分析从 CosmosDB 读取文档的 Python 函数似乎需要比预期更多的时间

Question

I am profiling a Python function which loads 3 very small documents from CosmosDB (using 'container.read_item') and seems to take more time than (subjectively) expected.

I am using the 'pyinstrument' profiler and the results are:

1.065 get_user_projects  main.py:147
└─ 1.065 get_user_projects  services/project_data_store_service.py:13
   ├─ 0.918 get_project  services/project_data_store_service.py:7
   │  └─ 0.918 wrapper_use_tracer  azure/core/tracing/decorator.py:75
   │        [124 frames hidden]  azure, requests, urllib3, http, socke...
   │           0.662 _SSLSocket.read  <built-in>:0
   │           0.220 _SSLSocket.read  <built-in>:0

Is my suspicion right, and is there some tracing enabled for CosmosDB which takes a lot of time?

Upon creating my CosmosDB client, I disabled logging and lowered the log level using:

client = CosmosClient(endpoint, credential=key, logging_enable=False)
   
logger = logging.getLogger("azure.core.pipeline.policies.http_logging_policy")
logger.setLevel(logging.WARNING)

Of course, my Azure CosmosDB container setup could be the cause, but I want to rule out tracing issues.

Answer 1

Thank you Matias Quaranta posting your suggestion as an answer to help other community members.

"Normally when doing performance testing we do not look at 1 operation, but rather, what is the P99 or P95 or PXX latency during a period of time, and the reason is we will always have initial latency when connections are established."

For more information please refer this SO THREAD : Slow performance on Azure DocumentDB