The request is to update the settings by setting the value of "velis" equal to the absolute value of "vls" plus one, where the name is "somename". The data is stored in an InnoDB table with up to 30 records. The server is hosted on a 1Gb VDS-1024 CentOS. The execution time of the request ranges from 0.003 to 10 seconds. The admin has exhausted all means of testing and configuring the server yet the problem persists.

Where could the issue be? This problem does not occur on local machines.

UPDATE

 settings

SET

 velis = ABS(vls) + 1

WHERE

 name = 'somename'

1. Could the issue be due to the MySQL server being overloaded? Perhaps the server's resources are being overused when trying to retrieve the 30 records requested, leading to slow response times.
2. Additionally, it is worth investigating whether any triggers executed during the request could be adding to the problem.

It is common for a VPS to experience random fluctuations, but instead of focusing on these minor issues, it would be more productive to collect statistics for the day and analyze the most inhibiting queries using the mysqlsla program.

While 10 seconds may seem like a long time for the request to execute, it is important to take into account the host's opinion and limitations.

If there are no issues when running the queries on the local machine, then perhaps the problem is not with the MySQL server. It would be helpful to know if you are running queries directly from the MySQL console or using a library such as libmysql or pdo.

Based on the information provided, it seems like the issue may not lie with the SQL query itself. Here are a few possibilities to consider:

1. Network Latency: Since the server is hosted on a remote 1Gb VDS-1024 CentOS, network latency could be a contributing factor to the varying execution times. This could be particularly noticeable when executing queries that involve I/O operations or accessing data over the network.

2. Server Load: If the server is experiencing high levels of traffic or resource usage, it can impact the execution time of queries. This could be due to other processes running concurrently and competing for resources.

3. Database Indexing: If the "name" column in the table is not indexed, searching for records with the matching name could be slower as the number of records grows. Adding an index on the "name" column might improve the query performance.

4. Hardware Limitations: While the server has 1GB of memory, it is possible that other hardware limitations, such as CPU speed or disk I/O performance, are impacting the execution time. Investigating the server's hardware specifications and ensuring they meet the requirements for the workload would be helpful.

5. Locking and Concurrency: If there are multiple concurrent transactions that modify the "settings" table, it can result in locking and contention issues, causing slower execution times. Analyzing the transaction isolation level and checking for any blocking or long-running transactions might help identify this issue.

6. Query Execution Plan: The query optimizer may be choosing suboptimal execution plans for the UPDATE query. Checking the query execution plan using the EXPLAIN statement can provide insights into how the query is being executed and whether any optimizations can be made.

7. Table Fragmentation: Over time, as data is inserted, updated, and deleted from the "settings" table, it can become fragmented, impacting query performance. Running periodic maintenance tasks like table optimization can help improve performance by reorganizing the data physically.

8. Database Statistics: Outdated or inaccurate database statistics can lead to poor query performance. Updating database statistics using the ANALYZE TABLE or OPTIMIZE TABLE commands can ensure the query optimizer has accurate data distribution information to make better execution plan decisions.

9. Server Configuration: Reviewing the server configuration parameters, such as the buffer pool size, query cache settings, and innodb_buffer_pool_instances, can help optimize the server for the workload. Adjusting these parameters based on the available memory and workload characteristics might improve performance.

It's also worth noting that since the problem does not occur on local machines, there could be differences between the local and remote environments that are causing the discrepancy. Consider comparing the hardware, network setup, database versions, and configurations between the local machines and the hosted server to identify any potential differences that might be contributing to the performance difference.

To address the issue, you can try the following steps:

- Check the server's system logs for any errors or warnings that might point towards the problem.
- Monitor the server's resource usage, including CPU, memory, and disk I/O, during query execution to identify any bottlenecks.
- Consider optimizing the query by adding appropriate indexes to the table or rewriting the query to improve its efficiency.
- If possible, test the query execution performance on a similar local environment to see if the issue persists. This will help eliminate or confirm any network-related problems.