What is the maximum network throughput of your EC2 instance? The answer to this question is key to choosing the type of an instance or defining monitoring alerts on network throughput. Unfortunately, you will only find very vague information about the networking capabilities of EC2 instances within AWS’s service description and documentation. That is why I run a network performance benchmark for almost all EC2 instance types within the last few days. The results are compiled into the following cheat sheet.
You may have heard of Amazon Aurora, a custom built MySQL and PostgreSQL compatible database born and built in the cloud. You may have also heard of serverless, which allows you to build and run applications and services without thinking about instances. These are two pieces of the growing AWS technology story that we’re really excited to be working on. Last year, at AWS re:Invent we announced a preview of a new capability for Aurora called Aurora Serverless. Today, I’m pleased to announce that Aurora Serverless for Aurora MySQL is generally available. Aurora Serverless is on-demand, auto-scaling, serverless Aurora. You don’t have to think about instances or scaling and you pay only for what you use.
This paradigm is great for applications with unpredictable load or infrequent demand. In production, you can save on costs by adjusting to scale based on actual load, in extremely granular increments – matching your demand curve almost perfectly. In development, you can save on costs by automatically pausing the cluster (scale to zero!) when it’s not in use. I’m excited to show you how this all works so let’s look at how we launch a Serverless Aurora cluster.
Amazon Web Services (AWS) recently announced that Simple Queue Service (SQS) is finally a supported event source for Lambda. This is extremely exciting news, as I have been waiting for this for two long years! It got me thinking about what other features I am desperately waiting to see from AWS Lambda. After some quick brainstorming, here is my wish list for Lambda for 2018. These items would address many recurring challenges Lambda users face in production, including:
- better monitoring at scale
- cold start performance
- scalability in spiky load scenarios
So, I hope someone from the Lambda team is reading this. Here we go!
We can now use Amazon Simple Queue Service (SQS) to trigger AWS Lambda functions! This is a stellar update with some key functionality that I’ve personally been looking forward to for more than 4 years. I know our customers are excited to take it for a spin so feel free to skip to the walk through section below if you don’t want a trip down memory lane.
SQS was the first service we ever launched with AWS back in 2004, 14 years ago. For some perspective, the largest commercial hard drives in 2004 were around 60GB, PHP 5 came out, Facebook had just launched, the TV show Friends ended, GMail was brand new, and I was still in high school. Looking back, I can see some of the tenets that make AWS what it is today were present even very early on in the development of SQS: fully managed, network accessible, pay-as-you-go, and no minimum commitments. Today, SQS is one of our most popular services used by hundreds of thousands of customers at absolutely massive scales as one of the fundamental building blocks of many applications.
AWS Lambda, by comparison, is a relative new kid on the block having been released at AWS re:Invent in 2014 (I was in the crowd that day!). Lambda is a compute service that lets you run code without provisioning or managing servers and it launched the serverless revolution back in 2014. It has seen immediate adoption across a wide array of use-cases from web and mobile backends to IT policy engines to data processing pipelines. Today, Lambda supports Node.js, Java, Go, C#, and Python runtimes letting customers minimize changes to existing codebases and giving them flexibility to build new ones. Over the past 4 years we’ve added a large number of features and event sources for Lambda making it easier for customers to just get things done. By adding support for SQS to Lambda we’re removing a lot of the undifferentiated heavy lifting of running a polling service or creating an SQS to SNS mapping.
Let’s take a look at how this all works.
This blog post will cover important considerations and strategies for choosing the right partition key while migrating from a relational database to DynamoDB. This is an important step in the design and building of scalable and reliable applications on top of DynamoDB.
What is a partition key?
DynamoDB supports two types of primary keys:
- Partition key: Also known as a hash key, the partition key is composed of a single attribute. Attributes in DynamoDB are similar in many ways to fields or columns in other database systems.
- Partition key and sort key: Referred to as a composite primary key or hash-range key, this type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key
Getting up-and-running with Amazon’s new machine learning-enabled camera
tl;dr It’s awesome. Get one.
At the end of 2017, Amazon announced DeepLens, a camera with specialized hardware that allows developers to deploy machine learning and computer vision models to “the edge,” and integrate the data it collects with other AWS services.
On a whim, I put in a one-click order on Prime (devices started shipping just last week); it arrived a couple days later and just hours from unboxing — with one or two minor hiccups — I got it up-and-running and integrated with other AWS services. I’ve been pleasantly surprised, to say the least.