Things to know — Amazon Aurora DB


Amazon Aurora is a relational database that was designed to take full advantage of the abundance of networking, processing, and storage resources available in the cloud. While maintaining compatibility with MySQL and PostgreSQL on the user-visible side, Aurora makes use of a modern, purpose-built distributed storage system under the covers. Your data is striped across hundreds of storage nodes distributed over three distinct AWS Availability Zones, with two copies per zone, on fast SSD storage. Here’s what this looks like:

Shared-Nothing Architecture

Aurora decouples its database engine from the storage backend. The database engine is a modified version with MySQL/InnoDB, where the storage backend consists of distributed replicas that span across different availability zones in AWS. The database engine will propagates logs to the backend storage, and the backend storage utilizes a quorum based synchronization scheme to ensure the consistency of the database.

Aurora architecture works on the basis of a cluster volume that manages the data for all the database instances in that particular cluster. A cluster volume spans across multiple availability zones and is effectively virtual database storage. The underlying storage volume is on top of multiple cluster nodes which are distributed across different availability zones. Separate from this, Aurora database can also have read-replicas. Only one instance usually serves as the primary instance and it supports reads as well as writes. The rest of the instances serve as read-replicas and load balancing needs to be handled by the user. This is different from the multiAZ deployment, where instances are located across the availability zone and support automatic failover.

Features that are worth exploring

Parallel query for Aurora MySQL

Aurora MySQL parallel query is an optimization that parallelizes some of the I/O and computation involved in processing data-intensive queries. The work that is parallelized includes retrieving rows from storage, extracting column values, and determining which rows match the conditions in the WHERE clause and join clauses. This data-intensive work is delegated (in database optimization terms, pushed down) to multiple nodes in the Aurora distributed storage layer. Without parallel query, each query brings all the scanned data to a single node within the Aurora MySQL cluster (the head node) and performs all the query processing there.

Cloning Aurora

Using database cloning, you can quickly and cost-effectively create clones of all of the databases within an Aurora DB cluster. The clone databases require only minimal additional space when first created.

Database cloning uses a copy-on-write protocol, in which data is copied at the time that data changes, either on the source databases or the clone databases. You can make multiple clones from the same DB cluster. You can also create additional clones from other clones. For more information on how the copy-on-write protocol works in the context of Aurora storage, see Copy-on-Write Protocol for Database Cloning.

Backtracking to an earlier point in time

Backtracking lets you rewind the Aurora DB cluster to the time you specify. With backtracking enabled, Aurora keeps a record of changes to your database and allows you to switch to a previous consistent state. With this feature you can easily undo mistakes. For example, if by accident you perform a destructive action, such as a DELETE without a WHERE clause, you can quickly backtrack to a state before the accident. Unlike restoring from a snapshot or automated backup — a slower operation, backtracking lets you move back and forth in time in a matter of minutes.

Backtracking is not a replacement for backing up your DB cluster so that you can restore it to a point in time.

Saving data into (Loading Data from) text files in Amazon S3

You can use the SELECT INTO OUTFILE S3 statement to query data from an Amazon Aurora MySQL DB cluster and save it directly into text files stored in an Amazon S3 bucket. You can use this functionality to skip bringing the data down to the client first, and then copying it from the client to Amazon S3. The LOAD DATA FROM S3 statement can use the files created by this statement to load data into an Aurora DB cluster.



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Deepak Sood

Deepak Sood

MTech in Computer Science | Senior Data Engineer @ AIML Data Analytics | Life Long Learner |