Added a graph of command time statistics to grafana

[Mixficsol]

Added a graph of command time statistics to grafana

1.Add averge milliseconds of the command time

2.Add Total milliseconds of the command

3.Add total number of commands

[cr-gpt]

Overall, the code patch you provided seems to address the addition of a new function called InfoCommandStats and the modification of the existing function void InfoCmd::Do(std::shared_ptr<Slot> slot).

Here are a few points to consider for your code review:

1. Naming: The function name InfoCommandStats suggests that it provides information related to command statistics. Make sure the function accurately represents its purpose.

2. String Concatenation: Instead of repeated info.append("\r\n"), you can use info += "\r\n" for brevity and readability.

3. Code Formatting: Maintain consistent code formatting for better readability. In the code you provided, indentation is inconsistent. Ensure proper indentation throughout the code.

4. Iteration: In the for loop, consider using a reference to avoid unnecessary object copying. Change auto iter to auto& iter in the for loop declaration.

5. Avoid Unnecessary Cast: In the line static_cast<double>(iter.second.cmd_time_consuming) == 0, casting the value to double to compare with zero is not necessary. You can directly compare it with zero without casting.

6. Division by Zero: Be cautious about potential division by zero. Make sure to handle the case where iter.second.cmd_count is zero to avoid division by zero. In the updated code, a check has been implemented to handle this scenario.

7. Code Documentation: Consider adding comments or documentation to clarify the purpose and functionality of the added code, especially if it's complex or not immediately obvious.

Remember to thoroughly test your code changes before deploying them to ensure they work as intended and do not introduce any unexpected bugs.

[cr-gpt]

Overall, the code patch you provided seems fine and does not contain any obvious bugs. However, there are a couple of improvement suggestions:

1. Formatting: The indentation in the patch appears inconsistent, which can make the code harder to read and maintain. Make sure to use consistent indentation throughout the code.

2. Clarify variable iteration with value reference: In the line for (auto iter : *g_pika_server->GetCommandStatMap()), it would be more efficient and clearer to iterate by reference using auto& instead of by value. This avoids unnecessary copying of map elements.

3. Simplification of division by 1000: To convert microseconds to milliseconds, you can simplify the code by dividing iter.second.cmd_time_consuming by 1000 directly in the loop, rather than repeating the division in each usage. This improves both readability and performance.

Here's an updated version of the code that incorporates these suggestions:

void InfoCmd::InfoCommandStats(std::string& info) {
    std::ostringstream tmp_stream;
    tmp_stream.precision(2);
    tmp_stream.setf(std::ios::fixed);

    tmp_stream << "# Commandstats" << "\r\n";
    for (const auto& iter : *g_pika_server->GetCommandStatMap()) {
        if (iter.second.cmd_count != 0) {
            tmp_stream << iter.first << ":"
                       << "calls=" << iter.second.cmd_count
                       << ", usec=" << iter.second.cmd_time_consuming / 1000.0
                       << ", usec_per_call="
                       << (iter.second.cmd_time_consuming / 1000.0) / iter.second.cmd_count
                       << "\r\n";
        }
    }

    info.append(tmp_stream.str());
}

Remember to apply consistent formatting and ensure appropriate error handling and boundary checks for your specific use case.

[yaoyinnan]

可以封装一个函数

[cr-gpt]

The code review of the given patch is as follows:

1. MethodofCommandStatistics():

   • It calculates the average time per command based on the time-consuming and frequency.
   • This method seems fine, but it would be better to provide a more descriptive name for this function.

2. MethodofTotalTimeCalculation():

   • It calculates the total time in seconds based on the time-consuming.
   • This method seems fine, but it would be better to provide a more descriptive name for this function.

3. InfoCmd::InfoCommandStats():

   • The loop iterating over *g_pika_server->GetCommandStatMap() can be improved by using a reference parameter instead of creating a copy.
   • A check is added before printing the "usec_per_call" value to handle potential division by zero errors.

4. Overall, the code looks good, and there are no apparent bug risks. However, consider the following suggestions for improvement:

   • Clarify the naming conventions of the methods to make their purpose clearer.
   • Add comments to explain the logic and purpose behind critical sections of code.
   • Consider using the const auto& syntax when iterating over containers if the objects are not modified.
   • Consider separating concerns into smaller, reusable functions to improve readability and maintainability.

Remember that the provided review is based on a brief analysis, and a deeper understanding may require more context about the codebase and its specific requirements.

[cr-gpt]

Based on the code patch you provided, here are a few observations:

1. It looks like you're adding new metrics for Pika command execution time and RocksDB metrics to your Prometheus configuration.
2. For the Pika Command Execution Time metrics, you have defined three counters: calls, usec, and usec_per_call with corresponding labels. Make sure that the metric values being recorded align with these definitions in your codebase.
3. The RocksDB metrics and their associated explanations look fine based on the provided code snippet.
4. The screenshots you included at the end of the patch demonstrate the visual representation of the network, RocksDB, and commandstats metrics.

Without additional context or the actual code implementation, it's difficult to identify specific bugs or suggest improvements. However, ensure that the metrics are correctly implemented, labeled, and associated with the appropriate data sources or calculations. Additionally, consider verifying that the Prometheus configuration interacts correctly with your monitoring stack and tools.

[yaoyinnan]

LGTM