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 256 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 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.

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.

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 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 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.

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 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.

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 “VolumeReadIOPs” and “VolumeWriteIOPs” 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.

There are no additional charges for Amazon Aurora Optimized Reads for Aurora PostgreSQL besides the price of Intel-based R6id and Graviton-based R6gd instances. For more information, visit the Aurora pricing page.

The minimum storage is 10 GiB. Based on your database usage, your Aurora storage will automatically grow, up to 256 TiB, in 10 GiB increments with no impact to database performance. There is no need to provision storage in advance. Aurora offers automated horizontal scaling with Amazon Aurora PostgreSQL Limitless Database, which scales storage beyond 256 TiB. To learn more, visit Using Aurora PostgreSQL Limitless Database.

There are three ways to scale the compute resources associated with your Amazon Aurora DB — using Amazon Aurora Serverless, Aurora PostgreSQL Limitless Database, or manual scaling. Regardless of which option you choose, you only pay for what you use.

You can use Aurora Serverless, an on-demand, autoscaling configuration for Aurora to scale database compute resources based on application demand. It helps you 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.

With Aurora PostgreSQL Limitless Database, you can automatically scale your compute resources horizontally based on your workload requirement to support high-scale applications. It helps you scale your applications beyond the write throughput and storage limits of a single database instance while maintaining the simplicity of operating inside a single database. 

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.

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%. 
• 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.

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.

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.

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 can use a mixed configuration of provisioned or serverless instance class types between your primary and secondary Regions. You can also configure your primary Region as the Aurora I/O-Optimized cluster configuration and your secondary Regions as Aurora Standard or the reverse. To learn more, visit Creating an 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 use the managed cross-region Aurora Global Database failover operation to promote a secondary Region to take full read and write capability. You can also use the Aurora Global Database writer endpoint to avoid the need to make application code changes to connect to the newly promoted Region. To learn more, visit Connecting to an Amazon Aurora Global Database.

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 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.

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.

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

In Aurora Serverless, database capacity is measured in Aurora Capacity Units (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.

Aurora PostgreSQL Limitless Database provides automated horizontal scaling to process millions of write transactions per second and manages petabytes of data while maintaining the simplicity of operating inside a single database. You can focus on building high-scale applications without having to build and maintain complex solutions for scaling your data across multiple database instances to support your workloads. Aurora PostgreSQL Limitless Database scales based on your application workload and you pay only for what your application consumes. To learn more, visit the Aurora PostgreSQL Limitless Database User Guide.

You should use Aurora PostgreSQL Limitless Database for applications that need to scale horizontally and require more write throughput or data storage capacity than a single Aurora database instance supports. For example, an accounting application can be horizontally partitioned by a user since each user’s accounting data is independent from the others. Aurora PostgreSQL Limitless Database automatically scales to support your largest and fastest growing applications. 

There are two existing features for scaling: Amazon Aurora Auto Scaling with Aurora Replicas and Aurora Serverless v2.

Aurora Replicas allow you to increase the read capacity of your Aurora cluster beyond the limits of what a single database instance can provide. Applications that can separate their read workload from their write workload can benefit from up to 15 read replicas to achieve higher overall read throughput. Aurora Replicas do not require the application to horizontally split their data. All data is available in every replica. Aurora Replicas do not increase the storage capacity of an Aurora cluster or write throughput.

Aurora Serverless v2 is an on-demand, vertical scaling configuration for Aurora that provides automatic scaling of database compute and memory based on application needs within the capacity constraints of a single compute instance. Aurora Serverless v2 is supported for both writer and reader instances. However, it does not increase the storage capacity of an Aurora cluster. If your application is designed to scale horizontally, Aurora PostgreSQL Limitless Database lets you scale the write throughput and storage capacity of your database beyond the limits of a single Aurora writer instance

Aurora PostgreSQL Limitless Database splits data across database instances using customer-specified values in a table column—also called the shard key. For example, a table storing user information might be split using the User-ID column as the shard key. Under the hood, Aurora PostgreSQL Limitless Database is a distributed deployment of serverless nodes. Nodes are either routers or shards. Routers manage the distributed nature of the database. Each shard stores a subset of your data, enabling parallel processing to achieve high write throughput.

As compute or storage requirements increase, Aurora first automatically scales up each instance and its associated storage and then scales out to serve the database workload for different shard key values. At any point, a shard key value is owned and served by a single serverless instance. When applications connect to Aurora PostgreSQL Limitless Database and issue a request, the request is first analyzed. Then, it is either sent to the compute instance that owns the shard key value specified by the request or a query across multiple instances is orchestrated.

Multiple compute instances, each serving distinct shard key values, can simultaneously serve application requests for the same Aurora PostgreSQL Limitless Database. Aurora PostgreSQL Limitless Database provides the same transaction semantics as single-writer Aurora PostgreSQL systems, removing the complexity of managing different transaction domains in your application.

Aurora PostgreSQL Limitless Database supports three types of tables that contain your data: sharded, reference, and standard.

Sharded tables: These tables are distributed across multiple shards. Data is split among the shards based on the values of designated columns in the table, called shard keys. They are useful for scaling the largest, most I/O-intensive tables in your application.

Reference tables: These tables copy data in full on every shard so that join queries can work faster by removing unnecessary data movement. They are commonly used for infrequently modified reference data, such as product catalogs and zip codes.

Standard tables: These tables are like regular Aurora PostgreSQL tables. Standard tables are all placed together on a single shard so join queries can work faster by removing unnecessary data movement. You can create sharded and reference tables from standard tables.

To learn more about PostgreSQL compatibility considerations, visit Aurora PostgreSQL Limitless Database requirements and considerations.

You can get started with Aurora PostgreSQL Limitless Database in the Amazon RDS console or Amazon APIs to create a new Aurora PostgreSQL cluster with the supported engine version. To learn more about getting started, visit Aurora PostgreSQL Limitless Database User Guide.

Your application connects to Aurora PostgreSQL Limitless Database the same way it would connect to a standard Aurora PostgreSQL cluster. You simply connect to the cluster endpoint. To learn more, visit Using Aurora PostgreSQL Limitless Database.

Yes, you might need to adjust your database schema to use Aurora PostgreSQL Limitless Database. All sharded tables are required to contain the shard key, so this data might need to be backfilled. For example, an accounting application may might split its data by user, using the User-ID column, since each user is independent from the others. While the user table itself naturally contains this
column, other tables might not, such as a table that holds the line items of invoices. Since these tables also need to be split by user to collocate the tables for optimal query performance, the User-ID column needs to be added to the table.

There are no naming constraints on the column that is used to split the data, but the column definition must match. You will need to add the shard key to application queries and you might also need to adjust your queries and transactions for optimal performance. For example, looking up an invoice using the Invoice-ID when the table is only split by User-ID would be slow because the query would need to execute on all database instances. However, if the query also specifies the User-ID, the query will be routed to the single database instance that contains all the orders for that User-ID, reducing the latency of the query.

Yes. You can choose a high availability option when you set compute redundancy to be greater than zero for your Aurora PostgreSQL Limitless Database, providing 99.99% availability. Each compute instance that stores and accesses data from your Aurora PostgreSQL Limitless Database can have one or two standbys that can take over requests if the primary is unavailable. The routers will automatically redirect the traffic for minimal impact on your application.

Aurora PostgreSQL Limitless Database is available for the Aurora I/O-Optimized cluster configuration with PostgreSQL 16.4 compatibility. Additional information regarding AWS Regional availability for Aurora PostgreSQL Limitless Database is available on the Aurora pricing page.

In Aurora PostgreSQL Limitless Database, database capacity is measured in ACUs. You pay a flat rate per second of ACU usage. Aurora I/O-Optimized configuration storage rates apply. For more information, visit the Aurora pricing page.

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.

Parallel Query is available for the MySQL 5.7 and MySQL 8.0 compatible version of Amazon Aurora.

Parallel Query is compatible with Aurora Serverless v2 and Backtrack.

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 Aurora Optimized Reads available for Aurora PostgreSQL is a new price-performance option that delivers up to 8x improved query latency and up to 30% cost savings compared to instances without it. It is ideal for applications with large datasets that exceed the memory capacity of a database instance.

Amazon Aurora Optimized Reads instances use local NVMe-based SSD block-level storage (available on Graviton-based r6gd and Intel-based r6id instances) to improve query latency of applications with data sets exceeding the memory capacity of a database instance. Optimized Reads includes performance enhancements such as tiered caching and temporary objects.

Tiered caching delivers up to 8x improved query latency and up to 30% cost savings for read-heavy, I/O-intensive applications such as operational dashboards, anomaly detection, and vector-based similarity searches. These benefits are realized by automatically caching data evicted from the in-memory database buffer cache onto local storage to speed up subsequent accesses of that data. Tiered caching is only available for Amazon Aurora PostgreSQL-Edition with the Aurora I/O-Optimized configuration.

Temporary objects achieve faster query processing by placing temporary tables generated by Aurora PostgreSQL on local storage, improving the performance of queries involving sorts, hash aggregations, high-load joins, and other data-intensive operations.

Amazon Aurora Optimized Reads for Aurora PostgreSQL offers customers with latency-sensitive applications and large working sets a compelling price-performance alternative to meet their business SLAs and do even more with their instances.

Amazon Aurora Optimized Reads is available on Intel-based R6id and Graviton-based R6gd instances. You can see Region availability for Aurora here.

Amazon Aurora Optimized Reads is available for the PostgreSQL-Compatible Edition of Aurora on R6id and R6gd instances. Supported engine versions are 15.4 and higher and 14.9 and higher on Aurora PostgreSQL.

Amazon Aurora Optimized Reads is unavailable on Aurora Serverless v2 (ASv2).

Yes, Amazon Aurora Optimized Reads is available with both configurations. On both configurations, Optimized Reads-enabled instances automatically map temporary tables to the NVMe-based local storage to improve the performance of analytical queries and index rebuilds.

For I/O intensive workloads which are read heavy, Optimized Reads-enabled instances on Aurora PostgreSQL configured to use Aurora I/O-Optimized automatically cache data evicted from memory on NVMe-based local storage to deliver up to 8x improved query latency and up to 30% cost savings compared to instances without it, for applications with large datasets that exceed the memory capacity of a database instance.

Customers can get started with Amazon Aurora Optimized Reads through the AWS Management Console, CLI, and SDK. Optimized Reads is available on all R6id and R6gd instances by default. To use this capability, customers can simply modify their existing Aurora database clusters to include R6id and R6gd instances, or create new database clusters using these instances. See the Amazon Aurora Optimized Reads documentation to get started.

Approximately 90% of the available local storage on R6id and R6gd instances is available for Optimized Reads, Aurora reserves 10% of the NVMe storage to reduce the impact of SSD write amplification. The allocation of the available storage depends on which Optimized Reads features are enabled.

When using Optimized Reads with both the Temporary Objects and the Tiered Caching features, the space available for temporary objects in local storage is equivalent to 2x the size of memory available on these database instances. This matches the current size of temporary object storage on Aurora PostgreSQL. The remaining local storage diskspace is available for caching data.

When using Optimized Reads only with the Temporary Objects feature, all available local storage diskspace is available for temporary objects. For example, when using an r6gd.8xlarge instance with both the Temporary Objects and Tiered Caching features, 534 GiB (2x memory capacity) are reserved for temporary objects, and 1054 GiB for tiered cache.

If the local storage fails, Aurora automatically performs a host replacement. In a multi-node database cluster, this triggers an in-region failover.

In the event of a database failover, the query latency will temporarily increase after failover. This latency increase will reduce over time and eventually catch up to the query latency prior to failover. This catch-up duration can be expedited by enabling cluster cache management (CCM). With CCM, customers can designate a specific Aurora PostgreSQL database instance as the failover target.

When CCM is enabled, the local storage cache of the designated failover target closely mirrors the local storage cache of the primary instance, reducing the catch-up time post failover. However, enabling CCM could impact the long-term efficacy of the local storage cache if the designated failover target is also being used to serve a read workload separate from the workload on the writer instance.

Therefore, customers running workloads that require a reader to be designated as the stand-by failover must enable CCM to increase their likelihood of quickly regaining their query latency post failover. Customers running separate workloads on their designated failover targets may want to balance their needs for immediate latency recovery post failover with long term effectiveness of the cache performance, prior to enabling CCM.

pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.

You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.

With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, chatbots, and customer service next best action recommendations based on successful transcripts or chat session dialogs, and more. 

Read our blog on vector database capabilities and learn how to store embeddings using the pgvector extension in an Aurora PostgreSQL database, create an interactive question answering chatbot, and use the native integration between pgvector and Aurora machine learning for sentiment analysis.

Yes. Aurora machine learning (ML) exposes ML models as SQL functions, allowing you to use standard SQL to call ML models, pass data to them, and return predictions as query results. pgvector requires vector embeddings to be stored in the database, which requires running the ML model on source text or image data to generate embeddings and then moving the embeddings in batch into Aurora PostgreSQL.

Aurora ML can make this a real-time process enabling embeddings to be kept up-to-date in Aurora PostgreSQL by making periodic calls to Amazon Bedrock or Amazon SageMaker which returns the most recent embeddings from your model.

Yes. There are two methods to integrate Amazon Aurora databases with Amazon Bedrock to power generative AI applications. First, Amazon Aurora ML provides access to foundation models available in Amazon Bedrock directly through SQL for both Aurora MySQL and Aurora PostgreSQL. Second, you can configure Aurora as your vector store in Amazon Bedrock Knowledge Bases in just one click and store embeddings generated from Bedrock on Aurora. Amazon Bedrock Knowledge Bases supports Aurora PostgreSQL as a vector store for use cases like Retrieval-Augmented Generation (RAG). Read our blog and documentation on Using Aurora PostgreSQL as a knowledge base for Amazon Bedrock.

Amazon Aurora PostgreSQL Optimized Reads with pgvector increases queries per second for vector search by up to 9x in workloads that exceed available instance memory. This is possible due to the tiered caching capability available in Optimized Reads that automatically caches data evicted from the in-memory database buffer cache onto local storage to speed up subsequent accesses of that data.

Read our blog and documentation on how to improve query performance for Aurora PostgreSQL with Aurora Optimized Reads.

You should use Amazon Aurora zero-ETL integration with Amazon Redshift when you need near real-time access to transactional data. This integration allows you to take advantage of Amazon Redshift ML with straightforward SQL commands.

Amazon Aurora MySQL-Compatible Edition zero-ETL integration with Amazon SageMaker enables bringing data from your operational databases and applications into your lakehouse in near real-time. With the lakehouse architecture of SageMaker, you can build an open lakehouse on your existing data investments, without changing your data architecture and use your preferred analytics tools and query engines such as SQL, Apache Spark, BI, and AI/ML tools.

Aurora zero-ETL integration with Amazon Redshift is available on the Aurora MySQL-Compatible Edition for Aurora MySQL 3.05.2 version (compatible with MySQL 8.0.32) and higher. Aurora zero-ETL integration with Amazon Redshift is available on the Aurora PostgreSQL-Compatible Edition for Aurora PostgreSQL 16.4 version and higher.

Aurora zero-ETL integration with Amazon SageMaker is available on the Aurora MySQL-Compatible Edition for Aurora MySQL 3.05.0 version (compatible with MySQL 8.0.32) and higher.

Visit Supported features in Aurora by AWS Region and Aurora DB engine to learn more about AWS Region availability for Aurora zero-ETL integrations.

Aurora zero-ETL integration with Amazon Redshift and Amazon SageMaker remove the need for you to build and maintain complex data pipelines. You can consolidate data from multiple tables from various Aurora database clusters to a single Amazon Redshift database cluster or the lakehouse architecture of SageMaker and run near real-time analytics and ML on operational data from Aurora.

Aurora zero-ETL integration with Amazon Redshift is compatible with Aurora Serverless v2. When using both Aurora Serverless v2 and Amazon Redshift Serverless you can generate near real-time analytics on transactional data without having to manage any infrastructure for data pipelines.

You can get started by using the Amazon RDS console to create the zero-ETL integration by specifying the Aurora source and target destination. Once the integration has been created, the Aurora database will be replicated to the target and you can start querying the data once initial seeding is completed. For more information, read the getting started guide for Aurora zero-ETL integrations with Amazon Redshift and Aurora zero-ETL integration with Amazon SageMaker.

Ongoing processing of data changes by zero-ETL integration is offered at no additional charge. You pay for existing resources used to create and process the change data generated as part of a zero-ETL integration. These resources could include:

• Additional I/O and storage used by enabling enhanced binlog
• Snapshot export costs for the initial data export to seed your databases
• Additional storage for storing replicated data
• Additional compute for processing data replication
• Cross-AZ data transfer costs for moving data from source to target

For more information, visit the Aurora pricing page.

Yes, you can manage and automate the configuration and deployment of resources needed for an Aurora zero-ETL integration using AWS CloudFormation. For more information, visit CloudFormation templates with the zero-ETL integration.

CloudWatch Database Insights is a monitoring and metrics solution that simplifies and enhances database troubleshooting. It automates telemetry collection, including metrics, logs, and traces, eliminating the need for manual setup and configuration. By consolidating this telemetry into Amazon CloudWatch, CloudWatch Database Insights provides a unified view of database performance and health.

Key benefits of CloudWatch Database Insights include:

1. Effortless Telemetry Collection: Automatically gathers database metrics, logs, and traces, minimizing setup time.
2. Curated Insights: Provides pre-built dashboards, alarms, and insights for monitoring and optimizing database performance, with minimal configuration needed to get started.
3. Unified CloudWatch View: Combines telemetry from multiple databases into one view for simplified monitoring.
4. AI/ML Capabilities: Uses AI/ML to detect anomalies, reducing manual troubleshooting efforts.
5. Application Context Monitoring: Allows users to correlate database performance with application performance.
6. Fleet and Instance-Level Views: Offers both high-level fleet monitoring and detailed instance views for root cause analysis.
7. Seamless AWS Integration: Integrates with Amazon CloudWatch Application Signals and AWS X- Ray, enabling comprehensive observability experience.

Amazon DevOps Guru for RDS is a 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.

CloudWatch Database Insights monitors Aurora resources and applications in real time and presents data through customizable dashboards. In contrast, Amazon DevOps Guru is a machine learning (ML) service that analyzes CloudWatch metrics to understand an application’s behavior over time, detect anomalies, and offer insights and recommendations for issue resolution. Additionally, DevOps Guru analyzes data from multiple sources, including AWS Config, AWS CloudFormation, and AWS X-Ray. You can use CloudWatch dashboards to monitor your DevOps Guru insights via the metrics published in the AWS/DevOps-Guru namespace. This helps you to view all insights and anomalies under a single pane of glass view in CloudWatch console.

RDS Performance Insights is a database performance tuning and monitoring feature which allows customers to assess the load on their database and determine when and where to take action. CloudWatch Database Insights is a new database observability feature that inherits all the capabilities of Performance Insights along with fleet-level monitoring, integration with application performance monitoring, and correlation of database metrics with logs and events.

You should use Data API for new modern applications, particularly those built with AWS Lambda that need to access Aurora in a request/response model. You should use database drivers instead of Data API and manage persistent database connections when an existing application is highly coupled with database drivers, when there are long-running queries, or when the developer wants to take advantage of database features such as temporary tables or use session variables.

Data API AWS Region and database version availability for Aurora Serverless v2 and Aurora provisioned instances may be found in our documentation.

Data API will enable you to simplify and accelerate modern application development. Data API is an easy-to-use secure HTTP based API that eliminates the need to deploy database drivers, manage client-side connection pools, or set up complex VPC networking between the application and the database. Data API also improves scalability by automatically pooling and sharing database connections, which reduces computational overhead from applications that open and close connections frequently. 

Data API for Aurora Serverless v2 and Aurora provisioned instances support Aurora Global Database writer instances.

Users can invoke Data API operations only if they are authorized to do so. Administrators can give a user permission to use the Data API by attaching an AWS Identity and Access Management (IAM) policy that defines their privileges. You can also attach the policy to a role if you're using IAM roles. When you call the Data API, you can pass credentials for the Aurora DB cluster by using a secret in AWS Secrets Manager. 

Data API for Aurora Serverless v2 and Aurora provisioned instances is priced by API request volume as described on the Aurora pricing page. Data API for Aurora Serveless v2 and Aurora provisioned instances uses AWS CloudTrail data plane events to log activity instead of management events.

You may enable data events logging through the CloudTrail console, CLI, or SDK if you want to track this activity. This will incur charges as set forth on the CloudTrail pricing page. Additionally, the use of AWS Secrets Manager will incur charges as set forth on the AWS Secrets Manager pricing page. 

AWS CloudTrail captures AWS API activity as management events or data events. CloudTrail management events (also known as "control plane operations") show management operations that are performed on resources in your AWS account, such as create, update, and delete a resource. CloudTrail data events (also known as "data plane operations") show the resource operations performed on or within a resource in your AWS account.

Data API performs data plane operations since it performs queries on data within your Aurora database. Therefore, we will be logging Data API activity as data events as this is the correct categorization of the events. Charges will only be incurred for CloudTrail data events if you enable data events logging.

Yes, the Data API free tier includes one million requests per month, aggregated across all AWS Regions, for the first year’s usage. After one year, customers will begin paying for Data API as described on the Aurora pricing page.

Amazon RDS Blue/Green Deployments are available in Amazon Aurora MySQL-Compatible Edition versions 5.6 and higher and Amazon Aurora PostgreSQL-Compatible Edition versions 11.21 and higher, 12.16 and higher, 13.12 and higher, 14.9 and higher, and 15.4 and higher. Learn more about available versions in the Aurora documentation.

Amazon RDS Blue/Green Deployments are available in all applicable AWS Regions and the AWS GovCloud Regions.

Amazon RDS Blue/Green Deployments allow you to make safer, simpler, and faster database changes. Blue/Green Deployments are ideal for use cases such as major or minor version database engine upgrades, operating system updates, schema changes on green environments that do not break logical replication, like adding a new column at the end of a table, or database parameter setting changes.

You can use Blue/Green Deployments to make multiple database updates at the same time using a single switchover. This allows you to stay current on security patches, improve database performance, and access newer database features with short, predictable downtime. If you are looking to perform just a minor version upgrade on Aurora, we recommend that you use Aurora Zero Downtime Patching (ZDP).

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.

If your blue environment is a self-managed logical replica, or subscriber, we will block switchover. We recommend that you first stop replication to the blue environment, proceed with the switchover, and then resume replication. In contrast, if your blue environment is a source for a self-managed logical replica, or publisher, you can continue to switchover. However, you will need to update the self-managed replica to replicate from the green environment post switchover.

No, Amazon RDS Blue/Green Deployments do not support Amazon Aurora Global Databases, Amazon RDS Proxy, or cross-Region read replicas. 

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

Trusted Language Extensions (TLE) for PostgreSQL enables developers to build high performance PostgreSQL extensions and run them safely on Amazon Aurora. In doing so, TLE improves your time to market and removes the burden placed on database administrators to certify custom and third-party code for use in production database workloads. You can move forward as soon as you decide an extension meets your needs. With TLE, independent software vendors (ISVs) can provide new PostgreSQL extensions to customers running on Aurora.

PostgreSQL extensions are executed in the same process space for high performance. However, extensions might have software defects that can crash the database.

TLE for PostgreSQL offers multiple layers of protection to mitigate this risk. TLE is designed to limit access to system resources. The rds_superuser role can determine who is permitted to install specific extensions. However, these changes can only be made through the TLE API. TLE is designed to limit the impact of an extension defect to a single database connection. In addition to these safeguards, TLE is designed to provide DBAs in the rds_superuser role fine-grained, online control over who can install extensions and they can create a permissions model for running them. Only users with sufficient privileges will be able to run and create using the “CREATE EXTENSION” command on a TLE extension. DBAs can also allow-list “PostgreSQL hooks” required for more sophisticated extensions that modify the database’s internal behavior and typically require elevated privilege.

TLE for PostgreSQL is available for Amazon Aurora PostgreSQL-Compatible Edition on versions 14.5 and higher. TLE is implemented as a PostgreSQL extension itself and you can activate it from the rds_superuser role similar to other extensions supported on Aurora.

You can run TLE for PostgreSQL in PostgreSQL 14.5 or higher in Amazon Aurora.

TLE for PostgreSQL is currently available in all AWS Regions (excluding AWS China Regions) and the AWS GovCloud Regions.

TLE for PostgreSQL is available to Aurora customers at no additional cost.

Aurora and Amazon RDS support a curated set of over 85 PostgreSQL extensions. AWS manages the security risks for each of these extensions under the AWS shared responsibility model. The extension that implements TLE for PostgreSQL is included in this set. Extensions that you write or that you obtain from third-party sources and install in TLE are considered part of your application code. You are responsible for the security of your applications that use TLE extensions.

You can build developer functions, such as bitmap compression and differential privacy (such as publicly accessible statistical queries that protect privacy of individuals).

TLE for PostgreSQL currently supports JavaScript, PL/pgSQL, Perl, and SQL.

Once the rds_superuser role activates TLE for PostgreSQL, you can deploy TLE extensions using the SQL CREATE EXTENSION command from any PostgreSQL client, such as psql. This is similar to how you would create a user-defined function written in a procedural language, such as PL/pgSQL or PL/Perl. You can control which users have permission to deploy TLE extensions and use specific extensions.

TLE for PostgreSQL access your PostgreSQL database exclusively through the TLE API. The TLE supported trusted languages include all functions of the PostgreSQL server programming interface (SPI) and support for PostgreSQL hooks, including the check password hook.

You can learn more about the TLE for PostgreSQL project on the official TLE GitHub page.