What You Need to Know About Amazon Aurora: A Comprehensive Guide
Amazon Aurora Looking Deeper
Amazon Aurora is a relational database service offered by Amazon Web Services (AWS) that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. It is fully compatible with MySQL and PostgreSQL engines, which are widely used for online transaction processing (OLTP) workloads. In this article, we will look deeper into what Amazon Aurora is, how it works, what are its features and benefits, how it compares to other databases, how to use it, and what are some of its use cases and customers.
Amazon Aurora – Looking Deeper
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How Amazon Aurora works with Amazon RDS
Amazon Aurora is part of the managed database service Amazon Relational Database Service (Amazon RDS), which makes it easier to set up, operate, and scale a relational database in the cloud. You can choose Aurora as the DB engine option when creating a new database server through Amazon RDS. You can also migrate your existing MySQL or PostgreSQL databases to or from Aurora using standard tools or services like AWS Database Migration Service (DMS).
Aurora takes advantage of the familiar Amazon RDS features for management and administration, such as:
Automated backups and snapshots
Point-in-time recovery
Monitoring and metrics
Scaling and load balancing
Patching and maintenance
Security groups and roles
Event notifications
Parameter groups
Option groups
Aurora also provides some additional features that are specific to its architecture and design, such as:
Aurora DB clusters
Aurora versions
Aurora connection management
Aurora DB instance classes
Aurora storage subsystem
Aurora security
Aurora high availability
Aurora replication
Aurora serverless
Aurora global database
Amazon Aurora storage and reliability
One of the key features of Amazon Aurora is its high-performance storage subsystem that is designed for OLTP workloads. The storage subsystem consists of a cluster volume that spans multiple Availability Zones (AZs) in a single AWS Region. The cluster volume is composed of hundreds of segments, each 10 GB in size. Each segment is replicated six times across three AZs, resulting in 11 nines (99.999999999%) of durability and an annual failure rate (AFR) of 0.1%.
The storage subsystem is also self-healing and can automatically detect and repair disk failures, data corruption, and network errors. It can also perform crash recovery and point-in-time recovery without affecting the availability or performance of the database. The storage subsystem is also scalable and can grow automatically as needed, up to a maximum size of 128 tebibytes (TiB).
Aurora also provides continuous backups to Amazon Simple Storage Service (Amazon S3) with no performance impact. You can restore your database from any backup or snapshot within minutes. You can also copy or share your backups or snapshots across AWS accounts or Regions.
Amazon Aurora performance and availability
Another key feature of Amazon Aurora is its high throughput and low latency that can handle millions of transactions per second with sub-millisecond response times. Aurora achieves this by optimizing the database engine for the cloud and leveraging the fast distributed storage. Aurora also uses a quorum-based commit protocol that only requires four out of six replicas to acknowledge a write operation, reducing the network latency and increasing the write throughput.
Aurora also provides high availability and fault tolerance by deploying multiple DB instances across multiple AZs in a single AWS Region. An Aurora DB cluster consists of one primary DB instance that handles read and write operations, and up to 15 read replicas that handle read-only operations. The read replicas share the same underlying storage as the primary DB instance, eliminating the need for data replication and synchronization. The read replicas also have their own endpoints and load balancers, allowing you to distribute your read workload across them.
If the primary DB instance fails, Aurora can automatically fail over to one of the read replicas within 30 seconds, minimizing the downtime and data loss. You can also enable multi-AZ deployment for your Aurora DB cluster, which creates a standby replica in a different AZ from the primary DB instance. The standby replica is constantly updated with the changes from the primary DB instance, and can take over as the new primary in case of a failure.
Amazon Aurora compatibility and migration
Aurora supports two major open-source database engines: MySQL and PostgreSQL. You can choose either Aurora MySQL or Aurora PostgreSQL as the DB engine option when creating a new database server through Amazon RDS. Aurora MySQL is compatible with MySQL 5.6, 5.7, and 8.0 versions, while Aurora PostgreSQL is compatible with PostgreSQL 9.6, 10, 11, 12, and 13 versions.
Aurora is designed to be fully compatible with MySQL and PostgreSQL engines, which means that you can use the same code, tools, and applications that you use today with your existing databases. You can also migrate your data from MySQL or PostgreSQL databases to Aurora using standard tools like mysqldump, pg_dump, or AWS DMS. You can also migrate your data from other databases like Oracle or SQL Server to Aurora using AWS DMS or AWS Schema Conversion Tool (SCT).
Aurora also supports a new feature called Babelfish for Aurora PostgreSQL, which enables you to run SQL Server applications on Aurora PostgreSQL with minimal code changes. Babelfish is a translation layer that converts SQL Server queries into PostgreSQL queries, and SQL Server data types into PostgreSQL data types. Babelfish allows you to migrate your SQL Server applications to Aurora PostgreSQL without rewriting them or using complex middleware.
Amazon Aurora security and compliance
Aurora protects your data with various security features, such as:
Encryption at rest: You can encrypt your data at rest using keys managed by AWS Key Management Service (AWS KMS) or your own keys stored in AWS CloudHSM.
Encryption in transit: You can encrypt your data in transit using SSL/TLS protocols.
Authentication: You can authenticate your users using passwords, IAM roles, Kerberos, or Active Directory.
Authorization: You can authorize your users using grants, roles, privileges, or policies.
Auditing: You can audit your user activity using logs, events, or AWS CloudTrail.
Aurora also complies with various standards and regulations, such as:
PCI DSS
HIPAA
ISO
SOC
FIPS
FedRAMP
GDPR
CIS
Amazon Aurora serverless and global database
Aurora offers two innovative features that enable you to build flexible and scalable applications: serverless and global database.
Aurora serverless allows you to run your database without managing any database capacity. You simply create a database and specify the minimum and maximum capacity range, and Aurora serverless automatically starts up, shuts down, and scales up or down based on your application's needs. You pay only for the capacity you consume on a per-second basis, and you can save up to 90% of your database cost compared to provisioning for peak load.
Aurora serverless is ideal for applications that have infrequent, intermittent, or unpredictable workloads, such as development and test environments, websites, and applications that experience sudden spikes or drops in traffic. Aurora serverless can scale from hundreds to hundreds of thousands of transactions in a fraction of a second, with no impact on performance or availability. Aurora serverless also supports the full breadth of Aurora features, including cloning, global database, Multi-AZ deployments, and read replicas.
Amazon Aurora global database
Aurora global database allows you to create a single Aurora database that spans multiple AWS Regions. You can have one primary AWS Region where you can perform read and write operations, and up to five secondary AWS Regions where you can perform read-only operations. The data is replicated from the primary AWS Region to the secondary AWS Regions with low latency using dedicated infrastructure that leaves your database resources fully available to serve your application.
Aurora global database is useful for applications that require global reach, disaster recovery, or low latency reads. For example, you can use Aurora global database to serve your customers across the world with fast local reads, or to fail over to a secondary AWS Region in case of a regional outage. You can also promote a secondary AWS Region to become the new primary AWS Region within minutes.
Amazon Aurora use cases and customers
Aurora supports various types of applications and workloads for different industries and sectors. Some of the common use cases for Aurora are:
Modernize enterprise applications: You can run your enterprise applications, such as customer relationship management (CRM), enterprise resource planning (ERP), supply chain, and billing applications, on Aurora with high availability and performance.
Build SaaS applications: You can support reliable, high-performance, and multi-tenant Software-as-a-Service (SaaS) applications on Aurora with flexible instance and storage scaling.
Deploy globally distributed applications: You can develop internet-scale applications, such as mobile games, social media apps, and online services, that require multi-Region scalability and resilience on Aurora with global database.
Go serverless: You can run your infrequently-used or variable workloads on Aurora with serverless compute and pay only for the capacity you consume.
Some of the customers who use Aurora are:
Netflix: Netflix uses Aurora to power its billing infrastructure that handles millions of customer accounts across 190 countries.
Dow Jones: Dow Jones uses Aurora to migrate its legacy Oracle databases to the cloud and reduce its operational costs by 90%.
Expedia: Expedia uses Aurora to scale its hotel-booking platform that serves over 150 million travelers per month.
ViacomCBS: ViacomCBS uses Aurora to power its streaming service Paramount+ that delivers live TV and on-demand content to millions of subscribers.
Amazon Aurora pricing and free tier
Aurora charges you for the following components:
DB instances: You pay for the compute resources that you use for your DB instances on an hourly basis. The price depends on the DB instance class, the DB engine option, and the AWS Region.
Storage: You pay for the storage space that you use for your cluster volume on a per-GB-month basis. The price depends on the DB engine option and the AWS Region.
Backup: You pay for the backup storage that you use for your automated backups and snapshots on a per-GB-month basis. The price depends on the DB engine option and the AWS Region.
Data transfer: You pay for the data transferred in or out of your DB instances on a per-GB basis. The price depends on the source and destination of the data transfer.
Other features: You pay for other features that you use with your DB instances, such as encryption, performance insights, enhanced monitoring, and custom endpoints on a per-hour or per-GB basis. The price depends on the feature and the AWS Region.
You can use the AWS Pricing Calculator to estimate your monthly bill for using Aurora. You can also use the Amazon RDS Free Tier to try Aurora for free for up to 12 months. The free tier includes 750 hours of Aurora Single-AZ db.t2.small DB instances, 20 GB of Aurora storage, and 10 GB of backup storage per month.
How to get started with Amazon Aurora
To get started with Amazon Aurora, you need to create an Aurora DB cluster, connect to it, and run queries using the AWS Management Console or the AWS Command Line Interface (AWS CLI). The following steps show you how to do that:
Sign in to the AWS Management Console and open the Amazon RDS console.
Choose Create database and select Amazon Aurora as the engine option.
Choose the DB engine option (Aurora MySQL or Aurora PostgreSQL) and the edition (provisioned or serverless).
Specify the DB instance identifier, DB instance class, master username, and master password for your DB cluster.
Choose the VPC, subnet group, security group, and availability mode for your DB cluster.
Choose Create database to launch your DB cluster.
After your DB cluster is available, choose its name to view its details.
Choose Connect to connect to your DB cluster using a query editor or a SQL client.
Enter your master username, password, and database name to connect to your DB cluster.
Run SQL queries on your DB cluster using the query editor or the SQL client.
Conclusion
In this article, we looked deeper into Amazon Aurora, a relational database service that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. We learned about how Amazon Aurora works with Amazon RDS, how it provides fast and durable storage, how it delivers high throughput and low latency, how it supports MySQL and PostgreSQL engines, how it protects data with encryption and compliance, how it offers serverless compute and cross-Region replication, how it supports various types of applications and workloads, how it charges for different components and features, and how to create and use an Aurora DB cluster. We hope that this article helped you to understand Amazon Aurora better and inspired you to try it out for your own projects.
FAQs
Here are some frequently asked questions about Amazon Aurora:
What are the differences between Aurora MySQL and Aurora PostgreSQL?
Aurora MySQL and Aurora PostgreSQL are two different DB engine options that you can choose when creating an Aurora DB cluster. They are both compatible with MySQL and PostgreSQL engines respectively, but they have some differences in terms of features, performance, pricing, and compatibility. For example, Aurora MySQL supports parallel query, global database, backtrack, fast database cloning, and Babelfish for SQL Server applications. Aurora PostgreSQL supports parallel query, global database, fast database cloning, logical replication, stored procedures, extensions, and PostgreSQL 13 features. For more details on the differences between Aurora MySQL and Aurora PostgreSQL, see Comparison of Amazon Aurora MySQL-Compatible Edition Features with MySQL Features and Comparison of Amazon Aurora PostgreSQL-Compatible Edition Features with PostgreSQL Features.
How does Aurora compare to other databases?
compatibility, security, and cost. Aurora is designed to be faster, more reliable, more scalable, more compatible, more secure, and more cost-effective than other databases in the cloud. For example, Amazon asserts Aurora is five times faster than standard MySQL databases and three times faster than PostgreSQL databases when used in the cloud. Comparing Amazon Aurora vs. SQL Server yields similar findings, since SQL Serverlike MySQLis designed to be used on-premises. Aurora also provides features like serverless, global database, and Babelfish that are not available in other databases.
However, Aurora may not be the best choice for every use case or scenario. Some of the reasons you might choose other databases over Aurora are:
You use a database engine that Aurora doesn't support. RDS supports more database engines and features than Aurora: RDS supports five database engines (MySQL, PostgreSQL, Oracle, SQL Server, and MariaDB), while Aurora supports only two (MySQL and PostgreSQL). RDS also supports features like Oracle Data Guard, SQL Server Always On Availability Groups, and MariaDB Galera Cluster that are not available in Aurora.
You have a low or fixed workload that doesn't require scaling. Aurora is optimized for high-performance and variable workloads that need to scale up or down quickly and frequently. If you have a low or fixed workload that doesn't need much scaling, you might be better off using a provisioned DB instance with RDS that has a fixed capacity and a lower cost.
You have strict latency or compliance requirements that limit your data location. Aurora is available only in certain AWS Regions and AZs, which may not meet your latency or compliance requirements for your data location. For example, if you need to store your data in a specific country or region that is not supported by Aurora, you might have to use another database that is available there.
How to monitor relational database performance
No matter which relational database you choose, monitoring its performance is an indispensable part of ensuring its functionality, reliability, and availability. Monitoring your database performance can help you to:
Detect and diagnose performance issues and bottlenecks
Optimize and tune your queries and indexes
Analyze and forecast your capacity and resource utilization
Identify and prevent potential failures and downtime
Improve your user experience and satisfaction
To monitor your relational database performance, you can use various tools and services provided by AWS or third-party vendors. Some of the common tools and services are:
Amazon RDS Performance Insights: This is a feature of Amazon RDS that helps you to quickly assess the load on your DB instances and determine when and where to take action. Performance Insights collects, analyzes, and displays database load data in an interactive dashboard that shows the breakdown of CPU utilization by SQL statements, waits, hosts, users, and applications.
Amazon CloudWatch: This is a service that monitors your AWS resources and applications in real time. You can use CloudWatch to collect metrics, logs, events, alarms, and dashboards for your DB instances and DB clusters. You can also use CloudWatch to set up alerts and notifications for when your metrics cross a threshold or when an event occurs.
AWS CloudTrail: This is a service that records AWS API calls for your account and delivers log files to you. You can use CloudTrail to track user activity and API usage for your DB instances and DB clusters. You can also use CloudTrail to audit changes made to your resources or configurations.
SolarWinds Database Performance Analyzer: This is a third-party tool that helps you to optimize the performance of your relational databases across multiple platforms. You can use Database Performance Analyzer to monitor real-time and historical performance metrics, identify root causes of performance issues, analyze query execution plans and wait events, compare performance trends over time, generate reports and recommendations, and more.
Relational database pricing comparison
such as the DB instance type, the DB engine option, the storage type and size, the backup and snapshot size, the data transfer amount, and the AWS Region. The pricing also varies depending on whether you use on-demand or reserved instances, and whether you use standard or serverless editions. To compare the pricing of different relational databas