Database load
Database load (DB load) measures the level of session activity in your
database. DBLoad is the key metric in Performance Insights, and Performance Insights collects DB load
every second.
Active sessions
A database session represents an application's dialogue with a relational database. An active session is a connection that has submitted work to the DB engine and is waiting for a response.
A session is active when it's either running on CPU or waiting for a resource to become available so that it can proceed. For example, an active session might wait for a page (or block) to be read into memory, and then consume CPU while it reads data from the page.
Average active sessions
The average active sessions (AAS) is the unit for the
DBLoad metric in Performance Insights. It measures how many sessions
are concurrently active on the database.
Every second, Performance Insights samples the number of sessions concurrently running a query. For each active session, Performance Insights collects the following data:
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SQL statement
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Session state (running on CPU or waiting)
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Host
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User running the SQL
Performance Insights calculates the AAS by dividing the total number of sessions by the number of samples for a specific time period. For example, the following table shows 5 consecutive samples of a running query taken at 1-second intervals.
| Sample | Number of sessions running query | AAS | Calculation |
|---|---|---|---|
| 1 | 2 | 2 | 2 total sessions / 1 sample |
| 2 | 0 | 1 | 2 total sessions / 2 samples |
| 3 | 4 | 2 | 6 total sessions / 3 samples |
| 4 | 0 | 1.5 | 6 total sessions / 4 samples |
| 5 | 4 | 2 | 10 total sessions / 5 samples |
In the preceding example, the DB load for the time interval was 2 AAS. This measurement means that, on average, 2 sessions were active at any given time during the interval when the 5 samples were taken.
Average active executions
The average active executions (AAE) per second is related to AAS. To calculate the AAE, Performance Insights divides the total execution time of a query by the time interval. The following table shows the AAE calculation for the same query in the preceding table.
| Elapsed time (sec) | Total execution time (sec) | AAE | Calculation |
|---|---|---|---|
| 60 | 120 | 2 | 120 execution seconds/60 elapsed seconds |
| 120 | 120 | 1 | 120 execution seconds/120 elapsed seconds |
| 180 | 380 | 2.11 | 380 execution seconds/180 elapsed seconds |
| 240 | 380 | 1.58 | 380 execution seconds/240 elapsed seconds |
| 300 | 600 | 2 | 600 execution seconds/300 elapsed seconds |
In most cases, the AAS and AAE for a query are approximately the same. However, because the inputs to the calculations are different data sources, the calculations often vary slightly.
Dimensions
The db.load metric is different from the other time-series metrics because you can break it into
subcomponents called dimensions. You can think of dimensions as "slice by" categories for
the different characteristics of the DBLoad metric.
When you are diagnosing performance issues, the following dimensions are often the most useful:
Topics
For a complete list of dimensions for the Aurora engines, see DB load sliced by dimensions.
Wait events
A wait event causes a SQL statement to wait for a specific event to happen before it can continue running. Wait events are an important dimension, or category, for DB load because they indicate where work is impeded.
Every active session is either running on the CPU or waiting. For example, sessions consume CPU when they search memory for a buffer, perform a calculation, or run procedural code. When sessions aren't consuming CPU, they might be waiting for a memory buffer to become free, a data file to be read, or a log to be written to. The more time that a session waits for resources, the less time it runs on the CPU.
When you tune a database, you often try to find out the resources that sessions are waiting for. For example, two or three wait events might account for 90 percent of DB load. This measure means that, on average, active sessions are spending most of their time waiting for a small number of resources. If you can find out the cause of these waits, you can attempt a solution.
Wait events vary by DB engine:
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For a list of the common wait events for Aurora MySQL, see Aurora MySQL wait events. To learn how to tune using these wait events, see Tuning Aurora MySQL.
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For information about all MySQL wait events, see Wait Event Summary Tables
in the MySQL documentation. -
For a list of common wait events for Aurora PostgreSQL, see Amazon Aurora PostgreSQL wait events. To learn how to tune using these wait events, see Tuning with wait events for Aurora PostgreSQL.
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For information about all PostgreSQL wait events, see The Statistics Collector > Wait Event tables
in the PostgreSQL documentation.
Top SQL
Where wait events show bottlenecks, top SQL shows which queries are contributing the most to DB load. For example, many queries might be currently running on the database, but a single query might consume 99 percent of the DB load. In this case, the high load might indicate a problem with the query.
By default, the Performance Insights console displays top SQL queries that are contributing to the database load. The console also shows relevant statistics for each statement. To diagnose performance problems for a specific statement, you can examine its execution plan.