AWS Lambda Event-Driven Architecture With Amazon SNS

Today, Amazon announced that AWS Lambda functions can be subscribed to Amazon SNS topics.

This means that any message posted to an SNS topic can trigger the execution of custom code you have written, but you don’t have to maintain any infrastructure to keep that code available to listen for those events and you don’t have to pay for any infrastructure when the code is not being run.

This is, in my opinion, the first time that Amazon can truly say that AWS Lambda is event-driven, as we now have a central, independent, event management system (SNS) where any authorized entity can trigger the event (post a message to a topic) and any authorized AWS Lambda function can listen for the event, and neither has to know about the other.

Making this instantly useful is the fact that there already are a number of AWS services and events that can post messages to Amazon SNS. This means there are a lot of application ideas that are ready to be implemented with nothing but a few commands to set up the SNS topic, and some snippets of nodejs code to upload as an AWS Lambda function.


Persistence Of The AWS Lambda Environment Between Function Invocations

AWS Lambda functions are run inside of an Amazon Linux environment (presumably a container of some sort). Sequential calls to the same Lambda function could hit the same or different instantiations of the environment.

If you hit the same copy (I don’t want to say “instance”) of the Lambda function, then stuff you left in the environment from a previous run might still be available.

This could be useful (think caching) or hurtful (if your code incorrectly expects a fresh start every run).

Here’s an example using lambdash, a hack I wrote that sends shell commands to a Lambda function to be run in the AWS Lambda environment, with stdout/stderr being sent back through S3 and displayed locally.

AWS Lambda: Pay The Same Price For Faster Execution

multiply the speed of compute-intensive Lambda functions without (much) increase in cost


  • AWS Lambda duration charges are proportional to the requested memory.

  • The CPU power, network, and disk are proportional to the requested memory.

One could conclude that the charges are proportional to the CPU power available to the Lambda function. If the function completion time is inversely proportional to the CPU power allocated (not entirely true), then the cost remains roughly fixed as you dial up power to make it faster.

If your Lambda function is primarily CPU bound and takes at least several hundred ms to execute, then you may find that you can simply allocate more CPU by allocating more memory, and get the same functionality completed in a shorter time period for about the same cost.

Exploring The AWS Lambda Runtime Environment

In the AWS Lambda Shell Hack article, I present a crude hack that lets me run shell commands in the AWS Lambda environment to explore what might be available to Lambda functions running there.

I’ve added a wrapper that lets me type commands on my laptop and see the output of the command run in the Lambda function. This is not production quality software, but you can take a look at it in the alestic/lambdash GitHub repo.

For the curious, here are some results. Please note that this is running on a preview and is in no way a guaranteed part of the environment of a Lambda function. Amazon could change any of it at any time, so don’t build production code using this information.

The version of Amazon Linux:

$ lambdash cat /etc/issue
Amazon Linux AMI release 2014.03
Kernel \r on an \m

The kernel version:

$ lambdash uname -a
Linux ip-10-0-168-157 3.14.19-17.43.amzn1.x86_64 #1 SMP Wed Sep 17 22:14:52 UTC 2014 x86_64 x86_64 x86_64 GNU/Linux

The working directory of the Lambda function:

lambdash: AWS Lambda Shell Hack

An updated version of this hack is now available:

Please follow the simpler instructions in the above article instead of the obsolete instructions listed below.

I spent the weekend learning just enough JavaScript and nodejs to hack together a Lambda function that runs arbitrary shell commands in the AWS Lambda environment.

This hack allows you to explore the current file system, learn what versions of Perl and Python are available, and discover what packages might be installed.

If you’re interested in seeing the results, then read following article which uses this AWS Lambda shell hack to examine the inside of the AWS Lambda run time environment.

Exploring The AWS Lambda Runtime Environment

Now on to the hack…

AWS Lambda Walkthrough Command Line Companion

The AWS Lambda Walkthrough 2 uses AWS Lambda to automatically resize images added to one bucket, placing the resulting thumbnails in another bucket. The walkthrough documentation has a mix of aws-cli commands, instructions for hand editing files, and steps requiring the AWS console.

For my personal testing, I converted all of these to command line instructions that can simply be copied and pasted, making them more suitable for adapting into scripts and for eventual automation. I share the results here in case others might find this a faster way to get started with Lambda.

These instructions assume that you have already set up and are using an IAM user / aws-cli profile with admin credentials.

The following is intended as a companion to the Amazon walkthrough documentation, simplifying the execution steps for command line lovers. Read the AWS documentation itself for more details explaining the walkthrough.

Set up

Set up environment variables describing the associated resources: