Thin client for Azure Monitor DataCollector API.
Project description
A thin client to send log data to Azure Monitor by using the HTTP Data Collector API.
Installation
$ pip install datacollector-api-client
Usage
from datacollector_api_client.client import DataCollectorWrapper wrapper = DataCollectorWrapper(LOGANALYTICS_WORKSPACE_ID, LOGANALYTICS_WORKSPACE_KEY) data = [{ "application": "my_app", "message": "my log message" }] response = wrapper.log_info(structured_log_message=data) print(response)
To log a message as error, use: wrapper.log_error(structured_log_message=data)
You can also override the default name of the tables by setting parameter: log_type=”your_preferred_table_name”
Enriching structured logging with Databricks information
Pass your dbutils instance from your Databricks session to the library
wrapper = DataCollectorWrapper(LOGANALYTICS_WORKSPACE_ID, LOGANALYTICS_WORKSPACE_KEY, dbutils) data = [{ "application": "Notebook", "message": f'Number of rows in dataframe: {df.count()}' }] response = wrapper.log_info(structured_log_message=data) print(response)
- The following data is also collected and appended to your log:
dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get()
dbutils.notebook.entry_point.getDbutils().notebook().getContext().tags().apply(‘clusterId’)
dbutils.notebook.entry_point.getDbutils().notebook().getContext().tags().apply(‘sessionId’)
dbutils.notebook.entry_point.getDbutils().notebook().getContext().tags().apply(‘user’)
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