Oracle to MySQL 2012

Oracle - MySQL
Migration
Marco Tusa
MySQL CTL
MySQL Conference 12 April 2012

Why Pythian
• Recognized Leader:
• Global industry-leader in remote database administration services and consulting for Oracle, Oracle
Applications, MySQL and SQL Server
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emergency response

3
© 2012 Pythian

Who am I?
• Cluster Technical Leader at Pythian for MySQL technology
• Previous manager Professional Service South EMEA at
MySQL/SUN/Oracle
• In MySQL before the SUN gets on us
• Lead the team responsible for Oracle & MySQL DBs service in
support to technical systems, at Food and Agriculture Organization
of United Nations (FAO of UN)
• Lead developer & system administrator teams in FAO managing the
Intranet/Internet infrastructure.
• Worked (a lot) in developing countries like (Ethiopia, Senegal,
Ghana, Egypt …)
• My Profile http://it.linkedin.com/in/marcotusa
• Email tusa@pythian.com marcotusa@tusacentral.net

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Why MySQL
I like to start from :*
•Scalability and Flexibility
•High Performance
•High Availability
•Robust Transactional Support
•Web and Data Warehouse Strengths
•Strong Data Protection
•Comprehensive Application Development
•Management Ease
•Open Source Freedom and 24 x 7 Support
•Lowest Total Cost of Ownership
*http://www.mysql.com/why-mysql/topreasons.html

5 © 2012 Pythian

Why MySQL?
MySQL TCO Savings Calculator (now)*

*From www.mysql.com TCO calculator

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Why MySQL?
MySQL TCO Savings Calculator (before)

*From www.mysql.com TCO calculator ancient time

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Why MySQL?
All good then? When should I migrate my
environment to MySQL?
Cost is not the only aspect to consider:
• Need to use MySQL correctly;
• Be aware of existing issues
• good list of them from Baron*
• Identify the real effort require for the migration.
*http://www.xaprb.com/blog/2009/03/13/50-things-to-know-before-migrating-oracle-
to-mysql/

8 © 2012 Pythian

12 things to know about MySQL (1)
1 Subqueries are poorly optimized (optimization expected in 5.6
http://dev.mysql.com/doc/refman/5.6/en/from-clause-subquery-optimization.html)

2 There is limited ability to audit (no user reference unless General log active).

3 Authentication is built-in. There is no LDAP, Active Directory, or other external
authentication capability. (New PAM module available for 5.5 but only enterprise)

4 Data integrity checking is very weak, and even basic integrity constraints
cannot always be enforced. (replication)

5 Most queries can use only a single index per table; some multi-index query
plans exist in certain cases, but the cost is usually underestimated by the query
optimizer, and they are often slower than a table scan.

6 Foreign keys are not supported in most storage engines.

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12 things to know about MySQL (2)
7 Execution plans are not cached globally, only per-connection.

8 There are no integrated or add-on business intelligence, OLAP cube, etc
packages.

9 There are no materialized views (also if we can use Event scheduler)

10 Replication is asynchronous and has many limitations and edge cases.

11 DDL such as ALTER TABLE or CREATE TABLE is non-transactional. It commits
open transactions and cannot be rolled back or crash-recovered.

12 Each storage engine can have widely varying behavior, features, and
properties. (positive and negative)

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Getting Started?
Prepare a plan, and do not improvise

• Analyze the source (from application to data design)
• Identify show stoppers
• Identify how to map what to what
• Identify how to organize the target

Most important:
Be ready to do not force migration. If it does not make sense to proceed,
STOP!

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The Motto
Use the right tool for the job

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Most common source cases
• Database is used only to store data all the logic reside in
the application

• Database contains logic such as stored procedure and
complex package

• Database containing data for data warehouse

• Real time data and historical records (telephone
company)

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Define the process
Analyze

Understand
Match
Src/dest
Something

Re/Design Extract src
fails

Convert

Import Schemas Logic data

Validate Test/POC Index Partition

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Mitigating risk of failure (analyze)
When analyzing the source database(s) what should be the outcome?

• Easy to understand excluding list
• Identify Source type (Simple data move; data + Intelligence; data mart)
• In detail review per schema of complexity
• Detailed assessment of modification and effort database objects
• Detailed assessment of functions/functionalities used (also in the
application)
• Application assessment and review

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Easy to understand excluding list

•Create a rank on the “impedance“
o Apply it to analyzed schema i.e.:
Issue Workaround Grade* Notes
Reference to external schemas
in the a different instance (db 10 Not portable
link)
…
Packages See Writing stored procedures 9 Require full recode

Procedures See Writing stored procedures 9 Require full recoding

Unique key longer then 255
See Key length limitations 4
characters
Columns alias must be added
Views alias Manually added 4
manually
Whenever possible convert to
Sequences See Migration of Sequences 3
autoincrement
Empty schemas See empty schema definitions 2 Convert to User definition

*The lower grade the better

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1. Identify and understand differences
- Oracle vs MySQL behavior
- DDL differences MySQL – Oracle
- DML differences
- Data formatting and encoding
- Data set dimensions

2. Identify and understand business logic differences
- map Oracle functions to MySQL
- convert Oracle logic to MySQL (if possible)

3. Realize a Proof of Concept
- involve an experienced Oracle DBA
- involve an experience MySQL DBA
- involve the developers
- use real data
- use real traffic

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Mitigating risk of failure (understand)
Understanding server behavior
Identify different behavior between Oracle and MySQL, some basic differences (cont.)

• Oracle is case insensitive in the schema object definition while MySQL is case
sensitive (remember to set lower_case_table_names)

• Oracle does not provide DEFAULT value for NOT NULL, MySQL does.

• Oracle supports millisecond MySQL only from 5.6

• Oracle does not apply silent conversion to data types MySQL does (set sql_mode)

• Oracle maximum VARCHAR2 dimension is 4,000 bytes, MySQL 65,535

18 © 2012 Pythian

Understanding server behavior
Identify different behavior between Oracle and MySQL, some basic differences
1.what is what, understanding the naming conventions
AUTO COMMIT
Default enabled in MySQL
- you can't ROLLBACK
- Non Transactional Storage Engines
- SET AUTOCOMMIT = {0 | 1};

2.securing the database
Database Authentication/Privileges
- MySQL Privileges (local; no roles)
- Oracle System Privileges (local/external; roles)

3.Dual in MySQL is not required
- e.g. SELECT 1+1
but we provided for Oracle Compatibility
- SELECT 1+1 FROM DUAL
- SELECT CURRENT_USER() FROM DUAL
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Understanding DDL differences
Key length limitations

Oracle handles index with a length up to the 40%(plus some overhead) of the
database block size (db_block_size), this could be a problem with MySQL.

MySQL can use 767/1000 bytes as a primary key or an index.

But because in UTF-8, one character is 3 bytes, a primary key or any key can be at
most 255 characters.

Work around only for InnoDB innodb_large_prefix in case of Dynamic/Compressed
ROW format.

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autoincrement/sequence

Oracle uses sequence, while MySQL is bound to AUTO_INCREMENT

AUTO_INCREMENT must be NOT NULL and part of the primary key

Oracle can retrieve sequence values MySQL need to use the function
LAST_INSERT_ID().

The LAST_INSERT_ID() is maintained per connection and is thus safe for concurrent
use.

Do not use “SELECT MAX(id)+1 FROM tab”

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Understanding Function Triggers difference

Given The relevance in a Migration of the presence of SP/Trigger it is worth to
talk about it a little bit more in details

Procedure and triggers difference
• one trigger for event in MySQL, all the different actions needs to be group
• no packages, workaround using a fake schema
• different behavior by storage engine and if transactional or not
• Security assignments and security definer/invoker
• Up to 5.5 very basic error handling and lack of “signal” . So version 5.5 is
almost mandatory if in the need to use decent error handling.

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MySQL stored programs can often add to application functionality and developer
efficiency, and there are certainly many cases where the use of a procedural
language such as the MySQL stored program language can do things that a non
procedural language like SQL cannot.

There are also a number of reasons why a MySQL stored program approach may
offer performance improvements over a traditional SQL approach

• It provides a procedural approach (SQL is a declarative, non procedural
language)
• It reduces client-server traffic
• It allows us to divide and conquer complex statements

But…

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One graph tells more then 1,000 words:

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IF and CASE Statements

When constructing IF and CASE statements, try to minimize the number of
comparisons that these statements are likely to make by testing for the most likely
scenarios first.

For instance, in the code in the next slide, the first statement maintains counts of
various percentages.

Assuming that the input data is evenly distributed, the first IF condition
(percentage>95) will match about once in every 20 executions.
On the other hand, the final condition will match in three out of four executions. So
this means that for 75% of the cases, all four comparisons will need to be
evaluated.

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Non Optimized
IF (percentage>95) THEN
SET Above95=Above95+1;
ELSEIF (percentage >=90) THEN
SET Range90to95=Range90to95+1;
ELSEIF (percentage >=75) THEN
ELSE
SET LessThan75=LessThan75+1;
END IF;
Optimized
IF (percentage<75) THEN
SET LessThan75=LessThan75+1;
ELSEIF (percentage >=75 AND percentage<90) THEN
ELSEIF (percentage >=90 and percentage <=95) THEN
ELSE
SET Above95=Above95+1;
END IF;

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Looks simple and the effect is relevant:

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Using Recursion

A recursive routine is one that invokes itself.

Recursive routines often offer elegant solutions to complex programming
problems, but they also tend to consume large amounts of memory.

They are also likely to be less efficient and less scalable than implementations
based on iterative execution.

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Recursive Not Recursive
CREATE PROCEDURE nonrec_fib(n INT,OUT
CREATE PROCEDURE rec_fib(n INT,OUT out_fib out_fib INT)
INT) BEGIN
BEGIN DECLARE m INT default 0;
DECLARE n_1 INT; DECLARE k INT DEFAULT 1;
DECLARE n_2 INT; DECLARE i INT;
DECLARE tmp INT;
IF (n=0) THEN
SET out_fib=0; SET m=0;
ELSEIF (n=1) then SET k=1;
SET out_fib=1; SET i=1;
ELSE
CALL rec_fib(n-1,n_1); WHILE (i<=n) DO
CALL rec_fib(n-2,n_2); SET tmp=m+k;
SET out_fib=(n_1 + n_2); SET m=k;
END IF; SET k=tmp;
END SET i=i+1;
END WHILE;
SET out_fib=m;
END

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The difference is quite impressive and evident

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When you need to retrieve only a single row from a SELECT statement,
using the INTO clause is far easier than declaring, opening, fetching from,
and closing a cursor. But does the INTO clause generate some additional
work for MySQL or could the INTO clause be more efficient than a cursor?

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Trigger Overhead
Every database trigger is associated with a specific DML operation (INSERT,
UPDATE, or DELETE) on a specific table the trigger code will execute whenever
that DML operation occurs on that table.

Furthermore, all MySQL 5.x triggers are of the FOR EACH ROW type, which
means that the trigger code will execute once for each row affected by the DML
operation.

Given that a single DML operation might potentially affect thousands of rows,
should we be concerned that our triggers might have a negative effect on DML
performance?

Absolutely yes!

32 © 2012 Pythian

Mitigating risk of failure (match)
Identify conversion between Oracle and MySQL for
MySQL Data Type Oracle Data Type
• Tables • Views INT NUMBER(10, 0)
• Procedures • Functions INTEGER NUMBER(10, 0)
• Packages • Triggers LONGBLOB BLOB, RAW
• Sequences, synonyms etc. LONGTEXT CLOB, RAW
I.e. data types: MEDIUMBLOB BLOB, RAW
MEDIUMINT NUMBER(7, 0)
MySQL Data Type Oracle Data Type MEDIUMTEXT CLOB, RAW
BIGINT NUMBER(19, 0) NUMERIC NUMBER
BIT RAW REAL FLOAT (24)
BLOB BLOB, RAW SET VARCHAR2
CHAR CHAR SMALLINT NUMBER(5, 0)
DATE DATE TEXT VARCHAR2, CLOB
DATETIME DATE TIME DATE
DECIMAL FLOAT (24) TIMESTAMP DATE
DOUBLE FLOAT (24) TINYBLOB RAW
DOUBLE PRECISION FLOAT (24) TINYINT NUMBER(3, 0)
TINYTEXT VARCHAR2
ENUM VARCHAR2 VARCHAR VARCHAR2, CLOB
FLOAT FLOAT
YEAR NUMBER

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Mitigating risk of failure (match)
Understanding DML differences
1.Join syntax
2.SQL_mode
3.Data comparison using collation
4.other common differences
• SQL macro differences
• NVL() --> IFNULL()
• ROWNUM --> LIMIT
• SEQ.CURRVAL --> LAST_INSERT_ID()
• SEQ.NEXTVAL --> NULL
• NO DUAL necessary (SELECT NOW())
• NO DECODE() --> IF() CASE()
• JOIN (+) Syntax --> INNER|OUTER LEFT|RIGHT
• No Hierarchical (connect to prior)

35 © 2012 Pythian

Mitigating risk of failure (convert)
Data export & Index redesign

• Re-organize the schema/table not just convert data types
• Storage engines
• Index full redesign
• Data organization
• Sharding
• Partition
• Logic rewrite
• Inside MySQL
• Move to application

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Mitigating risk of failure (POC)
Realize a Proof of Concept

Don’t work Alone
Involve Oracle experienced DBA
Involve MySQL experience DBA
Involve the developers
Use real data
Use real traffic
Take one source for each type; start with the easy one
Go Back to the analysis phase if you have to

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What should my migration doc contains?
General document

• Description of the main differences between platforms
• Description of the work around found
• Explanation of what to do to avoid most common issues
• Code write instructions
• Common function mapping
• List of the blocking issue(s)
• List and explanation of what cannot be migrated and why

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Per schema document
• Overview of the effort for the migration
Schema Name: Test
Objects Number Time(min) hrs Cost(0,50 cent/min)
Table 200 320 5,3 2,65
Views 50 5 0,08 0,04
Procedure 500 5000 83,33 41,67
Function 12 200 3,33 1,67
Trigger 200 2500 41,67 20,83
Package 3 5 0,08 0,04
Total Time 8030 133,80 66,90

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Per schema document

•Effort per table like:

Schema Name: Test Table City
Rows 2000
Estimated min 10
Attribute Data type source dim source Data type dest dim dest
Name VARCHAR2 50 varchar 50
lat FLOAT FLOAT
long FLOAT FLOAT
population Number 10,0 INT 10
SqKm Number 7,0 MEDIUMINT
Country CHAR 3 CHAR 3

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Trigger section
• Effort per schema Schema
Name: Test
Events Before Time(min) After Time(min)
Insert 50 600 20 300
Update 50 600 20 300
Delete 50 600 10 100
Total 150 50
Packages 3 3
Total Time 1800 700
• Effort per Table:
Schema Name: Test Table
Total 24
action time Trigger name Source Insert* Update* Delete* Trigger name dest
Before
Ins_change_ID 20 Ins_actions
Ins_change_ISO 10
upd_population 15 upd_population
After
del_died_male 10 del_died_male
avr_pop_calculation 15 avr_pop_calculation
Total 30 30 10
*time in minutes for conversion

41 © 2012 Pythian

Procedure - function section
• Effort per schema
Schema Name: Test
Package Number Time(min) Cost(0,50 cent/min)
Pack1 112 1200
Pack2 200 2000
Pack3 200 2000
Total 521
Packages 3
Total Time 5200

• Effort per Table:
Schema Name: Test Table
Total 3
Procedure name Code rows Impedance** Time* Packge comments
Proc_1 200 4 480 Pckg1 Complex Error handling
Func_1 50 0 120 Pckg2 No problem
Proc_2 300 10 - Pckg1 Use of connect by prior

Total 600
*time in minutes for conversion
** The lower the better 10 means no portable

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Document from the Proof of Concept per source type

• Expected results
• Real value from test
• Issues found
• Work around identify
• Time/Effort per schema
• Breakdown per object (Table, View, Trigger, SP)
• Redefine expectations
• Review efforts and costs

• Be ready to drop something from the migration list

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Should I do all this manually?
No! there are tools on the market but:

•Choose your product carefully !
•Better a simple one than something too complex
•Always double check before applying
•Nothing will replace human/professional knowledge/experience

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Oracle to MySQL 2012

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