---
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.
Triggers: "azure-monitor-ingestion", "LogsIngestionClient", "custom logs", "DCR", "data collection rule", "Log Analytics".
license: MIT
metadata:
author: Microsoft
version: "1.0.0"
package: azure-monitor-ingestion
---
# Azure Monitor Ingestion SDK for Python
Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.
## Installation
```bash
pip install azure-monitor-ingestion
pip install azure-identity
```
## Environment Variables
```bash
# 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
```
## 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 & Lifecycle
> **🔑 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.
```python
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)
...
```
## Upload Custom Logs
```python
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)
```
## Upload from JSON File
```python
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:
```python
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
```python
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
```python
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
```python
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(...)
...
```
## 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 tables
- `Microsoft-<TableName>` — For built-in tables
## Best Practices
1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
2. **Always use context managers for clients and async credentials.** Wrap every client in `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.
3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.
4. **Handle errors gracefully** — use `on_error` callback for partial failures
5. **Include TimeGenerated** — Required field for all logs
6. **Match DCR schema** — Log fields must match DCR column definitions
7. **Use async client** for high-throughput scenarios
8. **Batch uploads** — SDK handles batching, but send reasonable chunks
9. **Monitor ingestion** — Check Log Analytics for ingestion status
## Reference Files
| File | Contents |
|------|----------|
| [references/capabilities.md](references/capabilities.md) | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| [references/non-hero-scenarios.md](references/non-hero-scenarios.md) | Dedicated non-hero examples for secondary/advanced scenarios. |