Understanding Change Log: A Guide to Database Schema Evolution

As data continues to play an increasingly important role in modern business, the importance of effective database management cannot be overstated, as databases continue to grow and evolve, it’s essential to keep track of changes made to the schema, tables, and data. This is where a change log comes in – a critical component of database management that helps ensure data consistency, integrity, and recoverability. Recently, IBM Data Replication on CP4D has released a change log feature, enabling users to track and record changes made to their database schemas and data. In this blog post, we’ll take a closer look at change log, including what they are, how they work, and why they’re essential for database replication and management.

What is Change Log?

A change log is a standardized method for documenting and tracking modifications made to a database schema, tables, and data over time. It provides a historical view of what happened and when and includes DDL (data definition language) and DML (data manipulation language) operations, such as inserts, updates, and deletes. Change log is essential for ensuring data consistency, preserving backward compatibility, and easing schema migrations between different environments, such as development, testing, and production.

How Does a Change Log Differ from a Copy Scenario?

IBM Data Replication on CP4D supports copy scenario and change log scenario, A copy scenario, maintains an identical copy of the source table at the target database. While this approach ensures data consistency, it doesn’t provide a record of changes made to the database over time. A change log scenario, however, maintains a history of all changes, making it an essential tool for tracking database schema evolution by capturing and preserving every single change in the database and create a audit trail.

Example.

Let us perform the following operations

1.     INSERT INTO SOURCE_TABLE (ID, NAME) VALUES (1, 'abc');

2.     INSERT INTO SOURCE_TABLE (ID, NAME) VALUES (2, 'xyz');

3.     UPDATE SOURCE_TABLE SET NAME = 'upd' WHERE ID = 1;

4.     DELETE FROM SOURCE_TABLE WHERE ID = 2;

source table:

   id is primary key

change log table: (Change Log Scenario)

    sequence is primary key

target table: (Copy Scenario)

     id is primary key

Key Concepts and Terminology

Before we dive deeper into the world of change log, let’s take a moment to define some key concepts and terminology:

• DDL (Data Definition Language): A set of SQL commands used to define and modify the structure of a database.
• DML (Data Manipulation Language): A set of SQL commands used to manipulate data within a database.
• Change Log: A record of changes made to a database schema, tables, or data over time.
• Change Log Table: The mapped target table that stores the change log data, including change log columns such as sequence number, operation type, transaction ID, and timestamp.
• Change Log DDL Table: A metadata table that stores information about DDL operations applied to the source database.
• Schema Evolution: The process of modifying a database schema to accommodate changing data requirements.
• Data Lineage: The process of tracking and recording the origin, movement, and transformation of data throughout its lifecycle. Data lineage helps to provide transparency, accountability, and trustworthiness of data.
• Data Provenance: A specific type of data lineage that focuses on documenting the sources, processing, and transformations of data. Data provenance is essential for ensuring data quality, integrity, and regulatory compliance.

Change Log and Data Lineage

Change log plays a critical role in enabling data lineage by providing a detailed record of each change made to data over time. By analyzing the change log data, data stewards and analysts can:

• Reconstruct the history of changes made to data.
• Identify the sources of data and their transformations.
• Verify the accuracy and integrity of data. Improve data quality and regulatory compliance

With the recent release of IBM Data Replication on CP4D’s change log feature, users can now easily track, and record changes made to their database schema and data, ensuring data consistency, integrity, and recoverability.

Types of DDL Change Log

IBM Data Replication on CP4D’s change log feature supports three types of DDL change log, giving users the flexibility to choose the approach that best fits their needs.

1. Log and apply DDL: This type applies schema changes without any attempt to retain the schema history. DDLs are replicated, and previous state won’t be preserved.
2. Schema evolution with new table: This type creates a new table in the target database every time upon seeing a DDL with new structure. The previous table will be preserved, and new upcoming data will be replicated to the new table.
3. Schema evolution in same change log table: This type retains schema history by backing up columns within the table if a column attribute changes. If a table is dropped or renamed, the current state is backed up by renaming table, and a new table is created with the new name.

I will cover is each of these types in depth in my next blog.

Change Log Columns

The IBM Data Replication on CP4D’s change log feature allows users to specify extra metadata columns to include with each change log record, providing a more comprehensive view of the changes made. Following change log columns can be included in replication:

• SEQUENCE: An inter-transaction sequence counter starting at 1.
• OPTYPE: A column that indicates the type of DML operation.
• TRANSACTION_ID: A column that contains the transaction ID for each operation.
• TIMESTAMP: A column that contains the source commit timestamp for each operation.

Example: If we insert a row, then update and delete the same row, the state of the change log table will be

Metadata Tables

IBM Data Replication on CP4D’s change log creates following metadata tables 

• change_log_ddl: A table that stores information about DDLs applied to the source database.
• change_log_txid_to_commit_timestamp: A table that stores the mapping between transaction IDs and commit timestamps.
• change_log_ddltype_to_description: This table will contain description of ddl_type mentioned in change_log_ddl table

Real-World Scenario

1. Let’s say we’re working at an e-commerce company called “MyShop.” Our team is responsible for maintaining the company’s product database, which includes information about products, prices, and inventory. We want to ensure that any changes made to the database are tracked and recorded for auditing purposes.

Solution: As a member of the MyShop team, I want to be able to track changes made to the product database, including DDL operations, so that I can ensure data consistency and integrity. Your way forward is to make use of IBM Data Replication on CP4D’s change log.

Results: The company is now able to track all changes that have been made to their databases.

1.  A healthcare company is subject to strict regulations regarding patient data. The company wants to ensure compliance with regulatory requirements.

Solution: The company uses IBM Data Replication on CP4D’s change log to track all changes made to patient data. The change log ensures that all changes are captured and recorded, and that the company can demonstrate compliance with regulatory requirements.

Results: The company can demonstrate compliance with regulatory requirements. The change log provides a single source of truth for all data changes and enables the company to easily track and troubleshoot data inconsistencies.

Conclusions

In conclusion, change log are a critical component of database management that provide a detail of all changes made to a database schema, tables, and data over time. With the recent release of IBM Data Replication on CP4D’s change log feature, users can now easily track, and record changes made to their database schema and data, ensuring data consistency, integrity, recoverability and ensuring that they have a complete and auditable record of all changes made to their database. By understanding the concepts and terminology surrounding change logs, data lineage, and data provenance, data stewards and analysts can ensure the trustworthiness and transparency of their data assets.

Change Log Scenario Configured in IBM Data Replication on CP4D UI

The replication asset is configured with a change log that has a DDL change log type of Schema Evolution with a new table and is set up to track all changes for the change_log_demo1 schema.