Amazon Technologies, Inc.

Rande Anthony Blackman

Timothy Andrew Rath

Wei Xiao

Swaminathan Sivasubramanian

Stefano Stefani

Stuart Henry Seelye Marshall

Raymond S Bradford

Jakub Kulesza

Luis Felipe Cabrera

James Robert Hamilton

Grant Alexander Macdonald McAlister

David Alan Lutz

Chiranjeeb Buragohain

Maximiliano Maccanti

Grant A M McAlister

Akshat Vig

Kiran-Kumar Muniswamy-Reddy

Somasundaram Perianayagam

Miguel Mascarenhas Filipe

Bjorn Patrick Swift

Xianglong Huang

Jian Wang

Jiandan Zheng

Janani Narayanan

Yijun Lu

Chiranjeeb Buragahain

Grant A Mcalister

Jai Vasanth

Adam Douglas Morley

Grant Alexander MacDonald McAlister

Vincent Anthony Brancato

Grant A M Mcalister

A system that implements a scaleable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain data in partitions stored on respective computing nodes in the system. The service may support multiple throughput models, including a committed throughput model and a best effort throughput model. A service request to create a table may specify that requests directed to the table should be serviced under a committed throughput model and may specify the committed throughput level in terms of logical service request units. The service may reserve low-latency storage and other resources sufficient to meet the specified committed throughput level. A client/user may request a modification to the committed throughput level in anticipation of workload changes, such as an increase or decrease in traffic or data volume. In response, the system may increase or decrease the resources reserved for the table.

System and method for providing a committed throughput level in a data store

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

Methods and apparatus for conditional master election in a distributed database are described. A plurality of replicas of a database object are stored by a distributed database service. Some types of operations corresponding to client requests directed at the database object are to be coordinated by a master replica. Client access to the database object is enabled prior to election of a master replica. In response to a triggering condition, a particular replica is elected master. The master coordinates implementation of operations with one or more other replicas in response to client requests.

Conditional master election in distributed databases

Distributed database management systems may perform range queries over the leading portion of a primary key. Non-random distribution of data may improve performance related to the processing of range queries, but may tend to cause workload to be concentrated on particular partitions. Groups of partitions may be expanded and collapsed based on detection of disproportionate workload. Disproportionate write workload may be distributed among a group of partitions that can subsequently be queried using a federated approach. Disproportionate read workload may be distributed among a group of read-only replicated partitions.

Range query capacity allocation

A distributed database management system may comprise a plurality of computing nodes. A request to update an item maintained by the system may be acknowledged as durable and committed once an entry corresponding to the request has been written to a log file and quorum among the computing nodes has been achieved. Improved consistency may be achieved by maintaining snapshots of committed item states within queryable in-memory snapshot data structures. Range queries may be performed by merging a secondary index with the snapshots and applying filters. Projections may be completed by retrieving additional data from an item collection maintain on one or more storage devices.

Consistent query of local indexes

Partitions of a hosted computing service may be maintained on a computing node. Processing of requests to access the partition may be limited to constrain capacity utilization to a provisioned amount of capacity reserved for the partition. A second, additional amount of capacity may be associated with the partition and may reflect potential future changes to the provisioned amount of capacity. A sum of provisioned and additional capacities associated with partitions on a computing node may be calculated. The computing node may be ranked, relative to other computing nodes, for maintaining new or relocated partitions based on the sum.

Shadowed throughput provisioning

Methods and apparatus for resource silos at network-accessible services are disclosed. A subset of resources used for a database service, including at least one resource from each of a plurality of data centers, is selected for membership in a resource silo based on grouping criteria. A silo routing layer node identifies the resource silo as the target silo to which a client work request is to be directed. The client work request is sent to a front-end resource of the target silo either by the client, or by the silo routing layer node on behalf of the client. The front-end resource of the target silo transmits a representation of the work request to a back-end resource of the target silo, where a work operation corresponding to request is performed.

Resource silos at network-accessible services

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Seattle, WA, United States of America

Rande Anthony Blackman