Files
marc bfff0eeec0 Ingest Dashboard: 5min TTL cache + days->hours normalisation
Dashboard reloads on multi-day windows could take 30-60s and sometimes
returned HTTP 502 ("internal Scalyr error") when the SDL window was
expressed in days. Two-part fix:

1. In-process async TTL cache (services/async_cache.py)
   - 5 min TTL on top-sources, by-event-type, daily-volume.
   - Single-flight lock per cache key (no thundering herd).
   - Optional ?nocache=1 query param to force a refresh.
   - New endpoints: GET /api/ingest/cache-stats, DELETE /api/ingest/cache.

2. Normalise days -> hours upstream of the PowerQuery
   - SDL is unstable on day-scale windows for large group-by counts on
     busy tenants but stable on the equivalent hour-scale window.
   - top-sources?days=1 used to 502; now works.

Observed timings on a busy tenant:
  top-sources?days=7  cold ~55s -> warm ~13ms (~4300x)
  top-sources?days=1  was 502   -> ~4ms (cold) / ~1.4ms (warm)
2026-05-22 21:36:42 +02:00

174 lines
6.0 KiB
Python

from datetime import datetime, timedelta
from fastapi import APIRouter, Query, HTTPException
from pydantic import BaseModel
from services import s1_client
from services.async_cache import async_ttl_cache, cache_stats, cache_clear
router = APIRouter()
# Dashboard endpoints can be expensive on busy tenants. Cache results in-process
# for a short TTL so reloads and parallel widgets are instant. Pass ?nocache=1
# to bypass for a forced refresh.
_DASHBOARD_TTL_SECONDS = 300
def _date_range(days: int) -> tuple[str, str]:
now = datetime.utcnow()
return (
(now - timedelta(days=days)).strftime("%Y-%m-%dT%H:%M:%S.000Z"),
now.strftime("%Y-%m-%dT%H:%M:%S.000Z"),
)
def _date_range_hours(hours: int) -> tuple[str, str]:
now = datetime.utcnow()
return (
(now - timedelta(hours=hours)).strftime("%Y-%m-%dT%H:%M:%S.000Z"),
now.strftime("%Y-%m-%dT%H:%M:%S.000Z"),
)
@async_ttl_cache(ttl_seconds=_DASHBOARD_TTL_SECONDS)
async def _top_sources_cached(hours: int) -> dict:
"""Cache key: hours only. days is normalised to hours upstream."""
from_dt, to_dt = _date_range_hours(hours)
query = "| group events=count() by dataSource.name | sort -events | limit 25"
result = await s1_client.run_powerquery(query, from_dt, to_dt)
return {"data": result.get("events", [])}
@router.get("/top-sources")
async def get_top_sources(
days: int = Query(None, ge=1, le=90),
hours: int = Query(None, ge=1, le=720),
nocache: bool = Query(False, description="Bypass dashboard cache"),
):
"""Top log sources by event count.
Note: SDL returns 'internal Scalyr error' when this query uses day-scale
timestamps on busy tenants, but the same window expressed in hours runs
fine. We normalise days -> hours internally for stability.
"""
if hours is None and days is None:
days = 7
if hours is None:
hours = days * 24
period_label = f"{days}d"
else:
period_label = f"{hours}h"
try:
cached = await _top_sources_cached(hours, nocache=nocache)
except Exception as e:
raise HTTPException(502, f"PowerQuery error: {e}")
return {"period": period_label, "data": cached["data"]}
@async_ttl_cache(ttl_seconds=_DASHBOARD_TTL_SECONDS)
async def _by_event_type_cached(days: int) -> dict:
# Same days->hours normalisation as top-sources for tenant stability.
from_dt, to_dt = _date_range_hours(days * 24)
query = "| group events=count() by dataSource.name, event.type | sort -events | limit 100"
result = await s1_client.run_powerquery(query, from_dt, to_dt)
return {"data": result.get("events", [])}
@router.get("/by-event-type")
async def get_by_event_type(
days: int = Query(7, ge=1, le=90),
nocache: bool = Query(False),
):
"""Event counts grouped by source and event type."""
try:
cached = await _by_event_type_cached(days, nocache=nocache)
except Exception as e:
raise HTTPException(502, f"PowerQuery error: {e}")
return {"period_days": days, "data": cached["data"]}
@async_ttl_cache(ttl_seconds=_DASHBOARD_TTL_SECONDS)
async def _daily_volume_cached(days: int) -> list:
import asyncio
now = datetime.utcnow()
points = min(days, 7)
async def _fetch_day(i: int) -> dict:
day_from = (now - timedelta(days=i + 1)).strftime("%Y-%m-%dT00:00:00.000Z")
day_to = (now - timedelta(days=i)).strftime("%Y-%m-%dT00:00:00.000Z")
label = (now - timedelta(days=i + 1)).strftime("%Y-%m-%d")
try:
result = await s1_client.run_powerquery("| group total=count()", day_from, day_to)
events_list = result.get("events", []) if isinstance(result, dict) else []
count = events_list[0].get("total", 0) if events_list else 0
except Exception:
count = 0
return {"date": label, "events": count}
results = await asyncio.gather(*[_fetch_day(i) for i in range(points)])
return list(reversed(results))
@router.get("/daily-volume")
async def get_daily_volume(
days: int = Query(5, ge=1, le=7),
nocache: bool = Query(False),
):
"""Total event count per day — queries run in parallel."""
return await _daily_volume_cached(days, nocache=nocache)
@router.get("/cache-stats")
def ingest_cache_stats():
"""Inspect dashboard cache (entry count + TTL remaining per key)."""
return cache_stats()
@router.delete("/cache")
def ingest_cache_clear():
"""Forcefully wipe the dashboard cache (next call refetches from SDL)."""
return {"cleared": cache_clear()}
class FilterRule(BaseModel):
source: str = ""
event_type: str = ""
days: int = 7
gb_per_million_events: float = 0.5
@router.post("/simulate-filter")
async def simulate_filter(rule: FilterRule):
"""Estimate how many events and GB would be eliminated by an exclusion filter."""
from_dt, to_dt = _date_range(rule.days)
clauses = []
if rule.source:
clauses.append(f"dataSource.name=='{rule.source}'")
if rule.event_type:
clauses.append(f"event.type=='{rule.event_type}'")
if clauses:
filter_expr = " and ".join(clauses)
query = f"| filter {filter_expr} | group events=count()"
else:
query = "| group events=count()"
try:
result = await s1_client.run_powerquery(query, from_dt, to_dt)
events = (result.get("events") or [{}])[0].get("events", 0) if isinstance(result.get("events"), list) else 0
except Exception as e:
raise HTTPException(502, f"PowerQuery error: {e}")
estimated_gb = round(events / 1_000_000 * rule.gb_per_million_events, 3)
monthly_events = int(events / rule.days * 30)
monthly_gb = round(monthly_events / 1_000_000 * rule.gb_per_million_events, 2)
return {
"period_days": rule.days,
"matched_events": events,
"estimated_gb_period": estimated_gb,
"projected_monthly_events": monthly_events,
"projected_monthly_gb": monthly_gb,
"filter": {"source": rule.source, "event_type": rule.event_type},
}