In the world of code, how to efficiently organize and manage modules directly determines the maintainability of the project itself and the efficiency of development.
Basic concepts of module systems
Similar to the module system that builds traffic rules for the code world, it splits large programs into independent code blocks with clear functions, and each such code block is called a module. In this way, developers can divide and conquer, focus on a single function, and prevent all code from being messily placed in one file.
In the JavaScript ecosystem, CommonJS and ES Modules are the two most important sets of rules. CommonJS is mainly used in the Node.js environment. Its require() and module.exports syntax have long been the standard for back-end development, while ES Modules is the official standard of the JavaScript language. Using import and export statements, it has now become the first choice for browsers and modern Node.js applications.
How CommonJS works
What is synchronously loaded is the CommonJS module. When you use require('./moduleA') , Node.js will immediately locate the module file, then read it, then execute it, and then cache its exported object. Because module files are often stored on the local disk, the reading speed is very fast, so this design is very suitable for the server environment.
One of its key features is value copying. When a module is imported, what is obtained is a copy of the exported object at that moment. Even if the values inside the original module change later, the imported places will not be automatically updated. This mechanism is simple and intuitive, but it requires extra caution when dealing with circular dependencies.
Mechanism and advantages of ES Modules
ES Modules adopts a completely different asynchronous loading design. It performs static analysis during the compilation stage to determine all import and export relationships, rather than dynamic execution at runtime. This means that import statement must be placed at the top level of the module and cannot be nested in conditional statements. Although this limits flexibility, it brings better optimization space.
Unlike the copy of CommonJS, ES Modules export a dynamic read-only reference to the value. The importer and exporter point to the same address in memory. If the exported value itself is variable, such as an object, then if the object properties are modified, all importers can see the changes. This design is more in line with the expectations of modern programming.
Modular practice in Java
The Java platform introduced an official module system in JDK 9. This system is called JPMS. It uses the module-info.java file to declare the module and clearly indicate the packages that need to be exported and other dependent modules. This solves the problem of "JAR hell" under the traditional class path, and also enhances encapsulation and security.
In Java application development, developers often need to query data. Using IN and NOT IN operators is a common way to filter collections in SQL. In persistence layer frameworks such as JPA or MyBatis, this can be achieved by passing a List or array as a parameter. It should be noted that when the IN list is too long, it may have an impact on database query performance.
Elasticsearch search capabilities
Elasticsearch is an engine built based on Lucene with distributed search and analysis functions. It provides near-real-time data indexing and powerful full-text retrieval capabilities. Its core advantage lies in its distributed architecture, which can achieve horizontal expansion and handle PB-level data. It uses RESTful APIs to interact, lowering the threshold for use.
"In Elasticsearch, Term query and Match query are used in different scenarios. Term query is used for exact matching. It does not segment the search terms, but directly searches for exactly the same terms. It is often used in fields such as keywords and status codes. Match query will first segment the search terms and then perform matching based on the results of the segmentation. It is more suitable for full-text search, such as article content, product descriptions, etc."
Practical applications of data-driven decision-making
Take the report "Bawang Cha Ji Store Operation Analysis" as an example. It shows how to apply data analysis to actual business. The core purpose of this report is to use the analysis of sales data, user portraits and other information to achieve the goals of improving performance and optimizing operations. This shows an extension of modular thinking at the business level: that is, decomposing complex business problems into sales, users, products and other modules that can be analyzed independently.
Make reports, using Python as the primary analytical tool for descriptive statistics and visualization. For example, it can analyze changes in sales over time and at different stores, or identify the most popular product combinations. These concretely presented and quantified findings are more convincing than vague empirical judgments, and can provide a clear basis for product adjustment and marketing resource allocation.
Whether it is organizing code or conducting business analysis, systematic and modular thinking is a key weapon to deal with complexity. This weapon can help us sort out the chaos and build a structure that is stable and can continue to evolve. Which module specification do you prefer to use in your work, and what difficult problems have you encountered related to it? Welcome to the comment area to share your experiences with punctuation marks.
