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.


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 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.


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.

public DataSource dataSource() {
	final DriverManagerDataSource dataSource = new DriverManagerDataSource();
	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.

public class JpaConfiguration extends JpaBaseConfiguration {

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

	protected AbstractJpaVendorAdapter createJpaVendorAdapter() {
		return new EclipseLinkJpaVendorAdapter();

	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.

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

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

	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;

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



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;
---------- ---------------------------------------------------------------

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.



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.

Hazelcast Hot Cache with Striim

I previously introduced some articles about Hazelcast – an open source in memory data grid solution. In the first of them JPA caching with Hazelcast, Hibernate and Spring Boot I described how to set up 2nd level JPA cache with Hazelcast and MySQL. In the second In memory data grid with Hazelcast I showed more advanced sample how to use Hazelcast distributed queries to enable faster data access for Spring Boot application. Using Hazelcast as a cache level between your application and relational database is generally a very good solution under one condition – all changes are going across your application. If a data source is modified by other application which does not use your caching solution it causes problem with outdated data for your application. Did you have encountered this problem in your organization? In my organization we still use relational databases in almost all our systems. Sometimes it causes performance problems, even optimized queries are too slow for real time applications. Relational database is still required, so solutions like Hazelcast can help us.

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.

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

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.

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

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.

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

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 and to the Striim Dashboard which is available under (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:

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.

<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" />
			<id name="id" attribute-type="Integer">
				<column nullable="false" name="id" />
				<generated-value strategy="AUTO" />
			<basic name="personId" attribute-type="Integer">
				<column nullable="false" name="person_id" />
			<basic name="company" attribute-type="String">
				<column name="company" />

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.

public class Employee implements Serializable {

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

	public Employee() {


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


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.

Members [1] {
	Member []:5701 - d568a38a-7531-453a-b7f8-db2be4715132 this

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.

@SpringBootTest(webEnvironment = WebEnvironment.DEFINED_PORT)
public class CacheRefreshEmployeeTest {

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

	EmployeeRepository repository;

	TestRestTemplate template = new TestRestTemplate();

	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());

		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());


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.

JPA caching with Hazelcast, Hibernate and Spring Boot


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.



  • 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">
     <management-center enabled="true" update-interval="3"></management-center>

Hazelcast Dashboard is available under address. We can monitor there all running cluster members, maps and some other parameters.


  • 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.


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.

  • 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.

    name: person-service
    url: jdbc:mysql://
    username: datagrid
    password: datagrid
        show_sql: true
          use_query_cache: true
          use_second_level_cache: true
            use_native_client: true
            native_client_group: dev
            native_client_password: dev-pass
            factory_class: com.hazelcast.hibernate.HazelcastCacheRegionFactory
  • Application code

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

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

	private static final long serialVersionUID = 3214253910554454648L;

	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;

	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);


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> {

	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.

public class PersonApplication {

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

	HazelcastInstance hazelcastInstance() {
		ClientConfig config = new ClientConfig();
		HazelcastInstance instance = HazelcastClient.newHazelcastClient(config);
		return instance;

	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.



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, and for second, because it is exposed on 5702 port.


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

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

All Hazelcast running instances are visible in Management Center.



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.