Amazon Technologies, Inc.

David Charles Wein

Alexandre Olegovich Verbitski

Anand Kumar Thakur

Gaurav Kumar Gupta

Saleem Mohideen

Upendra Govindagowda

Murali Brahmadesam

Navaneetha Krishnan Thanka Nadar

Grant Alexander McAlister

Jin Chen

Marc John Brooker

Stefano Stefani

Michael Anthony Hall

Tanmoy Dutta

Dennis Tighe

Sankhyayan Debnath

James Laurence Finnerty

Baskar Durairaj

Gajanan Sharadchandra Chinchwadkar

Ahmad Mohammad Radi Ahmad Alsmair

Haritabh Gupta

Timothy Michael Galvin

Li Che David Hsiao

Shawn McCoy

James C Nasby

Khaled Sinno

Hong Yang

Sadagopan Nattamai Sathiyamoorthy

Mohammad Abu Obaida

Anton Shyrabokau

Bryce Jonathan Danz

Zhonghua Feng

Commit time logging is performed as part of implementing time-based Multi-Version Concurrency Control (MVCC). A query engine may determine a commit time for a database transaction is committed to a database by applying MVCC to select different versions of data in the database according to transaction commit times. The query engine may store the commit time for the database transaction in a log structure stored in a non-volatile data store. A segment describing the log structure maintained in a volatile memory maintained in a volatile memory may be updated to include the commit time for the database transaction.

Commit time logging for time-based multi-version concurrency control

Clock times may be stored at nodes of a database system that are used to provide access to database data using Multi-Version Concurrency Control (MVCC) and implement isolation for distributed transactions. When an access request is received at a processing node of a database, the snapshot time of the access request is determined. The snapshot may be a range of time, with an earliest and latest timestamp. If the snapshot time has a latest time stamp greater than a current timestamp in the clock time, then the clock time may be updated. The clock time and snapshot time may be used to implement time-based MVCC to select a version of a tuple stored in the database to use for performing the access request.

Increasing commit times at database shards for implementing consistency and isolation for distributed transactions

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

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

Working set ratio estimations of data items in a sliding time window are determined to dynamically allocate storage for the data items. A working set ratio may be determined by accessing a fixed-size array that stores respective timestamps of last accesses of data items to determine which data items are useful to determine an estimate of a working set for the application within a range of time. The working set ratio is then determined from an estimated working set and an amount of computing resources allocated to the application by the estimated working set. The amount of the computing resources allocated to the application may then be automatically scaled according to the determine working set ratio.

Working set ratio estimations of data items in a sliding time window for dynamically allocating computing resources for the data items

Lease-based consistency may be implemented for databases to handle failovers. A database node may obtain a consistency lease that describes a point in time determined from a time-to-live amount added to a consistent point in time for database data. While the consistency lease is valid, Multi-version Concurrency Control (MVCC) snapshots assigned by the database node can be used to handle requests to access the database data. Once expired, the database node may have to renew the consistency lease in order to continue to handle write and read requests.

Lease-based consistency management for handling failover in a database

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.

Aggregating and emitting database activity record batches

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Shoreline, WA, United States of America

David Charles Wein