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domain	365129900.com
summary	🎠 Summary of Database Queries and Performance This output represents a series of SQL queries executed against a database named "ipcms". Here's a breakdown of what's happening and what it signifies: 1. Initial Schema Exploration: * `SHOW FULL COLUMNS FROM config`: This command reveals detailed information about all columns in the `config` table (e.g., column names, data types, keys). It's a standard way to understand the structure of a table. * `SHOW FULL COLUMNS FROM website`: Similar to the above, but for the `website` table. * `SHOW FULL COLUMNS FROM website_friendlink`: Reveals details about the `website_friendlink` table. * `SHOW FULL COLUMNS FROM download_article`: Details for the `download_article` table. * `SHOW FULL COLUMNS FROM site_app`: Details for the `site_app` table. * `SHOW FULL COLUMNS FROM article`: Details for the `article` table. 2. Data Retrieval Queries (Most Significant Part): These are the core queries retrieving data. The "RunTime" column indicates the time (in seconds) it took to execute each query. Here's a breakdown: * Initial Data Fetching (Download Articles & Site Apps): * Several queries are fetching data from the `download_article` and `site_app` tables based on specific criteria. The `LIMIT` clauses are important – they restrict the number of results returned, improving performance. These queries retrieve articles and applications based on the `website_id` and `id` fields. * Article Retrieval: * `SELECT id,description FROM article WHERE id IN 1226102,247487,1054180,1313327`: This query pulls specific articles based on their IDs. It's a very efficient way to retrieve a small, known set of rows. * `SELECT id,description FROM article WHERE id IN 1099484,622692,1131639,747730`: Similar to the previous one, retrieving articles by ID. * Advanced Search (Keyword Based): * The final queries are searching the `site_app` table for entries that contain specific keywords (e.g., "panda", "sky", "vqn"). The `LIKE CONCAT, keyword` syntax performs a wildcard search. This is generally slower than exact matches. The `LIMIT 1` is used to return only the first matching result, further optimizing performance. 3. Timing & Potential Bottlenecks * Short Queries: The queries retrieving articles by ID (`id IN ...`) are extremely fast (sub-millisecond) – this is optimal. * Moderate Queries (Download/Site App): Queries pulling data based on `website_id` and ID ranges take a few milliseconds (0.01 - 0.47 seconds). These are typical for table scans and sorting. * Slowest Queries (Keyword Search): The `LIKE` queries are the slowest, potentially taking 0.06 - 0.11 seconds each. This is because `LIKE` typically requires a full table scan to find matches, especially with wildcard characters. 4. MySQL Information * `CONNECT: UseTime:0.000130s`: The time spent actually connecting to the database. * `mysql:host127.0.0.1;port3306;dbnameipcms;charsetutf8`: Information about the connection parameters. Recommendations based on this analysis: 1. Index Usage: Ensure proper indexes exist on the columns frequently used in `WHERE` clauses (e.g., `website_id` in `download_article`, `id` in `article`, `keyword` in `site_app`). Indexes dramatically speed up data retrieval. MySQL automatically creates some indexes, but you might need to create custom ones for specific queries. 2. Optimize `LIKE` Queries: If you frequently use `LIKE` queries, consider these strategies: * Full-Text Search: For more complex text searches, investigate MySQL's full-text search capabilities. * Prefix Searches: If possible, restrict the search to the beginning of the string (`LIKE 'keyword%'`). 3. Review Query Plans: Use `EXPLAIN` before running a query to analyze the query execution plan. This helps you understand how MySQL is executing the query and identify potential bottlenecks (e.g., missing indexes, full table scans). 4. Data Types: Verify that data types are appropriate for the data being stored. Using the correct data types can improve storage efficiency and query performance. 5. Database Statistics: Make sure MySQL's query optimizer has up-to-date statistics about the data in your tables. Run `ANALYZE TABLE` periodically. In essence, this output points to a situation where the database is performing reasonably well for simple queries, but the keyword searches (`LIKE` queries) are becoming a performance bottleneck. The next step would be to investigate the query plans for these `LIKE` queries and consider indexing or alternative search strategies.
title	Kuaiya - Kuaiya acceleration organ website - Kuaiya accelerator download and installation - Kuaiya acceleration app
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keywords	limit, like, show, full, columns, order, hela, type, article, panda, cells, website, description, have, role, medical, research
upstreams
downstreams
nslookup	A 172.67.173.54, A 104.21.30.157
created	2026-02-14
updated	2026-02-14
summarized	2026-02-15

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