Amazon Technologies, Inc.

Sainath Chowdary Mallidi

Piyush Gupta

Kawarjit Bedi

Andi Gutmans

Xingbo Wang

Divij Vaidya

Serkan Turgut

Ilie Gabriel Tanase

Ankesh Khandelwal

Andrew Edward Caldwell

Diwakar Chakravarthy

Bryan Thompson

Charles Porter Schermerhorn

Tim Januschowski

Bradley R Bebee

Valentin Flunkert

Steffen Rochel

Justin Canfield Crites

Divyakala Vel

Patrick Jing Ye

Navtanay Sinha

Arjun Ashok

Yejin Yoon

Guangqiang Li

Utkarsh Raj

Sudhanshu Gupta

Deep Saxena

Karthik Rajan

Michael Schmidt

Islam Mohamed Hatem A Atta

Geo T Varkey

Tiago Lima Salmito

Yigit Kiran

Herman Yusef Polloni Giacaman

Systems and methods are described for dynamically allocating memory for responding to requests. In some aspects, a thread may be obtained, where the thread includes a request to use a portion of system memory to perform a query of a database, such as a graph database. The system may determine that the portion of memory is greater than available memory of a maximum managed memory size of the system, and may block the thread until additional memory becomes available. The maximum managed memory size may be configurable, and may be less than a total memory of the system. The system may then determine, based at least in part on a change in the available system memory, that the available system memory is equal to or greater than the portion of memory to satisfy the request, and cause the system to process the thread to provide a response to the query.

Dynamic bounded memory allocation

Systems and methods are described for allocating computing resources to load data into a database. In some aspects, a database service obtains a request to load a number of data chunks. Responsive to the request, the database service scales a number of worker threads to load the data chunks. The scaling may include selecting a maximum number of worker threads to load (e.g., concurrently) the data chunks based on a number of data chunks, and assigning individual worker threads to load individual data chunks. The scaling may additionally include tracking a number of available worker threads relative to the maximum number of worker threads to reassign a first worker thread to load another data chunk upon the completing loading a prior data chunk, or assign a new worker thread that has become available based on a change in the maximum number of worker threads.

Bulk loader scaling

Methods, systems, and computer-readable media for query language interoperability in a graph database are disclosed. Data elements are inserted into a graph database using one or more of a plurality of graph database query languages. The graph database query languages comprise a first graph database query language associated with a first data model and a second graph database query language associated with a second data model. The data elements are stored in the graph database using an internal data model that differs from the first and second data models. One or more of the data elements are retrieved from the graph database based at least in part on a query. The query is expressed using a different graph database query language than the graph database query language used to insert the one or more retrieved data elements.

Query language interoperability in a graph database

Methods, systems, and computer-readable media for global column indexing in a graph database are disclosed. A plurality of data elements of a graph database are stored. The triples comprise identifiers, column names, and values. The column names are globally scoped in the graph database and are associated with data types. Indices corresponding to the column names are created. A particular one of the indices comprises one or more of the values associated with the corresponding column name. A query is performed on the graph database using one or more of the indices corresponding to one of more of the column names associated with the query.

Global column indexing in a graph database

Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

Making decisions for placing data in a multi-tenant cache

Query language interoperabtility in a graph database

Disclosed are various embodiments of systems, methods, and computer programs that can facilitate processing of large data sets from data sources. Examples of the disclosure can allow a client to perform data processing tasks in computing resources that are external to a data source from which the data is retrieved rather than relying upon the computing resources of the data source.

Processing large data sets from heterogeneous data sources using federated computing resources

Growing community of inventors

Seattle, WA, United States of America

Sainath Chowdary Mallidi