app/backend/fluksio/api/routes/observability.py

"""What the engine has been doing: health now, and history since.

The live state comes from the controller; everything older than the websocket's
memory comes from the rollups the metrics collector writes. Deliberately not
built on ``/utils/health/``: that endpoint answers 503 when something is wrong,
which the generated SDK turns into a thrown error — and a health page that
cannot render while the engine is degraded is the wrong way round.
"""

from datetime import UTC, datetime, timedelta
from typing import Any, Literal

from fastapi import APIRouter, Depends, Request
from fastapi.concurrency import run_in_threadpool
from pydantic import BaseModel
from sqlalchemy import ColumnElement, Integer, cast, func
from sqlalchemy import select as sa_select
from sqlmodel import col, select

from fluksio.api.deps import FlowControllerDep, SessionDep, get_current_user
from fluksio.core.config import settings
from fluksio.flow.controller import ADVISORY_ISSUES, NodeStatus
from fluksio.models import EngineEvent, FlowRun, MetricBucket

router = APIRouter(
    prefix="/observability",
    tags=["observability"],
    dependencies=[Depends(get_current_user)],
)

#: How many slices a per-flow sparkline is folded into.
SPARK_SLICES = 60


def _epoch(column: Any) -> ColumnElement[int]:
    """Seconds since 1970, as an integer the database can bin on.

    Binning is integer arithmetic on this rather than a dialect's own date
    function: buckets are whole minutes, so the cast is exact, and the
    expression is the same everywhere.
    """
    return cast(func.extract("epoch", column), Integer)


class HealthSummary(BaseModel):
    status: str
    problems: list[str]
    flows: dict[str, int]
    nodes: dict[str, int]
    queue: dict[str, Any]
    loop_lag: dict[str, float]


class SeriesPoint(BaseModel):
    ts: float
    executions: int
    errors: int
    messages: int
    avg_ms: float
    max_ms: float
    avg_lag_ms: float


class FlowRollup(BaseModel):
    flow: str
    executions: int
    errors: int
    messages: int
    avg_ms: float
    avg_lag_ms: float
    spark: list[int]
    last_error_ts: float | None = None


class RunRow(BaseModel):
    id: str
    flow: str
    source: str
    status: str
    started_at: datetime
    finished_at: datetime | None = None
    nodes: int
    errors: int
    duration_ms: float
    deliveries: int


class RunPage(BaseModel):
    data: list[RunRow]
    count: int


class EventRow(BaseModel):
    id: int
    ts: datetime
    type: str
    flow: str
    node: str
    detail: str
    actor: str


class DeadLetter(BaseModel):
    id: str
    ts: float
    flow: str
    node: str
    cause: str
    reason: str


def _since(hours: int) -> datetime:
    return datetime.now(UTC) - timedelta(hours=hours)


def _window_hours(hours: int) -> int:
    """A window the rollups can answer for: an hour at least, retention at most.

    Zero used to divide by nothing and answer 500, and nothing older than
    retention exists, so a larger window is a scan that can only find less.
    """
    return max(1, min(hours, settings.OBS_RETENTION_DAYS * 24))


def _aware(when: datetime) -> datetime:
    """A bound as the columns store it. A naive one is read as UTC."""
    return when if when.tzinfo else when.replace(tzinfo=UTC)


@router.get("/summary", response_model=HealthSummary)
async def read_summary(request: Request, controller: FlowControllerDep) -> Any:
    """How the engine is doing right now. Always 200, degraded or not."""
    watchdog = getattr(request.app.state, "watchdog", None)
    problems: list[str] = []
    if watchdog is not None and watchdog.degraded:
        problems.append("event loop lagging")

    queue = await run_in_threadpool(controller.queue_stats)
    if queue.get("oldest_pending_s", 0) > 120:
        problems.append("queue stalled")
    if queue.get("error"):
        problems.append("work queue unreachable")

    names = await run_in_threadpool(controller.store.list_flows)
    quarantined = controller.quarantined
    paused = set(controller.paused_flows())
    entries = list(controller.loaded.values())
    errored = [e for e in entries if e.status is NodeStatus.ERROR]
    if quarantined:
        problems.append(f"{len(quarantined)} flow(s) quarantined")
    if errored:
        problems.append(f"{len(errored)} node(s) failed to load")

    # What the canvas flags on a flow — a dependency loop, an input nothing
    # feeds — stops that flow running just as surely as a node that will not
    # load, and until now this screen was the one place it did not show.
    # `node_error` is left out: those are the nodes already counted above.
    invalid = sorted(
        {
            issue.flow
            for issue in controller.issues
            if issue.flow
            and issue.code != "node_error"
            and issue.code not in ADVISORY_ISSUES
        }
    )
    if invalid:
        problems.append(f"{len(invalid)} flow(s) cannot run: {', '.join(invalid)}")

    return HealthSummary(
        status="degraded" if problems else "ok",
        problems=problems,
        flows={
            "total": len(names),
            # What the engine will actually act on. Enabled is not enough:
            # validation stops a flow as surely as quarantine does, and a
            # paused one is holding its messages rather than running them.
            "running": len(
                [
                    n
                    for n in names
                    if controller.is_enabled(n)
                    and n not in quarantined
                    and n not in paused
                    and n not in invalid
                ]
            ),
            "paused": len(paused),
            "quarantined": len(quarantined),
            "invalid": len(invalid),
        },
        nodes={"total": len(entries), "error": len(errored)},
        queue=queue,
        loop_lag=(
            watchdog.snapshot()
            if watchdog is not None
            else {"ewma": 0.0, "max_60s": 0.0}
        ),
    )


@router.get("/timeseries", response_model=list[SeriesPoint])
def read_timeseries(
    session: SessionDep,
    flow: str | None = None,
    node: str | None = None,
    hours: int = 24,
    bucket_s: int = 60,
) -> Any:
    """Executions, errors and timings over time, summed across nodes."""
    hours = _window_hours(hours)
    # The database does the fold: a week of minute rows per node used to cross
    # the wire on every poll, and only the slices need to.
    stride = max(60, bucket_s)
    slot = (_epoch(col(MetricBucket.bucket)) // stride * stride).label("slot")
    statement = sa_select(
        slot,
        func.sum(col(MetricBucket.executions)).label("executions"),
        func.sum(col(MetricBucket.errors)).label("errors"),
        func.sum(col(MetricBucket.messages)).label("messages"),
        func.sum(col(MetricBucket.duration_sum_ms)).label("duration_sum_ms"),
        func.max(col(MetricBucket.duration_max_ms)).label("max_ms"),
        func.sum(col(MetricBucket.lag_sum_ms)).label("lag_sum_ms"),
        func.sum(col(MetricBucket.items)).label("items"),
    ).where(col(MetricBucket.bucket) >= _since(hours))
    if flow:
        statement = statement.where(col(MetricBucket.flow) == flow)
    if node:
        statement = statement.where(col(MetricBucket.node) == node)

    return [
        SeriesPoint(
            ts=float(row.slot),
            executions=int(row.executions),
            errors=int(row.errors),
            messages=int(row.messages),
            avg_ms=round(row.duration_sum_ms / (row.executions or 1), 2),
            max_ms=round(row.max_ms, 2),
            avg_lag_ms=round(row.lag_sum_ms / (row.items or 1), 2),
        )
        for row in session.execute(statement.group_by(slot).order_by(slot))
    ]


@router.get("/flows", response_model=list[FlowRollup])
def read_flow_rollups(session: SessionDep, hours: int = 24) -> Any:
    """One row per flow, with a coarse trend of how much it ran."""
    hours = _window_hours(hours)
    since = _since(hours)
    window = hours * 3600
    start = since.timestamp()

    # Binned to the sparkline slice rather than the minute, so a flow costs at
    # most SPARK_SLICES rows however long the window is. The slice is the
    # window over SPARK_SLICES, which for whole hours is whole minutes.
    stride = hours * 60
    origin = int(start)
    slot = (
        (_epoch(col(MetricBucket.bucket)) - origin) // stride * stride + origin
    ).label("slot")
    statement = (
        sa_select(
            col(MetricBucket.flow),
            slot,
            func.sum(col(MetricBucket.executions)).label("executions"),
            func.sum(col(MetricBucket.errors)).label("errors"),
            func.sum(col(MetricBucket.messages)).label("messages"),
            func.sum(col(MetricBucket.duration_sum_ms)).label("duration_sum_ms"),
            func.sum(col(MetricBucket.lag_sum_ms)).label("lag_sum_ms"),
            func.sum(col(MetricBucket.items)).label("items"),
        )
        .where(col(MetricBucket.bucket) >= since)
        .group_by(col(MetricBucket.flow), slot)
    )

    rollups: dict[str, dict[str, Any]] = {}
    for row in session.execute(statement):
        entry = rollups.setdefault(
            row.flow,
            {
                "executions": 0,
                "errors": 0,
                "messages": 0,
                "duration_sum_ms": 0.0,
                "lag_sum_ms": 0.0,
                "items": 0,
                "spark": [0] * SPARK_SLICES,
            },
        )
        entry["executions"] += row.executions
        entry["errors"] += row.errors
        entry["messages"] += row.messages
        entry["duration_sum_ms"] += row.duration_sum_ms
        entry["lag_sum_ms"] += row.lag_sum_ms
        entry["items"] += row.items
        # A slot sits a whole number of slices from `since`, so this rounds
        # rather than truncates: a float a hair short would lose a slice. The
        # clamp holds the bucket landing exactly on the far edge in range.
        index = min(
            SPARK_SLICES - 1,
            max(0, round((float(row.slot) - start) / window * SPARK_SLICES)),
        )
        entry["spark"][index] += row.executions

    # `.all()` first: a Result has `keys()`, so dict() would read it as a
    # mapping and subscript it.
    last_errors = dict(
        session.exec(
            select(col(EngineEvent.flow), func.max(col(EngineEvent.ts)))
            .where(col(EngineEvent.type) == "node_error", col(EngineEvent.ts) >= since)
            .group_by(col(EngineEvent.flow))
        ).all()
    )

    return [
        FlowRollup(
            flow=flow,
            executions=entry["executions"],
            errors=entry["errors"],
            messages=entry["messages"],
            avg_ms=round(entry["duration_sum_ms"] / (entry["executions"] or 1), 2),
            avg_lag_ms=round(entry["lag_sum_ms"] / (entry["items"] or 1), 2),
            spark=entry["spark"],
            last_error_ts=(
                last_errors[flow].timestamp() if flow in last_errors else None
            ),
        )
        for flow, entry in sorted(rollups.items())
    ]


@router.get("/runs", response_model=RunPage)
def read_runs(
    session: SessionDep,
    flow: str | None = None,
    status: str | None = None,
    since: datetime | None = None,
    until: datetime | None = None,
    limit: int = 50,
) -> Any:
    """Recent cascades, newest first, and how many there were in total.

    ``since`` is inclusive and ``until`` exclusive, so a window of one minute
    holds exactly the runs of the minute bucket the charts are drawn from.
    """
    filters: list[ColumnElement[bool]] = []
    if flow:
        filters.append(col(FlowRun.flow) == flow)
    if status:
        filters.append(col(FlowRun.status) == status)
    if since:
        filters.append(col(FlowRun.started_at) >= _aware(since))
    if until:
        filters.append(col(FlowRun.started_at) < _aware(until))

    capped = min(limit, 200)
    statement = select(FlowRun).where(*filters).order_by(col(FlowRun.started_at).desc())
    rows = list(session.exec(statement.limit(capped)))
    # A short page is its own total. The lists poll their whole range every
    # thirty seconds, and counting on each of those would hand back what
    # binning the metrics just saved — for a number that only ever says
    # "there is more here than fits".
    count = (
        len(rows)
        if len(rows) < capped
        else int(
            session.exec(
                select(func.count()).select_from(FlowRun).where(*filters)
            ).one()
        )
    )
    return {"data": rows, "count": count}


@router.get("/events", response_model=list[EventRow])
def read_events(
    session: SessionDep,
    kind: Literal["failure", "audit"] = "failure",
    flow: str | None = None,
    since: datetime | None = None,
    until: datetime | None = None,
    limit: int = 100,
) -> Any:
    """What went wrong, or who changed what. Newest first.

    ``since`` is inclusive and ``until`` exclusive, the same window ``/runs``
    takes, so a list can cover the span the charts beside it are drawn from.
    """
    statement = select(EngineEvent).order_by(col(EngineEvent.ts).desc())
    if kind == "audit":
        statement = statement.where(col(EngineEvent.type) == "audit")
    else:
        statement = statement.where(col(EngineEvent.type) != "audit")
    if flow:
        statement = statement.where(col(EngineEvent.flow) == flow)
    if since:
        statement = statement.where(col(EngineEvent.ts) >= _aware(since))
    if until:
        statement = statement.where(col(EngineEvent.ts) < _aware(until))
    return list(session.exec(statement.limit(min(limit, 500))))


@router.get("/dead-letter", response_model=list[DeadLetter])
async def read_dead_letters(controller: FlowControllerDep, limit: int = 50) -> Any:
    """Work the engine gave up on, which nothing else surfaces."""
    if controller.execution is None:
        return []
    return await run_in_threadpool(controller.execution.queue.dead_letters, limit)