AWS Database Blog

Introducing the Aurora Storage Engine

What Is Amazon Aurora?

Amazon Aurora is a MySQL and PostgreSQL compatible relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better throughput than MySQL and three times better throughput than PostgreSQL with the security, availability, and reliability of a commercial database at one tenth the cost.

In this post, we are going to dive deep into one of the innovations that allows Aurora to deliver this performance, availability and reliability: the storage layer.

The Evolution of Database Storage

Many customers use directly attached storage (DAS) to house their database data. Larger enterprise customers use storage area networks (SAN). These approaches introduce a number of issues:

  • SANs are expensive. Many smaller customers cannot afford the physical SAN infrastructure or storage expertise required to manage a SAN.
  • Disk storage is complex to scale out for performance. Even when done well, there are still limitations to how far DAS and SAN can scale.
  • Both DAS and SAN infrastructure exist in a single physical data center. If this data center is compromised by a power outage or network interruption, the database server is unavailable.
  • If a flood, earthquake, or other natural disaster destroys the data center, the data might be lost. If a backup of the data has been kept offsite, it can take anywhere from minutes to days to get the database server back online in a new location.

Introducing the Amazon Aurora Storage Engine

Amazon Aurora was designed to take advantage of the cloud.

Conceptually, the Amazon Aurora storage engine is a distributed SAN that spans multiple AWS Availability Zones (AZs) in a region. An AZ is a logical data center comprised of physical data centers. Each AZ is isolated from the others, except for a low latency link that allows for rapid communication with the other AZs in the region. The distributed, low latency storage engine at the center of Amazon Aurora depends on AZs.

Storage Allocation

Amazon Aurora currently builds its storage volumes in 10 GB logical blocks called protection groups. The data in each protection group is replicated across six storage nodes. Those storage nodes are then allocated across three AZs in the region in which the Amazon Aurora cluster resides.

When the cluster is created, it consumes very little storage. As the volume of data increases and exceeds the currently allocated storage, Aurora seamlessly expands the volume to meet the demand and adds new protection groups, as necessary. Amazon Aurora continues to scale in this way until it reaches its current limit of 64 TB.

How Amazon Aurora Handles Writes

When data is written to Amazon Aurora, it is sent to six storage nodes in parallel. Those storage nodes are spread across three Availability Zones, which significantly improves durability and availability.

On each storage node, the records first enter an in-memory queue. Log records in this queue are deduplicated. For example, in a case in which the master node successfully writes to the storage node, but the connection between the master and storage node is interrupted, the master node retransmits the log record, but the duplicate log record is discarded. The records that are to be kept are hardened to disk in the hot log.

After the records are persisted, the log record is written to an in-memory structure called the update queue. In the update queue, log records are first coalesced and then used to create data pages. If one or more log sequence numbers (LSNs) are determined to be missing, the storage nodes employ a protocol to retrieve the missing LSN(s) from other nodes in the volume. After the data pages are updated, the log record is backed up and then marked for garbage collection. Pages are then asynchronously backed up to Amazon S3.

When the write becomes durable by being written to the hot log, the storage node will acknowledge receipt of the data. After at least four of the six storage nodes acknowledge receipt, the write is considered successful, and acknowledgement is returned to the client application.

One of the reasons Amazon Aurora can write so much faster than other engines is that it only sends log records to the storage nodes and those writes are done in parallel. In fact, on average, compared to MySQL, Amazon Aurora requires approximately 1/8 the IOPS for a similar workload, even though data is being written to six different nodes.

All of the steps in this process are asynchronous. Writes are performed in parallel using group commit to reduce the latency and improve throughput.

Given the low write latency and the reduced I/O footprint, Amazon Aurora is ideally suited for write-intensive applications.

Fault Tolerance
The following diagram shows data stored in three AZs. The replicated data is continuously backed up to S3, which is designed to provide 99.999999999% durability.


This design makes it possible for Amazon Aurora to withstand the loss of an entire AZ or two storage nodes without any availability impact to the client applications.


During recovery, Aurora is designed to sustain the loss of an AZ and yet another storage node in a protection group without any loss of data. This is because a write is made durable in Aurora only when it has been persisted to four storage nodes in a protection group. Even if three storage nodes that received the write go down, Aurora can still recover the write from the fourth storage node. To achieve read quorum during recovery, Aurora makes sure that three storage nodes are caught up to the same LSN. However, in order to open the volume for writes, Aurora has to wait for four storage nodes to recover so that it can achieve write quorum for future writes.


For reads, Aurora tries to find the closest storage node to fulfill the read. Each read request is associated with a timestamp, namely an LSN. A storage node can fulfill the read if it is caught up to the LSN (in other words, if it has received all of the updates up to that LSN).

In the event that one or more storage nodes are down or are otherwise unable to communicate with the other storage nodes, those nodes will use the gossip protocol to resynchronize themselves when they come online. If a storage node is lost and a new node is created automatically to take its place, it will also by synchronized through the gossip protocol.

With Amazon Aurora, compute and storage is decoupled. This allows for Aurora replicas to act as compute interfaces to the storage layer without keeping any of the data on the replica itself. This enables Aurora replicas to start serving traffic as soon as the instance comes online without having to synchronize any data. Likewise, the loss of an Aurora replica has no impact on the underlying data. When an Aurora replica becomes the master node, there is no data loss. With support for up to 15 Aurora replicas and built-in load balancing across those Aurora replicas, Amazon Aurora is ideally suited for high availability, read-intensive workloads.

Backup and Recovery

With Amazon Aurora, data is continuously backed up to S3 in real time, with no performance impact to the end user. This eliminates the need for backup windows and automated backup scripts. It also allows users to restore to any point in the user-defined backup retention period. In addition, because all backups are stored in S3, which is highly durable storage, replicated across multiple data centers and designed to provide 99.999999999% durability, the risk of data loss is reduced significantly.

Sometimes users would like to take a snapshot of the data in their Aurora cluster at a specific point in time. Amazon Aurora allows users to do this with no impact on performance.

When restoring from an Amazon Aurora backup, each 10 GB protection group restores in parallel. Additionally, after the protection groups have been restored, there is no need to reapply logs. This means that Amazon Aurora can work at peak performance as soon as the protection groups are restored.

Amazon Aurora allows users to encrypt all data at rest using industry standard AES-256 encryption. Users can also manage keys by using the AWS Key Management Service (AWS KMS). Data in flight can be secured through a TLS connection to the Amazon Aurora cluster. In addition to these features, Amazon Aurora also holds multiple certifications/attestations, including SOC 1, SOC 2, SOC 3, ISO 27001/9001, ISO 27017/27018, and PCI.

Amazon Aurora was designed for the cloud. By distributing data across multiple Availability Zones, Amazon Aurora provides an extremely durable and highly available database engine. Due to many of the innovations in how and where data is stored, both read and write throughput is significantly improved over other relational database engines. So, too, is the speed with which database restore operations can occur. Finally, due to the fact that Amazon Aurora is a managed service, AWS handles all of the undifferentiated heavy lifting of backups, patching, and storage management.

Get started with Amazon Aurora today. For a deeper dive into Amazon Aurora including some of the latest features, please check out the Amazon Aurora webinar.

About the Author

Steve Abraham is a principal solutions architect for Amazon Web Services. He works with our customers to provide guidance and technical assistance on database projects, helping them improving the value of their solutions when using AWS.