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version: "1.0.1" name: azure-monitor-ingestion-py description: | Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API. Triggers: "azure-monitor-ingestion", "LogsIngestionClient", "custom logs", "DCR", "data collection rule", "Log Analytics". package: azure-monitor-ingestion

Azure Monitor Ingestion SDK for Python

Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.

Installation

bash

pip install azure-monitor-ingestion
pip install azure-identity

Environment Variables

bash

# Data Collection Endpoint (DCE)
AZURE_DCE_ENDPOINT=https://<dce-name>.<region>.ingest.monitor.azure.com
 
# Data Collection Rule (DCR) immutable ID
AZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
 
# Stream name from DCR
AZURE_DCR_STREAM_NAME=Custom-MyTable_CL

Prerequisites

Before using this SDK, you need:

Log Analytics Workspace — Target for your logs
Data Collection Endpoint (DCE) — Ingestion endpoint
Data Collection Rule (DCR) — Defines schema and destination
Custom Table — In Log Analytics (created via DCR or manually)

Authentication

python

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os
client = LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Upload Custom Logs

python

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os
client = LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)
rule_id = os.environ["AZURE_DCR_RULE_ID"]
stream_name = os.environ["AZURE_DCR_STREAM_NAME"]
logs = [
    {"TimeGenerated": "2024-01-15T10:00:00Z", "Computer": "server1", "Message": "Application started"},
    {"TimeGenerated": "2024-01-15T10:01:00Z", "Computer": "server1", "Message": "Processing request"},
    {"TimeGenerated": "2024-01-15T10:02:00Z", "Computer": "server2", "Message": "Connection established"}
]
client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)

Upload from JSON File

python

import json
with open("logs.json", "r") as f:
    logs = json.load(f)
client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)

Custom Error Handling

Handle partial failures with a callback:

python

failed_logs = []
def on_error(error):
    print(f"Upload failed: {error.error}")
    failed_logs.extend(error.failed_logs)
client.upload(
    rule_id=rule_id,
    stream_name=stream_name,
    logs=logs,
    on_error=on_error
)
# Retry failed logs
if failed_logs:
    print(f"Retrying {len(failed_logs)} failed logs...")
    client.upload(rule_id=rule_id, stream_name=stream_name, logs=failed_logs)

Ignore Errors

python

def ignore_errors(error):
    pass  # Silently ignore upload failures
client.upload(
    rule_id=rule_id,
    stream_name=stream_name,
    logs=logs,
    on_error=ignore_errors
)

Async Client

python

import asyncio
from azure.monitor.ingestion.aio import LogsIngestionClient
from azure.identity.aio import DefaultAzureCredential
async def upload_logs():
    async with LogsIngestionClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        await client.upload(
            rule_id=rule_id,
            stream_name=stream_name,
            logs=logs
        )
asyncio.run(upload_logs())

Sovereign Clouds

python

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.ingestion import LogsIngestionClient
# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
client = LogsIngestionClient(
    endpoint="https://example.ingest.monitor.azure.us",
    credential=credential,
    credential_scopes=["https://monitor.azure.us/.default"]
)

Batching Behavior

The SDK automatically:

Splits logs into chunks of 1MB or less
Compresses each chunk with gzip
Uploads chunks in parallel

No manual batching needed for large log sets.

Client Types

Client	Purpose
`LogsIngestionClient`	Sync client for uploading logs
`LogsIngestionClient` (aio)	Async client for uploading logs

Key Concepts

Concept	Description
DCE	Data Collection Endpoint — ingestion URL
DCR	Data Collection Rule — defines schema, transformations, destination
Stream	Named data flow within a DCR
Custom Table	Target table in Log Analytics (ends with `_CL`)

DCR Stream Name Format

Stream names follow patterns:

Custom-<TableName>_CL — For custom tables
Microsoft-<TableName> — For built-in tables

Best Practices

Use DefaultAzureCredential for authentication
Handle errors gracefully — use on_error callback for partial failures
Include TimeGenerated — Required field for all logs
Match DCR schema — Log fields must match DCR column definitions
Use async client for high-throughput scenarios
Batch uploads — SDK handles batching, but send reasonable chunks
Monitor ingestion — Check Log Analytics for ingestion status
Use context manager — Ensures proper client cleanup

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