2. Get the Best Out of Oracle Data Pump Functionality
Dean Gagne (Oracle)
Viljo Hakala (Nokia)

3. Agenda
Moving large amounts of data with Transportable Tablespaces
Filtering metadata using the INCLUDE and EXCLUDE parameters
Restarting stopped/failed jobs
Hear about Nokia Corporation's database environment
How Nokia uses the REMAP_DATA parameter to scramble data
How Nokia regenerates primary keys without having to use additional software or scripts

4. What is Oracle Data Pump?
New feature starting in Oracle Database 10g Release 1
Enables very fast bulk data and metadata movement between Oracle databases
High-speed, parallel Export and Import utilities (expdp and impdp) as well as a Web-based Oracle Enterprise Manager interface
Jobs can be restarted without loss of data, whether or not the stoppage was voluntary or involuntary
Jobs support fine-grained object selection. Virtually any type of object can be included or excluded
Supports the ability to load one instance directly from another (network import) and unload a remote instance (network export)

5. Moving Large Amounts of Data With Transportable Tablespace

6. What is Transportable Tablespace?
An Oracle Database feature that allows data file transfer from a database to another via a simple os copy and a light specific export/import
Data Pump will move metadata only
Data moves with data file copy
Much faster than using direct path or external tables

7. Moving Large Amounts of Data With Transportable Tablespaces
Restrictions:
Tablespaces need to be self contained
All dependent objects must be included in tablespace set
Tablespaces need to be read only for duration of export and datafile copying.
Not restartable
Must be privileged account
Notes:
Use RMAN CONVERT to change endianness (if needed)
Data Pump moves only metadata
Can use network link
For a full list of what is exported:
select unique seq_num, full_path from datapump_paths where het_type = 'TRANSPORTABLE_EXPORT‘ order by seq_num;

8. Transportable Tablespaces Self Containment Check
Create table part_tab(id number) tablespace a partition by range (id) (partition low value less than (100) tablespace b, partition hi values less than (1000) tablespace c);
Create index part_ind on part_tab.id tablespace d;
Transportable tablespace export requires all 4 tablespaces
(a, b, c, d)
DBMS_TTS.TRANSPORT_SET_CHECK procedure to verify
accepts a comma separated list of tablespace names
SELECT * FROM transport_set_violations;

9. Transportable Tablespace Steps
Source system tasks:
Optionally run DBMS_TTS.TRANSPORT_SET_CHECK
Set tablespaces read only
Run expdp command with tablespace list
Data Pump will list required dumpfiles and datafiles in log file
Copy datafiles and dumpfiles to target system
Optionally set tablespaces read write
Target system tasks:
Run impdp command
Optionally set tablespaces to read write

10. Table Mode with Transportable=always
Can export/import tables, partitions, and indexes regardless of tablespace closure
Can’t have any storage in system, sysaux, temp tablespaces
Data Pump export will list tablespaces that need to be read only if not already done
Data Pump export will list datafiles and dumpfiles that need to be copied to target
Data Pump import can use filters – don’t have to import the complete dumpfile set
Can import only one partition – Data Pump creates a non-partitioned table
Partition_options=departition – creates non-partitioned tables for every partition

11. ptab1,ptab2, etc or ptab1:lo,ptab1:hi
Export Table Mode with Transportable=always Expdp scott/tiger tables=pt1,pt2 transportable=always
Table filter needs to be complete tables or partitions from the same table
ptab1,ptab2, etc or ptab1:lo,ptab1:hi
ORA-29335: tablespace 'USER1' is not read only ORA-29335: tablespace 'USER2’ is not read only
Dump file set for SCOTT.SYS_EXPORT_TABLE_01 is: /oracle/work/tab_tts.dmp
Datafiles required for transportable tablespace USER1: /oracle/dbs/user1.f Datafiles required for transportable tablespace USER2: /oracle/dbs/user2.f

12. Import Using Transportable Table Mode Dumpfile
Impdp scott/tiger tables=ptab2:hi rename_table=ptab2:hi:my_tab1 transport_datafiles…
Created table is my_tab1
Impdp scott/tiger tables=ptab2 partition_options=departition transport_datafiles…
Created tables are ptab2_lo, ptab2_hi
Creates all tablespaces even if no objects created due to import filters
Cleans up any unused tablespace segments that are not imported

13. Filtering Metadata

14. Filtering Metadata Overview
Perform selective export/import job based on object type
Exp/imp had limited filtering
Grants, index, triggers, statistics, constraints
Data Pump has almost complete filtering capabilities

15. Filtering Metadata Using INCLUDE and EXCLUDE Parameters
If using exclude parameter, everything else is included
If using include parameter, everything else is excluded
Can’t use exclude and include in the same Data Pump job
Specify complete path or partial path. Objects matching the specified path will be excluded/included.
Query to find exclude/include object types:
select unique seq_num, full_path from sys.datapump_paths where het_type = 'DATABASE_EXPORT' order by seq_num;
Job_type: het_type:
FULL DATABASE_EXPORT
SCHEMA SCHEMA_EXPORT
TABLE TABLE_EXPORT
TRANSPORTABLE TRANSPORTABLE_EXPORT

16. Exclude Example: expdp system/manager schema=hr exclude=statistics … vs expdp system/manager schema=hr exclude= SCHEMA_EXPORT/TABLE/STATISTICS
select unique seq_num, full_path
from sys.datapump_paths
where het_type = 'SCHEMA_EXPORT' AND full_path like '%STATISTICS%‘
order by seq_num;
77 SCHEMA_EXPORT/TABLE/INDEX/STATISTICS
78 SCHEMA_EXPORT/TABLE/INDEX/STATISTICS/INDEX_STATISTICS
221 SCHEMA_EXPORT/TABLE/INDEX/STATISTICS
222 SCHEMA_EXPORT/TABLE/INDEX/STATISTICS/FUNCTIONAL_AND_BITMAP
223 SCHEMA_EXPORT/TABLE/INDEX/STATISTICS/FUNCTIONAL_AND_BITMAP/INDEX_STATISTICS
225 SCHEMA_EXPORT/TABLE/STATISTICS
226 SCHEMA_EXPORT/TABLE/STATISTICS/TABLE_STATISTICS
227 SCHEMA_EXPORT/TABLE/STATISTICS/USER_PREF_STATISTICS

17. Include Examples: impdp system/manager tables=hr.employees vs
impdp system/manager schemas=hr include=table:\"= \'EMPLOYEES\'\"
Same results
Includes all objects that have table in the path vs
impdp system/manager schemas=hr include=table/table:\"= \'EMPLOYEES\'\“
Only includes the table
Some of the TABLE object paths
SCHEMA_EXPORT/TABLE/TABLE
SCHEMA_EXPORT/TABLE/TABLE_DATA
SCHEMA_EXPORT/TABLE/GRANT

18. Restarting Stopped/Failed Jobs

19. Restarting Data Pump Jobs
Restart jobs intentionally or unintentionally stopped
User stopped
Dumpfile exhausted
Resumable wait
Optionally change value of PARALLEL parameter
Helps to know the job name
System generated job names
SYS_IMPORT_FULL_01
SYS_EXPORT_TABLE_05
What jobs are restartable
Select * from dba_datapump_jobs
Select * from user_datapump_jobs

20. Resets dumpfile pointers to last completed object type
Restarting Stopped Export Jobs Initial Export Command: expdp system/manager job_name=datapump_exp…
Restart command:
expdp system/manager attach=datapump_exp
Export> start_job
Resets dumpfile pointers to last completed object type
Restarts the job after the last completed object

21. Restarting Stopped Import Jobs initial import command: impdp system/manager job_name=datapump_imp…
Restart command:
Impdp system/manager attach=system.datapump_imp
Import> Start_job[=skip_current]
Detects the last object created
If creating an object and the object exists, checks to see if object was in progress in failed job
Data that was being loaded would have either been committed or not. Committed data marked complete, uncommitted data will be retried.

22. Restarting Stopped Network Import Jobs Initial Import Command impdp scott/tiger job_name=net_imp network_link=dbs1…
Restart command:
Impdp scott/tiger attach=net_imp
Import> Start_job[=skip_current]
Restarts on object type
If creating object and object exists, checks to see if object was in progress in failed job
Data that was being loaded would have either been committed or not. Committed data marked complete, uncommitted data will be retried.

23. Hear about Nokia Corporation's Database Environment and how they use Oracle Data Pump
Viljo Hakala (Nokia)

24. Nokia Nokia is a world leader in mobility
Head office in Finland; R&D, production, sales, marketing activities around the world
World’s #1 manufacturer of mobile devices, with estimated 40% share of global device market in 2009
Mobile device volumes 468 million units
Net sales EUR 50.7 billion
Operating profit EUR 5.0 billion
employees at year end (including Nokia Siemens Networks)
Strong R&D presence in 16 countries
R&D investment EUR 6.0 billion
Sales in more than 150 countries 24

25. Oracle @ Nokia 20 DBAs, 2300 databases
Grid Control: used for almost 5 years, #1 tool for all DBAs
Applications:
OLTP: 50%, Hybrid databases: 40%, DW: 10% + Teradata
About Oracle; also MySQL 200+, MS SQL 300+
11g: 4%
10g: 80%
9i: 15%
8i: 1%
Host platforms:
Solaris: 35%
HP-UX: 35%
Linux: 20%
Windows 10% 25

26. Oracle @ Nokia Production databases: single, HA Oracle, RAC, HA MySQL
5 MB – 7 TB range
Leverage virtually all Oracle database features
DBAs share responsibilities
Many federated databases: Streams, Advanced Replication, Materialized views, DB links
Common Oracle platform, Common MySQL platform and Common MS SQL platform
99% of databases backed up with RMAN
8 RMAN catalog databases
10 PB+ backed up / month 26

27. What is Data Masking? 27

28. Hints for Effective Data Masking
Be sure to mask all data that is not sensitive, but can be used to create sensitive data.
You should not be able to reverse the masking for it to be secure. Never should you be able to retrieve original values from masked values.
The masked or obfuscated data should follow the source data format. Masking should protect data, but still allow realistic looking data to be used for testing.
Integrity of relations in data should be followed. When masking a primary key, the foreign keys should also be masked using the same masking method.
You should be able to repeat the masking process for it to be an effective day to day process.

29. Data Masking with Oracle Data Pump
One of the best uses of Oracle Data Pump is to load test and development systems with data from production systems for realistic testing purposes.
Due to company rules or legal regulations, sensitive data in the non-production systems has to be replaced with realistic looking data to enable effective testing.
Effective data masking routines should support automation and Oracle Data Pump provides several ways to automate data imports and exports with masking. 29

30. Data Masking with Oracle Data Pump
In addition to Data Masking Pack in Grid Control, Oracle Data Pump provides a method to mask data: REMAP_DATA parameter introduced in Oracle Database 11g.
Oracle Data Pump’s REMAP_DATA feature uses a remapping function to rewrite data.
For example, a column with phone numbers could be replaced by a numbers generated by a REMAP_DATA function. 30

31. Data Masking with Oracle Data Pump
REMAP_DATA allows transformation of column’s data while exporting (expdp) or importing (impdp) by using a remapping function in the database.
REMAP_DATA with Data Pump is usually faster than a custom UPDATE statement for masking data.
To mask multiple columns in the same process and command, the REMAP_DATA parameter can be used multiple times. 31

32. Data Masking with Oracle Data Pump: Quick example
REMAP_DATA=[schema.]tablename.column_name:[schema.]pk g.function
impdp dumpfile=data.dmp REMAP_DATA=scott.orders.customer_name:scott.maskpkg.mas k
expdp dumpfile=data.dmp REMAP_DATA=scott.orders.customer_name:scott.maskpkg.mas k 32

33. Data Masking with Oracle Data Pump: Syntax
REMAP_DATA=
[schema1.]tablename.column_na me:
[schema2.]pkg.function
schema1 -- the schema with the table to be remapped. By default, this is the schema of the user doing the export.
tablename -- the table which column will be remapped.
column_name -- which is to be remapped.
schema2 -- with the PL/SQL package for remapping function. As a default, this is the schema of the user doing the export.
pkg -- the name of the PL/SQL package with the remapping function.
function -- the name of the remap function in the PL/SQL package 33

34. Data Masking with Oracle Data Pump: Restrictions
Data types must be same in the table column, masking function parameter, and function return value.
No commits or rollbacks should be done in the masking function.
No direct path loads can be used in import process with REMAP_DATA.
Note: Operation of long export/import data pump processes can be monitored from the v$session_longops view, but the estimated values do not take into account REMAP_DATA operations. 34

35. Data Masking with Oracle Data Pump: Example
Create a table in the CUSTOMERS schema called phones
SQL>
CREATE TABLE CUSTOMERS.PHONES (
MODELNAME VARCHAR(20) NOT NULL,
PHONENUMBER VARCHAR2(50) );
insert into CUSTOMERS.PHONES values(’N900’,’ ’);
insert into CUSTOMERS.PHONES values(’N8’,’ ’);
insert into CUSTOMERS.PHONES values(’N7’,’ ’);
Commit; 35

36. Data Masking with Oracle Data Pump: Example
We then need to create a function for remapping
create or replace package customers.maskpkg as
function masknumber(phonenumber varchar2) return varchar2;
end; /
create or replace package body customers.maskpkg as
function masknumber (phonenumber varchar2) return varchar2 is
begin return substr(phonenumber,1,4)||round(dbms_ra ndom.value (100,999))|| lpad(round(dbms_random.value (1,9999)),4,'0');
The function masknumber will accept a varchar2 type and returns a random phone number in varchar2 type 36

37. Data Masking with Oracle Data Pump: Example
This example will mask one column: phonenumber to the export output file
$ expdp customers/manager \
tables=customers.phones \
dumpfile=phones_masked.dmp \
directory=dumpdir \
remap_data=\
customers.phones.phonenumber: \
customers.maskpkg.masknumber
Export the data from the customers.phones table with expdp utility and use the REMAP_DATA option for masking the data in the dump file with the function customers.maskpkg.masknumber created earlier
By default the owner of the remap function is the user running expdp/impdp
Now the dumpfile phones_masked.dmp can be used for testing environments 37

38. Data Masking with Oracle Data Pump: Example, before masking
SQL> select * from customers.phones
MODELNAME PHONENUMBER N N N 38

39. Data Masking with Oracle Data Pump: import/replace table
This example will import the masked dump file
and replace the existing table with masked column data
$ impdp customers/manager \
tables=customers.phones \
dumpfile=phones_masked.dmp \
directory=dumpdir \
table_exists_action=REPLACE 39

40. Data Masking with Oracle Data Pump: Example, after import
SQL> select * from customers.phones
MODELNAME PHONENUMBER N N N 40

41. Data Masking with Oracle Data Pump: Import
REMAP_DATA parameter can also be used in the import process
Use impdp if you have an existing dump file and you want to mask the data when loading into the database
$ impdp customers/manager TABLE_EXISTS_ACTION=replace dumpfile=phones.dmp directory=dumpdir remap_data=customers.phones.phonenumber:customers.maskpkg.mask number 41

42. Primary Key Regeneration with REMAP_DATA
To change primary keys after development phase of an application, use REMAP_DATA parameter.
It is possible to use Data Pump’s REMAP_DATA to change the primary keys within a database by writing a remapping function which follows the new primary key format.
Primary key conflicts can happen when loading data to an existing table in a database.
With REMAP_DATA, conflicts can be avoided by changing the column values during import. 42

43. When not to use Masking
It can be very difficult and expensive to mask data especially in large BI/DWH systems due to the amount of data and refining operations.
It is often easier to use a data model in which sensitive data can be
limited to one location, referenced via surrogate keys, and
kept safe with strict access policies and auditing.
But when giving out data for simplified testing needs, masking is a good tool!

44. Other Methods to Secure Data in Oracle Database 11g Release 2
Oracle Virtual Private Database (VPD aka Fine Grained Access Control) feature allows filtering of data at row-level for runtime SQL statements according to a defined policy.
Data Masking Pack for Enterprise Manager. Centralized masking solution within Grid Control.
Oracle Advanced Security Option for encrypting data at tablespace or column level.
Oracle Label Security for restricting access to data based on policies.
Oracle Database Vault for separating roles and restricting access to data per roles.

45. Oracle Data Pump at Nokia: Summary
Oracle Data Pump is used extensively at Nokia to move data and metadata between systems.
REMAP_DATA option is a great way to mask sensitive data for security purposes.
REMAP_DATA option can also be used to change primary keys.
Oracle has many features for securing data at different levels.
Security for data is not just Obscurity. It is a process and requires time. 45

46. Oracle Data Pump Summary
Transportable Tablespaces
Tablespace mode
Table mode
Filtering metadata using the INCLUDE and EXCLUDE parameters
Restarting stopped/failed jobs
REMAP_DATA parameter to scramble data
How to generate primary keys without having to use additional software or scripts

47. Questions?
Oracle Data Pump
Demogrounds Booth – Moscone West W-025