SQL Networking and Distributed Databases in Software

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SQL Networking and Distributed Databases
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Note that the WHERE predicate doesn t affect the change log. All changes to the PRODUCTS table must still be logged because multiple materialized views can be refreshed from the change log, regardless of the predicates used in their definitions. The materialized view can also be created as a joined table, extracting its data from two or more master tables in the remote database: Create a local replica of salesperson data, refreshed weekly.
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CREATE MATERIALIZED VIEW SALESTEAM REFRESH FAST START WITH SYSDATE NEXT SYSDATE+7 AS SELECT NAME, QUOTA, SALES, CITY FROM SALESREPS@REMOTE, OFFICES@REMOTE WHERE REP_OFFICE = OFFICE;
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Adding to the complexity, the materialized view can be defined by a grouped query: Create a local summary of customer order volume, refreshed daily.
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CREATE MATERIALIZED VIEW CUSTORD REFRESH FAST START WITH SYSDATE NEXT SYSDATE+1 AS SELECT COMPANY, SUM(AMOUNT) FROM CUSTOMERS@REMOTE, ORDERS@REMOTE WHERE CUST = CUST_NUM;
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Of course, with each level of additional complexity, the overhead of managing the materialized view and the refresh process increases. Regardless of how simple or complex the definition of the materialized view, however, the overall principles remain the same. Instead of having queries against the replicated data travel across the network to the remote database, the remote data is brought down into the materialized view. The refreshes to the materialized view still generate network traffic, but the day-to-day queries against the materialized view data are carried out locally and do not generate network traffic. For situations where the query workload is much higher than the overhead of maintaining the materialized view, this can be an effective way to improve overall database performance.
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In the simplest implementations, a table and its replicas have a strict master/slave relationship, as shown in Figure 23-3. The central/master copy contains the real data. It is always up to date, and all updates to the table must occur on this copy of the table. The other slave copies are populated by periodic updates, managed by the DBMS. Between updates, they may become slightly out of date, but if the database is configured in this way, then it is an acceptable price to pay for the advantage of having a local copy of the data. Updates to the slave copies are not permitted. If they are attempted, the DBMS returns an error condition. By default, the Oracle CREATE MATERIALIZED VIEW statement creates this type of slave replica of a table. However, more advanced functions such as multiple updateable replicas of the same master table require use of the Oracle Advanced Replication facility, which is beyond the scope of this book. In the Microsoft SQL Server structure for replication, the master/slave relationship is implicit. The SQL server architecture defines the master as the publisher of the data and the slaves as subscribers to the data. In the default configuration, there is a single (updateable)
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PART VI
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Part VI:
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SQL Today and Tomorrow
publisher, and there may be multiple (read-only) subscribers. The SQL Server architecture carries this analogy one step further, supporting both the notion of push updates (the publisher actively sends the update data to the subscribers) and pull updates (the subscribers have primary responsibility for getting updates from the publisher). For some applications, table replication is an excellent technique without the master/ slave relationship. For example, applications that demand high availability use replicated tables to maintain identical copies of data on two different computer systems. If one system fails, the other contains current data and can carry on processing. An Internet application may demand very high database access rates, and achieve this scalability by replicating a table many times on different computer systems and then spreading out the workload across the systems. A sales force automation application will probably contain one central CUSTOMER table and hundreds of replicas on laptop systems, and individual salespeople should be able to enter new customers or change customer contact information on the laptop replicas. In these configurations (and others), the most efficient use of the computer resources is achieved if all of the replicas can accept updates to the table, as shown in Figure 23-4. A replicated table where multiple copies can accept updates creates a new set of data integrity issues. What happens if the same row of the table is updated in one or more replicas When the DBMS tries to synchronize the replicas, which of the two updates should apply, or should neither apply, or both What happens if a row is deleted from one copy of the table, but it is updated in another copy of the table In DBMS systems that support updateable replicas, these issues are addressed by creating a set of conflict resolution rules that the replication system applies. For example, when replication is set up between a central CUSTOMER table and laptop versions of the table, the replication rule may say that changes to the central customer database always win over changes entered on a laptop system. Alternatively, the replication rule might say that the most recent update always wins. In addition to the built-in rules provided by the DBMS itself, the replication definition may include the capability to pass conflicts to a user-written procedure (such as a stored procedure within the database) for selection of the winner and loser replicas.
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