Harold Geneen used to say: “It is much more difficult to measure nonperformance than performance”.
Without automation database management becomes increasingly difficult. An Exadata database can strongly benefit performance-wise from fully enabled Automatic SQL Tuning:
BEGIN DBMS_SQLTUNE.set_tuning_task_parameter( task_name => 'SYS_AUTO_SQL_TUNING_TASK', parameter => 'ACCEPT_SQL_PROFILES', value => 'TRUE'); END; /
I will explain why.
The SQL management base (SMB) is a part of the data dictionary that resides in the SYSAUX tablespace. It stores statement logs, plan histories, SQL plan baselines, and SQL profiles.
The process of automatic evolution of SQL Plan Baselines is described with a perfect example by Christian Antognini.
Converting baselines into profiles is almost straightforward but not simple, Sumit Bhatia explains how.
Now, for complex and long running SQL statements in Exadata, it is quite important to reduce the amount of data being transferred from the storage nodes to the database and to have the right degree of parallelism.
The former is achieved very much by smart scans which take place only when there are full scans or direct path reads. The blocks are not cached but they are returned to the PGA. The latter is even trickier. How in fact to determine the right degree of parallelism for an SQL statement?
Most often parallel_degree_policy is set to manual (default) and parallel_max_servers is sort of guessed. In my opinion, the right way is to use Oracle’s Auto DOP solution by enabling parallel_degree_policy to a non-default value. Let me quote Gwen Shapira:
There are two levels to Auto DOP:
– Limited: when accessing tables and indexes that have been declared with parallel clause, Oracle will decide on the degree of parallelism based on the query and system resources
– Auto: Oracle will decide on degree of parallelism for every query. In addition two new features are enabled: parallel statement queuing and in-memory parallel execution.
Even doing all that, because of Oracle’s unpredictable behavior at times, some SQL statements might not get the right number of parallel slaves or might not go for smart scans. For such statements creating an SPM baseline is a good thing to start from. Then, during the Automatic SQL Tuning maintenance window, the SQL Tuning Advisor might notice that there is a non-accepted plan which is better than the accepted one. The recommended SQL profile will simple do nothing else than to evolve the SPM baseline execution plan into an accepted plan.
All that happens automatically due to the fact that we have advised Oracle to automatically accept SQL Profiles.
But there is more to that: how to get a good degree of parallelism for the SQL statement? Oracle will automatically do so if needed by creating a parallel SQL Profile. Note that there are two types of profiles:
– REGULAR_PROFILE – profile without a change to parallel execution
– PX_PROFILE – regular profile with a change to parallel execution
Here is an example:
Automatically accepted parallel profiles can really boost the Exadata performance. But as PX_Profiles can consume also more resources than expected, it is important to have Instance Caging enabled.
The most important step for configuring instance caging is to determine the value of CPU_COUNT for each database instance and the sum of CPU_COUNTs across the server. There are two approaches:
– Partitioned approach for mission-critical pools sum(CPU_COUNT) < 75% x Total CPUs
– Over-subscribed approach for non-critical pools sum (CPU_COUNT) <= up to 3 x Total CPUs
Second step for instance caging: ensure resource_manager_plan is enabled.
Bottom line: tuning an Exadata database is not exactly the same things as tuning a traditional database. Actual tuning of an Exadata database is in fact a lot easier once understood.
And a lot of that tuning work can be really automated!