Amazon Technologies, Inc.

Cisco Technology, Inc.

Teradata Us, Inc.

Jeffrey Davis

Saileshwar Krishnamurthy

Anurag Windlass Gupta

Michael Ross Lewis

Neil Thombre

Wing Hang Li

Daniel Robert Farina

Stefano Stefani

Gopi K Attaluri

David Everett Simmen

Alexandre Olegovich Verbitski

Kamal Kant Gupta

Tengiz Kharatishvili

Yuri L Volobuev

Murali Brahmadesam

Debanjan Saha

Uttam Jain

Shriram Sridharan

Shant Hovsepian

Aakash Shah

Sailesh Krishnamurthy

Volodymyr Verovkin

Dhruva Ranjan Chakrabarit

Aleksandr Valerevich Feinberg

Namrata Bapat

Storage level query processing may be implemented for processing database queries. Nodes that can access a database may perform parallel processing for at least a portion of a database query. An indication may be received that specifies parallel processing for the database query. The nodes can then be caused to perform the portion of the query as part of providing a result in response to the database query instead of a node, such as a query engine node, that received the database query.

Storage level parallel query processing

A database engine may track granularity levels for accessing a spatial index. Granularity levels of a spatial index may be evaluated to identify those granularity levels that identify data objects in a data set. An indication of the identified granularity levels may be stored as part of metadata for the data set. When a spatial query directed to the data set is received, the spatial index may be accessed at the identified granularity levels indicated in the metadata for the data set as part of processing the spatial query.

Tracking granularity levels for accessing a spatial index

A storage engine may selectively maintain a spatial index for accessing spatial data. A spatial query may be received and the portions of the spatial index to evaluate may be determined by replacing those portions associated with regions identified for the query that do not exist in the spatial index with portions that do exist in the spatial index and are associated with a region that includes the identified regions. When inserting spatial objects into the spatial index, a determination may be made whether to create a new portion in the index if none currently exist that match a spatial index value for a new spatial object or to modify the spatial index value of the spatial object to insert the spatial object into an existing portion of the spatial index.

Selective maintenance of a spatial index

One or more transformation functions can be used in connection or together with one or more compression/decompression techniques. A transformation function can transform data (e.g., a data object) into a form more suitable for compression and/or decompression. As a result, data can be compressed and/or decompressed more effectively. In addition, multiple data objects can be associated with various transformation functions and/or compression/decompression techniques. As a result, different approaches can be taken with respect to compression and decompression of data objects in an effort to find an optimum approach for compression of data objects that may vary significantly from each other and change over time. It will be appreciated that the objects can be associated with transformation functions in a dynamic manner to accommodate changes to data. Also, an extendible and/or extensible system can allow for growth and adaption of new data in forms not currently present or expected.

Transformation functions for compression and decompression of data in computing environments and systems

In a system and method for order-independent stream query processing, one or more input streams of data are received, and the one or more input streams are analyzed to determine data which is older than an already emitted progress indicator. The data which is older than the already emitted progress indicator is partitioned into one or more partitions, and each of the one or more partitions are independently processed using out-of-order processing techniques. A query is received, rewritten and decomposed into one or more sub-queries that produce partial results for each of the one or more partitions, where each of the one or more sub-queries correspond to a partition. A view is also produced that consolidates the partial results for each partition. The partial results are consolidated at a consolidation time specified by the query to produce final results, and the final results are provided.

Order-independent stream query processing

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Jeffrey Davis