npx skills add ...
npx skills add microsoft/agent-skills --skill azure-monitor-ingestion-py
npx skills add microsoft/agent-skills --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".
The same skill content is published under more than one repo. The install counts are split across them; any of these commands works.
Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.
Before using this SDK, you need:
🔑 Two rules apply to every code sample below:
- Prefer
DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
- Local dev:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
- Sync:
with <Client>(...) as client:- Async:
async with <Client>(...) as client:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
Handle partial failures with a callback:
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 tablesazure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.with Client(...) as client: (sync) or async with Client(...) as client: (async) to ensure proper cleanup. For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.DefaultAzureCredential for code that runs locally. Use a specific token credential for code that runs in Azure.on_error callback for partial failures| File | Contents |
|---|---|
| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
import os
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
with LogsIngestionClient(
endpoint=os.environ["AZURE_DCE_ENDPOINT"],
credential=credential
) as client:
# Use `client.upload(...)` for all subsequent operations (see examples below)
...from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os
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"}
]
with LogsIngestionClient(
endpoint=os.environ["AZURE_DCE_ENDPOINT"],
credential=DefaultAzureCredential()
) as client:
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)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)
with LogsIngestionClient(
endpoint="https://example.ingest.monitor.azure.us",
credential=credential,
credential_scopes=["https://monitor.azure.us/.default"]
) as client:
# client.upload(...)
...