Amazon Technologies, Inc.

Slavcho Georgiev Slavchev

Sameer Choudhary

Vishwas Narendra

Nikhil Shah

Tyler Michael Jung

Christopher Andrew Stephens

Phillip H Pruett, Iv

James Zuber

Nishant Jain

Bryan T Wilkinson

Suchindra Yogendra Agarwal

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 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

Slavcho Georgiev Slavchev