Amazon Technologies, Inc.

Andrea Giuliano

Gary Taylor

Gavin Bramhill

Richard Nel-Boland

Robert Crossan

Gianluca Cacace

Hui Zhang

Described technologies generate a data structure corresponding to values sequenced based on a plurality of timestamps associated with the values. The data structure can include a first section identifying a first timestamp associated with the plurality of timestamps and a number representing how many timestamps are associated with the plurality of timestamps, and a second section including at least a value linked to the first timestamp, and an additional value representing an encoding type associated with the second section. The data structure can be stored in computer-implemented storage.

Metric data processing and storage

Data being identified includes a first portion of data and a second portion of data. Based on identifying the data, a data structure is generated. The data structure can include a first section having a first symbol associated with the first portion of data and a second symbol associated with the second portion of data. Further, the first section can include a first offset value corresponding to the first portion of data and a second offset value corresponding to the second portion of data. The data structure can include a second section with a plurality of pointers that reference at least a plurality of symbols including at least the first and second symbol. The data structure can be referenced to process one or more queries against the data.

A query including a time range can be received. The query can be processed to determine data associated with the time range of the query. Cached data can be analyzed to identify a first block of data including metadata identifying a first timestamp associated with the time range of the query. A communication can be generated to include the first timestamp associated with the first block of data. Based on the communication, a second block of data can be received with metadata identifying a second timestamp associated with the time range of the query. A query result can be generated to include the first block of data and the second block of data.

Techniques and systems can receive a query identifying a name linked to performance data of a computer system and a location of the performance data. The name linked to the performance data of the computer system and the location of the performance data can be communicated to a first computer-implemented system. The first computer-implemented system can include identifying data derived from the name and the location of the performance data. Identifying data derived from the name and the location of the performance data can be received from the first computer-implemented system. The identifying data derived from the name and the location of the performance data can be used to retrieve the performance data. The performance data can be hosted by a second computer-implemented system that is different than the first computer-implemented system.

Query language for metric data

Results of different programmatic interfaces may be paginated. A request that causes aggregation of results separately returned from different programmatic interfaces may be received. Attempts to obtain the results using the different programmatic interfaces to execute the request may be made. A token that describes result states for the different programmatic interfaces may be included in a response to the request.

Paginating results obtained from separate programmatic interfaces

Systems and methods for generating dynamic sparse exponential histograms. The system includes a network-based service and a data compression engine to generate a sparse exponential histogram (SEH) representation of a distribution of a plurality of data values of a performance metric of the network-based service. The data compression engine is configured to, map each data value to a bin of an exponential histogram. Responsive to a determination that the mapped bin is not indicated in the SEH representation and that a bin quantity limit would be exceeded by adding the mapped bin, the data compression engine is configured to increase a bin size parameter by a scaling factor to expand data value ranges of the bins, merge bins indicated in the SEH representation according to the expanded data value ranges to reduce the quantity of bins indicated in the SEH representation, and indicate the scaling factor in the SEH representation.

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Dublin, Ireland

Andrea Giuliano