Explanation of Each Step from Query Profiling Data in Detail
Once you have executed and stopped profiling using the DBMS_PROFILER.START_PROFILING and DBMS_PROFILER.STOP_PROFILING procedures, the next critical step is to analyze the profiling data to identify performance bottlenecks. You achieve this by querying the profiling tables, such as PROFILER_RUNS, PROFILER_DATA, PROFILER_LINES, and PROFILER_ERRORS. Let's break down the querying and analysis process step-by-step.
Step 1: Query the PROFILER_RUNS Table
Purpose:
The PROFILER_RUNS table provides metadata about each profiling run, including the start and end times of the profiling session. This step helps you identify the profiling run associated with your executed PL/SQL code.
Query:
SELECT *
FROM DBMS_PROFILER.PROFILER_RUNS;
Explanation:
run_id: A unique identifier for the profiling run. This is used to correlate all profiling data to a specific run. Each profiling session you start will have a distinct run_id.
start_time: The timestamp when the profiling started.
end_time: The timestamp when the profiling ended.
This query will return the general information about the profiling session, such as the time range in which the profiling took place.
Sample Output:
| run_id | start_time | end_time |
|---|---|---|
| 1001 | 2024-11-15 14:00:00 | 2024-11-15 14:05:00 |
Step 2: Query the PROFILER_LINES Table
Purpose:
The PROFILER_LINES table provides a line-by-line breakdown of the PL/SQL code execution, including the total time spent on each line and the number of times each line was executed. This is where you can analyze the performance of individual lines of code.
Query:
SELECT line#, total_time, call_count
FROM DBMS_PROFILER.PROFILER_LINES
WHERE run_id = (SELECT MAX(run_id) FROM DBMS_PROFILER.PROFILER_RUNS)
ORDER BY total_time DESC;
Explanation:
line#: The line number of the PL/SQL code that was executed.
total_time: The total time (in microseconds or seconds, depending on configuration) that was spent on executing that particular line of code.
call_count: The number of times that line was executed during the profiling session.
This query filters the data for the most recent profiling run by using MAX(run_id) and orders the result by total_time to identify the most time-consuming lines of code. The higher the total_time, the more likely the line is a performance bottleneck.
Sample Output:
| line# | total_time | call_count |
|---|---|---|
| 15 | 0.0304 | 1 |
| 25 | 0.0156 | 5 |
| 35 | 0.0047 | 3 |
This output tells you that line 15 took the most time, followed by line 25, and that line 25 was executed 5 times. You can now focus on optimizing the code on these lines.
Step 3: Query the PROFILER_DATA Table (Optional)
Purpose:
The PROFILER_DATA table provides detailed information about each statement within a given PL/SQL block. It shows how many times each individual line or statement was executed and its cumulative execution time.
Query:
SELECT statement_id, total_time, execution_count
FROM DBMS_PROFILER.PROFILER_DATA
WHERE run_id = (SELECT MAX(run_id) FROM DBMS_PROFILER.PROFILER_RUNS);
Explanation:
statement_id: The unique identifier for the SQL/PLSQL statement executed within the block.
total_time: The total time spent on the execution of that statement.
execution_count: The number of times the statement was executed.
This query provides more granular details than PROFILER_LINES, helping you pinpoint specific statements that may be consuming more resources.
Sample Output:
| statement_id | total_time | execution_count |
|---|---|---|
| 101 | 0.0220 | 3 |
| 103 | 0.0135 | 1 |
Here, statement 101 consumed a significant amount of time over 3 executions, while statement 103 was only executed once.
Step 4: Query the PROFILER_ERRORS Table (Optional)
Purpose:
The PROFILER_ERRORS table contains any errors encountered during the profiling session, such as exceptions or runtime issues in your PL/SQL code. It helps you identify problem areas where errors occurred during execution.
Query:
SELECT error_message, line_number
FROM DBMS_PROFILER.PROFILER_ERRORS
WHERE run_id = (SELECT MAX(run_id) FROM DBMS_PROFILER.PROFILER_RUNS);
Explanation:
error_message: The error message generated during profiling. This could be any exception, runtime error, or issue that prevented the code from executing as expected.
line_number: The specific line of the code where the error occurred.
This query will help you identify if your profiling run encountered any issues, and which lines of code might need attention due to errors.
Sample Output:
| error_message | line_number |
|---|---|
| "PL/SQL: numeric or value error" | 25 |
Here, line 25 has a numeric or value error, which you may need to resolve.
Step 5: Analyze the Output for Optimization Opportunities
Once you have the profiling data, you need to interpret the results to identify areas for optimization.
Key Indicators:
- High total_time:
Focus on lines or statements that have a high total_time. These are the parts of the code that consume the most time and may require optimization.
- High call_count:
If a line or statement has a high call_count but low total_time, it might indicate a frequently executed line that can be optimized for efficiency.
- Errors:
If any errors are found in the PROFILER_ERRORS table, you need to investigate and fix those errors before optimizing the performance.
Example of Optimization:
If line 25 takes the most time, consider reviewing the logic there. It might be performing an expensive operation, like a nested loop or a database query, which could be optimized.
If line 35 is executed frequently (high call_count) but takes relatively little time per execution, consider whether there’s a more efficient way to execute that operation in bulk.
Summary of the Querying Process
Query PROFILER_RUNS to identify the profiling session.
Query PROFILER_LINES to get a line-by-line breakdown of execution times and frequency.
Optionally, query PROFILER_DATA for statement-level profiling data.
Optionally, query PROFILER_ERRORS to detect errors during profiling.
Analyze the output: Identify high-cost operations (time-consuming lines, frequent executions, and errors), and optimize the code accordingly.
By following these steps and analyzing the output, you can pinpoint performance bottlenecks in your PL/SQL code and take corrective actions to optimize the code for better performance.
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