Q: What is Amazon Aurora?
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 TB 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).
Amazon Aurora is also a fully managed service that automates time-consuming administration tasks like hardware provisioning, database setup, patching, and backups while providing the security, availability, and reliability of commercial databases at 1/10th the cost.
Q: What does "MySQL compatible" mean?
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. When considering Aurora vs. MySQL, it is important to understand that the Amazon Aurora database engine is designed to be wire-compatible with MySQL 5.6 and 5.7 using the InnoDB storage engine. This makes it easy to move applications between the two engines. Certain MySQL features, like the MyISAM storage engine, are not available with Amazon Aurora.
Q: What does “PostgreSQL compatible” mean?
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.
Q: How should I think about Amazon Aurora vs. Amazon Relational Database Service (Amazon RDS)?
A: Amazon RDS is a fully managed, highly available, and secure database service that makes it simple to set up, operate, and run your choice of seven relational database engines: PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, Amazon Aurora MySQL-Compatible Edition, and Amazon Aurora PostgreSQL-Compatible Edition.
Amazon Aurora MySQL-Compatible Edition and Amazon Aurora PostgreSQL-Compatible Edition take advantage of the benefits of Amazon RDS, including automating time consuming database administrative tasks, and provide improved performance, scalability, and availability compared to community open-source MySQL and PostgreSQL.
Q: How do I try Amazon Aurora?
To try Amazon Aurora, sign in to the AWS Management Console, select RDS under the Database category, and choose Amazon Aurora as your database engine.
Q: How much does Amazon Aurora cost?
Please see our Aurora pricing page for current pricing information.
Q: Amazon Aurora replicates each chunk of my database volume six ways across three Availability Zones. Does that mean that my effective storage price will be three or six times what is shown on the pricing page?
No. Amazon Aurora’s 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 Amazon Aurora’s virtualized storage layer.
Q: In which AWS regions is Amazon Aurora available?
Please see our Aurora pricing page for current information on regions and prices.
Q: How can I migrate from MySQL to Amazon Aurora and vice versa?
You have several options. You can use the standard mysqldump utility to export data from MySQL and mysqlimport utility to import data to Amazon Aurora, and vice-versa. You can also use Amazon RDS’s DB Snapshot migration feature to migrate an Amazon RDS for MySQL DB Snapshot to Amazon Aurora using the AWS Management Console. Migration completes for most customers in under an hour, though the duration depends on format and data set size. For more information see Best Practices for Migrating MySQL Databases to Amazon Aurora.
Q: How can I migrate from PostgreSQL to Amazon Aurora and vice versa?
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 Amazon Aurora, and vice-versa. You can also use Amazon RDS’s DB Snapshot migration feature to migrate an Amazon RDS for PostgreSQL DB Snapshot to Amazon Aurora using the AWS Management Console. Migration completes for most customers in under an hour, though the duration depends on format and data set size. To migrate SQL Server applications to Aurora PostgreSQL-Compatible Edition, you can use Babelfish for Aurora PostgreSQL. See the Babelfish feature page for more information.
Q: Does Amazon Aurora participate in the AWS Free Tier?
Not at this time. The AWS Free Tier for Amazon RDS offers benefits for Micro DB Instances; Amazon Aurora does not currently offer Micro DB Instance support. Please see the Aurora pricing page for current pricing information.
Q: What are I/Os in Amazon Aurora and how are they calculated?
I/Os are input/output operations performed by the Aurora database engine against its solid state drive (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 in order 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 Aurora MySQL-Compatible Edition and 8 KB in Aurora PostgreSQL-Compatible Edition.
Aurora was designed to eliminate unnecessary I/O operations in order to reduce costs and to ensure resources are available for serving read/write traffic. Write I/Os 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/Os are counted in 4 KB units. For example, a log record that is 1024 bytes will count as one write I/O operation. However, if the log record is larger than 4 KB, more than one write I/O operation will be needed to persist it. Concurrent write operations whose log records are less than 4 KB may be batched together by the Aurora database engine in order to optimize I/O consumption, if they are persisted on the same storage protection groups. 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. You can see how many I/O requests your Aurora instance is consuming by checking the 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.
Q: Do I need to change client drivers to use Amazon Aurora PostgreSQL-Compatible Edition?
No, Amazon Aurora will work with standard PostgreSQL database drivers.
Q: What does "five times the performance of MySQL" mean?
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.
Q: What does "three times the performance of PostgreSQL" mean?
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.
Q: How do I optimize my database workload for Amazon Aurora MySQL-Compatible Edition?
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.
Q: How do I optimize my database workload for Amazon Aurora PostgreSQL-Compatible Edition?
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.
Hardware and Scaling
Q: What are the minimum and maximum storage limits of an Amazon Aurora database?
The minimum storage is 10 GB. Based on your database usage, your Amazon Aurora storage will automatically grow, up to 128 TB, in 10 GB increments with no impact to database performance. There is no need to provision storage in advance.
Q: How do I scale the compute resources associated with my Amazon Aurora DB Instance?
You can scale the compute resources allocated to your DB Instance in the AWS Management Console by selecting the desired DB Instance and clicking the Modify button. Memory and CPU resources are modified by changing your DB Instance class.
When you modify your DB Instance class, your requested changes will be applied during your specified maintenance window. Alternatively, you can use the "Apply Immediately" flag to apply your scaling requests immediately. Both of these options will have an availability impact for a few minutes as the scaling operation is performed. Bear in mind that any other pending system changes will also be applied.
Backup and Restore
Q: How do I enable backups for my DB Instance?
Automated backups are always enabled on Amazon Aurora DB Instances. Backups do not impact database performance.
Q: Can I take DB Snapshots and keep them around as long as I want?
Yes, and there is no performance impact when taking snapshots. Note that restoring data from DB Snapshots requires creating a new DB Instance.
Q: If my database fails, what is my recovery path?
Amazon Aurora automatically maintains six copies of your data across three Availability Zones (AZs) 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.
Q: What happens to my automated backups and DB Snapshots if I delete my DB Instance?
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).
Q: Can I share my snapshots with another AWS account?
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.
Q: Will I be billed for shared snapshots?
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.
Q: Can I automatically share snapshots?
We do not support sharing automatic DB snapshots. To share an automatic snapshot, you must manually create a copy of the snapshot, and then share the copy.
Q: How many accounts can I share snapshots with?
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.
Q: In which regions can I share my Aurora snapshots?
You can share your Aurora snapshots in all AWS regions where Aurora is available.
Q: Can I share my Aurora snapshots across different regions?
No. Your shared Aurora snapshots will only be accessible by accounts in the same region as the account that shares them.
Q: Can I share an encrypted Aurora snapshot?
Yes, you can share encrypted Aurora snapshots.
High Availability and Replication
Q: How does Amazon Aurora improve my database’s fault tolerance to disk failures?
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.
Q: How does Aurora improve recovery time after a database crash?
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.
Q: What kind of replicas does Aurora support?
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:
|Feature||Amazon Aurora Replicas
|Number of replicas||Up to 15||Up to 5|
|Replication type||Asynchronous (milliseconds)||Asynchronous (seconds)|
|Performance impact on primary||Low||High|
|Act as failover target||Yes (no data loss)||Yes (potentially minutes of data loss)|
|Support for user-defined replication delay||No||Yes|
|Support for different data or schema vs. primary||No||Yes|
You have two additional replication options in addition to the ones listed above. You can use Amazon Aurora 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.
Q: Can I have cross-region replicas with Amazon Aurora?
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.
Q: Can I create Aurora Replicas on the cross-region replica cluster?
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.
Q: Can I fail over my application from my current primary to the cross-region replica?
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.
Q: Can I prioritize certain replicas as failover targets over others?
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.
Q: Can I modify priority tiers for instances after they have been created?
Yes, you can modify the priority tier for an instance at any time. Simply modifying priority tiers will not trigger a failover.
Q: Can I prevent certain replicas from being promoted to the primary instance?
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.
Q: How can I improve upon the availability of a single Amazon Aurora database?
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.
Q: What happens during failover and how long does it take?
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 Amazon Aurora Replica, in the same or a different Availability Zone, when failing over, Aurora flips the canonical name record (CNAME) for your DB Instance to point at the healthy replica, which is in turn promoted to become the new primary. Start-to-finish, failover typically completes within 30 seconds.
- If you are running Aurora Serverless and the DB instance or AZ become unavailable, Aurora will automatically recreate the DB instance in a different AZ.
- If you do not have an Amazon 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.
Q: If I have a primary database and an Amazon Aurora Replica actively taking read traffic and a failover occurs, what happens?
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.
Q: How far behind the primary will my replicas be?
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 MySQL Read Replicas, the 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 logical replication will be influenced by the change/apply rate and delays in network communication between the specific regions selected. Cross-region replicas using Amazon Aurora Global Database will have a typical lag of under a second.
Q: Can I set up replication between my Aurora MySQL-Compatible Edition database and an external MySQL database?
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.
Q: What is Amazon Aurora Global Database?
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.
Q: How do I create an Amazon Aurora Global Database?
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.
Q: How many secondary regions can an Amazon Aurora Global Database have?
You can create up to five secondary regions for an Amazon Aurora Global Database.
Q: If I use Amazon Aurora Global Database, can I also use logical replication (binlog) on the primary 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.
Q: Will Aurora automatically fail over to a secondary region of an Amazon Aurora Global 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.
Q: What is Amazon Aurora Multi-Master?
Amazon Aurora Multi-Master is a new Aurora MySQL-Compatible Edition feature that adds the ability to scale out write performance across multiple Availability Zones, allowing applications to direct read/write workloads to multiple instances in a database cluster and operate with higher availability.
Q: How can I get started with Amazon Aurora Multi-Master?
Amazon Aurora Multi-Master is now generally available. You can read the Amazon Aurora documentation to learn more. You can create an Aurora Multi-Master cluster with just a few clicks in the Amazon RDS console or download the latest AWS SDK or CLI.
Q: Can I use Amazon Aurora in Amazon Virtual Private Cloud (Amazon VPC)?
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.
Q: Does Amazon Aurora encrypt my data in transit and at rest?
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.
Q: Can I encrypt an existing unencrypted database?
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.
Q: How do I access my Amazon Aurora database?
Amazon 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.
Q: Can I use Amazon Aurora with applications that require HIPAA compliance?
Yes, the MySQL- and PostgreSQL-compatible editions of Aurora are Health Insurance Portability and Accountability Act (HIPAA)-eligible, so you can use them to build HIPAA-compliant applications and store healthcare related information, including protected health information (PHI) under an executed Business Associate Agreement (BAA) with AWS. If you already have an executed BAA, no action is necessary to begin using these services in the account(s) covered by your BAA. For more information about building compliant applications on AWS, see Healthcare Providers & Insurers in the Cloud.
Q: Where can I access a list of Common Vulnerabilities and Exposures (CVE) entries for publicly known cybersecurity vulnerabilities for Amazon Aurora releases?
You can currently find a list of CVEs at Amazon Aurora Security Updates.
Q: What is Amazon Aurora Serverless?
Amazon Aurora Serverless is an on-demand, auto-scaling configuration that automatically adjusts database capacity based on application needs. With Aurora Serverless, you only pay for the database capacity, storage, and I/O your database consumes when it is active. Your database capacity automatically scales up or down to meet your application workload needs and shuts down during periods of inactivity, saving you money and administration time. Aurora Serverless measures database capacity in Aurora Capacity Units (ACUs) billed per second. One ACU has approximately 2 GB of memory with corresponding CPU and networking, similar to what is used in Aurora provisioned instances.
Aurora Serverless v2, currently in preview, instantly scales databases to support hundreds of thousands of transactions per second and supports all features of Aurora, including Multi-AZ deployments, Read Replicas, and Global Database. It is suitable for all manner of relational database workloads, up to and including the most demanding, business critical applications.
Amazon Aurora Serverless v1 is a simple, cost-effective option for infrequent, intermittent, or unpredictable workloads.
Q: Which versions of Amazon Aurora are supported for Aurora Serverless?
Aurora Serverless v1 is currently available for Aurora with MySQL 5.6 compatibility and for Aurora with PostgreSQL 10.7+ compatibility. Aurora Serverless v2 is currently available in preview for Aurora MySQL-Compatible Edition.
Q: Can I migrate an existing Aurora DB cluster to Aurora Serverless?
Yes, you can restore a snapshot taken from an existing Aurora provisioned cluster into an Aurora Serverless DB Cluster (and vice versa).
Q: How do I connect to an Aurora Serverless DB cluster?
You access an Aurora Serverless DB cluster from within a client application running in the same VPC. You can't give an Aurora Serverless DB cluster a public IP address.
Q: Can I explicitly set the capacity of an Aurora Serverless cluster?
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.
Q: Why isn't my Aurora Serverless DB Cluster automatically scaling?
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.
Q: How am I billed for Aurora Serverless?
In Aurora Serverless, database capacity is measured in Aurora Capacity Units (ACUs). You pay a flat rate per second of ACU usage, with a minimum of five minutes of usage each time the database is activated. Storage and I/O prices are the same for provisioned and Serverless configurations. View an Aurora Serverless pricing example.
Q: What is Amazon Aurora Parallel Query?
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.
Q: What's the target use case?
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.
Q: What benefits does Parallel Query provide?
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.
Q: What specific queries improve under Parallel Query?
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.
Q: What performance improvement can I expect?
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.
Q: Is there any chance that performance will be slower?
Yes, but we expect such cases to be rare.
Q: What changes do I need to make to my query to take advantage of Parallel Query?
No changes in query syntax are 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.
Q: How do I turn the feature on or off?
Parallel Query can be enabled and disabled dynamically at both the global and session level using the aurora_pq parameter.
Q: Are there any additional charges associated with using Parallel Query?
No. You aren’t charged for anything other than what you already pay for instances, I/O, and storage.
Q: Since Parallel Query reduces I/O, will turning it on reduce my Aurora IO charges?
No, I/O ince Parallel Query reduces 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.
Q: What versions of Amazon Aurora support Parallel Query?
Parallel Query is available for the MySQL 5.6-compatible version of Amazon Aurora, starting with v1.18.0. We plan to extend Parallel Query to Aurora with MySQL 5.7 compatibility, and to Aurora PostgreSQL-Compatible Edition.
Q: Is Parallel Query available with all instance types?
No. At this time, you can use Parallel Query with instances in the R* instance family.
Q: Is Parallel Query compatible with all other Aurora features?
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.
Q: If Parallel Query speeds up queries with only rare performance losses, should I simply turn it on all the time?
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.
Q: Can Aurora Parallel Query replace my data warehouse?
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.
Amazon DevOps Guru for RDS
Q: What is Amazon DevOps Guru for RDS?
Amazon DevOps Guru for RDS is a new ML-powered capability for Amazon RDS 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 notifiy 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.
Q: Why should I use DevOps Guru for RDS?
Amazon DevOps Guru for RDS is designed to remove manual effort and shortens 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.
Q: How does Amazon DevOps Guru for RDS work?
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.
Q: How can I get started with Amazon DevOps Guru for RDS?
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.
Q: What types of issues can Amazon DevOps Guru for RDS detect?
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.
Q: How is DevOps Guru for RDS different from Amazon RDS Performance insights?
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.