Capture Transaction Details
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You are about to design a replication link using Master-Subordinate Transactional Incremental Replication. For this purpose, you need access to transactional information on the source, and a logging system will not fulfill this need for one of the following reasons:
There is a logging system available at the source database but for some good reasons you do not want to use it.
You do not have access to a logging system.
In these cases, you need to design the recording of transactions on the source with your own artifacts.
Note: This pattern presumes knowledge of the concepts, terms, and definitions of the Data Replication architectural pattern, from which this pattern inherits concepts and terms.
How do you design a recording of transactional information for all changes to a source replication set?
Any of the following compelling forces justify using the solution described in this pattern:
No access to transactional information. You cannot access transactional information in the logging system because either you are not using a database system at the source, or the database system does not provide access to the transaction log.
Transactional information is not suitable. The information provided might be usable in the originating database only, for example, because it contains physical addresses instead of key values and thus cannot be applied on the target.
The following enabling force facilitates the adoption of the solution, and its absence might hinder such a move:
Recording for other purposes. Recording of transactions is required for other purposes, for example, auditing.
The solution is to create additional database objects, such as triggers and (shadow) tables, and to record changes of all tables belonging to the replication set.
The details of the solution are separated into:
Prerequisites for recording transactional information
Designing your own recording of transactions
Note: This pattern uses the terms "transactions" and "operations" with the following meanings:
A transaction is a collection of SQL commands that form a unit of work. Depending on the relational database management system (RDBMS), a transaction is started explicitly by a command like Begin Transaction, or implicitly by the first SQL command outside of a transaction. The transaction is ended either explicitly by a commit or a rollback, or implicitly at the end of every SQL command in autocommit mode.
An operation is the change (INSERT, UPDATE, or DELETE) of an individual row within a transaction.
This pattern depends on two features that the database management system (DBMS) must provide, and on a prerequisite for the data model:
Fine-grained clock. The order in which transactions are executed on the source must be the same as the order in which they are replayed on the target. Thus, the source clock must provide a sufficiently fine resolution to preserve the order. A clock grain of a millisecond is generally sufficient; many systems provide even microseconds. A clock that only has a resolution of whole seconds definitely prevents the use of this pattern.
Transaction Identifiers. The RDBMS must provide a means to identify the operations that belong to the same transaction. This is called a Transaction Identifier throughout the remaining discussion. It is typically an opaque data type, and is generally provided to handle distributed transactions.
Unique key. All tables of the replication set must have either unique keys or another combination of columns that identifies every row uniquely. The unique identifier of every row is referred to as the Replication Key throughout this document.
Designing Your Own Recording of Transactions
Since you cannot access the logging system of the source to acquire the transactional information, you have to implement the recording of the transactions using other DBMS services, such as triggers. Triggers are schema objects that perform additional operations on behalf of an initial operation. Triggered operations are also part of the initiating transaction and are logged in the same way as any other operation.
Hint: It is also possible to record transactions by changing the application to write a copy of the operation to a user-defined database, but this is very unusual.
The triggered function has to collect the following information for every committed transaction:
Tables written to by the transaction
For every table, the rows that have been written must be recorded. The data to be stored includes the current timestamp, the type of operation (INSERT, UPDATE, or DELETE) and additional information depending on the type of operation:
For INSERTs, the values of all fields must be recorded.
For UPDATEs that do not change the Replication Key, the new values of all changed columns, including the column names, must be recorded.
For UPDATEs that do change the Replication Key, the old and new values of the Replication Key must also be saved. Alternatively, you might record this as a DELETE of the old row followed by an INSERT of the new row, unless this approach violates integrity constraints.
For DELETEs, only the Replication Key of the deleted row is needed. If the DELETE fires cascade deletes of related rows, these additional deletes are recorded by further trigger invocations on those rows.
Timestamp of when the transaction has been completed on the source. If you cannot fire a trigger on the COMMIT, you can use the timestamp of the last operation within the transaction instead.
To store the above information, you need a table for the transactions and three additional shadow tables for each table that belongs to the replication set. The shadow tables store the inserted, updated, and deleted rows. The three shadow tables can be combined into one by adding a column to store the type of operation; depending on the type of operation, some of the columns will be empty. Figure 1 shows the corresponding data model.
Figure 1: Data model to store transactional information
When an INSERT, UPDATE or DELETE is triggered, the following steps must be taken:
Retrieve the Transaction Identifier of the current transaction.
UPDATE the current date and time in the EndTimestamp for the current transaction in the Transaction table. If the UPDATE statement returns no updated row, the transaction is new. Thus, INSERT a new row with the Transaction Identifier and the current date and time.
INSERT these values into the shadow table that corresponds to the table being written:
Current date and time
Type of operation (INSERT, UPDATE, or DELETE)
For an INSERT: the value of all columns For an UPDATE: the old and new key values plus the values of the remaining columns For a DELETE: the old key values
Transactions that are rolled back do not affect the source, and consequently must not affect the target. For this reason, you do not want to store information about rolled back transactions. The recording of the transaction details should be done within the same transaction that is being recorded. Then if the transaction is rolled back, the recording of the transaction is rolled back as well. Thus, information about rolled-back transactions is not recorded.
The use of this pattern has the following benefit and liability:
Other useful services. Recording transactions is very similar to other services, such as auditing. If the recorded information is enriched with data, such as current user or role, it can be the basis for auditing too.
Increasing space requirements. Recording transactions writes new information into the transaction table and the shadow tables. Thus, the space requirements of these tables are constantly increasing. You should design and schedule a housekeeping process that removes the transactional information from these tables once they have been transmitted to the targets.
The transactional information recorded by the use of this pattern can be used by Master-Subordinate Transactional Incremental Replication, which is a separate pattern.
If you feel that the resolution of your clock is fine enough to correctly order the transactions, but you do not trust the resolution to order the operations within the transaction, you can still use this pattern by following this variant. This variant also increases the efficiency of replaying the transactions on the target.
The concept behind this variant is that the result of a transaction does not depend on the order of its operations, but rather upon the net effect on any particular row within the transaction. So if an application on the source writes the same record twice within a transaction, the operations on that row can be aggregated to a single operation to be applied to the target. If the source application writes more than twice to the same row in a transaction, each of the other rows again aggregate with the previous aggregation to create a new aggregated row.
The following table presents the aggregated operation that has to be stored to achieve the correct net effect of two operations on the same row identified by the replication key in a single transaction:
Table 1: Net Effects of Two Operations on the Same Row
|First Operation||INSERT||Impossible||Insert||Do nothing|
The design of recording transactions on the source must now add these steps when storing the operation:
Determine if there is an earlier operation on the same row within the same transaction.
Determine the aggregated operation if an earlier operation is found.
Store the recorded or combined operation.
When applying this variant you must not have any referential integrity constraints on the target because the operations of the transaction might be executed in a different order. This would violate such constraints temporarily.
When combining several operations on the same row into a single operation, updates of the Replication Key might become a problem. However, because the target does not have referential integrity constraints for the reason just given, an update of key values can be converted into a delete of the old row, followed by an insert of the new row.
For more information, see the following related patterns:
Patterns That May Have Led You Here
Move Copy of Data. This pattern is the root pattern of this cluster. It presents the fundamental data movement building block consisting of source, data movement set, data movement link, and target. Transmissions in such a data movement building block are done asynchronously some time after the update of the source. Thus, the target applications must tolerate a certain amount of latency until changes are delivered.
Data Replication. This pattern presents the architecture of a replication, which is a specific type of data copy movement.
Master-Subordinate Replication. This pattern presents the high-level design for a replication where changes at the source are transmitted to the target by overwriting potential updates of the target.
Patterns That You Can Use Next
Master-Subordinate Transactional Incremental Replication. This pattern uses transactions to transmit changes from the source to the target. These changes might have been recorded using the Capture Transaction Details pattern.
This content is outdated and is no longer being maintained. It is provided as a courtesy for individuals who are still using these technologies. This page may contain URLs that were valid when originally published, but now link to sites or pages that no longer exist.