26Y01c5
"""
step6_excel_builder.py — 개정된 새 위계(cluster / workload_domain)가 수록된 마스터 리포터
"""
import boto3
import pandas as pd
import numpy as np
from pathlib import Path
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter
from openpyxl.drawing.image import Image as XLImage
from openpyxl.formatting.rule import ColorScaleRule, DataBarRule
# ─── 💡 [요청 반영] config를 완전 로드하여 AIStor 바인딩 원천 무결성 보장 ───
import config
from config import MERGED_DIR, PLOT_DIR, OUT_DIR
C = dict(
hdr_dark="1F4E79", hdr_mid="2E75B6", hdr_light="BDD7EE",
accent="ED7D31", red="C00000", green="70AD47",
yellow="FFC000", gray_row="F2F2F2", white="FFFFFF",
border="9DC3E6", summary_bg="EBF3FB", purple="7030A0",
teal="008080",
)
def ft(bold=False, size=10, color="000000", name="Arial"):
return Font(name=name, bold=bold, size=size, color=color)
def fill(hex_color):
return PatternFill("solid", fgColor=hex_color)
def thin_border():
t = Side(style="thin", color=C["border"])
return Border(left=t, right=t, top=t, bottom=t)
def center(wrap=False):
return Alignment(horizontal="center", vertical="center", wrap_text=wrap)
def left(wrap=False):
return Alignment(horizontal="left", vertical="center", wrap_text=wrap)
def set_col_widths(ws, widths):
for col, w in widths.items():
ws.column_dimensions[col].width = w
def apply_header_row(ws, row_idx, headers, bg=None, fg=C["white"], size=10):
bg = bg or C["hdr_dark"]
for c, h in enumerate(headers, 1):
cell = ws.cell(row=row_idx, column=c, value=h)
cell.font = ft(bold=True, size=size, color=fg)
cell.fill = fill(bg)
cell.alignment = center(wrap=True)
cell.border = thin_border()
def apply_data_rows(ws, df, start_row, num_formats=None, status_col_idx=None, zebra=True):
nf = num_formats or {}
for r_offset, row in enumerate(df.itertuples(index=False)):
row_num = start_row + r_offset
bg_hex = C["gray_row"] if (r_offset % 2 == 1 and zebra) else C["white"]
for c_idx, val in enumerate(row, 1):
cell = ws.cell(row=row_num, column=c_idx, value=val)
cell.font = ft(size=9)
cell.fill = fill(bg_hex)
cell.alignment = left()
cell.border = thin_border()
if c_idx in nf: cell.number_format = nf[c_idx]
if status_col_idx and c_idx == status_col_idx:
v = str(val)
if "OOM" in v or "Killed" in v:
cell.fill = fill("FFCCCC"); cell.font = ft(bold=True, size=9, color=C["red"])
elif "Shortage" in v or "부족" in v:
cell.fill = fill("FFF2CC"); cell.font = ft(bold=True, size=9, color="7F6000")
elif "Over" in v or "과다" in v:
cell.fill = fill("DDEEFF"); cell.font = ft(bold=True, size=9, color=C["hdr_dark"])
elif "Optim" in v or "최적" in v:
cell.fill = fill("E2EFDA"); cell.font = ft(bold=True, size=9, color="375623")
return start_row + len(df)
def freeze_and_filter(ws, row=2):
ws.freeze_panes = ws.cell(row=row+1, column=1)
ws.auto_filter.ref = ws.dimensions
def add_chart_image(ws, path_key, anchor_cell, w=860, h=400, label=None):
path = PLOT_DIR / path_key
if not path.exists(): return
row_num = int(''.join(filter(str.isdigit, anchor_cell)))
if label:
ws.cell(row=row_num-1, column=1, value=label).font = ft(bold=True, size=11, color=C["hdr_dark"])
img = XLImage(str(path))
img.width = w; img.height = h
ws.add_image(img, anchor_cell)
# 0번 시트 요약
def build_sheet_summary(wb, df_pod, df_ns, infra_tag):
ws = wb.active
ws.title = "0. 전사종합요약"
ws.sheet_view.showGridLines = False
ws.row_dimensions[1].height = 42
ws.merge_cells("A1:H1")
t = ws["A1"]
t.value = f"Resource Governance Master Report [{infra_tag}] (KST 기준)"
t.font = ft(bold=True, size=15, color=C["white"])
t.fill = fill(C["hdr_dark"]); t.alignment = center()
oom_cnt = int(df_pod["is_oom_killed"].sum())
no_req_cnt = int((df_pod["has_no_request"] | df_pod["has_no_limit"]).sum())
alloc_ch = df_pod["cpu_allocated_core_hours"].sum()
usage_ch = df_pod["cpu_usage_core_hours"].sum()
waste_ch = df_pod["cpu_waste_core_hours"].sum()
eff_pct = usage_ch / max(alloc_ch, 0.001) * 100
mem_waste = df_pod["mem_waste_gb_hours"].sum()
pv_waste = df_pod["pv_waste_gb_hours"].sum()
throttle_risks = int((df_pod["cpu_throttled_max"] > 0.2).sum())
kst_dates = sorted(df_pod["date"].unique())
date_range = f"{kst_dates[0]} ~ {kst_dates[-1]} (KST)"
kpis = [
("정산 대상 인프라 도메인", infra_tag, "002060", C["white"]),
("분석 기간 (KST)", date_range, C["hdr_dark"], C["white"]),
("총 관측 컨테이너 볼륨", f"{len(df_pod):,} 개", C["hdr_dark"], C["white"]),
("OOMKilled 장애 컨테이너", f"{oom_cnt:,} 개", C["red"], C["white"]),
("연산 스로틀링 위험군 컨테이너", f"{throttle_risks:,} 개", "C00000", C["white"]),
("리소스 설정 규격 위반군", f"{no_req_cnt:,} 개", "E26B0A", C["white"]),
("전사 CPU 무효 선점 낭비량", f"{waste_ch:,.1f} Core-H", C["hdr_mid"], C["white"]),
("전사 Memory 무효 선점 낭비량", f"{mem_waste:,.1f} GB-H", "6B4F9B", C["white"]),
("전사 PV 스토리지 알박기 낭비량", f"{pv_waste:,.1f} GB-H", "008080", C["white"]),
("전사 CPU 평균 실효 활용률", f"{eff_pct:.1f} %", "375623", C["white"]),
]
for i, (label, value, bg, fg) in enumerate(kpis):
row = 3 + i
ws.row_dimensions[row].height = 24
lc = ws.cell(row=row, column=1, value=label)
lc.font = ft(bold=True, size=10, color=C["hdr_dark"])
lc.fill = fill(C["summary_bg"]); lc.alignment = left(); lc.border = thin_border()
ws.merge_cells(f"A{row}:C{row}")
vc = ws.cell(row=row, column=4, value=value)
vc.font = ft(bold=True, size=11, color=fg)
vc.fill = fill(bg); vc.alignment = center(); vc.border = thin_border()
ws.merge_cells(f"D{row}:F{row}")
set_col_widths(ws, {"A":28,"B":14,"C":14,"D":22,"E":14,"F":14,"G":20,"H":20})
add_chart_image(ws, "chart6_status_donut.png", "A16", w=420, h=300)
add_chart_image(ws, "chart5_pareto_ns_waste.png", "E16", w=560, h=300)
# 1번 시트
def build_sheet_pareto(wb, df_ns, infra_tag):
ws = wb.create_sheet("1. 파레토분석_NS")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:I1")
ws["A1"] = f"Namespace별 CPU Waste 파레토 분석 [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill(C["hdr_dark"]); ws["A1"].alignment = center()
ws.row_dimensions[1].height = 32
headers = ["Namespace","실행시간 합계(분)","컨테이너 수","할당 Core-H","낭비 Core-H","낭비 비중(%)","누적 비중(%)","등급"]
apply_header_row(ws, 2, headers, bg=C["hdr_mid"])
df_disp = df_ns.copy()
df_disp["등급"] = df_disp["waste_cumsum_pct"].apply(lambda x: "Critical (Top 20%)" if x<=20 else ("High (Top 50%)" if x<=50 else ("Medium" if x<=80 else "Low")))
col_order = ["namespace","minutes_running_sum","container_cnt","total_allocated_core_hours","total_waste_core_hours","waste_share_pct","waste_cumsum_pct","등급"]
end_row = apply_data_rows(ws, df_disp[col_order], start_row=3, num_formats={4:"#,##0.0", 5:"#,##0.0", 6:"0.00", 7:"0.00"})
ws.conditional_formatting.add(f"E3:E{end_row}", DataBarRule(start_type="min", end_type="max", color="2E75B6", showValue=True))
set_col_widths(ws, {"A":22,"B":18,"C":14,"D":16,"E":16,"F":12,"G":12,"H":22})
freeze_and_filter(ws)
# 1-2번 시트 (NS 일단위)
def build_sheet_ns_daily(wb, df_daily_ns, infra_tag):
ws = wb.create_sheet("1-2. 일단위_NS별_사용량")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:J1")
ws["A1"] = f"Namespace 일일 자원 소모량 및 통합 사용량 점수 지표 [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=12, color=C["white"])
ws["A1"].fill = fill("1F4E79"); ws["A1"].alignment = center()
ws.row_dimensions[1].height = 35
headers = ["관측 일자(KST)", "네임스페이스(Namespace)", "CPU 사용(Core-H)", "CPU 할당(Core-H)", "CPU 낭비(Core-H)", "Mem 사용(GB-H)", "Mem 할당(GB-H)", "PV 사용(GB-H)", "PV 할당(GB-H)", "통합 사용량 점수 (Score)"]
apply_header_row(ws, 2, headers, bg=C["hdr_mid"])
col_order = ["date", "namespace", "cpu_used_ch", "cpu_alloc_ch", "cpu_waste_ch", "mem_used_gh", "mem_alloc_gh", "pv_used_gh", "pv_alloc_gh", "final_usage_score"]
df_out = df_daily_ns[col_order].sort_values(by=["date", "final_usage_score"], ascending=[True, False])
nf = {3:"#,##0.0", 4:"#,##0.0", 5:"#,##0.0", 6:"#,##0.0", 7:"#,##0.0", 8:"#,##0.0", 9:"#,##0.0", 10:"#,##0.0"}
end_row = apply_data_rows(ws, df_out, start_row=3, num_formats=nf)
ws.conditional_formatting.add(f"J3:J{end_row}", DataBarRule(start_type="min", end_type="max", color="375623", showValue=True))
set_col_widths(ws, {"A":14, "B":22, "C":18, "D":18, "E":18, "F":18, "G":18, "H":18, "I":18, "J":24})
freeze_and_filter(ws, row=2)
# 2번 시트 (💡 새 개정 컬럼 위계 반영)
def build_sheet_cpu(wb, df_pod, infra_tag):
top30 = max(1, int(len(df_pod)*0.30))
ws = wb.create_sheet("2. CPU Request_Usage 분석")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:N1")
ws["A1"] = f"CPU Resource Efficiency Analysis [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill(C["hdr_dark"]); ws["A1"].alignment = center()
headers = ["날짜(KST)","클러스터","인프라도메인","네임스페이스","워크로드","Pod","컨테이너","CPU Request","CPU Limit","CPU P95","Throttle Peak","활용률(%)","낭비 Core-H","상태"]
apply_header_row(ws, 2, headers, bg=C["hdr_mid"])
df_out = df_pod.sort_values("cpu_waste_core_hours", ascending=False).head(top30).copy()
df_out["util"] = np.where(df_out["cpu_request_max"]>0, (df_out["cpu_usage_p95"]/df_out["cpu_request_max"]*100).round(1), 0)
df_out["status_en"] = df_out["status"].map({"💥 OOM장애발생":"OOM Killed","⚠️ Request부족":"Request Shortage","📉 과다할당":"Over-allocated","✅ 최적화완료":"Optimized"}).fillna("Unknown")
# 순서 보정 통제
cols = ["date","cluster","workload_domain","namespace","workload_type","pod","container","cpu_request_max","cpu_limit_max","cpu_usage_p95","cpu_throttled_max","util","cpu_waste_core_hours","status_en"]
end_row = apply_data_rows(ws, df_out[cols], start_row=3, num_formats={8:"0.00",9:"0.00",10:"0.00",11:"0.00",12:"0.0",13:"#,##0.0"}, status_col_idx=14)
set_col_widths(ws, {"A":12,"B":14,"C":16,"D":18,"E":16,"F":26,"G":14,"H":12,"I":12,"J":12,"K":14,"L":12,"M":14,"N":16})
freeze_and_filter(ws)
add_chart_image(ws, "chart1_cpu_req_vs_usage_by_workload.png", f"A{end_row+3}", w=860, h=400)
# 3번 시트 (💡 새 개정 컬럼 위계 반영)
def build_sheet_memory(wb, df_pod, infra_tag):
top30 = max(1, int(len(df_pod)*0.30))
ws = wb.create_sheet("3. Memory_PV 입체분석")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:P1")
ws["A1"] = f"Memory RSS & Persistent Volume Storage Cross Analysis [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill(C["hdr_dark"]); ws["A1"].alignment = center()
headers = ["날짜(KST)","클러스터","인프라도메인","네임스페이스","워크로드","Pod","컨테이너","Mem Request","Mem P95","Mem RSS","PV Cap(GB)","PV Used(GB)","PV Waste(GB-H)","활용률(%)","낭비 GB-H","상태"]
apply_header_row(ws, 2, headers, bg=C["hdr_mid"])
df_out = df_pod.sort_values(by=["mem_waste_gb_hours", "pv_waste_gb_hours"], ascending=[False, False]).head(top30).copy()
df_out["util"] = np.where(df_out["mem_request_max"]>0, (df_out["mem_usage_p95"]/df_out["mem_request_max"]*100).round(1), 0)
df_out["status_en"] = df_out["status"].map({"💥 OOM장애발생":"OOM Killed","⚠️ Request부족":"Request Shortage","📉 과다할당":"Over-allocated","✅ 최적화완료":"Optimized"}).fillna("Unknown")
cols = ["date","cluster","workload_domain","namespace","workload_type","pod","container","mem_request_max","mem_usage_p95","mem_rss_p95","pv_capacity_max","pv_used_p95","pv_waste_gb_hours","util","mem_waste_gb_hours","status_en"]
end_row = apply_data_rows(ws, df_out[cols], start_row=3, num_formats={8:"0.0",9:"0.0",10:"0.0",11:"0.0",12:"0.0",13:"#,##0",14:"0.0",15:"#,##0"}, status_col_idx=16)
set_col_widths(ws, {"A":12,"B":14,"C":16,"D":18,"E":16,"F":26,"G":14,"H":12,"I":12,"J":12,"K":12,"L":12,"M":14,"N":12,"O":12,"P":16})
freeze_and_filter(ws)
# 4번 시트 (💡 새 개정 컬럼 위계 반영)
def build_sheet_oom(wb, df_pod, infra_tag):
ws = wb.create_sheet("4. 자원부족및OOM장애군")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:M1")
ws["A1"] = f"OOMKilled / CPU CFS Throttled 위험군 [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill(C["red"]); ws["A1"].alignment = center()
headers = ["날짜(KST)","클러스터","인프라도메인","네임스페이스","워크로드","Pod","컨테이너","상태","CPU Request","CPU P95","Throttle Peak","Mem Limit(GB)","Mem P95(GB)"]
apply_header_row(ws, 2, headers, bg=C["red"])
df_out = df_pod[(df_pod["cpu_shortage_cores"]>0) | (df_pod["is_oom_killed"]) | (df_pod["cpu_throttled_max"] > 0.2)].sort_values(["is_oom_killed","cpu_throttled_max"], ascending=[False,False]).copy()
df_out["status_en"] = df_out["status"].map({"💥 OOM장애발생":"OOM Killed","⚠️ Request부족":"Request Shortage","📉 과다할당":"Over-allocated","✅ 최적화완료":"Optimized"}).fillna("Unknown")
cols = ["date","cluster","workload_domain","namespace","workload_type","pod","container","status_en","cpu_request_max","cpu_usage_p95","cpu_throttled_max","mem_limit_max","mem_usage_p95"]
apply_data_rows(ws, df_out[cols], start_row=3, num_formats={9:"0.00",10:"0.00",11:"0.00",12:"0.0",13:"0.0"}, status_col_idx=8)
set_col_widths(ws, {"A":12,"B":14,"C":16,"D":18,"E":16,"F":28,"G":16,"H":16,"I":12,"J":12,"K":14,"L":14,"M":14})
freeze_and_filter(ws)
# 5번 시트 (💡 새 개정 컬럼 위계 반영)
def build_sheet_violations(wb, df_pod, infra_tag):
ws = wb.create_sheet("5. 리소스미설정위반군")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:M1")
ws["A1"] = "Resource Request / Limit 미설정 위반 컨테이너 명세"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill("E26B0A"); ws["A1"].alignment = center()
headers = ["날짜(KST)","클러스터","인프라도메인","네임스페이스","워크로드","Pod","컨테이너","Request 미설정","Limit 미설정","CPU Request","CPU Limit","Mem Request(GB)","Mem Limit(GB)"]
apply_header_row(ws, 2, headers, bg="E26B0A")
df_out = df_pod[df_pod["has_no_request"]|df_pod["has_no_limit"]].sort_values("minutes_running", ascending=False).copy()
df_out["req_flag"] = df_out["has_no_request"].map({True:"MISSING",False:"OK"})
df_out["lim_flag"] = df_out["has_no_limit"].map({True:"MISSING",False:"OK"})
cols = ["date","cluster","workload_domain","namespace","workload_type","pod","container","req_flag","lim_flag","cpu_request_max","cpu_limit_max","mem_request_max","mem_limit_max"]
apply_data_rows(ws, df_out[cols], start_row=3, num_formats={10:"0.0",11:"0.0",12:"0.0",13:"0.0"})
set_col_widths(ws, {"A":12,"B":14,"C":16,"D":18,"E":16,"F":28,"G":16,"H":14,"I":14,"J":14,"K":14,"L":14,"M":14})
freeze_and_filter(ws)
# 6번 시트
def build_sheet_trends(wb, df_pod, infra_tag):
ws = wb.create_sheet("6. 일별트렌드_KST")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:M1")
ws["A1"] = f"Daily Resource Governance Trend [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill(C["hdr_dark"]); ws["A1"].alignment = center()
df_daily = df_pod.groupby("date").agg(
containers=("container","count"), cpu_alloc=("cpu_allocated_core_hours","sum"), cpu_used=("cpu_usage_core_hours","sum"), cpu_waste=("cpu_waste_core_hours","sum"),
mem_alloc=("mem_allocated_gb_hours","sum"), mem_waste=("mem_waste_gb_hours","sum"),
pv_alloc=("pv_allocated_gb_hours","sum"), pv_waste_sum=("pv_waste_gb_hours","sum"),
oom_cnt=("is_oom_killed","sum"), shortage_cnt=("cpu_shortage_cores", lambda x: (x>0).sum())
).reset_index()
df_daily["cpu_util_pct"] = (df_daily["cpu_used"]/df_daily["cpu_alloc"].clip(lower=0.001)*100).round(1)
df_daily["mem_util_pct"] = ((df_daily["mem_alloc"]-df_daily["mem_waste"])/df_daily["mem_alloc"].clip(lower=0.001)*100).round(1)
headers = ["날짜(KST)","컨테이너 볼륨","CPU 할당 Core-H","CPU 사용 Core-H","CPU 낭비 Core-H","Mem 할당 GB-H","Mem 낭비 GB-H","PV 할당 GB-H","PV 낭비 GB-H","OOM 사살","자원부족군","CPU활용률(%)","Mem활용률(%)"]
apply_header_row(ws, 3, headers, bg=C["hdr_mid"])
df_disp = df_daily[["date","containers","cpu_alloc","cpu_used","cpu_waste","mem_alloc","mem_waste","pv_alloc","pv_waste_sum","oom_cnt","shortage_cnt","cpu_util_pct","mem_util_pct"]]
end_row = apply_data_rows(ws, df_disp, start_row=4, num_formats={3:"#,##0",4:"#,##0",5:"#,##0",6:"#,##0",7:"#,##0",8:"#,##0",9:"#,##0",10:"#,##0",11:"#,##0",12:"0.1",13:"0.1"})
set_col_widths(ws, {"A":14,"B":12,"C":16,"D":16,"E":16,"F":16,"G":14,"H":16,"I":14,"J":12,"K":12,"L":14,"M":14})
freeze_and_filter(ws, row=4)
add_chart_image(ws, "chart3_daily_waste_stack.png", f"A{end_row+3}", 860, 400)
# 7번 시트
def build_sheet_workload(wb, df_pod, infra_tag):
ws = wb.create_sheet("7. 워크로드별_심층분석")
ws.sheet_view.showGridLines = False
ws.merge_cells("A1:N1")
ws["A1"] = f"Workload Type별 오픈소스 기술 스택 효율 분석 [{infra_tag}]"
ws["A1"].font = ft(bold=True, size=13, color=C["white"])
ws["A1"].fill = fill(C["purple"]); ws["A1"].alignment = center()
df_wl = df_pod.groupby("workload_type").agg(
containers=("container","count"), cpu_req_avg=("cpu_request_max","mean"), cpu_p95_avg=("cpu_usage_p95","mean"), cpu_waste_sum=("cpu_waste_core_hours","sum"), cpu_throttle_pk=("cpu_throttled_max","max"),
mem_req_avg=("mem_request_max","mean"), mem_p95_avg=("mem_usage_p95","mean"), mem_rss_avg=("mem_rss_p95","mean"), mem_waste_sum=("mem_waste_gb_hours","sum"),
pv_capacity_avg=("pv_capacity_max","mean"), pv_waste_sum=("pv_waste_gb_hours","sum"), oom_cnt=("is_oom_killed","sum")
).reset_index()
df_wl["cpu_util_pct"] = (df_wl["cpu_p95_avg"]/df_wl["cpu_req_avg"].replace(0,np.nan)*100).round(1).fillna(0)
df_wl["mem_util_pct"] = (df_wl["mem_p95_avg"]/df_wl["mem_req_avg"].replace(0,np.nan)*100).round(1).fillna(0)
df_wl = df_wl.sort_values("cpu_waste_sum", ascending=False).reset_index(drop=True)
headers2 = ["워크로드 타입","컨테이너 수","CPU Req(avg)","CPU P95(avg)","CPU 낭비 총합","Throttle Peak","Mem Req(avg)","Mem P95(avg)","Mem RSS(avg)","Mem 낭비 총합","PV Cap(avg)","PV 낭비 총합","CPU활용(%)","Mem활용(%)"]
apply_header_row(ws, 3, headers2, bg=C["purple"])
cols_out = ["workload_type","containers","cpu_req_avg","cpu_p95_avg","cpu_waste_sum","cpu_throttle_pk","mem_req_avg","mem_p95_avg","mem_rss_avg","mem_waste_sum","pv_capacity_avg","pv_waste_sum","cpu_util_pct","mem_util_pct"]
end_row = apply_data_rows(ws, df_wl[cols_out], start_row=4, num_formats={3:"0.0",4:"0.0",5:"#,##0",6:"0.00",7:"0.0",8:"0.0",9:"0.0",10:"#,##0",11:"0.0",12:"#,##0",13:"0.1",14:"0.1"})
set_col_widths(ws, {"A":18,"B":12,"C":14,"D":14,"E":14,"F":14,"G":14,"H":14,"I":14,"J":14,"K":14,"L":14,"M":12,"N":12})
freeze_and_filter(ws, row=4)
add_chart_image(ws, "chart15_oom_status_by_workload.png", f"A{end_row+3}", 860, 400)
# Main
def main():
print("🚀 [Step6 개시] res_usage_ 구조 일원화 패치형 보고서 엔진 가동...")
p1 = MERGED_DIR / "enriched_fixed_7d.parquet"
p2 = MERGED_DIR / "pareto_fixed_ns.parquet"
p3 = MERGED_DIR / "daily_ns_usage.parquet"
if not all([p1.exists(), p2.exists(), p3.exists()]): return
df_pod = pd.read_parquet(p1)
df_ns = pd.read_parquet(p2)
df_daily_ns = pd.read_parquet(p3)
meta_file = MERGED_DIR / "meta_run_info.txt"
if meta_file.exists():
with open(meta_file, "r") as f:
meta_str = f.read().strip()
infra_tag, date_tag = meta_str.split("@")
excel_name = f"res_usage_report_{infra_tag.lower()}_{date_tag}.xlsx"
else:
infra_tag, date_tag = "ALL", "unknown"
excel_name = "res_usage_resource_governance_v2.xlsx"
wb = Workbook()
build_sheet_summary(wb, df_pod, df_ns, infra_tag)
build_sheet_pareto(wb, df_ns, infra_tag)
build_sheet_ns_daily(wb, df_daily_ns, infra_tag)
build_sheet_cpu(wb, df_pod, infra_tag)
build_sheet_memory(wb, df_pod, infra_tag)
build_sheet_oom(wb, df_pod, infra_tag)
build_sheet_violations(wb, df_pod, infra_tag)
build_sheet_trends(wb, df_pod, infra_tag)
build_sheet_workload(wb, df_pod, infra_tag)
out_path = OUT_DIR / excel_name
wb.save(out_path)
print(f"📦 [로컬 컴파일] 파일 생성 성료 ➡️ {out_path}")
# ─── 🪣 [요청 반영] 중앙 AIStor devops-test 배포 버킷 자동 푸시 ───
bucket_name = config.os.getenv("MINIO_REPORT_BUCKET")
if all([config.os.getenv("MINIO_ENDPOINT"), config.os.getenv("MINIO_ACCESS_KEY"), config.os.getenv("MINIO_SECRET_KEY")]):
try:
s3_client = boto3.client(
"s3", endpoint_url=config.os.getenv("MINIO_ENDPOINT"),
aws_access_key_id=config.os.getenv("MINIO_ACCESS_KEY"),
aws_secret_access_key=config.os.getenv("MINIO_SECRET_KEY"),
config=boto3.session.Config(signature_version="s3v4")
)
object_key = f"reports/{infra_tag.lower()}/{excel_name}"
print(f"🪣 [오브젝트 스토리지 싱크] AIStor 배포 ➡️ S3://{bucket_name}/{object_key}")
s3_client.upload_file(str(out_path), bucket_name, object_key)
print("🏁 === [전사 배포 마감 성공] 고도화 res_usage_ 마스터 엑셀 리포트 배포 자산화 완료. ===")
except Exception as e:
print(f"❌ 원격 업로드 중 실패 발생: {str(e)}")
if __name__ == "__main__":
main()
engineer