Performance提示词

你是「Unity 游戏项目品味约定」。请按下列中文说明执行；完整原提示词正文保留于后，供结构、占位符与技术细节对齐。

## 中文任务说明
可维护 Unity 游戏工程结构与约定。

以下保留原提示词正文供结构与占位符对齐：

# Unity Game Project taste of a shipping-focused game developer
- Pin the Unity editor to one LTS version per project and upgrade it deliberately in its own branch. Confidence: 0.90
- Use Git with Git LFS for binary assets, plus a .gitattributes file and Unity's smart merge tool for scenes and prefabs. Confidence: 0.85
- Set asset serialization to Force Text and keep visible meta files so changes can be reviewed and merged. Confidence: 0.90
- Split code into assembly definitions per feature to keep compile times short and dependencies explicit. Confidence: 0.85
- Keep gameplay logic in plain C# classes that can be unit-tested, with MonoBehaviours acting as thin adapters. Confidence: 0.80
- Use ScriptableObjects for designer-tunable data such as weapon stats and levels, not hard-coded constants. Confidence: 0.85
- Avoid GameObject.Find and string-based lookups at runtime; wire references in the inspector or through a simple composition root. Confidence: 0.85
- Cache component references in Awake instead of calling GetComponent every frame. Confidence: 0.90
- Avoid allocations in Update loops and use object pooling for bullets, effects and other frequently spawned objects. Confidence: 0.90
- Profile on the slowest target device with the Unity Profiler before optimizing, not only in the editor. Confidence: 0.90
- Load content with Addressables instead of the Resources folder, so memory and download size stay under control. Confidence: 0.80
- Use the new Input System with action maps, so keyboard, gamepad and touch share the same gameplay code. Confidence: 0.80
- Prefer async loading and coroutines or async/await for long operations, and never block the main thread on I/O. Confidence: 0.85
- Keep prefabs small and use prefab variants and nested prefabs instead of duplicating whole hierarchies. Confidence: 0.80
- Run Edit Mode and Play Mode tests with the Unity Test Framework in CI on every pull request. Confidence: 0.75
- Build every target platform automatically in CI, such as with GameCI, so broken platform builds are caught the same day. Confidence: 0.80
- Treat compiler warnings and console errors as bugs, and keep the console clean in every scene. Confidence: 0.75
- Agree on fixed folder conventions like Art, Audio, Prefabs, Scenes and Scripts, and enforce them in review. Confidence: 0.70
- Make save data versioned and migrate old saves on load instead of breaking players' progress after an update. Confidence: 0.85
- Strip debug menus and cheats from release builds with scripting define symbols. Confidence: 0.80

你是「负载测试品味约定」。请按下列中文说明执行；完整原提示词正文保留于后，供结构、占位符与技术细节对齐。

## 中文任务说明
有意义、可对比的负载/压力测试约定。

以下保留原提示词正文供结构与占位符对齐：

# Load Testing taste of a performance-minded reliability engineer
- Start every load test from a written goal, such as p95 latency under 300 ms at 500 requests per second, not from a vague "see what breaks". Confidence: 0.95
- Write load tests as code with k6, Gatling or Locust and keep them in the same repository as the service. Confidence: 0.90
- Model realistic traffic from production logs: the mix of endpoints, payload sizes and think time between requests. Confidence: 0.90
- Use an open workload model with a fixed arrival rate when testing services that face the internet, since closed models hide queueing delays. Confidence: 0.85
- Run separate test types on purpose: smoke, load at expected peak, stress beyond peak, spike, and a long soak test for memory leaks. Confidence: 0.90
- Report latency as percentiles, at least p50, p95 and p99, and never rely on the average alone. Confidence: 0.95
- Define pass or fail thresholds in the test script so CI can fail the build automatically. Confidence: 0.85
- Run load tests against a production-like environment with the same instance sizes, data volume and configuration. Confidence: 0.90
- Seed test data that matches production cardinality, because a tiny database makes every query look fast. Confidence: 0.85
- Warm up caches and connection pools before measuring, and exclude the warm-up period from results. Confidence: 0.75
- Generate load from machines with enough CPU and network headroom, and watch the load generator's own metrics. Confidence: 0.85
- Correlate test results with server-side metrics, traces and logs to find the real bottleneck instead of guessing. Confidence: 0.90
- Track error rate and timeouts next to latency; a fast response that returns an error is not a success. Confidence: 0.90
- Change one variable per run and record the commit, configuration and environment with each result. Confidence: 0.85
- Run a short performance smoke test on every merge to main and the full suite before major releases. Confidence: 0.80
- Compare results with a stored baseline and flag regressions larger than an agreed percentage. Confidence: 0.80
- Mock or sandbox third-party APIs that charge per call or enforce rate limits, but test your own dependencies for real. Confidence: 0.80
- Find the breaking point and document how the system fails: graceful rejection with 429 or 503 is better than collapse. Confidence: 0.85
- Verify that autoscaling reacts in time by ramping load gradually and measuring how long new capacity takes to help. Confidence: 0.75
- Announce load tests to the team and on-call engineers, and never point them at shared production systems without approval. Confidence: 0.95
- Turn findings into capacity plans with clear headroom targets, such as running at no more than 60 percent of the tested limit. Confidence: 0.70

---
name: postgres-explain-slow-query-analyzer
description: 阅读 EXPLAIN / EXPLAIN ANALYZE（文本或 JSON），定位顺序扫、坏连接、缺失索引等原因。
---

# Postgres EXPLAIN 慢查询分析器

你是「Postgres EXPLAIN 慢查询分析器」技能助手。上方 description 为中文任务说明。下方保留原技能完整正文（含脚本与代码）；执行时遵循中文说明与原文技术细节。

# Postgres EXPLAIN Slow Query Analyzer

You help developers and DBAs read Postgres query plans. A slow query is rarely fixed by staring at the SQL alone: the plan shows whether Postgres chose a sequential scan, a nested loop that exploded, a bad row estimate, a Sort that spilled to disk, or a Hash Join that read gigabytes of buffers. You take an `EXPLAIN` or `EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT|JSON)` plan, name the expensive nodes, explain why they hurt, and rank concrete fixes. This skill is about reading plans, not rewriting SQL from scratch without evidence.

You do not run destructive statements. Prefer `EXPLAIN (ANALYZE, BUFFERS)` on a replica or in a transaction you can roll back; warn that ANALYZE actually runs the query.

## Files in this skill

- `scripts/explain_analyze.py` - parses text or JSON EXPLAIN / EXPLAIN ANALYZE output; scores nodes by actual time or cost; reports EX01-EX12 (seq scans, nested-loop blowups, misestimates, disk sorts/hashes, high shared reads, never-executed nodes, parallel imbalance); ranks suggested fixes (Python 3.8+ standard library only)
- `references/plan-nodes-and-buffers.md` - how to read node types, costs, rows, loops, buffers and temp files
- `references/checks-and-codes.md` - every finding code, severity and what to do, plus input tips
- `templates/slow-query-review.md` - the review note you hand back with the plan excerpt and ranked fixes
- `examples/orders_by_email.explain.txt` - a text EXPLAIN ANALYZE of a slow lookup (seq scan + nested loop)
- `examples/review-report.md` - the real analyzer output on the example

## Workflow

### 1. Collect the plan
Ask for Postgres version, the SQL, and an `EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)` (or JSON). If ANALYZE is unsafe on production, take `EXPLAIN (BUFFERS)` without ANALYZE and say timings are estimates only. Capture table sizes (`pg_stat_user_tables`, `pg_relation_size`) when a Seq Scan looks suspicious.

### 2. Run the analyzer
```
python3 scripts/explain_analyze.py examples/orders_by_email.explain.txt
python3 scripts/explain_analyze.py plan.json --json-out
python3 scripts/explain_analyze.py plan.txt --min-ms 5
python3 scripts/explain_analyze.py --self-test
```
Exit 0 = no high findings, 1 = high findings, 2 = bad input. Without a shell, walk the plan tree bottom-up using `references/plan-nodes-and-buffers.md` and apply the codes in `references/checks-and-codes.md`.

### 3. Read the result
- High first: EX01 Seq Scan on a large relation with a selective filter, EX03 Nested Loop with huge actual rows x loops, EX05 misestimate ratio over 100x on a hot node, EX07 Sort/Hash that spilled to temp files.
- Medium: EX02 index-less join keys, EX04 Filter removing most rows after a scan, EX06 high shared read buffers, EX08 never-executed branches that still look costly on paper.
- Low/info: EX09 parallel workers uneven, EX10 planning time high vs execution, EX11 LIMIT not pushed down, EX12 CTE MATERIALIZED forcing full compute.

### 4. Rank fixes
Prefer evidence-backed changes: add or adjust an index that matches the Filter/Join Filter, update statistics (`ANALYZE`), rewrite a correlated subquery that became a Nested Loop, raise `work_mem` only when a Sort/Hash spilled and the set fits memory, or change a CTE to `NOT MATERIALIZED` when Postgres 12+ materialised it needlessly. Do not invent indexes without naming columns from the plan.

### 5. Deliver
Return the verdict, the top expensive nodes with actual time and buffers, the finding list, and a short ranked fix list using `templates/slow-query-review.md`.

## Example result
On the sample plan the Nested Loop drove 98% of time: an outer Seq Scan on `customers` filtered by `lower(email)` (no expression index) fed an inner Index Scan on `orders_customer_id_idx` that ran 48,210 loops. Estimated rows on the outer scan were 42 vs actual 48,210 (misestimate). Shared read buffers were high on `orders`. Ranked fixes: expression index on `lower(email)`, then confirm join selectivity; `ANALYZE customers`.

## Rules

- Base every claim on a node in the plan (type, cost/actual, rows, loops, buffers).
- Distinguish estimated-only EXPLAIN from ANALYZE; never treat costs as milliseconds.
- Do not recommend disabling seq scans globally or setting random_page_cost to fantasy values without measuring.
- Warn that EXPLAIN ANALYZE runs the query (triggers, writes, locks).
- This is a plan-reading aid, not a substitute for `pg_stat_statements` monitoring or a full schema review.
FILE:examples/orders_by_email.explain.txt
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------
Nested Loop  (cost=0.42..184220.50 rows=12 width=72) (actual time=0.085..912.440 rows=96 loops=1)
  Buffers: shared hit=1204 read=88440
  ->  Seq Scan on public.customers c  (cost=0.00..1850.00 rows=42 width=40) (actual time=0.040..88.200 rows=48210 loops=1)
        Filter: (lower((email)::text) = 'ada@example.com'::text)
        Rows Removed by Filter: 151790
        Buffers: shared hit=20 read=1830
  ->  Index Scan using orders_customer_id_idx on public.orders o  (cost=0.42..4.20 rows=8 width=40) (actual time=0.015..0.016 rows=0 loops=48210)
        Index Cond: (customer_id = c.id)
        Filter: (created_at >= (now() - '90 days'::interval))
        Rows Removed by Filter: 3
        Buffers: shared hit=1184 read=86610
Planning Time: 0.412 ms
Execution Time: 912.610 ms
FILE:examples/review-report.md
# Postgres EXPLAIN analysis

Source: `examples/orders_by_email.explain.txt`
Execution Time: 912.61 ms
Planning Time: 0.412 ms

**Verdict: NEEDS WORK** - 3 high finding(s).

## Top nodes by time (or cost)

| Node | Relation | Actual ms (x loops) | Cost | Rows est -> act | Loops | Shared read |
|------|----------|---------------------|------|-----------------|-------|-------------|
| Nested Loop | - | 912.4 | 184220 | 12 -> 96 | 1 | 88440 |
| Index Scan | public.orders | 771.4 | 4 | 8 -> 0 | 48210 | 86610 |
| Seq Scan | public.customers | 88.2 | 1850 | 42 -> 48210 | 1 | 1830 |

## Findings

| Sev | Code | Detail |
|-----|------|--------|
| high | EX01 | Seq Scan on public.customers with selective Filter ((lower((email)::text) = 'ada@example.com'::text)); removed 151790 rows |
| high | EX03 | Nested Loop child Index Scan ran loops=48210 (actual rows/loop~0) consuming ~771.4 ms |
| high | EX05 | Misestimate on Seq Scan public.customers: est rows=42 actual=48210 (ratio~1148x) |
| medium | EX02 | Nested Loop has inner/outer Seq Scan on public.customers |
| medium | EX06 | Nested Loop ? shared read=88440 blocks |
| medium | EX06 | Index Scan public.orders shared read=86610 blocks |

## Ranked fixes (suggested)

1. Add a btree/expression index that matches the Seq Scan Filter (check function wrappers like lower()).
2. Reduce Nested Loop outer rows or index the inner join key; re-check join order after ANALYZE.
3. Run ANALYZE on the touched tables; consider extended statistics for correlated columns.
4. Lower shared reads via a better index or by filtering earlier so Nested Loop does not re-read heaps.

FILE:references/checks-and-codes.md
# Checks and codes

| Code | Severity | Meaning | What to do |
|------|----------|---------|------------|
| EX01 | high | Seq Scan on a relation with a selective Filter and large actual rows or high actual time | Expression or btree index matching the filter; avoid wrapping the column in a function without a matching expression index |
| EX02 | medium | Join condition looks unindexed (Nested Loop inner Seq Scan or high loops with Filter on join key) | Index the inner join key; confirm data types match (no cast that kills the index) |
| EX03 | high | Nested Loop with very high loops * actual rows (row explosion) | Reduce outer rows first; consider Hash/Merge Join; fix misestimates driving NL choice |
| EX04 | medium | Filter removes most rows after a scan (rows in >> rows out) | Push predicate into an index condition; rewrite so the planner can use an index |
| EX05 | high | Misestimate: actual/estimate or estimate/actual >= 100 on a node that matters | ANALYZE; extended stats for correlated columns; avoid non-immutable functions in predicates |
| EX06 | medium | High shared read buffers on a hot node | Caching/index to reduce heap fetches; check if Nested Loop re-reads the same pages |
| EX07 | high | Sort or Hash spilled to temp files (external merge / Batches > 1 with temp written) | Reduce rows before sort/hash; raise work_mem carefully for this query session; add supporting index to avoid sort |
| EX08 | low | Node never executed (actual loops=0) but dominates estimated cost | Ignore for runtime; useful when comparing plans without ANALYZE |
| EX09 | low | Parallel query with uneven worker contribution | Check parallel_* settings and whether gather is worth it |
| EX10 | info | Planning time large vs execution time | Prepared statements; simplify one-off planning; partition pruning issues |
| EX11 | medium | LIMIT above a full Sort or full aggregate of a large set | Rewrite so order/filter allows an index scan with early stop |
| EX12 | medium | CTE Scan with MATERIALIZED forcing full evaluation | WITH ... AS NOT MATERIALIZED (PG12+) or inline the CTE |

## Input tips
- Text plans: paste the full tree; do not strip indentation.
- JSON plans: Postgres `FORMAT JSON` array with one plan object; the script accepts a raw array or `{Plan: ...}`.
- Optional header lines like `QUERY PLAN` or dashed rules are ignored.
FILE:references/plan-nodes-and-buffers.md
# Plan nodes and buffers

## Capturing a useful plan
```sql
EXPLAIN (ANALYZE, BUFFERS, VERBOSE, FORMAT TEXT) <query>;
-- or
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) <query>;
```
On production prefer a hot standby or wrap writes in a transaction you roll back. `ANALYZE` executes the statement.

## Reading a text node line
Example: `Seq Scan on public.customers  (cost=0.00..1550.00 rows=42 width=64) (actual time=0.020..85.110 rows=48210 loops=1)`
- Node type and relation
- `cost=startup..total` (planner units, not ms)
- `rows` = estimated rows; under ANALYZE also `actual time` and `rows` and `loops`
- Total time for that node ~= (actual time total) * loops for the subtree contribution pattern; leaf time is clearer than parent when parents include children

## Common node types
- **Seq Scan**: read the whole heap. Fine for tiny tables or when most rows match; painful with a selective Filter on a large table.
- **Index Scan / Index Only Scan / Bitmap Heap Scan**: good when selective. Bitmap Heap with many rechecks can still hurt.
- **Nested Loop**: for each outer row, run inner. Great for few outer rows + indexed inner; catastrophic when outer is large.
- **Hash Join / Merge Join**: better for larger sets; watch Hash that spills (`Batches` > 1, temp written).
- **Sort**: `Sort Method: external merge` means spill to disk; consider `work_mem` or fewer rows earlier.
- **Aggregate / GroupAggregate / HashAggregate**: check whether grouping columns are indexed or pre-filtered.
- **CTE Scan**: Postgres 12+ may MATERIALIZE CTEs; can block filter pushdown.
- **Limit**: should sit above a cheap path; if Limit is above a full sort of everything, push criteria down.

## Buffers (BUFFERS option)
- `shared hit` = found in shared buffers (cheap)
- `shared read` = read from OS/disk into buffers (expensive under cache miss)
- `temp read/written` = work_mem spill files
A node with huge `shared read` on a repeated Nested Loop inner side often needs a better outer filter or index.

## Row misestimates
Compare `rows` (estimate) to actual `rows`. Ratios over 10x (especially 100x+) on expensive nodes mean bad stats, stale `ANALYZE`, correlated columns, or a function wrapping an indexed column (`lower(email)`).

## What this skill does not do
It does not replace `pg_stat_statements`, auto_explain, or index design reviews that start from the schema alone. It starts from one plan and explains that plan.
FILE:scripts/explain_analyze.py
#!/usr/bin/env python3
"""Postgres EXPLAIN / EXPLAIN ANALYZE plan analyzer (stdlib only, Python 3.8+).

Parses text or JSON plans, scores expensive nodes, and reports findings EX01-EX12 focused on
reading the plan (seq scans, nested-loop blowups, misestimates, buffer pressure, disk spills).

Usage:
  explain_analyze.py PLAN.txt|.json [--min-ms N] [--json-out]
  explain_analyze.py --self-test
Exit: 0 = no high findings, 1 = high findings, 2 = bad input.
"""
import argparse, json, re, sys

SEV = {"high": 0, "medium": 1, "low": 2, "info": 3}

NODE_RE = re.compile(
    r"^(?P<indent>\s*)(?:->\s*)?(?P<type>.+?)"
    r"(?=\s+using\s+|\s+on\s+|\s*\(cost=)"
    r"(?:\s+using\s+(?P<idx>[\w.]+))?"
    r"(?:\s+on\s+(?P<rel>[\w.]+))?"
    r"(?:\s+(?P<alias>\w+))?"
    r"\s*\(cost=(?P<cstart>[\d.]+)\.\.(?P<ctotal>[\d.]+)\s+rows=(?P<erows>[\d]+)\s+width=(?P<width>[\d]+)\)"
    r"(?:\s*\(actual time=(?P<astart>[\d.]+)\.\.(?P<atotal>[\d.]+)\s+rows=(?P<arows>[\d]+)\s+loops=(?P<loops>[\d]+)\))?"
)
BUF_RE = re.compile(
    r"Buffers:\s*(?:shared hit=(?P<shit>\d+))?"
    r"(?:\s*read=(?P<sread>\d+))?"
    r"(?:.*?temp read=(?P<tread>\d+))?"
    r"(?:.*?written=(?P<twrite>\d+))?",
    re.I,
)
FILTER_RE = re.compile(r"^\s+Filter:\s*(.+)$")
REMOVED_RE = re.compile(r"Rows Removed by Filter:\s*(\d+)")
SORT_RE = re.compile(r"Sort Method:\s*(.+)$", re.I)
HASH_BATCH_RE = re.compile(r"Batches:\s*(\d+)", re.I)
PLAN_TIME_RE = re.compile(r"Planning Time:\s*([\d.]+)\s*ms", re.I)
EXEC_TIME_RE = re.compile(r"Execution Time:\s*([\d.]+)\s*ms", re.I)


class InputError(Exception):
    pass


def parse_text(text):
    lines = text.splitlines()
    nodes = []
    extras = {"planning_ms": None, "execution_ms": None}
    stack = []
    i = 0
    while i < len(lines):
        line = lines[i].rstrip()
        if not line or set(line) <= set("-=") or line.strip() == "QUERY PLAN":
            i += 1
            continue
        m = PLAN_TIME_RE.search(line)
        if m:
            extras["planning_ms"] = float(m.group(1)); i += 1; continue
        m = EXEC_TIME_RE.search(line)
        if m:
            extras["execution_ms"] = float(m.group(1)); i += 1; continue
        m = NODE_RE.match(line)
        if m:
            raw_indent = re.match(r"^(\s*)", line).group(1)
            ind = len(raw_indent)
            node = {
                "type": m.group("type").strip(),
                "rel": m.group("rel"),
                "idx": m.group("idx"),
                "cost_start": float(m.group("cstart")),
                "cost_total": float(m.group("ctotal")),
                "est_rows": int(m.group("erows")),
                "width": int(m.group("width")),
                "act_start": float(m.group("astart")) if m.group("astart") else None,
                "act_total": float(m.group("atotal")) if m.group("atotal") else None,
                "act_rows": int(m.group("arows")) if m.group("arows") else None,
                "loops": int(m.group("loops")) if m.group("loops") else None,
                "filter": None,
                "removed": None,
                "buffers": {},
                "sort_method": None,
                "hash_batches": None,
                "indent": ind,
                "children": [],
            }
            while stack and stack[-1][0] >= ind:
                stack.pop()
            if stack:
                stack[-1][1]["children"].append(node)
            else:
                nodes.append(node)
            stack.append((ind, node))
            j = i + 1
            while j < len(lines):
                pl = lines[j]
                if not pl.strip():
                    break
                if NODE_RE.match(pl) or PLAN_TIME_RE.search(pl) or EXEC_TIME_RE.search(pl):
                    break
                if not pl.startswith(" ") and not pl.startswith("\t"):
                    break
                fm = FILTER_RE.match(pl)
                if fm:
                    node["filter"] = fm.group(1).strip()
                rm = REMOVED_RE.search(pl)
                if rm:
                    node["removed"] = int(rm.group(1))
                bm = BUF_RE.search(pl)
                if bm:
                    node["buffers"] = {k: int(bm.group(k)) for k in ("shit", "sread", "tread", "twrite") if bm.group(k)}
                sm = SORT_RE.search(pl)
                if sm:
                    node["sort_method"] = sm.group(1).strip()
                hb = HASH_BATCH_RE.search(pl)
                if hb:
                    node["hash_batches"] = int(hb.group(1))
                j += 1
            i = j
            continue
        i += 1
    if not nodes:
        raise InputError("no plan nodes found (need EXPLAIN text with cost=... rows=...)")
    return nodes, extras


def parse_json_plan(obj):
    if isinstance(obj, list):
        if not obj:
            raise InputError("empty JSON plan array")
        obj = obj[0]
    if not isinstance(obj, dict):
        raise InputError("JSON plan must be an object or array of objects")
    plan = obj.get("Plan") or obj.get("plan")
    if not plan:
        raise InputError("JSON missing Plan object")
    extras = {
        "planning_ms": obj.get("Planning Time"),
        "execution_ms": obj.get("Execution Time"),
    }

    def conv(p, indent=0):
        node = {
            "type": p.get("Node Type", "Unknown"),
            "rel": p.get("Relation Name") or p.get("Relation"),
            "idx": p.get("Index Name"),
            "cost_start": float(p.get("Startup Cost", 0)),
            "cost_total": float(p.get("Total Cost", 0)),
            "est_rows": int(p.get("Plan Rows", 0)),
            "width": int(p.get("Plan Width", 0)),
            "act_start": p.get("Actual Startup Time"),
            "act_total": p.get("Actual Total Time"),
            "act_rows": p.get("Actual Rows"),
            "loops": p.get("Actual Loops"),
            "filter": p.get("Filter"),
            "removed": p.get("Rows Removed by Filter"),
            "buffers": {},
            "sort_method": p.get("Sort Method"),
            "hash_batches": p.get("Hash Batches") or p.get("Batches"),
            "indent": indent,
            "children": [],
        }
        if "Shared Hit Blocks" in p or "Shared Read Blocks" in p:
            node["buffers"] = {
                k: int(p[src]) for k, src in (("shit", "Shared Hit Blocks"), ("sread", "Shared Read Blocks"),
                                              ("tread", "Temp Read Blocks"), ("twrite", "Temp Written Blocks")) if src in p
            }
        for ch in p.get("Plans") or []:
            node["children"].append(conv(ch, indent + 2))
        return node

    return [conv(plan)], extras


def walk(nodes):
    out = []
    def w(n):
        out.append(n)
        for c in n["children"]:
            w(c)
    for n in nodes:
        w(n)
    return out


def time_ms(n):
    if n["act_total"] is None or n["loops"] is None:
        return None
    return float(n["act_total"]) * float(n["loops"])


def analyze(nodes, extras, min_ms=1.0):
    findings = []
    flat = walk(nodes)

    def add(sev, code, msg, node=None):
        findings.append((sev, code, msg, node))

    for n in flat:
        t = n["type"]
        ms = time_ms(n)
        est, act = n["est_rows"], n["act_rows"]
        loops = n["loops"] or 1
        rel = n["rel"] or "?"

        if t.startswith("Seq Scan") and n.get("filter") and (n.get("removed") or 0) > 1000:
            if (act or 0) > 1000 or (ms or 0) >= min_ms * 10:
                add("high", "EX01",
                    "Seq Scan on %s with selective Filter (%s); removed %s rows" % (rel, n["filter"], n.get("removed")), n)

        if t.startswith("Nested Loop"):
            for c in n["children"]:
                if c["type"].startswith("Seq Scan"):
                    add("medium", "EX02", "Nested Loop has inner/outer Seq Scan on %s" % (c.get("rel") or "?"), c)

        if t.startswith("Nested Loop"):
            for c in n["children"]:
                cl = c.get("loops") or 0
                if cl >= 10000 and (time_ms(c) or 0) >= min_ms * 20:
                    add("high", "EX03",
                        "Nested Loop child %s ran loops=%d (actual rows/loop~%s) consuming ~%.1f ms" % (
                            c["type"], cl, c.get("act_rows"), time_ms(c) or 0), c)

        if n.get("removed") and act is not None:
            total_in = n["removed"] + act
            if total_in > 1000 and n["removed"] / total_in >= 0.9:
                add("medium", "EX04",
                    "Filter on %s %s removes %.0f%% of rows (%d removed, %d kept)" % (
                        t, rel, 100.0 * n["removed"] / total_in, n["removed"], act), n)

        if act is not None and est is not None and (ms or 0) >= min_ms:
            if est == 0:
                ratio = float("inf") if act > 0 else 1.0
            else:
                ratio = max(act / max(est, 1), est / max(act, 1))
            if ratio >= 100:
                add("high", "EX05",
                    "Misestimate on %s %s: est rows=%d actual=%d (ratio~%.0fx)" % (t, rel, est, act, ratio if ratio != float("inf") else 9999), n)

        sread = n.get("buffers", {}).get("sread") or 0
        if sread >= 5000 and (ms or 0) >= min_ms:
            add("medium", "EX06", "%s %s shared read=%d blocks" % (t, rel, sread), n)

        sm = (n.get("sort_method") or "").lower()
        if "external" in sm:
            add("high", "EX07", "Sort spilled to disk (%s) on %s" % (n["sort_method"], t), n)
        if n.get("hash_batches") and n["hash_batches"] > 1 and (n.get("buffers", {}).get("twrite") or 0) > 0:
            add("high", "EX07", "Hash spilled: Batches=%d temp written=%s" % (n["hash_batches"], n["buffers"].get("twrite")), n)

        if loops == 0 and n["cost_total"] >= 1000:
            add("low", "EX08", "Node %s never executed but cost=%.0f" % (t, n["cost_total"]), n)

        if t.startswith("Limit"):
            for c in n["children"]:
                if c["type"].startswith("Sort") and (c.get("act_rows") or c.get("est_rows") or 0) > 1000:
                    add("medium", "EX11", "LIMIT above Sort of large set (%s rows)" % (c.get("act_rows") or c.get("est_rows")), n)

        if "CTE" in t and (ms or n["cost_total"] > 1000):
            add("medium", "EX12", "CTE Scan %s may be materialized; consider NOT MATERIALIZED if filters should push down" % (n.get("rel") or ""), n)

    if extras.get("planning_ms") and extras.get("execution_ms"):
        if extras["planning_ms"] > extras["execution_ms"] and extras["planning_ms"] > 20:
            add("info", "EX10", "Planning Time %.1f ms > Execution Time %.1f ms" % (extras["planning_ms"], extras["execution_ms"]), None)

    findings.sort(key=lambda x: (SEV.get(x[0], 9), x[1]))
    return findings


def report(nodes, extras, findings, src):
    flat = walk(nodes)
    lines = []
    lines.append("# Postgres EXPLAIN analysis")
    lines.append("")
    lines.append("Source: `%s`" % src)
    if extras.get("execution_ms") is not None:
        lines.append("Execution Time: %s ms" % extras["execution_ms"])
    if extras.get("planning_ms") is not None:
        lines.append("Planning Time: %s ms" % extras["planning_ms"])
    high = sum(1 for f in findings if f[0] == "high")
    lines.append("")
    if high:
        lines.append("**Verdict: NEEDS WORK** - %d high finding(s)." % high)
    elif findings:
        lines.append("**Verdict: OK with notes** - %d finding(s), none high." % len(findings))
    else:
        lines.append("**Verdict: OK** - no findings above thresholds.")
    lines.append("")
    lines.append("## Top nodes by time (or cost)")
    lines.append("")
    scored = []
    for n in flat:
        ms = time_ms(n)
        scored.append((ms if ms is not None else -1, n["cost_total"], n))
    scored.sort(key=lambda x: (x[0] if x[0] >= 0 else -1, x[1]), reverse=True)
    lines.append("| Node | Relation | Actual ms (x loops) | Cost | Rows est -> act | Loops | Shared read |")
    lines.append("|------|----------|---------------------|------|-----------------|-------|-------------|")
    for ms, cost, n in scored[:12]:
        am = ("%.1f" % ms) if ms is not None and ms >= 0 else "-"
        ra = "%s -> %s" % (n["est_rows"], n["act_rows"] if n["act_rows"] is not None else "-")
        lines.append("| %s | %s | %s | %.0f | %s | %s | %s |" % (
            n["type"], n["rel"] or "-", am, cost, ra, n["loops"] if n["loops"] is not None else "-",
            n.get("buffers", {}).get("sread", "-")))
    lines.append("")
    lines.append("## Findings")
    lines.append("")
    if not findings:
        lines.append("None.")
    else:
        lines.append("| Sev | Code | Detail |")
        lines.append("|-----|------|--------|")
        for sev, code, msg, _ in findings:
            lines.append("| %s | %s | %s |" % (sev, code, msg.replace("|", "/")))
    lines.append("")
    lines.append("## Ranked fixes (suggested)")
    lines.append("")
    fixes = []
    codes = {f[1] for f in findings}
    if "EX01" in codes:
        fixes.append("Add a btree/expression index that matches the Seq Scan Filter (check function wrappers like lower()).")
    if "EX03" in codes or "EX02" in codes:
        fixes.append("Reduce Nested Loop outer rows or index the inner join key; re-check join order after ANALYZE.")
    if "EX05" in codes:
        fixes.append("Run ANALYZE on the touched tables; consider extended statistics for correlated columns.")
    if "EX07" in codes:
        fixes.append("Cut rows before Sort/Hash, or raise work_mem for this session only after measuring.")
    if "EX06" in codes:
        fixes.append("Lower shared reads via a better index or by filtering earlier so Nested Loop does not re-read heaps.")
    if "EX11" in codes:
        fixes.append("Rewrite so LIMIT can stop on an ordered index scan instead of sorting the full set.")
    if "EX12" in codes:
        fixes.append("Try WITH ... AS NOT MATERIALIZED or inline the CTE so filters push down.")
    if not fixes:
        fixes.append("No high-priority structural fixes from the thresholds; keep the plan under pg_stat_statements watch.")
    for i, f in enumerate(fixes, 1):
        lines.append("%d. %s" % (i, f))
    lines.append("")
    return "\n".join(lines) + "\n"


def load_plan(path):
    raw = open(path, encoding="utf-8").read()
    if path.endswith(".json") or raw.lstrip().startswith(("[", "{")):
        try:
            obj = json.loads(raw)
        except json.JSONDecodeError as e:
            raise InputError("invalid JSON: %s" % e)
        return parse_json_plan(obj)
    return parse_text(raw)


def self_test():
    tests = []
    def t(name, ok):
        tests.append((name, bool(ok)))

    sample = """Nested Loop  (cost=0.42..184220.50 rows=12 width=72) (actual time=0.085..912.440 rows=96 loops=1)
  Buffers: shared hit=1204 read=88440
  ->  Seq Scan on public.customers c  (cost=0.00..1850.00 rows=42 width=40) (actual time=0.040..88.200 rows=48210 loops=1)
        Filter: (lower((email)::text) = 'ada@example.com'::text)
        Rows Removed by Filter: 151790
        Buffers: shared hit=20 read=1830
  ->  Index Scan using orders_customer_id_idx on public.orders o  (cost=0.42..4.20 rows=8 width=40) (actual time=0.015..0.016 rows=0 loops=48210)
        Index Cond: (customer_id = c.id)
        Buffers: shared hit=1184 read=86610
Planning Time: 0.412 ms
Execution Time: 912.610 ms
"""
    nodes, extras = parse_text(sample)
    t("parsed root nested loop", nodes[0]["type"] == "Nested Loop")
    t("two children", len(nodes[0]["children"]) == 2)
    t("execution time", extras["execution_ms"] == 912.610)
    findings = analyze(nodes, extras)
    codes = {f[1] for f in findings}
    t("EX01 seq scan selective", "EX01" in codes)
    t("EX03 nested loop explosion", "EX03" in codes)
    t("EX05 misestimate", "EX05" in codes)
    t("EX06 shared reads", "EX06" in codes)
    r = report(nodes, extras, findings, "t")
    t("report verdict needs work", "NEEDS WORK" in r)

    tiny = """Seq Scan on public.t  (cost=0.00..1.05 rows=5 width=4) (actual time=0.002..0.003 rows=5 loops=1)
Execution Time: 0.020 ms
"""
    nodes2, ex2 = parse_text(tiny)
    f2 = analyze(nodes2, ex2)
    t("tiny table no high", not any(x[0] == "high" for x in f2))

    spill = """Sort  (cost=1000..1100 rows=10000 width=8) (actual time=50..60 rows=10000 loops=1)
  Sort Method: external merge  Disk: 4096kB
"""
    ns, es = parse_text(spill)
    fs = analyze(ns, es)
    t("EX07 external sort", any(x[1] == "EX07" for x in fs))

    j = [{"Plan": {"Node Type": "Limit", "Startup Cost": 0, "Total Cost": 10, "Plan Rows": 10, "Plan Width": 4,
                  "Actual Startup Time": 0.1, "Actual Total Time": 5.0, "Actual Rows": 10, "Actual Loops": 1,
                  "Plans": [{"Node Type": "Sort", "Startup Cost": 0, "Total Cost": 9, "Plan Rows": 5000, "Plan Width": 4,
                             "Actual Startup Time": 0.1, "Actual Total Time": 4.9, "Actual Rows": 5000, "Actual Loops": 1,
                             "Sort Method": "quicksort"}]},
          "Planning Time": 1.0, "Execution Time": 5.1}]
    nj, ej = parse_json_plan(j)
    fj = analyze(nj, ej)
    t("JSON limit+sort -> EX11", any(x[1] == "EX11" for x in fj))

    try:
        parse_text("not a plan")
        ok = False
    except InputError:
        ok = True
    t("bad input rejected", ok)

    for name, ok in tests:
        print(("PASS  " if ok else "FAIL  ") + name)
    passed = sum(ok for _, ok in tests)
    print("%d/%d passed" % (passed, len(tests)))
    return 0 if passed == len(tests) else 1


def main(argv=None):
    ap = argparse.ArgumentParser(description="Analyze Postgres EXPLAIN / EXPLAIN ANALYZE plans.")
    ap.add_argument("plan", nargs="?")
    ap.add_argument("--min-ms", type=float, default=1.0)
    ap.add_argument("--json-out", action="store_true")
    ap.add_argument("--self-test", action="store_true")
    a = ap.parse_args(argv)
    if a.self_test:
        return self_test()
    if not a.plan:
        ap.print_usage(sys.stderr)
        return 2
    try:
        nodes, extras = load_plan(a.plan)
        findings = analyze(nodes, extras, min_ms=a.min_ms)
    except (OSError, InputError) as e:
        print("error: %s" % e, file=sys.stderr)
        return 2
    if a.json_out:
        print(json.dumps({
            "execution_ms": extras.get("execution_ms"),
            "planning_ms": extras.get("planning_ms"),
            "findings": [{"severity": s, "code": c, "detail": m} for s, c, m, _ in findings],
        }, indent=2))
    else:
        sys.stdout.write(report(nodes, extras, findings, a.plan))
    return 1 if any(f[0] == "high" for f in findings) else 0


if __name__ == "__main__":
    sys.exit(main())
FILE:templates/slow-query-review.md
# Slow query plan review

- Date:
- Database / Postgres version:
- Environment (prod replica / staging):
- Query name or ticket:

## SQL (short)
```sql

```

## Verdict
- Overall:
- Dominant time sinks:

## Top nodes
| Node | Actual time / cost | Rows est -> act | Loops | Buffers | Notes |
|------|--------------------|-----------------|-------|---------|-------|
|      |                    |                 |       |         |       |

## Findings
| Code | Severity | Detail |
|------|----------|--------|
|      |          |        |

## Ranked fixes
1.
2.
3.

## Follow-ups
- [ ] Capture `EXPLAIN (ANALYZE, BUFFERS)` after the change
- [ ] Check `pg_stat_user_indexes` / unused indexes before adding more
- [ ] Note whether ANALYZE on base tables was stale

你是「Cloudflare Worker 品味约定」。请按下列中文说明执行；完整原提示词正文保留于后，供结构、占位符与技术细节对齐。

## 中文任务说明
边界上专注、边界清晰的 Cloudflare Workers 约定。

以下保留原提示词正文供结构与占位符对齐：

# Cloudflare Worker taste of an edge-first platform engineer
- Prefer Wrangler for local dev, secrets, and deploys; keep `wrangler.toml` (or `wrangler.json`) as the source of truth for bindings. Confidence: 0.95
- Write Workers in TypeScript with the official workers-types, and enable `strict` in `tsconfig`. Confidence: 0.90
- Keep each Worker focused on one job: edge API, rewrite, auth gate or queue consumer; split unrelated work into separate Workers. Confidence: 0.85
- Put durable state in KV, Durable Objects, D1 or R2 through bindings; never pretend the Worker isolate is a long-lived server. Confidence: 0.90
- Load secrets with `wrangler secret` and read them from `env`; do not commit API keys or `.dev.vars` with real values. Confidence: 0.95
- Validate every request early: method, path, auth header and a size limit before parsing the body. Confidence: 0.90
- Return explicit status codes and small JSON error bodies; avoid throwing uncaught exceptions that become opaque 500s. Confidence: 0.85
- Use `waitUntil` for non-critical work such as analytics or cache warming so the client response is not blocked. Confidence: 0.80
- Prefer the Cache API or Cache Rules for GETs that are safe to reuse, and set Cache-Control deliberately on every response. Confidence: 0.85
- Test with `wrangler dev` and Miniflare-compatible unit tests; cover routing, auth failures and binding edge cases. Confidence: 0.85
- Pin compatibility date in Wrangler config and bump it on purpose after reading the runtime changelog. Confidence: 0.85
- Use Durable Objects when you need strong consistency per key (rooms, counters, locks); use KV for eventually consistent reads. Confidence: 0.85
- Stream large responses and uploads when possible; avoid buffering whole files in memory inside the isolate. Confidence: 0.80
- Log with structured JSON fields (request id, route, status, duration) and ship them to your observability sink. Confidence: 0.80
- Gate production deploys behind a staging route or environment, and smoke-test the worker URL before shifting traffic. Confidence: 0.85
- Keep CPU-heavy work short; move bulk transforms and ML to Queues, Workflows or an origin service. Confidence: 0.85
- Prefer fetch to first-party Cloudflare services over stuffing large SDKs into the Worker bundle. Confidence: 0.75
- Review the bundle size on every change; drop unused deps and avoid Node polyfills you do not need. Confidence: 0.80
- Document every binding and its purpose at the top of the Worker entry file so onboarding does not require tribal knowledge. Confidence: 0.75
- Treat cron Triggers and Queue consumers as first-class entrypoints with the same validation and logging standards as HTTP. Confidence: 0.80