In databases, change data capture (CDC) is a set of software design patterns used to determine (and track) the data that has changed so that action can be taken using the changed data. Also, Change data capture (CDC) is an approach to data integration that is based on the identification, capture and delivery of the changes made to enterprise data sources.
CDC solutions occur most often in data-warehouse environments since capturing and preserving the state of data across time is one of the core functions of a data warehouse, but CDC can be utilized in any database or data repository system.
System developers can set up CDC mechanisms in a number of ways and in any one or a combination of system layers from application logic down to physical storage.
In a simplified CDC context, one computer system has data believed to have changed from a previous point in time, and a second computer system needs to take action based on that changed data. The former is the source, the latter is the target. It is possible that the source and target are the same system physically, but that would not change the design pattern logically.
Not uncommonly, multiple CDC solutions can exist in a single system.
Tables whose changes must be captured may have a column that represents the time of last change. Names such as LAST_UPDATE, etc. are common. Any row in any table that has a timestamp in that column that is more recent than the last time data was captured is considered to have changed.
Database designers give tables whose changes must be captured a column that contains a version number. Names such as VERSION_NUMBER, etc. are common. When data in a row changes, its version number is updated to the current version. A supporting construct such as a reference table with the current version in it is needed. When a change capture occurs, all data with the latest version number is considered to have changed. When the change capture is complete, the reference table is updated with a new version number.
Three or four major techniques exist for doing CDC with version numbers, the above paragraph is just one.
Version numbers can be useful with optimistic locking in ACID transactional or relational database management systems (RDBMS). For an example in read-then-update scenarios for CRUD applications in relational database management systems, a row is first read along with the state of its version number; in a separate transaction, a SQL UPDATE statement is executed along with an additional WHERE clause that includes the version number found from the initial read. If no record was updated, it usually means that the version numbers didn't match because some other action/transaction had already updated the row and consequently its version number. Several object relational mapping tools use this method to detect for optimistic locking scenarios (including Hibernate).
This technique can either supplement or complement timestamps and versioning. It can configure an alternative if, for example, a status column is set up on a table row indicating that the row has changed (e.g. a boolean column that, when set to true, indicates that the row has changed). Otherwise, it can act as a complement to the previous methods, indicating that a row, despite having a new version number or a later date, still shouldn't be updated on the target (for example, the data may require human validation).
This approach combines the three previously discussed methods. As noted, it is not uncommon to see multiple CDC solutions at work in a single system, however, the combination of time, version, and status provides a particularly powerful mechanism and programmers should utilize them as a trio where possible. The three elements are not redundant or superfluous. Using them together allows for such logic as, "Capture all data for version 2.1 that changed between 6/1/2005 12:00 a.m. and 7/1/2005 12:00 a.m. where the status code indicates it is ready for production."
May include a publish/subscribe pattern to communicate the changed data to multiple targets. In this approach, triggers log events that happen to the transactional table into another queue table that can later be "played back". For example, imagine an Accounts table, when transactions are taken against this table, triggers would fire that would then store a history of the event or even the deltas into a separate queue table. The queue table might have schema with the following fields: Id, TableName, RowId, TimeStamp, Operation. The data inserted for our Account sample might be: 1, Accounts, 76, 11/02/2008 12:15am, Update. More complicated designs might log the actual data that changed. This queue table could then be "played back" to replicate the data from the source system to a target.
[More discussion needed]
An example of this technique is the pattern known as the log trigger.
Coding a change into an application at appropriate points is another method that can give intelligent discernment that data changed. Although this method involves programming vs. more easily implemented "dumb" triggers, it may provide more accurate and desirable CDC, such as only after a COMMIT, or only after certain columns changed to certain values - just what the target system is looking for.
Most database management systems manage a transaction log that records changes made to the database contents and to metadata. By scanning and interpreting the contents of the database transaction log one can capture the changes made to the database in a non-intrusive manner.
Using transaction logs for change data capture offers a challenge in that the structure, contents and use of a transaction log is specific to a database management system. Unlike data access, no standard exists for transaction logs. Most database management systems do not document the internal format of their transaction logs, although some provide programmatic interfaces to their transaction logs (for example: Oracle, DB2, SQL/MP, SQL/MX and SQL Server 2008).
Other challenges in using transaction logs for change data capture include:
CDC solutions based on transaction log files have distinct advantages that include:
As often occurs in complex domains, the final solution to a CDC problem may have to balance many competing concerns.
Change data capture both increases in complexity and reduces in value if the source system saves metadata changes when the data itself is not modified. For example, some Data models track the user who last looked at but did not change the data in the same structure as the data. This results in noise in the Change Data Capture.
Actually tracking the changes depends on the data source. If the data is being persisted in a modern database then Change Data Capture is a simple matter of permissions. Two techniques are in common use:
If the data is not in a modern database, Change Data Capture becomes a programming challenge.
Sometimes the Slowly changing dimension is used as a method.