Amazon Technologies, Inc.

Tyler Michael Jung

Sameer Choudhary

Nikhil Shah

Vishwas Narendra

Phillip H Pruett, Iv

James Zuber

Bryan T Wilkinson

Nishant Jain

Slavcho Georgiev Slavchev

Suchindra Yogendra Agarwal

Christopher Andrew Stephens

Arvinth Ravi

Prathiban Mohanasundaram

Hardik Navin Nagda

Nicholas Jacob Essenburg

Liming Ye

Rajesh Sampath

Pierre Vigneras

Janko Jerinic

Vijayasarathy Kannan

David Grant Emeny-Smith

Tianxing Dun

Ozan Okumusoglu

Alexander Koshelev

Systems and methods are provided for using request rate data to determine when and how to split storage partitions in a distributed storage system. Data objects in a given partition are stored and managed using a set of computing resources, such as caches, a witness service, and persistent storage nodes. When the request rate for the partition is too great, then the partition may be split in order to distribute the request load over two sets of computing resources. Some requests that would be able to be handled by the cache are passed through to the persistent storage nodes, which can track the requests at a granular level. Based on this data, specific partition splitting decisions can be made.

Data item and witness service partitioning in a distributed storage system

Systems and methods are provided for efficiently maintaining a transaction sequence witness service to ensure that requests to read data provide consistent results across all storage nodes in a distributed system. When a storage node receives a request for a data object, the node may obtain&#x2014;from transaction sequence witness service&#x2014;the latest sequence number associated with the object. If the sequence number indicates the object has been modified in the period of time since the currently stored version was stored, the node can obtain the current version from persistent storage.

Witness service for ensuring data consistency in a distributed storage system

Systems and methods are provided for using a distributed cache architecture with different methods to load balance requests depending upon whether a requested data item is a freely-requested item (e.g., a 'hot key'). The cache may be implemented as a consistent hash ring, and most keys may be assigned to particular node based on a consistent hash. For hot key requests, the requests may be distributed among a subset of nodes rather than being assigned to a specific node using consistent hashing. When a witness service is used to ensure that cached data is fresh, verification requests for data regarding hot keys may be batched to avoid overloading the witness service with hot key requests.

Multi-tier cache for a distributed storage system

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Seattle, WA, United States of America

Tyler Michael Jung