1

Practical Space Management in Data

Warehouse EnvironmentsHamid Minoui

Database Specialists, Inc.

www.dbspecialists.com

hminoui@dbspecialists.com

2

Objectives To point out data warehouse space

management issues Suggest resolutions Recommend space management

methodologies Provide proactive prevention strategies Cover both Oracle 9i and Oracle 10g

space management features

3

Characteristics of a Data Warehouse

The data:– Large amount of data loads and ETL operations– Very large size (Terabytes)– Change in structure of source data– Contains lots of historical data– Data massaging and aggregations– Multiple sources of data– Dynamic nature of data

4

Characteristics of a Data Warehouse (continued) Maintenance activities:

– Space management– Table re-organizations– Index rebuilds– Partition maintenance– Refresh maintenance on materialized views– Job and scheduling management

5

Characteristics of a Data Warehouse (continued) Typical issues:

– Data integrity issues– Data security issues– Space issues– Query performance issues– Duplicate rows

6

Characteristics of a Data Warehouse (continued) Database features frequently used:

– Materialized views (MV)– Bitmap indexes and bitmap-join indexes– Index organized tables (IOT)– Parallel execution– Table and index partitioning– Table and index compression– Load utilities and facilities

7

Other Characteristics Star schemas, snow flakes or 3rd Normal Form Have dimensions and hierarchy Frequent need to collect statistics Use of bulk and parallel loads Variety in the generated queries Dynamic nature of queries Divided into areas (staging, ODS, and target area) Often associated with smaller data marts

8

Performance Tuning and Resolutions

Frequent query tuning Star transformation De-normalization Pre-aggregations via materialized views B*Tree, IOT, function based, bitmap, bitmap-

join indexes Use of database resource management

9

Why is Space a Coveted Resource in a Data

Warehouse? Lots of disk space is consumed Stores all enterprise data Segments are mostly large Many indexes Years of historical data kept online Many versions of the same data Duplicated and de-normalized data Various levels and dimensions of data (monthly,

weekly, daily)

10

Why is Space a Coveted Resource in a Data

Warehouse? Enough reserve space needed: – For daily/weekly/monthly growth– Recall offline old data when needed– Data correction– Materialized views and their growth– Emergency needs– Data files and tablespace growth– Temporary tablespaces

11

Reacting to Space Issues Down sides:

– Often, not enough time to react– Delay in the load– Wasted resources to reload

Up sides:– Loads are usually scheduled– Once data is loaded, most of it won’t change

12

Issues with Database Backups

in a Data Warehouse Too many files to backup every night Backup takes a long time to complete System resources busy during backup Possible licensing issues with third-party

backup software Restoring and recovery after a failure can take

a long time

13

A Typical Backup Strategy Make non-current table spaces READONLY every

month Perform a special backup of READONLY

tablespaces Exclude the READONLY table spaces from regular

hot backups Never backup temporary tablespaces

Caveat: You must wait until all transactions are committed

14

Avoiding Unnecessary Redo Log Generation

Create some tables and all indexes with NOLOGGING for any segment that can be re-generated without doing a database recovery:

• SQL*Loader with direct path load• CREATE TABLE AS SELECT from external or transient tables• INSERT using +append hint• Use global temporary tables

insert /* +append */ into transiant_tableselect * from source_table ;

create table transient_tableas select * from source_table ;

15

Speeding Up Bulk Load Operations

Before the load:– Make all non-unique indexes unusable– Disable the primary and unique constraints if the

source data is trusted– Disable all triggers on the table– Set the session to skip unusable indexes

16

Speeding Up Bulk Load Operations

Implement the load:– Use append and parallel hints with insert– Commit the transaction

After the load:– Rebuild indexes– Enable triggers and constraints

17

Space Issues in Data Warehouses

Permanent tablespaces (data, indexes) Temporary tablespaces (temp segments) UNDO segments and tablespace

18

Space Issues with Permanent Tablespaces Caused by:

– Poor extent sizing– Setting maxextents– PCT_INCREASE > 0– Small data files (tablespaces)– User quota on tablespace

19

Space Issues with Temporary Tablespace

Caused by:– Not enough space for the sort segments– Other temp segments such as global temporary

tables– Multiple users sharing the same temporary space– Multiple queries with sort requirements running at

any time

20

Space Issues with Temporary Tablespace

Partially resolved by:– Oracle 9i - Dynamic PGA memory allocation

• PGA_AGGREGATE_TARGET=<integer value>• WORKAREA_SIZE_POLICY=AUTO

– Oracle 10g - Tablespace Group assignment

21

Space Issues Associated with Undo Segments

Long running queries causing ORA-1555 (snapshot too old)

Small UNDO tablespace Small rollback segments

22

Database Block Size (DB_BLOCK_SIZE)

Should seriously be considered An important decision with new data

warehouse projects Inappropriate value can be disastrous and

detrimental Small value can:

– Impact I/O efficiency for majority of queries– Negatively influence overall database performance

23

Appropriate DB_BLOCK_SIZE Value

Multiple of the OS block size As large as your I/O subsystem can handle in

a single read As large as supported by Oracle Best benefit from larger block size if:

– Database is configured on raw devices, or– Direct I/O is available to you.

24

Benefits of Larger DB_BLOCK_SIZE Value

Efficiency with index scan– A larger block size reduces the number of reads required to

probe an index and scan a range of values from its leaf blocks

Less memory requirement for buffer cache– Fewer buffers needed for index branch blocks

Better compression ratio for tables, indexes Improvement in block density

– Amount of space used by fixed portion

of bock header is reduced

25

Benefits of Larger DB_BLOCK_SIZE Value

Blocks can accommodate longer rows; less chance for row chaining

Less occurrence of ORA-1555– Increase in size of the transaction table in undo

segments header blocks

Fewer writes required for data loads– Because of the reduced block level overhead, less

redo logs are generated when blocks are modified sequentially

26

Disks, I/O and Database Files Configuration

A poorly configured I/O subsystem can badly impact I/O performance

Poor I/O performance can impair a data warehouse

Configure disk and distribute data for read and write efficiency

Use raw I/O if possible, otherwise use direct I/O Make use of asynchronous I/O, parallel read and

parallel writes

27

Disks, I/O and Database Files Configuration

Stripe and Mirror or Mirror and Stripe the disks – RAID-1+0 or RAID-0+1

Evenly spread your data and Stripe And Mirror Everything (SAME) on many disks

Reserve room on file systems for auto extendable files

28

Managing the UNDO Segments

Manual undo (rollback segments) management– Pre Oracle 9i practices– Too many manual interventions by DBA

29

Managing UNDO (continued)

Automatic Undo Management (AUM) – Much better – Highly recommended– Allows controlling retention of committed

transactions undo information (UNDO_RETENTION)

– Better monitoring statistics– Infrequent occurrence of ORA-1555– SMON periodically manage space and shrinks

undo segments

30

UNDO_RETENTION Parameter Setting

Set to a value equal to the time used by the longest running query

Undo is ‘expired’ when retention time is reached Expired undo will be de-allocated if needed by

new transactions Unexpired undo are re-used if space is needed

(undo reuse) Default value is 300 seconds

31

Undo Reuse and Undo Stealing

Undo Reuse:Unexpired undo of the same segment will be reused

Undo Stealing:Unexpired undo of another segment is used

Undo reuse is more common. Occurs when– UNDO tablespace is too small, or– UNDO_RETENTION value is too large

32

Monitoring the UNDO Segments Statistics

Statistics are gathered in V$UNDOSTAT every 10 minutes

Helps sizing UNDO tablespaces and tune UNDO_RETENTION

Statistics are retained for 7 days

33

V$UNDOSTAT

BEGIN_TIME Beginning time for this interval

END_TIME Ending time for this interval

UNDOTSN Tablespace ID of the last active undo within the interval

UNDOBLKS Number of consumed undo blocks within the period

MAXQUERYLEN The longest length of time (in seconds) a query took to complete within this period

TXNCOUNT Total number of transactions executed with the period

34

V$UNDOSTAT (continued)

UNXPSTEALCNT Number of attempts to obtain undo space by stealing unexpired extents from other undo segments

UNXPBLKRELCNT Number of unexpired blocks released from undo segments to be used by other transactions

SSOLDERRNT Number of times ORA-1555 occurred with the period

NOSPACERRCNT Number of times space was unavailable in the undo tablespace when requested and failed

35

Tuning UNDO_RETENTION Oracle 9i:

– Manually adjust to the time taken by the longest query

SELECT MAX (MAXQUERYLEN) FROM V$UNDOSTAT;

Oracle 10g: – Automatically tracked and tuned by RDBMS

36

The UNDO Tablespace Created at DB creation or with CREATE UNDO

TABLESPACE Use V$UNDOSTAT for sizing and monitoring Space issues if UNDO_RETENTION is too large Use AUTOEXTEND RETENTION_GUARANTEE clause Sizing formula:

Undo Segment Space Required (MB) = (undo_retention * undo_blcks/secs * DB_BLOCK_Size)/1024

37

Database Fragmentation Issues

Best to reduce or eliminate fragmentation to avoid wastage and improve performance– Tablespace level (or file level) fragmentation– Segment level fragmentation– Block level fragmentation

38

Tablespace Level Fragmentation

Bubble Fragmentation– Free block of space not large enough for another

extent

Honeycomb Fragmentation– Free un-coalesced space next to each other but

considered separate

39

Segment Level Fragmentation

Space allocated to segment is not fully utilized (wasted)– Space above the high water mark (unused blocks)– Free segment blocks below the high water mark

40

Block Level Fragmentation Blocks are not empty but there is space within

a block that is not used Caused by:

– Setting of PCTFREE and PCTUSED– Deletions– Row migrations

41

Tablespace Planning Use locally managed tablespaces (LMTs) with

UNIFORM size extents– 64K bitmaps on file header are used to manage

extents– Improves performance and significantly reduces

overhead associated with updating dictionary tables (recursive SQL)

– No need to use ST enqueue– No more tablespace fragmentation

42

Tablespace Planning Use Automatic Segment Space Management

(ASSM)– Set at the tablespace level– Tablespace must be locally managed– Uses bitmap instead of freelist to manage space

within segments

43

Benefits of ASSM No more need for FREELISTS, FREELIST

GROUPS and PCTUSED Reduces segment level and block level

fragmentations Reduces the number of buffer free waits Adds efficiency to space usage Provides better use of space within the blocks

44

LMT Considerations The bitmap is 64K

– Make the size of each file a multiple of UNIFORM extent+64K

Storage parameters– Avoid setting them– If already defined on segments reorganize, or

rebuild with storage parameters matching tablespace

45

Multiple Tablespace Size Models

SAFE (methodology) Group segments according to size (3 groups) Use 3 tablespace model having different

UNIFORM extents Assign each group to one of the size model Develop a naming convention

Segment Size Extent Size Size Model

< 128 M 128 KB Small

>= 128 M & < 4 GB 4 MB Medium

>= 4 GB 128 MB Large

46

Tablespaces for Different Types of Segments

Separate indexes and tables– Better manageability– Different type of usage– Reduces wastage (indexes are rebuilt often in data

warehouses)

47

Adjust Settings of PCTFREE and PCTUSED Parameters Avoid using default values Set according to usage Most of the times PCTFREE=0 and

PCTFREE=99 should be enough If ASSM, no need for PCTUSED More compact data in blocks reduces waste

and improves I/O

48

Use Index Organized Tables (IOTs)

When most of the columns are indexed When associated tables are used Columns are pre-sorted Makes better use of space and improve

performance Good for certain data warehouse tables

49

Table Compression– Introduced in Oracle 9i R2– Improves read only operations and factors out

repetitive values within a block– Replaces duplicate values in a block with a

reference to a symbol table in the block– Very low CPU overhead to reconstruct the block– Significantly fewer blocks, leading to better I/O– Very flexible (not all blocks are compressed)– Associated with bulk load operations

50

Table Compression To compress a table use:

ALTER TABLE t1 MOVE compress;

To compress a table partition use:ALTER TABLE T1 MOVE PARTTION P1 compress;

Alternative way CTAS compress:CREATE TABLE T1 compressAS SELECT * FROM T1_UNCOMPRESSED;

Table or partition not available (locked) during move

Use DBMS_REDEFINITION for online move

51

To Get the Best Results To achieve the best compression ratio:

1. Analyze the table to get column statistics SELECT COLUMN_NAME, NUM_DISTINCT, NUM_NULLS, AVG_COL_LENFROM DBA_TAB_COLUMNS ;

2. Identify best candidate columns for sorting as columns with• Lowest number of distinct values (low NUM_DISTINCT)• Least amount of null values (low NUM_NULLS)• Longest average length (high AVG_COL_LEN)

3. CTAS compress and use order by candidate_column

52

Table Compression Limitations

Can not be used on LOB field Can not be used for IOTs Can not compress tables with bitmap indexes With Oracle 9i, cannot drop or add columns to

compressed tables

53

Index Key Compression Introduced in Oracle 8i Compression of leading index columns Indexes are grouped into a suffix and prefix

entry– Suffix entry made out of unique pieces – Prefix entry consist of the grouping piece

Can offer significant space savings and better I/O performance

54

Index Key Compression Example

Current year’s Car Inventory table, index CAR_IND indexes columns are: Type, Color, Model

Before compression:

<SUV><Black><Rock Climber> <Sedan><Blue><Charisma>

<SUV><Black><Jungle Cruiser> <Sedan><Blue><Fantasy>

<SUV><Black><Mountaineer> <Sedan><Blue><Starlet>

…. …

55

Index Key Compression Example (continued)

ALTER INDEX CAR_IND compress 3; After compression:

<SUV><Black> <Rock Climber> <Jungle Cruiser> <Mountaineer>

<Sedan><Blue> <Charisma><Fantasy><Starlet>

….

56

Index Key Compression Partitioned indexes cannot be compressed Bitmap indexes cannot be compressed Can be defined on IOT Slight CPU overhead during index scan Consumes much less space Increases I/O throughput and buffer cache

efficiency Ideal for data warehouses

57

Identifying Keys to Compress

1. Validate or analyze the indexVALIDATE INDEX INDX1;

2. Query the index_stats viewSELECT NAME, OPT_CMPR_COUNT, OPT_CMPR_PCTSAVEFROM index_stats;

3. Examine outputNAME OPT_CMPR_COUNT OPT_CMPR_PCTSAVE------- -------------- ----------------------INDX1 2 57

58

De-Allocating Unused Space Segment Level:

– Blocks above the segment high water mark (unused blocks)

– Space below the segment high water mark (free blocks)

Tablespace Level– Free space within tablespace– Data file level– Unallocated space above the highest allocated

extent (file high water mark)

59

Identify Segment Space Usage

DBMS_SPACE.UNUSED_SPACE– Information about amount of unused space in

segment and position of high water mark

DBMS_SPACE.FREE_BLOCKS– Information about the number of blocks on the

freelist groups

DBMS_SPACE.SPACE_USAGE– Information about the space usage of blocks under

the high water mark

60

De-Allocate Segment Free Space

Unused blocks-ALTER [TABLE | INDEX | CLUSTER] segment_name

DEALLOCATE UNUSED [KEEP nK ]

– De-allocates only space above segment high water mark, retaining space specified by KEEP

Other Unused space-–Pre Oracle 10g – Reorganize table, rebuild index

• Table move, export/import, DBMS_REDEFINITION interface)

–Oracle 10g – Online segment shrink

61

Two-Phase Online Segment Shrink

ALTER TABLE table SHRINK SPACE;– Phase 1:

• A series of DELETE and INSERT statements applied to move data to the beginning of the segment

• DML-compatible changes are held on rows and blocks

– Phase 2:• High water mark adjusted to the appropriate location.• Exclusive lock is held• Unused blocks (above high water mark) are de-allocated

62

One-Phase Online Segment Shrink

ALTER TABLE table SHRINK SPACE COMPACT;

With COMPACT keyword only the first phase is executed.

To implement phase 2, issue it without COMPACT keyword at a later time

63

One-Phase Online Segment Shrink (continued)

Restrictions– Row movement must be enabled– Triggers based on ROWID of table must be

disabled

In data warehouses, locking might not be a problem on some tables

64

De-allocating Space at the Tablespace Level

Caused by tablespace fragmentation– Index rebuilds, table moves, partition move, etc.– Not having UNIFORM size extents

65

De-allocating Space at the Data File Level

File size larger than the last block used in the file

Size over-estimated Auto extended

66

Shrinking Data Files

The statement:ALTER DATABASE DATAFILE ‘file_name’ resize n (K | M);

– Attempts to size the data file to exactly n K (or M)– It is safe. It will fail with ORA-03297, if there are

blocks of data beyond the requested resize value

ORA-03297: File contains nnn blocks of data beyond requested resize value.

67

Steps to Shrink Data Files to High Water Mark Position

1) Create a temporary table preferably a GTT

CREATE global temporary table SPACE_ADMIN_GTTON COMMIT PRESERVE ROWS ASSELECT FILE_NAME, TABLESPACE_NAME, BYTES, BYTES, BYTESFROM DBA_DATAFILES WHERE 1=0;

2) Create another table with name of tablespace to shrink

CREATE GLOBAL TEMPORAY TABLE SHRINKING_TBS_GTTON COMMIT PRESERVE ROWSASSELECT TABLESPACE_NAME FROM DBA_TABLESPACESWHERE TABLESPACE_NAME in (‘TBS1’,’TBS2’,’TBS3’);COMMIT;

68

Steps to Shrink Data Files to High Water Mark Position

(continued)3) Get DB_BLOCK_SIZE

column value new_val blksizeselect value from v$parameterwhere name = 'db_block_size';

69

Steps to Shrink Data Files to High Water Mark Position

(continued)4) Calculate the file’s high water mark and save

INSERT INTO SPACE_ADMIN_GTTSELECT file_name, tablespace_name, ceil( (nvl(hwm,1)*&&blksize)/1024/1024 ) smallest, ceil( blocks*&&blksize/1024/1024) currsize, ceil( blocks*&&blksize/1024/1024) - ceil( (nvl(hwm,1)*&&blksize)/1024/1024 ) savingsFROM DBA_DATA_FILES a, ( SELECT file_id, max(block_id+blocks-1) hwm FROM DBA_EXTENTS GROUP BY file_id ) bWHERE a.file_id = b.file_id(+)AND a.tablespace_name IN (SELECT tablespace_name FROM SHRINKING_TBS_GTT);COMMIT;

70

Steps to Shrink Data Files to High Water Mark Position

(continued)5) Generate ALTER DATABASE commands

column cmd format a95 word_wrappedset trimspool onSPOOL c:\TMP\dbf_resize.sqlSELECT 'alter database datafile '''||file_name||''' resize ' || smallest || 'm;' cmdFROM SPACE_ADMIN_GTTWHERE savings >= 5;SPOOL OFF

71

Automatically Resolving Space Issues

Oracle 9i Feature called RESUMABLE SPACE ALLOCATION

Allows an active session to be suspended if a space issue is encountered

The session resumes automatically when – Space issue is fixed– A timeout period (default: 2 hours) is reached

Beneficial for data warehouse environments

72

Steps for Resumable Space Allocation

1. DBA grants RESUMABLE privilege to user

2. User makes session resumable withALTER SESSION ENABLE RESUMABLE ;

3. If session encounters space problem, it is suspended

73

Steps for Resumable Space Allocation

4. If AFTER SUPSPEND TRIGGER exists, it gets executed

5. If trigger does not exit (or disabled) or if the trigger does not fix the space problem, session remains suspended

6. Session resumes when space problem is fixed or timeout value is reached

74

Other Helpful Space-Related Features

Oracle-Managed Datafiles (OMF) DBA_ADVISOR family of views Oracle10g Workload Repository (AWR) and

segment advisor Oracle 10g Grid Control for monitoring

75

Conclusion Oracle is consistent in offering new space

management related features in every release Should be used by DBAs for best practices They enhance performance, reduce waste,

improve availability, reduce frequency of failures, and provide better monitoring

Data warehouse operations that rely heavily on space and I/O performance benefit the most from these features

76

Contact Information

Hamid MinouiDatabase Specialists, Inc.388 Market Street, Suite 400San Francisco, CA 94111

Tel: 415/344-0500Email: hminoui@dbspecialists.comWeb: www.dbspecialists.com