Database Posts

Where do you keep your DB credentials in your AWS Lambda functions that you use to access your Amazon RDS databases? Hardcoded? No! Environment variables? Well, you might be using AWS Secrets Manager, right? It would be great! But did you know that you can also use the IAM role of your AWS Lambda function and its temporary credentials to do this?

Yes, there are different ways to manage your database credentials in your AWS Lambda functions. In this post, I will talk about one of the most secure methods, using IAM authentication of Amazon RDS, with an example of an AWS Lambda function developed in Python. I will use an Amazon RDS - MySQL DB instance, but IAM authentication is also supported on PostgreSQL DB instances as well.

Continue reading the AWS Lambda IAM Authentication for Amazon RDS With Python: Secure, But Limited blog post.

Amazon RDS is the managed relational database solution of AWS. You leave the setup and maintenance of your database to AWS and focus on building applications using it. You can launch and maintain community edition MySQL, PostgreSQL databases as well as commercial Oracle and SQL Server databases on Amazon RDS. However, a few years ago, AWS developed its own cloud-native, enterprise-level database engine called Amazon Aurora, which provides MySQL and PostgreSQL compatibility. In this post, I will discuss some of Aurora’s unique features and why you should use it instead of an Amazon RDS DB instance with community edition MySQL or PostgreSQL databases.

Recently, AWS also launched the serverless and multi-master versions of Amazon Aurora, and any of these features can alone be the reason to choose it. However, in this post, we will focus on single-master Aurora deployment and its advantages over RDS.

Continue reading the Why Choose Amazon Aurora Over Regular RDS? blog post.

Having a Ruby on Rails background, I got excited to try Ruby to code my AWS Lambda functions when its support for AWS Lambda was announced at Re:Invent 2018. Finally, to try Ruby on AWS Lambda, I developed a simple API using AWS Serverless Application Model (SAM) to access an Amazon RDS MySQL database. I wanted to compare it with my experiences of doing this with Python and Node.js.

Although it may seem simple, installing mysql2 gem as a dependency proved itself to be challenging than others, because it has native extensions and depends on the environment you use bundler. As always, Docker simplifies our job by providing a similar environment that our AWS Lambda function runs inside.

In this post, I will make an introduction to AWS SAM CLI and using Ruby for AWS Lambda functions. Let’s begin!

Continue reading the Serverless APIs With Ruby, AWS Lambda & AWS SAM CLI: Accessing Amazon RDS blog post.

In recent days, I made some trials for connecting to MongoDB databases from AWS Lambda functions using Python. In today’s post I will share my experiences with you and take some notes about these trials for future reference. We will install MongoDB on an EC2 instance and develop simple Python functions to access it. Let’s start!

Continue reading the Accessing a MongoDB instance from AWS Lambda using Python blog post.

AWS Lambda allows us running code without maintaining servers and paying only for the resources allocated during the code run. In most cases, we do not need to run our Lambda function in a VPC and it is recommended not to run in a VPC in these situations as a best practice. Some of examples to these are sending emails using Amazon SES or accessing a DynamoDB table.

However, to access an RDS database in your Lambda function you have to run your function in the same VPC or in a VPC that has a peering connection to the VPC of your RDS instance. In this blog post I would like to discuss about running AWS Lambda functions in a VPC and accessing a RDS MySQL database.

Continue reading the Running AWS Lambda Functions in a VPC and Accessing RDS blog post.