Remote log based replication solution

Details Remote log based replication solution Remote log based replication solution

2000-05-09

2003-09-16

Robinson, Greta (Department: 2177)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C707S793000

Reexamination Certificate

active

06622152

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates in general to database management systems performed by computers, and in particular to the optimized remote log based data replication technique wherein Capture and Apply utilities are co-located on a target system, thereby eliminating communications overhead and providing an improved setting for building data warehouses and many other replication solutions.
2. Description of Related Art
Databases are computerized information storage and retrieval systems. A Relational Database Management System (RDBMS) is a database management system (DBMS) which uses relational techniques for storing and retrieving data. RDBMS software using a Structured Query Language (SQL) interface is well known in the art. The SQL interface has evolved into a standard language for RDBMS software and has been adopted as such by both the American National Standards Organization (ANSI) and the International Standards Organization (ISO).
In RDBMS software all data is externally structured into tables. The SQL interface allows users to formulate relational operations on the tables either interactively, in batch files, or embedded in host language, such as C, COBOL, etc. Operators are provided in SQL that allow the user to manipulate the data, wherein each operator operates on either one or two tables and produces a new table as a result. The power of SQL lies on its ability to link information from multiple tables or views together, to perform complex sets of procedures with a single statement.
A data warehouse is a subject-oriented, integrated, non-volatile, time-variant collection of data suited to the decision support needed at a certain network location of the enterprise data environment. For this purpose the IBM company created several replication products. One such product is named DataPropagator Relational Version 5.1, useable for AS/400 systems, having such features as efficient architecture for automatic Capture and asynchronous propagation of data changes to DB2 databases, applicable for building data warehouses and creating client/server, distributed, or mobile applications. This product provides for automatic maintenance of consistent copies of relational data in the databases, and has a change-Capture component that Captures all application table changes. It utilizes subscription sets for transaction consistency, and supports full refresh and update change replication, update-anywhere replication, DB2 views-based replication, event-driven and continuous replication. Replication can help decrease batch workloads by replicating data in the background throughout the day.
Businesses today benefit from replicating data across a range of applications and business sites, to improve business cycle times and customer responsiveness. Frequently, these applications must share data with legacy applications on the host. Data replication can automatically deliver the shared data to the target platform, improving data availability and data access performance, and accommodating data restructuring and minimizing network load. This can improve employees' decision making capabilities. The decision-support databases assist in day-to-day decision-making activities, from determining what items to stock in various stores, to identifying customer sets for new products.
Data replication can improve application deployment and the existing application backlog can be reduced, since the majority of these applications are based on the relational model and use standard SQL. Data replication enables use of these applications by mapping, reformatting, and delivering data from legacy environments to relational databases elsewhere. In order to increase online throughput, replication supports off-load query processing to make room for increasing transaction processing requirements. Off-loading query processing reduces contention that impacts online transaction processing (OLTP) response time. Businesses are migrating applications from legacy systems or replicating data between multivendor environments. This reduces application development time and reduces application maintenance costs.
DataPropagator Relational 5.1 provides read-only, update-anywhere, and on-demand replication between relational source and target processors. It consists of the following autonomous components and programs. Administration and replication control is performed by Control Center, Capture utility is used to Capture changes made to data on replication sources, and Apply utility reads previously Captured changed data and applies it to target tables. The use of Capture and Apply utilities is known in the art, as described, for example, in U.S. Pat. No. 5,995,980, incorporated herein by reference.
The Control Center is used to define tables as sources, called replication sources, define target table definitions, called replication subscriptions, clone replication subscriptions to other servers and remove replication sources or subscriptions no longer needed. Whenever a replication request from the Control Center is submitted, such as a command defining a replication source, the processing information is generated as SQL statements. The statements may be run immediately, or saved in a file, which can be edited and the statements can be run at a later time from a replication folder object in the Control Center. Deferred SQL files let a user customize the replication tasks for his application needs.
In a conventional system, the Capture component, located in a source computer, captures changes made to data in tables defined as replication sources by reading the database transaction log or journal, without making any changes to the sources. It is performed asynchronously to business applications using the same replication sources. The captured changes are placed in a staging area Change Data (CD) table, with transaction detail stored separately in a Unit of Work (UOW) table, both located in the source computer.
Ordinarily, the Apply component, located in a target computer, reads the changed data previously captured and stored in the staging area table and applies it to the target tables on the target computer. Apply components can also read data directly from the source tables, for example, for a full refresh. Supporting update and refresh copying provides greater flexibility and automation in a replication environment. The Apply component also massages the data to user specifications, as it copies data to the targets. SQL statements can be prepared to create new columns, summarize data, translate data, join data and do other data transactions.
The conventional Apply component allows the user to create read-only copies, user copy tables, which represent source data at a particular point in time, point-in-time tables, which represent source data at a particular point in time and some overhead columns, history tables, staging area tables (which can be used as a source for further copies without recapturing changes, thus supporting a consistent data environment and providing flexibility in data distribution across the network), updatable copies, and replica tables. Updates to a replica table are automatically applied to the original source table of the replica, provided no conflicts are detected.
The Apply component, running at the replica site, detects update conflicts after they occur during the subscription cycle. An Apply instance can process many subscriptions at a single site. Multiple Apply instances can run concurrently at different sites/platforms, each processing different numbers and types of subscriptions from the same source. Each subscription can have different definitions, refresh criteria, and timing.
One problem associated with data warehouses involves aggregating the extremely large amounts of data stored within application databases with the frequent input of large amounts of data. By aggregating the data stored within the data warehouse with newly inputted data, the raw data is translated into the most current meaningful information that can be relied upon by deci

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