This repository is the api gateway for ml services like sentiment analysis and machine translation, etc. We implement a REST API gateway with Java and Spring framework.
The mentioned ml services are separated in other repositories.
We also implement a front-end service that could take users' requests from browsers.[GitHubRepo]
With IntelliJ and directly run the mlserviceApplication main function. The default port is 8081 which can be modified in application.properties.
Currently, we have set up a sentiment analysis service. Follow the instruction in sentiment analysis repository to set up and run the service.
To test the ml service, using the following request in terminal:
curl -X POST localhost:8081/sentimentAnalysis -H 'Content-type:application/json' -d '{"requestId": "request_id_123", "content": "This is very good."}'
The response should be the following:
{"status":"200","requestId":"request_id_123","content":"{\"request\":{\"text\":\"This is very good.\"},\"response\":{\"word_num\":5,\"sentiment_score\":3.0}}"}
To communicate with other containers, we have to put these containers into a user-defined bridge network. We will use a user-defined bridge network named allyoushawn-net as an example. Check out the tutorial for more information.
Create the bridge network with the following
docker network create allyoushawn-net
Build image with Docker
docker build -t mlservice:base --target base -f deployment/Dockerfile .
Run with Docker, while using --net argument to put the container into the network.
docker run --name mlservice_local -it -p 8081:8081 --net=allyoushawn-net mlservice:base
Similarly, we have to make sure other containers we need to communicate with are put into the network, like
docker run --name sentiment_analysis_model_service_local -it -p 4460:4460 --net=allyoushawn-net sentiment_analysis_model_service:prod
We also have to make sure we are using the correct hostname in our code for communication.
For example, in MLServiceController.java, the SENTIMENT_ANALYSIS_POST_URL has to be the something like sentiment_analysis_model_service_local:4460/sentiment_analysis. sentiment_analysis_model_service_local:4460 represents the target container's name and port.
The following segment in the code enables us to dynamically set up the endpoint through application.properties. The hostname used for docker should be specified in application-docker.properties.
@Value("${sentiment_analysis_post_url}")
private String SENTIMENT_ANALYSIS_POST_URL;
Finally, to stop and remove the container.
docker container stop mlservice_local && docker container rm mlservice_local
The API gateway for ml services. Using Spring initializer for initializing.
- Project: Gradle-Groovy
- Language: Java
- Spring Boot: 2.7.7
- Packaging: Jar
- Java: 11