Ah, the classic problem: you’ve got a powerful algorithm—in your case, a simulated annealer—and you need to unleash it on a problem that demands massive parallelism, all while keeping it local and using the wonderful containerization magic of Docker! That’s a great setup.
You want a Spring Boot application to become the orchestrator, spinning up and tearing down Docker containers on demand to run your stock market simulations.
Here is the straightforward, Java-based way to do this using your local Docker daemon.
💻 The Local Orchestration Architecture
Before we dive into the code, let’s nail down the components:
- The Annealer Service (Your Spring Boot App): This is the master controller. It contains the simulated annealing logic, determines the next set of parameters to test (the “temperature,” the “move”), and, crucially, makes the programmatic calls to Docker.
- The Docker Daemon: The local engine that runs, manages, and builds containers.
- The Dockerized Simulation: This is your
Dockerfilethat packages:- The Stock Market Simulator.
- The Trading Robot.
- The Database (likely an in-memory or ephemeral one like H2/SQLite, or a containerized PostGreSQL/MySQL if the test needs persistence between simulation steps).
How to Programmatically Call Docker from Spring Boot
The most robust and Java-idiomatic way to talk to the Docker daemon without resorting to shell scripts is to use the official Docker client libraries. The best option for Java is the docker-java library.
1. ⚙️ Setup: Dependencies
First, you need to add the docker-java dependency to your pom.xml (if you’re using Maven) or build.gradle (if you’re using Gradle).
- Maven (
pom.xml):
<dependency>
<groupId>com.github.docker-java</groupId>
<artifactId>docker-java-core</artifactId>
<version>3.3.6</version> </dependency>
<dependency>
<groupId>com.github.docker-java</groupId>
<artifactId>docker-java-transport-httpclient5</artifactId>
<version>3.3.6</version>
</dependency>2. 🧱 The Docker-Java Client Configuration
The docker-java library needs to know how to connect to your local Docker daemon. Since you’re on your local machine, it should auto-configure the connection, but it’s good practice to set it up explicitly in a Spring @Configuration class or within your main service.
import com.github.dockerjava.api.DockerClient;
import com.github.dockerjava.core.DefaultDockerClientConfig;
import com.github.dockerjava.core.DockerClientConfig;
import com.github.dockerjava.core.DockerClientBuilder;
@Service
public class DockerOrchestrationService {
private final DockerClient dockerClient;
public DockerOrchestrationService() {
// --- 1. Configure the Docker Client ---
// This configuration tries to automatically find the correct socket
// for your local daemon (e.g., /var/run/docker.sock on Linux/Mac,
// or using the environment variables on Windows).
DockerClientConfig config = DefaultDockerClientConfig.createDefaultConfigBuilder()
.withDockerHost("unix:///var/run/docker.sock") // Example for Unix-like OS
// .withDockerHost("tcp://localhost:2375") // Example for a TCP host
.build();
// --- 2. Build the Client ---
this.dockerClient = DockerClientBuilder.getInstance(config).build();
}
// ... methods to build/run/destroy will go here
}3. 🚀 The Orchestration Logic
The core of your service needs three methods: Build, Run, and Destroy.
A. Building the Image (The One-Time Setup)
You’ll only need to do this once, but it’s good to know how to trigger a build programmatically. This takes your Dockerfile and creates a Docker image.
// Inside DockerOrchestrationService.java
public String buildImage(String imageTag, File dockerFileDir) throws InterruptedException {
// This tells the daemon to build an image from the context path
// and tag it with the given name.
BuildImageResultCallback resultCallback = dockerClient.buildImageCmd(dockerFileDir)
.withTags(Set.of(imageTag))
.start()
.awaitCompletion();
// In a real app, you'd process the output of the callback for logs/errors.
// For simplicity, we just return the image ID from the result.
return resultCallback.awaitImageId();
}
// Example usage:
// service.buildImage("annealer-sim:latest", new File("/path/to/your/simulator/Dockerfile/directory"));B. Running a Container (The “10x” Scale-Up)
This is the key step. You will call this method 10 times concurrently (or more!) for each round of the simulated annealer.
// Inside DockerOrchestrationService.java
public String runSimulationContainer(String imageTag, String containerName, Map<String, String> simParameters) {
// 1. Prepare Environment Variables
List
<String> envVars = simParameters.entrySet().stream()
.map(e -> e.getKey() + "=" + e.getValue())
.collect(Collectors.toList());
// 2. Create the container configuration
CreateContainerResponse containerResponse = dockerClient.createContainerCmd(imageTag)
.withName(containerName)
.withEnv(envVars) // Inject the simulation parameters (e.g., STARTING_TEMP=100)
.withHostConfig(HostConfig.createHostConfig().withAutoRemove(true)) // Auto-clean up!
.exec();
String containerId = containerResponse.getId();
// 3. Start the container
dockerClient.startContainerCmd(containerId).exec();
// 4. (Optional but important): Wait for the container to finish and get logs
// The simulation runs, processes, and should exit with a status code
// when it's done.
// This waits for the container to exit (blocking call)
WaitResponse waitResponse = dockerClient.waitContainerCmd(containerId).start().awaitStatusCode();
// Check for success and fetch the result (e.g., log the output/result file)
// You would typically need a way for the container to output its result
// (e.g., write to a mounted volume, or simply print the JSON result to STDOUT).
return containerId;
}C. Destroying the Container (The Clean-Up)
Since we used .withAutoRemove(true) in the run step, the container should automatically destroy itself after it exits. This is the clean way to handle build-and-destroy containers.
However, if you needed to stop a running container, or if you couldn’t use AutoRemove, here is how you’d manually stop and remove it:
// Inside DockerOrchestrationService.java
public void stopAndRemoveContainer(String containerId) {
try {
// Stop the container gracefully
dockerClient.stopContainerCmd(containerId).exec();
// Remove the container
dockerClient.removeContainerCmd(containerId)
.withForce(true) // Force stop/remove if it's stuck
.exec();
} catch (NotFoundException e) {
// Container was already removed or never existed
System.out.println("Container not found, already gone: " + containerId);
}
}📈 Tying it Back to Spring and Scaling
To achieve your 10x scaling, you need to call the runSimulationContainer method concurrently. Since you’re in a Spring Web App, you’re already in a multi-threaded environment.
You would typically use a ThreadPoolTaskExecutor (or just Java’s ExecutorService) to manage the 10 concurrent simulation runs and then use Java’s CompletableFuture to wait for all the results before the next annealing iteration.
// Inside your main Annealer Service
@Autowired
private DockerOrchestrationService dockerService;
// The simulated annealing loop
public void startAnnealingProcess() {
// 10 concurrent containers for one round
int scaleFactor = 10;
// Setup an Executor (e.g., Spring's TaskExecutor or a standard ExecutorService)
ExecutorService executor = Executors.newFixedThreadPool(scaleFactor);
while (/* Annealing condition is met */) {
// Determine 10 sets of parameters to test (e.g., slightly varied starting temps)
List<Map<String, String>> parameterSets = generateNextParameterSets(scaleFactor);
List<CompletableFuture<String>> futures = parameterSets.stream()
.map(params -> CompletableFuture.supplyAsync(() -> {
String containerName = "sim-run-" + UUID.randomUUID();
// **THE MAGIC CALL** - runs in a separate thread/container
return dockerService.runSimulationContainer("annealer-sim:latest", containerName, params);
}, executor))
.collect(Collectors.toList());
// Wait for all 10 simulations to finish
CompletableFuture.allOf(futures.toArray(new CompletableFuture[0])).join();
// Now, collect and evaluate the results from the containers (e.g., from logs or files)
// ... (Logic to update the best solution and current temperature) ...
}
executor.shutdown();
}This pattern—using an ExecutorService and CompletableFuture with the docker-java client—gives you the precise control and massive, local parallel scaling you are looking for. Good luck with your annealer!
