Traditional database systems have many performance, availability, and scalability limitations. Amazon Aurora is the solution to these limitations.
Databases are necessary to power the apps that run the business. They should be bigger, more reliable, and faster to provide better services. Amazon Web Services make it easy for businesses to manage data and improve customer satisfaction.
Amazon’s AWS Aurora web service is an example of such a service. It uses cluster volume to manage its data and allocate it for emergency backup.
What is Amazon Aurora?
Amazon Aurora, a cloud-native database, is fully compatible with Open-Source MySQL and PostgreSQL. Aurora supports all open-source MySQL functionality. It also allows for drop-in compatibility with applications that run on these databases.
Aurora was designed for customers who require a fully managed database service with the cost-effectiveness and simplicity of open-source databases but the same performance as a commercial database.
Aurora delivers performance five times faster than traditional MySQL and three times better than PostgreSQL. Amazon RDS handles tasks like provisioning, backup, and recovery. There is no upfront cost, and you only pay a monthly fee.
Aurora is unique in that it offers a track record for operational excellence and enterprise features at a fraction of the cost of commercial databases.
Aurora provides unparalleled performance, availability, security, and reliability globally. Aurora has been the fastest-growing service in AWS’s portfolio since its inception in 2014.
What is Amazon Aurora used for?
Aurora’s remarkable growth has been driven by strong interest from many industries. We have seen strong interest in Financial Services, Software, and Internet, as well as Entertainment and Games and Retail verticals over the past twelve months.
Customers migrate to Aurora MySQL and Aurora PostgreSQL to consolidate their MySQL and PostgreSQL databases. We also see many customers migrate from legacy databases like Oracle or Microsoft SQL Server to Aurora PostgreSQL.
These customers who are “break-free” have grown tired of paying high licensing fees and locking themselves into legacy databases. Customers with high growth want to scale quickly and seamlessly in all regions. They also want to be able to integrate across AWS services.
Benefits of Amazon Aurora
Below are the benefits of using Amazon Aurora:
You can enable autoscaling. It will increase the storage capacity when you have more storage. It will also decrease the size of your database if there is a decrease in storage space.
Only pay for the processing power and storage space you use. There are no upfront fees or other charges. It’s simple to make monthly payments.
Amazon VPC allows you to isolate the network from which your database is running.
Use AWS Key Management Service to create encryption keys that protect your data.
High availability and durability
You can create replicas of the Aurora in many Availability Zones.
It’s a global database that can easily be distributed across multiple AWS regions, which could decrease the local read/write speed.
Support for Migration
To migrate your local database to Aurora, you can use the pg-dump or MySQL dump commands.
Aurora is very easy to use. It is easy to set up an instance using the AWS RDS console. Aurora offers monitoring with Amazon Cloudwatch at no extra cost.
There is no need to worry about provisioning, updating, or upgrading. Amazon has complete control over all aspects of your software and will do any necessary patching.
Best features of Amazon Aurora
Below are the best features of Amazon Aurora:
Scalability: This is the best feature of Amazon Aurora. The database automatically increases in size according to storage requirements. Volume can grow in increments up to 10 GB and can reach a maximum of 128TB. This allows for seamless storage.
High throughput: SysBench benchmarks have shown a 5x increase in throughput. Amazon Aurora employs a variety of software and hardware techniques in order to maximize the available memory, computing, and networking. To improve performance consistency, input and output operations using distributed system techniques such as quorums.
Monitoring and repair of instances: Amazon RDS continually monitors the health and performance of the Amazon Aurora database as well as the underlying EC2 instances. Amazon RDS automatically restarts the database in case of a database failure and all associated processes.
Encryption: Amazon Aurora provides high security by allowing you to encrypt the database using keys you control and create through AWS Key Management Service. Amazon Aurora uses SSL encryption to protect data during transit.
Easy to use: It’s easy to use Amazon Aurora. Amazon RDS Management Console is used to create the new Amazon Aurora DB Instance. This API call, or CLI, only takes one API call. Amazon Aurora DB Instances come pre-configured with the appropriate parameters and settings for each DB Instance type.
Cost-effective: Only pay for what you use. There are no upfront costs or other fees. It’s simple to make monthly payments.
Migration support: To migrate your local database from Aurora to Aurora, you can use the pg-dump or MySQL dump commands.
Aurora is fully managed: You can start using it immediately. All you need to do is create an instance using the AWS RDS console or call an API from your code. There is no need to worry about provisioning, updating, or upgrading.
Amazon Aurora Architecture
The Aurora Database’s entire architecture is built on top of a traditional DBMS. It reuses the majority of DBMS components such as transaction manager, query execution engine, and recovery manager.
Amazon Aurora is a new DBMS that goes beyond the traditional game. It makes many improvements to existing DBMSs to increase their availability, reliability, and scalability.
These changes are:
A primary replica setup is used
Remote storage of the data can be replicated
Only changelogs are to be saved to the remote disk
Amazon Aurora’s architecture can scale out the storage layers of traditional relational databases.
Amazon Aurora’s primary database instance passes the redo log to its storage layer for processing. The logs are processed by the storage layer, which creates and stores new page versions and backs everything up to S3.
Amazon Aurora needs only to create additional database engine instances and connect them with the existing storage layer during a scale-out. This eliminates the bottleneck in data replication by delegating these tasks for parallel processing to the storage layer.
This allows for the creation of many new features, such as:
Instant crash recovery: You don’t need to replay logs from the last checkpoint
Rapid failovers: There’s no need to worry about which replica database has the most recent redo log record. The storage layer handles that.
Backtracking: Because the storage layer has streamed redo logs, it can “rewind” the data to specific points in the past without having to restore a checkpoint from the S3 backup
Setting up Amazon Aurora
Below are the steps to set up Amazon Aurora successfully.
#1.Login to the AWS management console and open RDS.
#2.Click on create database.
#3.Select Engine option as Amazon Aurora.
#4.Select the Aurora MySQL version you need.
#5.Create a DB cluster identifier and set the credential settings username and password.
#6.Select the instance configuration from the dropdown option and choose an option to create replica or not.
#7.Select the connectivity options as shown below.
#8.Choose from existing VPC security group or create a new one.
#9.Enable enhanced monitoring, select the granularity and monitoring role option.
#10.Finally, click on create database.
#11.You have successfully created Amazon Aurora database.
Pricing model: Amazon Aurora
Aurora Serverless ACUs v2 cost $0.12 an hour, twice the price of provisioned Aurora ACUs. This means:
Minimum current running cost: 4 ACUs, $0.48 an hour or $350 per monthly
For workloads that are pathologically triggering the auto-scaling function, there is a minimum scalability increase of 30 seconds or $0.0005 for a half ACU.
The monthly cost for Aurora Serverless V2 is $350. Each auto-scale event will be charged at least $0.0005. Although the equivalent Aurora capacity is $175 per month, it would not have responsive serverless auto-scaling.
Uses Cases: Amazon Aurora
#1. Software as a Service (SaaS)
It uses multi-talent architectures that can be flexible in storage and instance scaling. As a result, Amazon Aurora allows companies to focus on developing high-quality applications and not worry about the database.
#2. Gaming purpose
AWS Aurora functions in the same manner as Relational Database. It provides high throughput, massive storage capacity, high availability, and high availability.
#3. Enterprise Application
Amazon Aurora is compatible with any firm that is already filmier than Relational Database. Aurora is cost-effective as it reduces the price by up to 90% when compared with other options.
Aurora’s compatibility with MySQL and PostgreSQL
Amazon Aurora is built for high performance, global availability, and full MySQL/PostgreSQL compatibility. Standard tools make it easy to migrate MySQL and PostgreSQL databases from Aurora. You can also run legacy SQL Server apps with Babelfish for Aurora PostgreSQL, requiring minimal code changes.
Amazon Aurora works with standard PostgreSQL import/export tools, such as pg_dump or pg_restore. You can also create a new Amazon Aurora table using an Amazon RDS or MySQL DB Snapshot.
DB Snapshots are usually quick to complete, but it will depend on how much and what format of data is being migrated.
Amazon Aurora vs. Amazon RDS
RDS architecture is similar in that it allows you to install a database engine on Amazon EC2 manually but leaves the provisioning and maintenance to AWS. RDS provides many features like automatic failover, backups, etc. RDS uses Amazon EBS volumes to store log and database data.
The Aurora database storage system is reliable and fault-tolerant. The database storage for Aurora is independent of the instances. Aurora stores data in six copies, each with 10GB chunks. These copies are distributed to three Availability Zones. Even if you only have one Aurora instance, there will still be six copies of your data.
RDS uses SSDs storage for improved I/O throughput performance. There are two SSD-backed storage options available. One is for high-performance OLTP applications, while the other is for general-purpose, cost-effective use.
Aurora offers twice the performance of PostgreSQL and five times that of standard MySQL on comparable hardware. Aurora’s performance has been consistently higher and more consistent.
Database Engine Support
RDS is compatible with MySQL, PostgreSQL, and MariaDB, as well as Microsoft SQL Server and Oracle.
Aurora is compatible with both PostgreSQL as well as MySQL. This means that you can use your existing database tools and apps on both PostgreSQL and MySQL without making any changes.
Durability and Availability
Aurora has a unique storage model that allows for continuous backups and restores with very low RPO (recovery points objective). This makes it more reliable and durable than RDS.
Data is durable by design in Aurora. There are always multiple copies of your data. Each Aurora cluster has six storage nodes spread across three AZs. Even if you only have one compute node, there are still multiple copies of your data.
Because of its architectural design, Aurora is more resilient than RDS. It is quick to recover from failures. If a compute node crashes, Aurora can recover quickly.
RDS storage auto-scaling scales storage capacity to 64 TiB (except SQL Server’s 16 TiB) to accommodate growing database workloads. There is no downtime.
Aurora automatically increases storage by increasing it from a minimum of 10 GB to a maximum of 128 TiB. The storage is increased in 10 GB increments without any effect on database performance.
Vertical Scaling: RDS and Aurora both allow you to scale memory and compute resources to a maximum of 244 GiB RAM and 32 virtual CPUs. In a matter of seconds, you can scale operations.
Aurora Auto Scaling Dynamic: Aurora Auto Scaling dynamically adjusts how many Aurora Replicas are available for an Aurora DB cluster by using single-master replication. RDS does NOT support such Auto Scaling.
RDS can be used to provide up to five replicas. The process of replication is also slower than Aurora.
Aurora can provision up to 15 replicas, and the replication takes only a few seconds. Aurora scales quicker because it can quickly add new read replicas.
In RDS, failover to read the replica happens manually. This could cause data loss. Multi-AZ (Standby Instance) can be used to automate failover and prevent data loss.
To prevent data loss, Aurora uses failover to read replicas automatically. Aurora has a faster failover time.
RDS has a cluster endpoint that you can use to execute your write queries. It is the DNS pointer to your current master database instance. RDS routes the endpoint to the new master via a simple DNS change during a failover.
The cluster endpoint is still available in Aurora for writing queries. This endpoint also acts as a load balancer for your read replications. This endpoint can be used to answer your read queries.
RDS automatically creates and saves backups of your DB instances during your DB instance’s backup window. RDS makes a snapshot of your DB instance as a storage volume, backing up all databases and not just the ones you select.
Aurora automatically backs up cluster volumes and keeps restoring data for the duration of the backup retention period. Aurora backups are continuous and incremental so that you can quickly restore any point within the backup retention period.
Below is the summarised table for Amazon Aurora vs. Amazon RDS:
You can expand the storage from 10 GB to 64 GB
RDS allows 64 GBS for all engines, but only 16 GB for SQL server
You can scale memory and compute to a maximum of 32 vCPUs, and 244 GiB of RAM
Same as Aurora
Supports MySQL and PostgreSQL
Supports Microsoft SQL Server, MariaDB, Oracle Database, MySQL, PostgreSQL, and Amazon Aurora
Supports fifteen replicas
Supports five replicas
Pricing depends on I/O operations
You can try it free of charge with no minimum usage fee
Amazon Aurora vs. DynamoDB
Durability and availability
Each piece of DynamoDB data is replicated to multiple physical nodes. Amazon has placed these physical nodes in multiple zones (aka availability zones) to ensure high durability and availability in the event of a disaster like a fire or major power outage. The operation will continue even if one copy goes down. SSDs are also highly reliable and durable.
Backups and restore
DynamoDB offers an on-demand backup as well as PITR (Point in-time backups), which allows you to access any database state. DynamoDB is capable of backing up any amount of data. Performance and availability will not be affected. Backup takes very little time, and users don’t have to worry about backup schedules or background processes. AWS allows backup recovery with just one API call.
The Amazon Aurora backup process is similar to DynamoDB. It was created without any performance interruptions or interruptions to the database service. Amazon backups are saved in an S3 bucket. Aurora will automatically create a backup of 30 minutes if we do not specify a backup window.
DynamoDB supports IAM users and roles with fine-grained policies that limit database creation and query privileges. Amazon Aurora also has a similar permissions scheme.
However, Aurora also provides MySQL and PostgreSQL-compatible connection interfaces to connect to the database.
DynamoDB, a regional service, is not subject to the VPC boundary. However, Aurora DB clusters need to be established in a VPC that clearly defines its network boundary. You can also use the VPC security groups to control which devices or Amazon EC2 instances are allowed to connect to the database instance.
Below is the summarised table for Amazon Aurora vs. DynamoDB:
The relational database supporting MySQL and PostgreSQL
Replica can be promoted to primary in a minute
Replication latency is less than 1 second
Replication latency is sub milliseconds
That was it. We are all entering a new era in relational databases, and Aurora is only the beginning. Customers have responded with overwhelming agreement. Leaders in every industry–like Capital One, Dow Jones, Netflix, and Verizon–are migrating their relational database workloads to Aurora, including MySQL and PostgreSQL-compatible editions.
Avi is a tech enthusiast with expertise in trending technologies such as DevOps, Cloud Computing, Big Data and many more. He is passionate about learning cutting-edge technologies and sharing his knowledge with others through… read more