Amazon Technologies, Inc.

Lokendra Singh Panwar

Wei Xiao

Kiran Kumar Muniswamy Reddy

Rajaprabhu Thiruchi Loganathan

Adam Douglas Morley

Pejus Manoj Das

Stefano Stefani

Tate Andrew Certain

James Christopher Sorenson

Akshat Vig

Rama Krishna Sandeep Pokkunuri

Vaibhav Jain

Sudipto Das

Parikshit Shivajirao Pol

Xianglong Huang

Sharatkumar Nagesh Kuppahally

Alexander Richard Keyes

Ravi Math

Subramanian Sankara Subramanian

Lonnie J Princehouse

Aanchal Gupta

Shirley Xue Yi Ly

Harini Chandrasekharan

Rajesh Iyer

Anshul Gupta

Krishnan Seshadrinathan

Harish Tummalacherla

Methods, systems, and computer-readable media for adaptive querying of time-series data over tiered storage are disclosed. A distributed time-series database receives a query indicating spatial and temporal boundaries of requested time-series data in a table. The time-series database stores time-series data using a plurality of storage resources, wherein a first storage tier is accessible using a first access interface and a second storage tier is accessible using a second access interface. The time-series database determines, in the plurality of storage resources, one or more storage locations corresponding to the spatial and temporal boundaries of the query. Using the first access interface, the time-series database reads one or more elements of the requested time-series data from storage locations in the first storage tier. Using the second access interface, the time-series database reads one or more additional elements of the requested time-series data from storage locations in the second storage tier.

Adaptive querying of time-series data over tiered storage

Scalable architecture for propagating updates may be implemented for data replicated from a data set. A node may receive updates to items in a data set that have been committed to the data set. The node may determine whether the update should be applied to a replicated portion of the data set. For updates that should be applied, the node may identify another node that hosts the replicated portion of the data set and send a request to the other node to perform a conditional atomic operation to apply the update to the item in the replicated portion of the data set. The condition may compare a version identifier associated with an update and a current version identifier for the item at the other node. If the condition evaluates true, then the update to the item in the replicated portion may be performed.

Scalable architecture for propagating updates to replicated data

In response to determining that a secondary index is to be created for a particular table of a non-relational database service, a service component verifies that automated transmission of change records of the table to a log-structured journal has been configured. A given change record comprises a before-image and an after-image associated with a committed database write, and is assigned a unique sequence number. A change record propagator for the secondary index examines change records stored in the journal, and transmits selected change records to an index storage node. The index storage node builds the secondary index using the selected change records.

Scalable log-based secondary indexes for non-relational databases

A data storage system may implement generating creation performance metrics for a secondary index of a table. Metrics may be collected from different partitions of a table and different partitions of a secondary index in a table store upon initiating creation of the secondary index for the table that utilize provisioned resources for creating the secondary index. The metrics may be analyzed to generate performance metrics for the creation of the table. The performance metrics may be provided to clients of the data store or to monitoring systems. Monitoring systems may send performance event notifications to clients or automatically perform modifications to the provisioned throughput capacity.

Generating creation performance metrics for a secondary index of a table

A data storage system may implement rate-limiting secondary index creation for an online table. A secondary index may be generated for a table stored in a data store. The table may be incrementally indexed, maintaining the updates determined according to indexing different portions of the table in a queue of pending updates that are subsequently applied at the secondary index. Prior to indexing a portion of the table, an evaluation of a current number of pending updates in the queue of pending updates may be performed with respect to a throttle threshold. If the current number of pending updates exceeds the throttle threshold, then indexing the portion of the table may be throttled. Received updates to previously indexed portions of the table, may be applied to the table and placed in the queue of pending updates without an evaluation of the current number of pending requests.

Rate-limiting secondary index creation for an online table

A data storage system may implement implicit prioritization to rate-limit secondary index creation for an online table. A secondary index may be generated for a table stored in a data store. The table may be incrementally indexed, performing multiple indexing operations to populate the secondary index. Prior to performing an indexing operation, an evaluation of a capacity limitation for performing indexing operations may be made with respect to capacity to process access requests at the data store. If a determination is made that performance of the indexing operation exceeds the capacity limitation, then the indexing operation may be throttled. If a determination is made that performance of the indexing operation does not exceed the capacity limitation, then the indexing operation may be performed.

Implicit prioritization to rate-limit secondary index creation for an online table

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Seattle, WA, United States of America

Lokendra Singh Panwar