Amazon Technologies, Inc.

Shoreline Software, Inc.

Oracle International Corporation

Anurag Windlass Gupta

Samuel James McKelvie

Pradeep Jnana Madhavarapu

Yan Valerie Leshinsky

Mehul A Shah

James McClellan Corey

Prajakta Datta Damle

George Steven McPherson

Jakub Kulesza

Gopinath Duddi

Tengiz Kharatishvili

Stefano Stefani

Neal Thomas Fachan

Benjamin Tobler

Laurion Darrell Burchall

Andrew Edward Caldwell

Alexandre Olegovich Verbitski

Kamal Kant Gupta

Deepak Agarwal

Christopher Richard Newcombe

Saileshwar Krishnamurthy

Michail Petropoulos

Benjamin Albert Sowell

Debanjan Saha

Mengchu Cai

Sandor Loren Maurice

Alexey Kuznetsov

Alok Nandan Nikhil

Lawrence Douglas Webley

Bohan Liu

Charles Ian Ormsby Cary

Supratik Chakraborty

Timothy Andrew Rath

Grant Alexander Macdonald McAlister

Ippokratis Pandis

Santosh Kalki

Aleksandras Surna

Tushar Jain

Murali Brahmadesam

Raman Mittal

Srinivasan Sundar Raghavan

Stavros Harizopoulos

Neil Thombre

Martin Grund

Mushahid Alam

Yoni Shalom

Jagdeep Singh Sidhu

Yuhui Yuan

Yibo Zhu

Michael David Marr

Darin Lee Frink

Don Johnson

Ian Paul Nowland

Kumar Ramaiyer

Maximiliano Maccanti

Christopher Nathan Watson

Adam Douglas Morley

John Benjamin Tobler

Mehul Y Shah

Anthony A Virtuoso

Rahul Sharma Pathak

Joseph Andrew Tucek

Stephen Michael Ash

Timothy Jones

Andrew Borthwick

Xiaofeng Bao

Changan Han

Juliana Saussy

Jonathan Andrew Fritz

Jeffrey Davis

Bohou Li

John Xiao

Zelaine Fong

Aakash Shah

Carlos Garcia-Alvarado

Shehzad Qureshi

Pedrito Uriah Maynard-Zhang

Rui Liu

Gopi K Attaluri

Ashok Kumar

Andrea Olgiati

Nathan Lorenzo Binkert

Bhaven Avalani

Hakan Jakobsson

Yi Ding

Yuri L Volobuev

Naresh Chainani

George Constantin Caragea

Turkay Mert Hocanin

Christos Faloutsos

Vikram Singh Bisht

Saleem Mohideen

Punit Rajgaria

Niket Goel

Andrew Scott Richardson

Frederick Ryan Johnson

Uttam Jain

Shriram Sridharan

Ramkumar Kamalapuram Sugavanam

Seungmin Wei

A determination is made as to whether a workload associated with a collection of data items satisfies a first condition. If the first condition is satisfied, a determination is made as to whether a metric of dirty records to which delete operations have been directed within a portion of a data item satisfies a second condition. If the second condition is also satisfied, a cleanup operation may be initiated so that data blocks storing the portion of the data item do not include the deleted record.

Background incremental deletion cleanup techniques at storage services

A multi-column index is generated based on an interleaving of data bits for selectivity for efficient processing of data in a relational database system. Two or more columns may be identified for inclusion in the multi-column index for a relational database table. Based, at least in part, on the interleaving of data bits for selectivity from the identified columns, a multi-column index is generated for the relational database table that provides a respective index value for each entry in the relational database table. The entries of the relational database table may then be stored according to the index values of the multi-column index.

Generating a multi-column index for relational databases by interleaving data bits for selectivity

Proxy-based scaling may be performed for databases. A proxy may be implemented for a database that can establish a connection between the proxy and a database engine to perform a database queries received from a client at the proxy. A scaling event may be detected for the database responsive to which the proxy may establish a connection with a new database engine which may, in some embodiments, have different capabilities or resources that address the features or criteria that triggered the scaling event. Session state may be copied from the database engine to the new database engine so that the new database engine may be able to provide access to the database on behalf of requests received from the client through the proxy.

Proxy-based database scaling

Code may be dynamically routed to computing resources for execution. Code may be received for execution on behalf of a client. Execution criteria for the code may be determined and computing resources that satisfy the execution criteria may be identified. The identified computing resources may then be procured for executing the code and then the code may be routed to the procured computing resources for execution. Permissions or authorization to execute the code may be shared to ensure that computing resources executing the code have the same permissions or authorization when executing the code.

Dynamically routing code for executing

A database system may include a database service and a separate distributed storage service. The database service (or a database engine head node thereof) may be responsible for query parsing, optimization, and execution, transactionality, and consistency, while the storage service may be responsible for generating data pages from redo log records and for durability of those data pages. For example, in response to a write request directed to a particular data page, the database engine head node may generate a redo log record and send it, but not the data page, to a storage service node. The storage service node may store the redo log record and return a write acknowledgement to the database service prior to applying the redo log record. The server node may apply the redo log record and other redo log records to a previously stored version of the data page to create a current version.

Database system with database engine and separate distributed storage service

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

Extract, Transform, Load (ETL) processing may be initiated by detected events. A trigger event may be associated with an ETL process apply one or more transformations to a source data object. The trigger event may be detected for the ETL process and evaluated with respect to one or more execution conditions for the ETL process. If the execution conditions for the ETL process are satisfied, then the ETL process may be executed. At least some of the source data object may be obtained, the one or more transformations of the ETL process may be applied, and one or more transformed data objects may be stored.

Event driven extract, transform, load (ETL) processing

Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Using specified performance attributes to configure machine learning pipepline stages for an ETL job

Optimistic concurrency may be implemented for multiple writers to a database. Writes to a database from different writers may succeed unless a conflict is detected at the data store for the database. For detected conflicts, a request to resolve the conflict between writes may be submitted to a primary writer, which may determine and provide a resolution of the conflict to the conflicting writers. The data store may then be updated to commit the selected write identified by the resolution of the conflict.

Optimistic concurrency for a multi-writer database

Recognizing unknown data objects may be implemented for data objects stored in a data store. Data objects that are identified as unknown may be accessed to retrieve a portion of the data object. Different representations of the data object may be generated for recognizing different data schemas. An analysis of the representations may be performed to identify a data schema for the unknown data object. The data schema may be stored in a metadata store for the unknown data object.

Recognizing unknown data objects

Data transformation workflows may be generated to transform data objects. A source data schema for a data object and a target data format or target data schema for a data object may be identified. A comparison of the source data schema and the target data format or schema may be made to determine what transformations can be performed to transform the data object into the target data format or schema. Code to execute the transformation operations may then be generated. The code may be stored for subsequent modification or execution.

Generating data transformation workflows

A multi-framework blockchain service may be implemented with a common interface to manage different types of blockchain networks. Requests to create a blockchain network may be received via an interface for the control plane that triggers the creation of the blockchain network according to an identified workflow. Various operations to change the blockchain network, including membership changes, node additions, governance changes, analytics changes, and monitoring changes may be allowed or denied by the control plane according to a distributed governance policy in effect for the blockchain network.

Multi-framework managed blockchain service

A log-structured data store implementing data backup may implement variable data replication. Write requests may be received at different storage nodes maintaining respective replicas of a portion of a log for data maintained in the log-structured data store. Log records indicating the write requests may be stored in the respective replicas of the log portions at the different storage nodes. The log records may be sent to a backup data store to be durability persisted as part of an archived version of the log. At some of the storage nodes, in response to determining that the log records have been durably persisted in the backup data store, storage space for the log records may be reclaimed. In other remaining storage nodes, the log records may be retained and made accessible for servicing read requests.

Variable data replication for storage implementing data backup

Dynamic generation of data catalogs may be implemented for accessing data sets in different storage locations. Data sets may be accessed in order to extract portions of data. Structure recognition techniques may be applied to the extracted data in order to determine structural information for the data sets. The structural information may then be stored as part of a data catalog for the data sets. Requests to access the data catalog from different clients may be received and the requested structural data supplied so that the clients may access different data sets utilizing the supplied structural data. Data catalogs may be updated as changes to data sets are made.

Dynamic generation of data catalogs for accessing data

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Atherton, CA, United States of America

Anurag Windlass Gupta