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Get the Best Out of Oracle Data Pump Functionality. Dean Gagne (Oracle) Viljo Hakala (Nokia). Agenda. Moving large amounts of data with Transportable Tablespaces Filtering metadata using the INCLUDE and EXCLUDE parameters Restarting stopped/failed jobs

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Get the best out of oracle data pump functionality l.jpg

Get the Best Out of Oracle Data Pump Functionality

Dean Gagne (Oracle)

Viljo Hakala (Nokia)

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  • 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

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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)

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Moving Large Amounts of Data With Transportable Tablespace

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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

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Moving Large Amounts of Data With Transportable Tablespaces


  • 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


  • 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_pathfrom datapump_pathswhere het_type = 'TRANSPORTABLE_EXPORT‘order by seq_num;

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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 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;

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Transportable Tablespace Steps

Source system tasks:


  • 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

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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

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Export Table Mode with Transportable=alwaysExpdp 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 onlyORA-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.fDatafiles required for transportable tablespace USER2: /oracle/dbs/user2.f

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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

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Filtering Metadata

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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

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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_pathfrom sys.datapump_pathswhere het_type = 'DATABASE_EXPORT' order by seq_num;

    • Job_type:het_type:





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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;









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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


    impdp system/manager schemas=hr include=table/table:\"= \'EMPLOYEES\'\“

  • Only includes the table

    Some of the TABLE object paths




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Restarting Stopped/Failed Jobs

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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


  • What jobs are restartable

    • Select * from dba_datapump_jobs

    • Select * from user_datapump_jobs

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Restarting Stopped Export JobsInitial 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

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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.

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Restarting Stopped Network Import JobsInitial Import Commandimpdp 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.

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Hear about Nokia Corporation's Database Environment and how they use Oracle Data Pump

Viljo Hakala (Nokia)

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  • 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

  • 128 445 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


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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 1800+ 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%


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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


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What is Data Masking?


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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.

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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.


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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.


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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.


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Data Masking with Oracle Data Pump: Quick example

  • REMAP_DATA=[schema.]tablename.column_name:[schema.]pkg.function

  • impdp dumpfile=data.dmp REMAP_DATA=scott.orders.customer_name:scott.maskpkg.mask

  • expdp dumpfile=data.dmp REMAP_DATA=scott.orders.customer_name:scott.maskpkg.mask


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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

Data Masking with Oracle Data Pump:Syntax


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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.


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Data Masking with Oracle Data Pump: Example

  • Create a table in the CUSTOMERS schema called phones







    insert into CUSTOMERS.PHONES values(’N900’,’+3581234567’);

    insert into CUSTOMERS.PHONES values(’N8’,’+3589817654’);

    insert into CUSTOMERS.PHONES values(’N7’,’+3584834819’);



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We then need to create a function for remapping

create or replace package customers.maskpkg


function masknumber(phonenumber varchar2) return varchar2;



create or replace package body customers.maskpkg as

function masknumber (phonenumber varchar2) return varchar2 is

begin return substr(phonenumber,1,4)||round(dbms_random.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

Data Masking with Oracle Data Pump:Example


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This example will mask one column: phonenumber to the export output file

$ expdp customers/manager \

tables=customers.phones \

dumpfile=phones_masked.dmp \

directory=dumpdir \




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

Data Masking with Oracle Data Pump:Example


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Data Masking with Oracle Data Pump:Example, before masking

  • SQL> select * from customers.phones


    ---------- --------------------

    N900 +3591234567

    N8 +3589817654

    N7 +3584834819


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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 \



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Data Masking with Oracle Data Pump:Example, after import

  • SQL> select * from customers.phones


    ---------- --------------------

    N900 +35815499474

    N8 +3584800578

    N7 +3581247839


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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.masknumber


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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.


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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!

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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.

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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.


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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

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