Amazon Technologies, Inc.

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Tate Andrew Certain

Akshat Vig

Nicholas Gordon

James Christopher Sorenson

Vaibhav Jain

Sharatkumar Nagesh Kuppahally

Chase Kernan

Vaibhav Govil

Naveen Ganapathi Subramanian

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

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

Continuous data protection and restoration

A selective data restoration technique is disclosed. A request to recover data in a dataset is received along with an identifier of a particular portion of the data to be recovered. The request may specify a time that indicates a desired recovery state (e.g., recover the values as they were at the time). Change data is determined for the specified time. For example, data stream filters generate recovery data by applying filters (e.g., keys) to a time window batch of the stream data. The stream data may correspond to changes to a data set, and the recovery data may correspond to only a portion of the dataset (e.g., a portion of a table). The system performs a recovery action, based on the recovery data, using the recovery data to automatically restore data in a data store, or providing the recovery date to the requesting entity, for example.

Selective data restoration

Various systems and methods to efficiently store backups of partitions of a database are disclosed. An indication to take a current snapshot of a partition of the database is received. A log size and operation type statistics are determined for a portion of the change log for the partition. A snapshot type is determined for the partition based at least in part on the log size and the operation type statistics, where the snapshot type specifies a log-based snapshot or a full snapshot. The snapshot is stored as metadata indicating a point in the change log if the determined snapshot type is log-based snapshot or stored as complete data of the partition if the determined snapshot type is full snapshot.

Efficient database snapshot generation

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Seattle, WA, United States of America

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