Design and implementation of high-performance online translation system based on Swoole

In recent years, with people's pursuit of globalization and the rapid development of artificial intelligence, translation systems have become an indispensable tool. Traditional online translation systems are prone to bottlenecks and delays due to high concurrency and big data processing. In order to improve the quality and speed of online translation, this article builds a high-performance online translation system based on the Swoole framework. This article will introduce the design and implementation of this system.

1. Background

With the popularization of the Internet and the acceleration of globalization, more and more people need to communicate across languages. The popularity of translation systems has alleviated this problem to a great extent and satisfied people's information exchange needs. However, a large number of users and massive data will cause traditional translation systems to face many problems, such as performance bottlenecks, delays, system stability, etc. Therefore, we need to build a high-performance online translation system.

2. System design

1. Overall architecture

This system uses Swoole as the network communication framework to achieve high concurrency, high efficiency and high performance translation Serve. The specific framework is shown in Figure 1.

Figure 1 Overall Architecture Diagram

The traditional translation system uses the LAMP architecture that is easy to use, easy to expand, and easy to manage. However, with the increase in the number of users, traffic and concurrent requests, this There will be bottlenecks in the performance of this architecture, leading to problems such as latency. Therefore, we use the Swoole framework to replace the LAMP architecture to implement high-performance translation services. The Swoole framework is an asynchronous and efficient network communication framework. Compared with the traditional synchronous IO framework, a single process can support more requests. According to experimental results, the Swoole framework can increase throughput nearly 100 times compared to traditional synchronous io frameworks when processing equivalent HTTP requests.

2. Detailed implementation

(1) Client request

The content requested by the client is mainly a text to be translated. After the request is sent to the server via HTTP POST, the server parses the request, obtains the text to be translated, and checks and preprocesses the text.

(2) NLP preprocessing

In the text requested by the client, there are non-standard text contents, and there are also so-called "language" expressions and habits between different languages. obstacle". In order to improve the accuracy of translation and language coherence, the text to be translated needs to be pre-processed by NLP (Natural Language Processing). NLP preprocessing mainly includes the following operations:

Operation	Content
Clause	Decompose the text into multiple sentences
Word segmentation	Perform word segmentation for each sentence
Part-of-speech tagging	Perform part-of-speech tagging for each word to provide a basis for language conversion
Entity recognition	Identify the key in the sentence Information, such as time, location, etc.

(3) Language recognition

The translation system needs to first distinguish what language the text to be translated belongs to before it can update Good for translation. We used the Google Translate API library to detect the input language type. Pass the text to GoogleAPI and parse its response message to identify the language type of the input text.

(4) Call the translation service

After completing the above NLP preprocessing and language recognition, you need to call the translation service for translation. We used the Youdao online translation API service to request and send the preprocessed text, use the returned result as the translation result, and send it back to the client.

(5) Return results

After the translation service processes the request and returns the result, the server will encapsulate the result into an HTTP response and return it to the client.

3. Performance Optimization

1. Preheating

After the service is started, the Swoole framework needs to warm up for a period of time to ensure that the service can Reach a normal operating state to accept requests. Preheating mainly includes the following three aspects:

Aspect	Content
Code loading	Load all the code required by the translation system
Data connection	Establish a connection to the database or cache
Compilation Optimizer	Load the Swoole framework’s own code compilation optimizer

2. Process Control

The Swoole framework is a single-threaded asynchronous network communication framework that can support high concurrent connections using a single process. In order to better utilize the CPU and memory, controlling the number of processes is essential. We adopted the process management mechanism of the Swoole framework to meet the needs of server requests by controlling the number of processes.

3. Cache pool under multi-process

In order to reduce the response delay of the translation service and reduce the service response time, we adopt a caching mechanism. In order to make full use of the concurrent processing capabilities of the CPU, we use cache pools under multiple processes, and improve cache usage efficiency and reduce CPU idle time by controlling the available number and expiration time of the cache pool.

4. Conclusion

This article builds a high-performance online translation system based on the Swoole framework and achieves good results. The system adopts steps such as NLP preprocessing, language recognition and calling translation services. With the support of Swoole, it provides a highly concurrent and efficient translation service with many advantages such as stress testing and fault tolerance. In the future, we will continue to optimize the system, improve user experience and system performance, and try to expand the business scope of the service to meet the needs of more users.

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