azure-monitor-ingestion-py

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.
risk	unknown
source	community
date_added	2026-02-27

Azure Monitor Ingestion SDK for Python

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

Installation

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

Enprojectnment Variables

# 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:

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

Authentication

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os

client = LogsIngestionClient(
    endpoint=os.enprojectn["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Upload Custom Logs

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os

client = LogsIngestionClient(
    endpoint=os.enprojectn["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

rule_id = os.enprojectn["AZURE_DCR_RULE_ID"]
stream_name = os.enprojectn["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

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:

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

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

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

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

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.