Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Chiranjeeb Buragohain

Stefano Stefani

Sumit Lohia

John Walter Heitmann

Blake Meike

Christopher Reddy Kommareddy

Liang Chen

Alex Maclinovsky

Swaminathan Sivasubramanian

Timothy Andrew Rath

Wei Xiao

Jakub Kulesza

Geoffrey Scott Pare

Rande Anthony Blackman

Raymond S Bradford

Zachary S Musgrave

Luis Felipe Cabrera

James Robert Hamilton

Jai Vasanth

Grant A M McAlister

Geoffry Scott Pare

Peter F Hill

Grant Alexander Macdonald McAlister

Richendra Khanna

Dushyanth Narayanan

Knut Magne Risvik

Ming-Chuan Wu

Miguel Oom Temudo De Castro

Alexander Shamis

Aleksandar Dragojevic

Grant Alexander MacDonald McAlister

Matthew Renzelmann

Timothy Tan

Grant A Mcalister

Grant A M Mcalister

A system that implements a scalable data storage service may maintain tables in a non-relational data store on behalf of clients. The system may provide a Web services interface through which service requests are received, and an API usable to request that a table be created, deleted, or described; that an item be stored, retrieved, deleted, or its attributes modified; or that a table be queried (or scanned) with filtered items and/or their attributes returned. An asynchronous workflow may be invoked to create or delete a table. Items stored in tables may be partitioned and indexed using a simple or composite primary key. The system may not impose pre-defined limits on table size, and may employ a flexible schema. The service may provide a best-effort or committed throughput model. The system may automatically scale and/or re-partition tables in response to detecting workload changes, node failures, or other conditions or anomalies.

System and method for implementing a scalable data storage service

A system that implements a scaleable data storage service may maintain tables in a non-relational data store on behalf of clients. Each table may include multiple items. Each item may include one or more attributes, each containing a name-value pair. Attribute values may be scalars or sets of numbers or strings. The system may provide an API usable to request that values of one or more of an item's attributes be updated. An update request may be conditional on expected values of one or more item attributes (e.g., the same or different item attributes). In response to a request to update the values of one or more item attributes, the previous values and/or updated values may be optionally returned for the updated item attributes or for all attributes of an item targeted by an update request. Items stored in tables may be indexed using a simple or composite primary key.

System and method for conditionally updating an item with attribute granularity

Techniques are described for managing program execution capacity, such as for a group of computing nodes that are provided for executing one or more programs for a user. In some situations, dynamic program execution capacity modifications for a computing node group that is in use may be performed periodically or otherwise in a recurrent manner, such as to aggregate multiple modifications that are requested or otherwise determined to be made during a period of time, and with the aggregation of multiple determined modifications being able to be performed in various manners. Modifications may be requested or otherwise determined in various manners, including based on dynamic instructions specified by the user, and on satisfaction of triggers that are previously defined by the user. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Dynamically modifying program execution capacity

A distributed graph database that enables scaling and efficient processing is described. The distributed graph database can, for example, scale up to petabytes of data to enable transactional processing of graph data with low latency and low processing overhead. The distributed graph database can include a cluster of devices and a remote direct memory access (RDMA)-based communication layer to perform low latency messaging between devices of the cluster of devices. Additionally, the distributed graph database can include a shared memory layer that provides one or more data structures, a transaction layer to facilitate query processing, and a graph database layer stored in computer-readable media and executed on a processor to implement a graph data model. In at least one example, the graph data model can be mapped to the one or more data structures.

Distributed graph database

Techniques are described for managing program execution capacity, such as for a group of computing nodes that are provided for executing one or more programs for a user. In some situations, dynamic program execution capacity modifications for a computing node group that is in use may be performed periodically or otherwise in a recurrent manner, such as to aggregate multiple modifications that are requested or otherwise determined to be made during a period of time. In addition, various operations may be performed to attribute causality information or other responsibility for particular program execution capacity modifications that are performed, including by attributing a single event as causing one capacity modification, and a combination of multiple events as possible causes for another capacity modification. The techniques may in some situations be used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.

Attributing causality to program execution capacity modifications

A system that implements a scaleable data storage service may maintain tables in a non-relational data store on behalf of service clients. Each table may include multiple items. Each item may include one or more attributes, each containing a name-value pair. The system may provide an API through which clients can query tables maintained by the service. Items may be partitioned and indexed in a table according to a simple or composite primary key contained in all items in the table. A composite primary key may include a hash key attribute, and a range key attribute. The range key attribute may be usable to order items having the same hash key attribute value, and to partition them dependent on a range of range key attribute values. A query request may specify a logical or mathematical expression dependent on range key attribute values and may be directed to multiple partitions.

System and method for partitioning and indexing table data using a composite primary key

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Chiranjeeb Buragohain