This document discusses data compression in Oracle databases. It provides an overview of Oracle's data compression features in versions 9i, 10g, and 11g. Key topics covered include compressing tables and partitions, the effects of compression on performance and storage usage, and how compression interacts with operations like partition maintenance and bitmap indexes. The document aims to help database administrators understand when and how to use compression to improve I/O throughput and reduce storage requirements.

1. Data Compression in Oracle
Carl Dudley
University of Wolverhampton, UK
UKOUG Director
Oracle ACE Director
carl.dudley@wlv.ac.uk
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Carl Dudley – University of Wolverhampton, UK

2. Introduction
Working with Oracle since 1986
Oracle DBA - OCP Oracle7, 8, 9, 10
Oracle DBA of the Year – 2002
Oracle ACE Director
Regular Presenter at Oracle Conferences
Consultant and Trainer
Technical Editor for a number of Oracle texts
UK Oracle User Group Director
Member of IOUC
Day job – University of Wolverhampton, UK
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Carl Dudley – University of Wolverhampton, UK

3. Main Topics
 Oracle 9i and 10g Compression - major features
 Compression in data warehousing
 Sampling the data to predict compression
 Pre-sorting the data for compression
 Behaviour of DML/DDL on compressed tables
 Compression Internals
 Advanced Compression in Oracle11g (for OLTP operations)
 Shrinking unused space
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4. Oracle Data Compression – Main Features
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5. Compression : Characteristics
 Trades Physical I/O against CPU utilization
– Transparent to applications
– Can increase I/O throughput and buffer cache capacity
 Useful for 'read mostly' applications
– Decision Support and OLAP
 Compression is performed only when Oracle considers it worthwhile
– Depends on column length and amount of duplication
 Compression occurs only when duplicate values are present within and
across columns within a single database block
– Compression algorithms have caused little change to the Kernel code
• Modifications only to block formatting and accessing rows and columns
– No compression within individual column values or across blocks
– Blocks retrieved in compressed format in the buffer cache
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6. Getting Compressed
 Building a new compressed table
CREATE TABLE <table_name> ...
COMPRESS;
CREATE TABLE <table_name> COMPRESS AS SELECT ...
 Altering an existing table to be compressed
ALTER TABLE <table_name> MOVE COMPRESS;
– No additional copy created but temp space and exclusive table
level lock required for the compression activity
ALTER TABLE <table_name> COMPRESS;
– Future bulk inserts may be compressed – existing data is not
 Compressing individual partitions
ALTER TABLE <table_name>
MOVE PARTITION <partition_name> COMPRESS;
– Existing data and future bulk inserts compressed in a specific
partition
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7. Tablespace Level Compression
 Entire tablespaces can compress by default
CREATE | ALTER TABLESPACE < tablespace_name>
DEFAULT [ COMPRESS | NOCOMPRESS ] ...
– All objects in the tablespace will be compressed by default
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8. Compressing Table Data
 Uncompressed conventional emp table
CREATE TABLE emp
(empno NUMBER(4)
,ename VARCHAR2(12)
...);
 Compressed emp table
CREATE TABLE emp
(empno NUMBER(4)
,ename VARCHAR2(12)
...)
COMPRESS;
 Could consider sorting the data on columns which lend
themselves to compression
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9. Table Data Compression
 Uncompressed emp table
7369 CLERK 2000 1550 ACCOUNTING
7782 MANAGER 4975 1600 PLANT
7902 ANALYST 4000 2100 OPERATIONS
7900 CLERK 2750 1500 OPERATIONS
7934 CLERK 2200 1200 ACCOUNTING
7654 PLANT 3000 1100 RESEARCH
 Compressed emp table
– Similar to index compression
[SYMBOL TABLE] [A]=CLERK, [B]=ACCOUNTING, [C]=PLANT,
[D]=OPERATIONS
7369 [A] 2000 1550 [B]
7782 MANAGER 4975 1600 [C]
7902 ANALYST 4000 2100 [D]
7900 [A] 2750 1500 [D]
7934 [A] 2200 1200 [B]
7654 [C] 3000 1100 RESEARCH
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10. Scanning Compressed Tables – Tests(1)
 Compressing can significantly reduce disk I/O - good for queries?
– Possible increase in CPU activity
– May need to unravel the compression but logical reads will be reduced
SELECT table_name,compressed,num_rows FROM my_user_tables;
TABLE_NAME COMPRESSED NUM_ROWS
---------- ---------- --------
EMP_NC DISABLED 1835008
EMP_DSS ENABLED 1835008
SELECT COUNT(ename) FROM emp_nc
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.31 1.88 10974 10978 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.31 1.88 10974 10978 0 1
SELECT COUNT(ename) FROM emp_dss
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.56 0.71 3057 3060 0 1
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.56 0.71 3057 3060 0 1
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11. Scanning Compressed Tables – Tests (2)
 Force all rows to be uncompressed
– Increases logical I/O?
SELECT ename FROM emp_nc
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 122335 1.68 2.00 10974 132607 0 1835008
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 122337 1.68 2.00 10974 132607 0 1835008
SELECT ename FROM emp_dss
call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 122335 2.17 2.24 3057 125286 0 1835008
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 122337 2.17 2.24 3057 125286 0 1835008
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12. Space Reduction Due to Compression
 Space usage summary
Statistics for table EMP_NC Statistics for table EMP_DSS
Unformatted Blocks ...........0 Unformatted Blocks ...........0
FS1 Blocks (0-25) ............0 FS1 Blocks (0-25) ............0
FS2 Blocks (25-50) ...........0 FS2 Blocks (25-50) ...........0
FS3 Blocks (50-75) ...........0 FS3 Blocks (50-75) ...........0
FS4 Blocks (75-100) ..........0 FS4 Blocks (75-100) ..........0
Full Blocks .............10,974 Full Blocks ..............3,057
Total Blocks ............11,776 Total Blocks .............3,200
Total Bytes .........96,468,992 Total Bytes .........26,214,400
Total Mbytes ................92 Total Mbytes ................25
Unused Blocks ..............653 Unused Blocks ...............85
Unused Bytes .........5,349,376 Unused Bytes ...........696,320
Last Used Ext FileId .........4 Last Used Ext FileId .........4
Last Used Ext BlockId ..264,841 Last Used Ext BlockId ..263,561
Last Used Block ............371 Last Used Block .............43
– Summary routine adapted from Tom Kyte’s example
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13. Compression in Data Warehousing
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14. Compression in Data Warehousing
 Fact tables are good candidates for compression
– Large and have repetitive values
– Repetitive data tends to be clustered
 Dimension tables are often too small for compression
 Large block size leads to greater compression
– Typical in data warehouses
– More rows available for compression within each block
 Materialized views can be compressed (and partitioned)
– Naturally sorted on creation due to GROUP BY
– Especially good for ROLLUP views and join views
• Tend to contain repetitive data
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15. Compression of Individual Table Partitions
 Partition level
– Partitioning must be range or list (or composite)
CREATE TABLE sales
(sales_id NUMBER(8)
: :
,sales_date DATE)
PARTITION BY RANGE(sales_date)
(PARTITION sales_jan2009 VALUES LESS THAN
(TO_DATE('02/01/2009','DD/MM/YYYY')) COMPRESS,
PARTITION sales_feb2009 VALUES LESS THAN
(TO_DATE('03/01/2009','DD/MM/YYYY')),
PARTITION sales_mar2009 VALUES LESS THAN
(TO_DATE('04/01/2009','DD/MM/YYYY')),
PARTITION sales_apr2009 VALUES LESS THAN
(TO_DATE('05/01/2009','DD/MM/YYYY')));
– The first partition will be compressed
– Could consider compressing read only partitions of historical data
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16. Effect of Partition Operations
 Consider individual partitions compressed as shown
PARTITION p1 COMPRESS VALUES LESS THAN 100
PARTITION p2 COMPRESS VALUES LESS THAN 200
PARTITION p3 NOCOMPRESS VALUES LESS THAN 300
PARTITION p4 NOCOMPRESS VALUES LESS THAN 400
 Splitting a compressed partition
ALTER TABLE s1 SPLIT PARTITION p1 (50)
INTO (PARTITION p1a, PARTITION p1b);
– Produces two new compressed partitions
PARTITION p1a COMPRESS VALUES LESS THAN 50
PARTITION p1b COMPRESS VALUES LESS THAN 100
PARTITION p2 COMPRESS VALUES LESS THAN 200
PARTITION p3 NOCOMPRESS VALUES LESS THAN 300
PARTITION p4 NOCOMPRESS VALUES LESS THAN 400
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17. Effect of Partition Operations (contd)
 Effect of merging compressed partitions
PARTITION p1a COMPRESS VALUES LESS THAN 50
PARTITION p1b COMPRESS VALUES LESS THAN 100
PARTITION p2 COMPRESS VALUES LESS THAN 200
PARTITION p3 NOCOMPRESS VALUES LESS THAN 300
PARTITION p4 NOCOMPRESS VALUES LESS THAN 400
 Merge of two compressed partitions
ALTER TABLE s1 MERGE PARTITIONS p1b,p2
INTO PARTITION p1b_2;
PARTITION p1a COMPRESS VALUES LESS THAN 50
PARTITION p1b_2 NOCOMPRESS VALUES LESS THAN 200
PARTITION p3 NOCOMPRESS VALUES LESS THAN 300
PARTITION p4 NOCOMPRESS VALUES LESS THAN 400
– New partition p1b_2 is not compressed by default
• Same applies if any to be merged are initially uncompressed
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18. Forcing Compression During Partition Maintenance
 Force compression of the new partition(s) after a split operation
ALTER TABLE s1 SPLIT PARTITION p1 AT (50)
INTO (PARTITION p1a COMPRESS,PARTITION p2a);
 Force compression of new partition after a merge operation
ALTER TABLE s1 MERGE PARTITIONS p2,p3
INTO PARTITION p2_3 COMPRESS;
 Partitions may be empty or contain data during maintenance
operations involving compression
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19. Effect of Partitioned Bitmap Indexes
 Scenario :
– Table having no compressed partitions has bitmap locally partitioned
indexes
– The presence of usable bitmap indexes will prevent the first operation that
compresses a partition
SQL> ALTER TABLE sales MOVE PARTITION p4 COMPRESS;
ORA-14646: Specified alter table operation involving compression
cannot be performed in the presence of usable bitmap indexes
SQL> ALTER TABLE part2 SPLIT PARTITION p1a AT (25)
INTO ( PARTITION p1c COMPRESS,PARTITION p1d);
ORA-14646: Specified alter table operation involving compression
cannot be performed in the presence of usable bitmap indexes
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20. Compression of Partitions with
Bitmap Indexes in Place
 Uncompressed partitioned table with bitmap index in 3 partitions
CREATE TABLE emp_part
PARTITION BY RANGE (deptno)
(PARTITION p1 VALUES LESS THAN (11),
PARTITION p2 VALUES LESS THAN (21),
PARTITION p3 VALUES LESS THAN (31))
AS SELECT * FROM emp;
CREATE BITMAP INDEX part$empno
ON emp_part(empno) LOCAL;
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21. Compression of Partitions with
Bitmap Indexes in Place (continued)
 First compression operation requires the following
1. Mark bitmap indexes unusable (or drop them)
ALTER INDEX part$empno UNUSABLE;
2. Compress the first (and any subsequent) partition as required
ALTER TABLE emp_part MOVE PARTITION p1 COMPRESS;
3. Rebuild the bitmap indexes (or recreate them)
ALTER INDEX part$empno REBUILD PARTITION p1;
ALTER INDEX part$empno REBUILD PARTITION p2;
ALTER INDEX part$empno REBUILD PARTITION p3;
– Each index partition must be individually rebuilt
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22. Compression of Partitions with
Bitmap Indexes in Place (continued)
 Oracle needs to know maximum records per block
– Correct mapping of bits to blocks can then be done
– On compression this value increases
 Oracle has to rebuild bitmaps to accommodate potentially larger
number of values even if no data is present in the partition(s)
– Could result in larger bitmaps for uncompressed partitions
• Increase in size can be offset by the actual compression
 Once rebuilt, the indexes can cope with any compression
– Subsequent compression operations do not invalidate bitmap indexes
 Recommended to create each partitioned table with at least one
compressed (dummy/empty?) partition
– Can be subsequently dropped
 Compression activity does not affect Btree usability
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23. Table Level Compression for Partitioned Tables
 Compression can be the default for all partitions
CREATE TABLE sales
(sales_id NUMBER(8),
: :
sales_date DATE)
COMPRESS
PARTITION BY (sales_date)
...
– Can still specify individual partitions to be NOCOMPRESS
 Default partition maintenance actions on compressed tables
– Splitting non-compressed partitions results in non-compressed partitions
– Merging non-compressed partitions results in a compressed partition
– Adding a partition will result in a new compressed partition
– Moving a partition does not alter its compression
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24. Finding the Largest Tables
 Useful for finding candidates for compression
SELECT owner
,name
,SUM(gb)
,SUM(pct)
FROM (SELECT owner
,name
,TO_CHAR(gb,'999.99') gb
,TO_CHAR((RATIO_TO_REPORT(gb) OVER())*100,'999,999,999.99') pct
FROM (SELECT owner
,SUBSTR(segment_name,1,30) name
,SUM(bytes/(1024*1024*1024)) gb
FROM dba_segments
WHERE segment_type IN ('TABLE','TABLE PARTITION')
GROUP BY owner
,segment_name
)
)
WHERE pct > 3
GROUP BY ROLLUP(owner
,name)
ORDER BY 3;
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25. Finding the Largest Tables (contd)
OWNER NAME SUM(GB) SUM(PCT)
------------- -------------- ------------- ----------
SH COSTS .03 8.23
SH SALES .05 14.44
SH SALES_HIST .13 32.93
SH .21 55.61
SYS IDL_UB2$ .01 3.86
SYS SOURCE$ .02 6.43
SYS .03 10.29
.24 65.90
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26. Sampling Data to Predict Compression
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27. Compression Factor and Space Saving
 Compression Factor (CF)
non-compressed blocks
CF = * 100
compressed blocks
 Space Savings (SS)
non-compressed blocks - compressed blocks
SS = * 100
compressed blocks
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28. Predicting the Compression Factor
CREATE OR REPLACE FUNCTION compression_ratio (tabname VARCHAR2)
RETURN NUMBER IS
pct NUMBER := 0.000099; -- sample percentage
blkcnt NUMBER := 0; -- original block count (should be < 10K)
blkcntc NUMBER; -- compressed block count
BEGIN
EXECUTE IMMEDIATE ' CREATE TABLE temp_uncompressed PCTFREE 0 AS SELECT *
FROM ' || tabname || ' WHERE ROWNUM < 1';
WHILE ((pct < 100) AND (blkcnt < 1000)) LOOP -- until > 1000 blocks in sample
EXECUTE IMMEDIATE 'TRUNCATE TABLE temp_uncompressed';
EXECUTE IMMEDIATE 'INSERT INTO temp_uncompressed SELECT * FROM ' ||
tabname || ' SAMPLE BLOCK (' || pct || ',10)';
EXECUTE IMMEDIATE 'SELECT COUNT(DISTINCT(dbms_rowid.rowid_block_number(rowid)))
FROM temp_uncompressed' INTO blkcnt;
pct := pct * 10;
END LOOP;
EXECUTE IMMEDIATE 'CREATE TABLE temp_compressed COMPRESS AS SELECT * FROM
temp_uncompressed';
EXECUTE IMMEDIATE 'SELECT COUNT(DISTINCT(dbms_rowid.rowid_block_number(rowid)))
FROM temp_compressed' INTO blkcntc;
EXECUTE IMMEDIATE 'DROP TABLE temp_compressed';
EXECUTE IMMEDIATE 'DROP TABLE temp_uncompressed';
RETURN (blkcnt/blkcntc);
END;
/
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29. Predicting the Compression Factor (continued)
CREATE OR REPLACE PROCEDURE compress_test(p_comp VARCHAR2)
IS
comp_ratio NUMBER;
BEGIN
comp_ratio := compression_ratio(p_comp);
dbms_output.put_line('Compression factor for table ' ||
p_comp ||' is '|| comp_ratio );
END;
 Run the compression test for the emp table
EXEC compress_test('EMP')
Compression factor for table EMP is 1.6
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30. Compression Test – Clustered Data
CREATE TABLE noclust (col1
VARCHAR2(1000))
COMPRESS; CREATE TABLE clust (col1
VARCHAR2(1000))
COMPRESS;
INSERT INTO noclust VALUES ('VV...VV');
INSERT INTO noclust VALUES ('WW...WW'); INSERT INTO clust VALUES ('VV...VV');
INSERT INTO noclust VALUES ('XX...XX'); INSERT INTO clust VALUES ('VV...VV');
INSERT INTO noclust VALUES ('YY...YY'); INSERT INTO clust VALUES ('VV...VV');
INSERT INTO noclust VALUES ('ZZ...ZZ'); INSERT INTO clust VALUES ('VV...VV');
INSERT INTO noclust VALUES ('VV...VV'); INSERT INTO clust VALUES ('VV...VV');
INSERT INTO noclust VALUES ('WW...WW'); INSERT INTO clust VALUES ('WW...WW');
INSERT INTO noclust VALUES ('XX...XX'); : : :
INSERT INTO clust VALUES ('WW...WW');
INSERT INTO noclust VALUES ('YY...YY');
INSERT INTO clust VALUES ('XX...XX');
INSERT INTO noclust VALUES ('ZZ...ZZ'); : : :
INSERT INTO noclust VALUES ('VV...VV'); INSERT INTO clust VALUES ('YY...YY');
: : : : : :
INSERT INTO noclust VALUES ('ZZ...ZZ'); INSERT INTO clust VALUES ('ZZ...ZZ');
 Every value for column col1 is 390 bytes long
 Both tables have a total of 25 rows stored in blocks of size 2K
– So a maximum of four rows will fit in each block
 Both have same amount of repeated values but the clustering is different
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31. Compression Test (continued)
noclust - 4 rows per block. (7 blocks in total)
The 5th row to be inserted must go in the next block as it contains different data
header header header header
vv…vv zz…zz yy…yy xx…xx
ww…ww vv…vv zz…zz yy…yy
xx…xx ww…ww vv…vv zz…zz
yy…yy xx…xx ww…ww vv…vv
header header
clust - 20 rows per block.
vv…vv Rows 2,3,4,5 are duplicates of the first row
in the block.
ww…ww Rows 7,8,9,10 are duplicates of the 6th row
in the block, and this pattern is repeated.
xx…xx The residual space in the first block is used
by the compressed data
yy…yy zz…zz
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32. Compression Test - Compression Factors
 Compression test routine is accurate due to sampling of actual data
– Make sure default tablespace is correctly set
• Temporary sample tables are physically built for the testing
EXEC compress_test('CLUST')
Compression factor for table CLUST is 3.5
EXEC compress_test('NOCLUST')
Compression factor for table NOCLUST is 1
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Carl Dudley – University of Wolverhampton, UK

33. Testing Compression : Sampling Rows
 Tables can be sampled at row or block level
– Block level samples a random selection of whole blocks
– Row level (default) samples a random selection of rows
SELECT * FROM emp SAMPLE (10);
• Selects a 10% sample of rows
• If repeated, a different sample will be taken
 Samples can be 'fixed' in Oracle10g using SEED
– SEED can can have integer values from 0 -100
– Can also have higher numbers ending in '00'
SELECT * FROM emp SAMPLE (10) SEED (1);
– Shows a 10% sample of rows
– If repeated, the exact same sample will be taken
• Also applies to block level sampling
• The sample set will change if DML is performed on the table
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34. Pre-Sorting the Data for Compression
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35. Sorting the Data for Compression
 Reorganize (pre-sort) rows in segments that will be compressed
to cause repetitive data within blocks
 For multi-column tables, order the rows by the low cardinality column(s)
CREATE TABLE emp_comp COMPRESS AS
SELECT * FROM emp ORDER BY <some unselective column(s)>;
– For a single-column table, order the table rows by the column value
 Presort the data on a column which has :
no. of distinct values ~ no. of blocks (after compression)
 Information on column cardinality is shown in:
ALL_TAB_COL_STATISTICS
ALL_PART_COL_STATISTICS
ALL_SUBPART_COL_STATISTICS
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36. Sorting the Data for Compression (continued)
 Presort data on column having no. of distinct values ~ no. of blocks
~
SELECT * FROM large_emp; (114368 rows)
EMPNO ENAME JOB
------- ------------ ----------
43275 25***** CLERK
47422 128**** ANALYST
79366 6****** MANAGER
: : :
SELECT COUNT(DISTINCT job) FROM large_emp; 5 jobs
SELECT COUNT(DISTINCT ename) FROM large_emp; 170 enames
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37. Sorting the Data for Compression (continued)
CREATE TABLE nocomp
AS SELECT empno,ename,job FROM large_emp;
Non-compressed table : Number of used blocks = 360
CREATE TABLE cjob COMPRESS
AS SELECT empno,ename,job FROM large_emp ORDER BY job;
Compressed table sorted on job : Number of used blocks = 243
CREATE TABLE cename COMPRESS
AS SELECT empno,ename,job FROM large_emp ORDER BY ename;
Compressed table sorted on ename : Number of used blocks = 172
 Sorting on the job column is not the most effective
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38. Behaviour of DML/DDL
on Tables with Default (Direct Load)
Compression
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39. Default Compressed Table Behaviour
 Ordinary DML produces UNcompressed data
– UPDATE
• Wholesale updates lead to large increases in storage (>250%)
• Performance impact on UPDATEs can be around 400%
• Rows are migrated to new blocks (default value of PCTFREE is 0)
– DELETE
• Performance impact of around 15% for compressed rows
 Creating a compressed table can take 50% longer
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Carl Dudley – University of Wolverhampton, UK

40. Default Compressed Table Behaviour
(continued)
 Operations which 'perform' compression
– CREATE TABLE ... AS SELECT ...
– ALTER TABLE ... MOVE ...
– INSERT /*+APPEND*/ (single threaded)
– INSERT /*+PARALLEL(sales,4)*/
• Requires ALTER SESSION ENABLE PARALLEL DML;
• Both of the above inserts work with data from database tables and
external tables
– SQL*Loader DIRECT = TRUE
– Various partition maintenance operations
 Can not be used for:
– Certain LOB and VARRAY constructs (see 11g Docs)
– Index organized tables
• Can use index compression on IOTs
– External tables, index clusters, hash clusters
– Tables with more than 255 columns
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41. Compression Internals
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42. Hexadecimal Dump of Compressed Data
Symbol Table :
14 unique names
5 unique jobs
Start of next block
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43. Oracle Dump - Two Uncompressed Employee Rows
...
ALTER SYSTEM DUMP block_row_dump:
DATAFILE 8 BLOCK 35; tab 0, row 0, @0x4a1
tl: 41 fb: --H-FL-- lb: 0x2 cc: 8
col 0: [ 3] c2 03 38
 Creates a trace file in col 1: [ 5] 43 4c 41 52 4b
USER_DUMP_DEST (10g) col 2: [ 7] 4d 41 4e 41 47 45 52
col 3: [ 3] c2 4f 28
trace directory (11g) col 4: [ 7] 77 b5 06 09 01 01 01
col 5: [ 3] c2 19 33
col 6: *NULL*
col 7: [ 2] c1 0b
tab 0, row 1, @0x4ca
tl: 40 fb: --H-FL-- lb: 0x2 cc: 8
col 0: [ 3] c2 03 39
col 1: [ 5] 53 43 4f 54 54
col 2: [ 7] 41 4e 41 4c 59 53 54
col 3: [ 3] c2 4c 43
col 4: [ 7] 77 bb 04 13 01 01 01
col 5: [ 2] c2 1f
col 6: *NULL*
col 7: [ 2] c1 15
...
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44. Oracle Dump of Table of empno,ename,job
tl: 18 fb: --H-FL-- lb: 0x0 cc: 2
col 0: [ 9] 50 52 45 53 49 44 45 4e 54
col 1: [ 4] 4b 49 4e 47
bindmp: 00 0b 02 d1 50 52 45 53 49 44 45 4e 54 cc 4b 49 4e 47
tab 0, row 1, @0x746 PRESIDENT KING
tl: 9 fb: --H-FL-- lb: 0x0 cc: 2
col 0: [ 7] 41 4e 41 4c 59 53 54
col 1: [ 4] 46 4f 52 44
bindmp: 00 0a 02 11 cc 46 4f 52 44 FORD
.....
tab 0, row 13, @0x6cc
tl: 10 fb: --H-FL-- lb: 0x0 cc: 2
col 0: [ 5] 43 4c 45 52 4b
col 1: [ 5] 53 4d 49 54 48
bindmp: 00 0b 02 0e cd 53 4d 49 54 48 SMITH
tab 0, row 14, @0x780
tl: 8 fb: --H-FL-- lb: 0x0 cc: 1
col 0: [ 5] 43 4c 45 52 4b
bindmp: 00 04 cd 43 4c 45 52 4b CLERK
.....
tab 0, row 17, @0x761
tl: 10 fb: --H-FL-- lb: 0x0 cc: 1
col 0: [ 7] 41 4e 41 4c 59 53 54
bindmp: 00 02 cf 41 4e 41 4c 59 53 54 ANALYST
tab 1, row 0, @0x6c3
tl: 9 fb: --H-FL-- lb: 0x0 cc: 3
col 0: [ 5] 43 4c 45 52 4b
col 1: [ 5] 53 4d 49 54 48
col 2: [ 3] c2 02 34
bindmp: 2c 00 02 02 0d cb c2 02 34
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Carl Dudley – University of Wolverhampton, UK

45. Oracle Avoids Unnecessary Compression
 Create two tables with repeating small values in one column
CREATE TABLE tnocomp ( CREATE TABLE tcomp (
col1 VARCHAR2(1) col1 VARCHAR2(1)
,col2 VARCHAR2(6)) ,col2 VARCHAR2(6))
PCTFREE 0; COMPRESS;
 Insert data (320 rows) as follows
COL1 COL2
---- ------
1A 1ZZZZZ
2A 2ZZZZZ
3A 3ZZZZZ
4A 4ZZZZZ
5A 5ZZZZZ Values unique in col2
1A 6ZZZZZ Values repeat in col1 every 5 rows
2A 7ZZZZZ
...
4A 319ZZZ
5A 320ZZZ
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46. Evidence of Minimal Compression
SELECT dbms_rowid.rowid_block_number(ROWID) block
,dbms_rowid.rowid_relative_fno(ROWID) file
,COUNT(*) num_rows
FROM &table_name
GROUP BY dbms_rowid.rowid_block_number(ROWID)
,dbms_rowid.rowid_relative_fno(ROWID);
tnocomp tcomp
BLOCK FILE NUM_ROWS BLOCK FILE NUM_ROWS
----- ---- -------- ----- ---- --------
34 8 126 66 8 128
35 8 126 67 8 132
36 8 68 68 8 60
 Evidence of compression in the 'compressed' table
46
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47. Further Evidence of Compression
block_row_dump:
ALTER SYSTEM tab 0, row 0, @0x783
DUMP DATAFILE 8 tl: 5 fb: --H-FL-- lb: 0x0 cc: 1
col 0: [ 2] 31 41
BLOCK 67; bindmp: 00 1b ca 31 41
tab 0, row 1, @0x77e
tl: 5 fb: --H-FL-- lb: 0x0 cc: 1
col 0: [ 2] 32 41
bindmp: 00 1b ca 32 41 Symbol
tab 0, row 2, @0x779 Table (tab0)
tl: 5 fb: --H-FL-- lb: 0x0 cc: 1 (1A,2A.
col 0: [ 2] 33 41
bindmp: 00 1b ca 33 41 3A.4A.5A)
HEX(A) = 41 tab 0, row 3, @0x774
tl: 5 fb: --H-FL-- lb: 0x0 cc: 1
col 0: [ 2] 34 41
bindmp: 00 1a ca 34 41
tab 0, row 4, @0x76f
tl: 5 fb: --H-FL-- lb: 0x0 cc: 1
col 0: [ 2] 35 41
bindmp: 00 19 ca 35 41
tab 1, row 0, @0x763
tl: 12 fb: --H-FL-- lb: 0x0 cc: 2
col 0: [ 2] 31 41
col 1: [ 6] 31 30 31 30 30 30
bindmp: 2c 00 02 02 00 c9 31 30 31 30 30 30
tab 1, row 1, @0x757
tl: 12 fb: --H-FL-- lb: 0x0 cc: 2
col 0: [ 2] 32 41
col 1: [ 6] 31 30 32 30 30 30
bindmp: 2c 00 02 02 01 c9 31 30 32 30 30 30
47
Carl Dudley – University of Wolverhampton, UK

48. Compression not Performed on Unsuitable Data
 Both tables recreated with values in col1 now set to
TO_CHAR(MOD(ROWNUM,50))
– Much less repetition of values (only every 50 rows) allowing less
compression
tnocomp
COL1 COL2 BLOCK FILE NUM_ROWS
---- ------ ----- ---- --------
1 1ZZZZZ 34 8 128
2 2ZZZZZ 35 8 128
3 3ZZZZZ 36 8 64
4 4ZZZZZ Oracle decides
... 5ZZZZZ not to compress tcomp
49 49ZZZZ
50 50ZZZZ (if the compression BLOCK FILE NUM_ROWS
factor is likely to be ----- ---- --------
1 51ZZZZ 66 8 128
2 52ZZZZ less than 1.03)
67 8 128
3 53ZZZZ 68 8 64
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49. Comparison of Heap and IOT Compression
IOT = Index Organized table
49
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50. Comparison of IOT and Heap Tables
 Tests constructed using a standard set of data in emptest
– Six columns with absence of nulls
EMPNO ENAME JOB HIREDATE SAL DEPTNO
------- ---------- --------- --------- ------ ------
1 KING PRESIDENT 17-NOV-81 5000 10
2 FORD ANALYST 03-DEC-81 3000 20
3 SCOTT ANALYST 09-DEC-82 3000 20
4 JONES MANAGER 02-APR-81 2975 20
5 BLAKE MANAGER 01-MAY-81 2850 30
6 CLARK MANAGER 09-JUN-81 2450 10
7 ALLEN SALESMAN 20-FEB-81 1600 30
8 TURNER SALESMAN 08-SEP-81 1500 30
9 MILLER CLERK 23-JAN-82 1300 10
10 WARD SALESMAN 22-FEB-81 1250 30
11 MARTIN SALESMAN 28-SEP-81 1250 30
12 ADAMS CLERK 12-JAN-83 1100 20
13 JAMES CLERK 03-DEC-81 950 30
14 SMITH CLERK 17-DEC-80 800 20
15 KING PRESIDENT 17-NOV-81 5000 10
16 FORD ANALYST 03-DEC-81 3000 20
... ... ... ... ... ...
2000000 SMITH CLERK 17-DEC-80 800 20
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51. Creation of IOTs
 empi : Conventional IOT based on emptest data
CREATE TABLE empi (empno,ename,job,hiredate,sal,deptno,
CONSTRAINT pk_empi PRIMARY KEY(ename,job,hiredate,sal,deptno,empno))
ORGANIZATION INDEX
PCTFREE 0
AS SELECT * FROM emptest
 empic : First five columns compressed
CREATE TABLE empic (empno,ename,job,hiredate,sal,deptno,
CONSTRAINT pk_empic PRIMARY KEY(ename,job,hiredate,sal,deptno,empno))
ORGANIZATION INDEX
PCTFREE 0
COMPRESS 5
AS SELECT * FROM emptest
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52. Test tables
 Four tables built having heap/IOT structures and
compressed/noncompressed data
Table Table Compress Blocks Average
Name Type row length
emph Heap No 9669 33
emphc Heap Yes 3288 33
empi IOT No 10240 33
empic IOT Yes 2560 33
 Average row length obtained from user_tables (avg_row_len)
– Compressed tables show no reduction in average row length
52
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53. Compression Data
 Number of blocks in heap tables obtained using dbms_rowid
SELECT COUNT(
DISTINCT sys.dbms_rowid.rowid_block_number(ROWID)) BLOCKS
FROM &table_name;
 Number of blocks in IOTs obtained from index validation
VALIDATE INDEX &index_name;
SELECT * FROM index_stats;
 Compressed IOTs have compression shown as DISABLED in
user_tables, but ENABLED in user_indexes
53
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54. Timings to Scan Tables (1)
SELECT deptno FROM
<table_name>;
Table Table Compress CPU Elapsed DISK I/O Query
Name Type Time Time
emph Heap No 2.40 2.34 9670 142386
emphc Heap Yes 2.95 3.13 3289 136316
empi IOT No 2.59 3.85 9507 142531
empic IOT Yes 2.60 2.63 2398 135590
 Repeat queries on empi and empic have 0 physical reads
– Could be suffering from cold buffer flooding
54
Carl Dudley – University of Wolverhampton, UK

55. Updates to Heap and IOT Tables
UPDATE <table_name> SET ename = 'XXXXXXX‘;
 Lengthens same update on Oracle9i with 230,000 row table
 Results for each employee name by at least one character
TableTable
Table Compress Blocks
Table Compress Blocks Blocks PCT CPUElapsed
Blocks PCT Elapsed
Name type type
Name beforebefore afterIncrease time time time
after Increase
update update
update update
emphemph
HeapHeap
No No 9669 1092 1280 13% 17% 61.53 148.70
11020 5mins
emphc HeapHeap Yes
emphc Yes 3288 361 2291
20010 509%535% 112.54 224.13
15mins
empi empi
IOT IOT
No No 10240 1077 2218 94%
19864 106% 155.06 275.73
4mins
empic IOT IOT
empic Yes Yes 2560 261
4792 527 87%101% 52.45 208.82
12mins
 Note the ‘explosion’ in size of the compressed heap table
55
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56. Advanced Compression in Oracle11g
(for OLTP Operations)
56
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57. Advanced Compression in Oracle 11g
 Conventional DML maintains the compression
– Inserted and updated rows remain compressed
 The compress activity is kept at a minimum
 Known as the Advanced Compression option - extra cost
 Syntax :
COMPRESS [FOR OLTP]|[BASIC]
 Compression settings tracked in user_tables
– PCTFREE is 10 by default
SELECT table_name,compression,compress_for,pct_free
FROM user_tables;
TABLE_NAME COMPRESSION COMPRESS_FOR PCT_FREE
--------------- ----------- ------------ --------
EMP_TEST DISABLED 10
EMP_DSS_COMP ENABLED BASIC 0
EMP_OLTP_COMP ENABLED OLTP 10
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58. Compressing for OLTP Operations
 Requires COMPATIBILITY = 11.1.0 (or higher)
CREATE TABLE t1 ... COMPRESS FOR ALL OPERATIONS;
 Conventionally inserted rows stay uncompressed until PCTFREE is reached
– Mimimises compression operations
Header Header Header Header Header
Free space Free space Free space Free space Free space
Conventional Block Rows are now More uncompressed Rows are
inserts are not becomes full compressed Inserts fill the block compressed
compressed again
Transaction activity
58
Carl Dudley – University of Wolverhampton, UK

59. OLTP Compression Behaviour
 Table of employee data
 Rows repeat every 7th row – lots of duplication
 8K Block filled up to PCTFREE with 166 rows
– Rows are uncompressed
– Block dump reports 826 bytes of free space
 Additional 10 rows inserted into table
– Block dump reports 5636 bytes of free space
– Shows evidence of compression of 166 rows
59
Carl Dudley – University of Wolverhampton, UK

60. OLTP Compression
 Some early results
 Table containing 3million parts records
No Compression Compression
compression for direct path for all
operations operations
35M 12M 16M
— Avoid large scale (batch) updates on OLTP compressed tables
• Significant processing overheads
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Carl Dudley – University of Wolverhampton, UK

61. OLTP Compression Test
 Table containing 500000 sales records
PROD_ID CUST_ID TIME_ID CHANNEL_ID PROMO_ID AMOUNT_SOLD ACCOUNT_TYPE
------- ------- --------- ---------- -------- ----------- -------------
13 5590 10-JAN-04 3 999 1232.16 Minor account
19 6277 23-FEB-05 1 123 7690.00 Minor account
16 6859 04-NOV-05 3 999 66.16 Minor account
: : : : : : :
 Three tables created
CREATE TABLE s_non_c PCTFREE 0 AS SELECT * FROM sales;
— Non-compressed table with fully packed blocks
CREATE TABLE s_c COMPRESS AS SELECT * FROM sales;
— Compressed table for DSS operations
CREATE TABLE s_c_all PCTFREE 0 COMPRESS FOR OLTP
AS SELECT * FROM sales;
— Compressed table for OLTP operations
61
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62. OLTP Compression Test (continued)
 Update stress test – update 94% of the rows
— No lengthening of any data
UPDATE sales SET account_type = ‘Major account’
WHERE prod_id > 13;
Non-compressed Compressed Compressed Table
table Table for DSS for OLTP
Original size 3043 848 848
(blocks)
Size after update 3043 5475 5570
(blocks)
Elapsed time for 18 secs 40 secs 1:32:03 secs
update
 Test somewhat unfair on OLTP compression
— Update is large and batch orientated
— I/O subsystem was single disk
— But still interesting?
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Carl Dudley – University of Wolverhampton, UK

63. OLTP Compression Characteristics
 Tests performed on 500,000 row employee table with data repeating every 14th
row
 Transactions update 1500, 15000 and 50,000 rows with no increase in length
of any data
– Update performance of OLTP compression gets worse as we move more
into a batch environment
Time Initial Time Update Space Update Space Update Space
Type of to Space to 1500 after 15000 after 50000 after
Table create (blocks) Scan rows update rows update rows update
(secs) (blocks) (blocks) (blocks)
Regular 11.45 3712 1.29 0.07 3712 2.09 3712 3.30 3712
DSS 5.26 768 0.35 1.38 802 4.91 976 9.41 1280
OLTP 4.23 896 0.40 1.42 980 10.04 1024 1m18.50 1938
63
Carl Dudley – University of Wolverhampton, UK

64. Test for Conventional Inserts
 Two empty tables created
– Uncompressed (emp_ins)
– Compressed for all operations (emp_ins_c_all)
 1,900,000 employee rows inserted via conventional singe row inserts
– Elapsed time to insert into emp_ins : 3m 54s
– Elapsed time to insert into emp_ins_c_all : 4m.33s
 Further tests show a 40% overhead
64
Carl Dudley – University of Wolverhampton, UK

65. Introducing OLTP Compression
 Changing a non-compressed table to OLTP compressed
ALTER TABLE t1 COMPRESS FOR OLTP;
— Will not compress existing rows
— Compresses new rows including those inserted into partially full blocks below
the high water mark
 Compression advisor for OLTP compression (test kit) can be obtained from
— http://www.oracle.com/technology/products/database/compression/download.htm
— dbmscomp.sql package header build
— prvtcomp.plb package body build
65
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66. Shrinking Unused Space in Oracle10g
66
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67. Reclaiming Space in Oracle10g
 Unused space below the High Water Mark can be reclaimed online
ALTER TABLE <table_name> SHRINK SPACE;
― Space caused by delete operations can be returned to free space
― Object must be in a tablespace with ASSM
 Two-step operation
1. Rows are moved to blocks available for insert
• Requires row movement to be enabled
ALTER TABLE <table_name> ENABLE ROW MOVEMENT;
2. High water mark (HWM) is repositioned requiring table-level lock
 Newly freed blocks are not returned to free space if COMPACT is used
― HWM is not repositioned
ALTER TABLE <table_name> SHRINK SPACE COMPACT;
67
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68. Repositioning of HWM During shrinks
HWM
Allocated block
above the high
water mark
HWM
Free block Free block Free block
 Rows are physically moved to blocks that have free space
– Shrinking causes ROWIDs to change
– Indexes (bitmap and btree) are updated accordingly
 Shrinking does not work with compressed tables
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69. Shrinking Objects
 Why Shrink?
― To reclaim space
― To increase speed of full table scans
― To allow access to table during the necessary reorganisation
― The shrink operation can be terminated/interrupted at any time
• Can be continued at a later time from point of termination
 Objects that can be SHRINKed
― Tables
― Indexes
― Materialized views
― Materialized view logs
― Dependent objects may be shrunk when a table shrinks
ALTER TABLE <table_name> SHRINK SPACE [CASCADE];
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70. Compression Outside of the Database
 RMAN backups and LOB (Secure Files) can be compressed at three
levels LOW, MEDIUM and HIGH
 Data Pump exports can be compressed
― Inline operation with the actual imp/exp job
 Log Transport services in Data Guard can compress redo data to reduce
network traffic
 For further information, see
http://www.oracle.com/technology/products/database/
oracle11g/pdf/advanced-compression-whitepaper.pdf
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71. Data Compression in Oracle
Carl Dudley
University of Wolverhampton, UK
UKOUG SIG Director
carl.dudley@wlv.ac.uk
71
Carl Dudley – University of Wolverhampton, UK