Amazon Technologies, Inc.

Navaneetha Krishnan Thanka Nadar

Saleem Mohideen

Murali Brahmadesam

Alexandre Olegovich Verbitski

Changan Han

David Charles Wein

Grant Alexander McAlister

James Laurence Finnerty

Haritabh Gupta

Jan Engelsberg

Anupriya Mathur

Sanjay Shanthakumar

Sean Oczkowski

Aravind Kumar Kumar

Praveen Kannan

Ahmad Mohammad Radi Ahmad Alsmair

Gajanan Sharadchandra Chinchwadkar

Gaurav Kumar Gupta

Anand Kumar Thakur

Baskar Durairaj

Nanda Kaushik

Upendra Govindagowda

Apoorv Birthare

Awisha Makwana

Davor Prugovecki

Sambhavi Raghuraman

Kiran Pillarisetty

Amit Krishnan

Raghavendran Gururajan

Sadagopan Nattamai Sathiyamoorthy

Li Che David Hsiao

Andrew James Whitaker

Meet Kiritkumar Bhagdev

Replication of a client-managed table may be configured across shards of system-managed tables in a database system for improved query performance. A client-managed table may be identified to replicate as a complete copy of the table respectively collocated with two or more shards of one or more other system-managed tables. The complete copy may be stored in respective storage volumes of the two or more shards of the one or more other system-managed tables. Metadata for performing access requests at a database system may be updated to identify the client-managed table as collocated with the two or more shards of the one or more other system-managed tables.

Configuring replication across shards of scalable database tables for improved query performance

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.

Distributed transaction execution across shards of scalable database tables

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

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.

Adding a read-only query engine to perform queries to a point-in-time of a write-accessible database

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

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.

Automatic recovery from detected data errors in database systems

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Bothell, WA, United States of America

Navaneetha Krishnan Thanka Nadar