chore!: BREAKING CHANGE: remove sqlite from telemetry config (llamastack#3808)

# What does this PR do?
- Removed sqlite sink from telemetry config.
- Removed related code
- Updated doc related to telemetry

## Test Plan
CI

Author: ehhuang

docs/docs/building_applications/telemetry.mdx

@@ -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
 

docs/docs/distributions/self_hosted_distro/starter.md

@@ -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
 

docs/docs/providers/telemetry/inline_meta-reference.mdx

@@ -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:=}
 ```

llama_stack/apis/telemetry/telemetry.py

@@ -421,104 +421,3 @@ async def log_event(
         :param ttl_seconds: The time to live of the event.
         """
         ...
-
-    async def query_traces(
-        self,
-        attribute_filters: list[QueryCondition] | None = None,
-        limit: int | None = 100,
-        offset: int | None = 0,
-        order_by: list[str] | None = None,
-    ) -> QueryTracesResponse:
-        """Query traces.
-
-        :param attribute_filters: The attribute filters to apply to the traces.
-        :param limit: The limit of traces to return.
-        :param offset: The offset of the traces to return.
-        :param order_by: The order by of the traces to return.
-        :returns: A QueryTracesResponse.
-        """
-        ...
-
-    async def get_trace(self, trace_id: str) -> Trace:
-        """Get a trace by its ID.
-
-        :param trace_id: The ID of the trace to get.
-        :returns: A Trace.
-        """
-        ...
-
-    async def get_span(self, trace_id: str, span_id: str) -> Span:
-        """Get a span by its ID.
-
-        :param trace_id: The ID of the trace to get the span from.
-        :param span_id: The ID of the span to get.
-        :returns: A Span.
-        """
-        ...
-
-    async def get_span_tree(
-        self,
-        span_id: str,
-        attributes_to_return: list[str] | None = None,
-        max_depth: int | None = None,
-    ) -> QuerySpanTreeResponse:
-        """Get a span tree by its ID.
-
-        :param span_id: The ID of the span to get the tree from.
-        :param attributes_to_return: The attributes to return in the tree.
-        :param max_depth: The maximum depth of the tree.
-        :returns: A QuerySpanTreeResponse.
-        """
-        ...
-
-    async def query_spans(
-        self,
-        attribute_filters: list[QueryCondition],
-        attributes_to_return: list[str],
-        max_depth: int | None = None,
-    ) -> QuerySpansResponse:
-        """Query spans.
-
-        :param attribute_filters: The attribute filters to apply to the spans.
-        :param attributes_to_return: The attributes to return in the spans.
-        :param max_depth: The maximum depth of the tree.
-        :returns: A QuerySpansResponse.
-        """
-        ...
-
-    async def save_spans_to_dataset(
-        self,
-        attribute_filters: list[QueryCondition],
-        attributes_to_save: list[str],
-        dataset_id: str,
-        max_depth: int | None = None,
-    ) -> None:
-        """Save spans to a dataset.
-
-        :param attribute_filters: The attribute filters to apply to the spans.
-        :param attributes_to_save: The attributes to save to the dataset.
-        :param dataset_id: The ID of the dataset to save the spans to.
-        :param max_depth: The maximum depth of the tree.
-        """
-        ...
-
-    async def query_metrics(
-        self,
-        metric_name: str,
-        start_time: int,
-        end_time: int | None = None,
-        granularity: str | None = None,
-        query_type: MetricQueryType = MetricQueryType.RANGE,
-        label_matchers: list[MetricLabelMatcher] | None = None,
-    ) -> QueryMetricsResponse:
-        """Query metrics.
-
-        :param metric_name: The name of the metric to query.
-        :param start_time: The start time of the metric to query.
-        :param end_time: The end time of the metric to query.
-        :param granularity: The granularity of the metric to query.
-        :param query_type: The type of query to perform.
-        :param label_matchers: The label matchers to apply to the metric.
-        :returns: A QueryMetricsResponse.
-        """
-        ...

llama_stack/core/library_client.py

@@ -207,8 +207,9 @@ def __init__(
         super().__init__()
         # when using the library client, we should not log to console since many
         # of our logs are intended for server-side usage
-        current_sinks = os.environ.get("TELEMETRY_SINKS", "sqlite").split(",")
-        os.environ["TELEMETRY_SINKS"] = ",".join(sink for sink in current_sinks if sink != "console")
+        if sinks_from_env := os.environ.get("TELEMETRY_SINKS", None):
+            current_sinks = sinks_from_env.strip().lower().split(",")
+            os.environ["TELEMETRY_SINKS"] = ",".join(sink for sink in current_sinks if sink != "console")
 
         if in_notebook():
             import nest_asyncio

llama_stack/distributions/ci-tests/run.yaml

@@ -159,8 +159,7 @@ providers:
     provider_type: inline::meta-reference
     config:
       service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/trace_store.db
+      sinks: ${env.TELEMETRY_SINKS:=}
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   post_training:
   - provider_id: torchtune-cpu

llama_stack/distributions/dell/run-with-safety.yaml

@@ -50,8 +50,7 @@ providers:
     provider_type: inline::meta-reference
     config:
       service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/trace_store.db
+      sinks: ${env.TELEMETRY_SINKS:=}
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   eval:
   - provider_id: meta-reference

llama_stack/distributions/dell/run.yaml

@@ -46,8 +46,7 @@ providers:
     provider_type: inline::meta-reference
     config:
       service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/trace_store.db
+      sinks: ${env.TELEMETRY_SINKS:=}
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   eval:
   - provider_id: meta-reference

llama_stack/distributions/meta-reference-gpu/run-with-safety.yaml

@@ -61,8 +61,7 @@ providers:
     provider_type: inline::meta-reference
     config:
       service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/trace_store.db
+      sinks: ${env.TELEMETRY_SINKS:=}
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   eval:
   - provider_id: meta-reference

llama_stack/distributions/meta-reference-gpu/run.yaml

@@ -51,8 +51,7 @@ providers:
     provider_type: inline::meta-reference
     config:
       service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=sqlite}
-      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/trace_store.db
+      sinks: ${env.TELEMETRY_SINKS:=}
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   eval:
   - provider_id: meta-reference