Execute HudQL query.

Request: Only the query parameter is accepted.

Response: The response is a CSV string containing the query result or an error message if the query fails.

Database Schema

Session Context Common columns used by multiple tables. Columns:

• session_id (String): Session identifier
• session_tags (Map(String, String)): Custom tags attached to the session
• pod_name (String): Pod or runtime instance name

Service Context Common columns used by multiple tables. Columns:

• service_name (String): Service name
• service_id (UInt32): Service identifier
• environment_name (String): Environment name such as production or staging

Metric Aggregates Common aggregated metric columns used by function and endpoint metrics tables. Columns:

• invocations (UInt64): Number of invocations in the aggregation period
• total_duration_ms (Float64): Total execution time in milliseconds
• total_squared_duration_ms (Float64): Sum of squared durations for variance or standard deviation calculations
• duration_buckets (AggregateFunction): Pre-aggregated duration quantiles, use percentileMS(duration_buckets, percentile)
• exceptions (Map(String, Float64)): Exception or error counts grouped by type

Table: FunctionMetricsHighResolution Description: Minutes-level function metrics by session. Use for small time windows. Columns:

• Session Context: Shared session columns
• Service Context: Shared service columns
• service_runtime (String): Service runtime such as NODEJS, PYTHON, or JAVA
• timestamp_end (DateTime): End of the metric period
• function_id (UInt32): Function identifier
• caller_function_id (UInt32): Calling function identifier, or 0 if none
• wrapped_endpoint_id (UInt32): Endpoint this function is called within
• Metric Aggregates: Shared aggregated metric columns

Table: FunctionMetricsLowResolution Description: Hourly aggregated function metrics across sessions. Use for historical trends. Columns:

• hour_end (DateTime): Hour bucket timestamp
• function_id (UInt32): Function identifier
• caller_function_id (UInt32): Calling function identifier
• wrapped_endpoint_id (UInt32): Endpoint this function is called within
• Service Context: Shared service columns
• service_runtime (String): Service runtime such as NODEJS, PYTHON, or JAVA
• Metric Aggregates: Shared aggregated metric columns
• session_tags (Map(String, String)): Session tags for this aggregation

Table: EndpointMetricsHighResolution Description: Minutes-level endpoint metrics by session for HTTP, Kafka, SQS, GraphQL, and similar endpoint types. Columns:

• Session Context: Shared session columns
• Service Context: Shared service columns
• service_runtime (String): Service runtime such as NODEJS, PYTHON, or JAVA
• timestamp_end (DateTime): End of the metric period
• endpoint_id (UInt32): Endpoint identifier
• endpoint_type (String): Endpoint type such as HTTP, KAFKA, SQS, GRAPHQL, or ARQ
• msg_count (UInt64): Message count for queue-based endpoints
• status_codes (Map(String, Float64)): HTTP status code distribution
• Metric Aggregates: Shared aggregated metric columns
• total_e2e_duration_ms (Float64): Total end-to-end duration for queue endpoints
• total_squared_e2e_duration_ms (Float64): Sum of squared end-to-end durations
• e2e_duration_buckets (AggregateFunction): Pre-aggregated end-to-end duration quantiles
• fingerprints (Map(String, UInt64)): Error fingerprints with counts

Table: EndpointMetricsLowResolution Description: Hourly aggregated endpoint metrics. Use for historical analysis and capacity planning. Columns:

• hour_end (DateTime): Hour bucket timestamp
• Service Context: Shared service columns
• service_runtime (String): Service runtime such as NODEJS, PYTHON, or JAVA
• endpoint_id (UInt32): Endpoint identifier
• endpoint_type (String): Endpoint type such as HTTP, KAFKA, SQS, GRAPHQL, or ARQ
• msg_count (UInt64): Message count
• status_codes (Map(String, Float64)): HTTP status code distribution
• Metric Aggregates: Shared aggregated metric columns
• total_e2e_duration_ms (Float64): Total end-to-end duration for queue endpoints
• total_squared_e2e_duration_ms (Float64): Sum of squared end-to-end durations
• e2e_duration_buckets (AggregateFunction): Pre-aggregated end-to-end duration quantiles
• session_tags (Map(String, String)): Session tags for this aggregation
• fingerprints (Map(String, UInt64)): Error fingerprints with counts

Table: Functions Description: Function metadata table used to map function IDs to readable names and source locations. Columns:

• function_id (UInt32): Function identifier
• function_signature (String): Human-readable function signature
• file (String): Source file path
• line_number (UInt32, Nullable): Starting line number in the source file
• is_async (Boolean): Whether the function is async
• third_party (String): Third-party module name, empty if not third party
• services (Array(Tuple(UInt32, String))): Service IDs and service names where the function appears

Table: Endpoints Description: Endpoint metadata table that maps endpoint IDs to routing or queue information. Columns:

• endpoint_id (UInt32): Endpoint identifier
• type (String): Endpoint type such as HTTP, KAFKA, SQS, GRAPHQL, or ARQ
• endpoint_name (String): Unified endpoint name such as an HTTP path or queue name
• http_methods (Array(String)): HTTP methods for HTTP endpoints
• services (Array(Tuple(UInt32, String))): Service IDs and service names where the endpoint appears

Table: FunctionDeployments Description: Function deployment timestamps by service and environment. Columns:

• Service Context: Shared service columns
• function_id (UInt64): Function identifier
• deployment_timestamp (DateTime): Deployment timestamp

Table: EndpointDeployments Description: Endpoint deployment timestamps by service and environment. Columns:

• Service Context: Shared service columns
• endpoint_id (UInt32): Endpoint identifier
• deployment_timestamp (DateTime): Deployment timestamp

Table: MachineMetrics Description: Infrastructure metrics per session for CPU, memory, and event loop utilization. Columns:

• Session Context: Shared session columns
• service_name (String): Service name
• environment_name (String): Environment name
• service_runtime (String): Service runtime such as NODEJS, PYTHON, or JAVA
• timestamp (DateTime): Metric timestamp
• pid (UInt32): Process ID
• owner (String): Metric owner such as Process or MainThread
• uptime_seconds (Float64): Process uptime in seconds
• memory_rss_bytes (UInt64): Resident set size
• memory_utilization_percentage (Float64): Memory usage percentage
• event_loop_utilization_percentage (Float64): Node.js event loop utilization percentage, or -1 if not applicable
• cpu_usage_percentage (Float64): CPU usage percentage, or -1 if not applicable
• cpu_user_time (Float64): CPU user time in seconds
• cpu_system_time (Float64): CPU system time in seconds

Table: Forensics Description: Sampled execution traces for debugging slow requests or errors. Columns:

• timestamp_start (DateTime): Request start time
• endpoint_id (UInt32): Endpoint that initiated the flow
• Service Context: Shared service columns
• Session Context: Shared session columns
• trigger_type (String): Trace trigger type such as Error or Duration
• duration_ms (Float64): Total flow duration in milliseconds
• functions (Array(UInt32)): Function IDs in execution order
• exceptions (Array(String)): Exception messages
• exceptions_data_enriched (Array(Tuple(String, Array(UInt32)))): Exception messages enriched with function IDs
• fingerprint (String): Identifier used to group similar errors
• failure_type (String): Failure type for queue endpoints
• caught (Bool): Whether the exception was caught
• forensic_id (String): Unique investigation identifier