this is a WIP of the caching mechanism, heavily inspired by @rcobb-scwx work!
To test it, you can add the parameter "cache_path" to your query:
import msticpy as mp prov: mp.QueryProvider = mp.QueryProvider("LogAnalytics") prov.connect() data = prov.Azure.list_aad_signins_for_account(cache_path=<PATH_TO_CACHE>)
If it is executed from a notebook, and PATH_TO_CACHE is the path to the notebook, the cell's output will contain:
the HTML representation of the first few rows of the dataframe generated from the query result
metadata of the query result:
timestamp when the cache was generated
String representation of the executed query
name of the function called
dictionary representation of the parameters provided to the query's function
hash of the parameters provided to the query's function (required to return the right cached value)
compressed query result
If it is executed outside of a notebook, the same data will be stored in the file provided in cache_path
The path to the notebook is required as the kernel does not know which file it is receiving inputs from, hence cannot know which cell output to read to find the cached data
Hello,
this is a WIP of the caching mechanism, heavily inspired by @rcobb-scwx work!
To test it, you can add the parameter "cache_path" to your query:
import msticpy as mp prov: mp.QueryProvider = mp.QueryProvider("LogAnalytics") prov.connect() data = prov.Azure.list_aad_signins_for_account(cache_path=<PATH_TO_CACHE>)If it is executed from a notebook, and PATH_TO_CACHE is the path to the notebook, the cell's output will contain:If it is executed outside of a notebook, the same data will be stored in the file provided in cache_path
The path to the notebook is required as the kernel does not know which file it is receiving inputs from, hence cannot know which cell output to read to find the cached data
Things to be done: