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If you choose to scope your reserved instances to a specific availability zone, they are locked to a specific instance type . This requires you to understand and plan your compute requirements in two dimensions:

instance type availability zones

These require you to predict both how your application load will grow and how dense your cluster will be be years into the future. Any miscalculation means you’ll pay for reserved instances that you won’t actually use.

Compute power varies by the size of each instance: for example, nine c5.xlarge instances on AWS provide the equivalent computer power of one c5.9xlarge instance.

AWS divides its infrastructure into several regions, which include many availability zones. If you choose to scope your reserved instances more broadly by region, AWS allows you to deploy instances of any size , as long as the compute power matches what you’ve reserved. This allows you to purchase high-powered instances up-front and deploy lower-powered instances later on. Even better, AWS will automatically apply the budget you’ve allocated toward reserved instances to as many instances in that region as possible.

To adopt reserved instances, you first need to estimate your cluster’s total compute requirements. This is the hardest part of capacity planning. AWS defines a scale for compute power of all it’s server sizes: we can use this to calculate an aggregate value. (We’ve provided an example of a SQL query that could generate this report below.)

By choosing regional scope, we naturally define three properties across all our reserved instances: the scope, instance size, and instance family. Once we decide on an exact configuration, we execute a purchase in the AWS console and the reserved instance pricing is instantly applied to our fleet.

Because our fleet can dynamically grow, shrink, or change in compute requirements, we need to be more flexible with how we set the target number of reserved instances to purchase. Instead, we choose an acceptable range for a mix of reserved and on-demand instances in our fleet.

To automate this, we built an ETL process in SQL and Python that detects when we fall outside this band and automatically prepares a purchase for us to approve. This is an evergreen process: the ETL process will continue to analyze and suggest purchases over time as the fleet dynamically scales up and down in compute requirements. We purchase reserved instances once a month.

Here’s an example of the SQL query we regularly run to estimate our required compute power. First, we take a snapshot of our fleet with the cost and usage report:

Next, we select relevant data on usage for our existing reserved instances from our fleet’s total usage:

Finally, we compute the number of additional reserved instances we’ll need to purchase to remain within our acceptable range:

A complete example, including a Python notebook to render the output, can be found in the accompanying gist for this article.

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Getting Started

This guide walks you through the process of creating a Spring application connected with a MySQL Database, as opposed to an in-memory, embedded database, which all of the other guides and many sample apps use. It uses Spring Data JPA to access the database, but this is only one of many possible choices (e.g. you could use plain Spring JDBC).

What you’ll build

You’ll create a MySQL database, build a Spring application and connect it with the newly created database.

version 5.6 or better. If you have docker installed it might be useful to run the database as a .

About 15 minutes

A favorite text editor or IDE

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You can also import the code straight into your IDE:

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To skip the basics , do the following:

and unzip the source repository for this guide, or clone it using :

cd into

Jump ahead to .

When you’re finished , you can check your results against the code in .

Build with Gradle

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In a project directory of your choosing, create the following subdirectory structure; for example, with on *nix systems:

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provides many convenient features:

It collects all the jars on the classpath and builds a single, runnable "über-jar", which makes it more convenient to execute and transport your service.

It searches for the method to flag as a runnable class.

It provides a built-in dependency resolver that sets the version number to match . You can override any version you wish, but it will default to Boot’s chosen set of versions.

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