Unified trace table schema reference

The unified trace table follows the OpenTelemetry span data model. Each row is one span. The table is clustered by time.

Column	Description	Type
record_id	Unique identifier for this table row.	STRING
time	Timestamp when the span was recorded.	TIMESTAMP
date	UTC date when the span was recorded. Useful for partition pruning.	DATE
service_name	Name of the Unity AI Gateway service (endpoint name).	STRING
service_id	Identifier for the service named in service_name.	STRING
trace_id	Identifier shared by all spans in a single request tree. Filter on this to reconstruct a full trace.	STRING
span_id	Unique identifier for this span.	STRING
trace_state	OpenTelemetry tracestate header value, if present.	STRING
parent_span_id	span_id of the parent span. NULL for root spans (one per request).	STRING
flags	OpenTelemetry trace flags bitmask.	INT
name	Human-readable span name, for example my-endpoint-mlflow/v1/chat/completions.	STRING
kind	Span kind: SPAN_KIND_SERVER (root, one per request) or SPAN_KIND_CLIENT (downstream call).	STRING
start_time_unix_nano	Span start time in nanoseconds since Unix epoch.	BIGINT
end_time_unix_nano	Span end time in nanoseconds since Unix epoch.	BIGINT
attributes	Span attributes as a VARIANT object. Key names contain dots; use backtick syntax to access them: attributes:\enduser.id``. See Key attributes below.	VARIANT
dropped_attributes_count	Number of attributes dropped due to limits.	INT
events	Array of timed events within the span. The primary event type is policy_evaluated, which records per-policy evaluation detail. See Policy evaluation events.	ARRAY<STRUCT>
dropped_events_count	Number of events dropped due to limits.	INT
links	Array of links to other spans or traces.	ARRAY<STRUCT>
dropped_links_count	Number of links dropped due to limits.	INT
status	Span status with code (STATUS_CODE_OK or STATUS_CODE_ERROR) and optional message.	STRUCT
resource	Resource attributes describing the instrumented entity, for example service.name and SDK metadata.	STRUCT
resource_schema_url	Schema URL for the resource semantic conventions.	STRING
instrumentation_scope	Name and version of the instrumentation library that produced the span.	STRUCT
span_schema_url	Schema URL for the span semantic conventions.	STRING

Column	Description	Type
record_id	Unique identifier for this table row.	STRING
time	Timestamp when the span was recorded.	TIMESTAMP
date	UTC date when the span was recorded. Useful for partition pruning.	DATE
service_name	Name of the Unity AI Gateway service (endpoint name).	STRING
service_id	Identifier for the service named in service_name.	STRING
trace_id	Identifier shared by all spans in a single request tree. Filter on this to reconstruct a full trace.	STRING
span_id	Unique identifier for this span.	STRING
trace_state	OpenTelemetry tracestate header value, if present.	STRING
parent_span_id	span_id of the parent span. NULL for root spans (one per request).	STRING
flags	OpenTelemetry trace flags bitmask.	INT
name	Human-readable span name, for example my-endpoint-mlflow/v1/chat/completions.	STRING
kind	Span kind: SPAN_KIND_SERVER (root, one per request) or SPAN_KIND_CLIENT (downstream call).	STRING
start_time_unix_nano	Span start time in nanoseconds since Unix epoch.	BIGINT
end_time_unix_nano	Span end time in nanoseconds since Unix epoch.	BIGINT
attributes	Span attributes as a VARIANT object. Key names contain dots; use backtick syntax to access them: attributes:\enduser.id``. See Key attributes below.	VARIANT
dropped_attributes_count	Number of attributes dropped due to limits.	INT
events	Array of timed events within the span. The primary event type is policy_evaluated, which records per-policy evaluation detail. See Policy evaluation events.	ARRAY<STRUCT>
dropped_events_count	Number of events dropped due to limits.	INT
links	Array of links to other spans or traces.	ARRAY<STRUCT>
dropped_links_count	Number of links dropped due to limits.	INT
status	Span status with code (STATUS_CODE_OK or STATUS_CODE_ERROR) and optional message.	STRUCT
resource	Resource attributes describing the instrumented entity, for example service.name and SDK metadata.	STRUCT
resource_schema_url	Schema URL for the resource semantic conventions.	STRING
instrumentation_scope	Name and version of the instrumentation library that produced the span.	STRUCT
span_schema_url	Schema URL for the span semantic conventions.	STRING

The table also carries columns needed for its physical optimization, notably clustering, that are not useful for queries. These columns are named with an _ prefix. Treat them as implementation details that may change in future versions.

Key attributes

The attributes column is a VARIANT. Access fields using backtick syntax for keys that contain dots: attributes:\gen_ai.request.model``. The keys present depend on whether the span is a model service (LLM) call or an MCP service call.

Model service (LLM) spans:

Attribute	Description
databricks.api_type	Inbound API type, for example openai/v1/responses.
enduser.id	User or service principal that made the request.
databricks.requester_type	Requester type, for example USER.
databricks.request_id	Databricks-generated request ID.
databricks.url	Full request URL.
databricks.latency_ms	End-to-end request latency in milliseconds.
databricks.time_to_first_byte_ms	Time to first byte in milliseconds.
databricks.action	Attempt type, for example initial_attempt or retry.
databricks.destination_id	Target model, for example databricks-claude-sonnet-4-6.
databricks.outcome	success or failure.
gen_ai.operation.name	Operation, for example chat.
gen_ai.request.model	Requested model.
gen_ai.provider.name	Provider, for example databricks.
gen_ai.usage.input_tokens	Input tokens consumed.
gen_ai.usage.output_tokens	Output tokens generated.
http.response.status_code	HTTP status code.
error.type	Error type on failures, for example invalid_request.
mlflow.chat.tokenUsage	Detailed token usage as a JSON string.
mlflow.spanInputs	Serialized request payload.
mlflow.spanOutputs	Serialized response payload (empty on failure).

Attribute	Description
databricks.api_type	Inbound API type, for example openai/v1/responses.
enduser.id	User or service principal that made the request.
databricks.requester_type	Requester type, for example USER.
databricks.request_id	Databricks-generated request ID.
databricks.url	Full request URL.
databricks.latency_ms	End-to-end request latency in milliseconds.
databricks.time_to_first_byte_ms	Time to first byte in milliseconds.
databricks.action	Attempt type, for example initial_attempt or retry.
databricks.destination_id	Target model, for example databricks-claude-sonnet-4-6.
databricks.outcome	success or failure.
gen_ai.operation.name	Operation, for example chat.
gen_ai.request.model	Requested model.
gen_ai.provider.name	Provider, for example databricks.
gen_ai.usage.input_tokens	Input tokens consumed.
gen_ai.usage.output_tokens	Output tokens generated.
http.response.status_code	HTTP status code.
error.type	Error type on failures, for example invalid_request.
mlflow.chat.tokenUsage	Detailed token usage as a JSON string.
mlflow.spanInputs	Serialized request payload.
mlflow.spanOutputs	Serialized response payload (empty on failure).

MCP service spans:

Attribute	Description
mcp.method.name	MCP method, for example tools/list or tools/call.
gen_ai.operation.name	Operation, for example execute_tool for tools/call.
gen_ai.tool.name	Name of the tool being invoked, for example atlassianUserInfo.
gen_ai.tool.call.arguments	Extracted tool arguments, as a JSON string.
gen_ai.tool.call.result	Extracted tool result, as a JSON string.
databricks.requester_type	Requester type, for example USER.
databricks.request_id	Databricks-generated request ID.
databricks.tool.connection_type	MCP connection type, for example EXTERNAL_MCP.
tool_source	Backing connection or managed MCP server name, for example main.default.gh-conn (external) or code_interpreter (managed). The MCP service name is in the service_name column.
tool_type	Tool type, for example external_connection.
workspace_id	Workspace ID.
enduser.id	User or service principal that made the request.
http.response.status_code	HTTP status from the MCP call.
rpc.response.status_code	JSON-RPC status code, for example -32003. The primary MCP failure signal.
error.type	Failure classification, for example policy_deny, upstream_error, or internal_error.
mlflow.spanType	Span type. TOOL for MCP spans. Useful for WHERE filtering.
mlflow.spanInputs	Serialized JSON-RPC request.
mlflow.spanOutputs	Serialized MCP/JSON-RPC response.

Attribute	Description
mcp.method.name	MCP method, for example tools/list or tools/call.
gen_ai.operation.name	Operation, for example execute_tool for tools/call.
gen_ai.tool.name	Name of the tool being invoked, for example atlassianUserInfo.
gen_ai.tool.call.arguments	Extracted tool arguments, as a JSON string.
gen_ai.tool.call.result	Extracted tool result, as a JSON string.
databricks.requester_type	Requester type, for example USER.
databricks.request_id	Databricks-generated request ID.
databricks.tool.connection_type	MCP connection type, for example EXTERNAL_MCP.
tool_source	Backing connection or managed MCP server name, for example main.default.gh-conn (external) or code_interpreter (managed). The MCP service name is in the service_name column.
tool_type	Tool type, for example external_connection.
workspace_id	Workspace ID.
enduser.id	User or service principal that made the request.
http.response.status_code	HTTP status from the MCP call.
rpc.response.status_code	JSON-RPC status code, for example -32003. The primary MCP failure signal.
error.type	Failure classification, for example policy_deny, upstream_error, or internal_error.
mlflow.spanType	Span type. TOOL for MCP spans. Useful for WHERE filtering.
mlflow.spanInputs	Serialized JSON-RPC request.
mlflow.spanOutputs	Serialized MCP/JSON-RPC response.

Policy enforcement attributes (model service and MCP service spans):

When a policy blocks a request, the server span (the root span) sets the following scalar attributes. Because they are set only when a policy short-circuits the request, they also serve as a quick filter for "did any policy block this request?"

Attribute	Description
databricks.policy.name	The policy that blocked the request. The Unity Catalog function FQN for a custom policy, or the attachment label for a built-in policy. <unknown> if the policy is unnamed.
databricks.policy.action	Enforced action, either DENY or ASK.

Attribute	Description
databricks.policy.name	The policy that blocked the request. The Unity Catalog function FQN for a custom policy, or the attachment label for a built-in policy. <unknown> if the policy is unnamed.
databricks.policy.action	Enforced action, either DENY or ASK.

Policy evaluation events

The events column holds an array of policy_evaluated events, one per evaluated (policy, phase) pair. This is where per-policy detail lives, beyond the scalar databricks.policy.* attributes on the server span. Each event carries the following keys:

Key	Description
policy.name	Name of the evaluated policy.
policy.type	Policy type, either CUSTOM or BUILTIN.
policy.handler	Handler for the policy. Built-in policies only.
policy.options	Handler options. Built-in policies only.
policy.action	Evaluated action, one of ALLOW, DENY, or ASK.
policy.phase	Evaluation phase, either on_call or on_result. Custom policies only.

Key	Description
policy.name	Name of the evaluated policy.
policy.type	Policy type, either CUSTOM or BUILTIN.
policy.handler	Handler for the policy. Built-in policies only.
policy.options	Handler options. Built-in policies only.
policy.action	Evaluated action, one of ALLOW, DENY, or ASK.
policy.phase	Evaluation phase, either on_call or on_result. Custom policies only.

When a policy runs in dry-run (monitor or shadow) mode with dry_run=true, the enforced action is downgraded to ALLOW so the request proceeds, and the would-be verdict is recorded on the same event:

Key	Description
policy.dry_run_action	The action that would have been enforced, for example DENY. Its presence signals the policy ran in dry-run mode.
policy.dry_run_reason	Reason for the would-be action.
policy.dry_run_transformed_message	The payload a would-be transform or mask policy would have written.

Key	Description
policy.dry_run_action	The action that would have been enforced, for example DENY. Its presence signals the policy ran in dry-run mode.
policy.dry_run_reason	Reason for the would-be action.
policy.dry_run_transformed_message	The payload a would-be transform or mask policy would have written.