Microsoft Azure Monitor Ingestion Client Library for Python
The Azure Monitor Ingestion client library is used to send custom logs to Azure Monitor using the Logs Ingestion API.
This library allows you to send data from virtually any source to supported built-in tables or to custom tables that you create in Log Analytics workspace. You can even extend the schema of built-in tables with custom columns.
Resources:
Install the Azure Monitor Ingestion client library for Python with pip:
pip install azure-monitor-ingestion
An authenticated client is required to upload Logs to Azure Monitor. The library includes both synchronous and asynchronous forms of the clients. To authenticate, create an instance of a token credential. Use that instance when creating a LogsIngestionClient. The following examples use DefaultAzureCredential from the azure-identity package.
Consider the following example, which creates synchronous clients for uploading logs:
import os
from azure.identity import DefaultAzureCredential
from azure.monitor.ingestion import LogsIngestionClient
endpoint = os.environ['DATA_COLLECTION_ENDPOINT']
credential = DefaultAzureCredential()
logs_client = LogsIngestionClient(endpoint, credential)
The asynchronous forms of the client APIs are found in the .aio-suffixed namespace. For example:
import os
from azure.identity.aio import DefaultAzureCredential
from azure.monitor.ingestion.aio import LogsIngestionClient
endpoint = os.environ['DATA_COLLECTION_ENDPOINT']
credential = DefaultAzureCredential()
logs_client = LogsIngestionClient(endpoint, credential)
By default, LogsIngestionClient is configured to connect to the public Azure cloud. To connect to non-public Azure clouds, some additional configuration is required. The appropriate scope for authentication must be provided using the credential_scopes keyword argument. The following example shows how to configure the client to connect to Azure US Government:
logs_client = LogsIngestionClient(endpoint, credential_scopes=["https://monitor.azure.us//.default"])
Data Collection Endpoints (DCEs) allow you to uniquely configure ingestion settings for Azure Monitor. This article provides an overview of data collection endpoints including their contents and structure and how you can create and work with them.
Data collection rules (DCR) define data collected by Azure Monitor and specify how and where that data should be sent or stored. The REST API call must specify a DCR to use. A single DCE can support multiple DCRs, so you can specify a different DCR for different sources and target tables.
The DCR must understand the structure of the input data and the structure of the target table. If the two don't match, it can use a transformation to convert the source data to match the target table. You may also use the transform to filter source data and perform any other calculations or conversions.
For more information, see Data collection rules in Azure Monitor, and see this article for details about a DCR's structure. For information on how to retrieve a DCR ID, see this tutorial.
Custom logs can send data to any custom table that you create and to certain built-in tables in your Log Analytics workspace. The target table must exist before you can send data to it. The following built-in tables are currently supported:
The logs that were uploaded using this library can be queried using the Azure Monitor Query client library.
This example shows uploading logs to Azure Monitor.
import os
from azure.core.exceptions import HttpResponseError
from azure.identity import DefaultAzureCredential
from azure.monitor.ingestion import LogsIngestionClient
endpoint = os.environ['DATA_COLLECTION_ENDPOINT']
credential = DefaultAzureCredential()
client = LogsIngestionClient(endpoint=endpoint, credential=credential, logging_enable=True)
rule_id = os.environ['LOGS_DCR_RULE_ID']
body = [
{
"Time": "2021-12-08T23:51:14.1104269Z",
"Computer": "Computer1",
"AdditionalContext": "context-2"
},
{
"Time": "2021-12-08T23:51:14.1104269Z",
"Computer": "Computer2",
"AdditionalContext": "context"
}
]
try:
client.upload(rule_id=rule_id, stream_name=os.environ['LOGS_DCR_STREAM_NAME'], logs=body)
except HttpResponseError as e:
print(f"Upload failed: {e}")
To upload logs with custom error handling, you can pass a callback function to the on_error parameter of the upload method. The callback function is called for each error that occurs during the upload and should expect one argument that corresponds to an LogsUploadError object. This object contains the error encountered and the list of logs that failed to upload.
# Example 1: Collect all logs that failed to upload.
failed_logs = []
def on_error(error):
print("Log chunk failed to upload with error: ", error.error)
failed_logs.extend(error.failed_logs)
# Example 2: Ignore all errors.
def on_error_pass(error):
pass
client.upload(rule_id=rule_id, stream_name=os.environ['LOGS_DCR_STREAM_NAME'], logs=body, on_error=on_error)
For details on diagnosing various failure scenarios, see our troubleshooting guide.
To learn more about Azure Monitor, see the Azure Monitor service documentation.
The following code samples show common scenarios with the Azure Monitor Ingestion client library.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
logs parameter in the upload method. (#32591)on_error parameter to the upload method to allow users to handle errors in their own way.
LogsUploadError class was added to encapsulate information about the error. An instance of this class is passed to the on_error callback.logs parameter. (#28373)msrest dependency.isodate>=0.6.0 (isodate was required by msrest).typing-extensions>=4.0.1.~azure.monitor.ingestion.LogsIngestionClient to send logs to Azure Monitor along with ~azure.monitor.ingestion.aio.LogsIngestionClient.