游戏有点晚了，但认为分享不会有什么坏处。最佳实践建议将处理程序与 Lambda 的核心逻辑分开。不仅可以添加额外的定义，它可以导致更清晰的代码并减少浪费——例如。对 S3 的多个 API 调用。因此，尽管它可能会失控，但我不同意对您最初问题的一些批评。将您的处理程序用作将完成您的各种工作的附加功能的逻辑接口是有效的。在数据架构和工程领域，以这种方式工作通常成本更低，效率更高。特别是如果您正在构建 ETL 管道，遵循面向服务的架构模式。诚然，我有点特立独行，有些人可能会觉得这不守规矩/令人震惊，但出于各种原因，我什至在我的 Lambda 中构建类——例如。集中的、类似于数据湖的 S3 存储桶，可容纳各种文件类型，减少不必要的请求等……我支持它。这是我不久前放在集线器上的 CDK 示例项目中的一个处理程序文件的示例。希望它能给你一些有用的想法，或者至少在想要增强你的 Lambdas 时不会感到孤单。
import requests
import json
from requests.exceptions import Timeout
from requests.exceptions import HTTPError
from botocore.exceptions import ClientError
from datetime import date
import csv
import os
import boto3
import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
class Asteroids:
"""Client to NASA API and execution interface to branch data processing by file type.
Notes:
This class doesn't look like a normal class. It is a simple example of how one might
workaround AWS Lambda's limitations of class use in handlers. It also allows for
better organization of code to simplify this example. If one planned to add
other NASA endpoints or process larger amounts of Asteroid data for both .csv and .json formats,
asteroids_json and asteroids_csv should be modularized and divided into separate lambdas
where stepfunction orchestration is implemented for a more comprehensive workflow.
However, for the sake of this demo I'm keeping it lean and easy.
"""
def execute(self, format):
"""Serves as Interface to assign class attributes and execute class methods
Raises:
Exception: If file format is not of .json or .csv file types.
Notes:
Have fun!
"""
self.file_format=format
self.today=date.today().strftime('%Y-%m-%d')
# method call below used when Secrets Manager integrated. See get_secret.__doc__ for more.
# self.api_key=get_secret('nasa_api_key')
self.api_key=os.environ["NASA_KEY"]
self.endpoint=f"https://api.nasa.gov/neo/rest/v1/feed?start_date={self.today}&end_date={self.today}&api_key={self.api_key}"
self.response_object=self.nasa_client(self.endpoint)
self.processed_response=self.process_asteroids(self.response_object)
if self.file_format == "json":
self.asteroids_json(self.processed_response)
elif self.file_format == "csv":
self.asteroids_csv(self.processed_response)
else:
raise Exception("FILE FORMAT NOT RECOGNIZED")
self.write_to_s3()
def nasa_client(self, endpoint):
"""Client component for API call to NASA endpoint.
Args:
endpoint (str): Parameterized url for API call.
Raises:
Timeout: If connection not made in 5s and/or data not retrieved in 15s.
HTTPError & Exception: Self-explanatory
Notes:
See Cloudwatch logs for debugging.
"""
try:
response = requests.get(endpoint, timeout=(5, 15))
except Timeout as timeout:
print(f"NASA GET request timed out: {timeout}")
except HTTPError as http_err:
print(f"HTTP error occurred: {http_err}")
except Exception as err:
print(f'Other error occurred: {err}')
else:
return json.loads(response.content)
def process_asteroids(self, payload):
"""Process old, and create new, data object with content from response.
Args:
payload (b'str'): Binary string of asteroid data to be processed.
"""
near_earth_objects = payload["near_earth_objects"][f"{self.today}"]
asteroids = []
for neo in near_earth_objects:
asteroid_object = {
"id" : neo['id'],
"name" : neo['name'],
"hazard_potential" : neo['is_potentially_hazardous_asteroid'],
"est_diameter_min_ft": neo['estimated_diameter']['feet']['estimated_diameter_min'],
"est_diameter_max_ft": neo['estimated_diameter']['feet']['estimated_diameter_max'],
"miss_distance_miles": [item['miss_distance']['miles'] for item in neo['close_approach_data']],
"close_approach_exact_time": [item['close_approach_date_full'] for item in neo['close_approach_data']]
}
asteroids.append(asteroid_object)
return asteroids
def asteroids_json(self, payload):
"""Creates json object from payload content then writes to .json file.
Args:
payload (b'str'): Binary string of asteroid data to be processed.
"""
json_file = open(f"/tmp/asteroids_{self.today}.json",'w')
json_file.write(json.dumps(payload, indent=4))
json_file.close()
def asteroids_csv(self, payload):
"""Creates .csv object from payload content then writes to .csv file.
"""
csv_file=open(f"/tmp/asteroids_{self.today}.csv",'w', newline='\n')
fields=list(payload[0].keys())
writer=csv.DictWriter(csv_file, fieldnames=fields)
writer.writeheader()
writer.writerows(payload)
csv_file.close()
def get_secret(self):
"""Gets secret from AWS Secrets Manager
Notes:
Have yet to integrate into the CDK. Leaving as example code.
"""
secret_name = os.environ['TOKEN_SECRET_NAME']
region_name = os.environ['REGION']
session = boto3.session.Session()
client = session.client(service_name='secretsmanager', region_name=region_name)
try:
get_secret_value_response = client.get_secret_value(SecretId=secret_name)
except ClientError as e:
raise e
else:
if 'SecretString' in get_secret_value_response:
secret = get_secret_value_response['SecretString']
else:
secret = b64decode(get_secret_value_response['SecretBinary'])
return secret
def write_to_s3(self):
"""Uploads both .json and .csv files to s3
"""
s3 = boto3.client('s3')
s3.upload_file(f"/tmp/asteroids_{self.today}.{self.file_format}", os.environ['S3_BUCKET'], f"asteroid_data/asteroids_{self.today}.{self.file_format}")
def handler(event, context):
"""Instantiates class and triggers execution method.
Args:
event (dict): Lists a custom dict that determines interface control flow--i.e. `csv` or `json`.
context (obj): Provides methods and properties that contain invocation, function and
execution environment information.
*Not used herein.
"""
asteroids = Asteroids()
asteroids.execute(event)