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: Techniques for aggregating and emitting database activity record batches are described. Database activity records can be written to a shared memory queue and emitted to a destination using a remote procedure call (RPC). Individual database connection server processes can write client activity records to the queue. An activity monitor plugin in the database engine can monitor the audit records and aggregate the audit records into batches. Batches of audit records can be sent via RPC to their final or intermediate destination. Each instance host in a database service can include a client backend process configured to define how to submit audit records to shared memory. The activity monitor plugin can batch audit records in to messages and submit those messages via RPC to a security host manager and relaying response back to each relevant client backend.

Abstract: The first computing system may interface with an operator of the application and a plurality of hosts of the application distributed between different partitions. The second and third computing systems may host first and second portion of the application in first and second partitions, respectively. The second and third computing systems may poll the first computing system to identify first and second value, respectively, representing state conditions of the first and second partitions, respectively, wherein the first and second partition state conditions are the active state, the passive state, and the fenced state. The second and third computing systems may receive responses from the first computing system comprising the first and second values, respectively, and based on the respective values, initiate a transition to the corresponding partition state condition. The first computing system may assign one of the first and second values to indicate which is the active state.

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.

Abstract: The first computing system may interface with an operator of the application and a plurality of hosts of the application distributed between different partitions. The second and third computing systems may host first and second portion of the application in first and second partitions, respectively. The second and third computing systems may poll the first computing system to identify first and second value, respectively, representing state conditions of the first and second partitions, respectively, wherein the first and second partition state conditions are the active state, the passive state, and the fenced state. The second and third computing systems may receive responses from the first computing system comprising the first and second values, respectively, and based on the respective values, initiate a transition to the corresponding partition state condition. The first computing system may assign one of the first and second values to indicate which is the active state.