Lexical analysis: Divide user input into tokens and remove unnecessary symbols and stop words.

Syntax analysis: Build a query syntax tree and identify various structures in the query statement.

Semantic analysis: Understand the meaning of queries and transform abstract queries into executable operations.

Document collection: Obtain documents on the Internet through web crawlers.

Document preprocessing: remove HTML tags, extract text content, divide paragraphs, etc.

Inverted index construction: Divide the document into tokens, build an inverted index table, and record in which documents each token appears.

Index optimization: compress indexes, improve query speed and accuracy.

Similarity calculation: Calculate the similarity score between the query and the document.

Sorting algorithm: Sorts search results based on similarity score and other factors (such as weight, time, etc.).

Results filtering: Filter and adjust search results based on user needs, user portraits, etc.

The user enters query keywords and submits the query.

Display search results, including title, abstract, URL and other information.

Provides related searches, search history, search suggestions and other functions.

Load balancing: Distribute user requests to different servers to balance system load.

High availability: Ensure system stability and availability through mechanisms such as redundancy and backup.

Distributed storage: Distribute indexes, documents, and other data across multiple nodes to improve system capacity and performance.

Performance optimization: Monitor and tune system performance to improve search response speed.

Data sharding: Divide large-scale data into multiple small blocks and assign them to different computing nodes for parallel processing.

Task scheduling: Decompose complex computing tasks into multiple subtasks, assign them to different computing nodes, and coordinate execution.

Data synchronization: Maintain data consistency between computing nodes to ensure the accuracy of results.

Relational database: used to store metadata, user information and other structured data.

Distributed file system: used to store large-scale unstructured data, such as documents, pictures, videos, etc.

Cache system: used to cache popular data and improve query performance.

Web page discovery: Starting from the seed URL, through link analysis and URL queue, more web pages that need to be crawled are discovered.

Web crawling: download web content and save it to local or other storage media.

Anti-crawler strategy: Respond to the anti-crawler mechanism of the website, such as limiting request frequency, verification code, login, etc.

HTML parsing: Use a parser to parse HTML documents and convert them into operable data structures.

DOM operation: Obtain information such as tags, attributes, and content in web pages through DOM operations.

Text extraction: Extract text information from web pages and remove interference such as noise and advertisements.

Query analysis: Analyze user queries and extract keywords, filter words and other information.

Query expansion: Use synonyms, related words and other technologies to expand the query and obtain more relevant results.

Query optimization: Optimize query plans to improve query performance and accuracy.

Result feedback: Return the query results to the user and provide relevant search and error correction suggestions.