Abstract: Transaction consistency query support is available for replicated data from recovery log to external data stores. An external data store is populated with records using entries of a change data table. The change data table has entries for each transaction that has committed and is to be replicated, and each of the entries stores information for each log entry in a recovery log from a database management system. Each log entry identifies a transactional change of data and a transaction completion indicator of one of commit and abort. In response to receiving a query about a transaction of the transactions, a set of records are retrieved from the external data store for the transaction. From the set of records, records whose sequence identifier values are larger than a maximum transaction commit sequence identifier are removed. From the set of records, remaining records having transaction consistency are returned.

Abstract: Transaction consistency query support is available for replicated data from recovery log to external data stores. An external data store is populated with records using entries of a change data table. The change data table has entries for each transaction that has committed and is to be replicated, and each of the entries stores information for each log entry in a recovery log from a database management system. Each log entry identifies a transactional change of data and a transaction completion indicator of one of commit and abort. In response to receiving a query about a transaction of the transactions, a set of records are retrieved from the external data store for the transaction. From the set of records, records whose sequence identifier values are larger than a maximum transaction commit sequence identifier are removed. From the set of records, remaining records having transaction consistency are returned.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: Several replication subscriptions are defined for a table in a database management system. The table is divided into partitions. Each replication subscription replicates transactions to a range of partitions. Subscriptions are assignable to different consistency groups. Transaction consistency is preserved at the consistency group level. A persistent delay table is created for each of several apply functions. Each apply function processes replication subscriptions for one consistency group, to replicate the table to a target table in parallel. Transactions for a given range of a partition are executed in parallel. When an apply function upon a row of the target table results in an error, the row is stored in the delay table. Application of each row in the delay table is repeatedly retried, and if successful, the row is removed from the delay table.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: Provided are techniques for replicating relational transactional log data to a big data platform. Change records contained in change data tables are fetched. A relational change history with transaction snapshot consistency is rebuilt to generate consistent change records by joining the change data tables and a unit of work table based on a commit sequence identifier. The consistent change records are stored on the big data platform, and queries are answered on the big data platform using the consistent change records.

Abstract: Provided are techniques for replicating relational transactional log data to a big data platform. Change records contained in change data tables are fetched. A relational change history with transaction snapshot consistency is rebuilt to generate consistent change records by joining the change data tables and a unit of work table based on a commit sequence identifier. The consistent change records are stored on the big data platform, and queries are answered on the big data platform using the consistent change records.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: Several replication subscriptions are defined for a table in a database management system. The table is divided into partitions. Each replication subscription replicates transactions to a range of partitions. Subscriptions are assignable to different consistency groups. Transaction consistency is preserved at the consistency group level. A persistent delay table is created for each of several apply functions. Each apply function processes replication subscriptions for one consistency group, to replicate the table to a target table in parallel. Transactions for a given range of a partition are executed in parallel. When an apply function upon a row of the target table results in an error, the row is stored in the delay table. Application of each row in the delay table is repeatedly retried, and if successful, the row is removed from the delay table.

Abstract: Provided are techniques for transaction consistency query support for replicated data from recovery log to external data stores. An external data store is populated with records using entries of a change data table. The change data table has entries for each transaction that has committed and is to be replicated, and each of the entries stores information for each log entry in a recovery log from a database management system. Each log entry identifies a transactional change of data and a transaction completion indicator of one of commit and abort. In response to receiving a query about a transaction of the transactions, a set of records are retrieved from the external data store for the transaction. From the set of records, records whose sequence identifier values are larger than a maximum transaction commit sequence identifier are removed. From the set of records, remaining records having transaction consistency are returned.

Abstract: Provided are techniques for replicating relational transactional log data to a big data platform. Change records contained in change data tables are fetched. A relational change history with transaction snapshot consistency is rebuilt to generate consistent change records by joining the change data tables and a unit of work table based on a commit sequence identifier. The consistent change records are stored on the big data platform, and queries are answered on the big data platform using the consistent change records.

Abstract: Provided are techniques for replicating relational transactional log data to a big data platform. Change records contained in change data tables are fetched. A relational change history with transaction snapshot consistency is rebuilt to generate consistent change records by joining the change data tables and a unit of work table based on a commit sequence identifier. The consistent change records are stored on the big data platform, and queries are answered on the big data platform using the consistent change records.

Abstract: Provided are techniques for transaction consistency query support for replicated data from recovery log to external data stores. An external data store is populated with records using entries of a change data table. The change data table has entries for each transaction that has committed and is to be replicated, and each of the entries stores information for each log entry in a recovery log from a database management system. Each log entry identifies a transactional change of data and a transaction completion indicator of one of commit and abort. In response to receiving a query about a transaction of the transactions, a set of records are retrieved from the external data store for the transaction. From the set of records, records whose sequence identifier values are larger than a maximum transaction commit sequence identifier are removed. From the set of records, remaining records having transaction consistency are returned.

Abstract: Techniques are disclosed to determine differences between a source table and a target table in a database environment, as being persistent or transient. A first set of differences between the source table and the target table is determined at a first point in time. A second set of differences between the source table and the target table is determined at a second point in time subsequent to the first point in time. At least one of a set of persistent differences and a set of transient differences is determined. The set of persistent differences includes a set intersection of the first and second sets of differences, the set intersection being filtered based on matching non-key values of the differences. The set of transient differences includes a relative complement of the second set of differences in the first set of differences.

Abstract: According to one embodiment of the present invention, a system uses parallel transaction messages for database replication. The system receives transaction messages from a source system via a plurality of parallel send queues in a receive queue. Each transaction message includes a message identifier indicating a commit order for that transaction. The system reads transaction messages in the receive queue in order in which they were committed against a source database based on the message identifier, and applies changes described by the transaction messages to a target database. Two or more transaction messages in the receive queue are applied to the target database in parallel. The system deletes transaction messages from the receive queue asynchronously to applying changes described by those transaction messages to the target database. Embodiments of the present invention further include a method and computer program product for parallel transaction messages in substantially the same manners described above.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.

Abstract: A method for verifying data consistency between update-in-place data structures and append-only data structures containing change histories associated with the update-in-place data structures is provided. The method includes loading data from an update-in-place data structure to a first set of hash buckets in a processing platform, loading data from append-only data structures to a second set of hash buckets in the processing platform, performing a bucket-level comparison between the data in the first set of hash buckets and the data in the second set of has buckets, and generating a report based on the bucket-level comparison.