Amazon RDS Aurora.
Amazon aurora was used for audit purposes. The main purpose was to audit IoT device activities performed by end user. All the information …
Overview
What is Amazon Aurora?
Amazon Aurora is a global-scale relational database service built for the cloud with full MySQL and PostgreSQL compatibility.
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- Automated backups (25)9.494%
- Database scalability (26)9.494%
- Automatic software patching (26)8.989%
- Monitoring and metrics (25)8.787%
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What is Amazon Aurora?
Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, built to combine the performance and availability of enterprise databases with the simplicity and cost-effectiveness of open source databases. The vendor states Amazon Aurora is up to 5X faster than MySQL databases and 3X faster than PostgreSQL databases, and that it provides the security, availability, and reliability of commercial databases at 1/10th the cost.
Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones (AZs).
The vendor invites readers to learn more details on how they designed Amazon Aurora, from AWS CTO, Werner Vogels.
Amazon Aurora features a distributed, fault-tolerant, self-healing storage system that auto-scales up to 64TB per database instance. It delivers performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across three Availability Zones (AZs).
The vendor invites readers to learn more details on how they designed Amazon Aurora, from AWS CTO, Werner Vogels.
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How to create a first database cluster on Amazon Aurora.
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Amazon Aurora Availability
| Geography | NAMER, EMEA, APAC, LATAM |
|---|---|
| Supported Languages | English, French, Chinese, Korean, Japanese |
Frequently Asked Questions
Amazon Aurora is a global-scale relational database service built for the cloud with full MySQL and PostgreSQL compatibility.
Reviewers rate Database scalability and Automated backups highest, with a score of 9.4.
The most common users of Amazon Aurora are from Mid-sized Companies (51-1,000 employees).
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Reviews
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We use Aurora as the de-facto DBaaS product for hosting our Relational Databases, primarily MySQL. We have over 100 MySQL clusters (Master, Slave) across the microservices and the 5 global regions where Capillary's SaaS products are hosted. Prior to Aurora, all our databases were self-managed and hosted on EC2 instances with EBS volumes, having provisioned IOPS, for storage. As our system scaled, the management of the databases became a full-time job. Also, configuration management, upgrades, and regular maintenance started eating into the team's bandwidth. Furthermore, the chances of human errors and the consequent outages increased with the increase in the number of MySQL set-ups. To address these concerns, in early 2020, we migrated all our MySQL clusters from EC2 to Aurora. The Aurora service hosts over 400 TB of data, and the Aurora instances vary from 4 cores, 32GB RAM to 32 cores, 256 GB RAM configs. The storage layer varies anywhere from 200GB to 30 TB. In a nutshell, all relational, OLTP use-cases for the 700M odd end-consumers touched by Capillary's platform and served out of Aurora.
- Auto-expansion of the disks. The administrators don't have to worry about disk sizes anymore.
- Default configuration sets are designed for the majority of the OLTP use-cases. As a developer, I don't have to worry about tuning the MySQL configurations anymore.
- Better Performance than MySQL hosted on EC2 instances. The Aurora architecture allows faster replication as well.
- Access to slow query, and error logs is a little cumbersome. Maybe, stream that to an AWS Elasticsearch, and provide searching out of the box (even if it means additional costs).
- Upgrade to higher versions of MySQL is a problem.
- Failovers to replica, although, they are not needed often, they can be made more seamless.
- Well-defined Configuration Sets that take care of most workload requirements. No manual configuration is needed.
- Auto expansion of the disks that makes scaling easier.
- Better read/write performance as compared to self-hosted database instances.
- Improved performance leading to better product experience.
- As configurations are templated, fewer human errors, and higher stability.
- Increased costs of the overall infra, but the performance, and stability guarantees are compensating the higher costs.
- Amazon Relational Database Service (RDS) and Percona Server for MySQL
Aurora vs RDS: Better replication and performance of Aurora as compared to RDS. Almost zero replication lag in most cases which is a big improvement over RDS. Scaling, maintenance, and overall ROI are higher in Aurora.
Aurora vs Percona: Aurora comes well integrated with the AWS ecosystem. So, easier to integrate into the overall infrastructure if you are already on AWS.
Aurora vs Percona: Aurora comes well integrated with the AWS ecosystem. So, easier to integrate into the overall infrastructure if you are already on AWS.
150
Aurora is our primary database for all data entities requiring relational semantics and ACID properties. It is used across all the engineering groups, and services - which covers about 150 engineers across development, QA, devops. It is also used by our Data team for running ad-hoc analytics, reporting, and any asks from the business teams.
4
Our SRE/DevOps group is about 4 people who manage about 80 clusters of Aurora (1 Master, multiple Slaves). They are adept at systems provisioning, configuration, basic database administration skills, Infrastructure as Code technologies like Terraform, and have expert programming skills with Python. We use a lot of Boto for automation our infra management tasks. Although, for a small set-up of Aurora, we don't need special skills, other than basic understanding of database administration skills.
- Primary datastore for entities requiring relational semantics and ACID properties.
- Automated back-ups, and point in time recovery capabilities.
- Ability to auto-scale the readers (slaves) as the read query load increases.
- We are also evaluating serverless Aurora to handle the bursty traffic.
- Completely automating the provisioning of Aurora behind Terraform. We don't access the AWS console at all.
- Differential back-ups and master-slave redundancies depending on criticality of the service and keeping costs in control.
- Effective utilisation of the Key Management Service for encryption and decryption.
- Use more Aurora instances as a part of our Data Lake strategy, and couple it with S3, and Redshift.
- Evaluate the machine learning use-cases along with AWS Sagemaker and Comprehend as they have native integration with Aurora.
- Online training