Amazon Technologies, Inc.

Luca Natali

Abhishek Rajnikant Sinha

Bhargava Ram Kalathuru

Rahul Sharma Pathak

Xing Wu

Turkay Mert Hocanin

Jason Douglas Denton

Jian Fang

Pratik Bhagwat Gawande

Yuanyuan Yue

Sumeetkumar Veniklal Maru

Armen Tangamyan

Yufeng Jiang

Saurabh Dileep Baji

Jonathan Daly Einkauf

Andrew Edward Caldwell

Marc Howard Beitchman

A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

Automatic scaling of resource instance groups within compute clusters

Queries may be received and executed by a managed query service. A query directed to data sets that are separately stored in a remote data store may be received. Computing resources to execute the query may be provisioned from a pool of computing resources that are configured to execute queries. The query may be routed to the provisioned computing resources to execute the query. Results may be obtained from the computing resource and provided to a submitter of the query.

Managed query service

Query execution status may be tracked to selectively route queries to resources for execution. The completion of queries executing at computing resources obtained from a pool of computing resources configured to execute queries may be detected. Instead of returning the computing resources to the pool, the computing resources may be identified as available in resource management data. When another query is received, the resource management data may be evaluated to select an available computing resource. The query may then be routed to the selected computing resource for execution.

Tracking query execution status for selectively routing queries

The configuration of computing resources for executing queries may be selected. A comparison of the configuration of computing resources that executed previous queries may be made to select the configuration of computing resources for a received query. A historical query execution model maybe applied, in some embodiments, to determine a resource configuration for computing resources to execute a query. The computing resources may be selected from available computing resources according to the determined resource configuration.

Selecting resource configurations for query execution

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Kenmore, WA, United States of America

Luca Natali