如何在Lambda函数中创建图层 [英] How to create a layer in lambda function

问题描述

遵循的创建步骤

1. 创建一个虚拟环境并激活它
2. 点安装弹性搜索
3. 压缩site-packages.zip中的文件夹

4.在AWS中创建图层(假设名称为elastic)

5. 在lambda_handler中添加以下代码，并添加elastic
层

下面是代码

import json
from elasticsearch import Elasticsearch, RequestsHttpConnection
def lambda_handler(event, context):
    # TODO implement
    return {
        'statusCode': 200,
        'body': json.dumps('Hello from Lambda!')
    }

还是我得到了 "errorMessage": "Unable to import module 'lambda_function': No module named 'elasticsearch'",

解决方案

如果可以的话，我想推荐一种从未失败过的替代技术.该技术包括最近的 AWS博客中所述的 docker工具:

• 如何使用模拟Lambda创建Lambda层Docker环境?

因此，对于这个问题，我使用elasticsearch 验证了它，如下所示:

1. 创建空文件夹，例如mylayer.

2. 转到文件夹并创建内容为

   ['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'bulk', 'clear_scroll', 'close', 'count', 'create', 'delete', 'delete_by_query', 'delete_by_query_rethrottle', 'delete_script', 'exists', 'exists_source', 'explain', 'field_caps', 'get', 'get_script', 'get_script_context', 'get_script_languages', 'get_source', 'index', 'info', 'mget', 'msearch', 'msearch_template', 'mtermvectors', 'ping', 'put_script', 'rank_eval', 'reindex', 'reindex_rethrottle', 'render_search_template', 'scripts_painless_execute', 'scroll', 'search', 'search_shards', 'search_template', 'termvectors', 'update', 'update_by_query', 'update_by_query_rethrottle']
   

   的requirements.txt文件

elasticsearch

3. 运行以下docker命令(可以根据需要调整python版本):

docker run -v "$PWD":/var/task "lambci/lambda:build-python3.8" /bin/sh -c "pip install -r requirements.txt -t python/lib/python3.8/site-packages/; exit"

4. 将层创建为zip:

zip -r elastic.zip python > /dev/null

5. 在AWS控制台中基于elastic.zip创建lambda层.不要忘记将Compatible runtimes指定为python3.8.

6. 使用以下lambda函数在lambda中测试图层:

import json

from elasticsearch import Elasticsearch, RequestsHttpConnection

def lambda_handler(event, context):
    # TODO implement
    
    print(dir(Elasticsearch))
    
    return {
        'statusCode': 200,
        'body': json.dumps('Hello from Lambda!')
    }

该函数正确执行:

['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'bulk', 'clear_scroll', 'close', 'count', 'create', 'delete', 'delete_by_query', 'delete_by_query_rethrottle', 'delete_script', 'exists', 'exists_source', 'explain', 'field_caps', 'get', 'get_script', 'get_script_context', 'get_script_languages', 'get_source', 'index', 'info', 'mget', 'msearch', 'msearch_template', 'mtermvectors', 'ping', 'put_script', 'rank_eval', 'reindex', 'reindex_rethrottle', 'render_search_template', 'scripts_painless_execute', 'scroll', 'search', 'search_shards', 'search_template', 'termvectors', 'update', 'update_by_query', 'update_by_query_rethrottle']

Steps followed to create

1. Create a virtual environment and activate it
2. pip install elasticsearch
3. Zip the folder inside site-packages.zip

4.Create Layer in AWS (say name is elastic)

5. Add the code below in lambda_handler and add the Layer elastic

Below is code

import json
from elasticsearch import Elasticsearch, RequestsHttpConnection
def lambda_handler(event, context):
    # TODO implement
    return {
        'statusCode': 200,
        'body': json.dumps('Hello from Lambda!')
    }

Still I got "errorMessage": "Unable to import module 'lambda_function': No module named 'elasticsearch'",

解决方案

If I may, I would like to recommend an alternative technique which has never failed me. The technique includes docker tool described in the recent AWS blog:

• How do I create a Lambda layer using a simulated Lambda environment with Docker?

Thus for this question, I verified it using elasticsearch as follows:

1. Create empty folder, e.g. mylayer.

2. Go to the folder and create requirements.txt file with the content of

elasticsearch

3. Run the following docker command (may adjust python version to your needs):

docker run -v "$PWD":/var/task "lambci/lambda:build-python3.8" /bin/sh -c "pip install -r requirements.txt -t python/lib/python3.8/site-packages/; exit"

4. Create layer as zip:

zip -r elastic.zip python > /dev/null

5. Create lambda layer based on elastic.zip in the AWS Console. Don't forget to specify Compatible runtimes to python3.8.

6. Test the layer in lambda using the following lambda function:

import json

from elasticsearch import Elasticsearch, RequestsHttpConnection

def lambda_handler(event, context):
    # TODO implement
    
    print(dir(Elasticsearch))
    
    return {
        'statusCode': 200,
        'body': json.dumps('Hello from Lambda!')
    }

The function executes correctly:

['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'bulk', 'clear_scroll', 'close', 'count', 'create', 'delete', 'delete_by_query', 'delete_by_query_rethrottle', 'delete_script', 'exists', 'exists_source', 'explain', 'field_caps', 'get', 'get_script', 'get_script_context', 'get_script_languages', 'get_source', 'index', 'info', 'mget', 'msearch', 'msearch_template', 'mtermvectors', 'ping', 'put_script', 'rank_eval', 'reindex', 'reindex_rethrottle', 'render_search_template', 'scripts_painless_execute', 'scroll', 'search', 'search_shards', 'search_template', 'termvectors', 'update', 'update_by_query', 'update_by_query_rethrottle']

这篇关于如何在Lambda函数中创建图层的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！