Accessing an In-Memory H2 Database Across Two Spring Boot Applications
Sharing data between applications can be a complex challenge, especially when dealing with in-memory databases. This article explores a method to access the in-memory H2 database of one Spring Boot application from another.
The Problem:
Imagine you have two Spring Boot applications running independently. One (let's call it 'Data Provider') contains an in-memory H2 database holding critical information. The other application ('Data Consumer') needs to access this information. The challenge lies in accessing the in-memory database, which is not accessible through traditional network methods.
Scenario and Original Code:
Data Provider Application:
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class DataProviderApplication {
public static void main(String[] args) {
SpringApplication.run(DataProviderApplication.class, args);
}
}
Data Consumer Application:
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
@SpringBootApplication
public class DataConsumerApplication {
public static void main(String[] args) {
SpringApplication.run(DataConsumerApplication.class, args);
}
}
These are basic Spring Boot applications without any database interaction.
The Solution: Shared Memory Space
We can leverage a shared memory space to connect the two applications and access the in-memory H2 database. This space can be implemented using technologies like:
- Redis: A popular in-memory data store that allows data sharing across multiple applications.
- Apache Ignite: A powerful distributed in-memory data grid platform with advanced capabilities like persistence and querying.
- Hazelcast: A distributed in-memory data grid platform known for its ease of use and high performance.
Example with Redis:
1. Data Provider Application:
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.data.jpa.repository.config.EnableJpaRepositories;
import org.springframework.data.redis.connection.RedisStandaloneConfiguration;
import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.serializer.GenericJackson2JsonRedisSerializer;
import org.springframework.data.redis.serializer.StringRedisSerializer;
import javax.persistence.EntityManagerFactory;
import java.util.HashMap;
import java.util.Map;
@SpringBootApplication
@EnableJpaRepositories
public class DataProviderApplication {
public static void main(String[] args) {
SpringApplication.run(DataProviderApplication.class, args);
}
// Configure Redis
@Bean
JedisConnectionFactory jedisConnectionFactory() {
RedisStandaloneConfiguration configuration = new RedisStandaloneConfiguration("localhost", 6379);
return new JedisConnectionFactory(configuration);
}
// Create a RedisTemplate for accessing data
@Bean
RedisTemplate<String, Object> redisTemplate() {
RedisTemplate<String, Object> template = new RedisTemplate<>();
template.setConnectionFactory(jedisConnectionFactory());
template.setKeySerializer(new StringRedisSerializer());
template.setValueSerializer(new GenericJackson2JsonRedisSerializer());
return template;
}
// Populate Redis with H2 data
@Bean
public void populateRedis(EntityManagerFactory emf, RedisTemplate<String, Object> redisTemplate) {
// Get data from H2 database using EntityManagerFactory
// Convert data to a suitable format (e.g., JSON)
// Store data in Redis using redisTemplate.opsForHash().putAll(...)
}
}
2. Data Consumer Application:
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.data.redis.connection.RedisStandaloneConfiguration;
import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.serializer.GenericJackson2JsonRedisSerializer;
import org.springframework.data.redis.serializer.StringRedisSerializer;
@SpringBootApplication
public class DataConsumerApplication {
public static void main(String[] args) {
SpringApplication.run(DataConsumerApplication.class, args);
}
// Configure Redis
@Bean
JedisConnectionFactory jedisConnectionFactory() {
RedisStandaloneConfiguration configuration = new RedisStandaloneConfiguration("localhost", 6379);
return new JedisConnectionFactory(configuration);
}
// Create a RedisTemplate for accessing data
@Bean
RedisTemplate<String, Object> redisTemplate() {
RedisTemplate<String, Object> template = new RedisTemplate<>();
template.setConnectionFactory(jedisConnectionFactory());
template.setKeySerializer(new StringRedisSerializer());
template.setValueSerializer(new GenericJackson2JsonRedisSerializer());
return template;
}
// Read data from Redis
@Bean
public void readFromRedis(RedisTemplate<String, Object> redisTemplate) {
// Read data from Redis using redisTemplate.opsForHash().entries(...)
// Process data based on requirements
}
}
Key Points:
- The 'Data Provider' application reads data from its H2 database and publishes it to a shared memory space (Redis in this example).
- The 'Data Consumer' application subscribes to this shared space and retrieves the required data.
- Both applications need to be configured to connect to the same Redis server.
Advantages:
- Flexibility: This method allows for dynamic data sharing between applications without direct connection dependencies.
- Scalability: You can easily scale your applications and add more data consumers or providers without major modifications.
- Performance: In-memory data stores like Redis provide high-speed data access.
Limitations:
- Data Consistency: Maintaining data consistency across multiple applications requires careful coordination and synchronization mechanisms.
- Complexity: Implementing a shared memory space with features like data persistence and advanced querying can be complex.
Conclusion:
Accessing an in-memory H2 database across two Spring Boot applications is achievable through shared memory space technologies like Redis, Apache Ignite, or Hazelcast. This approach provides a flexible and scalable solution for inter-application data sharing while maintaining the benefits of in-memory databases. Remember to carefully consider the trade-offs and potential challenges, especially regarding data consistency and complexity, before implementing this solution.
