Amazon Technologies, Inc.

James Laurence Finnerty

Saleem Mohideen

Navaneetha Krishnan Thanka Nadar

Grant Alexander McAlister

Stefano Stefani

Sudipta Sengupta

Alexandre Olegovich Verbitski

Murali Brahmadesam

David Charles Wein

Sumeetkumar Veniklal Maru

Sanjay Shanthakumar

Baskar Durairaj

Julio Delgado Mangas

Jan Engelsberg

Ahmad Mohammad Radi Ahmad Alsmair

Haritabh Gupta

Praveen Kannan

Aravind Kumar Kumar

Gajanan Sharadchandra Chinchwadkar

Li Che David Hsiao

Sadagopan Nattamai Sathiyamoorthy

Andrei Victor Ilyashenko

Raghavendran Gururajan

Ronak Bharat Shah

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.

Shard management for scaling database tables

Techniques for performing machine learning inference calls in database query processing are described. A method for performing machine learning inference calls in database query processing may include generating a query plan to optimize a query for batch processing of data stored in a database service, the query plan including a batch mode operator to execute a function reference and an execution context associated with the batch mode operator, executing the query plan to invoke a function associated with the function reference, wherein the function sends a batch of requests, generated using the execution context, to a remote service and obtains a plurality of responses from the remote service, and generating a query response based on the plurality of responses.

Machine learning inference calls for database query processing

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.

Intelligent query routing across shards of scalable database tables

Techniques for batch mode execution for calls to remote services are described. A method of batch mode execution for calls to remote services may include generating, by a query service of a provider network, a query plan to optimize a query for batch processing of data, the query plan including at least a function reference to a function provided by at least one service of the provider network, executing the query plan to invoke the function associated with the function reference, wherein a batch function generates a request including a batch of service calls to be processed by the at least one service, sends the request including the batch of service calls to the at least one service, and obtains a plurality of machine learning responses from the at least one service, and generating a query response based on the plurality of responses.

Use of batch mode function execution in database engines to enable efficient calls to remote services

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Concord, MA, United States of America

James Laurence Finnerty