling binary log

Let’s start MySQL database using docker.

[code]
docker run -d –name mysql -e MYSQL_DATABASE=hz -e MYSQL_USER=hz -e MYSQL_PASSWORD=hz123 -e MYSQL_ALLOW_EMPTY_PASSWORD=yes -p 33306:3306 mysql
[/code]

Binary log is disabled by default. We have to enable it by including the following lines into mysqld.cnf. The configuration file is available on docker container under /etc/mysql/mysql.conf.d/mysqld.cnf.

[code]
log_bin = /var/log/mysql/binary.log
expire-logs-days = 14
max-binlog-size = 500M
server-id = 1
[/code]

If you are running MySQL on Docker you should restart your container using docker restart mysql.

2. Starting Hazelcast Dashboard and Striim

Same as for MySQL, I also used Docker.

[code]
docker run -d –name striim -p 39080:9080 striim/evalversion
docker run -d –name hazelcast-mgmt -p 38080:8080 hazelcast/management-center:3.7.7
[/code]

I selected 3.7.7 version of Hazelcast Management Center, because this version is included by default into the Spring Boot release I used in the sample application. Now, you should be able to login into Hazelcast Dashboard available under http://192.168.99.100:38080/mancenter/ and to the Striim Dashboard which is available under http://192.168.99.100:39080/ (admin/admin).

3. Starting sample application

Build sample application with mvn clean install and start using java -jar employee-service-1.0-SNAPSHOT.jar. You can test it by calling one of endpoint:
/employees/person/{id}
/employees/company/{company}
/employees/{id}

Before testing create table employee in MySQL and insert some test data (you can run my test class pl.piomin.services.datagrid.employee.data.AddEmployeeRepositoryTest).

4. Configure entity mapping in Striim

Before creating our first application in Striim we have to provide mapping configuration. The first step is to copy your entity ORM mapping file into docker container filesystem. You can perform it using Striim dashboard or with docker cp command. Here’s my orm.xml file – it is used by Striim HazelcastWriter while putting data into cache.

[code language=”xml”]
<entity-mappings xmlns="http://www.eclipse.org/eclipselink/xsds/persistence/orm" version="2.4">
<entity name="employee" class="pl.piomin.services.datagrid.employee.model.Employee">
<table name="hz.employee" />
<attributes>
<id name="id" attribute-type="Integer">
<column nullable="false" name="id" />
<generated-value strategy="AUTO" />
</id>
<basic name="personId" attribute-type="Integer">
<column nullable="false" name="person_id" />
</basic>
<basic name="company" attribute-type="String">
<column name="company" />
</basic>
</attributes>
</entity>
</entity-mappings>
[/code]

We also have to provide jar with entity class. It should be placed under /opt/Striim/lib directory on Striim docker container. What is important, the fields are public – do not make them private with setters, because it won’t work for HazelcastWriter. After all changes restart your container and proceed to the next steps. For the sample application just build employee-model module and upload to Striim.

[code language=”java”]
public class Employee implements Serializable {

private static final long serialVersionUID = 3214253910554454648L;
public Integer id;
public Integer personId;
public String company;

public Employee() {

}

@Override
public String toString() {
return "Employee [id=" + id + ", personId=" + personId + ", company=" + company + "]";
}

}
[/code]

5. Configuring MySQL CDC connection on Striim

If all the previous steps are completed we can finally begin to create our application in Striim. When creating a new app select Start with Template, and then MySQL CDC to Hazelcast. Put your MySQL connection data, security credentials and proceed. In addition to connection validation Striim also checks if binary log is enabled.

Then select tables for synchronization with cache.

6. Configuring Hazelcast on Striim

After starting employee-service application you should see the following fragment in the file logs.

[code]
Members [1] {
Member [192.168.8.205]:5701 – d568a38a-7531-453a-b7f8-db2be4715132 this
}
[/code]

This address should be provided as a Hazelcast Cluster URL. We should also put ORM mapping file location and cluster credentials (by default these are dev/dev-pass).

In the next screen you will see ORM mapping visualization and input selection. Your input is MySQL server you defined in the fifth step.

7. Deploy application on Striim

After finishing previous steps you see the flow diagram. I suggest you create log file where all input events will be stored as a JSON. My diagram is visible in the figure below. If your configuration is finished deploy and start application.  At this stage I had some problems. For example, if I deploy application after Striim restart I always have to change something and save, otherwise exception during deploy occurs. However, after a long struggle with Striim, I finally succeeded in running the application! So we can start testing.

8. Checking out

I created JUnit test to illustrate cache refresh performed by Striim. Inside this test I invoke employees/company/{company} REST API method and collect entities. Then I modified entities with EmployeeRepository which commits changes directly to the database bypassing Hazelcast cache. I invoke REST API again and compare results with entities collected with previous invoke. Field personId should not be equal with value for previously invoked entity. You also can test it manually by calling REST API endpoint and change something in the database using the client like MySQL Workbench.

[code language=”java”]
@SpringBootTest(webEnvironment = WebEnvironment.DEFINED_PORT)
@RunWith(SpringRunner.class)
public class CacheRefreshEmployeeTest {

protected Logger logger = Logger.getLogger(CacheRefreshEmployeeTest.class.getName());

@Autowired
EmployeeRepository repository;

TestRestTemplate template = new TestRestTemplate();

@Test
public void testModifyAndRefresh() {
Employee[] e = template.getForObject("http://localhost:3333/employees/company/{company}", Employee[].class, "Test001");
for (int i = 0; i < e.length; i++) {
Employee eMod = repository.findOne(e[i].getId());
eMod.setPersonId(eMod.getPersonId()+1);
repository.save(eMod);
}

Employee[] e2 = template.getForObject("http://localhost:3333/employees/company/{company}", Employee[].class, "Test001");
for (int i = 0; i < e2.length; i++) {
Assert.assertNotEquals(e[i].getPersonId(), e2[i].getPersonId());
}

}
}
[/code]

Here’s the picture with Striim dashboard monitor. We can check out how many events were processed, what is actual memory and CPU usage etc.

Final Thoughts

I have no definite opinion about Striim. On the one hand it is an interesting solution with many integration possibilities and a nice dashboard for configuration and monitoring. But on the other hand it is not free from errors and bugs. My application crashed when an exception was thrown for the lack of a matching serializer for the entity in Hazelcast’s cache. This stopped processing any further events. It may be a deliberate action, but in my opinion subsequent events should be processed as they may affect other tables. The application management with web dashboard is not very comfortable at all. Every time I restarted the container, I had to change something in the configuration, because the application threw not intuitive exception on startup. From this type of application I would expect first of all reliability if the application would require updating of the data on the Hazelcast. However, despite some drawbacks, it is worth a closer look at Striim.

The post Hazelcast Hot Cache with Striim appeared first on Piotr's TechBlog.

Spring Boot has a built-in auto configuration for Hazelcast. It is enabled if the library available under application classpath and @Bean Config is declared.

@Bean
Config config() {
   Config c = new Config();
   c.setInstanceName("cache-1");
   c.getGroupConfig().setName("dev").setPassword("dev-pass");
   ManagementCenterConfig mcc = new ManagementCenterConfig().setUrl("http://192.168.99.100:38080/mancenter").setEnabled(true);
   c.setManagementCenterConfig(mcc);
   SerializerConfig sc = new SerializerConfig().setTypeClass(Employee.class).setClass(EmployeeSerializer.class);
   c.getSerializationConfig().addSerializerConfig(sc);
   return c;
}

In the code fragment above we declared cluster name and password credentials, connection parameters to Hazelcast Management Center and entity serialization configuration. Entity is pretty simple – it has @Id and two fields for searching personId and company.

@Entity
public class Employee implements Serializable {

   private static final long serialVersionUID = 3214253910554454648L;

   @Id
   @GeneratedValue
   private Integer id;
   private Integer personId;
   private String company;

   public Integer getId() {
      return id;
   }

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

   public Integer getPersonId() {
      return personId;
   }

   public void setPersonId(Integer personId) {
      this.personId = personId;
   }

   public String getCompany() {
      return company;
   }

   public void setCompany(String company) {
      this.company = company;
   }

}

Every entity needs to have a serializer declared if it is to be inserted and selected from the cache. There are same default serializers available inside the Hazelcast library, but I implemented the custom one for our sample. It is based on StreamSerializer and ObjectDataInput.

public class EmployeeSerializer implements StreamSerializer<Employee> {

   @Override
   public int getTypeId() {
      return 1;
   }

   @Override
   public void write(ObjectDataOutput out, Employee employee) throws IOException {
      out.writeInt(employee.getId());
      out.writeInt(employee.getPersonId());
      out.writeUTF(employee.getCompany());
   }

   @Override
   public Employee read(ObjectDataInput in) throws IOException {
      Employee e = new Employee();
      e.setId(in.readInt());
      e.setPersonId(in.readInt());
      e.setCompany(in.readUTF());
      return e;
   }

   @Override
      public void destroy() {
   }

}

There is also a DAO interface for interacting with the database. It has two searching methods and extends Spring Data CrudRepository.

public interface EmployeeRepository extends CrudRepository<Employee, Integer> {

   public Employee findByPersonId(Integer personId);
   public List<Employee> findByCompany(String company);

}

Hazelcast instance is embedded in the application. When starting the Spring Boot application we have to provide VM argument -DPORT which is used for exposing service REST API. Hazelcast automatically detects other running member instances and its port will be incremented out of the box. Here’s REST @Controller class with exposed API.

@RestController
public class EmployeeController {

   private Logger logger = Logger.getLogger(EmployeeController.class.getName());

   @Autowired
   EmployeeService service;

   @GetMapping("/employees/person/{id}")
   public Employee findByPersonId(@PathVariable("id") Integer personId) {
      logger.info(String.format("findByPersonId(%d)", personId));
      return service.findByPersonId(personId);
   }

   @GetMapping("/employees/company/{company}")
   public List<Employee> findByCompany(@PathVariable("company") String company) {
      logger.info(String.format("findByCompany(%s)", company));
      return service.findByCompany(company);
   }

   @GetMapping("/employees/{id}")
   public Employee findById(@PathVariable("id") Integer id) {
      logger.info(String.format("findById(%d)", id));
      return service.findById(id);
   }

   @PostMapping("/employees")
   public Employee add(@RequestBody Employee emp) {
      logger.info(String.format("add(%s)", emp));
      return service.add(emp);
   }

}

@Service is injected into the EmployeeController. Inside EmployeeService there is a simple implementation of switching between Hazelcast cache instance and Spring Data DAO @Repository. In every find method, we are trying to find data in the cache and in case it’s not there we are searching it in database and then putting found entity into the cache.

@Service
public class EmployeeService {

   private Logger logger = Logger.getLogger(EmployeeService.class.getName());

   @Autowired
   EmployeeRepository repository;
   @Autowired
   HazelcastInstance instance;

   IMap<Integer, Employee> map;

   @PostConstruct
   public void init() {
      map = instance.getMap("employee");
      map.addIndex("company", true);
      logger.info("Employees cache: " + map.size());
   }

   @SuppressWarnings("rawtypes")
   public Employee findByPersonId(Integer personId) {
      Predicate predicate = Predicates.equal("personId", personId);
      logger.info("Employee cache find");
      Collection<Employee> ps = map.values(predicate);
      logger.info("Employee cached: " + ps);
      Optional<Employee> e = ps.stream().findFirst();
      if (e.isPresent())
      return e.get();
      logger.info("Employee cache find");
      Employee emp = repository.findByPersonId(personId);
      logger.info("Employee: " + emp);
      map.put(emp.getId(), emp);
      return emp;
   }

   @SuppressWarnings("rawtypes")
   public List<Employee> findByCompany(String company) {
      Predicate predicate = Predicates.equal("company", company);
      logger.info("Employees cache find");
      Collection<Employee> ps = map.values(predicate);
      logger.info("Employees cache size: " + ps.size());
      if (ps.size() > 0) {
         return ps.stream().collect(Collectors.toList());
      }
      logger.info("Employees find");
      List<Employee> e = repository.findByCompany(company);
      logger.info("Employees size: " + e.size());
      e.parallelStream().forEach(it -> {
         map.putIfAbsent(it.getId(), it);
      });
      return e;
   }

   public Employee findById(Integer id) {
      Employee e = map.get(id);
      if (e != null)
         return e;
      e = repository.findOne(id);
      map.put(id, e);
      return e;
   }

   public Employee add(Employee e) {
      e = repository.save(e);
      map.put(e.getId(), e);
      return e;
   }

}

If you are interested in running sample application you can clone my repository on GitHub. In person-service module there is an example for my previous article about Hibernate 2nd cache with Hazelcast, in employee-module there is an example for that article.

Testing

Let’s start three instances of employee service on different ports using VM argument -DPORT. In the first figure visible in the beginning of article these ports are 2222, 3333 and 4444. When starting the last third service’s instance you should see the fragment visible below in the application logs. It means that the Hazelcast cluster of three members has been set up.

2017-05-09 23:01:48.127  INFO 16432 --- [ration.thread-0] c.h.internal.cluster.ClusterService      : [192.168.1.101]:5703 [dev] [3.7.7]

Members [3] {
   Member [192.168.1.101]:5701 - 7a8dbf3d-a488-4813-a312-569f0b9dc2ca
   Member [192.168.1.101]:5702 - 494fd1ac-341b-451c-b585-1ad58a280fac
   Member [192.168.1.101]:5703 - 9750bd3c-9cf8-48b8-a01f-b14c915937c3 this
}

Here is a picture from the Hazelcast Management Center for two running members (only two members are available in the freeware version of Hazelcast Management Center).

Then run docker containers with MySQL and Hazelcast Management Center.

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

Now, you could try to call endpoint http://localhost:/employees/company/{company} on all of your services. You should see that data is cached in the cluster and even if you call endpoint on different service it find entities put into the cache by different service. After several attempts, my service instances put about 100k entities into the cache. The distribution between the two Hazelcast members is 50% to 50%.

Final Words

Probably we could implement smarter solution for the problem described in that article, but I just wanted to show you the idea. I tried to use Spring Data Hazelcast for that, but I’ve got a problem to run it on Spring Boot application. It has HazelcastRepository interface, which something similar to Spring Data CrudRepository but basing on cached entities in Hazelcast grid and also uses Spring Data KeyValue module. The project is not well document and like I said before it didn’t worked with Spring Boot so I decided to implement my simple solution

In my local environment, queries on the cache were about 10 times faster than similar queries on database. I inserted 2M records into the employee table. Hazelcast data grid could not only be a 2nd level cache but even a middleware between your application and database. If your priority is a performance of queries on the large amounts of data and you need to have a lot of RAM reserved for your Hazelcast in memory data grid. It is the right solution for you

The post In memory data grid with Hazelcast appeared first on Piotr's TechBlog.

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 databases. 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 an in-memory data grid while we considered moving to Oracle Coherence in one of organizations I had been working for 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 a MySQL database accessed by Spring Data DAO objects. Here’s the figure illustrating the architecture of the presented solution.

Implementation

1. 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.

2. 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 the 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 the person-service module we declared some other dependencies to Hazelcast artifacts and Spring Data JPA. I had to override the managed hibernate-core version for Spring Boot 1.5.3.RELEASE, because Hazelcast didn’t work 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>
 

3. 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 a YAML configuration 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 – the 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
 

4. 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 endpoint 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 methods 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 to find a 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.

Clustering

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

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

Now we should perform one change in hazelcast.xml configuration file. Because the data grid is run inside the 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 a 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 the Management Center.

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 an entity by another index like the PESEL number I had to cache findByPesel query. Even if the entity was cached before by id query will not find it in the cache but perform SQL on the database. Only the 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.

The post JPA caching with Hazelcast, Hibernate and Spring Boot appeared first on Piotr's TechBlog.