AWS is the most popular DBaaS cloud service provider. Due to the increased demand for DBaaS, individuals and businesses are frequently required to choose between Aurora and RDS.
When deciding which database solution is ideal for your company, consider multiple aspects. First, we will discuss the similarities and differences between RDS and Amazon Aurora.
But what is DBaaS? That’s the main question. Let’s see the definition of DBaaS.
What is DBaaS?
Database-as-a-service (DBaaS) cloud services allow customers to access databases without setting up any physical infrastructure or installing any software.
What is AWS Aurora?
Aurora is a database offering of AWS that aims to give a high level of performance and dependability like any high-end commercial database platform, but with more convenience and reliability.
AWS Aurora is MySQL 5.6 compliant and provides five times the performance of MySQL on equivalent hardware. In addition, it saves time for DBAs when planning backup storage drives since it continually backs up data to AWS S3 in real-time without sacrificing performance. It also eliminates the requirement for backup windows and automated backup scripts.
Why should you choose AWS Aurora?
Some of the advantages of using AWS Aurora are:
Scalability
It adjusts the size of the database volume based on the amount of storage required. For example, the volume increases in 10 GB increments to 128 TB. This makes it easier to meet the storage requirements seamlessly.
Fully managed
Getting started with Aurora is a breeze. You have to use the AWS RDS interface to launch an instance or access an API from your code. Aurora uses Amazon Cloudwatch to offer monitoring at no additional cost. And no need to be concerned about upgrades or provisioning. Everything is under AWS’s control, and it does all software patching as necessary.
Encryption
Amazon Aurora provides strong security by allowing you to encrypt your database with keys created and managed using the AWS Key Management Service (KMS.) Data kept at rest in the underlying storage on an Amazon Aurora-enabled database instance is encrypted. In addition, SSL is used by Amazon Aurora to encrypt data in transit.
High throughput
Amazon Aurora employs software and hardware solutions to guarantee that the database engine can fully utilize available computation, memory, and networking. To boost performance consistency, input or output procedures employ distributed systems strategies such as quorums.
What is RDS?
Amazon Relational Database Service (RDS) stands for Amazon Relational Database Service. It makes it simple to set up, scale, and manage relational databases in the cloud.
It gives you more time to focus on your company and applications by providing a scalable and cost-effective capacity while executing database management activities. You may pick from six database engines: Amazon Aurora, MySQL, Oracle, MariaDB, PostgreSQL, and Microsoft SQL Server.
Why should you choose Amazon RDS?
Availability
Amazon RDS is a highly available database with a feature called Multi-AZ that ensures a 99.95 percent SLA uptime. Also, there is no risk of data loss since the database and its replica are in sync.
AWS also provides a domain name server (DNS) for RDS access. If the master database instance fails, an RDS automated failover mechanism will switch the master DNS to a replica to maintain high availability.
Performance
To achieve fast, consistent, and predictable I/O performance, AWS RDS offers Provisioned IOPS or PIOPS. This is appropriate for online transaction processing (OLTP) databases or applications with a lot of input/output.
You may set up as many as 30,000 IOPS using MySQL RDS. The more IOPS you set up, the more concurrent request processes you’ll have, which will boost throughput and lower latency, resulting in better database performance.
Backup
Amazon RDS has two types of backup mechanisms: automated and point-in-time snapshots, simple to set up. Automated backup creates a complete daily snapshot of a database’s data (you can choose the time slot).
It also keeps track of your transaction logs and any changes to your RDS database. Unlike automatic backups, which are only done once a day, point-in-time snapshots may be done as often as needed. For example, they’re often used to back up certain database states before a big release or an application upgrade.
AWS Aurora vs RDS: Main Difference
The main difference between AWS Aurora and RDS is that RDS architecture is like installing a database engine on Amazon EC2 and the provisioning and maintenance are handled by AWS, whereas Aurora database storage is built to be reliable and fault-tolerant. The database storage for Aurora is independent of the instances.
Many features, like automated failover and backups, are available with RDS. For database and log storage, RDS uses Amazon EBS volumes. You must enable the Multi-AZ capability on your RDS instance and duplicate it synchronously to a replica in another AZ. You must enable the Multi-AZ ability on your RDS instance and duplicate it synchronously to a replica in another AZ.
In Aurora, the data is replicated 6 times across three AZs. So, as a result, even if you only have one Aurora instance, your data will be duplicated six times.
RDS is a hosted database service that includes PostgreSQL, Aurora, MariaDB, and other relational database management systems. Aurora has even more tricks on its sleeve, although there are performance advantages to hosting databases on RDS for businesses.
AWS Aurora vs RDS: Performance
For greater IO throughput, RDS leverages SSDs in its database services. You may select between two SSD-backed storage choices with Amazon RDS, one geared for high-performance OLTP applications and the other that is cost-effective for general-purpose use.
On comparable hardware, Amazon Aurora provides two times the throughput of PostgreSQL and five times the throughput of regular MySQL. In addition, Aurora’s performance has improved and has become more constant. Aurora does not store log buffers and writes logs directly to storage. Also, RDS has an SLA of 99.95%, and Aurora has a 99.99% SLA.
AWS Aurora vs RDS: Scalability
Amazon RDS and Aurora both offer the ability to scale compute resources and memory up or down to 32 vCPUs and 244 GB of RAM. As a result, you can finish your compute scaling tasks in minutes with only a few clicks.
AWS Aurora vs RDS: Replication
AWS Aurora supports two types of replicas, which share the same volume as the parent instance. All Aurora clones are updated when the original instance is updated. You can have up to 15 replicas with Aurora, and replication takes just a few milliseconds. In addition, failover is handled automatically in Aurora to avoid data loss.
On the other hand, RDS only allows for five copies, and the replication procedure is slower than Amazon Aurora. Furthermore, failover in RDS is done manually, which might result in data loss at the last minute.
AWS Aurora vs RDS: Backup
Amazon RDS takes a daily snapshot of your database and preserves transaction logs to provide point-in-time recovery. The snapshot takes place during a backup window that you can set. Storage I/O may be halted while data is replicated during the snapshot, reducing database performance. Backups are stored to S3 and have a very long lifespan.
Amazon Aurora has incremental and automatic backup, so it does not impact performance. Furthermore, no regular snapshots are required for point-in-time recovery.
AWS Aurora vs RDS: Pricing
For the same workloads, Aurora is often more costly than RDS. It is charged according to the instance type, size, and EBS volume. Aurora cost is mainly determined by instance size, with storage charges dependent on actual usage. Remember, both platforms come at an additional expense.
AWS Aurora vs RDS: Database support
Database support refers to using your existing database tools and applications without making any changes. MySQL, Oracle, PostgreSQL, Microsoft SQL, and MariaDB are all supported by RDS. PostgreSQL and MySQL are both supported by Aurora.
Final words
Compared to Amazon RDS, Aurora’s unique design provides higher durability, scalability, robustness, and performance. As a result, Aurora is recommended for enterprise-level applications, despite the slight price increase. Aurora is an excellent choice if you need a native high, availability solution and a read-intensive workload.
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Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Machine Learning frameworks like PyTorch and Tensorflow is a testament to his versatility and commitment to the craft.