This chapter describes Oracle Database automatic features for performance diagnosing and tuning.
This chapter contains the following topics:
See Also:
Oracle Database 2 Day + Performance Tuning Guide for information about using Oracle Enterprise Manager Cloud Control (Cloud Control) to diagnose and tune the database with the Automatic Database Diagnostic MonitorWhen problems occur with a database, it is important to perform accurate and timely diagnosis of the problem before making any changes. Often a database administrator (DBA) simply looks at the symptoms and immediately starts changing the system to fix those symptoms. However, an accurate diagnosis of the actual problem in the initial stage significantly increases the probability of success in resolving the problem.
With Oracle Database, the statistical data needed for accurate diagnosis of a problem is stored in Automatic Workload Repository (AWR). Automatic Database Diagnostic Monitor (ADDM):
Analyzes AWR data on a regular basis
Diagnoses the root causes of performance problems
Provides recommendations for correcting any problems
Identifies non-problem areas of the system
Because AWR is a repository of historical performance data, ADDM can analyze performance issues after the event, often saving time and resources in reproducing a problem.
In most cases, ADDM output should be the first place that a DBA looks when notified of a performance problem. ADDM provides the following benefits:
Automatic performance diagnostic report every hour by default
Problem diagnosis based on decades of tuning expertise
Time-based quantification of problem impacts and recommendation benefits
Identification of root cause, not symptoms
Recommendations for treating the root causes of problems
Identification of non-problem areas of the system
Minimal overhead to the system during the diagnostic process
Tuning is an iterative process, and fixing one problem can cause the bottleneck to shift to another part of the system. Even with the benefit of ADDM analysis, it can take multiple tuning cycles to reach acceptable system performance. ADDM benefits apply beyond production systems; on development and test systems, ADDM can provide an early warning of performance issues.
This section contains the following topics:
Note:
Data visibility and privilege requirements may differ when using ADDM features with pluggable databases (PDBs). For information about how manageability features—including ADDM features—work in a multitenant container database (CDB), see Oracle Database Administrator's Guide.An ADDM analysis can be performed on a pair of AWR snapshots and a set of instances from the same database. The pair of AWR snapshots define the time period for analysis, and the set of instances define the target for analysis.
If you are using Oracle Real Application Clusters (Oracle RAC), then ADDM has three analysis modes:
Database
In Database mode, ADDM analyzes all instances of the database.
Instance
In Instance mode, ADDM analyzes a particular instance of the database.
Partial
In Partial mode, ADDM analyzes a subset of all database instances.
If you are not using Oracle RAC, then ADDM can only function in Instance mode because only one instance of the database exists.
An ADDM analysis is performed each time an AWR snapshot is taken and the results are saved in the database. The time period analyzed by ADDM is defined by the last two snapshots (the last hour by default). ADDM will always analyze the specified instance in Instance mode. For non-Oracle RAC or single instance environments, the analysis performed in the Instance mode is the same as a database-wide analysis. If you are using Oracle RAC, then ADDM also analyzes the entire database in Database mode, as described in "Using ADDM with Oracle Real Application Clusters". After an ADDM completes its analysis, you can view the results using Cloud Control, or by viewing a report in a SQL*Plus session.
ADDM analysis is performed top down, first identifying symptoms, and then refining them to reach the root causes of performance problems. The goal of the analysis is to reduce a single throughput metric called DB time. DB time is the fundamental measure of database performance, and is the cumulative time spent by the database in processing user requests. It includes wait time and CPU time of all non-idle user sessions. DB time is displayed in the V$SESS_TIME_MODEL and V$SYS_TIME_MODEL views.
See Also:
Oracle Database Reference for information about the V$SESS_TIME_MODEL and V$SYS_TIME_MODEL views
"Time Model Statistics" for a discussion of time model statistics and DB time
Oracle Database Concepts for information about server processes
By reducing DB time, the database is able to support more user requests using the same resources, which increases throughput. The problems reported by ADDM are sorted by the amount of DB time they are responsible for. System areas that are not responsible for a significant portion of DB time are reported as non-problem areas.
The types of problems that ADDM considers include the following:
CPU bottlenecks - Is the system CPU bound by Oracle Database or some other application?
Undersized Memory Structures - Are the Oracle Database memory structures, such as the SGA, PGA, and buffer cache, adequately sized?
I/O capacity issues - Is the I/O subsystem performing as expected?
High-load SQL statements - Are there any SQL statements which are consuming excessive system resources?
High-load PL/SQL execution and compilation, and high-load Java usage
Oracle RAC specific issues - What are the global cache hot blocks and objects; are there any interconnect latency issues?
Sub-optimal use of Oracle Database by the application - Are there problems with poor connection management, excessive parsing, or application level lock contention?
Database configuration issues - Is there evidence of incorrect sizing of log files, archiving issues, excessive checkpoints, or sub-optimal parameter settings?
Concurrency issues - Are there buffer busy problems?
Hot objects and top SQL for various problem areas
Note:
This is not a comprehensive list of all problem types that ADDM considers in its analysis.ADDM also documents the non-problem areas of the system. For example, wait event classes that are not significantly impacting the performance of the system are identified and removed from the tuning consideration at an early stage, saving time and effort that would be spent on items that do not impact overall system performance.
If you are using Oracle RAC, then run ADDM in Database analysis mode to analyze the throughput performance of all instances of the database. In Database mode, ADDM considers DB time as the sum of the database time for all database instances. Using the Database analysis mode enables you to view all findings that are significant to the entire database in a single report, instead of reviewing a separate report for each instance.
The Database mode report includes findings about database resources (such as I/O and interconnect). The report also aggregates findings from the various instances if they are significant to the entire database. For example, if the CPU load on a single instance is high enough to affect the entire database, then the finding appears in the Database mode analysis, which points to the particular instance responsible for the problem.
See Also:
Oracle Database 2 Day + Real Application Clusters Guide for information about using ADDM with Oracle RACIntroduced in Oracle Enterprise Manager Cloud Control 12c, Real-Time ADDM helps you to analyze and resolve problems in unresponsive or hung databases that traditionally require you to restart the database. Real-Time ADDM runs through a set of predefined criteria to analyze the current performance of the database. After analyzing the problem, Real-Time ADDM helps you to resolve the identified issues—such as deadlocks, hangs, shared pool connections, and other exception situations—without having to restart the database.
This section describes Real-Time ADDM and contains the following topics:
See Also:
Oracle Database 2 Day + Performance Tuning Guide for information about using Real-Time ADDM with Cloud ControlDepending on the database state, Real-Time ADDM uses two different types of connection modes when connecting to the database using Enterprise Manager:
Normal connection
In this mode, Real-Time ADDM performs a normal JDBC connection to the database. This mode is intended to perform extensive performance analysis of the database when some connectivity is available.
Diagnostic connection
In this mode, Real-Time ADDM performs a latch-less connection to the database. This mode is intended for extreme hang situations when a normal JDBC connection is not possible.
Starting with Oracle Database 12c, Real-Time ADDM proactively detects transient database performance issues. To do this, Real-Time ADDM runs automatically every 3 seconds and uses in-memory data to diagnose any performance spikes in the database.
Real-Time ADDM triggers an analysis automatically when a performance problem is detected, as described in the following steps:
Every 3 seconds, the manageability monitor process (MMON) performs an action to obtain performance statistics without lock or latch.
The MMON process checks these statistics and triggers a Real-Time ADDM analysis if any of the issues listed in Table 7-1 are detected.
The MMON slave process creates the report and stores it in the AWR.
To view metadata for the report, use the DBA_HIST_REPORTS view.
Table 7-1 lists the issues and conditions that trigger a Real-Time ADDM analysis.
Table 7-1 Triggering Issues and Conditions for Real-Time ADDM
| Issue | Condition |
|---|---|
|
High load |
Average active sessions are greater than 3 times the number of CPU cores |
|
I/O bound |
I/O impact on active sessions based on single block read performance |
|
CPU bound |
Active sessions are greater than 10% of total load and CPU utilization is greater than 50% |
|
Over-allocated memory |
Memory allocations are over 95% of physical memory |
|
Interconnect bound |
Based on single block interconnect transfer time |
|
Session limit |
Session limit is close to 100% |
|
Process limit |
Process limit is close to 100% |
|
Hung session |
Hung sessions are greater than 10% of total sessions |
|
Deadlock detected |
Any deadlock is detected |
To ensure that the automatic triggers do not consume too many system resources and overwhelm the system, Real-Time ADDM employs the following controls:
Duration between reports
If a Real-Time ADDM report was created in the past 5 minutes by the automatic trigger, then no new reports will be generated.
Oracle RAC control
Automatic triggers are local to the database instance. For Oracle RAC, only one database instance can create a Real-Time ADDM report at a given time because a lock is required and a query is performed by the MMON slave process before the report is actually generated.
Repeated triggers
An automatic trigger for any issue must have an impact of 100% or higher than the previous report with the same triggering issue within the past 45 minutes. For example, if a report is triggered for active sessions with an impact of 8 sessions, then in order for another report to trigger within the next 45 minutes, there must be at least 16 active sessions. In this case, the reported problem with the database is becoming more severe over time. On the other hand, if the same report is being generated once every 45 minutes, then the database is experiencing a persistent problem that has a consistent impact.
Newly identified issues
If a new issue is detected (that was not previously detected within the past 45 minutes), then a new report is generated. For example, if a report is triggered for 8 active sessions and a new deadlock issue is detected, then a new report is generated regardless of the new active sessions load.
In addition to problem diagnostics, ADDM recommends possible solutions. ADDM analysis results are represented as a set of findings. See Example 7-1 for an example of an ADDM analysis result. Each ADDM finding can belong to one of the following types:
Problem findings describe the root cause of a database performance problem.
Symptom findings contain information that often lead to one or more problem findings.
Information findings are used for reporting information that are relevant to understanding the performance of the database, but do not constitute a performance problem (such as non-problem areas of the database and the activity of automatic database maintenance).
Warning findings contain information about problems that may affect the completeness or accuracy of the ADDM analysis (such as missing data in AWR).
Each problem finding is quantified by an impact that is an estimate of the portion of DB time caused by the finding's performance issue. A problem finding can be associated with a list of recommendations for reducing the impact of the performance problem. The types of recommendations include:
Hardware changes: adding CPUs or changing the I/O subsystem configuration
Database configuration: changing initialization parameter settings
Schema changes: hash partitioning a table or index, or using automatic segment-space management (ASSM)
Application changes: using the cache option for sequences or using bind variables
Using other advisors: running SQL Tuning Advisor on high-load SQL or running Segment Advisor on hot objects
A list of recommendations can contain various alternatives for solving the same problem; you do not have to apply all the recommendations to solve a specific problem. Each recommendation has a benefit which is an estimate of the portion of DB time that can be saved if the recommendation is implemented. Recommendations are composed of actions and rationales. You must apply all the actions of a recommendation to gain the estimated benefit. The rationales are used for explaining why the set of actions were recommended and to provide additional information to implement the suggested recommendation.
Consider the following section of an ADDM report in Example 7-1.
Example 7-1 Example ADDM Report
FINDING 1: 31% impact (7798 seconds)
------------------------------------
SQL statements were not shared due to the usage of literals. This resulted in
additional hard parses which were consuming significant database time.
RECOMMENDATION 1: Application Analysis, 31% benefit (7798 seconds)
ACTION: Investigate application logic for possible use of bind variables
instead of literals. Alternatively, you may set the parameter
"cursor_sharing" to "force".
RATIONALE: SQL statements with PLAN_HASH_VALUE 3106087033 were found to be
using literals. Look in V$SQL for examples of such SQL statements.
In Example 7-1, the finding points to a particular root cause, the usage of literals in SQL statements, which is estimated to have an impact of about 31% of total DB time in the analysis period.
The finding has a recommendation associated with it, composed of one action and one rationale. The action specifies a solution to the problem found and is estimated to have a maximum benefit of up to 31% DB time in the analysis period. Note that the benefit is given as a portion of the total DB time and not as a portion of the finding's impact. The rationale provides additional information on tracking potential SQL statements that were using literals and causing this performance issue. Using the specified plan hash value of SQL statements that could be a problem, a DBA could quickly examine a few sample statements.
When a specific problem has multiple causes, ADDM may report multiple problem and symptom findings. In this case, the impacts of these multiple findings can contain the same portion of DB time. Because the performance issues of findings can overlap, the sum of the impacts of the findings can exceed 100% of DB time. For example, if a system performs many reads, then ADDM might report a SQL statement responsible for 50% of DB time due to I/O activity as one finding, and an undersized buffer cache responsible for 75% of DB time as another finding.
When multiple recommendations are associated with a problem finding, the recommendations may contain alternatives for solving the problem. In this case, the sum of the recommendations' benefits may be higher than the finding's impact.
When appropriate, an ADDM action may have multiple solutions for you to choose from. In the example, the most effective solution is to use bind variables. However, it is often difficult to modify the application. Changing the value of the CURSOR_SHARING initialization parameter is much easier to implement and can provide significant improvement.
Automatic database diagnostic monitoring is enabled by default and is controlled by the CONTROL_MANAGEMENT_PACK_ACCESS and the STATISTICS_LEVEL initialization parameters.
The CONTROL_MANAGEMENT_PACK_ACCESS parameter should be set to DIAGNOSTIC or DIAGNOSTIC+TUNING to enable automatic database diagnostic monitoring. The default setting is DIAGNOSTIC+TUNING. Setting CONTROL_MANAGEMENT_PACK_ACCESS to NONE disables ADDM.
The STATISTICS_LEVEL parameter should be set to the TYPICAL or ALL to enable automatic database diagnostic monitoring. The default setting is TYPICAL. Setting STATISTICS_LEVEL to BASIC disables many Oracle Database features, including ADDM, and is strongly discouraged.
See Also:
Oracle Database Reference for information about theCONTROL_MANAGEMENT_PACK_ACCESS and STATISTICS_LEVEL initialization parametersADDM analysis of I/O performance partially depends on a single argument, DBIO_EXPECTED, that describes the expected performance of the I/O subsystem. The value of DBIO_EXPECTED is the average time it takes to read a single database block in microseconds. Oracle Database uses the default value of 10 milliseconds, which is an appropriate value for most modern hard drives. If your hardware is significantly different—such as very old hardware or very fast RAM disks—then consider using a different value.
To determine the correct setting for the DBIO_EXPECTED parameter:
Measure the average read time of a single database block read for your hardware.
Note that this measurement is for random I/O, which includes seek time if you use standard hard drives. Typical values for hard drives are between 5000 and 20000 microseconds.
Set the value one time for all subsequent ADDM executions.
For example, if the measured value if 8000 microseconds, you should execute the following command as SYS user:
EXECUTE DBMS_ADVISOR.SET_DEFAULT_TASK_PARAMETER(
'ADDM', 'DBIO_EXPECTED', 8000);
To diagnose database performance problems, first review the ADDM analysis results that are automatically created each time an AWR snapshot is taken. If a different analysis is required (such as a longer analysis period, using a different DBIO_EXPECTED setting, or changing the analysis mode), you can run ADDM manually as described in this section.
ADDM can analyze any two AWR snapshots (on the same database), as long as both snapshots are still stored in AWR (have not been purged). ADDM can only analyze instances that are started before the beginning snapshot and remain running until the ending snapshot. Additionally, ADDM will not analyze instances that experience significant errors when generating AWR snapshots. In such cases, ADDM will analyze the largest subset of instances that did not experience these problems.
The primary interface for diagnostic monitoring is Cloud Control. Whenever possible, run ADDM using Cloud Control, as described in Oracle Database 2 Day + Performance Tuning Guide. If Cloud Control is unavailable, then run ADDM using the DBMS_ADDM package. To run the DBMS_ADDM APIs, the user must be granted the ADVISOR privilege.
This section contains the following topics:
See Also:
Oracle Database PL/SQL Packages and Types Reference for information about theDBMS_ADDM packageFor Oracle RAC configurations, you can run ADDM in Database mode to analyze all instances of the databases. For single-instance configurations, you can still run ADDM in Database mode; ADDM will behave as if running in Instance mode.
To run ADDM in Database mode, use the DBMS_ADDM.ANALYZE_DB procedure:
BEGIN DBMS_ADDM.ANALYZE_DB ( task_name IN OUT VARCHAR2, begin_snapshot IN NUMBER, end_snapshot IN NUMBER, db_id IN NUMBER := NULL); END; /
The task_name parameter specifies the name of the analysis task that will be created. The begin_snapshot parameter specifies the snapshot number of the beginning snapshot in the analysis period. The end_snapshot parameter specifies the snapshot number of the ending snapshot in the analysis period. The db_id parameter specifies the database identifier of the database that will be analyzed. If unspecified, this parameter defaults to the database identifier of the database to which you are currently connected.
The following example creates an ADDM task in database analysis mode, and executes it to diagnose the performance of the entire database during the time period defined by snapshots 137 and 145:
VAR tname VARCHAR2(30); BEGIN :tname := 'ADDM for 7PM to 9PM'; DBMS_ADDM.ANALYZE_DB(:tname, 137, 145); END; /
To analyze a particular instance of the database, you can run ADDM in Instance mode. To run ADDM in Instance mode, use the DBMS_ADDM.ANALYZE_INST procedure:
BEGIN DBMS_ADDM.ANALYZE_INST ( task_name IN OUT VARCHAR2, begin_snapshot IN NUMBER, end_snapshot IN NUMBER, instance_number IN NUMBER := NULL, db_id IN NUMBER := NULL); END; /
The task_name parameter specifies the name of the analysis task that will be created. The begin_snapshot parameter specifies the snapshot number of the beginning snapshot in the analysis period. The end_snapshot parameter specifies the snapshot number of the ending snapshot in the analysis period. The instance_number parameter specifies the instance number of the instance that will be analyzed. If unspecified, this parameter defaults to the instance number of the instance to which you are currently connected. The db_id parameter specifies the database identifier of the database that will be analyzed. If unspecified, this parameter defaults to the database identifier of the database to which you are currently connected.
The following example creates an ADDM task in instance analysis mode, and executes it to diagnose the performance of instance number 1 during the time period defined by snapshots 137 and 145:
VAR tname VARCHAR2(30); BEGIN :tname := 'my ADDM for 7PM to 9PM'; DBMS_ADDM.ANALYZE_INST(:tname, 137, 145, 1); END; /
To analyze a subset of all database instances, you can run ADDM in Partial mode. To run ADDM in Partial mode, use the DBMS_ADDM.ANALYZE_PARTIAL procedure:
BEGIN DBMS_ADDM.ANALYZE_PARTIAL ( task_name IN OUT VARCHAR2, instance_numbers IN VARCHAR2, begin_snapshot IN NUMBER, end_snapshot IN NUMBER, db_id IN NUMBER := NULL); END; /
The task_name parameter specifies the name of the analysis task that will be created. The instance_numbers parameter specifies a comma-delimited list of instance numbers of instances that will be analyzed. The begin_snapshot parameter specifies the snapshot number of the beginning snapshot in the analysis period. The end_snapshot parameter specifies the snapshot number of the ending snapshot in the analysis period. The db_id parameter specifies the database identifier of the database that will be analyzed. If unspecified, this parameter defaults to the database identifier of the database to which you are currently connected.
The following example creates an ADDM task in partial analysis mode, and executes it to diagnose the performance of instance numbers 1, 2, and 4, during the time period defined by snapshots 137 and 145:
VAR tname VARCHAR2(30); BEGIN :tname := 'my ADDM for 7PM to 9PM'; DBMS_ADDM.ANALYZE_PARTIAL(:tname, '1,2,4', 137, 145); END; /
To display a text report of an executed ADDM task, use the DBMS_ADDM.GET_REPORT function:
DBMS_ADDM.GET_REPORT ( task_name IN VARCHAR2 RETURN CLOB);
The following example displays a text report of the ADDM task specified by its task name using the tname variable:
SET LONG 1000000 PAGESIZE 0; SELECT DBMS_ADDM.GET_REPORT(:tname) FROM DUAL;
Note that the return type of a report is a CLOB, formatted to fit line size of 80. For information about reviewing the ADDM analysis results in an ADDM report, see "ADDM Analysis Results".
Typically, you should view output and information from ADDM using Cloud Control or ADDM reports.
However, you can display ADDM information through the DBA_ADVISOR views. This group of views includes:
DBA_ADVISOR_FINDINGS
This view displays all the findings discovered by all advisors. Each finding is displayed with an associated finding ID, name, and type. For tasks with multiple executions, the name of each task execution associated with each finding is also listed.
DBA_ADDM_FINDINGS
This view contains a subset of the findings displayed in the related DBA_ADVISOR_FINDINGS view. This view only displays the ADDM findings discovered by all advisors. Each ADDM finding is displayed with an associated finding ID, name, and type.
DBA_ADVISOR_FINDING_NAMES
This view lists all finding names registered with the advisor framework.
DBA_ADVISOR_RECOMMENDATIONS
This view displays the results of completed diagnostic tasks with recommendations for the problems identified in each execution. The recommendations should be reviewed in the order of the RANK column, as this relays the magnitude of the problem for the recommendation. The BENEFIT column displays the benefit to the system you can expect after the recommendation is performed. For tasks with multiple executions, the name of each task execution associated with each advisor task is also listed.
DBA_ADVISOR_TASKS
This view provides basic information about existing tasks, such as the task ID, task name, and when the task was created. For tasks with multiple executions, the name and type of the last or current execution associated with each advisor task is also listed.
See Also:
Oracle Database Reference for information about static data dictionary views