问题
I upload my lambda function sources from AWS codebuild. My Python script uses NLTK so it needs a lot of data. My .zip package is too big and an RequestEntityTooLargeException occurs. I want to know how to increase the size of the deployment package sent via the UpdateFunctionCode command.
I use AWS CodeBuild to transform the source from a GitHub repository to AWS Lambda. Here is the associated buildspec file:
version: 0.2
phases:
install:
commands:
- echo "install step"
- apt-get update
- apt-get install zip -y
- apt-get install python3-pip -y
- pip install --upgrade pip
- pip install --upgrade awscli
# Define directories
- export HOME_DIR=`pwd`
- export NLTK_DATA=$HOME_DIR/nltk_data
pre_build:
commands:
- echo "pre_build step"
- cd $HOME_DIR
- virtualenv venv
- . venv/bin/activate
# Install modules
- pip install -U requests
# NLTK download
- pip install -U nltk
- python -m nltk.downloader -d $NLTK_DATA wordnet stopwords punkt
- pip freeze > requirements.txt
build:
commands:
- echo 'build step'
- cd $HOME_DIR
- mv $VIRTUAL_ENV/lib/python3.6/site-packages/* .
- sudo zip -r9 algo.zip .
- aws s3 cp --recursive --acl public-read ./ s3://hilightalgo/
- aws lambda update-function-code --function-name arn:aws:lambda:eu-west-3:671560023774:function:LaunchHilight --zip-file fileb://algo.zip
- aws lambda update-function-configuration --function-name arn:aws:lambda:eu-west-3:671560023774:function:LaunchHilight --environment 'Variables={NLTK_DATA=/var/task/nltk_data}'
post_build:
commands:
- echo "post_build step"
When I launch the pipeline, I have RequestEntityTooLargeException because there are too many data in my .zip package. See the build logs below:
[Container] 2019/02/11 10:48:35 Running command aws lambda update-function-code --function-name arn:aws:lambda:eu-west-3:671560023774:function:LaunchHilight --zip-file fileb://algo.zip
An error occurred (RequestEntityTooLargeException) when calling the UpdateFunctionCode operation: Request must be smaller than 69905067 bytes for the UpdateFunctionCode operation
[Container] 2019/02/11 10:48:37 Command did not exit successfully aws lambda update-function-code --function-name arn:aws:lambda:eu-west-3:671560023774:function:LaunchHilight --zip-file fileb://algo.zip exit status 255
[Container] 2019/02/11 10:48:37 Phase complete: BUILD Success: false
[Container] 2019/02/11 10:48:37 Phase context status code: COMMAND_EXECUTION_ERROR Message: Error while executing command: aws lambda update-function-code --function-name arn:aws:lambda:eu-west-3:671560023774:function:LaunchHilight --zip-file fileb://algo.zip. Reason: exit status 255
Everything works correctly when I reduce the NLTK data to download (I tried with only the packages stopwords and wordnet.
Does anyone have an idea to solve this "size limit problem"?
回答1:
You cannot increase the deployment package size for Lambda. AWS Lambda limits are described in AWS Lambda devopler guide. More information on how those limits work can be seen here. In essence, your unzipped package size has to be less than 250MB (262144000 bytes).
PS: Using layers doesn't solve sizing problem, though helps with management & maybe faster cold start. Package size includes the layers - Lambda layers.
A function can use up to 5 layers at a time. The total unzipped size of the function and all layers can't exceed the unzipped deployment package size limit of 250 MB.
回答2:
I have not tried this myself, but the folks at Zappa describe a trick that might help. Quoting from https://blog.zappa.io/posts/slim-handler:
Zappa zips up the large application and sends the project zip file up to S3. Second, Zappa creates a very minimal slim handler that just contains Zappa and its dependencies and sends that to Lambda.
When the slim handler is called on a cold start, it downloads the large project zip from S3 and unzips it in Lambda’s shared /tmp space. All subsequent calls to that warm Lambda share the /tmp space and have access to the project files; so it is possible for the file to only download once if the Lambda stays warm.
This way you should get 500MB in /tmp.
Update:
I have used the following code in the lambdas of a couple of projects, it is based on the method zappa used, but can be used directly.
# Based on the code in https://github.com/Miserlou/Zappa/blob/master/zappa/handler.py
# We need to load the layer from an s3 bucket into tmp, bypassing the normal
# AWS layer mechanism, since it is too large, AWS unzipped lambda function size
# including layers is 250MB.
def load_remote_project_archive(remote_bucket, remote_file, layer_name):
# Puts the project files from S3 in /tmp and adds to path
project_folder = '/tmp/{0!s}'.format(layer_name)
if not os.path.isdir(project_folder):
# The project folder doesn't exist in this cold lambda, get it from S3
boto_session = boto3.Session()
# Download zip file from S3
s3 = boto_session.resource('s3')
archive_on_s3 = s3.Object(remote_bucket, remote_file).get()
# unzip from stream
with io.BytesIO(archive_on_s3["Body"].read()) as zf:
# rewind the file
zf.seek(0)
# Read the file as a zipfile and process the members
with zipfile.ZipFile(zf, mode='r') as zipf:
zipf.extractall(project_folder)
# Add to project path
sys.path.insert(0, project_folder)
return True
This can then be called as follows (I pass the bucket with the layer to the lambda function via an env variable):
load_remote_project_archive(os.environ['MY_ADDITIONAL_LAYERS_BUCKET'], 'lambda_my_extra_layer.zip', 'lambda_my_extra_layer')
At the time when I wrote this code, tmp was also capped, I think to 250MB, but the call to zipf.extractall(project_folder) above can be replaced with extracting directly to memory: unzipped_in_memory = {name: zipf.read(name) for name in zipf.namelist()}
which I did for some machine learning models, I guess the answer of @rahul is more versatile for this though.
回答3:
AWS Lambda functions can mount EFS. You can load libraries or packages that are larger than the 250 MB package deployment size limit of AWS Lambda using EFS.
Detailed steps on how to set it up are here: https://aws.amazon.com/blogs/aws/new-a-shared-file-system-for-your-lambda-functions/
On a high level, the changes include:
- Create and setup EFS file system
- Use EFS with lambda function
- Install the pip dependencies inside EFS access point
- Set the PYTHONPATH environment variable to tell where to look for the dependencies
回答4:
The following are hard limits for Lambda (may change in future):
- 3 MB for in-console editing
- 50 MB zipped as package for upload
- 250 MB when unzipped including layers
A sensible way to get around this is to mount EFS from your Lambda. This can be useful not only for loading libraries, but also for other storage.
Have a look through these blogs:
- https://aws.amazon.com/blogs/compute/using-amazon-efs-for-aws-lambda-in-your-serverless-applications/
- https://aws.amazon.com/blogs/aws/new-a-shared-file-system-for-your-lambda-functions/
回答5:
You can try the workaround used in the awesome serverless-python-requirements plugin.
Ideal solution is to use lambda layers if it solves the purpose. If the total dependency is greater than 250MB then you can sideload lesser used dependencies from S3 bucket during run time by utilizing the 512 MB provided in /tmp directory. The zipped dependencies are stored in S3 and lambda can fetch the files from S3 during initialisation. Unzip the dependecy pacakge and add the path to sys path.
Please note that the python dependencies need to be built on the Amazon Linux, which is the operating system for lambda containers. I used a EC2 instance to create the zip package.
You check the code used in serverless-python-requirements here
回答6:
Actually, you can request to increase the limit on deployment package size
1. Go to AWS Support after logging in AWS console
https://docs.aws.amazon.com/awssupport/latest/user/getting-started.html
2. Then ask for service limit increase.
3. And fill in desired max size.
AWS support engineer will get back to you about limit increase approval
回答7:
You cannot increase the package size, but you can use AWS Lambda layers to store some application dependencies.
https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html#configuration-layers-path
Before this layers a common used pattern to workaround this limitation was to download huge dependencies from S3.
来源：https://stackoverflow.com/questions/54632009/how-to-increase-the-maximum-size-of-the-aws-lambda-deployment-package-requesten