Amazon Aurora FAQs

Amazon Aurora is a modern relational database service offering performance and high availability at scale, fully open-source MySQL- and PostgreSQL-compatible editions, and a range of developer tools for building serverless and machine learning (ML)-driven applications.

Aurora features a distributed, fault-tolerant, and self-healing storage system that is decoupled from compute resources and auto-scales up to 128 TiB per database instance. It delivers high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon Simple Storage Service (Amazon S3), and replication across three Availability Zones (AZs).

Aurora is also a fully managed service that automates time-consuming administration tasks such as hardware provisioning, database setup, patching, and backups while providing the security, availability, and reliability of commercial databases at one-tenth of the cost.

Amazon Aurora is drop-in compatible with existing MySQL open-source databases and adds support for new releases regularly. This means you can easily migrate MySQL databases to and from Aurora using standard import/export tools or snapshots. It also means that most of the code, applications, drivers, and tools you already use with MySQL databases today can be used with Aurora with little or no change. This makes it easy to move applications between the two engines.

You can see the current Amazon Aurora MySQL release compatibility information in the documentation.

Amazon Aurora is drop-in compatible with existing PostgreSQL open-source databases and adds support for new releases regularly. This means you can easily migrate PostgreSQL databases to and from Aurora using standard import/export tools or snapshots. It also means that most of the code, applications, drivers, and tools you already use with PostgreSQL databases today can be used with Aurora with little or no change.

You can see the current Amazon Aurora PostgreSQL release compatibility information in the documentation.

Amazon fully supports Aurora PostgreSQL and all extensions available with Aurora. If you need support for Aurora PostgreSQL, reach out to AWS Support. If you have an active AWS Premium Support account, you can contact AWS Premium Support for Aurora specific issues.

To try Aurora, sign in to the AWS Management Console, select RDS under the Database category, and choose Amazon Aurora as your database engine. For detailed guidance and resources, check out our Getting started with Aurora page.

You can see Region availability for Aurora here.

If you want to migrate from MySQL to Aurora and the other way around, you have several options:

• You can use the standard mysqldump utility to export data from MySQL and mysqlimport utility to import data to Aurora, and the other way around.
• You can also use Amazon RDS’s DB Snapshot migration feature to migrate an Amazon RDS for MySQL DB Snapshot to Aurora using the AWS Management Console.

Migration to Aurora completes for most customers in under an hour, though the duration depends on format and dataset size. For more information see Best Practices for Migrating MySQL Databases to Amazon Aurora.

If you want to migrate from PostgreSQL to Aurora and the other way around, you have several options:

• You can use the standard pg_dump utility to export data from PostgreSQL and pg_restore utility to import data to Aurora, and the other way around.
• You can also use RDS’s DB Snapshot migration feature to migrate an Amazon RDS for PostgreSQL DB Snapshot to Aurora using the AWS Management Console.

Migration to Aurora completes for most customers in under an hour, though the duration depends on format and dataset size.

To migrate SQL Server databases to Amazon Aurora PostgreSQL-Compatible Edition, you can use Babelfish for Aurora PostgreSQL. Your applications will work without any changes. See the Babelfish documentation for more information.

No, Aurora works with standard PostgreSQL database drivers.

Amazon Aurora delivers significant increases over MySQL performance by tightly integrating the database engine with an SSD-based virtualized storage layer purpose-built for database workloads, reducing writes to the storage system, minimizing lock contention, and eliminating delays created by database process threads.

Our tests with SysBench on r3.8xlarge instances show that Amazon Aurora delivers over 500,000 SELECTs/sec and 100,000 UPDATEs/sec, five times higher than MySQL running the same benchmark on the same hardware. Detailed instructions on this benchmark and how to replicate it yourself are provided in the Amazon Aurora MySQL-Compatible Edition Performance Benchmarking Guide.

Amazon Aurora delivers significant increases over PostgreSQL performance by tightly integrating the database engine with an SSD-based virtualized storage layer purpose-built for database workloads, reducing writes to the storage system, minimizing lock contention, and eliminating delays created by database process threads.

Our tests with SysBench on r4.16xlarge instances show that Amazon Aurora delivers SELECTs/sec and UPDATEs/sec over three times higher than PostgreSQL running the same benchmark on the same hardware. Detailed instructions on this benchmark and how to replicate it yourself are provided in the Amazon Aurora PostgreSQL-Compatible Edition Performance Benchmarking Guide.

Amazon Aurora is designed to be compatible with MySQL so that existing MySQL applications and tools can run without requiring modification. However, one area where Amazon Aurora improves upon MySQL is with highly concurrent workloads. In order to maximize your workload’s throughput on Amazon Aurora, we recommend building your applications to drive a large number of concurrent queries and transactions.

Amazon Aurora is designed to be compatible with PostgreSQL so that existing PostgreSQL applications and tools can run without requiring modification. However, one area where Amazon Aurora improves upon PostgreSQL is with highly concurrent workloads. In order to maximize your workload’s throughput on Amazon Aurora, we recommend building your applications to drive a large number of concurrent queries and transactions.

See the Aurora pricing page for current pricing information.

There is no AWS Free Tier offering for Aurora at this time. However, Aurora durably stores your data across three Availability Zones in a Region and charges for only one copy of data. You are not charged for backups of up to 100% of the size of your database cluster. You are also not charged for snapshots during the backup retention period that you’ve configured for your database cluster.

No, Aurora replication is bundled into the price. You are charged based on the storage your database consumes at the database layer, not the storage consumed in the virtualized storage layer of Aurora.

I/O operations are performed by the Aurora database engine against its SSD-based virtualized storage layer. Every database page read operation counts as one I/O.
The Aurora database engine issues reads against the storage layer to fetch database pages not present in memory in the cache:

• If your query traffic can be totally served from memory or the cache, you will not be charged for retrieving any data pages from memory.
• If your query traffic cannot be served entirely from memory, you will be charged for any data pages that need to be retrieved from storage.
  Each database page is 16 KB in Amazon Aurora MySQL-Compatible Edition and 8 KB in Aurora PostgreSQL-Compatible Edition.

Aurora was designed to remove unnecessary I/O operations to reduce costs and ensure resources are available for serving read/write traffic. Write I/O operations are only consumed when persisting redo log records in Aurora MySQL-Compatible Edition or write ahead log records in Aurora PostgreSQL-Compatible Edition to the storage layer for the purpose of making writes durable.

Write I/O operations are counted in 4 KB units. For example, a log record that is 1,024 bytes counts as one write I/O operation. However, if the log record is larger than 4 KB, more than one write I/O operation is needed to persist it.

Concurrent write operations whose log records are less than 4 KB might be batched together by the Aurora database engine in order to optimize I/O consumption. Unlike traditional database engines, Aurora never flushes dirty data pages to storage.

You can see how many I/O requests your Aurora instance is consuming by checking the AWS Management Console. To find your I/O consumption, go to the Amazon RDS section of the console, look at your list of instances, select your Aurora instances, then look for the “Billed read operations” and “Billed write operations” metrics in the monitoring section.

For more information on the pricing of I/O operations, visit the Aurora pricing page. You are charged for read and write I/O operations when you configure your database clusters to the Aurora Standard configuration. You are not charged for read and write I/O operations when you configure your database clusters to Amazon Aurora I/O-Optimized.

Aurora offers you the flexibility to optimize your database spend by choosing between two configuration options based on your price-performance and price-predictability needs. The two configuration options are Aurora Standard and Aurora I/O-Optimized. Neither option requires upfront I/O or storage provisioning and both can scale I/O operations to support your most demanding applications.

Aurora Standard is a database cluster configuration that offers cost-effective pricing for the vast majority of applications with low to moderate I/O usage. With Aurora Standard, you pay for database instances, storage, and pay-per-request I/O.

Aurora I/O-Optimized is a database cluster configuration that delivers improved price performance for I/O-intensive applications such as payment processing systems, ecommerce systems, and financial applications. Also, if your I/O spend exceeds 25% of your total Aurora database spend, you can save up to 40% on costs for I/O-intensive workloads with Aurora I/O-Optimized. Aurora I/O-Optimized offers predictable pricing for all applications as there are no charges for read and write I/O operations, making this configuration ideal for workloads with high I/O variability.

Aurora I/O-Optimized is the ideal choice when you need predictable costs for any application. It delivers improved price performance for I/O-intensive applications, which require a high write throughput or run analytical queries processing large amounts of data. For customers with an I/O spend that exceeds 25% of their Aurora bill, you can save up to 40% on costs for I/O-intensive workloads with Aurora I/O-Optimized.

You can use the one-click experience available in the AWS Management Console to change the storage type of your existing database clusters to be Aurora I/O-Optimized. You can also invoke the AWS Command Line Interface (AWS CLI) or AWS SDK to make this change.

You can switch your existing database clusters once every 30 days to Aurora I/O-Optimized. You can switch back to Aurora Standard at any time.

Yes, Aurora I/O-Optimized works with existing Aurora Reserved Instances. Aurora automatically accounts for the price difference between Aurora Standard and Aurora I/O-Optimized with Reserved Instances. With Reserved Instance discounts with Aurora I/O-Optimized, you can gain even more savings on your I/O spend.

There are no changes to the price of backtrack, snapshot, export, or continuous backup with Aurora I/O-Optimized.

Yes, the charges for the I/O operations required to replicate data across Regions continue to apply. Aurora I/O-Optimized does not charge for read and write I/O operations, which is different from data replication.

The minimum storage is 10 GB. Based on your database usage, your Amazon Aurora storage will automatically grow, up to 128 TiB, in 10 GB increments with no impact to database performance. There is no need to provision storage in advance.

There are two ways to scale the compute resources associated with my Amazon Aurora DB Instance – via Aurora Serverless and via manual adjustment.

You can use Aurora Serverless, an on-demand, autoscaling configuration for Amazon Aurora to scale database compute resources based on application demand. It enables you to run your database in the cloud without worrying about database capacity management. You can specify the desired database capacity range and your database will scale based on your application’s needs. Read more in the Aurora Serverless User Guide.

You can also manually scale your compute resources associated with your database by selecting the desired DB instance type in the AWS Management Console. Your requested change will be applied during your specified maintenance window or you can use the "Apply Immediately" flag to change the DB instance type immediately.

Both of these options will have an availability impact for a few minutes as the scaling operation is performed. Note that any other pending system changes will also be applied.

Automated continuous backups are always enabled on Amazon Aurora DB Instances. Backups do not impact database performance.

Yes, and there is no performance impact when taking snapshots. Note that restoring data from DB Snapshots requires the creation of a new DB Instance.

Amazon Aurora automatically makes your data durable across three Availability Zones (AZs)  in a Region and will automatically attempt to recover your database in a healthy AZ with no data loss. In the unlikely event your data is unavailable within Amazon Aurora storage, you can restore from a DB Snapshot or perform a point-in-time restore operation to a new instance. Note that the latest restorable time for a point-in-time restore operation can be up to five minutes in the past.

You can choose to create a final DB Snapshot when deleting your DB Instance. If you do, you can use this DB Snapshot to restore the deleted DB Instance at a later date. Amazon Aurora retains this final user-created DB Snapshot along with all other manually created DB Snapshots after the DB Instance is deleted. Only DB Snapshots are retained after the DB Instance is deleted (i.e., automated backups created for point-in-time restore are not kept).

Yes. Aurora gives you the ability to create snapshots of your databases, which you can use later to restore a database. You can share a snapshot with a different AWS account, and the owner of the recipient account can use your snapshot to restore a DB that contains your data. You can even choose to make your snapshots public – that is, anybody can restore a DB containing your (public) data.

You can use this feature to share data between your various environments (production, dev/test, staging, etc.) that have different AWS accounts, as well as keep backups of all your data secure in a separate account in case your main AWS account is ever compromised.

There is no charge for sharing snapshots between accounts. However, you may be charged for the snapshots themselves, as well as any databases you restore from shared snapshots. Learn more about Aurora pricing.

We do not support automatic sharing of DB snapshots. To share a snapshot, you must manually create a copy of the snapshot, and then share the copy.

You may share manual snapshots with up to 20 AWS account IDs. If you want to share the snapshot with more than 20 accounts, you can either share the snapshot as public, or contact support for increasing your quota.

You can share your Aurora snapshots within each AWS region where Aurora is available.

No. Your shared Aurora snapshots will only be accessible by accounts in the same region as the account that shares them.

Yes, you can share encrypted Aurora snapshots.

Amazon Aurora automatically divides your database volume into 10 GB segments spread across many disks. Each 10 GB chunk of your database volume is replicated six ways, across three AZs. Amazon Aurora is designed to transparently handle the loss of up to two copies of data without affecting database write availability and up to three copies without affecting read availability.

Amazon Aurora storage is also self-healing. Data blocks and disks are continuously scanned for errors and repaired automatically.

Unlike other databases, after a database crash Amazon Aurora does not need to replay the redo log from the last database checkpoint (typically five minutes) and confirm that all changes have been applied before making the database available for operations. This reduces database restart times to less than 60 seconds in most cases.

Amazon Aurora moves the buffer cache out of the database process and makes it available immediately at restart time. This prevents you from having to throttle access until the cache is repopulated to avoid brownouts.

Amazon Aurora MySQL-Compatible Edition and Amazon Aurora PostgreSQL-Compatible Edition support Amazon Aurora replicas, which share the same underlying volume as the primary instance in the same AWS region. Updates made by the primary are visible to all Amazon Aurora Replicas.

With Amazon Aurora MySQL-Compatible Edition, you can also create cross-region MySQL Read Replicas based on MySQL’s binlog-based replication engine. In MySQL Read Replicas, data from your primary instance is replayed on your replica as transactions. For most use cases, including read scaling and high availability, we recommend using Amazon Aurora Replicas.

You have the flexibility to mix and match these two replica types based on your application needs:

You have two additional replication options in addition to the ones listed above. You can use Amazon Global Database for much faster physical replication between Aurora clusters in different regions. And for replication between Aurora and non-Aurora MySQL-Compatible Edition databases (even outside of AWS), you can set up your own, self-managed binlog replication.

Yes, you can set up cross-region Aurora replicas using either physical or logical replication. Physical replication, called Amazon Aurora Global Database, uses dedicated infrastructure that leaves your databases entirely available to serve your application, and can replicate up to five secondary regions with typical latency of under a second. It's available for both Aurora MySQL-Compatible Edition and Aurora PostgreSQL-Compatible Edition.

For low-latency global reads and disaster recovery, we recommend using Amazon Aurora Global Database.
Aurora supports native logical replication in each database engine (binlog for MySQL and PostgreSQL replication slots for PostgreSQL), so you can replicate to Aurora and non-Aurora databases, even across Regions.

Aurora MySQL-Compatible Edition also offers an easy-to-use logical cross-region read replica feature that supports up to five secondary AWS regions. It is based on single threaded MySQL binlog replication, so the replication lag will be influenced by the change/apply rate and delays in network communication between the specific regions selected.

Yes, you can add up to 15 Aurora Replicas on each cross-region cluster, and they will share the same underlying storage as the cross-region replica. A cross-region replica acts as the primary on the cluster and the Aurora Replicas on the cluster will typically lag behind the primary by tens of milliseconds.

Yes, you can promote your cross-region replica to be the new primary from the Amazon RDS console. For logical (binlog) replication, the promotion process typically takes a few minutes depending on your workload. The cross-region replication will stop once you initiate the promotion process.

With Amazon Aurora Global Database, you can promote a secondary region to take full read/write workloads in under a minute.

Yes. You can assign a promotion priority tier to each instance on your cluster. When the primary instance fails, Amazon RDS will promote the replica with the highest priority to primary. If two or more Aurora Replicas share the same priority, then Amazon RDS promotes the replica that is largest in size. If two or more Aurora Replicas share the same priority and size, then Amazon RDS promotes an arbitrary replica in the same promotion tier.

For more information on failover logic, read the Amazon Aurora User Guide.

Yes, you can modify the priority tier for an instance at any time. Simply modifying priority tiers will not trigger a failover.

You can assign lower priority tiers to replicas that you don’t want promoted to the primary instance. However, if the higher priority replicas on the cluster are unhealthy or unavailable for some reason, then Amazon RDS will promote the lower priority replica.

You can add Amazon Aurora Replicas. Aurora Replicas in the same AWS Region share the same underlying storage as the primary instance. Any Aurora Replica can be promoted to primary without any data loss, and therefore can be used to enhance fault tolerance in the event of a primary DB Instance failure.

To increase database availability, simply create one to 15 replicas, in any of three AZs, and Amazon RDS will automatically include them in failover primary selection in the event of a database outage. You can use Amazon Aurora Global Database if you want your database to span multiple AWS Regions. This will replicate your data with no impact on database performance and provide disaster recovery from region-wide outages.

Failover is handled automatically by Amazon Aurora so your applications can resume database operations as quickly as possible without manual administrative intervention.

• If you have an Aurora Replica in the same or a different AZ when failing over, Aurora flips the canonical name record (CNAME) for your DB Instance to point at the healthy replica, which is promoted to become the new primary. Start-to-finish, failover typically completes within 30 seconds. For improved resiliency and faster failovers, consider using Amazon RDS Proxy which automatically connects to the failover DB instance while preserving application connections. Proxy makes failovers transparent to your applications and reduces failover times by up to 66%.
• If you are running Aurora Serverless v1 and the DB instance or AZ become unavailable, Aurora will automatically recreate the DB instance in a different AZ. Aurora Serverless v2 works like provisioned for failover and other high availability features. For more information, see Aurora Serverless v2 and high availability.. 
• If you do not have an Aurora Replica (i.e., single instance) and are not running Aurora Serverless, Aurora will attempt to create a new DB Instance in the same Availability Zone as the original instance. This replacement of the original instance is done on a best-effort basis and may not succeed, for example, if there is an issue that is broadly affecting the Availability Zone.
  

Your application should retry database connections in the event of connection loss. Disaster recovery across regions is a manual process, where you promote a secondary region to take read/write workloads.

Amazon Aurora will automatically detect a problem with your primary instance and trigger a failover. If you are using the Cluster Endpoint, your read/write connections will be automatically redirected to an Amazon Aurora Replica that will be promoted to primary.

In addition, the read traffic that your Aurora Replicas were serving will be briefly interrupted. If you are using the Cluster Reader Endpoint to direct your read traffic to the Aurora Replica, the read only connections will be directed to the newly promoted Aurora Replica until the old primary node is recovered as a replica.

Since Amazon Aurora Replicas share the same data volume as the primary instance in the same AWS Region, there is virtually no replication lag. We typically observe lag times in the tens of milliseconds.

For cross-region replication, binlog-based logical replication lag can grow indefinitely based on change/apply rate as well as delays in network communication. However, under typical conditions, under a minute of replication lag is common. Cross-region replicas using Amazon Aurora Global Database’s physical replication will have a typical lag of under a second.

Yes, you can set up binlog replication between an Aurora MySQL-Compatible Edition instance and an external MySQL database. The other database can run on Amazon RDS, or as a self-managed database on AWS, or completely outside of AWS.

If you're running Aurora MySQL-Compatible Edition 5.7, consider setting up GTID-based binlog replication. This will provide complete consistency so your replication won’t miss transactions or generate conflicts, even after failover or downtime.

Amazon Aurora Global Database is a feature that allows a single Amazon Aurora database to span multiple AWS regions. It replicates your data with no impact on database performance, enables fast local reads in each Region with typical latency of less than a second, and provides disaster recovery from region-wide outages. In the unlikely event of a regional degradation or outage, a secondary region can be promoted to full read/write capabilities in less than one minute. This feature is available for both Aurora MySQL-Compatible Edition and Aurora PostgreSQL-Compatible Edition.

You can create an Aurora Global Database with just a few clicks in the Amazon RDS console. Alternatively, you can use the AWS Software Development Kit (SDK) or AWS Command-Line Interface (CLI). You need to provision at least one instance per region in your Amazon Aurora Global Database.

You can create up to five secondary regions for an Amazon Aurora Global Database.

Yes. If your goal is to analyze database activity, consider using Aurora advanced auditing, general logs, and slow query logs instead, to avoid impacting the performance of your database.

No. If your primary region becomes unavailable, you can manually remove a secondary region from an Amazon Aurora Global Database and promote it to take full reads and writes. You will also need to point your application to the newly promoted region.

Yes, all Amazon Aurora DB Instances must be created in a VPC. With Amazon VPC, you can define a virtual network topology that closely resembles a traditional network you might operate in your own datacenter. This gives you complete control over who can access your Amazon Aurora databases.

Yes. Amazon Aurora uses SSL (AES-256) to secure the connection between the database instance and the application. Amazon Aurora allows you to encrypt your databases using keys you manage through AWS Key Management Service (AWS KMS).

On a database instance running with Amazon Aurora encryption, data stored at rest in the underlying storage is encrypted, as are its automated backups, snapshots, and replicas in the same cluster. Encryption and decryption are handled seamlessly. For more information about the use of AWS KMS with Amazon Aurora, see the Amazon RDS User's Guide.

Currently, encrypting an existing unencrypted Aurora instance is not supported. To use Amazon Aurora encryption for an existing unencrypted database, create a new DB Instance with encryption enabled and migrate your data into it.

Aurora databases must be accessed through the database port entered on database creation. This provides an additional layer of security for your data. Step-by-step instructions on how to connect to your Amazon Aurora database are provided in the Amazon Aurora Connectivity Guide.

Yes, the MySQL- and PostgreSQL-compatible editions of Aurora are HIPAA-eligible. You can use them to build HIPAA-compliant applications and store healthcare-related information, including protected health information (PHI) under an executed Business Associate Addendum (BAA) with AWS. If you have already entered into a BAA with AWS, no further action is necessary to begin using these services in the account(s) covered by your BAA. For more information about using AWS to build compliant applications, see Healthcare Providers.

You can currently find a list of CVEs at Amazon Aurora Security Updates.

Aurora is integrated with Amazon GuardDuty to help you identify potential threats to data stored in Aurora databases. GuardDuty RDS Protection profiles and monitors login activity and new databases in your account, and uses tailored ML models to detect suspicious logins to Aurora databases. For more information, see Monitoring threats with GuardDuty RDS Protection and the GuardDuty RDS Protection User Guide.

Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora. With Aurora Serverless, you can run your database in the cloud without managing database capacity. Manually managing database capacity can be time consuming and lead to inefficient use of database resources. With Aurora Serverless, you create a database, specify the desired database capacity range, and connect your application. Aurora automatically adjusts the capacity within the range specified based on your application’s needs.

You pay on a per-second basis for the database capacity you use when the database is active. Learn more about Aurora Serverless and get started in a few steps in the Amazon RDS Management Console.

Aurora Serverless v2 supports every type of database workload, from development and test environments, websites, and applications that have infrequent, intermittent, or unpredictable workloads to the most demanding, business critical applications that require high scale and high availability. It scales in place by adding more CPU and memory without having to failover the database to a larger or smaller database instance. As a result, it can scale even when there are long running transactions, table locks, and more.

In addition, it scales database capacity in increments as small as 0.5 Aurora Capacity Units (ACUs) so your database capacity closely matches your application’s needs.

Aurora Serverless v1 is a simple, cost-effective option for infrequent, intermittent, or unpredictable workloads. It automatically starts up, scales compute capacity to match your application's usage, and shuts down when it's not in use. Visit the Aurora User Guide to learn more.

Aurora Serverless v2 supports all features of provisioned Aurora, including read replica, Multi-AZ configuration, Aurora Global Database, RDS Proxy, and Performance Insights.

Yes, you can start using Aurora Serverless v2 to manage database compute capacity in your existing Aurora DB cluster. A cluster containing both provisioned instances as well as Aurora Serverless v2 is referred to as a mixed-configuration cluster. You can choose to have any combination of provisioned instances and Aurora Serverless v2 in your cluster.

To test Aurora Serverless v2, you add a reader to your Aurora DB cluster and select Serverless v2 as the instance type. Once the reader is created and available, you can start using it for read-only workloads. Once you confirm that the reader is working as expected, you can initiate a failover to start using Aurora Serverless v2 for both reads and writes. This option provides a minimal downtime experience to get started with Aurora Serverless v2.

Yes, you can migrate from Aurora Serverless v1 to Aurora Serverless v2. Refer to the Aurora User Guide to learn more.

Aurora Serverless v1 compatibility information can be seen here. Aurora Serverless v2 compatibility information can be seen here.

Yes, you can restore a snapshot taken from an existing Aurora provisioned cluster into an Aurora Serverless DB Cluster and the other way around.

You access an Aurora Serverless DB cluster from within a client application running in the same VPC. You can't give a public IP address to an Aurora Serverless DB.

While Aurora Serverless automatically scales based on the active database workload, in some cases, capacity might not scale fast enough to meet a sudden workload change, such as a large number of new transactions. In these cases, you can set the capacity explicitly to a specific value with the AWS Management Console, the AWS CLI, or the Amazon RDS API.

Once a scaling operation is initiated, Aurora Serverless attempts to find a scaling point, which is a point in time at which the database can safely complete scaling. Aurora Serverless might not be able to find a scaling point if you have long-running queries or transactions in progress, or temporary tables or table locks in use.

In Aurora Serverless, database capacity is measured in ACUs. You pay a flat rate per second of ACU usage. Compute costs for running your workloads on Aurora Serverless will depend on the database cluster configuration that you choose: Aurora Standard or Aurora I/O-Optimized. Visit the Aurora pricing page for information about pricing and Regional availability.

Amazon Aurora Parallel Query refers to the ability to push down and distribute the computational load of a single query across thousands of CPUs in Aurora’s storage layer. Without Parallel Query, a query issued against an Amazon Aurora database would be executed wholly within one instance of the database cluster; this would be similar to how most databases operate.

Parallel Query is a good fit for analytical workloads requiring fresh data and good query performance, even on large tables. Workloads of this type are often operational in nature.

Parallel Query results in faster performance, speeding up analytical queries by up to two orders of magnitude. It also delivers operational simplicity and data freshness as you can issue a query directly over the current transactional data in your Aurora cluster. And, Parallel Query enables transactional and analytical workloads on the same database by allowing Aurora to maintain high transaction throughput alongside concurrent analytical queries.

Most queries over large data sets that are not already in the buffer pool can expect to benefit. The initial version of Parallel Query can push down and scale out of the processing of more than 200 SQL functions, equijoins, and projections.

The improvement to a specific query’s performance depends on how much of the query plan can be pushed down to the Aurora storage layer. Customers have reported more than an order of magnitude improvement to query latency.

Yes, but we expect such cases to be rare.

Changes in query syntax are not required. The query optimizer will automatically decide whether to use Parallel Query for your specific query. To check if a query is using Parallel Query, you can view the query execution plan by running the EXPLAIN command. If you wish to bypass the heuristics and force Parallel Query for test purposes, use the aurora_pq_force session variable.

Parallel Query can be enabled and disabled dynamically at both the global and session level using the aurora_pq parameter.

No. You aren’t charged for anything other than what you already pay for instances, I/O, and storage.

No, Parallel Query I/O costs for your query are metered at the storage layer, and will be the same or larger with Parallel Query turned on. Your benefit is the improvement in query performance.

There are two reasons for potentially higher I/O costs with Parallel Query. First, even if some of the data in a table is in the buffer pool, Parallel Query requires all data to be scanned at the storage layer, incurring I/O. Second, a side effect of avoiding contention in the buffer pool is that running a Parallel Query does not warm up the buffer pool. As a result, consecutive runs of the same Parallel Query query will incur the full I/O cost.

Learn more about Parallel Query in the Documentation.

No. At this time, you can use Parallel Query with instances in the R* instance family.

Not initially. At this time, you can only turn it on for database clusters that aren't running the Serverless or Backtrack features. Further, it doesn’t support functionality specific to Aurora with MySQL 5.7 compatibility.

No. While we expect Parallel Query to improve query latency in most cases, you may incur higher I/O costs. We recommend that you thoroughly test your workload with the feature enabled and disabled. Once you're convinced that Parallel Query is the right choice, you can rely on the query optimizer to automatically decide which queries will use Parallel Query. In the rare case when the optimizer doesn’t make the optimal decision, you can override the setting.

Aurora Parallel Query is not a data warehouse and doesn’t provide the functionality typically found in such products. It’s designed to speed up query performance on your relational database and is suitable for use cases such as operational analytics, when you need to perform fast analytical queries on fresh data in your database.

For an exabyte scale cloud data warehouse, please consider Amazon Redshift.

Amazon DevOps Guru for RDS is a new ML-powered capability for Amazon RDS (which includes Amazon Aurora) that is designed to automatically detect and diagnose database performance and operational issues, enabling you to resolve issues in minutes rather than days.

Amazon DevOps Guru for RDS is a feature of Amazon DevOps Guru, which is designed to detect operational and performance issues for all Amazon RDS engines and dozens of other resource types. DevOps Guru for RDS expands the capabilities of DevOps Guru to detect, diagnose, and remediate a wide variety of database-related issues in Amazon RDS (e.g. resource over-utilization, and misbehavior of certain SQL queries).

When an issue occurs, Amazon DevOps Guru for RDS is designed to immediately notify developers and DevOps engineers and provides diagnostic information, details on the extent of the problem, and intelligent remediation recommendations to help customers quickly resolve database-related performance bottlenecks and operational issues.

Amazon DevOps Guru for RDS is designed to remove manual effort and shorten time (from hours and days to minutes) to detect and resolve hard to find performance bottlenecks in your relational database workload.

You can enable DevOps Guru for RDS for every Amazon Aurora database, and it will automatically detect performance issues for your workloads, send alerts to you on each issue, explain findings, and recommend actions to resolve.
DevOps Guru for RDS helps make database administration more accessible to non-experts and assists database experts so that they can manage even more databases.

Amazon DevOps Guru for RDS uses ML to analyze telemetry data collected by Amazon RDS Performance Insights (PI). DevOps Guru for RDS does not use any of your data stored in the database in its analysis. PI measures database load, a metric that characterizes how an application spends time in the database and selected metrics generated by the database, such as server status variables in MySQL and pg_stat tables in PostgreSQL.

To get started with DevOps Guru for RDS, ensure Performance Insights is enabled through the RDS console, and then simply enable DevOps Guru for your Amazon Aurora databases. With DevOps Guru, you can choose your analysis coverage boundary to be your entire AWS account, prescribe the specific AWS CloudFormation stacks that you want DevOps Guru to analyze, or use AWS tags to create the resource grouping you want DevOps Guru to analyze.

Amazon DevOps Guru for RDS helps identify a wide range of performance issues that may affect application service quality, such as lock pile-ups, connection storms, SQL regressions, CPU and I/O contention, and memory issues.

Amazon RDS Performance Insights is a database performance tuning and monitoring feature that collects and visualizes Amazon RDS database performance metrics, helping you quickly assess the load on your database, and determine when and where to take action. Amazon DevOps Guru for RDS is designed to monitor those metrics, detect when your database is experiencing performance issues, analyze the metrics, and then tell you what’s wrong and what you can do about it.

Amazon RDS Blue/Green Deployments are available in Amazon Aurora MySQL-Compatible Edition versions 5.6. Learn more about available versions in the Aurora documentation.

Amazon RDS Blue/Green Deployments are available in all AWS Regions (excluding AWS China Regions) and the AWS GovCloud Regions.

You will incur the same price for running your workloads on green instances as you do for blue instances. The cost of running on blue and green instances include our current standard pricing for db.instances, cost of storage, cost of read/write I/Os, and any enabled features, such as cost of backups and Amazon RDS Performance Insights. Effectively, you are paying approximately 2x the cost of running workloads on db.instance for the lifespan of the blue-green-deployment.

For example: You have Aurora MySQL-Compatible Edition 5.7 cluster running on two r5.2xlarge db.instances, a primary writer instance and a reader instance, in us-east-1 AWS region. Each of the r5.2xlarge db.instances are configured for 40 GiB Storage and have 25 Million I/Os per month. You create a clone of the blue instance topology using Amazon RDS Blue/Green Deployments, run it for 15 days (360 hours) and each green instance has 3 million I/O reads during that time. You then delete the blue instances after a successful switchover. The blue instances (writer and reader) cost $849.2 for 15 days at an on-demand rate of $1.179/hr (Instance + Storage+ I/O). The green instances (writer and reader) cost $840.40 for 15 days at an on-demand rate of $1.167/hr (Instance +Storage+ I/O). The total cost to you for using Blue/Green Deployments for those 15 days is $1689.60, which is approximately 2x the cost of running blue instances for that time period.

Amazon RDS Blue/Green Deployments help you make safer, simpler, and faster database changes, such as major or minor version upgrades, schema changes, instance scaling, engine parameter changes, and maintenance updates.

In Amazon RDS Blue/Green Deployments, the blue environment is your current production environment. The green environment is your staging environment that will become your new production environment after switchover.

When Amazon RDS Blue/Green Deployments initiate a switchover, they block writes to both the blue and green environments, until switchover is complete. During switchover, the staging environment, or green environment, catches up with the production system, ensuring data is consistent between the staging and production environment. Once the production and staging environment are in complete sync, Blue/Green Deployments promote the staging environment as the new production environment by redirecting traffic to the newly promoted production environment. Amazon RDS Blue/Green Deployments are designed to enable writes on the green environment after switchover is complete, ensuring zero data loss during the switchover process.

Amazon RDS Blue/Green Deployments do not delete your old production environment. If needed, you can access it for additional validations and performance/regression testing. If you no longer need the old production environment, you can delete it. Standard billing charges apply on old production instances until you delete them.

Amazon RDS Blue/Green Deployments switchover guardrails block writes on your blue and green environments until your green environment catches up before switching over. Blue/Green Deployments also perform health checks of your primary and replicas in your blue and green environments. They also perform replication health checks, for example, to see if replication has stopped or if there are errors. They detect long running transactions between your blue and green environments. You can specify your maximum tolerable downtime, as low as 30 seconds, and if you have an ongoing transaction that exceeds this your switchover will time out.

No, Amazon RDS Blue/Green Deployments do not support Amazon Aurora Global Databases.

No, at this time you cannot use Amazon RDS Blue/Green Deployments to rollback changes.