Amazon Technologies, Inc.

Nicholas Gordon

Akshat Vig

Somasundaram Perianayagam

Ravi Math

Tate Andrew Certain

Go Hori

Chase Kernan

James Christopher Sorenson

Vaibhav Jain

Sharatkumar Nagesh Kuppahally

Sroaj Sosothikul

Mazen Moez Ali

Avinash Kodakandla

Sharan Rajesh Munyal

Amit Gupta

Divyank Duvedi

Mrithyunjaya Kumar Annapragada

Peter Zhivkov

Remi Paucher

James Christopher Sorenson, Iii

Almero Gouws

Hao He

Jeffrey John Wierer

Amit Purohit

Lakshmi Narasimha Guptha Munuhur Rajagopal

Joseph Robert Idziorek

Arturo Hinojosa

Sangeeth Prem

Christopher John Anderson

Shahzeb Farrukh

Steven A Mayszak

Olaf Anders Grette

Robert Kenny

Elie Gharios

Naveen Ganapathi Subramanian

Time and value ordering may be applied for items stored in data backups. A change log that persists changes to a data set may be updated with changes and used to update an in-memory table for the data set, which describes changes to items up to a current time. An event may be detected to seal the in-memory table from subsequent updates and a persistent data object that orders the items in the in-memory according to both keys of the respective items and the respective time values of the items, as stored in the change log, may be generated and stored as part of a backup for the data set.

Time and value ordered data objects for a backup of a data set

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

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

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

Offline building of a projected data subset may be performed. A request to create a data set that is a projected subset of data from a source data set may be received. A data store separate from the data store storing the source data set may store a copy of the source data set that is used to replicate items to the projected subset of data according to a schema for the projected data subset. Updates made to the source data set may also be replicated to the projected data subset according to the schema. Conflicts between replicated items and replicated updates to the projected data set may be resolved by comparing a version identifier for the replicated update and replicated item to determine what to store in the projected data subset.

Offline index builds for database tables

A system receives a request to import data file(s) from a source data store into a target database. The system reserves a first portion of computing resources that host the target database to import the data file(s). The reservation of the first portion of computing resources permits the import throughput rate of the data file(s) through the first portion of computing resources while maintaining a second portion of the computing resources to support client access to the target database at an access throughput rate. The system initiates import of the data file(s) from the source data store to the target database through one or more storage nodes at the import throughput rate according to the first portion of computing resources. The target database is able to receive access requests from one or more clients during the import of the data file(s) to the target database.

Serverless managed bulk import on a global NoSQL database with selective back pressure

A distributed database encrypts a table using a table encryption key protected by a client master encryption key. The encrypted table is replicated among a plurality of nodes of the distributed database. The table encryption key is replicated among the plurality of nodes, and is stored on each node in a respective secure memory. In the event of node failure, a copy of the stored key held by another member of the replication group is used to restore a node to operation. The replication group may continue operation in the event of a revocation of authorization to access the client master encryption key.

A distributed database encrypts tables using table encryption keys protected by a client master encryption key. The client may revoke authorization to access the client master encryption key. Subsequent to a revocation of authority to access the client master encryption key, the distributed database generates interim snapshots of the table using the table encryption key. Also subsequent to the revocation, the distributed database generates a backup of the table using a backup encryption key protected by the client master encryption key.

Methods, systems, and computer-readable media for renaming a database table with minimized application downtime are disclosed. A database receives a table rename request for a table having a first table name. Routing metadata comprises a mapping of the first table name to a table identifier and a mapping of the table identifier to one or more partitions. The table rename request indicates a second table name. The database generates updated routing metadata responsive to the table rename request. The updated routing metadata comprises a mapping of the second table name to the table identifier and the mapping of the table identifier to the one or more partitions. The database receives, from a client application, one or more access requests indicating the second table name. The database routes the one or more access requests to the one or more partitions based at least in part on the updated routing metadata.

Renaming a database table with minimized application downtime

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Seattle, WA, United States of America

Nicholas Gordon