Amazon Technologies, Inc.

Richard David Krog

Mario Vargas Adoc, Jr

Christopher Lawrence Lavin

Jeremy Stephen Hynoski

Kerry Chang

Ethan Zane Evans

Ian M Bone

Anthony Warren Sajec

Eden Jn Baptiste

Jun Zhou

Xiaochuan Qin

Alexandre P Moura

Adel Gawdat

Mario Vargas Adoc

Akhilesh Mritunjai

Mostafa Elhemali

Kai Zhao

Yogesh Kansal

Wan Man Ryan Chan

Tushar Rakheja

Jason Kroll

Xi Huang

Jonathan L Meed

Saumil Ramesh Hukerikar

Dolev Ish-am

Throughput capacity may be changed to sustain throughput for accessing individual items in a database. A table hosted at storage nodes that provide access to the table in a database may be identified as allocated with a client-specified throughput capacity for accessing the table. Performance of access requests to the table at the storage nodes may be tracked. Based on the performance of the access requests, a change may be determined that modifies a throughput capacity for the table to sustain a guaranteed throughput for each access request independent of other access requests received for the table.

Changing throughput capacity to sustain throughput for accessing individual items in a database

Techniques are described for monitoring and enabling access to information about key access patterns for nonrelational database tables managed by a nonrelational database service of a service provider network. Storage nodes store data as key-value pairs (or key-document pairs), where the records or documents of a database are addressed by a unique key for each record. The storage nodes over time receive requests to perform various operations (for example, to get, put, update, or delete data in a table) associated with records stored by the storage nodes. The storage nodes collect information indicating a frequency with which such requests are received for each key for which a storage node is responsible. This frequency information for each key can be normalized relative to a maximum frequency supported by the storage nodes to obtain an access frequency score, where the maximum frequency indicates a frequency at which additional requests to the storage node may be throttled.

Monitoring key access patterns for nonrelational databases

Disclosed are various embodiments for synchronizing application state information across devices. More specifically, embodiments of the disclosure are related to resolving conflicts between application state information. A synchronization rule, an event name and/or event value are embedded within application state information obtained from devices associated with a user, from which conflicts can be resolved by an application synchronization service.

Resolving conflicts within saved state data

Disclosed are various embodiments for synchronizing application state information across devices. More specifically, embodiments of the disclosure are related to facilitating idempotency of application state information. Idempotency is maintained by using a timestamp embedded within application state information and/or by determining that the application state information is associated with an accumulating value.

Idempotency of application state data

Disclosed are various embodiments of a server application that facilitates selecting a computing device in which to host execution of an application. The selected application host provides application output to a client, and the client provides application input to the application host. In some embodiments, the application server may be a communications proxy between the client and a remote application host.

Remote execution of applications over a dispersed network

Disclosed are various embodiments for synchronizing application state information across devices. More specifically, embodiments of the disclosure are related to generating and storing of application state information. Key-value pairs are stored on a client device and synchronized with an application synchronization service.

Management of application state data

Disclosed are various embodiments for facilitating anticipatory distribution of applications to a network of remote hosts. A demand for each of the applications is calculated. Based on criteria within the demand and computing resources available, remote hosts are selected to receive the applications. Transmissions of the applications to the selected remote hosts are scheduled and monitored for completion according to the schedule.

Distribution of applications over a dispersed network

Idempotentcy of application state data

Growing community of inventors

Seattle, WA, United States of America

Richard David Krog