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Donald K. Burleson

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Concurrent Database Access

Before we discuss failover, database clusters, and parallel database clusters in detail, it is interesting to see how the database maintains consistent data in a multi-user environment. Consistent data and guaranteed data integrity are crucial for the database in a clustered environment.

Relational database systems allow concurrent access to database content such as rows and tables. The same data is retrieved and updated by many users. Concurrent access requires meaningful access control and should provide consistent results.

There are two major concepts for database access: data concurrency and data consistency. Data concurrency allows unhindered access by many users to the same data at the same time. Data consistency means that each user sees a consistent view of data, including visible changes made by the users own transactions, as well as the transactions of other users.

To provide consistent transaction behavior, database systems follow appropriate transaction isolation models. For example, Oracle automatically provides read consistency for queries so that all the data a query sees comes from a single point in time (statement level read consistency). As an option, it can also provide transaction-level read consistency.

Oracle makes use of rollback segments to provide these consistent views. The local cache of the instance has all the relevant data blocks to satisfy consistent results for database operations. Fig 3.7 shows the simultaneous access of data blocks by many users through identical and different instances.

Figure 3.7 Data Concurrency and Data Consistency

In a failover database cluster environment, all nodes actively access the disk storage unit that provides data volumes or file systems. The active node is where the database instance is running. The database instance, with memory structures and processes, is nothing but a front end for physical data blocks or data pages. The database instance’s local cache on the active node is the place where blocks are fetched, modified, and flushed back to the physical storage unit. The local cache is where active buffers are handled for processing by SQL statements. Since we are dealing with a single instance and only one set of cache buffers, the consistency mechanism is confined to this local cache.

However, in case of the parallel database clustered environment, where there are multiple instances located on multiple nodes, data consistency mechanisms go beyond one instance and cover the database caches of all the nodes. Multiple caches are joined virtually to provide a single cache image that processes SQL operations. When a user modifies a set of data blocks on one node, another user accessing the same set of blocks on a second node still gets read consistent blocks.

The caches from both these nodes act as if they are one single entity. For instance, Oracle Real Application Clusters use cache-to-cache block transfer, known as cache fusion, to move read-consistent images of data blocks from one instance cache to another instance cache. To support such an activity, there has to be some form of data locking.

We will explore more details about cache fusion and locking mechanisms in Chapter 7, Internals of Real Application Clusters.


For more information, see the book Oracle 11g Grid and Real Application Clusters 30% off if you buy it directly from Rampant TechPress . 

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