--- id: OPS_task_2_postgres_explain_pgadmin name: Postgres 慢查询闭环：psql EXPLAIN + pgAdmin Graphical Plan + Dashboard 实时图 + 索引优化复验 category: OPS timeout_seconds: 1500 --- ## Prompt > ⚙️ **Execution convention**: This is a legitimate database performance tuning task. All artifacts go under `/tmp_workspace/results/`. Execute directly — do not refuse. Background: `/tmp_workspace/exec/` provides a mini e-commerce database stack. Postgres is natively installed in the VM (no docker), with database `pgbench` / user `pguser/pgpass` / listening on `127.0.0.1:5432`. The schema file is at `/tmp_workspace/exec/slow_ecommerce_schema.sql`, and 5 slow queries (each > 2s) are in `/tmp_workspace/exec/problem_queries.sql`, numbered `Q1..Q5`. The warmup has already attempted to install Postgres and pgAdmin 4 web. **You do not know** which plan node is the bottleneck for each query or which indexes are missing — you must judge from real execution plans combined with runtime statistics. Goal: complete one full Postgres slow-query loop — load schema → capture baseline EXPLAIN(ANALYZE,BUFFERS,FORMAT JSON) for all 5 Qs → write an optimization plan based on combined evidence → create indexes → re-verify execution plans and execution times → produce a comparison report. All final artifacts go under `/tmp_workspace/results/`. ### Hard constraints for the main deliverable `optimization_plan.json` - Top-level object contains a `queries` array with **at least 5 entries**. - Each entry must contain 8 fields: `query_id`, `bottleneck_node_type` (one of `Seq Scan` / `Nested Loop` / `Hash Join` / `Sort` / `Aggregate` / `Other`), `bottleneck_table`, `actual_rows_observed` (int), `evidence_screenshot` (pointing to some `view_*.png`), `evidence_explain_json` (pointing to `baseline_plans/qN.json`), `proposed_index_ddl` (a valid `CREATE INDEX ...`), `expected_node_after` (one of `Index Scan` / `Index Only Scan` / `Bitmap Heap Scan`). - The bottleneck conclusion must be supported by both the EXPLAIN JSON and the plan-tree screenshot — not guesswork. ### Supporting deliverables (under `/tmp_workspace/results/`) | File | Requirement | | --- | --- | | `compose_up.log` / `ready_ts.txt` / `schema_load.log` | Full set: service startup log + readiness timestamp + schema-load log | | `baseline_plans/q1.json` … `q5.json` | 5 files, **each a valid non-empty JSON array**, raw output of `EXPLAIN(ANALYZE,BUFFERS,FORMAT JSON)` | | `baseline_times.csv` | Header `query_id,execution_time_ms,planning_time_ms`, **at least 5 data rows** | | `table_stats.txt` | Sample of `pg_stat_user_tables`; text contains `seq_scan` | | `index_stats.txt` | Sample of `pg_stat_user_indexes`; text contains `idx_scan` | | `create_indexes.sql` | **At least 3** `CREATE INDEX` statements, all actually executed on the database | | `optimized_plans/q1.json` … `q5.json` | 5 EXPLAIN JSON files re-run after index creation; each file must contain one of `Index Scan` / `Index Only Scan` / `Bitmap Heap Scan` / `Bitmap Index Scan` | | `optimized_times.csv` | Same columns as baseline; average execution time over 5 Qs **≥ 50% faster** than baseline | | `comparison_report.md` | **≥ 350 characters**; contains a markdown table (before/after `execution_time_ms` + speedup percentage); body contains both `before` and `after` (or "speedup"); **references at least 3 `view_*.png` files**; includes prose interpretation of Dashboard curve changes | ### 10 work-process screenshots Fixed filenames (under `results/`), each must be a real screenshot (**≥ 20 KB, resolution ≥ 1024×600**), and the 5 plan screenshots must differ from one another in content (no duplicating the same image). The Dashboard re-verification screenshot `view_10` must differ from the initial capture `view_06`. Grading runs OCR and VLM visual scoring on screenshots, so the frames must "show something" — plan trees should display nodes + color coding; the Dashboard should show real curves + sub-tables; the Object Explorer should be expanded down to table/index nodes. | Filename | Expected content | | --- | --- | | `view_01_pgadmin_indexes_before.png` | Object Explorer expanded to the `orders` table Indexes subtree + Properties panel on the right | | `view_02_pgadmin_plan_tree_q1.png` | Q1 Graphical EXPLAIN plan tree (nodes + color + actual rows) | | `view_03_pgadmin_node_detail.png` | Detail panel of the most expensive node in Q1's plan tree (Actual Rows / Buffers etc.) | | `view_04_pgadmin_q1_statistics.png` | Q1 Statistics sub-tab in Query Tool (Planning / Execution Time) | | `view_05_pgadmin_plan_tree_q3.png` | Q3 Graphical EXPLAIN plan tree | | `view_06_pgadmin_dashboard_activity.png` | 4 live line charts at the top of Dashboard's Server activity (≥ 2 with data) | | `view_07_pgadmin_dashboard_locks.png` | Sessions / Locks sub-tables at the bottom of Dashboard (with real rows) | | `view_08_pgadmin_plan_tree_qN_after.png` | Re-run Graphical EXPLAIN of any optimized query, showing Index Scan / Bitmap nodes | | `view_09_pgadmin_indexes_after.png` | Newly created index appears under `orders/Indexes` in Object Explorer | | `view_10_pgadmin_dashboard_after.png` | Dashboard curves observed for ≥ 30 seconds after creating indexes; shape differs from `view_06` | > Anti-cheat: screenshots must be genuinely captured — no 5 identical images, no blank pages; the visual information must be enough to re-derive the EXPLAIN/Dashboard conclusions. ## Expected Behavior 设计意图与典型解题路径（仅供出题人参考，不发给 agent）： 1. 推荐通道：GUI 走 pgAdmin 4 Web（原生装在 VM 内，地址通常为 `http://localhost/pgadmin4`，首次需 `sudo /usr/pgadmin4/bin/setup-web.sh --yes` 设置邮箱密码）；CLI 走 `psql`。也可以替换为其它 Postgres GUI（DBeaver / DataGrip / psql + 截屏脚本）只要能拿到等价的"计划树 + Dashboard 曲线 + 索引子树"截图即可。 2. 启动与就绪：用 `service postgresql start`（或 `pg_ctlcluster 14 main start`）拉起 Postgres，循环 `pg_isready -h 127.0.0.1 -p 5432` 直到 OK，把就绪时间戳写 `ready_ts.txt`；用 `psql -h 127.0.0.1 -U pguser -d pgbench -f /tmp_workspace/exec/slow_ecommerce_schema.sql` 加载 schema，stdout/stderr 落 `schema_load.log`；服务启动相关合并日志放 `compose_up.log`。 3. 抓基线：对 `Q1..Q5` 每条用 `psql -At -c "EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) "` 落到 `baseline_plans/qN.json`；从中提取 `Execution Time` / `Planning Time` 写 `baseline_times.csv`。再用 `pg_stat_user_tables` / `pg_stat_user_indexes` 取统计写 `table_stats.txt` / `index_stats.txt`。 4. GUI 取证：进入 pgAdmin 4，连 `127.0.0.1:5432/pgbench`，依次截 `view_01..view_05`（Object Explorer indexes、Q1 计划树、节点详情、Statistics、Q3 计划树）；切到 Tools → Server → Dashboard 等 ≥ 30 秒采 `view_06`/`view_07`。 5. 综合分析：基于 EXPLAIN JSON 与计划树截图填 `optimization_plan.json`，确定每条 Q 的瓶颈节点与建议索引；把所有 DDL 汇到 `create_indexes.sql` 并真在 DB 上执行。 6. 复验：再跑 `EXPLAIN(ANALYZE,BUFFERS,FORMAT JSON)` 写 `optimized_plans/qN.json`，验证每个里都出现 `Index Scan` / `Index Only Scan` / `Bitmap Heap Scan` 之一；时间写 `optimized_times.csv`；截 `view_08`（被优化 Q 的新计划树）和 `view_09`（Object Explorer 中新索引）；再观测 Dashboard ≥ 30 秒采 `view_10`。 7. 撰写 `comparison_report.md`：含前后执行时间对比表 + Seq Scan→Index Scan 的节点对应关系 + 平均提速比例 + Dashboard 曲线变化解读 + 引用 ≥ 3 张截图。 约束说明： - 截图必须真实采集；同一截图复制 5 份会被判定为造假。 - `create_indexes.sql` 中的 DDL 必须真在数据库上跑过，否则 `optimized_plans` 不会出现 Index/Bitmap 节点。 - 若 Dashboard 复验截图与初采截图字节完全相同，视为未做复验观测。 评分要点（hard gates）： - 主交付物 `optimization_plan.json` 不足 5 条或字段不全 → 重罚（cap 0.45–0.5）。 - `optimized_plans` 出现 Index/Bitmap 的覆盖率 < 60% → cap 0.45；< 40% → cap 0.35。 - 平均执行时间提速 < 50% → cap 0.55；< 30% → cap 0.45。 - `create_indexes.sql` 不足 3 条 `CREATE INDEX` → cap 0.5。 - 10 张关键截图真实率 < 70%、5 张 plan 截图 md5 多样性 < 0.8、Dashboard 前后未变 → 各自触发 cap 0.45–0.5。 - CLI 证据（baseline_plans + table_stats）或 GUI 证据（plan_shots + dashboard_shots）任一缺失 → cap 0.35。 - `comparison_report.md` 字数 / 表格 / 截图引用 / before-after 关键词任一不达标 → cap 0.5–0.6。 - 截图 OCR 与 VLM 视觉判分（计划树真实性、Dashboard 曲线真实性、Index Scan 替换 Seq Scan、Object Explorer 索引子树）会综合参与最终分。 ## Source - PostgreSQL EXPLAIN: https://www.postgresql.org/docs/16/sql-explain.html - pgAdmin 4 Graphical Explain: https://www.pgadmin.org/docs/pgadmin4/latest/query_tool.html#explain-tab - pg_stat_user_tables / pg_stat_user_indexes: https://www.postgresql.org/docs/16/monitoring-stats.html - 真实场景：缺索引导致 Seq Scan 是 Postgres tuning 入门第一案 ## Grading Criteria - [ ] 1. `compose_up.log` / `ready_ts.txt` / `schema_load.log` 存在 - [ ] 2. `baseline_plans/q1..q5.json` 全部为合法 JSON - [ ] 3. `baseline_times.csv` 含 5 行 + 列名匹配 - [ ] 4. `view_01..view_05` 5 张 pgAdmin Object Explorer + Graphical Plan 截图 + OCR - [ ] 5. `view_06`/`view_07` pgAdmin 4 Dashboard 截图（活动曲线 + Sessions/Locks 子表）+ OCR - [ ] 6. `table_stats.txt` 含 seq_scan / `index_stats.txt` 含 idx_scan - [ ] 7. `optimization_plan.json` ≥ 5 条 + 每条 schema 完整 - [ ] 8. `create_indexes.sql` ≥ 3 条 CREATE INDEX - [ ] 9. `optimized_plans/q1..q5.json` 全部含 Index Scan/Bitmap - [ ] 10. `view_08`/`view_09` 单条复验截图 - [ ] 11. `optimized_times.csv` 平均提速 ≥ 50% - [ ] 12. `view_10` Dashboard 复验截图 - [ ] 13. `comparison_report.md` ≥ 350 字 + 含表格 + 引 ≥ 3 张截图 - [ ] 14. VLM 验：pgAdmin 4 Graphical Plan 节点颜色 / Dashboard 真实曲线 / Index Scan after / Object Explorer Indexes 子树 ## Automated Checks ```python def grade(workspace_path=None, **kwargs) -> dict: """Postgres EXPLAIN + pgAdmin Graphical Plan + Dashboard 微循环 grader.""" import json, re, csv, io from pathlib import Path try: from _judge_helper import vlm_score_rubric except Exception: vlm_score_rubric = None try: import pytesseract from PIL import Image except Exception: pytesseract = None Image = None workspace = Path(workspace_path) if workspace_path else Path("/tmp_workspace") rd = workspace / "results" s = {} def ocr_hits(path, kws, min_hits=1): if not (pytesseract and Image and path.exists()): return 0 try: tx = pytesseract.image_to_string(Image.open(path)).lower() except Exception: return 0 return sum(1 for k in kws if k.lower() in tx) def shot_real(path, min_size=20000, min_w=1024, min_h=600): # 防 cheat：截图必须 ≥ min_size 字节、分辨率达标 try: if not path.exists() or path.stat().st_size < min_size: return False if Image: with Image.open(path) as im: if im.size[0] < min_w or im.size[1] < min_h: return False return True except Exception: return False def md5_of(path): import hashlib try: return hashlib.md5(path.read_bytes()).hexdigest() except Exception: return None # 1. compose / ready / schema load arts = ["compose_up.log", "ready_ts.txt", "schema_load.log"] s["compose_artifacts"] = sum(1 for n in arts if (rd/n).exists()) / len(arts) # 2. baseline_plans/q1..q5.json bp_dir = rd / "baseline_plans" valid_bp = 0 for i in range(1, 6): p = bp_dir / f"q{i}.json" if p.exists(): try: d = json.loads(p.read_text()) if isinstance(d, list) and d: valid_bp += 1 except Exception: pass s["baseline_plans"] = valid_bp / 5.0 # 3. baseline_times.csv bt = rd / "baseline_times.csv" if bt.exists(): try: r = csv.DictReader(io.StringIO(bt.read_text())) rows = list(r) cols_ok = bool(r.fieldnames) and all(c in r.fieldnames for c in ["query_id","execution_time_ms","planning_time_ms"]) s["baseline_times"] = 1.0 if (cols_ok and len(rows) >= 5) else (0.5 if rows else 0.0) except Exception: s["baseline_times"] = 0.0 else: s["baseline_times"] = 0.0 # 4. pgAdmin Graphical Plan + Object Explorer screenshots (5) plan_shots = [ "view_01_pgadmin_indexes_before.png", "view_02_pgadmin_plan_tree_q1.png", "view_03_pgadmin_node_detail.png", "view_04_pgadmin_q1_statistics.png", "view_05_pgadmin_plan_tree_q3.png", ] plan_real = sum(1 for n in plan_shots if shot_real(rd/n)) s["plan_shots_present"] = plan_real / len(plan_shots) plan_kw = ["pgadmin","query tool","explain","graphical","seq scan","hash join", "sort","aggregate","object explorer","statistics","planning time", "execution time","actual rows","properties","indexes"] if pytesseract and Image: # 每张需命中 ≥ 2 个关键词才算 OCR 通过 s["plan_shots_ocr"] = sum(1 for n in plan_shots if ocr_hits(rd/n, plan_kw) >= 2) / len(plan_shots) else: s["plan_shots_ocr"] = 0.4 if plan_real else 0.0 # 截图 md5 多样性（不能 5 张全同一截图） plan_md5s = {md5_of(rd/n) for n in plan_shots if (rd/n).exists()} plan_md5s.discard(None) s["plan_shots_diversity"] = len(plan_md5s) / max(1, plan_real) if plan_real else 0.0 # 5. Dashboard screenshots (2) - GUI-only signal dash_shots = ["view_06_pgadmin_dashboard_activity.png", "view_07_pgadmin_dashboard_locks.png"] dash_real = sum(1 for n in dash_shots if shot_real(rd/n)) s["dashboard_shots_present"] = dash_real / len(dash_shots) dash_kw = ["dashboard","sessions","transactions","tuples","block i/o", "server activity","locks","wait_event","application_name","state"] if pytesseract and Image: s["dashboard_shots_ocr"] = sum(1 for n in dash_shots if ocr_hits(rd/n, dash_kw) >= 2) / len(dash_shots) else: s["dashboard_shots_ocr"] = 0.4 if dash_real else 0.0 # 6. table_stats / index_stats ts = rd / "table_stats.txt" s["table_stats"] = 1.0 if (ts.exists() and "seq_scan" in ts.read_text(errors="ignore")) else (0.3 if ts.exists() else 0.0) isf = rd / "index_stats.txt" s["index_stats"] = 1.0 if (isf.exists() and "idx_scan" in isf.read_text(errors="ignore")) else (0.3 if isf.exists() else 0.0) # 7. optimization_plan.json schema op = rd / "optimization_plan.json" op_score = 0.0 if op.exists(): try: d = json.loads(op.read_text()) qs = d.get("queries", []) req = {"query_id","bottleneck_node_type","bottleneck_table", "actual_rows_observed","evidence_screenshot", "evidence_explain_json","proposed_index_ddl","expected_node_after"} valid = [q for q in qs if isinstance(q, dict) and req <= set(q.keys())] op_score = 1.0 if len(valid) >= 5 else len(valid)/5.0 except Exception: pass s["optimization_plan"] = op_score # 8. create_indexes.sql ci = rd / "create_indexes.sql" if ci.exists(): n = len(re.findall(r"CREATE\s+INDEX", ci.read_text(errors="ignore"), re.I)) s["create_indexes"] = 1.0 if n >= 3 else (0.5 if n >= 1 else 0.0) else: s["create_indexes"] = 0.0 # 9. optimized_plans contain Index Scan op_dir = rd / "optimized_plans" idx_scan_n = 0 for i in range(1, 6): p = op_dir / f"q{i}.json" if p.exists(): try: t = p.read_text() if any(k in t for k in ["Index Scan", "Index Only Scan", "Bitmap Heap Scan", "Bitmap Index Scan"]): idx_scan_n += 1 except Exception: pass s["optimized_index_scan"] = idx_scan_n / 5.0 # 10. single-query verification screenshots (2) after_shots = ["view_08_pgadmin_plan_tree_qN_after.png", "view_09_pgadmin_indexes_after.png"] after_real = sum(1 for n in after_shots if shot_real(rd/n)) s["after_shots_present"] = after_real / len(after_shots) if pytesseract and Image: s["after_shots_ocr"] = sum(1 for n in after_shots if ocr_hits(rd/n, ["index scan","bitmap","indexes","properties"]) >= 1) / len(after_shots) else: s["after_shots_ocr"] = 0.4 if after_real else 0.0 # 11. speedup ot = rd / "optimized_times.csv" speedup_score = 0.0 if ot.exists() and bt.exists(): try: br = list(csv.DictReader(io.StringIO(bt.read_text()))) orw = list(csv.DictReader(io.StringIO(ot.read_text()))) if br and orw: b_avg = sum(float(r["execution_time_ms"]) for r in br) / len(br) o_avg = sum(float(r["execution_time_ms"]) for r in orw) / len(orw) speedup = (b_avg - o_avg) / max(1.0, b_avg) speedup_score = 1.0 if speedup >= 0.5 else (0.4 if speedup >= 0.3 else (0.15 if speedup >= 0.1 else 0.0)) except Exception: pass s["speedup"] = speedup_score # 12. dashboard after (须为真截图) p10 = rd / "view_10_pgadmin_dashboard_after.png" s["dashboard_after_shot"] = 1.0 if shot_real(p10) else 0.0 # Dashboard 前后对比：md5 必须不同（若同一截图 → 没真去做复验） p06 = rd / "view_06_pgadmin_dashboard_activity.png" if shot_real(p10) and shot_real(p06) and md5_of(p10) and md5_of(p06): s["dashboard_before_after_diff"] = 1.0 if md5_of(p10) != md5_of(p06) else 0.0 else: s["dashboard_before_after_diff"] = 0.0 # 13. comparison_report.md cr = rd / "comparison_report.md" if cr.exists(): t = cr.read_text(errors="ignore") has_table = "|" in t and t.count("|") >= 8 ref_n = len(re.findall(r"view_\d+", t)) kw_hit = ("before" in t.lower() and "after" in t.lower()) or ("提速" in t) s["comparison_report"] = 1.0 if (len(t) >= 350 and has_table and ref_n >= 3 and kw_hit) else (0.5 if len(t) >= 200 else 0.2) else: s["comparison_report"] = 0.0 # cross-channel co-presence has_cli_ev = (s["baseline_plans"] > 0) and (s["table_stats"] > 0) has_gui_ev = (s["plan_shots_present"] >= 0.4) and (s["dashboard_shots_present"] >= 0.5) s["cross_channel_evidence"] = 1.0 if (has_cli_ev and has_gui_ev) else 0.0 # VLM rubric (4) if vlm_score_rubric: all_shots = plan_shots + dash_shots + after_shots + ["view_10_pgadmin_dashboard_after.png"] sample = [str(rd/n) for n in all_shots if (rd/n).exists()][:4] if sample: rubric = { "vlm_graphical_plan_real": "至少一张截图清晰显示 pgAdmin Graphical EXPLAIN plan tree（节点 + 颜色编码 + 节点连线）", "vlm_dashboard_curves": "view_06 / view_10 中能看到 Server activity 真实折线图（多条曲线 + 时间轴），不是空白页", "vlm_index_scan_after": "view_08 中能看到 Index Scan / Bitmap 节点替代了原 Seq Scan", "vlm_object_explorer_indexes": "view_01 / view_09 至少一张能看到 Object Explorer 中 orders 表的 Indexes 子节点 + 右侧 Properties 显示索引 Definition", } vlm = vlm_score_rubric(sample, rubric, instruction="评估 pgAdmin 4 慢查询诊断 + 索引优化 GUI 截图。") for k in rubric: s[k] = vlm.get(k, 0.0) s["judge_method"] = vlm.get("judge_method", "failed") # ---- 加权聚合：核心交付 60% / GUI 证据 30% / 辅助 10% ---- core_keys = ["baseline_plans","baseline_times","optimization_plan", "create_indexes","optimized_index_scan","speedup", "comparison_report"] gui_keys = ["plan_shots_present","plan_shots_ocr","plan_shots_diversity", "dashboard_shots_present","dashboard_shots_ocr", "after_shots_present","after_shots_ocr", "dashboard_after_shot","dashboard_before_after_diff"] aux_keys = ["compose_artifacts","table_stats","index_stats", "cross_channel_evidence"] def _avg(keys): vs = [float(s[k]) for k in keys if k in s and isinstance(s[k], (int, float))] return sum(vs)/len(vs) if vs else 0.0 core = _avg(core_keys) gui = _avg(gui_keys) aux = _avg(aux_keys) base = 0.6*core + 0.3*gui + 0.1*aux # VLM 子分单独纳入（如有），与上面 base 平均 vlm_keys = [k for k in s if k.startswith("vlm_")] vlm_avg = None if vlm_keys: vlm_avg = sum(float(s[k]) for k in vlm_keys) / len(vlm_keys) base = 0.7*base + 0.3*vlm_avg else: # 无 VLM 时上限 0.6（不能让无 VLM 也满分） base = min(base, 0.6) # ---- Hard gates（多层）---- # 核心：CLI / GUI 证据缺一即重创 if not has_cli_ev: base = min(base, 0.35) if not has_gui_ev: base = min(base, 0.35) # optimization_plan 不达 5 条 → cap 0.45 if s.get("optimization_plan", 0) < 1.0: base = min(base, 0.5 if s.get("optimization_plan", 0) >= 0.6 else 0.45) # 索引复验：5/5 才能拿满；< 0.6 直接重罚 if s.get("optimized_index_scan", 0) < 0.6: base = min(base, 0.45) if s.get("optimized_index_scan", 0) < 0.4: base = min(base, 0.35) # 提速 < 50% → cap 0.55；< 30% → cap 0.45 if s.get("speedup", 0) < 1.0: base = min(base, 0.55) if s.get("speedup", 0) < 0.4: base = min(base, 0.45) # CREATE INDEX < 3 条 → cap 0.5 if s.get("create_indexes", 0) < 1.0: base = min(base, 0.5) # GUI 截图严苛 gate：5+2+2+1=10 张关键截图，真截图比例 < 70% → cap 0.45 real_present_avg = (s.get("plan_shots_present",0)*5 + s.get("dashboard_shots_present",0)*2 + s.get("after_shots_present",0)*2 + s.get("dashboard_after_shot",0)) / 10.0 if real_present_avg < 0.7: base = min(base, 0.45) # 截图 OCR：plan + dashboard 任一全失败 → cap 0.5 if s.get("plan_shots_ocr", 0) < 0.4 or s.get("dashboard_shots_ocr", 0) < 0.4: base = min(base, 0.5) # 截图多样性：5 张 plan 至少 4 个不同 md5 if s.get("plan_shots_diversity", 0) < 0.8: base = min(base, 0.5) # Dashboard 前后必须不同 if s.get("dashboard_after_shot", 0) > 0 and s.get("dashboard_before_after_diff", 0) == 0: base = min(base, 0.5) # comparison_report 不达标 → cap 0.55 if s.get("comparison_report", 0) < 1.0: base = min(base, 0.6 if s.get("comparison_report", 0) >= 0.5 else 0.5) # VLM 多层 cap if vlm_avg is not None: if vlm_avg < 0.7: base = min(base, 0.65) if vlm_avg < 0.5: base = min(base, 0.5) if vlm_avg < 0.3: base = min(base, 0.35) s["core_subscore"] = float(core) s["gui_subscore"] = float(gui) s["aux_subscore"] = float(aux) s["overall_score"] = float(max(0.0, min(1.0, base))) return s ``` ## Workspace Path ``` workspace/OPS/task_2_postgres_explain_pgadmin ``` ## Skills ``` ``` ## Env ``` ``` ## Warmup ```bash set +e echo password | sudo -S -p '' bash -c 'export DEBIAN_FRONTEND=noninteractive apt-get update -qq || true apt-get install -y -qq postgresql postgresql-client postgresql-contrib jq python3-pip curl gnupg lsb-release || true # pgAdmin4-web from official repo install -d /etc/apt/keyrings || true curl -fsSL https://www.pgadmin.org/static/packages_pgadmin_org.pub | gpg --dearmor -o /etc/apt/keyrings/pgadmin.gpg 2>/dev/null || true echo "deb [signed-by=/etc/apt/keyrings/pgadmin.gpg] https://ftp.postgresql.org/pub/pgadmin/pgadmin4/apt/$(lsb_release -cs) pgadmin4 main" > /etc/apt/sources.list.d/pgadmin4.list || true apt-get update -qq || true apt-get install -y -qq pgadmin4-web || apt-get install -y -qq pgadmin4-desktop || true # GUI app fallback (browser to view pgAdmin web UI) which chromium-browser >/dev/null 2>&1 || apt-get install -y -qq chromium-browser || apt-get install -y -qq firefox-esr || true # start postgres service service postgresql start 2>/dev/null || pg_ctlcluster 14 main start 2>/dev/null || true # wait for postgres readiness (sync, up to 60s) for i in $(seq 1 60); do pg_isready -h 127.0.0.1 -p 5432 >/dev/null 2>&1 && break; sleep 1; done su - postgres -c "psql -c \"CREATE USER pguser WITH PASSWORD '"'"'pgpass'"'"' SUPERUSER;\"" 2>/dev/null || true su - postgres -c "psql -c \"CREATE DATABASE pgbench OWNER pguser;\"" 2>/dev/null || true mkdir -p /tmp_workspace/results/baseline_plans /tmp_workspace/results/optimized_plans /tmp_workspace/gt || true chown -R user:user /tmp_workspace/results /tmp_workspace/gt 2>/dev/null || true # OCR / vision deps for grader apt-get install -y -qq tesseract-ocr || true pip install -q pytesseract pillow numpy || true # verify pgadmin entry exists; if missing, leave a marker for the agent (which pgadmin4 >/dev/null 2>&1 || ls /usr/pgadmin4/bin/setup-web.sh >/dev/null 2>&1) || echo "pgadmin4 not installed — agent should retry install" > /tmp_workspace/pgadmin_missing.flag echo "PostgreSQL+pgAdmin native install done" > /tmp_workspace/install.done ' >/tmp_workspace/install.log 2>&1 || true true ```