microsoft/azure-monitor-ingestion-py
| Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
npx skills add https://github.com/microsoft/skills --skill azure-monitor-ingestion-py
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 # Required for all auth methods
# Data Collection Rule (DCR) immutable ID
AZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx # Required for all auth methods
# Stream name from DCR
AZURE_DCR_STREAM_NAME=Custom-MyTable_CL # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
Before using this SDK, you need:
> 🔑 Two rules apply to every code sample below:
>
> 1. 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: DefaultAzureCredential works as-is.
> - Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
> 2. 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: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)
>
> Snippets may abbreviate this setup, but production code should always follow both rules.
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)
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)
with LogsIngestionClient(
endpoint="https://example.ingest.monitor.azure.us",
credential=credential,
credential_scopes=["https://monitor.azure.us/.default"]
) as client:
# client.upload(...)
...
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. |
Take microsoft/azure-monitor-ingestion-py from the repository into ~/.claude/skills for personal
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same name cannot sit side by side — one of them will be ignored.
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Without those the skill loads but fails at the first command.