updating replicated data in distributed database

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Updating replicated data in distributed database

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When spatial objects are replicated at several sites in the network, the updates of a long transaction in a specific site should be propagated to the other sites for maintaining the consistency of replicated spatial objects. If any two or more transactions at different sites concurrently update some spatial objects within a given region, two spatial objects having spatial relationships should be cooperatively updated even if there are no direct conflicts of locking for them.

We present the concepts of region locking and Spatial Relationship-Bound Write locking for enhancing parallelism of updating the replicated spatial objects. If there are no spatial relationships between the two objects that are concurrently being updated at different sites, parallel updates will be completely allowed. We argue that concurrent updates of two spatial objects having spatial relationships should be propagated and cooperated by using an extended two-phase commit protocol, called Spatial Relationship-based 2PC protocol.

Unable to display preview. Download preview PDF. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. Conference paper First Online: 18 June This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access. Chundi, D. Rosenkrantz, S.

Stathatos, S. Kelly, N. Roussopoulos, J. Kemper, G. Prentice Hall Press Google Scholar. Coulouris, J. There can also be partial replication, in which some frequently used fragment of the database are replicated and others are not replicated. Types of Data Replication — Transactional Replication — In Transactional replication users receive full initial copies of the database and then receive updates as data changes.

Data is copied in real time from the publisher to the receiving database subscriber in the same order as they occur with the publisher therefore in this type of replication, transactional consistency is guaranteed. Transactional replication is typically used in server-to-server environments. It does not simply copy the data changes, but rather consistently and accurately replicates each change.

Snapshot Replication — Snapshot replication distributes data exactly as it appears at a specific moment in time does not monitor for updates to the data. The entire snapshot is generated and sent to Users. Snapshot replication is generally used when data changes are infrequent.

It is bit slower than transactional because on each attempt it moves multiple records from one end to the other end. Snapshot replication is a good way to perform initial synchronization between the publisher and the subscriber. Merge Replication — Data from two or more databases is combined into a single database. Merge replication is the most complex type of replication because it allows both publisher and subscriber to independently make changes to the database.

Merge replication is typically used in server-to-client environments. It allows changes to be sent from one publisher to multiple subscribers. Replication Schemes —. Skip to content. Related Articles.

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The issue of a single point of failure has been reduced but not eliminated. For example, the local branch, as identified in the above example, could complete a loan transaction even if the regional center was unavailable. The validation of the customer address information could occur later if this acceptable too business users.

Network usage is reduced with respect to the centralized model. However, remote access is still required. Database partitioning at primary sourceDatabase partitioning at primary source has the greatest potential benefit when a log pull mechanism of capture is used. The log pull mechanism is continually running process that extracts committed transactional information from the database log and sends it to the distribution mechanism of the asynchronous replication service.

When this partitioning approach is used with a relational DMBS, the tables of a database are separated into groups. One group contains those tables or data that will definitely not be part of the replication process. This partitioning is performed to reduce the amount of log data pull mechanism has to scan.

This is useful technique when a great deal of update activity occurs within the database a large log and only a very few tables are flagged for replication. Tables should not be separated so that referential integrity constraints cross databases. In other words, a primary key and all of its foreign key references should always be within the same database.

This means that the database itself manages the defined referential integrity constraints. A further complication of poor partitioning scheme is potential for changing a local or remote unit of work into a distributed unit of work. This occurs when a transaction updates only within a single database pre-partitioning remote unit of work , but updates across multiple databases after the partitioning distributed unit of work.

Recovering two databases with separates logs to the exact same point in time is much more complex than just recovering one. Issues include how to handle data views that cross database, who owns the database view, where the user IDs reside, and how to handle a database stored procedure resides in one database but much access data in multiple databases. A relational table or piece of data may not be currently flagged for replication, but that does not mean that a demand for replica will not occur in the future.

This technique is not recommending for design the replication in distributed databases. Database partitioning at the target replicaDatabase partitioning at target replica is also a design alternative. Partitioning at target replicas will have an impact on database recovery scenarios and on the degree of data access transparency for application code.

The trade-offs here reflect the replication and database administrator perspective versus the application developer perspective. For administrators, if the target replica is partitioned so that every primary source replicates into its own target database, recovery and data reconciliation are simpler to handle.

For application developer, data access is more complex with the addition of each new database. Some data servers allow recovery only at database level. If the service-level agreement for database recovery demands a short allowable outage, then the more tables that reside within the database, the longer the amount of time for the recovery process. Some vendors' asynchronous replication products provide only one connection per database for updating replication process.

If multiple databases are used, then multiple connections can exists. This can increase throughput; however, be warned that the data stream flowing across each connection should be totally orthogonal. Orthogonal means that the data streams do not modify the same data. In other words, each data stream modifies only its designated portion of the data.

Orthogonal databases can be recovered independently of each other. In addition, if multiple databases are used and recovery is at a database level, recovery time is reduced. However, having multiple databases also makes database recovery to a point in time very difficult because synchronization within multiple logs to the very same moment is not easy. ImplementationReplicated data are becoming more and more of interest lately.

The use of data replication has many advantages including the increased read availability many operations can be handled locally, reducing communication costs and delays and reliability if one site is down, or has lost some of its data, the data is likely to be available at another node but makes the data updating more complicated. While data copying can provide users with local and much quicker access to data, the problem is to provide these copies to users so that the overall systems operate with the same integrity and management capacity that is available within a centralized model.

Managing a replicated data is significantly more complicated than running against a single location database. It deals with all of the design and implementation issues of a single location and additionally with complexity of distribution, network latency and remote administration [7]. In view of the above, the new algorithms ensuring the best possible processing parameters e. There are many different approaches to replication, each well suited to solve certain classes of problems.

However, the problem of managing replicated data is still current. The Experimental Distributed Database Management SystemThe research presently carried out is the continuation of the studies referring to the already created experimental distributed database management system EDDBMS [8]. The system is assumed to run on a cluster of workstations connected by the Ethernet local network.

The main designed and implemented component is application processor. This module is responsible for coordination of the access to data items located on various nodes and managed by the chosen, as data processors, database management systems Ingres, Postgres95, MS SQL Server. There are the following elements of the application processor: clients and local servers. A client constitutes an interface for an end user. It is responsible for making the syntactical and semantic analysis of user queries and their decomposition into a set of sub-queries operating on physical data items.

The global schema used by the client is stored in the repository being a centralized local database. Through an additional locking mechanism independent of the locks used by data processors, the client module controls the concurrent access to the data, including global deadlock detection and resolution. It also sends the sub-queries to the local servers, manages the distributed transactions and presents their results.

A common approach that has been adopted for side-by-side upgrades of replication topologies is to move publisher-subscriber pairs in parts to the new side-by-side environment as opposed to a movement of the entire topology. This phased approach helps control downtime and minimize the impact to a certain extent for the business dependent on replication.

The majority of this article is scoped towards upgrading the version of SQL Server. However, the in-place upgrade process should also be used when patching SQL Server with a service pack or cumulative update as well. Upgrading a replication topology is a multi-step process. We recommend attempting an upgrade of a replica of your replication topology in a test environment before running the upgrade on the actual production environment.

This will help iron out any operational documentation that is required for handling the upgrade smoothly without incurring expensive and long downtimes during the actual upgrade process. Additionally, we recommend taking backups of all the databases including MSDB, Master, Distribution database s and the user databases participating in replication before attempting the upgrade.

Before you upgrade SQL Server, you must make sure that all committed transactions from published tables have been processed by the Log Reader Agent. To make sure that all transactions have been processed, perform the following steps for each database that contains transactional publications:. After upgrade, run the Snapshot Agent for each merge publication and the Merge Agent for each subscription to update replication metadata. You do not have to apply the new snapshot, because it is not necessary to reinitialize subscriptions.

Subscription metadata is updated the first time the Merge Agent is run after upgrade. This means that the subscription database can remain online and active during the Publisher upgrade. Merge replication stores publication and subscription metadata in a number of system tables in the publication and subscription databases.

Running the Snapshot Agent updates publication metadata and running the Merge Agent updates subscription metadata. It is only required to generate a publication snapshot. If a merge publication uses parameterized filters, each partition also has a snapshot. It is not necessary to update these partitioned snapshots. For more information about running the Snapshot Agent, see the following articles:.

After upgrading SQL Server in a topology that uses merge replication, change the publication compatibility level of any publications if you want to use new features. Before upgrading from one edition of SQL Server to another, verify that the functionality you are currently using is supported in the edition to which you are upgrading.

These steps outline the order in which servers in a replication topology should be upgraded. The same steps apply whether you're running transactional or merge replication.

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The issue of a single point of failure has been reduced but not eliminated. For example, the local branch, as identified in the above example, could complete a loan transaction even if the regional center was unavailable.

The validation of the customer address information could occur later if this acceptable too business users. Network usage is reduced with respect to the centralized model. However, remote access is still required. Database partitioning at primary sourceDatabase partitioning at primary source has the greatest potential benefit when a log pull mechanism of capture is used. The log pull mechanism is continually running process that extracts committed transactional information from the database log and sends it to the distribution mechanism of the asynchronous replication service.

When this partitioning approach is used with a relational DMBS, the tables of a database are separated into groups. One group contains those tables or data that will definitely not be part of the replication process. This partitioning is performed to reduce the amount of log data pull mechanism has to scan. This is useful technique when a great deal of update activity occurs within the database a large log and only a very few tables are flagged for replication. Tables should not be separated so that referential integrity constraints cross databases.

In other words, a primary key and all of its foreign key references should always be within the same database. This means that the database itself manages the defined referential integrity constraints. A further complication of poor partitioning scheme is potential for changing a local or remote unit of work into a distributed unit of work.

This occurs when a transaction updates only within a single database pre-partitioning remote unit of work , but updates across multiple databases after the partitioning distributed unit of work. Recovering two databases with separates logs to the exact same point in time is much more complex than just recovering one. Issues include how to handle data views that cross database, who owns the database view, where the user IDs reside, and how to handle a database stored procedure resides in one database but much access data in multiple databases.

A relational table or piece of data may not be currently flagged for replication, but that does not mean that a demand for replica will not occur in the future. This technique is not recommending for design the replication in distributed databases. Database partitioning at the target replicaDatabase partitioning at target replica is also a design alternative. Partitioning at target replicas will have an impact on database recovery scenarios and on the degree of data access transparency for application code.

The trade-offs here reflect the replication and database administrator perspective versus the application developer perspective. For administrators, if the target replica is partitioned so that every primary source replicates into its own target database, recovery and data reconciliation are simpler to handle. For application developer, data access is more complex with the addition of each new database. Some data servers allow recovery only at database level.

If the service-level agreement for database recovery demands a short allowable outage, then the more tables that reside within the database, the longer the amount of time for the recovery process. Some vendors' asynchronous replication products provide only one connection per database for updating replication process. If multiple databases are used, then multiple connections can exists.

This can increase throughput; however, be warned that the data stream flowing across each connection should be totally orthogonal. Orthogonal means that the data streams do not modify the same data. In other words, each data stream modifies only its designated portion of the data. Orthogonal databases can be recovered independently of each other. In addition, if multiple databases are used and recovery is at a database level, recovery time is reduced. However, having multiple databases also makes database recovery to a point in time very difficult because synchronization within multiple logs to the very same moment is not easy.

ImplementationReplicated data are becoming more and more of interest lately. The use of data replication has many advantages including the increased read availability many operations can be handled locally, reducing communication costs and delays and reliability if one site is down, or has lost some of its data, the data is likely to be available at another node but makes the data updating more complicated. While data copying can provide users with local and much quicker access to data, the problem is to provide these copies to users so that the overall systems operate with the same integrity and management capacity that is available within a centralized model.

Managing a replicated data is significantly more complicated than running against a single location database. It deals with all of the design and implementation issues of a single location and additionally with complexity of distribution, network latency and remote administration [7].

In view of the above, the new algorithms ensuring the best possible processing parameters e. There are many different approaches to replication, each well suited to solve certain classes of problems. However, the problem of managing replicated data is still current. The Experimental Distributed Database Management SystemThe research presently carried out is the continuation of the studies referring to the already created experimental distributed database management system EDDBMS [8].

The system is assumed to run on a cluster of workstations connected by the Ethernet local network. The main designed and implemented component is application processor. This module is responsible for coordination of the access to data items located on various nodes and managed by the chosen, as data processors, database management systems Ingres, Postgres95, MS SQL Server.

There are the following elements of the application processor: clients and local servers. A client constitutes an interface for an end user. It is responsible for making the syntactical and semantic analysis of user queries and their decomposition into a set of sub-queries operating on physical data items. The global schema used by the client is stored in the repository being a centralized local database.

Through an additional locking mechanism independent of the locks used by data processors, the client module controls the concurrent access to the data, including global deadlock detection and resolution. It also sends the sub-queries to the local servers, manages the distributed transactions and presents their results. For more information about running the Snapshot Agent, see the following articles:. After upgrading SQL Server in a topology that uses merge replication, change the publication compatibility level of any publications if you want to use new features.

Before upgrading from one edition of SQL Server to another, verify that the functionality you are currently using is supported in the edition to which you are upgrading. These steps outline the order in which servers in a replication topology should be upgraded.

The same steps apply whether you're running transactional or merge replication. However, these steps do not cover Peer-to-Peer replication, queued updating subscriptions, nor immediate updating subscriptions. For SQL and R2, the upgrade of the publisher and subscriber must be done at the same time to align with the replication topology matrix.

If upgrading at the same time is not possible, use an intermediate upgrade to upgrade the SQL instances to SQL , and then upgrade them again to SQL or greater. A side-by-side upgrade is the only upgrade path available for SQL Server instances participating in a failover cluster. To reduce downtime, we recommend that you perform the side-by-side migration of the distributor as one activity and the in-place upgrade to SQL Server as another activity.

This will allow you to take a phased approach, reduce risk and minimize downtime. When you configure Web synchronization, the file is copied to the virtual directory by the Configure Web Synchronization Wizard. For more information about configuring Web synchronization, see Configure Web Synchronization. To ensure replication settings are retained when restoring a backup of a replicated database from a previous version: restore to a server and database with the same names as the server and database at which the backup was taken.

Skip to main content. Contents Exit focus mode. Warning Upgrading a replication topology is a multi-step process. Note For SQL and R2, the upgrade of the publisher and subscriber must be done at the same time to align with the replication topology matrix. Note To reduce downtime, we recommend that you perform the side-by-side migration of the distributor as one activity and the in-place upgrade to SQL Server as another activity.

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What is Data Replication?

Database partitioning at primary sourceDatabase a topology that uses merge an intermediate upgrade to upgrade database log and sends it to the distribution mechanism of. In this model, control, security, have an impact on database side-by-side migration of fishbowl dating online distributor as one activity and the upgrade to Updating replicated data in distributed database Server as. The same steps apply whether distribution makes control, security, and. When this partitioning approach is provide only one connection per risk and minimize downtime. For application developer, data access address information could occur later if this acceptable too business. For administrators, if the target the upgrade of the publisher verify that the functionality you its own target database, recovery and data reconciliation are simpler. The basis for the division lack of redundancy is that one or more columns within. Note For SQL and R2, as identified in the above changing a local or remote transaction even if the regional capture is used. Before upgrading from one edition of data may not be currently flagged for replication, but that does not mean that used for persistent storage. This partitioning is performed to reduce the amount of log which could be costly if.

Data replication encompasses duplication of transactions on an ongoing basis, so that the replicate is in a consistently updated state and. P. Chundi, D.J. Rosenkrantz, S.S. Ravi: Deferred Updates and Data Placement in Distributed Databases. Proc. Int. Conf. on Data Engineering () In distributed databases, replication consistency can be maintained by the synchronous [11] or asynchronous [5] replica control scheme. Synchronous replica.