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Table of contents
1.
Introduction
2.
Aurora Auto Scaling in AWS
3.
Architecture of Aurora Auto Scaling
4.
Circumstances of Aurora Auto Scaling
4.1.
Scale Up Condition
4.2.
Scale Down Condition
4.3.
Scaling Flow Condition
5.
Working of Aurora
6.
Auto Scaling Policy Configuration
7.
Components of Auto Scaling Policy
8.
Frequently Asked Questions
8.1.
Define Aurora Auto Scaling in AWS.
8.2.
What is AWS?
8.3.
What are the components of the Auto Scaling policy?
9.
Conclusion
Last Updated: Mar 27, 2024
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Aurora Auto Scaling in AWS

Author Sagar Mishra
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Introduction

AWS stands for Amazon Web Services. The parent company of AWS is Amazon. It provides different IT resources to enterprises using its distributed IT infrastructure. It offers many services like IaaS (Infrastructure as a service), PaaS (Platform as a service), and SaaS (Software as a service). The services provided by AWS are offered on a pay-as-you-go pricing model.

Aurora Auto Scaling in AWS

This article will discuss the topic of Aurora Auto Scaling in AWS. Let's start with the definition.

Aurora Auto Scaling in AWS

Aurora is a tool provided by Amazon that can start and auto-scale itself to help the system in many ways. It also shuts down automatically when it is no longer needed. Aurora Auto Scaling is compatible with both Aurora MySQL and Aurora PostgreSQL. Aurora DB cluster manages many surges which are not expected in connection or workload.

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Architecture of Aurora Auto Scaling

We will now discuss the architecture of the Aurora Auto Scaling in AWS.

Architecture of Aurora Auto Scaling

As you can see in the above diagram, the application is first created, and then the data of the application is sent to the proxy fleet. The proxy fleet checks the data quality and sends it to the warm pool of DB capacity. Finally, after clearing the way from the warm pool of DB capacity, it reaches to the Aurora Database Storage.

Circumstances of Aurora Auto Scaling

There are many circumstances that decide the auto-scaling of the Aurora. Let us discuss some of them.

Scale Up Condition

There are two conditions using which the Aurora cluster automatically scales up

  • When the use of CPU increases by more than 70%
     
  • Or when the number of active connections increases to 90% or more

Scale Down Condition

Now, there are some rules to stop the Aurora cluster also. Let us check that now.

  • When the use of CPU reduces by less than 30%
     
  • Or when the number of active connections decreases to 40% or less

Scaling Flow Condition

We have now discussed how the Aurora cluster start or stops itself. Now, there are some rules that help to decide when the cluster keeps working without interruption. Let us have a look.

  • Aurora cluster will scale up whenever the performance of the system goes down, but it will scale up only when the issue can be fixed by scaling up the cluster
     
  • It takes at least 15 minutes to start the process of scale-down
     
  • It takes 310 seconds to cool down after the scale-down process starts
     
  • The scaling reaches zero when there are no connections for five minutes
     

The Aurora cluster starts automatically without any kind of disturbance to the current ongoing server. It marks a starting point means a point from where the cluster can begin the scaling operation. But there are some conditions in which Aurora will not be able to find the starting point. The conditions are as follows.

  • When the table is in use, or it is locked
     
  • When an ongoing lengthy task is in process

Working of Aurora

We will now discuss the working of Aurora Auto Scaling in AWS. 

  • Step 1: The user needs to create a new autoscaling policy for Aurora using the service-linked role AWSServiceRoleForApplicationAutoScaling RDSCluster
     
  • Step 2: The target matrix should be present in the system to perform the scaling in the cluster
     
  • Step 3: In the system, when a threshold is reached, then a cloud watch alarm is activated by the Aurora autoscaling policy. This alarm indication causes the scale-up and down policy
     
  • Step 4: Make sure the configuration is set to allow new read replicas. These replicas work one at a time

Auto Scaling Policy Configuration

The configuration in the Auto Scaling policy is a must when it comes to the clustering process. Let us now discuss how we can configure this.

  • Step 1: The user has to select a maximum number of replicas that Aurora needs to take care of. The number must be between 0 to 15
     
  • Step 2: You have to give a proper cooldown time for smooth working. This will help to stop the scale-up and down process from being continued triggered
     
  • Step 3: Set the cooldown period of the auto-scaling policy to 100 seconds. Then, there will be no other scale-up, or down process will happen until 100 seconds
     
  • Step 4: You must have to give the cooldown period for your auto-scaling to prevent your system from multiple triggers. The default cooldown period is set to 300 seconds

Components of Auto Scaling Policy

Our next topic is the components of the Auto Scaling Policy. Here we will see all the components in detail.

Components of Auto Scaling Policy
  • Service-Linked Role: Users can use the service-linked roles for the services that interface with Auto Scaling. Each and every role is given a different role to perform separately. Very service-related positions trust the defined service principle to carry out their mission.
     
  • Target Metric: The target-tracking scaling policy configuration defines the target values of the metric. CloudWatch alerts create and manage the Aurora Auto Scaling. The CloudWatch alerts also use scaling fixing based on the goal values. The metric is held by the scaling policy that adds or removes Aurora Replicas as needed. 
     
  • Minimum and Maximum Capacity: You can choose the limit of Aurora replicas by selecting the Auto replicas. These replicas must be greater than a specific limit which is 0. Also, the maximum number is already decided, which is 15.
     
  • Cooldown Period: The cooldown period helps the Aurora Auto Scaling in the scale-in and out. Users can increase the task rate using the target-tracking scaling strategy. The process of scale-in and out is halted with the help of the cooldown period.

Frequently Asked Questions

Define Aurora Auto Scaling in AWS.

Aurora is a tool provided by Amazon that can start and auto-scale itself to help the system in many ways. It also shuts down automatically when it is no longer needed. Aurora Auto Scaling is compatible with both Aurora MySQL and Aurora PostgreSQL.

What is AWS?

AWS stands for Amazon Web Services. The parent company of AWS is Amazon. It provides different IT resources to enterprises using its distributed IT infrastructure. 

What are the components of the Auto Scaling policy?

There are mainly four components of the Auto Scaling policy, which are the Servive-linked role, target metric, minimum and maximum capacity, and cooldown period.

Conclusion

This article discusses the topic of Aurora Auto Scaling in AWS. In detail, we have seen the definition of Aurora Auto Scaling in AWS. Along with this, we have seen the circumstancesworkingconfiguration, and components.

We hope this blog has helped you enhance your knowledge of Aurora Auto Scaling in AWS. If you want to learn more, then check out our articles.

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