File size: 3,703 Bytes
d8ad0fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | import functools
import time
from concurrent.futures import Future, ThreadPoolExecutor
from datetime import datetime
from typing import List, Optional, Tuple
import boto3
import requests
from mypy_boto3_lambda.client import LambdaClient
from mypy_boto3_lambda.type_defs import InvocationResponseTypeDef
from pydantic import BaseModel, ConfigDict
from requests import Request, Response
from requests.exceptions import RequestException
from retry import retry
GetLambdaResponse = Tuple[InvocationResponseTypeDef, datetime]
class GetLambdaResponseOptions(BaseModel):
lambda_arn: str
payload: Optional[str] = None
client: Optional[LambdaClient] = None
raise_on_error: bool = True
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
def get_lambda_response(
lambda_arn: str,
payload: Optional[str] = None,
client: Optional[LambdaClient] = None,
raise_on_error: bool = True,
) -> GetLambdaResponse:
"""Invoke function synchronously
Parameters
----------
lambda_arn : str
Lambda function ARN to invoke
payload : Optional[str], optional
JSON payload for Lambda invocation, by default None
client : Optional[LambdaClient], optional
Boto3 Lambda SDK client, by default None
raise_on_error : bool, optional
Whether to raise exception upon invocation error, by default True
Returns
-------
Tuple[InvocationResponseTypeDef, datetime]
Function response and approximate execution time
Raises
------
RuntimeError
Function invocation error details
"""
client = client or boto3.client("lambda")
payload = payload or ""
execution_time = datetime.utcnow()
response: InvocationResponseTypeDef = client.invoke(
FunctionName=lambda_arn,
InvocationType="RequestResponse",
Payload=payload,
)
has_error = response.get("FunctionError", "") == "Unhandled"
if has_error and raise_on_error:
error_payload = response["Payload"].read().decode()
raise RuntimeError(f"Function failed invocation: {error_payload}")
return response, execution_time
@retry(RequestException, delay=2, jitter=1.5, tries=5)
def get_http_response(request: Request) -> Response:
session = requests.Session()
result = session.send(request.prepare())
result.raise_for_status()
return result
def get_lambda_response_in_parallel(
get_lambda_response_options: List[GetLambdaResponseOptions],
) -> List[GetLambdaResponse]:
"""Invoke functions in parallel
Parameters
----------
get_lambda_response_options : List[GetLambdaResponseOptions]
List of options to call get_lambda_response with
Returns
-------
List[GetLambdaResponse]
Function responses and approximate execution time
"""
result_list = []
with ThreadPoolExecutor() as executor:
running_tasks: List[Future] = []
for options in get_lambda_response_options:
# Sleep 0.5, 1, 1.5, ... seconds between each invocation. This way
# we can guarantee that lambdas are executed in parallel, but they are
# called in the same "order" as they are passed in, thus guaranteeing that
# we can assert on the correct output.
time.sleep(0.5 * len(running_tasks))
get_lambda_response_callback = functools.partial(get_lambda_response, **options.model_dump())
running_tasks.append(
executor.submit(get_lambda_response_callback),
)
executor.shutdown(wait=True)
result_list.extend(running_task.result() for running_task in running_tasks)
return result_list
|