Amazon Technologies, Inc.

Shriram Sridharan

Alexandre Olegovich Verbitski

Kamal Kant Gupta

Stefano Stefani

Tengiz Kharatishvili

Yuri L Volobuev

Anurag Windlass Gupta

Gopi K Attaluri

Saileshwar Krishnamurthy

Murali Brahmadesam

Eric Boutin

Jaeyun Noh

Jeffrey Davis

Debanjan Saha

Aakash Shah

Damien Katz

Sangil Song

Nanda Kaushik

Volodymyr Verovkin

Pallavi Maheshwara Kakunje

Dhruva Ranjan Chakrabarit

Aleksandr Valerevich Feinberg

Namrata Bapat

Sungheun Wi

Madhura Madhukar Parikh

Remote grouping operations may be used to perform queries for distinct values. A query may be received at a query engine that includes a request for distinct items from a data set stored in a data store. To perform the query, a request may be made to the data store to group items from the data set by value when returned from the data store to the query engine. The query engine may then evaluate the items grouped by value that are returned from the data store to determine distinct items as part of performing the query.

Remote grouping operations to perform queries for distinct values

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

This application relates to multi-level replication filtering for a distributed database. A primary node of the database can generate log records that describe write requests performed to the database. The primary node can, in some embodiments, identify a portion of the data of the database that is stored at a second node, such as data stored by a read replica node that can be used in responding to read requests from clients. The primary node can select log records that are applicable to this identified portion of the data to send to the second node. Log records are not selected based on a determination that those unselected log records are not applicable to the identified portion of the data. The primary node can then send the selected log records to the second node, for the second node to apply the log records to its stored database data.

Multi-level replication filtering for a distributed database

A replica database may receive a replication instruction from a master. The replica may maintain a version of a collection of data in database pages stored on a storage device. A replication thread may process the instruction. The replication thread may determine that an item affected by the instruction is located in a page that is both loaded into a memory buffer and subject to contention. The page may be modified with information indicating that the instruction has not been processed. A subsequent reader thread may, while processing a request to access the page, apply the instruction and complete processing of the request.

Cooperative log application

A lock-free data structure may be used to provide concurrent access to a data retention index for a data store. Different processes may obtain keys to the data retention index from the lock-free data structure. The different processes may update entries in the data retention index corresponding to the keys to identify data objects to retain in the data store. The data retention index may be evaluated to determine data objects not identified in the data retention index and reclaim portions of the data store that store the determined data objects for storing other data.

Lock-free updates to a data retention index

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Sunnyvale, CA, United States of America

Shriram Sridharan