Highly Available Database Management Systems

Highly Available Database Management Systems

Wenbing Zhao (Cleveland State University, USA)
DOI: 10.4018/978-1-60566-026-4.ch272
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Abstract

In the Internet age, real-time Web-based services are becoming more pervasive every day. They span virtually all business and government sectors, and typically have a large number of users. Many such services require continuous operation, 24 hours a day, seven days a week. Any extended disruption in services, including both planned and unplanned downtime, can result in significant financial loss and negative social effects. Consequently, the systems providing these services must be made highly available. A Web-based service is typically powered by a multi-tier system, consisting of Web servers, application servers, and database management systems, running in a server farm environment. The Web servers handle direct Web traffic and pass requests that need further processing to the application servers. The application servers process the requests according to the predefined business logic. The database management systems store and manage all mission-critical data and application states so that the Web servers and application servers can be programmed as stateless servers. (Some application servers may cache information, or keep session state. However, the loss of such state may reduce performance temporarily or may be slightly annoying to the affected user, but not critical.) This design is driven by the demand for high scalability (to support a large number of users) and high availability (to provide services all the time). If the number of users has increased, more Web servers and application servers can be added dynamically. If a Web server or an application server fails, the next request can be routed to another server for processing. Inevitably, this design increases the burden and importance of the database management systems. However, this is not done without good reason. Web applications often need to access and generate a huge amount of data on requests from a large number of users. A database management system can store and manage the data in a well-organized and structured way (often using the relational model). It also provides highly efficient concurrency control on accesses to shared data. While it is relatively straightforward to ensure high availability for Web servers and application servers by simply running multiple copies in the stateless design, it is not so for a database management system, which in general has abundant state. The subject of highly available database systems has been studied for more than two decades, and there exist many alternative solutions (Agrawal, El Abbadi, & Steinke, 1997; Kemme, & Alonso, 2000; Patino-Martinez, Jimenez- Peris, Kemme, & Alonso, 2005). In this article, we provide an overview of two of the most popular database high availability strategies, namely database replication and database clustering. The emphasis is given to those that have been adopted and implemented by major database management systems (Davies & Fisk, 2006; Ault & Tumma, 2003).
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Background

A database management system consists of a set of data and a number of processes that manage the data. These processes are often collectively referred to as database servers. The core programming model used in database management systems is called transaction processing. In this programming model, a group of read and write operations on a data set are demarcated within a transaction. A transaction has the following ACID properties (Gray & Reuter, 1993):

  • Atomicity: All operations on the data set agree on the same outcome. Either all the operations succeed (the transaction commits) or none of them do (the transaction aborts).

  • Consistency: If the database is consistent at the beginning of a transaction, then the database remains consistent after the transaction commits.

  • Isolation: A transaction does not read or overwrite a data item that has been accessed by another concurrent transaction.

  • Durability: The update to the data set becomes permanent once the transaction is committed.

Key Terms in this Chapter

Transaction: A group of read/write operations on the same data set that succeeds or fails atomically. More accurately, a transaction that has atomicity, consistency, isolation, and durability properties.

Database Recovery (Roll-Backward, Roll-Forward): Recovery is needed when a database instance that has failed is restarted or a surviving database instance takes over a failed one. In roll-backward recovery, the active transactions at the time of failure are aborted and the resourced allocated for those transactions are released. In roll-forward recovery, the updates recorded in the redo log are transferred to the database so that they are not lost.

Two-Phase Commit Protocol (2PC): This protocol ensures atomic commitment of a transaction that spans multiple nodes in two phases. During the first phase, the coordinator (often the primary replica) queries the prepare status of a transaction. If all participants agree to commit, the coordinator decides to commit. Otherwise, the transaction is aborted. The second phase is needed to propagate the decision to all participants.

Database Replication (Eager, Lazy): Multiple instances of a database management system are deployed in different computers (often located in different sites). Their state is synchronized closely to ensure replica consistency. In eager replication, the updates are propagated and applied to all replicas within the transaction boundary. In lazy replication, the changes are propagated from one replica to others asynchronously.

Database Cluster (Shared-Everything, Shared-Nothing): A database management system runs on a group of computers interconnected by a high-speed network. In the cluster, multiple database server instances are deployed. If one instance fails, another instance takes over very quickly to ensure high availability. In the shared-everything design, all nodes can access a shared stable storage device. In the shared-nothing design, each node has its own cache buffer and stable storage.

Split-Brain Syndrome: This problem may happen if the network partitions in a database cluster, and if each partition makes incompatible decisions on the outcome of transactions or their orders. To prevent this problem, typically only the main partition is allowed to survive.

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