azure-monitor-ingestion-py – skill DevOps & Hạ tầng cho agent AI

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

Environment 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.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Use an authorized endpoint/DCR/stream and schema, with scoped identity permissions; DCE versus DCR ingestion endpoints depend on setup. The 2024 timestamps below are historical sample data, not a current live-ingestion probe. Use approved synthetic current timestamps for validation, redact PII/secrets before upload and confirm retention/charges. Close both client and credential, including failure paths; no logs should be uploaded simply to validate this guide.

Upload Custom Logs

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

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)

Incomplete uploads

An on_error callback can suppress the default exception. Persist a bounded, protected failure summary and report the upload as partial/failed; never silently discard failed logs or label the whole batch successful. Retry only classified transient failures with bounded backoff, reconciling ambiguous outcomes and duplicate-ingestion impact.

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

SDK chunking does not bound the application's input memory or total ingestion cost. Limit input bytes/rows, confirm the installed version's batching/concurrency and handle partial success.

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 environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.