synops/maskinrommet/src/usage_overview.rs

// Forbruksoversikt — aggregert ressursforbruk (oppgave 15.8)
//
// Admin-API for å se totalt forbruk per samling, per ressurstype,
// per tidsperiode. Drill-down til jobbtype og modellnivå for AI.
//
// Spør mot resource_usage_log (oppgave 15.7) og ai_usage_log (oppgave 15.4).
//
// Ref: docs/features/ressursforbruk.md

use axum::extract::State;
use axum::http::StatusCode;
use axum::Json;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use sqlx::PgPool;
use uuid::Uuid;

use crate::auth::AuthUser;
use crate::AppState;

// =============================================================================
// Datatyper
// =============================================================================

/// Aggregert forbruk per samling og ressurstype.
#[derive(Serialize, sqlx::FromRow)]
pub struct CollectionUsageSummary {
    pub collection_id: Option<Uuid>,
    pub collection_title: Option<String>,
    pub resource_type: String,
    pub event_count: i64,
    /// Hovedmetrikk i naturlig enhet (tokens, sekunder, bytes, tegn, minutter).
    pub total_value: f64,
    /// Sekundær metrikk (f.eks. tokens_out for AI, 0 for andre).
    pub secondary_value: f64,
}

/// Drill-down for AI: forbruk per jobbtype og modellnivå.
#[derive(Serialize, sqlx::FromRow)]
pub struct AiDrillDown {
    pub collection_id: Option<Uuid>,
    pub collection_title: Option<String>,
    pub job_type: Option<String>,
    pub model_level: Option<String>,
    pub tokens_in: i64,
    pub tokens_out: i64,
    pub event_count: i64,
}

/// Tidsserie: forbruk per dag for en gitt ressurstype.
#[derive(Serialize, sqlx::FromRow)]
pub struct DailyUsage {
    pub day: DateTime<Utc>,
    pub resource_type: String,
    pub event_count: i64,
    pub total_value: f64,
}

/// Samlet respons for forbruksoversikten.
#[derive(Serialize)]
pub struct UsageOverviewResponse {
    pub by_collection: Vec<CollectionUsageSummary>,
    pub ai_drilldown: Vec<AiDrillDown>,
    pub daily: Vec<DailyUsage>,
}

#[derive(Deserialize)]
pub struct UsageOverviewParams {
    pub days: Option<i32>,
    pub collection_id: Option<Uuid>,
}

#[derive(Serialize)]
pub struct ErrorResponse {
    pub error: String,
}

fn internal_error(msg: &str) -> (StatusCode, Json<ErrorResponse>) {
    (
        StatusCode::INTERNAL_SERVER_ERROR,
        Json(ErrorResponse {
            error: msg.to_string(),
        }),
    )
}

// =============================================================================
// GET /admin/usage — forbruksoversikt
// =============================================================================

pub async fn usage_overview(
    State(state): State<AppState>,
    _user: AuthUser,
    axum::extract::Query(params): axum::extract::Query<UsageOverviewParams>,
) -> Result<Json<UsageOverviewResponse>, (StatusCode, Json<ErrorResponse>)> {
    let days = params.days.unwrap_or(30).clamp(1, 365);

    let by_collection = fetch_by_collection(&state.db, days, params.collection_id)
        .await
        .map_err(|e| internal_error(&format!("Feil i samlingsoversikt: {e}")))?;

    let ai_drilldown = fetch_ai_drilldown(&state.db, days, params.collection_id)
        .await
        .map_err(|e| internal_error(&format!("Feil i AI drill-down: {e}")))?;

    let daily = fetch_daily(&state.db, days, params.collection_id)
        .await
        .map_err(|e| internal_error(&format!("Feil i daglig oversikt: {e}")))?;

    Ok(Json(UsageOverviewResponse {
        by_collection,
        ai_drilldown,
        daily,
    }))
}

// =============================================================================
// Spørringer
// =============================================================================

/// Aggregert forbruk per samling og ressurstype.
///
/// Hovedmetrikk (total_value) er type-spesifikk:
///   ai      → tokens_in (detail->>'tokens_in')
///   whisper  → duration_seconds
///   tts      → characters
///   cas      → size_bytes (kun store)
///   bandwidth→ size_bytes
///   livekit  → participant_minutes
async fn fetch_by_collection(
    db: &PgPool,
    days: i32,
    collection_filter: Option<Uuid>,
) -> Result<Vec<CollectionUsageSummary>, sqlx::Error> {
    sqlx::query_as::<_, CollectionUsageSummary>(
        r#"
        SELECT
            r.collection_id,
            n.title AS collection_title,
            r.resource_type,
            COUNT(*)::BIGINT AS event_count,
            COALESCE(SUM(
                CASE r.resource_type
                    WHEN 'ai'        THEN (r.detail->>'tokens_in')::FLOAT8
                    WHEN 'whisper'   THEN (r.detail->>'duration_seconds')::FLOAT8
                    WHEN 'tts'       THEN (r.detail->>'characters')::FLOAT8
                    WHEN 'cas'       THEN (r.detail->>'size_bytes')::FLOAT8
                    WHEN 'bandwidth' THEN (r.detail->>'size_bytes')::FLOAT8
                    WHEN 'livekit'   THEN (r.detail->>'participant_minutes')::FLOAT8
                    ELSE 0
                END
            ), 0) AS total_value,
            COALESCE(SUM(
                CASE r.resource_type
                    WHEN 'ai' THEN (r.detail->>'tokens_out')::FLOAT8
                    ELSE 0
                END
            ), 0) AS secondary_value
        FROM resource_usage_log r
        LEFT JOIN nodes n ON n.id = r.collection_id
        WHERE r.created_at >= now() - make_interval(days := $1)
          AND ($2::UUID IS NULL OR r.collection_id = $2)
        GROUP BY r.collection_id, n.title, r.resource_type
        ORDER BY total_value DESC
        "#,
    )
    .bind(days)
    .bind(collection_filter)
    .fetch_all(db)
    .await
}

/// AI drill-down: per jobbtype og modellnivå (fra resource_usage_log).
async fn fetch_ai_drilldown(
    db: &PgPool,
    days: i32,
    collection_filter: Option<Uuid>,
) -> Result<Vec<AiDrillDown>, sqlx::Error> {
    sqlx::query_as::<_, AiDrillDown>(
        r#"
        SELECT
            r.collection_id,
            n.title AS collection_title,
            r.detail->>'job_type' AS job_type,
            r.detail->>'model_level' AS model_level,
            COALESCE(SUM((r.detail->>'tokens_in')::BIGINT), 0)::BIGINT AS tokens_in,
            COALESCE(SUM((r.detail->>'tokens_out')::BIGINT), 0)::BIGINT AS tokens_out,
            COUNT(*)::BIGINT AS event_count
        FROM resource_usage_log r
        LEFT JOIN nodes n ON n.id = r.collection_id
        WHERE r.resource_type = 'ai'
          AND r.created_at >= now() - make_interval(days := $1)
          AND ($2::UUID IS NULL OR r.collection_id = $2)
        GROUP BY r.collection_id, n.title, r.detail->>'job_type', r.detail->>'model_level'
        ORDER BY tokens_in DESC
        "#,
    )
    .bind(days)
    .bind(collection_filter)
    .fetch_all(db)
    .await
}

/// Daglig tidsserie per ressurstype.
async fn fetch_daily(
    db: &PgPool,
    days: i32,
    collection_filter: Option<Uuid>,
) -> Result<Vec<DailyUsage>, sqlx::Error> {
    sqlx::query_as::<_, DailyUsage>(
        r#"
        SELECT
            date_trunc('day', r.created_at) AS day,
            r.resource_type,
            COUNT(*)::BIGINT AS event_count,
            COALESCE(SUM(
                CASE r.resource_type
                    WHEN 'ai'        THEN (r.detail->>'tokens_in')::FLOAT8
                    WHEN 'whisper'   THEN (r.detail->>'duration_seconds')::FLOAT8
                    WHEN 'tts'       THEN (r.detail->>'characters')::FLOAT8
                    WHEN 'cas'       THEN (r.detail->>'size_bytes')::FLOAT8
                    WHEN 'bandwidth' THEN (r.detail->>'size_bytes')::FLOAT8
                    WHEN 'livekit'   THEN (r.detail->>'participant_minutes')::FLOAT8
                    ELSE 0
                END
            ), 0) AS total_value
        FROM resource_usage_log r
        WHERE r.created_at >= now() - make_interval(days := $1)
          AND ($2::UUID IS NULL OR r.collection_id = $2)
        GROUP BY date_trunc('day', r.created_at), r.resource_type
        ORDER BY day DESC, resource_type
        "#,
    )
    .bind(days)
    .bind(collection_filter)
    .fetch_all(db)
    .await
}