القائمة
Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
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| 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. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | data |
| category | data-engineering |
| risk | unknown |
| source | community |
| tags | ["skill","data","data-engineering","azure","monitor","ingestion"] |
Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.
pip install azure-monitor-ingestion
pip install azure-identity
# 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
Before using this SDK, you need:
from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os
client = LogsIngestionClient(
endpoint=os.environ["AZURE_DCE_ENDPOINT"],
credential=DefaultAzureCredential()
)
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"},
{: , : , : },
{: , : , : }
]
client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)
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)
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)
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
)
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())
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"]
)
The SDK automatically:
No manual batching needed for large log sets.
| Client | Purpose |
|---|---|
LogsIngestionClient | Sync client for uploading logs |
LogsIngestionClient (aio) | Async client for uploading logs |
| 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) |
Stream names follow patterns:
Custom-<TableName>_CL — For custom tablesMicrosoft-<TableName> — For built-in tableson_error callback for partial failuresThis skill is applicable to execute the workflow or actions described in the overview.