Amazon Technologies, Inc.

Akshat Vig

Somasundaram Perianayagam

Rama Krishna Sandeep Pokkunuri

Rajaprabhu Thiruchi Loganathan

Nicholas Gordon

Tate Andrew Certain

James Christopher Sorenson

Timothy Andrew Rath

Alexander Richard Keyes

Go Hori

Wei Xiao

Stefano Stefani

Maximiliano Maccanti

William Alexander Stevenson

Vaibhav Jain

Srivaths Badrinath Copparam

Almero Gouws

Clarence Wing Yin Ng

Amit Gupta

Parikshit Shivajirao Pol

Ravi Math

Rishabh Jain

Subramanian Sankara Subramanian

Derek William Richard Chen-Becker

Douglas Brian Terry

Sharatkumar Nagesh Kuppahally

Lewis Bruck

Stuart Henry Seelye Marshall

Akhilesh Mritunjai

Chase Kernan

Haifeng He

Nathan Pellegrom Riley

Craig Wesley Howard

David Alan Lutz

Rande Anthony Blackman

Gopinath Duddi

Oren Yossef

Jian Wang

Jiandan Zheng

Arijit Choudhury

Amit Purohit

Sroaj Sosothikul

Safeer Mohiuddin

Rashmi Krishnaiah Setty

Harini Chandrasekharan

Lakshmi Narasimha Guptha Munuhur Rajagopal

Janani Narayanan

Avinash Kodakandla

Mazen Moez Ali

Sharan Rajesh Munyal

Sudarshan Narasimhan

Hao He

Tony Petrossian

Divyank Duvedi

Akanksha Fouzdar

Benjamin Donald Wood

Palak Agrawal

Mark Roper

Sudhir Konduru

Joon Ahn

Joseph Robert Idziorek

Arturo Hinojosa

Aishwarya Kamal

Colin Laird Lazier

Yosseff Levanoni

Kiran-Kumar Muniswamy-Reddy

Yijun Lu

Qiang Liu

Dumanshu Goyal

Pejus Manoj Das

Ming-Chuan Wu

Barry Bailey Hunter, Jr

Fahad Ahmed

Jeffrey John Wierer

Lokendra Singh Panwar

Sanjay Shanthakumar

Adel Gawdat

Benny Tran

Ying Lin

Anshul Gupta

Shitanshu Aggarwal

Mohamed Emad El Din Fathalla

Richard Krog

Andrey Markin

Kapil Singh

Vaibhav Govil

Maximliano Maccanti

Zoe Wheeler

Naveen Ganapathi Subramanian

Elie Gharios

Robert Kenny

Jin Kyoung Kwon

Varsha Ganesh

Yang Nan

Olaf Anders Grette

Steven A Mayszak

Christopher John Anderson

Bradley James Curran

Jeffrey Alan Duffy

Wen Han Albert Huang

Queries may be made against a non-relational database without impacting the capacities of the non-relational database by enabling performance of queries to a non-relational database via an external query management, backup management, and a non-relational database backup hosted in an external data storage. Query units may be provided to an external query engine to provide parallel units of work for accessing and performing queries on a backup of a database.

Providing query units to support external analytics queries to a backup of a database

A database table may be synchronously replicated across regions of a provider network. When a write request to an item of the table is received at one region, a local version of the item may be obtained as well as any additional versions of the item corresponding to inflight writes that target the item from a multi-region replication append-only log. An updated version of the item is then generated based on the local version of the item and the additional versions, which is appended to the multi-region replication append-only log. A conditional write is performed to update the local version of the item with the updated version of the item. If successful, a response indicates that the write request was successful.

Synchronous cross-region replication for a multi-writer database

Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

Obtaining inferences to perform access requests at a non-relational database system

In response to determining that continuous data protection is to be enabled for a particular table of a database service, a service component verifies that automated transmission of change records of the table to a log-structured journal has been configured. A given change record comprises a before-image and an after-image associated with a committed database write, and is assigned a unique sequence number. In response to a determination to restore the table as of a specified point in time, a restore record set is identified from the journal with respect to a selected snapshot of the table. The restore record set includes change records which are not represented in the snapshot and are to be represented in the restored table. A restore result table is created using the selected snapshot and the restore record set.

Scalable log-based continuous data protection for distributed databases

A materialized view management service (MVMS) is capable of monitoring resource allocation changes of a source data object at a source data store and responsively generating resource change alerts to the owner of a target data object (the materialized view) in the target data store. Resource allocation changes may include autoscaling changes to the source data object's partition scheme, throughput limit, storage limit, and the like. The MVMS generates resource change alerts in response to these detected events and pushes the alerts to interested subscribers. Depending on the embodiment, the alerts may be pushed to human administrators, or the target data store itself, which may be configured to automatically adjust the resource allocation of the target data object to match the source data object. Advantageously, the disclosed alerts allow view owners to gain real time visibility of resource auto-scaling at the data source and appropriately react to such changes.

Propagating resource scaling information between source and target data stores of a materialized view

A resource-based authorization control may be added to a data processing service. A control-plane action requested by an account or entity creates a resource-based authorization policy. The resource-based authorization policy pertaining to respective resources allows recurrent data-plane actions to maintain a data flow in a data pipeline to be authorized independent of a separate authorization service.

Separate authorization for managing stages in a data pipeline

Changes made to a database table are accumulated, in durable storage, and snapshots of partitions of the table are obtained. For successive snapshots of a partition, the system accesses a previous snapshot, applies changes from the accumulated changes, and stores the updated snapshot to a durable data store. The accumulated changes and the successive partition snapshots are made available to restore the database to any point in time across a continuum between successive snapshots. Although each partition of the table may have a backup snapshot that was generated at a time different from when other partition snapshots were generated, changes from respective change logs may be selectively log-applied to distinct partitions of a table to generate an on-demand backup of the entire table at common point-in-time across partitions. Point-in-time restores of a table may rely upon a similar process to coalesce partition snapshots that are not aligned in time.

Continuous data protection

Different types of index structures are used for a replica group of a database. A leader node of a replica group performs receives updates to a copy of the database using a first type of index structure. A follower node performs updates received from the leader node as a log of updates to a copy of the database in an external storage system when a size of the received updates exceeds a threshold. The follower node performs requests to read data from the database using the copy in the external storage.

Different types of index structures for storing database data in a replica group

Queries may be made against a non-relational database without impacting the capacities of the non-relational database by enabling performance of queries to a non-relational database via an external query management, backup management, and a non-relational database backup hosted in an external data storage. The query management and backup management of the non-relational database service may split transactional load to the non-relational database and analytical load, and furthermore allow selection of a desired data freshness for queries including various time ranges, such as seconds, minutes, or days.

Analytic query processing using a backup of a database

An interface for requesting, and technique for generation of, a backup of a past state of a database table are provided. Changes made to a database table are accumulated, in durable storage, and snapshots of partitions of the table are obtained. The accumulated changes and the successive partition snapshots are used to generate a past state of the database at any point in time across a continuum between successive snapshots. Although each partition of the table may have a snapshot that was generated at a time different from when other partition snapshots were generated, changes from respective change logs may be selectively log-applied to distinct partitions of a table to generate backup in the past of the entire table at common point-in-time across partitions.

Past-state backup generator and interface for database systems

Index management for non-relational database systems may be automatically performed. Performance of queries to a non-relational database may be evaluated to determine whether to create or remove an additional index. An additional index may be automatically created to store a subset of data projected from the non-relational database to utilize when performing a query to the non-relational database instead of accessing data in the non-relational database.

Automatic index management for a non-relational database

A system that implements a data storage service may store data for a database table in multiple replicated partitions on respective storage nodes. In response to a request to back up a table, the service may back up individual partitions of the table to a remote storage system independently and (in some cases) in parallel, and may update (or create) and store metadata about the table and its partitions on storage nodes of the data storage service and/or in the remote storage system. Backing up each partition may include exporting it from the database in which the table is stored, packaging and compressing the exported partition for upload, and uploading the exported, packaged, and compressed partition to the remote storage system. The remote storage system may be a key-value durable storage system in which each backed-up partition is accessible using its partition identifier as the key.

Backup of partitioned database tables

A distributed database encrypts tables using table encryption keys protected by a client master encryption key. The client may revoke and subsequently restore authorization to access the client master encryption key. A sweeper process of the distributed database examines encrypted tables and identifies changes to the status of a corresponding client master encryption key. A response to an identified change in status is initiated.

Database with client-controlled encryption key

Columns of a non-relational data store may be efficiently dropped. A drop column request for a table stored in a non-relational database may be received. In response to the drop column request, schema information for the table may be updated to filter out the column from subsequent read responses to read requests. A backup of the table is created and evaluated to identify those items in the table that include the column. The column is then deleted from the identified items.

Efficient drop column requests in a non-relational data store

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Akshat Vig