Amazon Technologies, Inc.

Chiranjeeb Buragahain

Stefano Stefani

Timothy Andrew Rath

Jakub Kulesza

Wei Xiao

Yan Valerie Leshinsky

David Alan Lutz

Jai Vasanth

Swaminathan Sivasubramanian

Luis Felipe Cabrera

James Robert Hamilton

Grant Alexander Macdonald McAlister

Rande Anthony Blackman

Raymond S Bradford

A system that implements a scalable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain table data in multiple replicas of partitions that are stored on respective computing nodes in the system. In response to detecting an anomaly in the system, detecting a change in data volume on a partition or service request traffic directed to a partition, or receiving a service request from a client to split a partition, the data storage service may create additional copies of a partition replica using a physical copy mechanism. The data storage service may issue a split command defined in an API for the data store to divide the original and additional replicas into multiple replica groups, and to configure each replica group to maintain a respective portion of the table data that was stored in the partition before the split.

System and method for performing live partitioning 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

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Issaquah, WA, United States of America

Chiranjeeb Buragahain