Amazon Technologies, Inc.

Martin Grund

Ippokratis Pandis

Anurag Windlass Gupta

Mengchu Cai

Naresh Chainani

Andrew Edward Caldwell

Bhaven Avalani

Michail Petropoulos

Naveen Muralimanohar

William Michael McCreedy

Nathan Lorenzo Binkert

Gaurav Saxena

George Constantin Caragea

Venkatraman Govindaraju

Frederick Ryan Johnson

Fabian Oliver Nagel

Kiran Kumar Chinta

Davide Pagano

Srividhya Srinivasan

Luxiang Yin

Maor Kleider

Michalis Petropoulos

Nikolaos Armenatzoglou

Mikhail Krestyaninov

Burst performance of a database query may be determined according to a size of the database query. A query to a database may be received. A size may be determined for the query. If the size is less than a size threshold assigned to a first query engine, then the query may be performed at the first query engine. If the size is greater than or equal to the size threshold assigned to the first query engine, then the query may be performed at a second query engine.

Burst performance of database queries according to query size

Compiled portions of code generated to perform a query plan at a query engine may be shared with other query engines. A data store, separate from the query engines, may store compiled portions of query code generated for different queries. If a query engine does not have a locally stored compiled portion of query code, then the separate data store may be accessed in order to obtain a compiled portion of query code, allowing reuse of compiled query code across different queries engines for queries directed to different databases.

Sharing compiled code for executing queries across query engines

Caching results of sub-queries to different locations in a data store may be performed. A database query may be received that causes different storage engines to perform sub-queries to different locations in a data store that stores data for a database. The results of the sub-queries may be stored in a cache. When another database query is received, sub-queries generated to perform the other database query that are the same as one or more of the previously performed sub-queries may obtain the results of the sub-queries from the cache instead of performing the sub-queries again.

Caching results for sub-queries to different data store locations

Techniques for rapid secure authentication and communications through multitenant components in a provider network are described. A main database cluster can request a burst cluster from a burst service, which can provide the main cluster with a shared secret associated with only a selected burst cluster. The main cluster can use the shared secret to encrypt a value that can be passed, via a proxy, to the selected burst cluster in a connection request. The selected burst cluster can validate that the connection request was truly originated by the main cluster by validating the encrypted value using the shared secret.

Rapid secure authentication and communications through multitenant components in provider networks

Data processing engines implement format independent data processing for distributed data in local and remote data stores. Requests execute at least a portion of an access request are received from a data processing engine that does not support a data format for data stored in a data store. The data format for the data is identified and the data accessed according to the identified data format. The portion of the access request is applied to the accessed data in order to generate results. The generated results for the portion of the access request are then sent to the data processing engine.

Format independent processing for distributed data

Database queries may be performed using resources based on a determined size of the database query. Database query size may be dynamically determined for a database query when the query is received. The database query may be assigned to resources used for database queries of the determined size. In some embodiments, timeouts may be applied to reassign database queries to different resources if the performance of the database query exceeds a timeout threshold.

Using computing resources to perform database queries according to a dynamically determined query size

Relocating data sharing operations for query processing may be implemented when generating plans to perform a query. A query operation that causes a node to share data obtained from a different set of nodes with other nodes in a same set of nodes may be identified. The identified query operation may be replaced with another operation that causes the different set of nodes to share the data directly with the other nodes in the same set of nodes.

Relocating data sharing operations for query processing

Data processing engines implement tiered data processing for distributed data in local and remote data stores. Requests to access distributed data including a data object in a remote data store are received at a data processing engine. A query plan is generated to service the access request. Different operations in the query plan are identified and assigned to one or more remote query processing engines that may access the remote data object. Requests to perform the different operations are sent to the one or more remote query processing engines. A final result is generated for the request based on the results received for the different operations from the remote query processing engine and results from operations performed with respect to locally stored data.

Tiered data processing for distributed data

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Lafayette, CA, United States of America

Martin Grund