Springboot JMS
A production JMS server must be a stand alone server. What we have here is for demonstration purposes.
Add dependency
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-activemq</artifactId>
</dependency>
Add configuration class
@Configuration
@EnableJms
public class JmsConfig {
@Bean
public JmsListenerContainerFactory<?> jmsFactory(ConnectionFactory connectionFactory,DefaultJmsListenerContainerFactoryConfigurer configurer) {
DefaultJmsListenerContainerFactory factory = new DefaultJmsListenerContainerFactory();
configurer.configure(factory, connectionFactory);
return factory;
}
@Bean // Serialize message content to json using TextMessage
public MessageConverter jacksonJmsMessageConverter() {
MappingJackson2MessageConverter converter = new MappingJackson2MessageConverter();
converter.setTargetType(MessageType.TEXT);
converter.setTypeIdPropertyName("_type");
return converter;
}
}
Add a Sender
@Slf4j
@Component
public class Sender {
@Autowired
private JmsTemplate jmsTemplate;
@Value("${queue.sender-receiver}")
private String queue;
public void sendMail(Mail mail) {
try {
jmsTemplate.convertAndSend(queue, mail);
log.info("Mail sent to queue, {}, at {}", queue, LocalDateTime.now());
} catch (Exception e) {
log.error("Mail not sent");
log.error("Exception, msg: {}", e.getMessage());
}
}
}
Add a Receiver
@Slf4j
@Component
public class Receiver {
@JmsListener(destination = "${queue.sender-receiver}", containerFactory = "jmsFactory")
public void receiveMessage(Mail mail) {
log.info("Received mail {}", LocalDateTime.now());
try {
log.info("mail={}", ObjectUtils.toJson(mail));
} catch (Exception e) {
System.err.println(e.getMessage());
}
}
}
Springboot Cache
An application without cache is typically slow. Even great code will crawl when handling heavy traffic without cache. Caching can be a fast and relatively cheap way to increase performance.
Caching is a performance boost strategy you must implement into your application. Caching is nothing more than a hash table.
For Redis, all Redis data resides in-memory, in contrast to databases that store data on disk or SSDs. By eliminating the need to access disks, in-memory data stores such as Redis avoid seek time delays and can access data in microseconds. Redis features versatile data structures, high availability, geospatial, Lua scripting, transactions, on-disk persistence, and cluster support making it simpler to build real-time internet-scale apps.
I am going to show you how to implement caching into your Spring Boot application.
1. Include the following dependencies in your pom file.
<dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-cache</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-redis</artifactId> </dependency>
In your application.properties file, set Redis configurations. Also set spring.cache.type=redis.
2. Create a configuration class
@Configuration
@EnableCaching
public class CachingConfig{
@Bean("customKeyGenerator")
public KeyGenerator keyGenerator() {
return new CustomKeyGenerator();
}
private class CustomKeyGenerator implements KeyGenerator {
public Object generate(Object target, Method method, Object... params) {
StringBuilder strParams = new StringBuilder(target.getClass().getSimpleName());
strParams.append("_");
strParams.append(method.getName());
for(Object param: params) {
strParams.append("_");
strParams.append(param);
}
System.out.println("key="+strParams.toString());
return strParams.toString();
}
}
}
3. Use @Cacheable to retrieve data
@Cacheable(value="users", key = "#id")
public User getById(Long id) {
// retrieve User from db
...
return user;
}
4. Use @CachePut to update cached data
@CachePut(value="users", key = "#user.id")
public User update(User user) {
....
return user;
}
5. Use @CacheEvict to delete cached data
@CacheEvict(value="users", key = "#id")
public Boolean delete(Long id) {
....
return true;
}
August 6, 2019 Dealing with Failure
Relaxing
Excellent Reporting