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domain	feiyijiasuqi.com
summary	Okay, let's analyze this information. This output represents a series of database queries executed against a database named `ipcms`. The data provides details about the execution time of each query. Here's a breakdown of what we can gather: 1. Overall Observations: * Query Complexity: Many of the queries are `SELECT` statements retrieving data based on `WHERE` clauses. The use of `LIKE` with wildcards (`%`) and `IN` clauses suggests pattern matching or multiple value selection, which can be slower than precise equality comparisons. * Performance Bottlenecks: The `download_article` queries, especially those using `ORDER BY id DESC LIMIT` and `WHERE` clauses with `LIKE`, appear to have the highest execution times. * Database Schema: The database schema includes tables like `config`, `website`, `website_friendlink`, `download_article`, `site_app`, `article`, and potentially others, depending on the application's functionality. 2. Specific Query Performance Analysis (Rough Estimates Based on Time): * Shortest Queries (0.000039 - 0.000077s): These are simple `SELECT` queries using `LIKE` with single wildcards. They are likely the fastest. * Moderate Queries (0.000077 - 0.000154s): Queries using `LIKE` with multiple wildcards, or queries selecting from the `article` table. These have moderate execution times. * Slow Queries (0.013694s - 0.014359s): The `download_article` queries with `ORDER BY id DESC LIMIT` are by far the slowest. This indicates an inefficient way to retrieve data. Using `ORDER BY` with a large dataset and then `LIMIT` can be costly. If the `id` column is not indexed properly, the database has to scan the entire table to find the rows for the `LIMIT`. * General Performance (0.000100 - 0.000263s): These are standard `SELECT` queries, including retrieving column names, and returning specific values, and include a small latency. 3. Key Optimization Opportunities: * Indexing: Crucially, the `id` column in the `download_article`, `article`, `site_app`, `website` and `website_friendlink` tables should be indexed. An index allows the database to quickly locate rows based on the `id` column without scanning the entire table. This will significantly speed up the `ORDER BY id DESC LIMIT` queries. * Optimize `LIKE` Queries: If possible, avoid using leading wildcards (`%`) in `LIKE` clauses. Leading wildcards force the database to perform a full table scan. If you need to search for partial strings, consider alternative search methods if your database supports them (e.g., full-text search). * Reduce `IN` Clause Size: The `IN` clause in the `article` query selects multiple specific IDs. If the number of IDs in the `IN` clause grows significantly, consider alternative approaches. * Query Design: Consider if the data retrieval logic can be redesigned to be more efficient. For example, can you filter by multiple criteria in a way that avoids repeated `SELECT` statements? 4. Additional Notes: * Database Server: The details of the database server (e.g., MySQL version, hardware) are not provided, so this analysis is based solely on the query execution times. * Data Volume: The size of the tables would heavily influence performance. * Execution Time Measurement: The time measurements are relative within the execution of the overall script. In summary, the most pressing optimization task is to add an index to the `id` columns in the relevant tables. Addressing the slow `ORDER BY` queries by potentially redesigning their logic (e.g., using an indexed column for sorting) would also significantly improve performance. To provide more tailored advice, please provide: * The database system being used (e.g., MySQL, PostgreSQL, etc.). * The sizes of the tables. * The purpose of the application and how the queries are used. * The indexes that are already in place.
title	One-day trial of the accelerator - special accelerator for wall climbing chrome - permanent access to foreign browsers
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keywords	limit, like, show, full, columns, order, article, type, potato, website, description, concept, african, philosophy, highlights, interconnectedness, individuals
upstreams
downstreams
nslookup	A 172.67.201.94, A 104.21.44.166
created	2026-02-16
updated	2026-02-16
summarized	2026-02-16

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