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chore!: BREAKING CHANGE: remove sqlite from telemetry config

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# What does this PR do?


## Test Plan
This commit is contained in:
Eric Huang 2025-10-14 15:31:01 -07:00
parent d875e427bf
commit 49e9b53e00
21 changed files with 26 additions and 1026 deletions
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136
docs/docs/building_applications/telemetry.mdx
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@ -10,58 +10,8 @@ import TabItem from '@theme/TabItem';
# Telemetry
The Llama Stack telemetry system provides comprehensive tracing, metrics, and logging capabilities. It supports multiple sink types including OpenTelemetry, SQLite, and Console output for complete observability of your AI applications.
The Llama Stack uses OpenTelemetry to provide comprehensive tracing, metrics, and logging capabilities.
## Event Types
The telemetry system supports three main types of events:
<Tabs>
<TabItem value="unstructured" label="Unstructured Logs">
Free-form log messages with severity levels for general application logging:
```python
unstructured_log_event = UnstructuredLogEvent(
message="This is a log message",
severity=LogSeverity.INFO
)
```
</TabItem>
<TabItem value="metrics" label="Metric Events">
Numerical measurements with units for tracking performance and usage:
```python
metric_event = MetricEvent(
metric="my_metric",
value=10,
unit="count"
)
```
</TabItem>
<TabItem value="structured" label="Structured Logs">
System events like span start/end that provide structured operation tracking:
```python
structured_log_event = SpanStartPayload(
name="my_span",
parent_span_id="parent_span_id"
)
```
</TabItem>
</Tabs>
## Spans and Traces
- **Spans**: Represent individual operations with timing information and hierarchical relationships
- **Traces**: Collections of related spans that form a complete request flow across your application
This hierarchical structure allows you to understand the complete execution path of requests through your Llama Stack application.
## Automatic Metrics Generation
@ -129,21 +79,6 @@ Send events to an OpenTelemetry Collector for integration with observability pla
- Compatible with all OpenTelemetry collectors
- Supports both traces and metrics
</TabItem>
<TabItem value="sqlite" label="SQLite">
Store events in a local SQLite database for direct querying:
**Use Cases:**
- Local development and debugging
- Custom analytics and reporting
- Offline analysis of application behavior
**Features:**
- Direct SQL querying capabilities
- Persistent local storage
- No external dependencies
</TabItem>
<TabItem value="console" label="Console">
@ -174,9 +109,8 @@ telemetry:
provider_type: inline::meta-reference
config:
service_name: "llama-stack-service"
sinks: ['console', 'sqlite', 'otel_trace', 'otel_metric']
sinks: ['console', 'otel_trace', 'otel_metric']
otel_exporter_otlp_endpoint: "http://localhost:4318"
sqlite_db_path: "/path/to/telemetry.db"
```
### Environment Variables
@ -185,7 +119,7 @@ Configure telemetry behavior using environment variables:
- **`OTEL_EXPORTER_OTLP_ENDPOINT`**: OpenTelemetry Collector endpoint (default: `http://localhost:4318`)
- **`OTEL_SERVICE_NAME`**: Service name for telemetry (default: empty string)
- **`TELEMETRY_SINKS`**: Comma-separated list of sinks (default: `console,sqlite`)
- **`TELEMETRY_SINKS`**: Comma-separated list of sinks (default: `[]`)
### Quick Setup: Complete Telemetry Stack
@ -248,37 +182,10 @@ Forward metrics to other observability systems:
</TabItem>
</Tabs>
## SQLite Querying
The `sqlite` sink allows you to query traces without an external system. This is particularly useful for development and custom analytics.
### Example Queries
```sql
-- Query recent traces
SELECT * FROM traces WHERE timestamp > datetime('now', '-1 hour');
-- Analyze span durations
SELECT name, AVG(duration_ms) as avg_duration
FROM spans
GROUP BY name
ORDER BY avg_duration DESC;
-- Find slow operations
SELECT * FROM spans
WHERE duration_ms > 1000
ORDER BY duration_ms DESC;
```
:::tip[Advanced Analytics]
Refer to the [Getting Started notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) for more examples on querying traces and spans programmatically.
:::
## Best Practices
### 🔍 **Monitoring Strategy**
- Use OpenTelemetry for production environments
- Combine multiple sinks for development (console + SQLite)
- Set up alerts on key metrics like token usage and error rates
### 📊 **Metrics Analysis**
@ -293,45 +200,8 @@ Refer to the [Getting Started notebook](https://github.com/meta-llama/llama-stac
### 🔧 **Configuration Management**
- Use environment variables for flexible deployment
- Configure appropriate retention policies for SQLite
- Ensure proper network access to OpenTelemetry collectors
## Integration Examples
### Basic Telemetry Setup
```python
from llama_stack_client import LlamaStackClient
# Client with telemetry headers
client = LlamaStackClient(
base_url="http://localhost:8000",
extra_headers={
"X-Telemetry-Service": "my-ai-app",
"X-Telemetry-Version": "1.0.0"
}
)
# All API calls will be automatically traced
response = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=[{"role": "user", "content": "Hello!"}]
)
```
### Custom Telemetry Context
```python
# Add custom span attributes for better tracking
with tracer.start_as_current_span("custom_operation") as span:
span.set_attribute("user_id", "user123")
span.set_attribute("operation_type", "chat_completion")
response = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=[{"role": "user", "content": "Hello!"}]
)
```
## Related Resources

3
docs/docs/distributions/self_hosted_distro/starter.md
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@ -119,7 +119,7 @@ The following environment variables can be configured:
### Telemetry Configuration
- `OTEL_SERVICE_NAME`: OpenTelemetry service name
- `TELEMETRY_SINKS`: Telemetry sinks (default: `console,sqlite`)
- `TELEMETRY_SINKS`: Telemetry sinks (default: `[]`)
## Enabling Providers
@ -216,7 +216,6 @@ The starter distribution uses SQLite for local storage of various components:
- **Files metadata**: `~/.llama/distributions/starter/files_metadata.db`
- **Agents store**: `~/.llama/distributions/starter/agents_store.db`
- **Responses store**: `~/.llama/distributions/starter/responses_store.db`
- **Trace store**: `~/.llama/distributions/starter/trace_store.db`
- **Evaluation store**: `~/.llama/distributions/starter/meta_reference_eval.db`
- **Dataset I/O stores**: Various HuggingFace and local filesystem stores

6
docs/docs/providers/telemetry/inline_meta-reference.mdx
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@ -16,14 +16,12 @@ Meta's reference implementation of telemetry and observability using OpenTelemet
|-------|------|----------|---------|-------------|
| `otel_exporter_otlp_endpoint` | `str \| None` | No | | The OpenTelemetry collector endpoint URL (base URL for traces, metrics, and logs). If not set, the SDK will use OTEL_EXPORTER_OTLP_ENDPOINT environment variable. |
| `service_name` | `<class 'str'>` | No | ​ | The service name to use for telemetry |
| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [&lt;TelemetrySink.SQLITE: 'sqlite'&gt;] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console) |
| `sqlite_db_path` | `<class 'str'>` | No | ~/.llama/runtime/trace_store.db | The path to the SQLite database to use for storing traces |
| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, console) |
## Sample Configuration
```yaml
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/trace_store.db
sinks: ${env.TELEMETRY_SINKS:=}
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
```