Abstract: Distributed execution of transactions may be performed across shards of a scalable database table. Instructions to perform as part of a transaction with respect to one or more database tables may be received. The instructions may be evaluated with respect to metadata obtained for the database tables to determine an assignment distribution of computing resources of the database system to data that can satisfy the access request. The commit protocol for the transaction may be determined according to the assignment distribution of the computing resources. After a request to commit the transaction is received, the transaction may be committed according to the determined commit protocol.

Abstract: Shard management may be implemented for scalable database tables. A table may be identified as aligned with another table of the database according to a common shard key across multiple shards that are mapped to respective storage volumes of a data store of the database according to a placement hierarchy. Different portions of the table may be stored across the shards as respective table slices with corresponding portions of the other table as respective other table slices with same values of the shard key. Metadata for performing access requests to the database may be updated to identify individual ones of the table slices stored with individual ones of the other table slices as respective table slice groups that are mapped to respective ones of the shards in the placement hierarchy.

Abstract: A database system may add a read-only query engine to perform read-only queries associated with points-in-time of a database. In various embodiments, the read-only query engine may be added in response to a manual request, an automatic refresh of a network endpoint, a query specifying a point-in-time, or a connection request. The read-only query engine may perform the point-in-time queries on a version the database at the point-in-time and return results for the queries. Upon completion of the queries or at a determined time, the database system may remove the read-only query engine. The specified point-in-time may refer to a current time, a prior time, or a future time with respect to the current time.

Abstract: Intelligent query routing may be performed across shards of a scalable database table. A router of a database system may receive an access request directed to one or more database tables. The router may evaluate the access request with respect to metadata obtained for the database tables to determine an assignment distribution of computing resources of the database system to data that can satisfy the access request. The router can select planning locations to perform the access request based on the assignment distribution of the computing resources. The router can cause the access request to be performed according to planning at the selected planning locations.

Abstract: A database system may add a read-only query engine to perform read-only queries associated with points-in-time of a database. In various embodiments, the read-only query engine may be added in response to a manual request, an automatic refresh of a network endpoint, a query specifying a point-in-time, or a connection request. The read-only query engine may perform the point-in-time queries on a version the database at the point-in-time and return results for the queries. Upon completion of the queries or at a determined time, the database system may remove the read-only query engine. The specified point-in-time may refer to a current time, a prior time, or a future time with respect to the current time.

Abstract: Techniques for fast online scaling of a database via a split architecture including decoupled storage and compute tiers in a database service are described. A cluster of database (DB) nodes is scaled to add a new DB node. The scaling includes determining a split for data of a first volume managed by an existing DB node. A second DB node is obtained, and the first volume is cloned according to a lightweight copy technique to yield a second volume for use by the second DB node. After the cloning, a set of database modifications are applied to the second volume based on modifications caused by database traffic received by the first DB node, involving the the volume, during the cloning of the first volume. Each DB node may drop the portion of the volume that it does not need according to the split.

Abstract: A database system may add a read-only query engine to perform read-only queries associated with points-in-time of a database. In various embodiments, the read-only query engine may be added in response to a manual request, an automatic refresh of a network endpoint, a query specifying a point-in-time, or a connection request. The read-only query engine may perform the point-in-time queries on a version the database at the point-in-time and return results for the queries. Upon completion of the queries or at a determined time, the database system may remove the read-only query engine. The specified point-in-time may refer to a current time, a prior time, or a future time with respect to the current time.

Abstract: Systems and methods are disclosed to implement an automatic recovery technique for a detected corruption of stored data in a database system. In embodiments, the database system implements a physical replication process that replicates the contents of a primary volume to a mirror volume, with a specified mirror lag. The database system also stores a log of updates that it has received. The database system is configured to detect an error caused by a recent write operation received during the mirror lag. Upon detection, physical replication to the mirror volume is stopped, and a logical replication is performed where write operations logged during the mirror lag are replayed on the mirror volume. The mirror volume is then promoted to replace the primary volume in the database system. Advantageously, the disclosed recovery technique can be performed very quickly, and with zero loss of client requests received before initiation of the recovery.

Abstract: Fine-grained virtualization provisioning may be performed for in-place database scaling. Computing resource utilization for a database on a host system is obtained for a period of time. The computing resource utilization may be evaluated with respect to a target capacity for the database. If a scaling event is detected based on the evaluation, a modified target capacity may be determined and used to make an adjustment of the computing resources permitted to be used by the database.