Abstract:
Techniques are provided for dependency-aware transaction batching for data replication. A plurality of change records corresponding to a plurality of transactions is read. Inter-transaction dependency data is generated, the inter-transaction dependency data including at least one inter-transaction dependency relationship between a plurality of pending transactions. Each inter-transaction dependency relationship indicates that a first transaction is dependent on a second transaction. A batch transaction is generated based on the inter-transaction dependency data. The batch transaction includes at least one pending transaction of the plurality of pending transactions. The batch transaction is assigned to an apply process of a plurality of apply processes configured to apply batch transactions in parallel.
Abstract:
Techniques are provided for dependency-aware transaction batching for data replication. A plurality of change records corresponding to a plurality of transactions is read. Inter-transaction dependency data is generated, the inter-transaction dependency data including at least one inter-transaction dependency relationship between a plurality of pending transactions. Each inter-transaction dependency relationship indicates that a first transaction is dependent on a second transaction. A batch transaction is generated based on the inter-transaction dependency data. The batch transaction includes at least one pending transaction of the plurality of pending transactions. The batch transaction is assigned to an apply process of a plurality of apply processes configured to apply batch transactions in parallel.
Abstract:
Techniques are provided for client and server integration for scalable replication. A replication client transmits change records to a database server over a stream. The database server determines at least one batch comprising change records for at least one transaction. The database server generates dependency data for at least one change record in a batch based on at least one constraint identifier for at least one column. The database server determines an ordered grouping of the change records based on an operation type of each change record and the dependency data of each change record, wherein change records sharing operation types are grouped together unless a division based on the dependency data is determined. The database server generates a reordered transaction comprising a plurality of reordered operations based on the ordered grouping of the change records of the particular batch.
Abstract:
Transient duplicate key violations of unique key constraints are handled during row updates. Row changes are buffered until a point is reached that those changes are safe to execute. Row changes are effectively reordered to avoid constraint violations during execution of updates. In response to receiving a constraint key violation from a server after an attempted update, a client locally stores a record containing information regarding the failed update. Later, in response to the lack of receipt of an error in response to another update to the same column of the same table, the client uses the information in this record to instruct the server to attempt to repeat a failed update that previously attempted to change the value of a row to a value that was present in a uniqueness-constrained column at the time of the failure, but is no longer present due to the successful update.
Abstract:
Transient duplicate key violations of unique key constraints are handled during row updates. Row changes are buffered until a point is reached that those changes are safe to execute. Row changes are effectively reordered to avoid constraint violations during execution of updates. In response to receiving a constraint key violation from a server after an attempted update, a client locally stores a record containing information regarding the failed update. Later, in response to the lack of receipt of an error in response to another update to the same column of the same table, the client uses the information in this record to instruct the server to attempt to repeat a failed update that previously attempted to change the value of a row to a value that was present in a uniqueness-constrained column at the time of the failure, but is no longer present due to the successful update.
Abstract:
Techniques are provided for client and server integration for scalable replication. A replication client transmits change records to a database server over a stream. The database server determines at least one batch comprising change records for at least one transaction. The database server generates dependency data for at least one change record in a batch based on at least one constraint identifier for at least one column. The database server determines an ordered grouping of the change records based on an operation type of each change record and the dependency data of each change record, wherein change records sharing operation types are grouped together unless a division based on the dependency data is determined. The database server generates a reordered transaction comprising a plurality of reordered operations based on the ordered grouping of the change records of the particular batch.