Wednesday May 25, 2011

Parallel Load: Uniform or AutoAllocate extents?

Over the last couple of years there has been a lot of debate about space management in Data Warehousing environments and the benefits of having fewer larger extents. Many believe the easiest way to achieve this is to use uniform extents but the obvious benefits can often be out weighed by some not so obvious drawbacks.

For performance reasons most loads leverage direct path operations where the load process directly formats and writes Oracle blocks to disk instead of going through the buffer cache. This means that the loading process allocates extents and fills them with data during the load. During parallel loads, each loader process will allocate it own extent and  no two processes work on the same extent. When loading data into a table with UNIFORM extents each loader process will allocate its own Uniform extent and begin loading the data, if the extents  are not fully populated the table is left with a lot of partially filled extents, effectively creating ‘HOLES’ in the table. Not only is this space wastage but it also impacts query performance as subsequent queries that scan the table have to scan all of the extents even if they are not fully filled.

What is different with AUTOALLOCATE? AUTOALLOCATE will dynamically adjust the size of an extent and trim the extent after the load in case it is not fully loaded (Extent Trimming)


Tom Kyte covers this problem in great details in his post Loading and Extents but below is a simple example just to illustrate what a huge difference there can be in space management when you load into a table with uniform extents versus a table with autoallocated extents.

1) Create two tablespaces: Test_Uniform (using uniform extent management), Test_Allocate (using auto allocate)

create tablespace test_uniform datafile '+DATA/uniform.dbf' SIZE 1048640K

AUTOEXTEND ON NEXT 100M

EXTENT management local uniform size 100m;

create tablespace test_allocate datafile '+DATA/allocate2.dbf' SIZE 1048640K

AUTOEXTEND ON NEXT 100M

EXTENT management local autoallocate segment space management auto;

2)Create a flat file with a 10,000,000 records.

-rw-r--r-- 1 oracle dba 1077320689 May 17 16:59 TEST.dat

3)Do a parallel direct path load of this file into each tablespace:

create table UNIFORM_TEST                                                                         parallel
tablespace Test_
_uniform                                                                        as  select * from big_table_ext;

create table AUTOALLOCATE_TEST
parallel
tablespace Test_allocate
as select * from big_table_ext;

Let's view the space usage using a PL/SQL package called show_space  .

 

 As you can see from the results there are no unformatted blocks in the autoallocate table as extent trimming has taken place after the load was complete. The same can not be said for the uniform_test table. It is quite evident from the numbers that there is substantial space wastage in the uniform_test table. Although the count of full blocks are the same in both cases, the Total Mbytes used is 10x greater in the Uniform table.

Conclusion

Space utilization is much better with autoallocate becuase of extent trimming. As I said before more information on this topic can be found on Tom's Kyte post.

 

Wednesday Mar 04, 2009

Managing Optimizer statistics in an Oracle Database 11g

Knowing when and how to gather optimizer statistics has become somewhat of dark art especially in a data warehouse environment where statistics maintenance can be hindered by the fact that as the data set increases the time it takes to gather statistics will also increase. By default the DBMS_STATS packages will gather global (table level), partition level, and sub-partition statistics for each of the tables in the database. The only exception to this is if you have hash sub-partitions. Hash sub-partitions do not need statistics, as the optimizer can accurately derive any necessary statistics from the partition level statistic because the hash partitions are all approximately the same size due to linear hashing algorithm.
As mentioned above the length of time it takes to gather statistics will grow proportionally with your data set, so you may now be wondering if the optimizer truly need statistics at every level for a partitioned table or if time could be saved by skipping one or more levels? The short answer is "no" as the optimizer will use statistics from one or more of the levels in different situations.

The optimizer will use global or table level statistics if one or more of your queries touches two or more partitions.

The optimizer will use partition level statistics if your queries do partition elimination, such that only one partition is necessary to answer each query. If your queries touch two or more partitions the optimizer will use a combination of global and partition level statistics.

The optimizer will user sub-partition level statistics if your queries do partition elimination, such that only one sub-partition is necessary. If your queries touch two more sub-partitions the optimizer will use a combination of sub-partition and partition level statistics.

How to gather statistics?
Global statistics are by far the most important statistics but they also take the longest time to collect because a full table scan is required. However, in Oracle Database 11g this issue has been addressed with the introduction of Incremental Global statistics. Typically with partitioned tables, new partitions are added and data is loaded into these new partitions. After the partition is fully loaded, partition level statistics need to be gathered and the global statistics need to be updated to reflect the new data. If the INCREMENTAL value for the partition table is set to TRUE, and the DBMS_STATS GRANULARITY parameter is set to AUTO, Oracle will gather statistics on the new partition and update the global table statistics by scanning only those partitions that have been modified and not the entire table. Below are the steps necessary to do use incremental global statistics

SQL> exec dbms_stats.set_table_prefs('SH', 'SALES', 'INCREMENTAL', 'TRUE');

SQL> exec dbms_stats.gather_table_stats( Owname=>'SH', Tabname=>'SALES', Partname=>'23_MAY_2008', Granularity=>'AUTO');

Incremental Global Stats works by storing a synopsis for each partition in the table. A synopsis is statistical metadata for that partition and the columns in the partition. Each synopsis is stored in the SYSAUX tablespace and takes approximately 10KB. Global statistics are generated by aggregating the synopses from each partition, thus eliminating the need for the full table scan (see Figure below). When a new partition is added to the table you only need to gather statistics for the new partition. The global statistics will be automatically updated by aggregating the new partition synopsis with the existing partitions synopsis.

incremental_stats_gathering.bmp

But what if you are not using Oracle Database 11g and you can't afford to gather partition level statistic (not to mention global statistics) after data is loaded? In Oracle Database 10g (10.2.0.4) you can use the DBMS_STATS.COPY_TABLE_STATS procedure. This procedure enables you to copy statistics from an existing [sub] partition to the new [sub] partition and will adjust statistics to account for the additional partition of data (for example the number of blks, number of rows). It sets the new partition's high bound partitioning value as the maximum value of the first partitioning column and high bound partitioning value of the previous partition as the minimum value of the first partitioning column for a range partitioned table. For a list-partitioned table it will find the maximum and minimum from the list of values.

SQL>exec dbms_stats.copy_table_stats('sh','sales','sales_q3_2000','sales_q44_2000', force=>TRUE);

When should you gather Statistics?
If you use the automatic stats job or dbms_stats.gather_schema_stats with the option "GATHER AUTO", Oracle only collect statistics at the global level if the table has changed more than 10% or if the global statistics have not yet been collected. Partition level statistics will always be gathered if they are missing. For most tables this frequency is fine.
However, in a data warehouse environment there is one scenario where this is not the case. If a partition table is constantly having new partitions added and then data is loaded into the new partition and users instantly begin querying the new data, then it is possible to get a situation where an end-users query will supply a value in one of the where clause predicate that is outside the [min,max] range for the column according to the optimizer statistics. For predicate values outside the statistics [min,max] range the optimizer will prorates the selectivity for that predicate based on the distance between the value the max (assuming the value is higher than the max). This means, the farther the value is from the maximum value the lower is the selectivity will be, which may result in sub-optimal execution plans.
You can avoid this "Out of Range" situation by using the new incremental Global Statistics or the copy table statistics procedure.

More information on Incremental Global Statistics or the copy table statistics procedure can be found on the Optimizer developers blog.

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The data warehouse insider is written by the Oracle product management team and sheds lights on all thing data warehousing and big data.

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