Since I’ve started using Rust quite a bit more at work and in some personal projects, I’ve been wondering how well Rust would fair when used in AWS Lambda functions. Back in 2018, AWS published a blog post announcing a runtime for Rust on AWS Lambda. More recently, I found the comments on this reddit post to be quite interesting. Aleksandr Filichkin also published in September last year an AWS Lambda Battle that showed the performance of Rust on AWS Lambda was quite impressive. In a later post in November, Aleksandr wrote about the performance of x86 vs ARM on AWS Lambda, which again showed Rust to be quite performant, and even more so on ARM.
A few months ago I decided to change the analytics for this website over to a custom analytics implementation, replacing my use of Simple Analytics. The analytics are now provided by a couple of lambda functions: one which provides the API for collecting statistics and reporting, and another that processes DynamoDB events to provide simple aggregation.
I’ve been fairly pleased with this approach: it means I don’t really pay anything for the analytics, as AWS Lambda and DynamoDB are extremely cheap for small use cases like this. Moreover, the reporting provided the minimal amount of information that I currently desire from site analytics.
As a first attempt using Rust on AWS Lambda I felt that these two functions were a good starting point for a few reasons:
- The API function is behind an API Gateway proxy, and so I could make use of the lambda-http crate to handle HTTP requests and responses.
- The trigger function processes events from a DynamoDB stream, which is another fairly nice use-case for a Lambda function.
- There’s little pressure for these to be performant or stable, as it only effects this site 😆
Building Rust for AWS Lambda
I initially had a number of issues compiling Rust code for AWS Lambda using the method described in the
README in the AWS Lambda Rust
runtime repository. The main issue I came across was a segfault from LLVM when compiling the
crate. This seemed to only arise when I compiled in Docker on the M1 when targeting x86-64. As AWS
support for ARM in Lambda is quite recent, none of the AWS Lambda Rust build images I looked at
currently seemed to support it.
I’m sure that at some point they will support ARM, but for the time being it was necessary for me
to create a Dockerfile that used the
al2-arm64 image provided by AWS in the ECR. This
Dockerfile simply installs a Rust version (currently 1.58.1) and copies over a build script. When
executed, the build script invokes
cargo to compile a release build of the lambda functions, and
then bundles the compiled executables into a ZIP file, with each executable named
required by AWS Lambda.
One caveat that I came across was the use of
libssl indirectly from the fernet crate I use to
encrypt the session tokens for the analytics reporting API. The
libssl library appears not to be
present on AWS Lambda, and therefore I needed to include it in the zip package. I considered deploying
a Lambda layer for this purpose, however I thought it overkill for just one function. However, the
same folder structure can be used to provide library dependencies in a zip package. The somewhat
evil trick I use is to invoke
ldd to find the resolved library for
libssl on the Docker
container and copy that file into a
lib directory in the zip package.
Now that my executables could be run by Lambda, I could start iterating the API and trigger functions.
Implementing the API
The initial implementation of the API in Rust has gone very easily, mostly due to the structures provided in various crates available to Rust, including the lambda-http crate. I was able to finish most of the API code on one Sunday morning.
Making the calls to DynamoDB in Rust was a very nice experience. The AWS client crate makes use of “builder” syntax, which works quite nicely. For example, to build a query for a particlar path and section (the primary and sort keys for the analytics table) is quite easily comprehended, if somewhat verbose:
Implementing the API was not entirely a matter of simply translating the Python code. Mostly this
was due to the fact that Rust tends to encourage us to better handle errors than Python. I found
that there were a number of times in the Python code where I was simply assuming that a value was
as I expected. As an example, when we query DynamoDB we get back a
dict of attributes in Python,
where each attribute is another
dict containing the DynamoDB named-value pair. This tends to
lead me to write Python code like:
Unfortunately I’m making a lot of assumptions in the above code, such as:
- There is a
ViewCountattribute is a
- There is an
Nattribute in the
- The value can be parsed as an integer with
Rust forced me to be more aware of this: Each item from DynamoDB is a
HashMap mapping a
key to an AttributeValue. The equivalent Rust code
item["ViewCount"] makes use of the
HashMap which will panic if the given key is not found in the mapping (see
here). This encouraged
me to use the
method to access the attributes of an item returned from DynamoDB. The
provides a number of methods that help with unwrapping the enumeration, which all return a
Result, which we need to match against (or just lazily
This results in somewhat cleaner code. That being said, there are times where I felt justified in
expect and allowing the Lambda function to panic.
Implementing the Trigger
Once I had come to understand the structures and functions in the Rust AWS client crates, I had a
far easier time building the trigger function. This function simply responds to events received
from a DynamoDB stream, which it receives as a
serde_json::Value. Depending on the contents of
the event, it performs various aggregations by updating items to increment the
attribute for weekly and monthly totals.
I was somewhat worried about the parsing of user agent strings: in Python I did this using the user-agents library. However I found a rather nice crate woothee that performs the same operation just as well for my use case.
I was pleasantly suprised at how well the process went. Apart from the somewhat slow start getting Rust code compiled for AWS Lambda on ARM, once I had my bearings it was quite easy going.
I am somewhat worried that the error handling is not as graceful as it should be. I’m somewhat
nervous of cases where I’m using the
Index trait of a
HashMap, or just
expect-ing a value where I should be using
if let. I think I’d better go over the
Lambda functions again some other time to round off the edges.
Cover image courtesy of Jay Heike (@jayrheike on unsplash).