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Validate database objects after migrating from IBM Db2 LUW to Amazon RDS for MySQL, Amazon RDS for MariaDB, or Amazon Aurora MySQL

Migrating your database from IBM Db2 LUW to Amazon Relational Database Service (Amazon RDS) for MySQL, Amazon RDS for MariaDB, or Amazon Aurora MySQL-Compatible Edition is a complex, multistage process, which usually includes assessment, database schema conversion, data migration, functional testing, performance tuning, and many other steps spanning across the stages.

You can use AWS Schema Conversion Tool (AWS SCT) to convert your database schema into a format compatible with your target database. AWS Database Migration Service (AWS DMS) supports many of the most popular source and target database engines to help you migrate databases to AWS quickly and securely.

Schema migration with AWS SCT is a semi-automated process, so there is a chance of missing objects or key features in the target database. Therefore, schema validation is a crucial milestone that prevents missing objects during schema conversion and certifies that everything intended for migration has been migrated successfully.

In this post, we walk you through how to validate the database schema objects migrated from Db2 LUW to Amazon RDS for MySQL, Amazon RDS for MariaDB, or Aurora MySQL. A similar validation between Db2 LUW to Amazon Aurora PostgreSQL-Compatible Edition will be covered in another post.

Validating database objects

You should perform schema validation right after you successfully convert the source schema objects from Db2 LUW to their equivalent MySQL or MariaDB schema objects. To perform the validations, we first need to understand the different types of Db2 LUW database objects and their equivalent MySQL or MariaDB database object type.

The following list shows database objects that you can compare between Db2 LUW (source) and the corresponding MySQL or MariaDB database (target). We should validate these objects thoroughly to reduce issues during later stages of database migration.

Schema
Tables
Views
Primary keys
Foreign keys
Indexes
Triggers
Procedures
Functions

In following sections, we deep dive into each of these object types and validate using their corresponding SQL queries to help us identify any missing migrated schema objects.

If you find differences for any of the schema objects, identify the reason of failure from the AWS SCT logs, convert the objects to the target database equivalent manually, and create the objects on the target database. For example, the SQL syntax to create data partition tables in Db2 LUW is different than that of MySQL or MariaDB. As a result, these tables aren’t created on the target database. You need to manually correct the SQL scripts to replace the target equivalent syntax for table partitions before running them on the target database.

The queries we use in these sections exclude system schemas in the source and target databases. We cover both summary-level as well as detail-level validations, wherever applicable. You can further modify these queries to include more scrutiny as required.

Schema

Schemas are used to represent a collection of database objects that serve a related functionality in an application or microservice. You should validate the schemas at the source and target databases using the following SQL queries.

Use the following SQL query for Db2 LUW:

select schemaname as schema_name
from syscat.schemata
where schemaname not like ‘SYS%’
and schemaname not IN (‘SQLJ’, ‘NULLID’)
order by schema_name;

The following screenshot is an example of the Db2 LUW output.

The following SQL query is for MySQL or MariaDB:

select schema_name
from INFORMATION_SCHEMA.schemata
where SCHEMA_NAME not in (‘information_schema’, ‘performance_schema’, ‘sys’, ‘mysql’)
and SCHEMA_NAME not like ‘aws_db2%’
order by schema_name;

You get the following output.

Verify if the schema results match between the source and target database using the aforementioned SQL queries. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In this example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Extension packs

When you convert your database or data warehouse schema, AWS SCT may add additional schemas to your target database. These schemas implement SQL system functions of the source database that are required when writing your converted schema to your target database. These additional schemas are called extension packs.

When migrating your database from Db2 LUW to Amazon RDS for MySQL or MariaDB or Aurora MySQL, AWS SCT creates two extension packs: aws_db2_ext and aws_db2_ext_data, as shown in the following examples.

The following SQL query is for MySQL or MariaDB:

select schema_name
from INFORMATION_SCHEMA.schemata
where SCHEMA_NAME in (‘aws_db2_ext’,’aws_db2_ext_data’)
order by schema_name;

You get the following output.

You can directly deploy these extension pack schemas to the target database after verifying the native equivalent options in both the source and target databases.

Tables

AWS SCT converts source Db2 LUW tables to the equivalent MySQL or MariaDB target tables with appropriate data types and relative table definitions using the default or custom mapping rules. The following scripts return the counts and detail-level information for all the tables, assuming the source database doesn’t have any partitioned tables.

Use the following Db2 LUW query:

select tab.tabschema as schema_name,
count(tab.tabname) as table_count
from syscat.tables tab
where tab.type = ‘T’
and tab.tabschema not like ‘SYS%’
group by tab.tabschema
order by tab.tabschema;

The following screenshot shows your output.

Use the following SQL query for MySQL or MariaDB:

SELECT table_schema AS Schema_name,
Count(table_name) AS Tables_Count
FROM information_schema.tables
WHERE table_type = ‘BASE TABLE’
AND table_schema not in (‘information_schema’, ‘performance_schema’, ‘sys’, ‘mysql’)
and table_schema not like ‘aws_db2%’
GROUP BY table_schema
ORDER BY table_schema;

You get the following output.

Verify the table counts between the source and target database using the aforementioned SQL queries. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In the preceding example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Views

A view is a virtual table created by a query joining one or more tables. You can validate the views count converted by AWS SCT by using the following queries on the source and target databases.

Use the following SQL query for Db2 LUW:

select tab.tabschema as schema_name,
count(tab.tabname) as table_count
from syscat.tables tab
where tab.type = ‘V’
and tab.tabschema not like ‘SYS%’
group by tab.tabschema
order by tab.tabschema;

You get the following output.

Use the following SQL query for MySQL or MariaDB:

SELECT TABLE_SCHEMA AS OBJECT_SCHEMA,
count(TABLE_NAME) AS View_Count
FROM information_schema.VIEWS
where table_schema not in (‘information_schema’, ‘performance_schema’, ‘sys’, ‘mysql’)
and table_schema not like ‘aws_db2%’
group by TABLE_SCHEMA
order by TABLE_SCHEMA;

The following screenshot shows the output.

Verify the view counts between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In this example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Primary keys

A primary key is a column or group of columns whose values uniquely identify every row in the table. The following queries help you extract the counts and details of primary keys in source and target databases.

The following is SQL query for Db2 LUW:

select tab.tabschema as schema_name,
count(*) as PK_Count
from syscat.tables tab
inner join syscat.tabconst const
on const.tabschema = tab.tabschema
and const.tabname = tab.tabname and const.type = ‘P’
inner join syscat.keycoluse key
on const.tabschema = key.tabschema
and const.tabname = key.tabname
and const.constname = key.constname
where tab.type = ‘T’
and tab.tabschema not like ‘SYS%’
group by tab.tabschema
order by tab.tabschema;

The following screenshot shows the output.

Use the following SQL query for MySQL or MariaDB:

SELECT cons.CONSTRAINT_SCHEMA as SCHEMA_NAME
, count(*) as PK_Count
FROM information_schema.TABLE_CONSTRAINTS cons
join information_schema.KEY_COLUMN_USAGE col_use on cons.CONSTRAINT_SCHEMA=col_use.CONSTRAINT_SCHEMA
and cons.CONSTRAINT_NAME=col_use.CONSTRAINT_NAME
and cons.TABLE_SCHEMA=col_use.TABLE_SCHEMA
and cons.TABLE_NAME=col_use.TABLE_NAME
where cons.constraint_type in (‘PRIMARY KEY’)
and cons.table_schema not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and cons.table_schema not like ‘aws_db2%’
group by cons.CONSTRAINT_SCHEMA
order by cons.CONSTRAINT_SCHEMA;

You get the following output.

To verify details including the column names in the constraint along with their ordinal position, you can use the following queries.

Use the following SQL query for Db2 LUW:

Select tab.tabschema as schema_name,
tab.tabname as table_name,
‘PRIMARY’ as constraint_name,
key.colname as column_name,
key.colseq as position
from syscat.tables tab
inner join syscat.tabconst const
on const.tabschema = tab.tabschema
and const.tabname = tab.tabname and const.type = ‘P’
inner join syscat.keycoluse key
on const.tabschema = key.tabschema
and const.tabname = key.tabname
and const.constname = key.constname
where tab.type = ‘T’
and tab.tabschema not like ‘SYS%’
order by tab.tabschema, tab.tabname, key.colseq, key.colname;

You get the following output.

The following is SQL query for MySQL or MariaDB:

SELECT cons.CONSTRAINT_SCHEMA as SCHEMA_NAME
, cons.TABLE_NAME
, cons.CONSTRAINT_NAME as CONSTRAINT_NAME
, col_use.COLUMN_NAME
, col_use.ORDINAL_POSITION as POSITION
FROM information_schema.TABLE_CONSTRAINTS cons
join information_schema.KEY_COLUMN_USAGE col_use on cons.CONSTRAINT_SCHEMA=col_use.CONSTRAINT_SCHEMA
and cons.CONSTRAINT_NAME=col_use.CONSTRAINT_NAME
and cons.TABLE_SCHEMA=col_use.TABLE_SCHEMA
and cons.TABLE_NAME=col_use.TABLE_NAME
where cons.constraint_type in (‘PRIMARY KEY’)
and cons.table_schema not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and cons.table_schema not like ‘aws_db2%’
order by cons.TABLE_SCHEMA, cons.TABLE_NAME, col_use.ORDINAL_POSITION, col_use.COLUMN_NAME;

The following screenshot shows the output.

Verify the count and details of the primary keys between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In the preceding example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Foreign keys

Foreign keys link data in a parent table to the data in another child table. Foreign key constraints help maintain referential integrity between tables. You can use the following queries to get the counts and detail-level information about the foreign keys in both the source and target databases.

Use the following Db2 LUW query:

select tabschema as schema_name, count(*) as fk_count
from syscat.references
where tabschema not like ‘SYS%’
group by tabschema
order by tabschema;

You get the following output.

Use the following MySQL or MariaDB query:

SELECT TABLE_SCHEMA, count(*) as FK_COUNT
FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE
WHERE TABLE_SCHEMA not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and TABLE_SCHEMA not like ‘aws_db2%’
and REFERENCED_TABLE_SCHEMA IS NOT NULL
group by TABLE_SCHEMA
order by TABLE_SCHEMA;

You get the following output.

For detailed information, use the following queries.

The following SQL query is for Db2 LUW:

select ref.reftabschema as schema_name ,
ref.reftabname as table_name,
ref.constname as fk_constraint_name,
ref.tabname as foreign_table_name,
trim(key.colname) as fk_column_name
from syscat.references ref
left outer join syscat.keycoluse key on key.tabschema = ref.tabschema
and key.tabname = ref.tabname
and key.constname = ref.constname
where ref.tabschema not like ‘SYS%’
order by ref.reftabschema,ref.reftabname,ref.constname;

The following screenshot shows the output.

The following SQL query is for MySQL or MariaDB:

select referenced_table_schema as schema_name
, referenced_table_name as table_name
, constraint_name as fk_constraint_name
, table_name as foreign_table_name
, column_name as fk_column_name
FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE
WHERE TABLE_SCHEMA not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and TABLE_SCHEMA not like ‘aws_db2%’
and REFERENCED_TABLE_SCHEMA IS NOT NULL
order by referenced_table_schema, referenced_table_name, constraint_name;

You get the following output.

Verify the count and the details of foreign keys between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In the preceding example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Indexes

Indexes play a key role in improving query performance. Because tuning methodologies differ from database to database, the number of indexes and their types vary between Db2 LUW and MySQL or MariaDB databases.

With the following queries, you can get the counts of indexes and their types in both Db2 LUW and MySQL or MariaDB databases.

Unique indexes

For unique indexes, use the following Db2 LUW query:

select ind.tabschema as schema_name,
count(cols.colname) as unique_count
from syscat.indexes ind
join syscat.indexcoluse cols
on ind.indname = cols.indname
and ind.indschema = cols.indschema
where ind.tabschema not like ‘SYS%’
and ind.uniquerule in (‘U’)
group by ind.tabschema
order by schema_name;

The following screenshot shows the output.

Use the following SQL query for MySQL or MariaDB:

SELECT cons.table_schema as SCHEMA_NAME
, count(col_use.COLUMN_NAME) as unique_count
FROM information_schema.TABLE_CONSTRAINTS cons
join information_schema.KEY_COLUMN_USAGE col_use on cons.CONSTRAINT_SCHEMA=col_use.CONSTRAINT_SCHEMA
and cons.CONSTRAINT_NAME=col_use.CONSTRAINT_NAME
and cons.TABLE_SCHEMA=col_use.TABLE_SCHEMA
and cons.TABLE_NAME=col_use.TABLE_NAME
where cons.constraint_type in (‘UNIQUE’)
and cons.table_schema not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and cons.table_schema not like ‘aws_db2%’
group by cons.table_schema
order by cons.table_schema;

You get the following output.

For detailed information, use the following queries.

The following SQL query is for Db2 LUW:

select ind.tabschema as schema_name,
ind.tabname as table_name,
ind.indname as CONSTRAINT_NAME,
‘Unique Index’ as constraint_type,
cols.colname as column_name
from syscat.indexes ind
join syscat.indexcoluse cols
on ind.indname = cols.indname
and ind.indschema = cols.indschema
where ind.tabschema not like ‘SYS%’
and ind.uniquerule in (‘U’)
order by schema_name,
ind.tabname,
ind.indname;

You get the following output.

Use the following SQL query for MySQL or MariaDB:

SELECT cons.table_schema as SCHEMA_NAME
, cons.TABLE_NAME
, cons.CONSTRAINT_NAME as CONSTRAINT_NAME
,’Unique Index’ as constraint_type
, col_use.COLUMN_NAME
FROM information_schema.TABLE_CONSTRAINTS cons
join information_schema.KEY_COLUMN_USAGE col_use on cons.CONSTRAINT_SCHEMA=col_use.CONSTRAINT_SCHEMA
and cons.CONSTRAINT_NAME=col_use.CONSTRAINT_NAME
and cons.TABLE_SCHEMA=col_use.TABLE_SCHEMA
and cons.TABLE_NAME=col_use.TABLE_NAME
where cons.constraint_type in (‘UNIQUE’)
and cons.table_schema not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and cons.table_schema not like ‘aws_db2%’
order by cons.table_schema, cons.TABLE_NAME, cons.CONSTRAINT_NAME, col_use.COLUMN_NAME;

The following screenshot shows the output.

Non-unique indexes

MySQL and MariaDB create implicit indexes on foreign keys and referenced keys so that foreign key checks can be fast and not require a table scan. The MySQL and MariaDB database queries used in this section exclude such indexes so that you can perform the validations against the source without any mismatch.

Use the following SQL query for Db2 LUW:

select ind.tabschema as schema_name,
count(cols.colname) as index_count
from syscat.indexes ind
join syscat.indexcoluse cols
on ind.indname = cols.indname
and ind.indschema = cols.indschema
where ind.tabschema not like ‘SYS%’
and ind.uniquerule in (‘D’)
group by ind.tabschema
order by schema_name;

The output is as follows.

Use the following SQL query for MySQL or MariaDB:

SELECT idx.table_schema
,count(idx.column_name) as index_count
FROM INFORMATION_SCHEMA.STATISTICS idx
WHERE idx.TABLE_SCHEMA not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’, ‘DB2INST1’)
and idx.table_schema not like ‘aws_db2%’
and idx.index_name not in (‘PRIMARY’)
and idx.non_unique = 1
and index_name not like ‘FK_%’
group by idx.table_schema
order by idx.table_schema;

You get the following output.

For detailed information, use the following queries.

The following SQL query is for Db2 LUW:

select ind.tabschema as schema_name,
ind.tabname as table_name,
ind.indname as index_name,
cols.colname as column_name
from syscat.indexes ind
join syscat.indexcoluse cols
on ind.indname = cols.indname
and ind.indschema = cols.indschema
where ind.tabschema not like ‘SYS%’
and ind.uniquerule in (‘D’)
order by schema_name,
ind.tabname,
ind.indname,
cols.colname;

The following screenshot shows the output.

Use the following SQL query for MySQL or MariaDB:

SELECT idx.table_schema
, idx.table_name
, idx.index_name
, idx.column_name
FROM INFORMATION_SCHEMA.STATISTICS idx
WHERE idx.TABLE_SCHEMA not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’, ‘DB2INST1’)
and idx.table_schema not like ‘aws_db2%’
and idx.index_name not in (‘PRIMARY’)
and idx.non_unique = 1
and index_name not like ‘FK_%’
order by idx.table_schema
, idx.table_name
, idx.index_name
, idx.column_name;

The output is as follows.

Verify the count and the details of the indexes between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In this example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Triggers

Triggers define a set of actions that are performed in response to an insert, update, or delete operation on a specified table. The following queries give you the count and details of triggers for both the source and target databases.

Use the following Db2 LUW query:

Select tabschema as table_schema
, count(trigname) as trigger_count
From syscat.triggers t
where tabschema not like ‘SYS%’
group by tabschema
order by tabschema;

You get the following output.

Use the following MySQL or MariaDB query:

SELECT tgr.EVENT_OBJECT_SCHEMA as TABLE_SCHEMA
, count(tgr.TRIGGER_NAME) as trigger_count
FROM information_schema.triggers tgr
where tgr.TRIGGER_SCHEMA not in (‘sys’,’performance_schema’,’mysql’)
and tgr.TRIGGER_SCHEMA not like ‘aws_db2%’
group by tgr.EVENT_OBJECT_SCHEMA
order by tgr.EVENT_OBJECT_SCHEMA;

The output is as follows.

For detail-level information, use the following queries.

The following SQL query is for Db2 LUW:

select
tabschema as table_schema,
trigname as trigger_name,
tabname as table_name,
case trigtime
when ‘B’ then ‘before’
when ‘A’ then ‘after’
when ‘I’ then ‘instead of’
end as activation,
rtrim(case when eventupdate =’Y’ then ‘update ‘ else ” end
||
case when eventdelete =’Y’ then ‘delete ‘ else ” end
||
case when eventinsert =’Y’ then ‘insert ‘ else ” end)
as event
from syscat.triggers t
where tabschema not like ‘SYS%’
order by table_name, trigger_name;

You get the following output.

Use the following MySQL or MariaDB query:

SELECT tgr.EVENT_OBJECT_SCHEMA as TABLE_SCHEMA
,tgr.TRIGGER_NAME
,tgr.EVENT_OBJECT_TABLE as Table_name
,tgr.ACTION_TIMING as activation
,tgr.EVENT_MANIPULATION as event
FROM information_schema.triggers tgr
where tgr.TRIGGER_SCHEMA not in (‘sys’, ‘performance_schema’, ‘mysql’)
and tgr.TRIGGER_SCHEMA not like ‘aws_db2%’;

The following screenshot shows the output.

Verify the trigger count and details between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In the preceding example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Procedures

A stored procedure is a collection of precompiled SQL statements stored in a database that can be called from an application program. Stored procedures have better performance compared to inline queries because the SQL queries are precompiled with reusable execution plans. It also improves productivity because similar SQL statements or the business logic are consolidated and reused across applications or other programs. The following queries give you the count and details of stored procedures for both the source and target databases.

The following is SQL query for Db2 LUW:

select routineschema as schema_name
,count(*) as proc_count
from syscat.routines
where routinetype = ‘P’
and routineschema not like ‘SYS%’
and routineschema not like ‘SQLJ%’
group by routineschema
order by routineschema;

You get the following output.

Use the following SQL query for MySQL or MariaDB:

SELECT rtn.ROUTINE_SCHEMA
,count(*) as proc_count
FROM information_schema.ROUTINES rtn
where rtn.ROUTINE_TYPE = ‘PROCEDURE’
and rtn.ROUTINE_SCHEMA not in (‘information_schema’,’sys’, ‘performance_schema’, ‘mysql’)
and rtn.ROUTINE_SCHEMA not like ‘aws_db2%’
group by rtn.ROUTINE_TYPE
order by rtn.ROUTINE_TYPE;

The output is as follows.

For detail-level information, use the following queries.

The following is SQL query for Db2 LUW:

select routineschema as schema_name,
routinename as procedure_name
from syscat.routines
where routinetype = ‘P’
and routineschema not like ‘SYS%’
and routineschema not like ‘SQLJ%’
order by schema_name,
procedure_name;

You get the following output.

Use the following SQL query for MySQL or MariaDB:

SELECT rtn.ROUTINE_TYPE
,rtn.ROUTINE_SCHEMA
,rtn.ROUTINE_NAME
FROM information_schema.ROUTINES rtn
where rtn.ROUTINE_TYPE = ‘PROCEDURE’
and rtn.ROUTINE_SCHEMA not in (‘information_schema’,’sys’, ‘performance_schema’, ‘mysql’)
and rtn.ROUTINE_SCHEMA not like ‘aws_db2%’;

The following screenshot shows the output.

Verify the count and the details of procedures between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In this example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Functions

Functions implement specific business or functional logic based on the given input and return a predefined output. The following queries give you the count and details of functions for both the source and target databases.

Use the following SQL query for Db2 LUW:

select routineschema as schema_name,
count(*) as proc_count
from syscat.routines
where routinetype = ‘F’
and routineschema not like ‘SYS%’
and routineschema not like ‘SQLJ%’
group by routineschema
order by routineschema;

The following screenshot shows the output.

The following is SQL query for MySQL or MariaDB:

SELECT rtn.ROUTINE_SCHEMA
,count(*) as proc_count
FROM information_schema.ROUTINES rtn
where rtn.ROUTINE_TYPE = ‘FUNCTION’
and rtn.ROUTINE_SCHEMA not in (‘information_schema’, ‘sys’, ‘performance_schema’, ‘mysql’)
and rtn.ROUTINE_SCHEMA not like ‘aws_db2%’
group by rtn.ROUTINE_TYPE
order by rtn.ROUTINE_TYPE;

You get the following output.

For detail-level information, use the following queries.

Use the following SQL query for Db2 LUW:

select routineschema as schema_name,
routinename as procedure_name
from syscat.routines
where routinetype = ‘F’
and routineschema not like ‘SYS%’
and routineschema not like ‘SQLJ%’
order by schema_name,
procedure_name;

The output is as follows.

Use the following SQL query for MySQL or MariaDB:

SELECT rtn.ROUTINE_SCHEMA
,rtn.ROUTINE_NAME
FROM information_schema.ROUTINES rtn
where rtn.ROUTINE_TYPE = ‘FUNCTION’
and rtn.ROUTINE_SCHEMA not in (‘information_schema’,’sys’, ‘performance_schema’, ‘mysql’)
and rtn.ROUTINE_SCHEMA not like ‘aws_db2%’;

The following screenshot shows the output.

Verify the count and the details of functions between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In the preceding example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Validating data-partitioned tables

Partitioned tables use a data organization scheme in which table data is divided across multiple storage objects, called data partitions or ranges, according to values in one or more table partitioning key columns of the table. You can use the following queries to compare the partitioned tables between the source and target.

The following is SQL query for Db2 LUW:

select dp.tabschema as table_schema
, dp.tabname as table_name
, datapartitionname as partition_name
, char(datapartitionexpression,30) as partition_expression
, highvalue as partition_description
from SYSCAT.DATAPARTITIONS dp
inner join syscat.datapartitionexpression dpe on dp.tabschema=dpe.tabschema
and dp.tabname=dpe.tabname
where dp.tabschema=’GOASALESDW’;

You get the following output.

The following SQL query is for MySQL or MariaDB:

select table_schema
, table_name
, partition_name
, partition_expression
, partition_description
FROM INFORMATION_SCHEMA.PARTITIONS
where table_schema=’GOASALESDW’;

The output is as follows.

Verify the count and the details of partitioned data tables between the source and target database. If any differences are found, identify the cause of failure from the deployment logs and fix them accordingly. In the preceding example, the output is as expected. No issues were detected, so you can move to the next level of validation.

Useful MySQL or MariaDB catalog tables

The following table summarizes some of the Db2 LUW objects and their corresponding objects on MySQL and MariaDB that are helpful for database object validation. For MySQL databases with many objects and concurrent workloads, the queries provided in this post can take longer duration to complete. This can be improved to some extent using data dictionary changes in MySQL 8.0.

Db2 LUW
MySQL or MariaDB
syscat.tables/ syscat.columns
information_schema.tables
syscat.tables/ syscat.columns
information_schema.views
syscat.tables/ syscat.tabconst / syscat.references / syscat.keycoluse
information_schema.TABLE_CONSTRAINTS
syscat.routines
information_schema.ROUTINES
syscat.triggers
information_schema.triggers
syscat.datapartitions / datapartitionexpression
Information_schema.partitions

Handling objects not supported in MySQL or MariaDB

For the Db2 LUW objects not supported by MySQL or MariaDB (like aliases, sequences, or materialized query tables), you must perform the migration from source to target database manually to achieve the functionality that exists in the source database. You can use the queries provided in this post to iteratively validate the migrated objects to identify gaps and fix them.

Conclusion

Database object validation is an essential step that provides an in-depth view of the migration accuracy. It confirms whether all the database objects are migrated appropriately and the integrity of target database. It ensures business continuity of the dependent application processes.

In this post, we discussed post-migration validation of database objects with the metadata queries for a Db2 LUW source and Amazon RDS for MySQL, Amazon RDS for MariaDB, or Aurora MySQL target database. You can run the queries provided in this post on your source and target database to retrieve the metadata and compare the output to confirm if your migration was successful.

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About the Authors

Sai Parthasaradhi is a Database Migration Specialist with AWS Professional Services. He works closely with customers to help them migrate and modernize their databases on AWS.

Vikas Gupta is a Lead Database Migration Consultant with AWS Professional Services. He loves to automate manual processes and enhance the user experience. He helps customers migrate and modernize workloads in the AWS Cloud, with a special focus on modern application architectures and development best practices.

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