How to build python code bundles for AWS Lambda quickly and easily

AWS Lambda is conceptually really cool but as soon as your code creeps beyond a single python file that uses botocore things start to get messy and cumbersome. It’s tempting to add an entirely new tool to your workflow, but theres really no need.

The approach I use is good old make. It’s a perfect fit really. We have input files:

We want to take these and assemble a

One of the nice things about this setup is that when you run make it will only update the things that have changed. This means that the requirements.txt gather step only needs to be run once - rebuilding the zip files can actually be really quick.

Make 101

If you are familiar with how a Makefile is plumbed together you can skip this bit. A Makefile is a collection of build targets and the rules for how to build those targets.
	mkdir -p build/lambda_zip
	cp build_lambda_zip/
	rm -f
	cd build_lambda_zip/ && zip -q -X -9 -r ../ *

In this example, is the target. make is responsible for generating that target, and if any of the dependencies listed ( in this example) are newer than it knows it needs to recreate the zip.

One very important thing is that a Makefile must be tab indented.

Sometimes there isn’t a single file that is generated by a build step. Sometimes there might not even be a file. For example, you might want to upload a build artifact only when something has changed. The make idiom for this is to use a stamp file. A stamp file is a 0 byte marker that indicates some process has been completed at a give date and time. So for example:

	aws lambda update-function-code --function-name MyFunction --zip-file
	touch $@

The build target is upload.stamp. The target needs building every time is updated. awscli is used to do a code upload, then touch $@ creates the stamp file (or updates its modification timestamp). This upload is now idempotent.

There are some special rules in make. These are rules that don’t have targets on disk. For example, make clean. Without some configuration hint make would believe that you wanted to create a file called clean. If you happened to have a file called clean then make would think that the build was up to date and that it didn’t need to clean anything. What this means is that we need targets that are always built. These are called .PHONY targets, and you need to include a declaration in your Makefile like this:

.PHONY: all clean

Basic Makefile structure

We’ll look at the basic skaffold first before delving into specifics.

I declare a bunch of paths at the top of my Makefile. They are all relative to the cwd which i grab with $(shell pwd):

SRC_DIR=$(shell pwd)

The all target defines what should happen if you just run make with no arguments. We let make know about our .PHONY rules too:

all: $(OUTPUT_ZIP)
.PHONY: all clean

make clean needs to delete any files that were created by running make:

	rm -f $(OUTPUT_ZIP)

We have a build step to generate a staging directory when the code changes:

	touch $@

And then we zip it up as $cwd/build/

	rm -f $(OUTPUT_ZIP)
	cd $(STAGING_DIRECTORY) && zip -q -9 -r $(OUTPUT_ZIP) *

Collecting and extracting wheels

We want to collect all the eggs in requirements.txt. We’ll use the pip wheel command to do any compilation and build a wheelhouse. Subsequent builds can reuse the same wheels and avoid compilation:

$(CACHE_WHEELHOUSE_STAMP): $(SRC_DIR)/requirements.txt
	pip wheel -q -r requirements.txt . --wheel-dir=$(CACHE_WHEELHOUSE) --find-links=$(CACHE_WHEELHOUSE)
	touch $@

We want to preserve the built wheels as much as we can, but we don’t have a mechanism to purge old wheels. Because we want to be able to get just the wheels related to the current requirements.txt we use a second wheelhouse that we delete before repopulating it. By using the first wheelhouse as a --find-links this is pretty much a straight copy and fast:

	pip wheel -q -r requirements.txt . --wheel-dir=$(STAGING_WHEELHOUSE) --find-links=$(CACHE_WHEELHOUSE)
	touch $@

Now the best part of collecting wheels like this is that we can just unzip them into the build directory and they will be in the correct location:

	touch $@

Reproducibility and Idempotence

One nice property of this is theoretically if a build is run twice on the same base OS then you should get the same output, bit for bit. And this should mean use can use the CodeSha256 property returned from the Lambda API to not only prove what is deployed is what you think it is but also build in idempotence. However its not that simple.

If your zip building process is not creating identical output you can use the Debian diffoscope utility to help figure out what went wrong. Here are some things we spotted and fixed.

First we need to add an extra parameter to our zip incantation:

	rm -f $(OUTPUT_ZIP)
	cd $(STAGING_DIRECTORY) && zip -q -X -9 -r $(OUTPUT_ZIP) *

This turns on --no-extra mode. This tells zip to ignore non-essential extra file attributes. By default these extra attributes introduce some non-determinism, so we just get rid of them.

Next up is that when a wheel is unpacked the mtime of the directories that are created are the current time. This metadata is preserved in the zip, but isn’t interesting or useful to us. I pick an arbitrary date (in this case the mtime of the last commit) and clamp the modification timestamps:

BUILD_DATE=$(shell git log --date=local -1 --format="@%ct")

	find "$(STAGING_DIRECTORY)" -newermt "$(BUILD_DATE)" -print0 | xargs -0r touch --no-dereference --date="$(BUILD_DATE)"
	touch $@

The next problem are .so files that are generated by the build process. Hopefully you don’t have any, in which case you are done. Right now if you run a based compilation of an .so twice you will get different outputs. Some of this is the use of random /tmp directories. Right now the easiest way to work around this is just to pre-compile your binary dependencies as wheels and upload them to a private repository. The right fix involves using the learnings of the Reproducible Builds team to make python wheels repeatable.

You should now have reproducible lambda zips.

Bonus targets

As alluded to earlier, we can upload the zip directly to AWS by calling out to awscli. And why not add a make invoke to deploy, upload and run our function?


	aws lambda update-function-code --function-name MyFunction --zip-file
	touch $@


	aws lambda invoke \
      --function-name MyFunction \
      --invocation-type RequestResponse \
      --payload file://example-payload.json

.PHONY: all clean upload invoke

Because of the dependencies invoke will build a new if somethings changed and then deploy it, before finally running it. Perfect when developing!