This is just a short note prompted by a recent thread on the OTN forums. In recent versions Oracle changes the costs of a full table scan (FTS or index fast full scan / IFFS) quite dramatically if the "flashback query" clause gets used.
It looks like that it simply uses the number of blocks of the segment as I/O cost for the FTS operation, quite similar to setting the "db_file_multiblock_read_count" ("dbfmbrc"), or from 10g on more precisely the "_db_file_optimizer_read_count", to 1 (but be aware of the MBRC setting of WORKLOAD System Statistics, see comments below) for the cost estimate of the segment in question.
This can lead to some silly plans depending on the available other access paths as can be seen from the thread mentioned.
The Cost Based Optimizer (CBO) supports since at least Oracle 9i the automatic generation of additional predicates based on transitive closure.
In principle this means:
If a = b and b = c then the CBO can infer a = c
As so often with these optimizations the purpose of these automatically generated additional predicates is to allow the optimizer finding potentially more efficient access paths, like an index usage or earlier filtering reducing the amount of data to process.
Introduction
In the first part of this post I've explained some of the details and underlying reasons of bug 6918210. The most important part of the bug is that it can only be hit if many row migrations happen during a single transaction. However, having excessive row migrations is usually a sign of poor design, so this point probably can't be stressed enough:
If you don't have excessive row migrations the bug can not become significant
Of course, there might be cases where you think you actually have a sound design but due to lack of information about the internal workings it might not be obvious that excessive row migrations could be caused by certain activities.
Continuing from the previous installment of this series I'll cover in this post some of the inevitable classics regarding Parallel Execution Control. So forgive me if you're bored by the repetition of known facts - however I still see these things too often used incorrectly, therefore I decided: This is worth to mention and remember!
- Up to and including version 10.2 PARALLEL (without any parameters) and NOLOGGING are valid keywords only in DDL commands
- Applies to all versions: NOLOGGING can not be used as a hint. It can only be specified as part of DDL, for example ALTER INDEX ... REBUILD PARALLEL NOLOGGING.
In a recent discussion I've mentioned that I thought to remember that the DML part of conventional load as select inserts will always be executed serially, even with parallel DML enabled and requesting parallel DML execution. It's important to understand in this context that this is not the same as the parallel query execution of the SELECT part, which is possible independently from the parallel DML part.
After that discussion I realized that it was quite some time ago that I tested this scenario, probably it was back then with some 10.2 version.
So I quickly put together a small test case that I ran on 11g versions and the results were quite surprising which motivated me to take a closer look.
When I read the recent post by the optimizer group about the new concurrent gather stats feature added in 11.2.0.2 it reminded me of the fact that I intended to publish something based on the same idea already some time ago.
The Problem
It was motivated by a client's regular need during a transition phase from non-Exadata to Exadata to create literally thousands of indexes with potentially a multitude of (sub-)partitions as fast as possible - as part of a full datapump import job of a multi-terabyte database running 11.1.0.7 and 11.2.0.1 (Exadata V2).
There are actually two issues regarding the index creation part of a large database import:
1. The datapump import performs the index creation only by a single worker thread even when using the PARALLEL worker thread import feature. Although an index could be created in parallel if you have thousands of smaller index objects this single worker thread potentially does not make efficient use of the available hardware resources with high-end configurations, including and in particular Exadata.
2. There is a nasty bug 8604502 that has been introduced with 11.1.0.7 that affects also 11.2.0.1 (fixed in 11.2.0.2 and a generic one-off patch is available on My Oracle Support for 11.1.0.7 and 11.2.0.1): The IMPDP creates all indexes serially, even those supposed to be created in parallel, and only after the creation ALTERs them to the defined PARALLEL degree. Note that the fix actually only fixes the problem at actual execution time, even with the fix installed (and in 11.2.0.2) the SQLFILE option of IMPDP still generates CREATE INDEX DDLs that will always have the parallel degree set to PARALLEL 1 (see MOS document 1289032.1 and bug 10408313 - INDEXES ARE CREATED WITH PARALLEL DEGREE 1 DURING IMPORT which has been closed as not being a bug). This "not-being-a-bug" also affects all other versions that support the datapump utility - the SQLFILE option always generates CREATE INDEX scripts with the parallel degree set to 1 no matter what the actual degree of the index is supposed to be. It's only the ALTER INDEX DDL command following the CREATE INDEX command that sets the parallel degree correctly.
These two issues in combination meant to them that a full database import job took ages to complete the index creation step after loading quite quickly the vast amount of table data in parallel.
In case of partitioned indexes there is another complication independently from the mentioned issues: Oracle uses only one parallel slave per partition for creation - in case of large and/or few partitions this again doesn't make efficient use of the available resources.
Oracle therefore provides several means to speed up index creation and rebuild tasks, in particular the documented DBMS_PCLXUTIL package that is around since the Oracle 8 days to overcome the above mentioned limitation of partitioned index creation by spawning multiple jobs each rebuilding an index partition in parallel.
Another, undocumented feature is the DBMS_INDEX_UTL package that is obviously used internally as part of several maintenance operations, for example those DDLs that include the "UPDATE INDEXES" clause. According to the spec it allows to rebuild multiple indexes concurrently by spawning multiple jobs - however since it is undocumented it might not be safe to use in production-like configurations - furthermore it might be changed in future releases without further notice and therefore is potentially unreliable.
A Solution
Since the client wanted a quick solution that ideally addressed all of the above issues I came up with a simple implementation that uses Advanced Queueing and background jobs to create as many indexes as desired concurrently.
The solution is targeted towards the client's scenario, so the following is assumed:
- There is a SQL file that contains the CREATE INDEX statements. This can easily be generated via IMPDP based on the dump files using the SQLFILE option.
- To address the CREATE INDEX (not-being-a-)bug (the bugfix for the bug 8604502 still generates incorrect CREATE INDEX DDLs with the SQLFILE option of IMPDP as mentioned above) I've created a combination of "sed" and "awk" unix scripts that take the IMPDP SQLFILE potentially including all DDLs commands as input and create a output file that consists solely of the CREATE INDEX commands with correct PARALLEL clauses based on the ALTER INDEX command following the CREATE INDEX in the script
- To address the lengthy index creation process I've created a small PL/SQL package that sets up the required AQ infrastructure, takes the CREATE INDEX DDL file as input, populates a queue with the index creation commands and spawns as many worker threads as specified that will take care of the actual index creation (that in turn might be a parallel index creation)
As a side note it is interesting that Oracle actually allows to build several indexes concurrently on the same segment (which makes totally sense but does probably not happen too often in practice).
Note that in principle this code could be used as a general template to execute arbitrary DDLs concurrently (of course with corresponding modifications).
The following link allows to download an archive that contains the following subdirectories:
- correct_parallel_clause: This directory contains the Unix scripts mentioned above that allow to process a SQLFILE generated by IMPDP and output a DDL file that solely consists of the CREATE INDEX commands contained in the SQLFILE. The generated CREATE INDEX statements also use a correct PARALLEL clause - the degree is taken from the ALTER INDEX DDL command following the CREATE INDEX in the SQLFILE. For further details refer to the README.txt in that directory. Note that the script at present does not handle Domain Indexes, only conventional and bitmap.
- source: Contains the package source for the concurrent index creation, furthermore a package that is required by the provided automated unit testing (see below for more details) and a script that prompts for the required details to initiate a concurrent index creation. The README.txt in that directory provides a quick start guide how to use the concurrent index creation.
- test: Contains two flavours of test harnesses for automated unit testing of the package. One based on the unit testing feature implemented in SQLDeveloper 2.1.1, and another one based on "dbunit", an open-source unit testing framework based on jUnit. The README.txt in the respective subdirectories explain how to use these unit tests.
How to use it
The usage is split into two parts: The first part deals with preparing a suitable text file that consists of the CREATE INDEX commands, the second part is about processing this text file with as many worker threads as desired.
Preparing the file is straightforward: You can use the "transform_all_sql.sh" script to generate the required CREATE INDEX script from a DDL script created via IMPDP SQLFILE.
The script has been tested primarily with bash, sed and awk under Cygwin 1.7.1 and OEL5, different Unix flavors might have different versions of the shell, awk or sed and therefore might behave differently.
Simply put all four Unix scripts in the "correct_parallel_clause" directory into the same directory, mark them as executable and run the "transform_all_sql.sh" like that:
where "input_file" is the file generated via IMPDP SQLFILE option and "output_file" will be the result.
In order to perform the parallel index creation, you need an account that has suitable privileges granted. Since it is assumed that the indexes will have to be created in different schemas this account will have to have extended privileges granted. The package is implemented using invoker's rights so granting these privileges via roles is sufficient. A quick and dirty solution could be creating a temporary account and granting simply the DBA role to it (this is what I used to do to test it). Note that the account also requires EXECUTE privileges on the DBMS_AQ and DBMS_AQADM packages for the AQ stuff. It also needs a simple logging table where errors and progress will be written to as well as a type that is used as payload of the queue. Obviously the account also needs to be able to create jobs - in this version of the package this is done via DBMS_SCHEDULER. At execution time the package is going to create a queue plus queue table that also needs to be stored in a tablespace - so you should make sure that the account (or at least the database) that executes the index creation has an appropriate default tablespace defined.
You can simply run the "pk_create_index_concurrent.sql" script (located in the "source" directory) in such a suitable account which will deinstall/install all required objects.
The execution of the index creation is then straightforward (taken from the package specification):
Note that the "p_job_submit_delay" parameter is currently not used - there were some odd locking issues on the AQ table in case of a RAC environment when using that option so I have commented out its usage at present - I haven't had a chance yet to investigate further what the problem actually was.
So the only required input to the CREATE_INDEX_CONCURRENT procedure is the name of the directory object that points to the directory where the file to process resides and the name of the file itself.
You probably want to specify the number of worker threads for the two sets: The idea here is to distinguish between the creation of serial and parallel indexes. The first parameter specifies the number of worker threads used for serial indexes, the second one the number of concurrent threads for parallel indexes.
The default is CPU_COUNT * INSTANCES threads for serial indexes and a single thread for parallel indexes.
If you don't want/need this separation of serial and parallel indexes simple use the same "worker_set_id" for both parameters "p_worker_set_id_1" and "p_worker_set_id_2" and specify the desired total parallel degree in one of the degree parameters and set the other one to 0 (the 0 is required otherwise one of the DBMS_SCHEDULER.CREATE_JOB calls will fail with a "duplicate job name/job name already exists").
The "p_sleep_seconds" parameter is only used to allow the jobs spawned to put a lock on the queue table - the teardown is then going to wait until all locks have been removed and therefore all queue processing has ended. The default of 10 seconds was sufficient in all cases I've encountered.
Since the package requires as prerequisite a directory where the file to process resides, I've prepared the script "create_index_concurrent.sql" that guides through the required inputs and takes care of that step as well.
It takes the full O/S path to the file and the file name as input, creates a directory CREATE_INDEX_CONCURRENT_DIR pointing to that directory and prompts then for the two degrees as input and the names of the two worker thread sets before calling the CREATE_INDEX_CONCURRENT stored procedure.
Caveats
Please note that you should double-check not to pass a non-transformed SQLFILE generated via IMPDP to the procedure - the results may be dire since the generated SQLFILE always contains much more than the bare CREATE INDEX commands, no matter what options you use for IMPDP. Always use the provided Unix scripts to post-process the SQLFILE before initiating the index creation.
Furthermore you need to be aware of the current limitation of the package that it does not attempt to tokenize the file contents. It simply uses a semicolon as delimiter to separate the DDL commands. This should be sufficient for most cases, but in case you have a function-based index using a string expression containing a semicolon as part of the index definition this will not work as expected. Also if you plan to use this package for other DDL execution activities like CTAS statements you might again hit this limitation if the DDL text contains semicolons.
Note that creating indexes using this tool results potentially in different index statistics than creating the indexes using IMPDP since IMPDP by default also imports the index statistics whereas the indexes created using this tool will end up with the current index statistics automatically generated during index creation (from 10g onwards, and the code requires at least 10.2). If you want to have the index statistics imported you can run IMPDP after the index creation using the INCLUDE=INDEX_STATISTICS option. This should complete fairly quickly and will import the index statistics only.
If you have SERVEROUTPUT enabled by default then you will very likely see some errors that will be printed by the initial attempt to tear down the AQ infrastructure. These errors are expected if the previous run was completed successfully or in case of the initial run and can be ignored (and will be catched/ignored by the default implementation).
Note also that all provided scripts except for the Unix shell scripts use DOS file format - under OEL this isn't a problem but it might be on your platform.
Finally the inevitable disclaimer: Although this has been tested thoroughly it comes with absolutely no warranty. Use it at your own risk and test it in your environment before attempting any runs against anything important.
Monitoring the execution
The code logs errors and progress into the table CREATE_INDEX_CONCURRENT_LOG. At present the code logs every attempt to execute DDL into the table as well as any errors that are raised during that DDL execution.
So the table can be used for both, monitoring the progress as well as checking for errors. The code currently continues the execution in case of errors encountered using the dreaded WHEN OTHERS THEN NULL construct, but the code is already prepared for a more granular error handling if required - see the defined exceptions and commented out exception handler.
You can view the queue contents in the corresponding queue view created by the AQ setup (AQ$CREATE_INDEX_QUEUE) in order to see the data to process. Note that due to the fact that all worker threads do not commit the queue transaction you won't be able to see the progress in the queue table until all worker threads committed. If you don't like that you can remove the wait and "teardown_aq" call at the end of the main procedure "create_index_concurrent" and uncomment the dequeue option "visibility=immediate" in the "create_index_thread" procedure. You would need then to call "teardown_aq" in a separate step as desired. With this modification you can monitor the progress by monitoring the queue, but the provided automated unit testing won't work with that variant since it relies on the main call to wait for all worker threads to complete before validating the results.
However you can see the progress also in the log table using the following sample query:
If you want to perform more sophisticated queries on the that table you might need to use some casts similar to the following, because the text columns are defined as CLOBs in order to be able to hold the complete DDLs and error messages in case of errors. The casts allow you to perform for example GROUP BYs etc.
The Unit Testing
Here we come to a completely different issue that is off-topic for this post, however in my experience so far it seems to be a very important one and I hopefully will have the time to cover it in the future with separate posts.
Generally speaking I've seen to many shops that don't follow best-practice when it comes to database deployment and development, therefore here is what you should know/do about it ideally - in a nutshell:
- Treat your database like source code, which means put everything related to the database under version control. This includes not only the obvious database source code but also DDL and DML scripts for schema evolution
- Use unit testing to test database code. Automate this unit testing
- Automate the deployment of your database related changes
- Install a continuous integration environment that runs the automated deployment and unit tests regularly, for example every night
- Automate deployment everywhere - starting from the development databases up to the production environment
- Follow your guidelines strictly - for example any hotfix-like adhoc change should still go through the established processes - code changes, testing, deployment etc.
I've helped several clients in the past to setup corresponding tools and processes for implementing above - if you are interested, get in touch with me.
So as a bonus, if you haven't spent too much time yet with above mentioned topics, in order to get you started at least with automated unit testing, I've included two different examples for this small source provided, one using the built-in unit test feature of SQLDeveloper and the other one using "dbunit". You can find both in the corresponding subdirectories of the "test" folder in the archive.
The unit testing is based on the "pk_create_index_concur_test.sql" package that is used to setup and teardown the environment for running the unit test. It assumes at present the existence of a directory "C:\app\oracle\admin\orcl112\dpdump" on O/S level. It will create a directory object for the path and attempt to create/write a file used for the unit test runs. You can pass any valid O/S directory path to the "pk_create_index_concur_test.setup" procedure if you want/need to use a different one.
All provided automated tests assume that both scripts, "pk_create_index_concurrent.sql" and "pk_create_index_concur_test.sql" have been run in the schema that should be used for test runs.
You can use the SQLDeveloper Unit Test feature to run the provided Unit Test. You can either use the GUI to import and run the test, or you can use a command line version that is actually using ANT to run the UTUTIL command line tool that comes with SQLDeveloper. You can read and follow the instructions in the "README.txt" in the test/SQLDeveloper directory how to do so. You'll need to setup a unit test repository initially if you want to use SQLDeveloper's unit testing feature either way (GUI or UTUTIL command line). See the SQLDeveloper's user's guide or online help how to do that (Hint: Menu item "Extras->Unit Testing" gets you started).
If you don't like the SQLDeveloper unit test approach or you are simply to lazy to install the tool, the unit test repository etc., you can alternatively try the automated unit testing using "dbunit". Follow the instructions in the "README.txt" in the test/dbunit directory how to run the unit tests using "dbunit".
This version of the package has successfully been tested using these unit tests on 10.2.0.4, 10.2.0.5, 11.1.0.7, 11.2.0.1 and 11.2.0.2 (after all it's dead easy with automated unit testing :-).
Summary
The provided tool set should represent a solid foundation for the given task of concurrent index creation. In particular it has been designed with the following in mind:
- Efficient use of privileges granted via roles: The package uses invoker's rights and most operations use dynamic SQL to avoid compilation issues, therefore granting the required privileges to the account used via roles should be sufficient
- The Unix scripts should be able to deal with table-, schema- and database-level datapump formats from Oracle 10g and 11g (all these variants use slightly different texts to identify the relevant sections of the generated SQLFILE by IMPDP)
- Optional use of two separate worker thread sets: This allows the concurrent creation of a multitude of indexes, be it serial or parallel, with clear distinction between the handling of serial (possibly many worker threads) and parallel indexes (usually only a few worker threads)
- Support for arbitrarily sized SQL: The DDL commands for (sub-)partitioned indexes can become quite large due to the way the Oracle meta data API generates the SQL. Therefore these generated SQLs can easily exceed the usual 32KB limit for PL/SQL character strings. The implementation uses CLOBs for the processed SQLs (and DBMS_SQL in versions lower than 11 to handle these piecewise) to support these potentially very large SQLs
- RAC/Grid/Cluster support via DBMS_SCHEDULER: The usage of DBMS_SCHEDULER allows a fine grained control of the resource consumption by the optional use of job classes (not implemented yet but can easily be added - it is a simple additional parameter to the CREATE_JOB procedure) that allow to specify a resource consumer group and a specific service name for the spawned worker threads
- Automated Unit Testing support: The provided unit test harness allows for easy testing of modifications to the code
This is just a quick heads-up to those that plan to use the Pending Statistics feature that has been introduced in Oracle 11.1.
It looks like that in all currently available versions that support this feature Pending Statistics have not been implemented consequently for all possible DBMS_STATS calls, so you have to be very careful which calls you use. Having enabled the pending statistics for a particular table you might start to manipulate the statistics under the impression that the modifications performed are not reflected in the actual dictionary statistics (by "dictionary statistics" in this case I don't mean the statistics of the data dictionary objects themselves but the actual statistics of database objects stored in the data dictionary) but only in the pending statistics area allowing you to test statistics modifications in an isolated environment using the OPTIMIZER_USE_PENDING_STATISTICS parameter on session level.
You therefore might be in for a surprise to find out that this holds true only for a limited set of DBMS_STATS calls, but not for all.
This effectively means that particular changes to the statistics will be effective immediately although pending statistics have been enabled.
In particular manipulations of the statistics using the SET_*_STATS procedures of DBMS_STATS seem to ignore the pending statistics settings and still update the dictionary statistics immediately without further notice.
This is rather unfortunate since this means that Pending Statistics can not be used in a straightforward way to test user-defined statistics which can be very helpful under certain circumstances but require extensive testing before using them on a live system.
But also other calls, like gathering statistics only for a particular set of columns show an unexpected behaviour: It looks like that both statistics get modified, the pending statistics area, but also the dictionary statistics.
Note that setting the GLOBAL preferences (DBMS_STATS.SET_GLOBAL_PREFS) for PUBLISH to FALSE seems to fix this particular issue - in that case only the pending statistics get updated, but the dictionary statistics are left unchanged. This fix does not apply to the SET_*_STATS procedures unfortunately, those seem to always update the dictionary statistics.
The worst thing however is that the statistics history that is automatically maintained since Oracle 10g does not reflect these (unintended) changes properly, so you can not easily recover from the potentially unwanted modifications by calling DBMS_STATS.RESTORE_TABLE_STATS.
Finally I was obviously able to activate the pending statistics using DBMS_STATS.RESTORE_TABLE_STATS - you can rather clearly see this behaviour when using the SET_GLOBAL_PREFS('PUBLISH', 'FALSE') variant of the following script.
The following is a small demonstration of the issues encountered - please note that it modifies the GLOBAL preferences for the PUBLISH setting if you intend to run this test by yourself.
alter session set nls_date_format = 'DD.MM.YYYY HH24:MI:SS';
drop table t purge;
create table t
as
select * from all_objects
where rownum <= 1000;
exec dbms_stats.set_global_prefs('PUBLISH', 'TRUE')
select dbms_stats.get_prefs('PUBLISH', null, 'T') from dual;
-- Our baseline, no histograms, basic column statistics for all columns
exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR ALL COLUMNS SIZE 1')
-- Verify the result
select
num_distinct
, density
, last_analyzed
, num_buckets
, user_stats
from
user_tab_col_statistics
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- Enable pending statistics for table T
-- You can try these different calls
--
-- The GATHER_*_STATS procedures seem to behave correctly
-- only when setting the GLOBAL PREFS to FALSE
--
-- "Correctly" means that the results are reflected in the
-- pending area only but not in the dictionary statistics
--
-- Note that the SET_*_STATS procedures seem to ignore the setting
-- always and publish directly to the dictionary
-- no matter what the PUBLISH setting is on any level (TABLE, GLOBAL)
--
-- exec dbms_stats.set_global_prefs('PUBLISH', 'FALSE')
exec dbms_stats.set_table_prefs(null, 'T', 'PUBLISH', 'FALSE')
-- exec dbms_stats.set_schema_prefs(user, 'PUBLISH', 'FALSE')
-- Verify the current setting, statistics will not be published
select dbms_stats.get_prefs('PUBLISH', null, 'T') from dual;
-- Wait for two seconds to make the LAST_ANALYZED column meaningful
exec dbms_lock.sleep(2)
-- This is supposed to go to the pending statistics area
exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR ALL COLUMNS SIZE 254')
-- Yes, it worked, the dictionary statistics are not modified
select
num_distinct
, density
, last_analyzed
, num_buckets
, user_stats
from
user_tab_col_statistics
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- The pending statistics area contains now the new statistics including histograms
select
num_distinct
, density
, last_analyzed
from
user_col_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histgrm_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histograms
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- Wait for two seconds to make the LAST_ANALYZED column meaningful
exec dbms_lock.sleep(2)
-- Let's gather statistics only for the OBJECT_NAME column
exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR COLUMNS OBJECT_NAME SIZE 1')
-- Oops, why do my dictionary statistics reflect that change (Note the LAST_ANALYZED column)
-- Except for you set the GLOBAL preferences for PUBLISH to FALSE
select
num_distinct
, density
, last_analyzed
, num_buckets
, user_stats
from
user_tab_col_statistics
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- I do have now the statistics updated in both, pending statistics and dictionary statistics
-- Except for you set the GLOBAL preferences for PUBLISH to FALSE
select
num_distinct
, density
, last_analyzed
from
user_col_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histgrm_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histograms
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- Wait for two seconds to make the LAST_ANALYZED column meaningful
exec dbms_lock.sleep(2)
-- Let's recreate the histogram only on the OBJECT_NAME column
exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR COLUMNS OBJECT_NAME SIZE 254')
-- Oops, I did it again...
-- Except for you set the GLOBAL preferences for PUBLISH to FALSE
select
num_distinct
, density
, last_analyzed
, num_buckets
, user_stats
from
user_tab_col_statistics
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
num_distinct
, density
, last_analyzed
from
user_col_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histgrm_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histograms
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- Wait for two seconds to make the LAST_ANALYZED column meaningful
exec dbms_lock.sleep(2)
-- Let's define a manually crafted NDV and DENSITY value
-- Again I expect this to go to the pending statistics area
declare
srec dbms_stats.statrec;
novals dbms_stats.numarray;
distcnt number;
avgclen number;
nullcnt number;
density number;
begin
dbms_stats.get_column_stats(null, 't', 'object_name', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
dbms_stats.set_column_stats(
ownname=>null,
tabname=>'t',
colname=>'object_name',
distcnt=>distcnt*100,
nullcnt=>nullcnt,
srec=>srec,
avgclen=>avgclen,
density=>density/100
);
end;
/
-- Nope, no change here
select
num_distinct
, density
, last_analyzed
from
user_col_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- But I just changed it in the dictionary statistics
-- Even in case of setting the GLOBAL preference to FALSE
select
num_distinct
, density
, last_analyzed
, num_buckets
, user_stats
from
user_tab_col_statistics
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
-- And what is even worse: The statistics history does not reflect all these changes to the dictionary statistics
select table_name, stats_update_time from USER_TAB_STATS_HISTORY where table_name = 'T';
exec dbms_stats.restore_table_stats(null, 'T', systimestamp)
-- But which statistics have been restored now?
-- It looks like this actually restored the PENDING statistics
-- according to the LAST_ANALYZED information??
select
num_distinct
, density
, last_analyzed
, num_buckets
, user_stats
from
user_tab_col_statistics
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histgrm_pending_stats
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
select
count(*)
from
user_tab_histograms
where
table_name = 'T'
and column_name = 'OBJECT_NAME';
exec dbms_stats.set_global_prefs('PUBLISH', 'TRUE')
And this is what I get from running this on 11.1.0.7, 11.2.0.1 or 11.2.0.2:
SQL>
SQL> alter session set nls_date_format = 'DD.MM.YYYY HH24:MI:SS';
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> drop table t purge;
Table dropped.
Elapsed: 00:00:00.20
SQL>
SQL> create table t
2 as
3 select * from all_objects
4 where rownum <= 1000;
Table created.
Elapsed: 00:00:00.35
SQL>
SQL> exec dbms_stats.set_global_prefs('PUBLISH', 'TRUE')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.03
SQL>
SQL> select dbms_stats.get_prefs('PUBLISH', null, 'T') from dual;
DBMS_STATS.GET_PREFS('PUBLISH',NULL,'T')
----------------------------------------------------------------------------------------------------------------------------------
TRUE
Elapsed: 00:00:00.00
SQL>
SQL> -- Our baseline, no histograms, basic column statistics for all columns
SQL> exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR ALL COLUMNS SIZE 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.09
SQL>
SQL> -- Verify the result
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 , num_buckets
6 , user_stats
7 from
8 user_tab_col_statistics
9 where
10 table_name = 'T'
11 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED NUM_BUCKETS USE
------------ ---------- ------------------- ----------- ---
995 .001005025 18.01.2011 18:58:24 1 NO
Elapsed: 00:00:00.01
SQL>
SQL> -- Enable pending statistics for table T
SQL> -- You can try these different calls
SQL> --
SQL> -- The GATHER_*_STATS procedures seem to behave correctly
SQL> -- only when setting the GLOBAL PREFS to FALSE
SQL> --
SQL> -- "Correctly" means that the results are reflected in the
SQL> -- pending area only but not in the dictionary statistics
SQL> --
SQL> -- Note that the SET_*_STATS procedures seem to ignore the setting
SQL> -- always and publish directly to the dictionary
SQL> -- no matter what the PUBLISH setting is on any level (TABLE, GLOBAL)
SQL> --
SQL> -- exec dbms_stats.set_global_prefs('PUBLISH', 'FALSE')
SQL> exec dbms_stats.set_table_prefs(null, 'T', 'PUBLISH', 'FALSE')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.01
SQL> -- exec dbms_stats.set_schema_prefs(user, 'PUBLISH', 'FALSE')
SQL>
SQL> -- Verify the current setting, statistics will not be published
SQL> select dbms_stats.get_prefs('PUBLISH', null, 'T') from dual;
DBMS_STATS.GET_PREFS('PUBLISH',NULL,'T')
----------------------------------------------------------------------------------------------------------------------------------
FALSE
Elapsed: 00:00:00.01
SQL>
SQL> -- Wait for two seconds to make the LAST_ANALYZED column meaningful
SQL> exec dbms_lock.sleep(2)
PL/SQL procedure successfully completed.
Elapsed: 00:00:02.01
SQL>
SQL> -- This is supposed to go to the pending statistics area
SQL> exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR ALL COLUMNS SIZE 254')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.99
SQL>
SQL> -- Yes, it worked, the dictionary statistics are not modified
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 , num_buckets
6 , user_stats
7 from
8 user_tab_col_statistics
9 where
10 table_name = 'T'
11 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED NUM_BUCKETS USE
------------ ---------- ------------------- ----------- ---
995 .001005025 18.01.2011 18:58:24 1 NO
Elapsed: 00:00:00.01
SQL>
SQL> -- The pending statistics area contains now the new statistics including histograms
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 from
6 user_col_pending_stats
7 where
8 table_name = 'T'
9 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED
------------ ---------- -------------------
995 .00101 18.01.2011 18:58:26
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histgrm_pending_stats
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
255
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histograms
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
2
Elapsed: 00:00:00.01
SQL>
SQL> -- Wait for two seconds to make the LAST_ANALYZED column meaningful
SQL> exec dbms_lock.sleep(2)
PL/SQL procedure successfully completed.
Elapsed: 00:00:02.01
SQL>
SQL> -- Let's gather statistics only for the OBJECT_NAME column
SQL> exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR COLUMNS OBJECT_NAME SIZE 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.14
SQL>
SQL> -- Oops, why do my dictionary statistics reflect that change (Note the LAST_ANALYZED column)
SQL> -- Except for you set the GLOBAL preferences for PUBLISH to FALSE
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 , num_buckets
6 , user_stats
7 from
8 user_tab_col_statistics
9 where
10 table_name = 'T'
11 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED NUM_BUCKETS USE
------------ ---------- ------------------- ----------- ---
995 .001005025 18.01.2011 18:58:29 1 NO
Elapsed: 00:00:00.01
SQL>
SQL> -- I do have now the statistics updated in both, pending statistics and dictionary statistics
SQL> -- Except for you set the GLOBAL preferences for PUBLISH to FALSE
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 from
6 user_col_pending_stats
7 where
8 table_name = 'T'
9 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED
------------ ---------- -------------------
995 .001005025 18.01.2011 18:58:29
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histgrm_pending_stats
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
0
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histograms
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
2
Elapsed: 00:00:00.01
SQL>
SQL> -- Wait for two seconds to make the LAST_ANALYZED column meaningful
SQL> exec dbms_lock.sleep(2)
PL/SQL procedure successfully completed.
Elapsed: 00:00:02.01
SQL>
SQL> -- Let's recreate the histogram only on the OBJECT_NAME column
SQL> exec dbms_stats.gather_table_stats(null, 'T', estimate_percent => null, cascade => false, method_opt => 'FOR COLUMNS OBJECT_NAME SIZE 254')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.34
SQL>
SQL> -- Oops, I did it again...
SQL> -- Except for you set the GLOBAL preferences for PUBLISH to FALSE
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 , num_buckets
6 , user_stats
7 from
8 user_tab_col_statistics
9 where
10 table_name = 'T'
11 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED NUM_BUCKETS USE
------------ ---------- ------------------- ----------- ---
995 .00101 18.01.2011 18:58:32 254 NO
Elapsed: 00:00:00.01
SQL>
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 from
6 user_col_pending_stats
7 where
8 table_name = 'T'
9 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED
------------ ---------- -------------------
995 .00101 18.01.2011 18:58:32
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histgrm_pending_stats
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
255
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histograms
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
255
Elapsed: 00:00:00.01
SQL>
SQL> -- Wait for two seconds to make the LAST_ANALYZED column meaningful
SQL> exec dbms_lock.sleep(2)
PL/SQL procedure successfully completed.
Elapsed: 00:00:02.01
SQL>
SQL> -- Let's define a manually crafted NDV and DENSITY value
SQL> -- Again I expect this to go to the pending statistics area
SQL> declare
2 srec dbms_stats.statrec;
3 novals dbms_stats.numarray;
4 distcnt number;
5 avgclen number;
6 nullcnt number;
7 density number;
8 begin
9 dbms_stats.get_column_stats(null, 't', 'object_name', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
10 dbms_stats.set_column_stats(
11 ownname=>null,
12 tabname=>'t',
13 colname=>'object_name',
14 distcnt=>distcnt*100,
15 nullcnt=>nullcnt,
16 srec=>srec,
17 avgclen=>avgclen,
18 density=>density/100
19 );
20 end;
21 /
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.17
SQL>
SQL> -- Nope, no change here
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 from
6 user_col_pending_stats
7 where
8 table_name = 'T'
9 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED
------------ ---------- -------------------
995 .00101 18.01.2011 18:58:32
Elapsed: 00:00:00.01
SQL>
SQL> -- But I just changed it in the dictionary statistics
SQL> -- Even in case of setting the GLOBAL preference to FALSE
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 , num_buckets
6 , user_stats
7 from
8 user_tab_col_statistics
9 where
10 table_name = 'T'
11 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED NUM_BUCKETS USE
------------ ---------- ------------------- ----------- ---
99500 .0000101 18.01.2011 18:58:34 254 YES
Elapsed: 00:00:00.01
SQL>
SQL> -- And what is even worse: The statistics history does not reflect all these changes to the dictionary statistics
SQL> select table_name, stats_update_time from USER_TAB_STATS_HISTORY where table_name = 'T';
TABLE_NAME STATS_UPDATE_TIME
------------------------------ ---------------------------------------------------------------------------
T 18-JAN-11 06.58.24.391000 PM +01:00
Elapsed: 00:00:00.01
SQL>
SQL> exec dbms_stats.restore_table_stats(null, 'T', systimestamp)
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.29
SQL>
SQL> -- But which statistics have been restored now?
SQL> -- It looks like this actually restored the PENDING statistics
SQL> -- according to the LAST_ANALYZED information??
SQL> select
2 num_distinct
3 , density
4 , last_analyzed
5 , num_buckets
6 , user_stats
7 from
8 user_tab_col_statistics
9 where
10 table_name = 'T'
11 and column_name = 'OBJECT_NAME';
NUM_DISTINCT DENSITY LAST_ANALYZED NUM_BUCKETS USE
------------ ---------- ------------------- ----------- ---
995 .00101 18.01.2011 18:58:32 254 NO
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histgrm_pending_stats
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
255
Elapsed: 00:00:00.01
SQL>
SQL> select
2 count(*)
3 from
4 user_tab_histograms
5 where
6 table_name = 'T'
7 and column_name = 'OBJECT_NAME';
COUNT(*)
----------
255
Elapsed: 00:00:00.01
SQL>
SQL> exec dbms_stats.set_global_prefs('PUBLISH', 'TRUE')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.01
SQL>
It is also interesting to note that, although Oracle has added an array of new dictionary views that allow to access the pending statistics area, these views are inconsistent with the existing statistics-related views in terms of naming conventions, columns and behaviour.
For example the *_col_pending_stats view does not have a NUM_BUCKETS column, and the corresponding *_tab_histgrm_pending_stats view for histogram details shows 0 rows if basic column statistics have been defined but no histogram whereas the original *_tab_histograms returns two rows if basic column statistics have been collected representing the low and high value of the column.
Summary
The Pending Statistics feature clearly shows some unexpected behaviour. In particular you better don't rely on it preventing the dictionary statistics from being updated by DBMS_STATS calls with the PUBLISH attribute set to FALSE.
Some of this clearly looks like a bug but I couldn't find a corresponding bug entry yet in My Oracle Support.
Note that this post does not cover the actual usage of Pending Statistics by the optimizer - I haven't done any extensive testing in this regard, but some quick checks showed that it seems to work as expected, which means that the optimizer picks statistics for those tables that have Pending Statistics defined when setting OPTIMIZER_USE_PENDING_STATISTICS = TRUE, but still uses the statistics from the dictionary for those that don't have any pending statistics defined.
Oracle introduced in Oracle 10g the hash aggregation as a new feature. It can be used for both GROUP BY and UNIQUE operations (HASH GROUP BY and HASH UNIQUE respectively) and is by default the preferred aggregation method if there is no particular reason that lets the cost based optimizer prefer the sort based aggregation (SORT GROUP BY and SORT UNIQUE), for example if the GROUP BY is followed by an ORDER BY on the same expression (using the SORT GROUP BY in such cases is not always beneficial by the way, see Guy Harrison's blog for an example).
Ever since its introduction from time to time I've heard complaints about performance degradations of aggregation operations that are based on the new hash aggregation algorithm compared to the previously used sort based aggregations.
Now there may be many potential reasons for such performance degradations (and possibly many of them might not have anything to do with the hash aggregation) but here is a surprising revelation that might explain why some of them were indeed caused by the switch to the new algorithm: The hash aggregation operation does not work very well together with the automatic PGA management (WORKAREA_SIZE_POLICY = AUTO, default since 9i). The fundamental defect is that it is not able to dynamically resize to a larger workarea size when using automatic PGA management and therefore remains more or less at its initial expected size based on the estimates at optimization time.
This effectively means that the efficiency of the hash aggregation operation when using automatic PGA management is heavily dependant on the cardinality estimates at optimization time - in case of estimates in the right ballpark, the memory used at execution time will correspond to the actual requirements at runtime, but in case of bad estimates, the operation potentially uses far less memory than available and unnecessarily spills to disk.
Let's start with a simple script to demonstrate the issue:
show parameter pga
show parameter processes
-- alter session set workarea_size_policy = manual sort_area_size = 40000000;
drop table t1 purge;
drop table t2 purge;
create table t1
as
select mod(rownum, 27) as user_id, rownum as object_id from dual connect by level <= 30000;
create table t2
as
select distinct user_id from t1;
exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
column low_val new_value low_value
column high_val new_value high_value
select
max(user_id) + 1 as low_val
, max(user_id) + max(user_id) - min(user_id) + 1 as high_val
from
t2;
-- Invalidate any cursors using T1
comment on table t1 is '';
alter session set statistics_level = all;
-- alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
-- alter session set "_smm_trace" = 65535;
select /*+ full(@inner t1)
full(@inner t2)
full(@inner t3)
leading(@inner t2 t3 t1)
use_nl(@inner t3)
use_hash(@inner t1)
no_swap_join_inputs(@inner t1)
use_hash_aggregation(@inner)
*/
max(cnt)
from
(
select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
from t1, t2, t2 t3
where t1.user_id = t2.user_id
group by t1.object_id, t3.user_id
)
;
-- alter session set "_smm_trace" = 0;
set pagesize 0 linesize 200 trimspool on tab off
select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
set pagesize 14
-- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
declare
srec dbms_stats.statrec;
novals dbms_stats.numarray;
distcnt number;
avgclen number;
nullcnt number;
density number;
srec2 dbms_stats.statrec;
begin
dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
srec2.epc := 2;
novals := dbms_stats.numarray(
&low_value,
&high_value
);
srec2.bkvals := null;
dbms_stats.prepare_column_values(srec2,novals);
dbms_stats.set_column_stats(
ownname=>null,
tabname=>'t1',
colname=>'user_id',
distcnt=>distcnt,
nullcnt=>nullcnt,
srec=>srec2,
avgclen=>avgclen,
density=>density
);
end;
/
-- Invalidate any cursors using T1
comment on table t1 is '';
alter session set statistics_level = all;
-- alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
-- alter session set "_smm_trace" = 65535;
select /*+ full(@inner t1)
full(@inner t2)
full(@inner t3)
leading(@inner t2 t3 t1)
use_nl(@inner t3)
use_hash(@inner t1)
no_swap_join_inputs(@inner t1)
use_hash_aggregation(@inner)
*/
max(cnt)
from
(
select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
from t1, t2, t2 t3
where t1.user_id = t2.user_id
group by t1.object_id, t3.user_id
)
;
-- alter session set "_smm_trace" = 0;
set pagesize 0 linesize 200 trimspool on tab off
select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
I first start with showing the current PGA_AGGREGATE_TARGET (P_A_T) and PROCESSES setting.
I create then two tables as sample data that will be joined and already collect information about the minimum and maximum value of the join column.
Any cursors that are dependent on table T1 will then be invalidated to make sure that a reoptimization of the next query will take place (of course running the complete script invalidates such cursors anyway due to the drop and re-create of the tables).
I then run a simple join followed by a group by operation. I've added a cartesian join by the way, but its only purpose is to ensure that the generated row source is sufficiently large to give the group by something to do.
I have used hints to ensure that I always get the same execution plan even if I later on manipulate the statistics to see the effect of incorrect cardinality estimates.
After getting the actual runtime execution plan along with execution statistics I modify the column statistics of one of the tables' join column in such a way that the optimizer thinks that there is no overlap between the join column values and therefore computes a very low join cardinality.
After making sure again that a reoptimization will take place I run the same statement again with the same data volume and the same hints in place.
And this is what I get for all major versions that are currently out there (10.2.0.4, 10.2.0.5, 11.1.0.7, 11.2.0.1 and 11.2.0.2):
SQL>
SQL> show parameter pga
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
pga_aggregate_target big integer 200M
SQL> show parameter processes
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
aq_tm_processes integer 0
db_writer_processes integer 1
gcs_server_processes integer 0
job_queue_processes integer 10
log_archive_max_processes integer 2
processes integer 150
SQL>
SQL> -- alter session set workarea_size_policy = manual sort_area_size = 40000000;
SQL>
SQL> drop table t1 purge;
drop table t1 purge
*
ERROR at line 1:
ORA-00942: table or view does not exist
Elapsed: 00:00:00.01
SQL>
SQL> drop table t2 purge;
drop table t2 purge
*
ERROR at line 1:
ORA-00942: table or view does not exist
Elapsed: 00:00:00.00
SQL>
SQL> create table t1
2 as
3 select mod(rownum, 27) as user_id, rownum as object_id from dual connect by level <= 30000;
Table created.
Elapsed: 00:00:00.07
SQL>
SQL> create table t2
2 as
3 select distinct user_id from t1;
Table created.
Elapsed: 00:00:00.06
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.09
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.04
SQL>
SQL> column low_val new_value low_value
SQL> column high_val new_value high_value
SQL>
SQL> select
2 max(user_id) + 1 as low_val
3 , max(user_id) + max(user_id) - min(user_id) + 1 as high_val
4 from
5 t2;
LOW_VAL HIGH_VAL
---------- ----------
27 53
Elapsed: 00:00:00.00
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.04
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> -- alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
SQL>
SQL> -- alter session set "_smm_trace" = 65535;
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:00.71
SQL>
SQL> -- alter session set "_smm_trace" = 0;
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 2s1sdfhvhajv3, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner
t2 t3 t1) use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner
t1) use_hash_aggregation(@inner) */ max(cnt) from ( select /*+ qb_name(inner) */ count(*)
as cnt, t1.object_id, t3.user_id from t1, t2, t2 t3 where t1.user_id = t2.user_id group by t1.object_id,
t3.user_id )
Plan hash value: 3134842094
--------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.72 | 139 | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.72 | 139 | | | |
| 2 | VIEW | | 1 | 570K| 810K|00:00:00.43 | 139 | | | |
| 3 | HASH GROUP BY | | 1 | 570K| 810K|00:00:00.43 | 139 | 35M| 5521K| 38M (0)|
|* 4 | HASH JOIN | | 1 | 807K| 810K|00:00:00.02 | 139 | 1348K| 1348K| 1093K (0)|
| 5 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | | | |
| 7 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | | | |
| 8 | TABLE ACCESS FULL | T1 | 1 | 29907 | 30000 |00:00:00.01 | 55 | | | |
--------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("T1"."USER_ID"="T2"."USER_ID")
29 rows selected.
Elapsed: 00:00:00.12
SQL>
SQL> set pagesize 14
SQL>
SQL> -- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
SQL> declare
2 srec dbms_stats.statrec;
3 novals dbms_stats.numarray;
4 distcnt number;
5 avgclen number;
6 nullcnt number;
7 density number;
8 srec2 dbms_stats.statrec;
9 begin
10 dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
11 srec2.epc := 2;
12 novals := dbms_stats.numarray(
13 &low_value,
14 &high_value
15 );
16 srec2.bkvals := null;
17 dbms_stats.prepare_column_values(srec2,novals);
18 dbms_stats.set_column_stats(
19 ownname=>null,
20 tabname=>'t1',
21 colname=>'user_id',
22 distcnt=>distcnt,
23 nullcnt=>nullcnt,
24 srec=>srec2,
25 avgclen=>avgclen,
26 density=>density
27 );
28 end;
29 /
old 13: &low_value,
new 13: 27,
old 14: &high_value
new 14: 53
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.03
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.03
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.01
SQL>
SQL> -- alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
SQL>
SQL> -- alter session set "_smm_trace" = 65535;
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:01.37
SQL>
SQL> -- alter session set "_smm_trace" = 0;
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 2s1sdfhvhajv3, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner t1) use_hash_aggregation(@inner)
*/ max(cnt) from ( select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id from t1, t2, t2 t3 where t1.user_id =
t2.user_id group by t1.object_id, t3.user_id )
Plan hash value: 3134842094
------------------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | Used-Mem | Used-Tmp|
------------------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:01.37 | 139 | 2715 | 2715 | | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:01.37 | 139 | 2715 | 2715 | | | | |
| 2 | VIEW | | 1 | 1 | 810K|00:00:00.62 | 139 | 2715 | 2715 | | | | |
| 3 | HASH GROUP BY | | 1 | 1 | 810K|00:00:00.62 | 139 | 2715 | 2715 | 35M| 5521K| 7010K (1)| 23552 |
|* 4 | HASH JOIN | | 1 | 1 | 810K|00:00:00.01 | 139 | 0 | 0 | 1348K| 1348K| 1167K (0)| |
| 5 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | 0 | 0 | | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | 0 | 0 | | | | |
| 7 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | 0 | 0 | | | | |
| 8 | TABLE ACCESS FULL | T1 | 1 | 29907 | 30000 |00:00:00.01 | 55 | 0 | 0 | | | | |
------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("T1"."USER_ID"="T2"."USER_ID")
28 rows selected.
Elapsed: 00:00:00.03
SQL>
As it can be seen with an estimate in the right ballpark the HASH GROUP BY operation completes in memory (very close to the 20% PGA_AGGREGATE_TARGET maximum size of a single workarea from 10.2 on with automatic PGA management for PGA_AGGREGATE_TARGET < 500M, for more information see Joze Senegacnik's paper on the internals of automatic PGA management).
However repeating exactly the same operation with the fudged statistics it spills to disk and uses only 7M, although it could have used up to 40M (given that there is no concurrent workload).
The same does not happen when repeating this experiment with other operations that use a workarea - the most obvious one being a SORT GROUP BY, as can be seen from this output:
SQL>
SQL> show parameter pga
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
pga_aggregate_target big integer 200M
SQL> show parameter processes
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
aq_tm_processes integer 0
db_writer_processes integer 1
gcs_server_processes integer 0
job_queue_processes integer 10
log_archive_max_processes integer 2
processes integer 150
SQL>
SQL> drop table t1 purge;
Table dropped.
Elapsed: 00:00:00.04
SQL>
SQL> drop table t2 purge;
Table dropped.
Elapsed: 00:00:00.04
SQL>
SQL> create table t1
2 as
3 select mod(rownum, 15) as user_id, rownum as object_id from dual connect by level <= 30000;
Table created.
Elapsed: 00:00:00.06
SQL>
SQL> create table t2
2 as
3 select distinct user_id from t1;
Table created.
Elapsed: 00:00:00.03
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.09
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.04
SQL>
SQL> column low_val new_value low_value
SQL> column high_val new_value high_value
SQL>
SQL> select
2 max(user_id) + 1 as low_val
3 , max(user_id) + max(user_id) - min(user_id) + 1 as high_val
4 from
5 t2;
LOW_VAL HIGH_VAL
---------- ----------
15 29
Elapsed: 00:00:00.00
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> -- alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
SQL>
SQL> -- alter session set "_smm_trace" = 65535;
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 no_use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:00.54
SQL>
SQL> -- alter session set "_smm_trace" = 0;
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 94tqnspjuzk8x, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner
t2 t3 t1) use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner
t1) no_use_hash_aggregation(@inner) */ max(cnt) from ( select /*+ qb_name(inner) */
count(*) as cnt, t1.object_id, t3.user_id from t1, t2, t2 t3 where t1.user_id = t2.user_id group by
t1.object_id, t3.user_id )
Plan hash value: 2068941295
--------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.53 | 103 | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.53 | 103 | | | |
| 2 | VIEW | | 1 | 315K| 450K|00:00:00.37 | 103 | | | |
| 3 | SORT GROUP BY | | 1 | 315K| 450K|00:00:00.37 | 103 | 28M| 1913K| 25M (0)|
|* 4 | HASH JOIN | | 1 | 446K| 450K|00:00:00.01 | 103 | 1348K| 1348K| 1097K (0)|
| 5 | NESTED LOOPS | | 1 | 225 | 225 |00:00:00.01 | 48 | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 15 | 15 |00:00:00.01 | 3 | | | |
| 7 | TABLE ACCESS FULL| T2 | 15 | 15 | 225 |00:00:00.01 | 45 | | | |
| 8 | TABLE ACCESS FULL | T1 | 1 | 29787 | 30000 |00:00:00.01 | 55 | | | |
--------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("T1"."USER_ID"="T2"."USER_ID")
29 rows selected.
Elapsed: 00:00:00.03
SQL>
SQL> set pagesize 14
SQL>
SQL> -- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
SQL> declare
2 srec dbms_stats.statrec;
3 novals dbms_stats.numarray;
4 distcnt number;
5 avgclen number;
6 nullcnt number;
7 density number;
8 srec2 dbms_stats.statrec;
9 begin
10 dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
11 srec2.epc := 2;
12 novals := dbms_stats.numarray(
13 &low_value,
14 &high_value
15 );
16 srec2.bkvals := null;
17 dbms_stats.prepare_column_values(srec2,novals);
18 dbms_stats.set_column_stats(
19 ownname=>null,
20 tabname=>'t1',
21 colname=>'user_id',
22 distcnt=>distcnt,
23 nullcnt=>nullcnt,
24 srec=>srec2,
25 avgclen=>avgclen,
26 density=>density
27 );
28 end;
29 /
old 13: &low_value,
new 13: 15,
old 14: &high_value
new 14: 29
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.01
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> -- alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
SQL>
SQL> -- alter session set "_smm_trace" = 65535;
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 no_use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:00.56
SQL>
SQL> -- alter session set "_smm_trace" = 0;
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 94tqnspjuzk8x, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner
t2 t3 t1) use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner
t1) no_use_hash_aggregation(@inner) */ max(cnt) from ( select /*+ qb_name(inner) */
count(*) as cnt, t1.object_id, t3.user_id from t1, t2, t2 t3 where t1.user_id = t2.user_id group by
t1.object_id, t3.user_id )
Plan hash value: 2068941295
--------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.55 | 103 | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.55 | 103 | | | |
| 2 | VIEW | | 1 | 1 | 450K|00:00:00.38 | 103 | | | |
| 3 | SORT GROUP BY | | 1 | 1 | 450K|00:00:00.38 | 103 | 28M| 1913K| 25M (0)|
|* 4 | HASH JOIN | | 1 | 1 | 450K|00:00:00.01 | 103 | 1348K| 1348K| 1053K (0)|
| 5 | NESTED LOOPS | | 1 | 225 | 225 |00:00:00.01 | 48 | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 15 | 15 |00:00:00.01 | 3 | | | |
| 7 | TABLE ACCESS FULL| T2 | 15 | 15 | 225 |00:00:00.01 | 45 | | | |
| 8 | TABLE ACCESS FULL | T1 | 1 | 29787 | 30000 |00:00:00.01 | 55 | | | |
--------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("T1"."USER_ID"="T2"."USER_ID")
29 rows selected.
Elapsed: 00:00:00.03
SQL>
All I've done is to reduce the data set, because the SORT GROUP BY in this case required more memory for the same amount of data than the HASH GROUP BY, in order to prevent the operation to spill to disk with a 200M PGA_AGGREGATE_TARGET setting and change the USE_HASH_AGGREGATION hint to NO_USE_HASH_AGGREGATION.
As you can see, the operation completes both times in memory and uses the same amount of memory no matter what the estimates look like.
I've tested the serial execution of the most common workarea based operations like HASH JOIN, SORT ORDER BY, WINDOW SORT, SORT UNIQUE and all of them were able to dynamically resize the workarea in cases where the initial estimated size was too small.
If you carefully check then you'll notice that I haven't mentioned the HASH UNIQUE operation yet, and later on you'll see why.
A cunning feature of the automatic PGA management comes to help, however, which is a kind of "feedback loop" for the workareas based on statistics maintained by the automatic PGA memory management, and indeed when the HASH GROUP BY cursor based on the incorrect cardinality estimate gets shared (not invalidated) and re-executed, the next execution will look like this:
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_wrong_cardinality_repeated_execution';
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:00.76
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 2s1sdfhvhajv3, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner t1)
use_hash_aggregation(@inner) */ max(cnt) from ( select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id,
t3.user_id from t1, t2, t2 t3 where t1.user_id = t2.user_id group by t1.object_id, t3.user_id )
Plan hash value: 3134842094
------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | Used-Tmp|
------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.75 | 139 | | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.75 | 139 | | | | |
| 2 | VIEW | | 1 | 1 | 810K|00:00:00.44 | 139 | | | | |
| 3 | HASH GROUP BY | | 1 | 1 | 810K|00:00:00.44 | 139 | 35M| 5521K| 38M (0)| |
|* 4 | HASH JOIN | | 1 | 1 | 810K|00:00:00.01 | 139 | 1348K| 1348K| 1412K (0)| |
| 5 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | | | | |
| 7 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | | | | |
| 8 | TABLE ACCESS FULL | T1 | 1 | 29836 | 30000 |00:00:00.01 | 55 | | | | |
------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("T1"."USER_ID"="T2"."USER_ID")
28 rows selected.
Elapsed: 00:00:00.03
SQL>
So Oracle this time based the workarea memory requirements on the feedback from the previous execution and therefore allocated sufficient memory to complete the operation without spilling to disk.
Notice however that the feedback loop unfortunately does not work as desired when the workarea execution fails due to insufficient TEMP space. Ideally the feedback loop should allow the subsequent executions to grab more memory specifically in such cases, but at present it doesn't - the failure obviously prevents an update of the statistics and therefore subsequent executions continue to fail since they still use the same amount of memory.
You can test this by simply assigned a very small TEMP tablespace to the user executing the query so that the second execution fails due to insufficient TEMP space. If you repeat the execution in this case, it will fail again and keeps doing so.
What happens if I invalidate the cursor and repeat the execution?
Comment created.
Elapsed: 00:00:00.01
SQL> set echo on timing on
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_wrong_cardinality_repeated_exec_invalid';
Session altered.
Elapsed: 00:00:00.01
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
1
Elapsed: 00:00:01.29
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 2s1sdfhvhajv3, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner t1) use_hash_aggregation(@inner)
*/ max(cnt) from ( select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id from t1, t2, t2 t3 where t1.user_id =
t2.user_id group by t1.object_id, t3.user_id )
Plan hash value: 3134842094
------------------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | Used-Mem | Used-Tmp|
------------------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:01.28 | 139 | 2715 | 2715 | | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:01.28 | 139 | 2715 | 2715 | | | | |
| 2 | VIEW | | 1 | 1 | 810K|00:00:00.61 | 139 | 2715 | 2715 | | | | |
| 3 | HASH GROUP BY | | 1 | 1 | 810K|00:00:00.61 | 139 | 2715 | 2715 | 35M| 5521K| 7067K (1)| 23552 |
|* 4 | HASH JOIN | | 1 | 1 | 810K|00:00:00.01 | 139 | 0 | 0 | 1348K| 1348K| 1129K (0)| |
| 5 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | 0 | 0 | | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | 0 | 0 | | | | |
| 7 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | 0 | 0 | | | | |
| 8 | TABLE ACCESS FULL | T1 | 1 | 29836 | 30000 |00:00:00.01 | 55 | 0 | 0 | | | | |
------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
4 - access("T1"."USER_ID"="T2"."USER_ID")
28 rows selected.
Elapsed: 00:00:00.03
SQL>
Back to square one - with the invalidation the statistics are gone, too, so the bad cardinality estimate again lead to the suboptimal execution of the HASH GROUP BY.
So you might think, why bother? Only the first execution of a cursor without workarea execution statistics will be affected by the problem, subsequent execution of the same cursor will benefit from the statistics from the previous executions.
The problem however is, that this is fine for applications that share cursors. Unfortunately applications that peform heavy duty aggregations like data warehouses typically do not share cursors, since they do not care about the optimization overhead and deliberately use literals to provide as much information to the optimizer as possible.
Also these heavy duty aggregations usually use Parallel Execution features, and as you'll see from the output of the same test case, if I run the HASH GROUP BY in parallel by simply setting table T1 to parallel degree 2, a similar problem occurs - so Parallel Execution is affected as well.
SQL>
SQL> show parameter pga
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
pga_aggregate_target big integer 200M
SQL> show parameter processes
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
aq_tm_processes integer 0
db_writer_processes integer 1
gcs_server_processes integer 0
job_queue_processes integer 10
log_archive_max_processes integer 2
processes integer 150
SQL>
SQL> -- alter session set workarea_size_policy = manual sort_area_size = 40000000;
SQL>
SQL> drop table t1 purge;
Table dropped.
Elapsed: 00:00:00.01
SQL>
SQL> drop table t2 purge;
Table dropped.
Elapsed: 00:00:00.04
SQL>
SQL> create table t1
2 as
3 select mod(rownum, 27) as user_id, rownum as object_id from dual connect by level <= 30000;
Table created.
Elapsed: 00:00:00.06
SQL>
SQL> alter table t1 parallel 2;
Table altered.
Elapsed: 00:00:00.03
SQL>
SQL> create table t2
2 as
3 select distinct user_id from t1;
Table created.
Elapsed: 00:00:00.03
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.04
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.04
SQL>
SQL> column low_val new_value low_value
SQL> column high_val new_value high_value
SQL>
SQL> select
2 max(user_id) + 1 as low_val
3 , max(user_id) + max(user_id) - min(user_id) + 1 as high_val
4 from
5 t2;
LOW_VAL HIGH_VAL
---------- ----------
27 53
Elapsed: 00:00:00.00
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
Session altered.
Elapsed: 00:00:00.01
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:00.42
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS'));
SQL_ID 2s1sdfhvhajv3, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_hash(@inner t1) no_swap_join_inputs(@inner t1)
use_hash_aggregation(@inner) */ max(cnt) from ( select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id,
t3.user_id from t1, t2, t2 t3 where t1.user_id = t2.user_id group by t1.object_id, t3.user_id )
Plan hash value: 464898991
--------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | O/1/M |
--------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.43 | 87 | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.43 | 87 | | | |
| 2 | PX COORDINATOR | | 1 | | 2 |00:00:00.43 | 87 | | | |
| 3 | PX SEND QC (RANDOM) | :TQ10002 | 0 | 1 | 0 |00:00:00.01 | 0 | | | |
| 4 | SORT AGGREGATE | | 2 | 1 | 2 |00:00:00.81 | 0 | | | |
| 5 | VIEW | | 2 | 575K| 810K|00:00:00.48 | 0 | | | |
| 6 | HASH GROUP BY | | 2 | 575K| 810K|00:00:00.48 | 0 | 35M| 5521K| 2/0/0|
| 7 | PX RECEIVE | | 2 | 814K| 810K|00:00:00.01 | 0 | | | |
| 8 | PX SEND HASH | :TQ10001 | 0 | 814K| 0 |00:00:00.01 | 0 | | | |
|* 9 | HASH JOIN | | 2 | 814K| 810K|00:00:00.01 | 89 | 1348K| 1348K| 2/0/0|
| 10 | BUFFER SORT | | 2 | | 1458 |00:00:00.01 | 0 | 29696 | 29696 | 2/0/0|
| 11 | PX RECEIVE | | 2 | 729 | 1458 |00:00:00.01 | 0 | | | |
| 12 | PX SEND BROADCAST | :TQ10000 | 0 | 729 | 0 |00:00:00.01 | 0 | | | |
| 13 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | | | |
| 14 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | | | |
| 15 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | | | |
| 16 | PX BLOCK ITERATOR | | 2 | 30164 | 30000 |00:00:00.01 | 89 | | | |
|* 17 | TABLE ACCESS FULL | T1 | 18 | 30164 | 30000 |00:00:00.01 | 89 | | | |
--------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
9 - access("T1"."USER_ID"="T2"."USER_ID")
17 - access(:Z>=:Z AND :Z<=:Z)
38 rows selected.
Elapsed: 00:00:00.03
SQL>
SQL> set pagesize 14
SQL>
SQL> -- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
SQL> declare
2 srec dbms_stats.statrec;
3 novals dbms_stats.numarray;
4 distcnt number;
5 avgclen number;
6 nullcnt number;
7 density number;
8 srec2 dbms_stats.statrec;
9 begin
10 dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
11 srec2.epc := 2;
12 novals := dbms_stats.numarray(
13 &low_value,
14 &high_value
15 );
16 srec2.bkvals := null;
17 dbms_stats.prepare_column_values(srec2,novals);
18 dbms_stats.set_column_stats(
19 ownname=>null,
20 tabname=>'t1',
21 colname=>'user_id',
22 distcnt=>distcnt,
23 nullcnt=>nullcnt,
24 srec=>srec2,
25 avgclen=>avgclen,
26 density=>density
27 );
28 end;
29 /
old 13: &low_value,
new 13: 27,
old 14: &high_value
new 14: 53
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.00
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_hash(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 use_hash_aggregation(@inner)
9 */
10 max(cnt)
11 from
12 (
13 select /*+ qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 group by t1.object_id, t3.user_id
17 )
18 ;
MAX(CNT)
----------
1
Elapsed: 00:00:01.17
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS'));
SQL_ID 2s1sdfhvhajv3, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2) full(@inner t3) leading(@inner t2 t3 t1) use_nl(@inner t3)
use_hash(@inner t1) no_swap_join_inputs(@inner t1) use_hash_aggregation(@inner) */ max(cnt) from ( select /*+
qb_name(inner) */ count(*) as cnt, t1.object_id, t3.user_id from t1, t2, t2 t3 where t1.user_id = t2.user_id group by t1.object_id, t3.user_id )
Plan hash value: 464898991
------------------------------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | O/1/M | Max-Tmp |
------------------------------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:01.16 | 87 | 0 | 0 | | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:01.16 | 87 | 0 | 0 | | | | |
| 2 | PX COORDINATOR | | 1 | | 2 |00:00:01.16 | 87 | 0 | 0 | | | | |
| 3 | PX SEND QC (RANDOM) | :TQ10002 | 0 | 1 | 0 |00:00:00.01 | 0 | 0 | 0 | | | | |
| 4 | SORT AGGREGATE | | 2 | 1 | 2 |00:00:02.25 | 0 | 2460 | 2460 | | | | |
| 5 | VIEW | | 2 | 1 | 810K|00:00:01.53 | 0 | 2460 | 2460 | | | | |
| 6 | HASH GROUP BY | | 2 | 1 | 810K|00:00:01.53 | 0 | 2460 | 2460 | 35M| 5521K| | 11264 |
| 7 | PX RECEIVE | | 2 | 1 | 810K|00:00:00.01 | 0 | 0 | 0 | | | | |
| 8 | PX SEND HASH | :TQ10001 | 0 | 1 | 0 |00:00:00.01 | 0 | 0 | 0 | | | | |
|* 9 | HASH JOIN | | 2 | 1 | 810K|00:00:00.01 | 89 | 0 | 0 | 1348K| 1348K| 2/0/0| |
| 10 | BUFFER SORT | | 2 | | 1458 |00:00:00.01 | 0 | 0 | 0 | 29696 | 29696 | 2/0/0| |
| 11 | PX RECEIVE | | 2 | 729 | 1458 |00:00:00.01 | 0 | 0 | 0 | | | | |
| 12 | PX SEND BROADCAST | :TQ10000 | 0 | 729 | 0 |00:00:00.01 | 0 | 0 | 0 | | | | |
| 13 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | 0 | 0 | | | | |
| 14 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | 0 | 0 | | | | |
| 15 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | 0 | 0 | | | | |
| 16 | PX BLOCK ITERATOR | | 2 | 30164 | 30000 |00:00:00.01 | 89 | 0 | 0 | | | | |
|* 17 | TABLE ACCESS FULL | T1 | 18 | 30164 | 30000 |00:00:00.01 | 89 | 0 | 0 | | | | |
------------------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
9 - access("T1"."USER_ID"="T2"."USER_ID")
17 - access(:Z>=:Z AND :Z<=:Z)
37 rows selected.
Elapsed: 00:00:00.04
SQL>
So, if your database uses automatic PGA management and
- uses the hash aggregation HASH GROUP BY (or HASH UNIQUE, more on that in a moment)
- and does not share cursors
every execution that is based on bad cardinality estimates potentially has a problem with the hash aggregation because it might not make efficient use of the available memory.
The same applies to applications that share cursors, however in that case only the first execution after re-optimization / invalidation is affected.
So you might want to carefully check the runtime execution statistics of your critial hash aggregations.
Mind you, things could be worse, and that is where the HASH UNIQUE operation comes into the picture.
When I realised the issue with the HASH GROUP BY operation I was quite certain that the HASH UNIQUE operation will be affected in a similar way, since internally Oracle seems to use the same mechanism for both operations (In the SMM trace files both are called HASH GROUP BY).
To my surprise I noticed that in 10g versions below 10.2.0.5 and 11g versions below 11.2.0.1 (which means in this case 10.2.0.4 and 11.1.0.7 respectively, I didn't test other versions) the HASH UNIQUE operation suffers from an even more dramatic problem: The cardinality estimate is not considered and the initial workarea size is always based on minimum assumptions.
In passing, according to the SMM trace files that can be activated using the undocumented "_smm_trace" parameter it looks like that many of the sort-based workareas like SORT ORDER BY or WINDOW SORT seem to suffer from the same defect, since they however are able to dynamically resize and make use of the workarea statistics feedback they effectively work as expected, they just start with a too low workarea size estimate every time they are executed for the first time after re-optimization / invalidation.
The combination of the two issues - inability to dynamically resize and ignoring the optimizer estimates - leads to a dire result: Every first execution of a HASH UNIQUE operation in those versions will only use the minimum amount of memory. The following test case shows the problem:
show parameter pga
show parameter processes
-- alter session set workarea_size_policy = manual sort_area_size = 40000000;
drop table t1 purge;
drop table t2 purge;
create table t1
as
select mod(rownum, 27) as user_id, rownum as object_id from dual connect by level <= 30000;
create table t2
as
select distinct user_id from t1;
exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
column low_val new_value low_value
column high_val new_value high_value
select
max(user_id) + 1 as low_val
, max(user_id) + max(user_id) - min(user_id) + 1 as high_val
from
t2;
-- Invalidate any cursors using T1
comment on table t1 is '';
alter session set statistics_level = all;
alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
alter session set "_smm_trace" = 65535;
select /*+ full(@inner t1)
full(@inner t2)
full(@inner t3)
leading(@inner t2 t3 t1)
use_nl(@inner t3)
use_nl(@inner t1)
use_hash_aggregation(@inner)
*/
max(user_id)
from
(
select /*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id
from t1, t2, t2 t3
where t1.user_id = t2.user_id
)
;
alter session set "_smm_trace" = 0;
set pagesize 0 linesize 200 trimspool on tab off
select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
set pagesize 14
-- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
declare
srec dbms_stats.statrec;
novals dbms_stats.numarray;
distcnt number;
avgclen number;
nullcnt number;
density number;
srec2 dbms_stats.statrec;
begin
dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
srec2.epc := 2;
novals := dbms_stats.numarray(
&low_value,
&high_value
);
srec2.bkvals := null;
dbms_stats.prepare_column_values(srec2,novals);
dbms_stats.set_column_stats(
ownname=>null,
tabname=>'t1',
colname=>'user_id',
distcnt=>distcnt,
nullcnt=>nullcnt,
srec=>srec2,
avgclen=>avgclen,
density=>density
);
end;
/
-- Invalidate any cursors using T1
comment on table t1 is '';
alter session set statistics_level = all;
alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
alter session set "_smm_trace" = 65535;
select /*+ full(@inner t1)
full(@inner t2)
full(@inner t3)
leading(@inner t2 t3 t1)
use_nl(@inner t3)
use_nl(@inner t1)
use_hash_aggregation(@inner)
*/
max(user_id)
from
(
select /*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id
from t1, t2, t2 t3
where t1.user_id = t2.user_id
)
;
alter session set "_smm_trace" = 0;
set pagesize 0 linesize 200 trimspool on tab off
select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
Obviously it is very similar to the HASH GROUP BY test case, this time however I've used a DISTINCT clause and replaced the HASH JOIN with a NESTED LOOP which makes the generated trace files easier to read, since there is exactly one workarea involved in this execution.
And this is what I get from 11.1.0.7, this time using AMM, and therefore the PGA_AGGREGAT_TARGET has been set to 0 (You'll get the same result from 10.2.0.4 with a corresponding P_A_T setting):
SQL>
SQL> show parameter pga
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
pga_aggregate_target big integer 0
SQL> show parameter processes
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
aq_tm_processes integer 0
db_writer_processes integer 1
gcs_server_processes integer 0
global_txn_processes integer 1
job_queue_processes integer 1000
log_archive_max_processes integer 4
processes integer 150
SQL>
SQL> -- alter session set workarea_size_policy = manual sort_area_size = 40000000;
SQL>
SQL> drop table t1 purge;
Table dropped.
Elapsed: 00:00:00.01
SQL>
SQL> drop table t2 purge;
Table dropped.
Elapsed: 00:00:00.00
SQL>
SQL> create table t1
2 as
3 select mod(rownum, 27) as user_id, rownum as object_id from dual connect by level <= 30000;
Table created.
Elapsed: 00:00:00.04
SQL>
SQL> create table t2
2 as
3 select distinct user_id from t1;
Table created.
Elapsed: 00:00:00.04
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.06
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.01
SQL>
SQL> column low_val new_value low_value
SQL> column high_val new_value high_value
SQL>
SQL> select
2 max(user_id) + 1 as low_val
3 , max(user_id) + max(user_id) - min(user_id) + 1 as high_val
4 from
5 t2;
LOW_VAL HIGH_VAL
---------- ----------
27 53
Elapsed: 00:00:00.00
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_nl(@inner t1)
7 use_hash_aggregation(@inner)
8 */
9 max(user_id)
10 from
11 (
12 select /*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id
13 from t1, t2, t2 t3
14 where t1.user_id = t2.user_id
15 )
16 ;
MAX(USER_ID)
------------
26
Elapsed: 00:00:01.79
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 7cnqz02uwcs1a, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2)
full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_nl(@inner t1)
use_hash_aggregation(@inner) */ max(user_id) from ( select /*+
qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id from t1,
t2, t2 t3 where t1.user_id = t2.user_id )
Plan hash value: 1541846686
------------------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | Used-Mem | Used-Tmp|
------------------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:01.78 | 40179 | 1470 | 1470 | | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:01.78 | 40179 | 1470 | 1470 | | | | |
| 2 | VIEW | | 1 | 572K| 810K|00:00:00.97 | 40179 | 1470 | 1470 | | | | |
| 3 | HASH UNIQUE | | 1 | 572K| 810K|00:00:00.97 | 40179 | 1470 | 1470 | 25M| 3296K| 6029K (1)| 13312 |
| 4 | NESTED LOOPS | | 1 | 810K| 810K|00:00:00.01 | 40179 | 0 | 0 | | | | |
| 5 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | 0 | 0 | | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | 0 | 0 | | | | |
| 7 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | 0 | 0 | | | | |
|* 8 | TABLE ACCESS FULL | T1 | 729 | 1111 | 810K|00:00:00.03 | 40095 | 0 | 0 | | | | |
------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
8 - filter("T1"."USER_ID"="T2"."USER_ID")
30 rows selected.
Elapsed: 00:00:00.03
SQL>
SQL> set pagesize 14
SQL>
SQL> -- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
SQL> declare
2 srec dbms_stats.statrec;
3 novals dbms_stats.numarray;
4 distcnt number;
5 avgclen number;
6 nullcnt number;
7 density number;
8 srec2 dbms_stats.statrec;
9 begin
10 dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
11 srec2.epc := 2;
12 novals := dbms_stats.numarray(
13 &low_value,
14 &high_value
15 );
16 srec2.bkvals := null;
17 dbms_stats.prepare_column_values(srec2,novals);
18 dbms_stats.set_column_stats(
19 ownname=>null,
20 tabname=>'t1',
21 colname=>'user_id',
22 distcnt=>distcnt,
23 nullcnt=>nullcnt,
24 srec=>srec2,
25 avgclen=>avgclen,
26 density=>density
27 );
28 end;
29 /
old 13: &low_value,
new 13: 27,
old 14: &high_value
new 14: 53
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.03
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
Session altered.
Elapsed: 00:00:00.01
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_nl(@inner t1)
7 use_hash_aggregation(@inner)
8 */
9 max(user_id)
10 from
11 (
12 select /*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id
13 from t1, t2, t2 t3
14 where t1.user_id = t2.user_id
15 )
16 ;
MAX(USER_ID)
------------
26
Elapsed: 00:00:01.26
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID 7cnqz02uwcs1a, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2)
full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_nl(@inner t1)
use_hash_aggregation(@inner) */ max(user_id) from ( select /*+
qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id from t1,
t2, t2 t3 where t1.user_id = t2.user_id )
Plan hash value: 1541846686
------------------------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | Used-Mem | Used-Tmp|
------------------------------------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:01.26 | 40179 | 1470 | 1470 | | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:01.26 | 40179 | 1470 | 1470 | | | | |
| 2 | VIEW | | 1 | 1 | 810K|00:00:00.98 | 40179 | 1470 | 1470 | | | | |
| 3 | HASH UNIQUE | | 1 | 1 | 810K|00:00:00.98 | 40179 | 1470 | 1470 | 25M| 3296K| 6093K (1)| 13312 |
| 4 | NESTED LOOPS | | 1 | 1 | 810K|00:00:00.01 | 40179 | 0 | 0 | | | | |
| 5 | NESTED LOOPS | | 1 | 729 | 729 |00:00:00.01 | 84 | 0 | 0 | | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 27 | 27 |00:00:00.01 | 3 | 0 | 0 | | | | |
| 7 | TABLE ACCESS FULL| T2 | 27 | 27 | 729 |00:00:00.01 | 81 | 0 | 0 | | | | |
|* 8 | TABLE ACCESS FULL | T1 | 729 | 1 | 810K|00:00:00.01 | 40095 | 0 | 0 | | | | |
------------------------------------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
8 - filter("T1"."USER_ID"="T2"."USER_ID")
30 rows selected.
Elapsed: 00:00:00.03
SQL>
As you can see no matter what the estimates looks like, on this system the first execution of a HASH UNIQUE will not get more than 6M - subsequent executions of the same cursor benefit from the statistics from the previous run as seen before.
Again, switching to a SORT UNIQUE and reducing the data set accordingly, the problem can not be reproduced:
SQL>
SQL> show parameter pga
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
pga_aggregate_target big integer 0
SQL> show parameter processes
NAME TYPE VALUE
------------------------------------ ----------- ------------------------------
aq_tm_processes integer 0
db_writer_processes integer 1
gcs_server_processes integer 0
global_txn_processes integer 1
job_queue_processes integer 1000
log_archive_max_processes integer 4
processes integer 150
SQL>
SQL> drop table t1 purge;
Table dropped.
Elapsed: 00:00:00.15
SQL>
SQL> drop table t2 purge;
Table dropped.
Elapsed: 00:00:00.07
SQL>
SQL> create table t1
2 as
3 select mod(rownum, 15) as user_id, rownum as object_id from dual connect by level <= 30000;
Table created.
Elapsed: 00:00:00.06
SQL>
SQL> create table t2
2 as
3 select distinct user_id from t1;
Table created.
Elapsed: 00:00:00.04
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't1', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.04
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't2', method_opt => 'for all columns size 1')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.03
SQL>
SQL> column low_val new_value low_value
SQL> column high_val new_value high_value
SQL>
SQL> select
2 max(user_id) + 1 as low_val
3 , max(user_id) + max(user_id) - min(user_id) + 1 as high_val
4 from
5 t2;
LOW_VAL HIGH_VAL
---------- ----------
15 29
Elapsed: 00:00:00.00
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_correct_cardinality';
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_nl(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 no_use_hash_aggregation(@inner)
9 */
10 max(user_id)
11 from
12 (
13 select /*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 )
17 ;
MAX(USER_ID)
------------
14
Elapsed: 00:00:00.73
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.01
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID ckb2sbz3y2z14, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2)
full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_nl(@inner t1)
no_swap_join_inputs(@inner t1)
no_use_hash_aggregation(@inner) */ max(user_id) from ( select
/*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id from
t1, t2, t2 t3 where t1.user_id = t2.user_id )
Plan hash value: 3828303002
--------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.71 | 12423 | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.71 | 12423 | | | |
| 2 | VIEW | | 1 | 318K| 450K|00:00:00.62 | 12423 | | | |
| 3 | SORT UNIQUE | | 1 | 318K| 450K|00:00:00.62 | 12423 | 22M| 1744K| 20M (0)|
| 4 | NESTED LOOPS | | 1 | 450K| 450K|00:00:00.01 | 12423 | | | |
| 5 | NESTED LOOPS | | 1 | 225 | 225 |00:00:00.01 | 48 | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 15 | 15 |00:00:00.01 | 3 | | | |
| 7 | TABLE ACCESS FULL| T2 | 15 | 15 | 225 |00:00:00.01 | 45 | | | |
|* 8 | TABLE ACCESS FULL | T1 | 225 | 2000 | 450K|00:00:00.01 | 12375 | | | |
--------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
8 - filter("T1"."USER_ID"="T2"."USER_ID")
31 rows selected.
Elapsed: 00:00:00.06
SQL>
SQL> set pagesize 14
SQL>
SQL> -- Fudge the statistics so that the join cardinality between t1 and t2 will be 0 (rounded up to 1)
SQL> declare
2 srec dbms_stats.statrec;
3 novals dbms_stats.numarray;
4 distcnt number;
5 avgclen number;
6 nullcnt number;
7 density number;
8 srec2 dbms_stats.statrec;
9 begin
10 dbms_stats.get_column_stats(null, 't1', 'user_id', distcnt => distcnt, avgclen => avgclen, nullcnt => nullcnt, density => density, srec => srec);
11 srec2.epc := 2;
12 novals := dbms_stats.numarray(
13 &low_value,
14 &high_value
15 );
16 srec2.bkvals := null;
17 dbms_stats.prepare_column_values(srec2,novals);
18 dbms_stats.set_column_stats(
19 ownname=>null,
20 tabname=>'t1',
21 colname=>'user_id',
22 distcnt=>distcnt,
23 nullcnt=>nullcnt,
24 srec=>srec2,
25 avgclen=>avgclen,
26 density=>density
27 );
28 end;
29 /
old 13: &low_value,
new 13: 15,
old 14: &high_value
new 14: 29
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.06
SQL>
SQL> -- Invalidate any cursors using T1
SQL> comment on table t1 is '';
Comment created.
Elapsed: 00:00:00.01
SQL>
SQL> alter session set statistics_level = all;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> alter session set tracefile_identifier = 'smm_trace_wrong_cardinality';
Session altered.
Elapsed: 00:00:00.01
SQL>
SQL> alter session set "_smm_trace" = 65535;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> select /*+ full(@inner t1)
2 full(@inner t2)
3 full(@inner t3)
4 leading(@inner t2 t3 t1)
5 use_nl(@inner t3)
6 use_nl(@inner t1)
7 no_swap_join_inputs(@inner t1)
8 no_use_hash_aggregation(@inner)
9 */
10 max(user_id)
11 from
12 (
13 select /*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id
14 from t1, t2, t2 t3
15 where t1.user_id = t2.user_id
16 )
17 ;
MAX(USER_ID)
------------
14
Elapsed: 00:00:00.74
SQL>
SQL> alter session set "_smm_trace" = 0;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> set pagesize 0 linesize 200 trimspool on tab off
SQL>
SQL> select * from table(dbms_xplan.display_cursor(null, null, 'ALLSTATS LAST'));
SQL_ID ckb2sbz3y2z14, child number 0
-------------------------------------
select /*+ full(@inner t1) full(@inner t2)
full(@inner t3) leading(@inner t2 t3 t1)
use_nl(@inner t3) use_nl(@inner t1)
no_swap_join_inputs(@inner t1)
no_use_hash_aggregation(@inner) */ max(user_id) from ( select
/*+ qb_name(inner) no_merge */ distinct t1.object_id, t3.user_id from
t1, t2, t2 t3 where t1.user_id = t2.user_id )
Plan hash value: 3828303002
--------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem |
--------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | | 1 |00:00:00.73 | 12423 | | | |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |00:00:00.73 | 12423 | | | |
| 2 | VIEW | | 1 | 1 | 450K|00:00:00.61 | 12423 | | | |
| 3 | SORT UNIQUE | | 1 | 1 | 450K|00:00:00.61 | 12423 | 22M| 1744K| 20M (0)|
| 4 | NESTED LOOPS | | 1 | 1 | 450K|00:00:00.01 | 12423 | | | |
| 5 | NESTED LOOPS | | 1 | 225 | 225 |00:00:00.01 | 48 | | | |
| 6 | TABLE ACCESS FULL| T2 | 1 | 15 | 15 |00:00:00.01 | 3 | | | |
| 7 | TABLE ACCESS FULL| T2 | 15 | 15 | 225 |00:00:00.01 | 45 | | | |
|* 8 | TABLE ACCESS FULL | T1 | 225 | 1 | 450K|00:00:00.01 | 12375 | | | |
--------------------------------------------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
8 - filter("T1"."USER_ID"="T2"."USER_ID")
31 rows selected.
Elapsed: 00:00:00.01
SQL>
SQL> set doc off
SQL> doc
SQL>
In 10.2.0.5 and 11.2.0.1/11.2.0.2 this issue is apparently fixed and the cardinality estimates are used for an initial workarea size calculation - however this doesn't mean that the dynamic resize problem is fixed - it simply means that the HASH UNIQUE in those versions behaves exactly as the HASH GROUP BY and the memory usage for the first execution depends on the cardinality estimates.
Summary
If your database uses automatic PGA management then for the hash aggregation HASH GROUP BY / HASH UNIQUE operations every initial execution that is based on bad cardinality estimates potentially has a problem because it might not make efficient use of the available memory.
The same applies to applications that share cursors, however in that case only the initial execution after re-optimization / invalidation is affected, subsequent executions benefit from the workarea statistics feedback mechanism.
Furthermore in 10g versions below 10.2.0.5 and 11g versions below 11.2.0.1 the initial execution of a HASH UNIQUE operation ignores the cardinality estimates and will always be based on minimum assumptions.
So you might want to carefully check the runtime execution statistics of your critical hash aggregations.
Possible Workarounds
Different strategies are available as workarounds, depending on the situation:
- Upgrading to versions where the HASH UNIQUE operation at least considers the optimizer estimates might be beneficial
- Obviously good cardinality estimates are crucial, in that case and when using the correct Oracle versions you should be fine. Using the undocumented OPT_ESTIMATE hint (or the deprecated undocumented CARDINALITY hint) might help in cases where other options like manually crafted statistics are not able to help the optimizer to come up with reasonable cardinality estimates.
- Applications that are able to share cursors might not be too much affected due to the ability to use the workarea executions for subsequent executions of the same cursor
- The described problems disappear when switching to manual workarea size policy and allowing for sufficient memory of workarea. Interestingly the less obvious SORT_AREA_SIZE is used for the manual control of the HASH GROUP BY operation, and not the HASH_AREA_SIZE. Of course using manual PGA memory management system-wide is highly unrecommended, so this might only be a workaround in certain cases of large batch jobs where manual workarea sizes are used anyway. Also note, when switching to manual workareas be aware of a nasty bug that was introduced in the 10.2.0.3 patchset. For more information see MOS note "6053134.8: ALTER SESSION to set SORT_AREA_SIZE no honoured" and for example mine and Jonathen Lewis' post about the issue. According to the MOS note the bug has been fixed in 10.2.0.4.3, 10.2.0.5 and 11.1.0.7 / 11.2
- Obviously the hash aggregation can be avoided on statement level using the NO_USE_HASH_AGGREGATION hint or on session / system level using the _GBY_HASH_AGGREGATION_ENABLED parameter. Since the sort based aggregation can be less efficient (but note that it doesn't have to be, it depends on the individual grouping and data pattern) again this doesn't necessarily solve the problem since aggregate workareas might spill to disk which wouldn't be necessary when hash aggregation was used and worked as expected.
Final Note
Of course there are a lot of things that I haven't touched yet or only briefly, like Parallel Execution of the hash aggregations, workareas used for index sorts, and all the other details when the workarea spills to disk.
If you want to get an idea what kind of nasty things can happen in that case you might want to read Jonathan Lewis' post on analytic functions.
I also haven't tried yet to fiddle around with the undocumented _smm* related parameters if setting them to non-default values allows to work around the issue.
And of course then there are bugs like this one: Bug 6817844 - Multi pass sort with auto memory management even with plenty of PGA [ID 6817844.8], which is also mentioned in Jonathan's post.
As a final note, the odd TEMP tablespace I/O pattern issue that Jonathan describes in his post (Bug 9041800) is marked as fixed in the 11.2.0.2 patch set, but I haven't tested this yet.
This is just a short note that some of the new features added in 11.2 have some subtle restrictions that are not documented properly.
1. Hybrid Columnar Compression and Function-Based Indexes
The Hybrid Columnar Compression (HCC) introduced in 11.2 that is only enabled in tablespaces residing on Exadata Storage in 11.2.0.1 has the subtle restriction that the mere presence of a function-based index either explicitly created or indirectly via an index on a virtual column disables HCC completely on that segment with direct path / parallel DML inserts. It silently falls back to no compression at all, rather than using for example basic or advanced oltp compression instead.
This can be easily reproduced using the following simple test case:
spool hcc_function_based_indexes_testcase.log
drop table t_hcc_fbi purge;
-- Create table with HCC enabled
create table t_hcc_fbi compress for query low as select * from dba_objects where 1 = 2;
truncate table t_hcc_fbi;
-- Direct path inserts support HCC by default
insert /*+ append */ into t_hcc_fbi select * from dba_objects;
commit;
-- This is supposed to return DBMS_COMPRESSION.COMP_FOR_QUERY_LOW = 8
select distinct dbms_compression.get_compression_type(USER, 'T_HCC_FBI', rowid) from t_hcc_fbi;
-- So it is supported to have an FBI on HCC data
create index t_hcc_fbi_idx on t_hcc_fbi (lower(object_type));
truncate table t_hcc_fbi;
-- Let's make the FBI unusable, so it doesn't have to be maintained by the load
alter index t_hcc_fbi_idx unusable;
insert /*+ append */ into t_hcc_fbi select * from dba_objects;
commit;
-- But this will return DBMS_COMPRESSION.COMP_NOCOMPRESS = 1
select distinct dbms_compression.get_compression_type(USER, 'T_HCC_FBI', rowid) from t_hcc_fbi;
-- So the mere existence of a FBI, no matter if usable or unusable will prevent HCC with direct-path inserts / parallel DML
-- It is however fully supported to have them on HCC data, but you need to drop / re-create them in order to load the data
-- via direct-path inserts / parallel DML with HCC enabled
spool off
And this is the output I get from 11.2.0.1:
Table dropped.
Elapsed: 00:00:00.13
SQL>
SQL> -- Create table with HCC enabled
SQL> create table t_hcc_fbi compress for query low as select * from dba_objects where 1 = 2;
Table created.
Elapsed: 00:00:00.09
SQL>
SQL> truncate table t_hcc_fbi;
Table truncated.
Elapsed: 00:00:00.01
SQL>
SQL> -- Direct path inserts support HCC by default
SQL> insert /*+ append */ into t_hcc_fbi select * from dba_objects;
72545 rows created.
Elapsed: 00:00:01.24
SQL>
SQL> commit;
Commit complete.
Elapsed: 00:00:00.00
SQL>
SQL> -- This is supposed to return DBMS_COMPRESSION.COMP_FOR_QUERY_LOW = 8
SQL> select distinct dbms_compression.get_compression_type(USER, 'T_HCC_FBI', rowid) from t_hcc_fbi;
DBMS_COMPRESSION.GET_COMPRESSION_TYPE(USER,'T_HCC_FBI',ROWID)
-------------------------------------------------------------
8
Elapsed: 00:00:09.51
SQL>
SQL> -- So it is supported to have an FBI on HCC data
SQL> create index t_hcc_fbi_idx on t_hcc_fbi (lower(object_type));
Index created.
Elapsed: 00:00:00.23
SQL>
SQL> truncate table t_hcc_fbi;
Table truncated.
Elapsed: 00:00:00.03
SQL>
SQL> -- Let's make the FBI unusable, so it doesn't have to be maintained by the load
SQL> alter index t_hcc_fbi_idx unusable;
Index altered.
Elapsed: 00:00:00.02
SQL>
SQL> insert /*+ append */ into t_hcc_fbi select * from dba_objects;
72546 rows created.
Elapsed: 00:00:01.01
SQL>
SQL> commit;
Commit complete.
Elapsed: 00:00:00.01
SQL>
SQL> -- But this will return DBMS_COMPRESSION.COMP_NOCOMPRESS = 1
SQL> select distinct dbms_compression.get_compression_type(USER, 'T_HCC_FBI', rowid) from t_hcc_fbi;
DBMS_COMPRESSION.GET_COMPRESSION_TYPE(USER,'T_HCC_FBI',ROWID)
-------------------------------------------------------------
1
Elapsed: 00:00:08.93
SQL>
I've been told that this restriction is going to be lifted in some future version, but I haven't had the chance yet to test this with the 11.2.0.2 patch set, in particular because the patch set is not yet released for existing Exadata installations as upgrade - it is only available for new Exadata deployments.
2. Deferred Segment Creation and Parallel DML
11.2 introduced the deferred segment creation that interestingly is enabled by default when doing a fresh installation. I haven't done a migration from previous versions to 11.2 yet so I don't know if this is also enabled in migrated environments but I assume so.
Note that the 11.2.0.2 patch set extends the deferred segment creation to partitioned objects which wasn't supported in the initial 11.2.0.1 release.
The deferred segment creation has some subtle side effects, for example sequences seem not to start with the defined START WITH value because the recursive transaction fails initially, the segment gets created and the next sequence value will be used for the actual DML insert operation.
Another side effect that is not properly documented is the fact that parallel DML is not supported on "segment-less" objects.
So if you have a freshly created segment that is supposed to be populated via parallel DML inserts then this will silently fall back to serial direct-path inserts for the first time executed.
Once the segment has been created from the next time on parallel DML is going to be used which might help to confuse the issue since it is only reproducible under particular circumstances.
So if you have migrated an application to 11.2 and wonder why sometimes load operations take significantly longer than before but mostly do not then this might be a viable explanation.
This behaviour is not part of the deferred segment creation restrictions described in the official documentation, but it is mentioned towards the end of the MOS note "11.2 Database New Feature Deferred Segment Creation [ID 887962.1]"
According to the mentioned MOS note this still seems to apply to 11.2.0.2, but I haven't tested this yet myself. So it looks like this significant restriction is not yet lifted with the current patch set.
Update July 2011: See comment below by Radoslav, the problem seems to be fixed in 11.2.0.2.
Here is a simple test case demonstrating the issue:
set linesize 160 trimspool on tab off pagesize 0
spool deferred_segment_creation_parallel_dml_testcase.log
drop table t_source purge;
drop table t_deferred_parallel_dml purge;
-- Create a simple table as source for the insert
create table t_source parallel nologging
as
select * from dba_objects;
exec dbms_stats.gather_table_stats(null, 't_source')
-- No AUTO DOP for this session to prevent any side-effects of this feature
alter session set parallel_degree_policy = manual;
-- Note that even a CTAS operation can be segment-less
-- if the query doesn't return any rows
create table t_deferred_parallel_dml
segment creation deferred
as
select * from t_source where 1 = 2;
-- No segment although CTAS has been used
select count(*) from user_segments where segment_name = 'T_DEFERRED_PARALLEL_DML';
alter session enable parallel dml;
explain plan for
insert /*+ append parallel(t) */ into t_deferred_parallel_dml t select * from t_source;
-- This plan does not show parallel DML
select * from table(dbms_xplan.display);
drop table t_deferred_parallel_dml purge;
create table t_deferred_parallel_dml
segment creation immediate
as
select * from t_source where 1 = 2;
-- One segment
select count(*) from user_segments where segment_name = 'T_DEFERRED_PARALLEL_DML';
explain plan for
insert /*+ append parallel(t) */ into t_deferred_parallel_dml t select * from t_source;
-- This plan does show parallel DML
select * from table(dbms_xplan.display);
spool off
And this is the output I get from 11.2.0.1:
Table dropped.
Elapsed: 00:00:00.04
SQL>
SQL> drop table t_deferred_parallel_dml purge;
Table dropped.
Elapsed: 00:00:00.01
SQL>
SQL> -- Create a simple table as source for the insert
SQL> create table t_source parallel nologging
2 as
3 select * from dba_objects;
Table created.
Elapsed: 00:00:03.32
SQL>
SQL> exec dbms_stats.gather_table_stats(null, 't_source')
PL/SQL procedure successfully completed.
Elapsed: 00:00:00.64
SQL>
SQL> -- No AUTO DOP for this session to prevent any side-effects of this feature
SQL> alter session set parallel_degree_policy = manual;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> -- Note that even a CTAS operation can be segment-less
SQL> -- if the query doesn't return any rows
SQL> create table t_deferred_parallel_dml
2 segment creation deferred
3 as
4 select * from t_source where 1 = 2;
Table created.
Elapsed: 00:00:00.01
SQL>
SQL> -- No segment although CTAS has been used
SQL> select count(*) from user_segments where segment_name = 'T_DEFERRED_PARALLEL_DML';
0
Elapsed: 00:00:00.00
SQL>
SQL> alter session enable parallel dml;
Session altered.
Elapsed: 00:00:00.00
SQL>
SQL> explain plan for
2 insert /*+ append parallel(t) */ into t_deferred_parallel_dml t select * from t_source;
Explained.
Elapsed: 00:00:00.01
SQL>
SQL> -- This plan does not show parallel DML
SQL> select * from table(dbms_xplan.display);
Plan hash value: 1453990251
------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | TQ |IN-OUT| PQ Distrib |
------------------------------------------------------------------------------------------------------------------------------
| 0 | INSERT STATEMENT | | 73251 | 6938K| 117 (1)| 00:00:02 | | | |
| 1 | LOAD AS SELECT | T_DEFERRED_PARALLEL_DML | | | | | | | |
| 2 | PX COORDINATOR | | | | | | | | |
| 3 | PX SEND QC (RANDOM)| :TQ10000 | 73251 | 6938K| 117 (1)| 00:00:02 | Q1,00 | P->S | QC (RAND) |
| 4 | PX BLOCK ITERATOR | | 73251 | 6938K| 117 (1)| 00:00:02 | Q1,00 | PCWC | |
| 5 | TABLE ACCESS FULL| T_SOURCE | 73251 | 6938K| 117 (1)| 00:00:02 | Q1,00 | PCWP | |
------------------------------------------------------------------------------------------------------------------------------
12 rows selected.
Elapsed: 00:00:00.06
SQL>
SQL> drop table t_deferred_parallel_dml purge;
Table dropped.
Elapsed: 00:00:00.03
SQL>
SQL> create table t_deferred_parallel_dml
2 segment creation immediate
3 as
4 select * from t_source where 1 = 2;
Table created.
Elapsed: 00:00:00.01
SQL>
SQL> -- One segment
SQL> select count(*) from user_segments where segment_name = 'T_DEFERRED_PARALLEL_DML';
1
Elapsed: 00:00:00.01
SQL>
SQL> explain plan for
2 insert /*+ append parallel(t) */ into t_deferred_parallel_dml t select * from t_source;
Explained.
Elapsed: 00:00:00.01
SQL>
SQL> -- This plan does show parallel DML
SQL> select * from table(dbms_xplan.display);
Plan hash value: 4111574378
------------------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | TQ |IN-OUT| PQ Distrib |
------------------------------------------------------------------------------------------------------------------------------
| 0 | INSERT STATEMENT | | 73251 | 6938K| 117 (1)| 00:00:02 | | | |
| 1 | PX COORDINATOR | | | | | | | | |
| 2 | PX SEND QC (RANDOM) | :TQ10000 | 73251 | 6938K| 117 (1)| 00:00:02 | Q1,00 | P->S | QC (RAND) |
| 3 | LOAD AS SELECT | T_DEFERRED_PARALLEL_DML | | | | | Q1,00 | PCWP | |
| 4 | PX BLOCK ITERATOR | | 73251 | 6938K| 117 (1)| 00:00:02 | Q1,00 | PCWC | |
| 5 | TABLE ACCESS FULL| T_SOURCE | 73251 | 6938K| 117 (1)| 00:00:02 | Q1,00 | PCWP | |
------------------------------------------------------------------------------------------------------------------------------
12 rows selected.
Elapsed: 00:00:00.06
SQL>
SQL> spool off
In general I would advise to disable deferred segment creation and enable it only in environments where a clear benefit can be seen and the side effects are known, understood and tested.
This can be accomplished by simply setting "deferred_segment_creation = false" on instance level.
If you have the scenario that a large database that contains historical data - typically a data warehouse with partitioned tables where the partitions reside in different tablespaces depending on their age - is supposed to be duplicated but the target environment, typically a lower environment, like duplicating Production to a UAT environment, doesn't have sufficient space to hold the complete database and in particular the whole historic partitions, then you somehow need to deal with that partial clone.
Of course, in an ideal world the lower environment is supposed to have sufficient space to hold the complete database, but we don't live in an ideal world. Sometimes, depending on the storage technology, you might be able to do some kind of temporary "split mirror" operation that allows you to start up a complete clone of the database and drop all the affected objects / tablespaces before putting the cleaned up / partial database on the final target system, but this is not always applicable, for instance when using ASM-based storage.
The issue in that particular case is that you can do for instance a partial RMAN clone that omits the historical tablespaces, but the resulting database then obviously has some tablespaces that are unavailable and can't be onlined since the datafiles are simply missing.
In case of objects that are completely residing in the offlined tablespaces the solution is simple and straightforward: The objects can be dropped or the affected tablespaces can simply be dropped with the "including contents" clause.
Things get more complicated however in case of partitioned objects if some of the partitions reside in the offlined tablespaces.
In that case you can't simply drop the offlines tablespaces - you end up with Oracle error message "ORA-14404: partitioned table contains partitions in a different tablespace" for example that tells you that objects exist that have some of their partitions not residing in the offlined tablespace and Oracle obviously won't do the cleanup job for you in that case.
Oracle allows to drop partitions of a table that reside in offlined tablespaces, but there is an important limitation: You can only do so if no index has been defined on the table. Otherwise you get the error message "ORA-14117/ORA-14630: [sub]partition resides in offlined tablespace" when attempting to drop the partition. It's interesting to note that the mere existence of indexes is sufficient - it doesn't matter if the index is residing in an offlined or onlined tablespace, if it is unusable or not - you just need to have at least one index defined on the table, and the drop partition operation errors out. It looks like a hard coded "if index exists then error" code path. I'm not sure why exactly this restriction is in place, but it has serious consequences if you need to do this sort of cleanup for a large database with hundreds of thousands of partitions.
By the way it is interesting to know that Oracle allows to drop segments from offlined tablespaces that are locally managed - think about the consequence: The local bitmaps that represent the allocated and free space in the tablespace can not be updated at the time of the drop operation. This is one of the few disadvantages compared to the old-fashioned dictionary managed tablespaces where an operation like that potentially could have been limited to a pure dictionary operation.
The approach Oracle chooses is straightforward: The segments dropped are converted into "temporary" objects (kind of a special "recyclebin" if you want to say so) and if the offlined tablespace should get onlined again the local bitmaps will be updated according to these temporary objects and finally the temporary objects will be removed from the dictionary. Otherwise if the offlined tablespace gets dropped the corresponding temporary objects consequently can also be removed.
Tanel Poder recently also blogged about this in the context of read-only tablespaces.
So you could now drop all indexes from the affected tables and recreate them afterwards, but this is not a good idea for several reasons:
* If the remaining partitions still represent multiple terabytes it will take quite long to rebuild all indexes
* Things might go wrong and you end up with missing indexes
Since these are partitioned objects, another Oracle best practice might come to rescue: Exchange the affected partitions with an exchange table excluding indexes - this allows to drop the exchanged partition residing in an offlined tablespace without any error message. Note that this means that after you've exchanged the partition you need to drop the unpartitioned object (that doesn't have indexes) in order to get rid of the affected segment.
Now if you only have a few objects that are affected or only a single partition to clean up per table then you're basically done, but if you need to clean up multiple partitions and the number of partitions is high then this approach is not feasible, because it sounds like quite an overhead to drop / create a table for each partition that needs to be cleaned up - in particular when talking about hundred thousands of partitions.
The approach of creating a table to exchange partitions with adds another complexity to the problem: Many large databases make usage of the option in Oracle to set columns unused instead of actually dropping them, since the former is a simple meta data operation in the dictionary (renames the column and marks the column as hidden) and independent from the data volume and therefore almost instant but the latter requires Oracle to process the whole segment which can take quite some time in case of huge segments (and is not a very efficient operation by the way, but that is something for a different post).
Now creating a table that can be exchanged with such an evolved table that contains unused columns can not be accomplished by simply doing a handy CTAS operation - the unused columns will be missing from the copy and therefore the exchange operation will fail with "column mismatch" errors.
So you need to deal with these unused columns somehow in that moment you need to create a table dynamically, and you need to do that if you need to drop more than a single partition per table.
The only other viable option that I'm aware of is to maintain this exchange table as a partitioned table itself permanently - which means create two exact copies (in terms of dictionary meta data) of every potentially affected table and make sure to apply the same DDL in the same order which will ensure that the exchange operation with those tables will succeed.
It will need two copies, one unpartitioned and one partitioned, if you want to avoid the drop and dynamic create table operation, since Oracle allows to exchange partitions only with unpartitioned tables. So it would take two exchange operations per affected partition - the first with the unpartitioned table, and the second with the partitioned table. Both exchange operations would have to be done excluding indexes and at least the final partitioned table has to be created excluding indexes - note that Oracle allows the exchange operation even if one table is missing a potentially created primary key constraint if you use the "EXCLUDING INDEXES" clause.
Then you can drop the partition that has been exchanged into the second table since the table doesn't have any indexes defined on it.
This allows you to accomplish two objectives:
1. Avoid a costly (and potentially complex in case of unused columns) drop / create table operation per partition
2. Manage the drop / create cycle by simply dropping and adding partitions in the second table which is far less overhead compared to a drop / create table operation which is important when performance matters
However maintaining two copies for every table that potentially needs to be cleaned up sounds like a huge overhead, and even in Oracle 11.2 with the deferred segment creation at least the partitioned table copy will allocate space since the deferred segment creation option is not (yet) applicable to partitioned objects.
So here a few ideas how to accomplish such a cleanup operation:
1. For each table to process create the required table copies on the fly dynamically once. If you don't have to deal with unused columns then a simple CTAS operation can be used to create those tables. If you need to handle unused columns, a more complex approach is required. You will have to use custom queries against the data dictionary to extract the required information since the official DBMS_METADATA interface as far as I know doesn't expose information about unused columns.
The following query could be used as a starting point to extract the column definition of a table:
This will generate SYS$
Note that it hasn't been tested with virtual columns yet, but should deal with most of the available data types and also handle columns using char semantics correctly (important for NCHAR / NVARCHAR based columns and databases running with multi-byte characters, in particular AL32UTF8). It also can handle virtual columns added via function-based indexes.
Of course the extraction process potentially needs to handle much more than shown here, for instance check the table properties like index organized tables, partition keys etc.
2. Process each affected table partition using the following approach:
* Add a new "clean" partition to the partitioned copy table in a tablespace that is online. The partition key is a "dummy" key that can be the same for each iteration of this process. This is the segment that is going to end up in the affected partitioned table and will be finally dropped since it then resides in a available tablespace. Remember, we want to get rid of those partitions residing in unavailable tablespaces, since we have not sufficient space in the clone of the database to make them available)
* Exchange the affected partition with the unpartitioned copy table EXCLUDING INDEXES (The unpartitioned table copy has also been initially created in an available tablespace)
* Exchange the unpartitioned copy table (that now holds the defect partition) with the newly created "clean" partition of the partitioned copy table EXCLUDING INDEXES
* Now the defect partition resides in the partitioned copy table and can simply be dropped since we don't have any indexes defined on this table
* Finally we can drop now the partition from the affected table (that is now the partition that has been added to the partitioned copy table in the previous iteration of the process) since the partition resides now in an available tablespace
The same process can be applied to a subpartitioned table. There are of course some more details to consider, for example, you can't drop the last subpartition or partition from a partition / table, but since we assume that our affected table in general still has some partitions left over that reside in current and available tablespaces it should be possible to handle these scenarios.
Here is a code snippet / template that applies this process:
-- Step 1: Add a clean partition to the temporary cleanup table
s_sql := 'alter table ' || p_temp_exc_table_name || ' add partition p_cleanup values (42) tablespace ' || p_clean_tablespace_name;
execute(p_object.owner, s_sql);
-- Get the name of the unpartitioned table copy
s_exchange_table := get_exchange_table(p_object);
-- Step 2: Exchange the defect (sub-)partition with the unpartitioned copy table EXCLUDING indexes
s_sql := '
alter table ' || p_object.segment_name || '
exchange ' || s_partition_type || ' ' || p_partition_name || '
with table ' || s_exchange_table || '
excluding indexes without validation';
execute(p_object.owner, s_sql);
-- Step 3: Exchange the unpartitioned copy table with the partitioned copy table partition again EXCLUDING indexes
s_sql := '
alter table ' || p_temp_exc_table_name || '
exchange partition p_cleanup
with table ' || s_exchange_table || '
excluding indexes without validation';
execute(p_object.owner, s_sql);
-- Step 4: Drop the defect partition now residing in partitioned "waste-bin"table partition
-- Since we don't have any indexes this is possible now
s_sql := 'alter table ' || p_temp_exc_table_name || ' drop partition p_cleanup';
begin
execute(p_object.owner, s_sql);
exception
-- Any tables with LOBs will cause an exception since they will implicitly have a LOB INDEX added
-- Therefore the DROP PARTITION will fail with ORA-14117
-- The simplest solution is to drop and recreate the entire working table - note the serious side effects
-- of the underlying ORA-01110 error in 11.1.0.7. See below comments about the potential impact
-- of the health check monitoring
-- A smoother way of handling this to avoid these potential side-effects is to check if the
-- table has any indexes defined on it and then do not attempt to drop the partition but
-- immediately drop the entire table
when e_partition_in_offline_ts then
drop_temporary_exc_table(p_object, p_temp_exc_table_name);
p_temp_exc_table_name := create_temporary_exc_table(p_object, p_clean_tablespace_name, p_segment_type);
end;
-- Step 5: Drop the partition / subpartition which completes the cleanup
-- TODO: Are really all subpartitions of a partition affected?
-- If not, this logic needs to be revised
if (s_partition_type = 'SUBPARTITION' and p_last_partition_indicator = 'Y') then
s_sql := 'alter table ' || p_object.segment_name || ' drop partition ' || get_parent_partition(p_object, p_partition_name);
execute(p_object.owner, s_sql);
elsif s_partition_type = 'PARTITION' then
s_sql := 'alter table ' || p_object.segment_name || ' drop partition ' || p_partition_name;
execute(p_object.owner, s_sql);
end if;
end drop_partition;
Note that tables with LOBs need special treatment since the logic used for tables without LOBs fails with an error that the partition resides in an offline tablespace when the "defect" partition gets dropped from the temporary cleanup table.
This is the error that is typically raised when attempting to drop a partition from a table having indexes defined on it. Very likely this error is caused by the fact that a LOB always has a corresponding LOB INDEX (that will be always exchanged when doing an EXCHANGE PARTITION even if EXCLUDING INDEXES is specified).
Since this index cannot be dropped from the temporary cleanup table we actually do not gain anything by performing the exchange exercise.
The simplest solution to the problem is to drop and recreate the cleanup table which is possible without the ORA-14117 error message being raised.
Note that attempting to drop the partition raising the ORA-14117 error has a serious side effect in 11.1.0.7 and 11.2.0.1: If the "Health Monitoring" detects a "flooding" ORA-1110 error (something is wrong with a datafile) any operation on these datafiles from then on seem to have tracing turned on - this leads to the odd situation that onlining such a tablespace again (which we can't do in our actual case since the datafiles are not there, but I've done for testing purposes) takes minutes and generates trace files in size between 20 and 500 MB (!).
I haven't investigated much into this, but in my tests this couldn't be avoided by setting the undocumented parameter "_disable_health_checks=TRUE".
The MMON_SLAVE process will dump something similar to this:
Also for the sessions that cause the error something like this will be written to trace files:
A smoother way of handling this to avoid these potential side-effects is to check if the table has any indexes defined on it and then do not attempt to drop the partition but immediately drop the entire table.
Since each iteration of this process takes some time (in a database with hundreds of thousands of partitions this can take a few seconds per partition due to the underlying recursive data dictionary operations) and is according to some traces mostly CPU bound you can speed up the processing by using multiple processes it if you have multiple CPUs available.
Using an approach with Advanced Queueing having multiple consumers (for instance as many consumers as CPUs) we were able to cleanup more than 200,000 partitions (table plus index (sub)partitions) in less than one hour which was within the available timeframe for that operation.
Recent comments
17 weeks 21 hours ago
26 weeks 6 days ago
28 weeks 4 days ago
31 weeks 5 days ago
34 weeks 9 hours ago
43 weeks 4 days ago
45 weeks 23 hours ago
46 weeks 1 day ago
46 weeks 2 days ago
49 weeks 3 hours ago