Building an Automated Labeling System Based on Spring Boot

In this tutorial, we will build an automated labeling system using Spring Boot. We will cover configuration, implementing the core functions, and optimizing deployment.The system is designed to integrate a tagging rules engine with the database, enabling efficient content classification. It is suitable for developers who need to process large amounts of data.

Why do we need an automatic labeling system?

Nowadays, both content platforms and e-commerce sites generate huge amounts of data every day.To manually tag all this data would not only be inefficient, it would also be a headache, requiring overtime.For instance, before we were processing 100,000 user comments, and three people spent two days on it, and still weren't finished.Later, we used Spring Boot to make a tagging automation tool, which did the same amount of work in 20 minutes. That's the productivity that technology brings.

Environmental readiness and infrastructure.

Don't compromise on development tools.

I recommend the community edition of IntelliJ IDEA, which is free and very good.You can use MySQL or MongoDB, whichever your team is more familiar with.There is a small trick: If you use MySQL, remember to set the time zone in application.properties, otherwise you might have trouble debugging at midnight.

Maven relies on Bello.

Besides the Spring Boot Web base package, the following two dependencies are particularly important.

1. Spring Data JPA (easy database operations)

2. HanLP Chinese word segmentation tool (a magic tool for processing text).

Don't ask me how I know this--last week I forgot to add the JPA annotation, and the field just wouldn't go into the database. It took half an hour to find the bug.

Steps to achieve core functions.

Label rule engine design.

First, we need a flexible rule configuration table. For example, we can store matching keywords and corresponding tags in JSON.For example:

Q: What kind of person would you like to be?

The following is a translation of the article.

Please translate the following Chinese into English.

Keywords: ["Java","Spring","microservices"].

Programming and development.

(Chang Chao-hsuan / photos by Chung Li-chi / tr.

Please translate the following into English.

Don't write rules into the code, or you will have to redeploy every time you change the rules.The right solution is to use a database or configuration file to dynamically load the information.

Asynchronous processing improves performance.

Use the @Async annotation to implement asynchronous tagging, and use a thread pool to control the number of concurrent processes.In actual testing, the system can process up to 3000 lines of information in a second, 15 times faster than the previous system.But you need to be careful, and adjust the thread pool parameters according to the configuration of the server. You can't just use the default values.

It will also provide suggestions for optimizing the deployment of the system.

Dockerization is the answer.

After packaging them into Docker images, one can use docker-compose to start them up with one click.Remember to tune the JVM memory parameters, especially the heap size.We've been burned before--we just assumed that the default configuration would crash with an OutOfMemoryError when running batch tasks, so we had to add -Xmx2048m to keep it stable.

Monitoring cannot be neglected.

The Spring Boot Actuator can be used to check on the health state, while Prometheus can be used to monitor the success rate of the labeling.Once, there was a sudden problem with labeling, and it turned out to be caused by someone mistakenly deleting a part-of-speech dictionary file.

A guide to common problems.

1. Chinese word segmentation is not precise? Try loading a custom dictionary.

2. Label repeat matching? Priority field arrangement.

3. Slow to transfer historical data? Break it up into batches and resume interrupted transfers.

Last week he helped a friend's company with the third problem--they lost all the data they had just transferred over the network, but after adding a checkpoint recovery function they were able to continue.