Files
bambuddy/backend/app/services/failure_analysis.py
T
maziggy f645bd2bbe fix(stats): per-event data for all widgets, not just Quick Stats (#1390)
After #1378 moved Quick Stats to print_log_entries, six widgets and
  Failure Analysis still iterated the archive list. That made reprints
  multiply event-based widgets while leaving archive-based ones unchanged,
  and made hard-deleted archives drop from archive-based widgets while
  their orphan events kept feeding Quick Stats.

  Swap the data source in two places:

  - GET /archives/slim now reads PrintLogEntry, LEFT JOINs the archive for
    the sliced print_time_seconds estimate, prefers PrintLogEntry's own
    duration_seconds as the measured-time field. StatsPage is the only
    caller -- every widget realigns in one step.
  - FailureAnalysisService swapped from PrintArchive to PrintLogEntry for
    every aggregation. project_id filter still resolves through archives
    but counts matching events.

  Conftest archive_factory now syncs the synthesized event's created_at
  with the archive's so backdated test data survives the change.
2026-05-17 14:38:31 +02:00

224 lines
9.0 KiB
Python

from collections import defaultdict
from datetime import date, datetime, time, timedelta, timezone
from sqlalchemy import and_, func, select
from sqlalchemy.ext.asyncio import AsyncSession
from backend.app.models.print_log import PrintLogEntry
from backend.app.models.printer import Printer
class FailureAnalysisService:
"""Service for analyzing print failure patterns.
Reads from print_log_entries (per-event data) rather than print_archives
so reprints contribute each run and orphan events (archive deleted, log
row survived via ON DELETE SET NULL) still count consistently with
Quick Stats. The archive-based predecessor diverged from Quick Stats
after #1378 moved the rest of the page to per-event aggregation.
"""
def __init__(self, db: AsyncSession):
self.db = db
async def analyze_failures(
self,
days: int | None = None,
date_from: date | None = None,
date_to: date | None = None,
printer_id: int | None = None,
project_id: int | None = None,
created_by_id: int | None = None,
) -> dict:
"""Analyze failure patterns across logged print events."""
# Build base query — separate date vs non-date filters for trend reuse
base_filter = []
non_date_filter = []
if date_from or date_to:
if date_from:
dt_from = datetime.combine(date_from, time.min, tzinfo=timezone.utc)
base_filter.append(PrintLogEntry.created_at >= dt_from)
if date_to:
dt_to = datetime.combine(date_to, time.max, tzinfo=timezone.utc)
base_filter.append(PrintLogEntry.created_at <= dt_to)
range_start = dt_from if date_from else datetime.now(timezone.utc) - timedelta(days=365)
range_end = dt_to if date_to else datetime.now(timezone.utc)
effective_days = max((range_end - range_start).days, 1)
else:
effective_days = days if days is not None else 30
cutoff_date = datetime.now(timezone.utc) - timedelta(days=effective_days)
base_filter.append(PrintLogEntry.created_at >= cutoff_date)
if printer_id:
non_date_filter.append(PrintLogEntry.printer_id == printer_id)
# project_id is an archive-level concept; PrintLogEntry has no project
# link, so we resolve it by archive_id where present.
if project_id:
from backend.app.models.archive import PrintArchive
project_archive_ids = await self.db.execute(
select(PrintArchive.id).where(PrintArchive.project_id == project_id)
)
archive_ids = [row[0] for row in project_archive_ids.fetchall()]
if archive_ids:
non_date_filter.append(PrintLogEntry.archive_id.in_(archive_ids))
else:
# No archives in this project → nothing to count
non_date_filter.append(PrintLogEntry.id.is_(None))
if created_by_id is not None:
if created_by_id == -1:
non_date_filter.append(PrintLogEntry.created_by_id.is_(None))
else:
non_date_filter.append(PrintLogEntry.created_by_id == created_by_id)
base_filter.extend(non_date_filter)
# Total counts
total_result = await self.db.execute(select(func.count(PrintLogEntry.id)).where(and_(*base_filter)))
total_prints = total_result.scalar() or 0
failed_result = await self.db.execute(
select(func.count(PrintLogEntry.id)).where(
and_(*base_filter, PrintLogEntry.status.in_(["failed", "aborted"]))
)
)
failed_prints = failed_result.scalar() or 0
failure_rate = (failed_prints / total_prints * 100) if total_prints > 0 else 0
# Failures by reason
reason_result = await self.db.execute(
select(
PrintLogEntry.failure_reason,
func.count(PrintLogEntry.id).label("count"),
)
.where(and_(*base_filter, PrintLogEntry.status.in_(["failed", "aborted"])))
.group_by(PrintLogEntry.failure_reason)
.order_by(func.count(PrintLogEntry.id).desc())
)
failures_by_reason = {(row[0] or "Unknown"): row[1] for row in reason_result.fetchall()}
# Failures by filament type
filament_result = await self.db.execute(
select(
PrintLogEntry.filament_type,
func.count(PrintLogEntry.id).label("count"),
)
.where(and_(*base_filter, PrintLogEntry.status.in_(["failed", "aborted"])))
.group_by(PrintLogEntry.filament_type)
.order_by(func.count(PrintLogEntry.id).desc())
)
failures_by_filament = {(row[0] or "Unknown"): row[1] for row in filament_result.fetchall()}
# Failures by printer
printer_result = await self.db.execute(
select(
PrintLogEntry.printer_id,
func.count(PrintLogEntry.id).label("count"),
)
.where(
and_(
*base_filter,
PrintLogEntry.status.in_(["failed", "aborted"]),
PrintLogEntry.printer_id.isnot(None),
)
)
.group_by(PrintLogEntry.printer_id)
.order_by(func.count(PrintLogEntry.id).desc())
)
failures_by_printer_id = {row[0]: row[1] for row in printer_result.fetchall()}
# Get printer names
if failures_by_printer_id:
printers_result = await self.db.execute(
select(Printer.id, Printer.name).where(Printer.id.in_(failures_by_printer_id.keys()))
)
printer_names = {row[0]: row[1] for row in printers_result.fetchall()}
failures_by_printer = {
printer_names.get(pid, f"Printer {pid}"): count for pid, count in failures_by_printer_id.items()
}
else:
failures_by_printer = {}
# Failures by hour of day
failed_events_result = await self.db.execute(
select(PrintLogEntry.started_at).where(
and_(
*base_filter,
PrintLogEntry.status.in_(["failed", "aborted"]),
PrintLogEntry.started_at.isnot(None),
)
)
)
failures_by_hour = defaultdict(int)
for (started_at,) in failed_events_result.fetchall():
if started_at:
hour = started_at.hour
failures_by_hour[hour] += 1
failures_by_hour_complete = {h: failures_by_hour.get(h, 0) for h in range(24)}
# Recent failures
recent_result = await self.db.execute(
select(PrintLogEntry)
.where(and_(*base_filter, PrintLogEntry.status.in_(["failed", "aborted"])))
.order_by(PrintLogEntry.created_at.desc())
.limit(10)
)
recent_failures = [
{
"id": e.archive_id,
"print_name": e.print_name,
"failure_reason": e.failure_reason,
"filament_type": e.filament_type,
"printer_id": e.printer_id,
"created_at": e.created_at.isoformat() if e.created_at else None,
}
for e in recent_result.scalars().all()
]
# Failure rate trend (by week)
trend_data = []
num_weeks = max(effective_days // 7, 1)
for i in range(num_weeks):
week_end = datetime.now(timezone.utc) - timedelta(weeks=i)
week_start = week_end - timedelta(weeks=1)
week_filter = [
PrintLogEntry.created_at >= week_start,
PrintLogEntry.created_at < week_end,
*non_date_filter,
]
week_total = await self.db.execute(select(func.count(PrintLogEntry.id)).where(and_(*week_filter)))
week_failed = await self.db.execute(
select(func.count(PrintLogEntry.id)).where(
and_(*week_filter, PrintLogEntry.status.in_(["failed", "aborted"]))
)
)
total = week_total.scalar() or 0
failed = week_failed.scalar() or 0
rate = (failed / total * 100) if total > 0 else 0
trend_data.append(
{
"week_start": week_start.date().isoformat(),
"total_prints": total,
"failed_prints": failed,
"failure_rate": round(rate, 1),
}
)
trend_data.reverse() # Oldest first
return {
"period_days": effective_days,
"total_prints": total_prints,
"failed_prints": failed_prints,
"failure_rate": round(failure_rate, 1),
"failures_by_reason": failures_by_reason,
"failures_by_filament": failures_by_filament,
"failures_by_printer": failures_by_printer,
"failures_by_hour": failures_by_hour_complete,
"recent_failures": recent_failures,
"trend": trend_data,
}