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Agent skill
azure-monitor-ingestion-py
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".
Install this agent skill to your Project
npx add-skill https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py
SKILL.md
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:
- 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
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
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)
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 tablesMicrosoft-<TableName>— For built-in tables
Best Practices
- Use DefaultAzureCredential for authentication
- Handle errors gracefully — use
on_errorcallback 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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