Amazon Technologies, Inc.

Murali Brahmadesam

Punit Rajgaria

Navaneetha Krishnan Thanka Nadar

Hemanth Satyanarayana

Stefano Stefani

Alexandre Olegovich Verbitski

Saleem Mohideen

David Charles Wein

Ramesh Chander

Gokul Soundararajan

Ippokratis Pandis

Gaurav Saxena

Yuri L Volobuev

Changan Han

Richard Shawn Bice

Jaeyun Noh

Matthew Perry Abrams

Sanuj Basu

Grant Alexander McAlister

Shriram Sridharan

Gaurav Kumar Gupta

James Laurence Finnerty

Anand Kumar Thakur

Baskar Durairaj

Sangil Song

Upendra Govindagowda

Gopal Paliwal

Nanda Kaushik

Todd Jeffrey Green

Ahmad Mohammad Radi Ahmad Alsmair

Haritabh Gupta

Anupriya Mathur

Vadim Skipin

Gajanan Sharadchandra Chinchwadkar

Li Che David Hsiao

Sadagopan Nattamai Sathiyamoorthy

Sungheun Wi

Michael Higgins Dowling

Omar Farhat

Aakash Ashwin Shah

Suprio Pal

Jan Wieck

Korry Allen Douglas

Johannes Wust

Techniques for fast database recovery in a multi-volume database environment via transactional awareness are described. In the event of a failure associated with a first volume storing database page data, the first volume can be restored to a point in time and transactional metadata from a second volume storing logical change data can be obtained for a limited number of transactions occurring at/after that point in time, as opposed to analyzing extremely large change log files. These transactions can be checked to ensure that they have all been persisted, and if not, change data for those transactions can be obtained from the second volume and used to replay these transactions on the restored first volume.

Fast database recovery in a multi-volume database environment via transactional awareness

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

Techniques for volume durable log sequence number movement in a multi-volume database environment are described. Blocks of database updates are persisted in an atomic and durable manner, where the blocks may include updates to a first volume, a second volume, or both. Backlinks from one block of updates to the previous block of updates may be set, in addition to backlinks between updates to the first volume and backlinks between updates to the second volume. Upon durably persisting a block, the block backlinks can be followed to help verify that all contiguous blocks—of changes to one or both volumes—have all been persisted. Thereafter, a volume durable log sequence number (VDL) pointer value can be updated to point to a known 'last' durably persisted log update.

Volume durable log sequence number movement in a multi-volume database environment

Distributed execution of transactions may be performed across shards of a scalable database table. Instructions to perform as part of a transaction with respect to one or more database tables may be received. The instructions may be evaluated with respect to metadata obtained for the database tables to determine an assignment distribution of computing resources of the database system to data that can satisfy the access request. The commit protocol for the transaction may be determined according to the assignment distribution of the computing resources. After a request to commit the transaction is received, the transaction may be committed according to the determined commit protocol.

Distributed transaction execution across shards of scalable database tables

Methods for replicating transactional tables of a transactional database to an analytical database and maintaining updates to those transactional table representations are disclosed. Snapshots of the transactional tables are provided to the analytical database via a transport mechanism, such as a data storage service or a data streaming service, and stored at the analytical database. Then, checkpoints comprising portions of a change-data-capture log that has recorded transactional changes to the transactional tables of the transactional database are provided to the analytical database via the same or different transport mechanism and used to commit those transactional changes to the snapshot representations. The snapshot representations may be used to respond to incoming analytical queries in order to provide real-time querying results.

Hybrid transactional and analytical processing architecture for optimization of real-time analytical querying

A database system may add a read-only query engine to perform read-only queries associated with points-in-time of a database. In various embodiments, the read-only query engine may be added in response to a manual request, an automatic refresh of a network endpoint, a query specifying a point-in-time, or a connection request. The read-only query engine may perform the point-in-time queries on a version the database at the point-in-time and return results for the queries. Upon completion of the queries or at a determined time, the database system may remove the read-only query engine. The specified point-in-time may refer to a current time, a prior time, or a future time with respect to the current time.

Adding a read-only query engine to perform queries to a point-in-time of a write-accessible database

Techniques for change data capture (CDC) log augmentation are described. In some examples, a user configures CDC log augmentation by indicating which data should be included in a CDC log, and the database, when generating a CDC log associated with this configuration, can obtain the associated data and augment the CDC log by inserting this data into it. The augmented data can include one or more fields from a record in a separate database table, where the record can be identified based on the changed record represented by the CDC log.

Change data capture log augmentation for streamlined ETL processing

Intelligent query routing may be performed across shards of a scalable database table. A router of a database system may receive an access request directed to one or more database tables. The router may evaluate the access request with respect to metadata obtained for the database tables to determine an assignment distribution of computing resources of the database system to data that can satisfy the access request. The router can select planning locations to perform the access request based on the assignment distribution of the computing resources. The router can cause the access request to be performed according to planning at the selected planning locations.

Intelligent query routing across shards of scalable database tables

Techniques for implementing a multi-wire protocol and multi-dialect database engine are described. A database engine exposes multiple interfaces in the form of ports that support different database wire protocols. The database engine supports multiple query dialects that can be passed over any one of the supported wire protocols. The database engine can support multiple different query dialects within a single database session.

Multi-wire protocol and multi-dialect database engine for database compatability

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Tiruchirappalli, India

Murali Brahmadesam