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:
-
SQL statement
-
Session state (running on CPU or waiting)
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Host
-
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 Amazon RDS 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:
-
For information about all MariaDB and MySQL wait events, see Wait Event Summary Tables
in the MySQL documentation. -
For information about all PostgreSQL wait events, see The Statistics Collector > Wait Event tables
in the PostgreSQL documentation. -
For information about all Oracle wait events, see Descriptions of Wait Events
in the Oracle documentation. -
For information about all SQL Server wait events, see Types of Waits
in the SQL Server documentation.
Note
For Oracle, background processes sometimes do work without an associated SQL statement.
In these cases, Performance Insights reports the type of background process
concatenated with a colon and the wait class associated with that background
process. Types of background process include LGWR,
ARC0, PMON, and so on.
For example, when the archiver is performing I/O, the Performance Insights report for it is
similar to ARC1:System I/O. Occasionally, the background process type is also
missing, and Performance Insights only reports the wait class, for example :System
I/O.
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.
Plans
An execution plan, also called simply a plan, is a sequence of steps that
access data. For example, a plan for joining tables t1 and t2 might loop through
all rows in t1 and compare each row to a row in t2. In a relational database, an
optimizer is built-in code that determines the most efficient plan for a SQL
query.
For DB instances, Performance Insights collects execution plans automatically. To diagnose SQL performance problems, examine the captured plans for high-resource SQL queries. The plans show how the database has parsed and run queries.
To learn how to analyze DB load using plans, see:
Plan capture
Every five minutes, Performance Insights identifies the most resource-intensive queries and captures their plans. Thus, you don't need to manually collect and manage a huge number of plans. Instead, you can use the Top SQL tab to focus on the plans for the most problematic queries.
Note
Performance Insights doesn't capture plans for queries whose text exceeds the maximum collectable query text limit. For more information, see Accessing more SQL text in the Performance Insights dashboard.
The retention period for execution plans is the same as for your Performance Insights data. The retention setting in the free tier is Default (7 days). To retain your performance data for longer, specify 1–24 months. For more information about retention periods, see Pricing and data retention for Performance Insights.
Digest queries
The Top SQL tab shows digest queries by default. A digest query
doesn't itself have a plan, but all queries that use literal values have plans. For example, a
digest query might include the text WHERE `email`=?. The digest might contain two queries,
one with the text WHERE email=user1@example.com and another with WHERE
email=user2@example.com. Each of these literal queries might include multiple plans.
When you select a digest query, the console shows all plans for child statements of the selected digest. Thus, you don't need to look through all the child statements to find the plan. You might see plans that aren’t in the displayed list of top 10 child statements. The console shows plans for all child queries for which plans have been collected, regardless of whether the queries are in the top 10.
