Intro
Reading and interpreting Automatic Workload Repository (AWR) reports can often seem like a daunting task, especially considering the sheer volume of information that they contain. While setting up basic monitoring and alarms for your Oracle database via the OCI console is recommended, there will come a time when a deeper understanding and interpretation of the AWR report will be required for advanced analysis.
In this first part of our three-part blog series on AWR reports, we will start slow and cover some basic aspects of AWR analysis.
Elapsed Time vs DB Time
The first thing you should look at in your AWR report is the difference between elapsed time and DB time.High elapsed time often indicates high concurrency, where multiple sessions are running concurrently, each taking a fraction of the total time.
This scenario is quite common in environments with multiple cores running in parallel, such as Exadata Cloud Service (ExaCS).
Inefficient SQL queries often come to the surface here. By identifying and optimizing these queries, you can significantly improve performance.
The use of Oracle's parallel execution feature can also help manage and reduce elapsed time.
Physical Reads vs Logical Reads
Understanding the difference between physical and logical reads is key to diagnosing performance issues.A physical read occurs when data is read into the database buffer cache from disk. If the data is not available in the buffer cache, a physical read will need to take place. This operation is slower than a logical read.
A logical read happens when data is read from the buffer cache, avoiding the need for a disk read. This operation is faster and more efficient.
Wait Events
Wait events provide valuable information about performance issues, as they show where the database is spending its time.High CPU utilization or frequent direct path reads are common wait events. You can avoid these through indexing and partitioning.
Interpreting wait events can help you to identify and resolve potential bottlenecks in your database.
SQL Ordered by Gets
This section of the AWR report lists SQL statements, ordered by the number of logical reads (buffer gets). This is an excellent place to identify inefficient SQL that may be causing performance issues. By analyzing these SQL statements, you can identify areas for optimization.
Time Model and Hard Parses
The Time Model section of the AWR report gives you detailed time usage statistics for different types of database operations. In this context, 'hard parses' are particularly important. A hard parse occurs when Oracle parses a SQL statement for the first time or when it cannot find a reusable SQL area in the shared pool. High hard parse counts can significantly impact database performance.
SQL Tuning
Companies can optimize SQL performance in a variety of ways. These include:Indexing: Proper indexing can improve the speed of data retrieval operations in a database.
Partitioning: By dividing large tables into smaller, more manageable pieces, query performance can be significantly improved.
Using Bind Variables: Bind variables can help to avoid hard parsing of SQL statements, improving performance.
Using Materialized Views: These allow you to store the results of a query physically and update them periodically. They can be a great performance booster for complex and long-running queries.
In the next part of our series on AWR reports, we will dive deeper into some more advanced aspects of AWR analysis. Stay tuned!
Remember, it's not the amount of data in your AWR report that counts, it's how you use it!
In this first part of our three-part blog series on AWR reports, we will start slow and cover some basic aspects of AWR analysis.
Elapsed Time vs DB Time
The first thing you should look at in your AWR report is the difference between elapsed time and DB time.High elapsed time often indicates high concurrency, where multiple sessions are running concurrently, each taking a fraction of the total time.
This scenario is quite common in environments with multiple cores running in parallel, such as Exadata Cloud Service (ExaCS).
Inefficient SQL queries often come to the surface here. By identifying and optimizing these queries, you can significantly improve performance.
The use of Oracle's parallel execution feature can also help manage and reduce elapsed time.
Physical Reads vs Logical Reads
Understanding the difference between physical and logical reads is key to diagnosing performance issues.A physical read occurs when data is read into the database buffer cache from disk. If the data is not available in the buffer cache, a physical read will need to take place. This operation is slower than a logical read.
A logical read happens when data is read from the buffer cache, avoiding the need for a disk read. This operation is faster and more efficient.
Wait Events
Wait events provide valuable information about performance issues, as they show where the database is spending its time.High CPU utilization or frequent direct path reads are common wait events. You can avoid these through indexing and partitioning.
Interpreting wait events can help you to identify and resolve potential bottlenecks in your database.
SQL Ordered by Gets
This section of the AWR report lists SQL statements, ordered by the number of logical reads (buffer gets). This is an excellent place to identify inefficient SQL that may be causing performance issues. By analyzing these SQL statements, you can identify areas for optimization.
Time Model and Hard Parses
The Time Model section of the AWR report gives you detailed time usage statistics for different types of database operations. In this context, 'hard parses' are particularly important. A hard parse occurs when Oracle parses a SQL statement for the first time or when it cannot find a reusable SQL area in the shared pool. High hard parse counts can significantly impact database performance.
SQL Tuning
Companies can optimize SQL performance in a variety of ways. These include:Indexing: Proper indexing can improve the speed of data retrieval operations in a database.
Partitioning: By dividing large tables into smaller, more manageable pieces, query performance can be significantly improved.
Using Bind Variables: Bind variables can help to avoid hard parsing of SQL statements, improving performance.
Using Materialized Views: These allow you to store the results of a query physically and update them periodically. They can be a great performance booster for complex and long-running queries.
In the next part of our series on AWR reports, we will dive deeper into some more advanced aspects of AWR analysis. Stay tuned!
Remember, it's not the amount of data in your AWR report that counts, it's how you use it!
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