The document discusses techniques for optimizing storage in Oracle databases for data warehousing workloads. It covers data segment compression, which can reduce the storage space required by eliminating repeated column values within database blocks. It also discusses partitioning tables, which can improve query performance, manageability, scalability and availability by dividing tables into smaller, more manageable parts. Key recommendations include ordering data to maximize compression, using appropriate partitioning strategies like partitioning on a transaction date for fact tables, and ensuring statistics are gathered after maintenance operations.

1. Stack It & Pack It Partitioning And Compression For Warehouses / VLDB Jeff Moss

2. Who Dunnit ?

3. Agenda My background Squeeze your data with data segment compression Partition for success Questions

4. My Background Independent Consultant 13 years Oracle experience Blog: http://oramossoracle.blogspot.com/ Focused on warehousing / VLDB since 1998 First project UK Music Sales Data Mart Produces BBC Radio 1 Top 40 chart and many more 2 billion row sales fact table 1 Tb total database size Currently working with Eon UK (Powergen) 4Tb Production Warehouse, 8Tb total storage Oracle Product Stack

5. What Is Data Segment Compression ? Compresses data by eliminating intra block repeated column values Reduces the space required for a segment …but only if there are appropriate repeats! Self contained Lossless algorithm

6. Where Can Data Segment Compression Be Used ? Can be used with a number of segment types Heap & Nested Tables Range or List Partitions Materialized Views Can’t be used with Subpartitions Hash Partitions Indexes – but they have row level compression IOT External Tables Tables that are part of a Cluster LOBs

7. How Does Segment Compression Work ? Database Block Symbol Table Row Data Area Block Common Header (20 bytes) Transaction Header (24 bytes fixed + 24 bytes per ITL) Data Header (14 bytes) Compressed Data Header (16 bytes - variable ) Tail (4 bytes) 100 Call to discuss bill amount TEL NO YES 3 TEL 4 NO 5 YES 2 Call to discuss bill amount 1 100 1 2 3 4 5 101 Call to discuss new product MAIL NO N/A 8 MAIL 9 N/A 7 Call to discuss new product 6 101 6 7 8 4 9 102 Call to discuss new product TEL YES N/A 10 7 3 5 9 10 102 ID DESCRIPTION CONTACT TYPE OUTCOME FOLLOWUP Table Directory (8 bytes) Row Directory (2 bytes per row )

8. What Affects Compression ? Undisclosed Algorithm I asked but support wouldn’t play ball! Many Factors Block size Anything which affects block overhead Interested Transaction Lists ( INITRANS ) Number of columns Number of rows PCTFREE Number of repeats ( in the block ) Length of column value(s)

9. Compression v Block Size 200K rows, Non ASSM Uniform Local extents More chance of repeats in any given block

10. Compression v ITL 10K rows, Non ASSM Uniform Local extents More ITL = more overhead = less repeats

11. Compression v Number Of Columns 500K rows, Non ASSM Uniform Local extents Same amount of data to store More columns = more overhead = less repeats

12. Compression v PCTFREE 200K rows, Non ASSM Uniform Local extents Higher PCTFREE = less space = less repeats

13. Compression v NDV 200K rows, Non ASSM Uniform Local extents Higher NDV = less repeats

14. Compression v Column Length 80K rows, Non ASSM Uniform Local extents Minimum 6 characters for compression Longer Length = more compression savings

15. Compression v Ordering Colocate data to maximise compression benefits For maximum compression Minimise the total space required by the segment Identify most “compressable” column(s) For optimal access We know how the data is to be queried Order the data by Access path columns Then the next most “compressable” column(s) Uniformly distributed Colocated 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5

16. Get Max Compression Order Package PROCEDURE mgmt_p_get_max_compress_order Argument Name Type In/Out Default? ------------------------------ ----------------------- ------ -------- P_TABLE_OWNER VARCHAR2 IN DEFAULT P_TABLE_NAME VARCHAR2 IN P_PARTITION_NAME VARCHAR2 IN DEFAULT P_SAMPLE_SIZE NUMBER IN DEFAULT P_PREFIX_COLUMN1 VARCHAR2 IN DEFAULT P_PREFIX_COLUMN2 VARCHAR2 IN DEFAULT P_PREFIX_COLUMN3 VARCHAR2 IN DEFAULT BEGIN mgmt_p_get_max_compress_order(p_table_owner => ‘AE_MGMT’ ,p_table_name =>’BIG_TABLE’ ,p_sample_size =>10000); END: / Running mgmt_p_get_max_compress_order... ---------------------------------------------------------------------------------------------------- Table : BIG_TABLE Sample Size : 10000 Unique Run ID: 25012006232119 ORDER BY Prefix: ---------------------------------------------------------------------------------------------------- Creating MASTER Table : TEMP_MASTER_25012006232119 Creating COLUMN Table 1: COL1 Creating COLUMN Table 2: COL2 Creating COLUMN Table 3: COL3 ---------------------------------------------------------------------------------------------------- The output below lists each column in the table and the number of blocks/rows and space used when the table data is ordered by only that column, or in the case where a prefix has been specified, where the table data is ordered by the prefix and then that column. From this one can determine if there is a specific ORDER BY which can be applied to to the data in order to maximise compression within the table whilst, in the case of a a prefix being present, ordering data as efficiently as possible for the most common access path(s). ---------------------------------------------------------------------------------------------------- NAME COLUMN BLOCKS ROWS SPACE_GB ============================== ============================== ============ ============ ======== TEMP_COL_001_25012006232119 COL1 290 10000 .0022 TEMP_COL_002_25012006232119 COL2 345 10000 .0026 TEMP_COL_003_25012006232119 COL3 555 10000 .0042

17. Pros & Cons Pros Saves space Reduces LIO / PIO Speeds up backup/recovery Improves query response time Transparent To readers …and writers Decreases time to perform some DML Deletes should be quicker Bulk inserts may be quicker

18. Pros & Cons Cons Increases CPU load Can only be used on Direct Path operations CTAS Serial Inserts using INSERT /*+ APPEND */ Parallel Inserts (PDML) ALTER TABLE…MOVE… Direct Path SQL*Loader Increases time to perform some DML Bulk inserts may be slower Updates are slower

19. Data Warehousing Specifics Star Schema compresses better than Normalized More redundant data Focus on… Fact Tables and Summaries in Star Schema Transaction tables in Normalized Schema Performance Impact 1 Space Savings Star schema: 67% Normalized: 24% Query Elapsed Times Star schema: 16.5% Normalized: 10% 1 - Table Compression in Oracle 9iR2: A Performance Analysis

20. Things To Watch Out For DROP COLUMN is awkward ORA-39726: Unsupported add/drop column operation on compressed tables Uncompress the table and try again - still gives ORA-39726! After UPDATEs data is uncompressed Performance impact Row migration Use appropriate physical design settings PCTFREE 0 - pack each block Large blocksize - reduce overhead / increase repeats per block Minimise INITRANS - reduce overhead Order data for best compression / access path

21. A Funny Thing… Block dump trace files still show 9iR2 even in 10g releases… ALTER SYSTEM DUMP DATAFILE x BLOCK y; Thanks to Julian Dyke for the block dumping information – http://www.juliandyke.com

22. What Is Partitioning ? “ Partitioning addresses key issues in supporting very large tables and indexes by letting you decompose them into smaller and more manageable pieces called partitions .” Oracle Database Concepts Manual, 10gR2 Introduced in Oracle 8.0 Numerous improvements since Subpartitioning adds another level of decomposition Partitions and Subpartitions are logical containers

23. Partition To Tablespace Mapping Partitions map to tablespaces Partition can only be in One tablespace Tablespace can hold many partitions Highest granularity is One tablespace per partition Lowest granularity is One tablespace for all the partitions Tablespace volatility Read / Write Read Only P_JAN_2005 P_FEB_2005 P_MAR_2005 P_APR_2005 P_MAY_2005 P_JUN_2005 P_JUL_2005 P_AUG_2005 P_SEP_2005 P_OCT_2005 P_NOV_2005 P_DEC_2005 T_Q1_2005 T_Q2_2005 T_Q3_2005 T_Q4_2005 T_Q1_2006 P_JAN_2006 P_FEB_2006 P_MAR_2006 T_Q3_2005 Read / Write Read Only

24. Read Only Tablespaces Quicker checkpointing Quicker backup Quicker recovery Reduced space use via compression But… … depends on granularity… Partition Tablespace

25. Why Partition ? - Performance Improved query performance Pruning or elimination Partition wise joins Full Partial Selective Compression By Partition Selective Reorganisation Index Partition REBUILD Table Partition MOVE SELECT SUM(sales) FROM part_tab WHERE sales_date BETWEEN ‘01-JAN-2005’ AND ’30-JUN-2005’ Sales Fact Table * Oracle 10gR2 Data Warehousing Manual JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

26. Why Partition ? - Manageability Archiving Use a rolling window approach ALTER TABLE … ADD/SPLIT/DROP PARTITION… Easier ETL Processing Build a new dataset in a staging table Add indexes and constraints Collect statistics Then swap the staging table for a partition on the target ALTER TABLE…EXCHANGE PARTITION… Easier Maintenance Table partition move, e.g. to compress data Local Index partition rebuild

27. Why Partition ? - Scalability Partition is generally consistent and predictable Assuming an appropriate partitioning key is used …and data has an even distribution across the key Read only approach Scalable backups - read only tablespaces are ignored …so partitions in those tablespaces are ignored Pruning allows consistent query performance

28. Why Partition ? - Availability Offline data impact minimised … depending on granularity Quicker recovery Pruned data not missed EXCHANGE PARTITION Allows offline build Quick swap over P_JAN_2005 P_FEB_2005 P_MAR_2005 P_APR_2005 P_MAY_2005 P_JUN_2005 P_JUL_2005 P_AUG_2005 P_SEP_2005 P_OCT_2005 P_NOV_2005 P_DEC_2005 T_Q1_2005 T_Q2_2005 T_Q3_2005 T_Q4_2005 T_Q1_2006 P_JAN_2006 P_FEB_2006 P_MAR_2006 T_Q3_2005 Read / Write Read Only

29. Fact Table Partitioning Transaction Date Load Date Easier ETL Processing Each load deals with only 1 partition No use to end user queries! Can’t prune – Full scans! Harder ETL Processing But still uses EXCHANGE PARTITION Useful to end user queries Allows full pruning capability 07-JAN-2005 Customer 1 09-JAN-2005 15-JAN-2005 Customer 2 17-JAN-2005 January Partition February Partition 22-JAN-2005 Customer 3 01-FEB-2005 02-FEB-2005 Customer 4 05-FEB-2005 26-FEB-2005 Customer 5 28-FEB-2005 March Partition 06-MAR-2005 Customer 2 07-MAR-2005 12-MAR-2005 Customer 3 15-MAR-2005 Tran Date Customer Load Date April Partition 21-JAN-2005 Customer 7 04-APR-2005 09-APR-2005 Customer 9 10-APR-2005 07-JAN-2005 Customer 1 09-JAN-2005 15-JAN-2005 Customer 2 17-JAN-2005 21-JAN-2005 Customer 7 04-APR-2005 22-JAN-2005 Customer 3 01-FEB-2005 January Partition February Partition 02-FEB-2005 Customer 4 05-FEB-2005 26-FEB-2005 Customer 5 28-FEB-2005 March Partition 06-MAR-2005 Customer 2 07-MAR-2005 12-MAR-2005 Customer 3 15-MAR-2005 Tran Date Customer Load Date April Partition 09-APR-2005 Customer 9 10-APR-2005

30. Watch out for… Partition exchange and table statistics 1 Partition stats updated … but Global stats are NOT! Affects queries accessing multiple partitions Solution Gather stats on staging table prior to EXCHANGE Partition exchange Gather stats on partitioned table using GLOBAL Jonathan Lewis: Cost-Based Oracle Fundamentals, Chapter 2

31. Partitioning Feature: Characteristic Reason Matrix    Partition Truncation     Exchange Partition    Archiving    Pruning (Partition Elimination)   Partition wise joins  Parallel DML     Local Indexes    Read Only Partitions Availability Scalability Manageability Performance Characteristic: Feature:

32. Questions ?

33. References: Papers Table Compression in Oracle 9iR2: A Performance Analysis Table Compression in Oracle 9iR2: An Oracle White Paper “ Scaling To Infinity, Partitioning In Oracle Data Warehouses”, Tim Gorman Decision Speed: Table Compression In Action

34. References: Online Presentation / Code http://www.oramoss.demon.co.uk/presentations/stackitandpackit.ppt http://www.oramoss.demon.co.uk/Code/mgmt_p_get_max_compression_order.prc http://www.oramoss.demon.co.uk/Code/test_dml_performance_delete.sql http://www.oramoss.demon.co.uk/Code/test_dml_performance_insert.sql http://www.oramoss.demon.co.uk/Code/test_dml_performance_update.sql http://www.oramoss.demon.co.uk/Code/test_block_size_compression.sql http://www.oramoss.demon.co.uk/Code/test_column_length_compression.sql http://www.oramoss.demon.co.uk/Code/test_itl_compression.sql http://www.oramoss.demon.co.uk/Code/test_ndv_compression.sql http://www.oramoss.demon.co.uk/Code/test_num_cols_compression.sql http://www.oramoss.demon.co.uk/Code/test_pctfree_compression.sql