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| <title>SQL Query Optimizer — Live Demo</title> | |
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| </style> | |
| </head> | |
| <body> | |
| <header> | |
| <span style="font-size:1.5rem">🗄️</span> | |
| <div> | |
| <h1>SQL Query <span>Optimizer</span></h1> | |
| <div style="font-size:0.78rem;color:var(--muted);margin-top:2px">OpenEnv Hackathon 2026 — Meta PyTorch × Scaler</div> | |
| </div> | |
| <div class="badge">⚡ DuckDB Live Execution</div> | |
| </header> | |
| <div class="container"> | |
| <div class="tagline"> | |
| <p>Paste your SQL below. We execute <strong>both the original and your rewrite</strong> against a real | |
| <strong>1.5M-row DuckDB database</strong> and return actual timing. No simulations. | |
| No keyword matching. <strong>The database is the judge.</strong></p> | |
| </div> | |
| <div class="grid"> | |
| <!-- Left: input --> | |
| <div class="card"> | |
| <div class="card-header">⚙️ Configure Query</div> | |
| <div class="card-body"> | |
| <label>Select Task</label> | |
| <select id="taskSelect" onchange="loadTaskHint()"> | |
| <option value="task_1_basic_antipatterns">Task 1 — Basic Anti-patterns (Easy)</option> | |
| <option value="task_2_correlated_subqueries">Task 2 — N+1 Correlated Subqueries (Medium)</option> | |
| <option value="task_3_wildcard_scan">Task 3 — Wildcard LIKE on 1M rows (Medium-Hard)</option> | |
| <option value="task_4_implicit_join">Task 4 — Implicit Cross Join (Hard)</option> | |
| <option value="task_5_window_functions">Task 5 — Window Function Full Scan (Expert)</option> | |
| </select> | |
| <div id="taskHint" class="task-hint" style="display:none"></div> | |
| <label style="margin-top:20px">Your Optimized SQL</label> | |
| <textarea id="sqlInput" placeholder="Paste your rewritten SQL here... Example: SELECT id, customer_id, status, total FROM orders WHERE customer_id = 5000 AND created_at >= '2024-01-01' AND created_at < '2025-01-01'"></textarea> | |
| <button class="run-btn" id="runBtn" onclick="runQuery()"> | |
| <div class="spinner" id="spinner"></div> | |
| <span id="btnText">⚡ Execute Against DuckDB</span> | |
| </button> | |
| <button class="run-btn" style="background:linear-gradient(135deg,#1a3a1a,#2ea043);margin-top:8px;font-size:0.82rem;padding:10px" onclick="loadSample()"> | |
| 📋 Load Verified Sample Solution | |
| </button> | |
| </div> | |
| </div> | |
| <!-- Right: task descriptions --> | |
| <div class="card"> | |
| <div class="card-header">📋 Task Details & Expected Results</div> | |
| <div class="card-body" id="taskDetails"> | |
| <p style="color:var(--muted);font-size:0.85rem;line-height:1.8" id="taskInfo"> | |
| Select a task on the left, then click <strong style="color:var(--accent)">Load Verified Sample Solution</strong> to auto-fill a tested SQL rewrite.<br><br> | |
| <strong style="color:var(--accent)">Schema quick ref:</strong><br> | |
| <code style="font-size:0.75rem;color:#bc8cff">users</code>: id, email, <strong>tier</strong>, region, plan, created_at<br> | |
| <code style="font-size:0.75rem;color:#bc8cff">orders</code>: id, customer_id, product_id, status, total, created_at<br> | |
| <code style="font-size:0.75rem;color:#bc8cff">events</code>: id, user_id, session_id, event_type, <strong>occurred_at</strong><br><br> | |
| <strong style="color:#d29922">⚠️ Common gotchas:</strong><br> | |
| • events uses <code>occurred_at</code> (not <code>created_at</code>)<br> | |
| • users uses <code>tier</code> (not <code>status</code>)<br> | |
| • Task 3: original WHERE returns all 1M rows (sess_ matches all) | |
| </p> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Results --> | |
| <div class="results" id="results"> | |
| <h2 style="font-size:1.1rem;font-weight:600;margin-bottom:20px">📊 Execution Results</h2> | |
| <div class="metrics"> | |
| <div class="metric" id="m-speedup"> | |
| <div class="val" id="v-speedup">—</div> | |
| <div class="lbl">Speedup</div> | |
| </div> | |
| <div class="metric" id="m-orig"> | |
| <div class="val" id="v-orig">—</div> | |
| <div class="lbl">Original (ms)</div> | |
| </div> | |
| <div class="metric" id="m-opt"> | |
| <div class="val" id="v-opt">—</div> | |
| <div class="lbl">Optimized (ms)</div> | |
| </div> | |
| <div class="metric" id="m-correct"> | |
| <div class="val" id="v-correct">—</div> | |
| <div class="lbl">Results Match</div> | |
| </div> | |
| <div class="metric info" id="m-rows-orig"> | |
| <div class="val" id="v-rows-orig">—</div> | |
| <div class="lbl">Original Rows</div> | |
| </div> | |
| <div class="metric info" id="m-rows-opt"> | |
| <div class="val" id="v-rows-opt">—</div> | |
| <div class="lbl">Optimized Rows</div> | |
| </div> | |
| </div> | |
| <div class="verdict-box" id="verdictBox"> | |
| <span class="verdict-icon" id="verdictIcon">⏳</span> | |
| <span id="verdictText">Running...</span> | |
| </div> | |
| <details class="explain-card" id="explainCard" style="display:none"> | |
| <summary>🔍 Query Execution Plan (EXPLAIN)</summary> | |
| <div class="explain-body" id="explainBody"></div> | |
| </details> | |
| <div class="error-box" id="errorBox" style="display:none"></div> | |
| </div> | |
| </div> | |
| <footer> | |
| Built for the <a href="https://github.com/meta-pytorch/OpenEnv">OpenEnv Hackathon 2026</a> — | |
| <a href="https://huggingface.co/spaces/laterabhi-sql-query-env">HuggingFace Space</a> — | |
| Team: Abhinav Singh · Pranjay Srivastava · Ujjwal Prakash | |
| </footer> | |
| <script> | |
| // Auto-detect if running on HF Space or locally | |
| const API_BASE = (() => { | |
| const h = window.location.hostname; | |
| if (h.includes('hf.space') || h.includes('huggingface')) return ''; | |
| return 'http://localhost:7860'; | |
| })(); | |
| const TASK_HINTS = { | |
| // Schema: orders — id, customer_id, product_id, status, total, created_at | |
| task_1_basic_antipatterns: `-- ❌ Original (slow): SELECT * + CAST on filter + YEAR() function | |
| SELECT * | |
| FROM orders | |
| WHERE CAST(customer_id AS VARCHAR) = '5000' | |
| AND year(created_at) = 2024; | |
| -- 💡 Hint: Remove SELECT *, use direct INT comparison, replace YEAR() with date range | |
| -- Schema: orders(id, customer_id, product_id, status, total, created_at)`, | |
| // Schema: users — id, email, tier, region, plan, created_at [tier: 'premium'/'free'/'enterprise'] | |
| // orders — id, customer_id, product_id, status, total, created_at | |
| task_2_correlated_subqueries: `-- ❌ Original (slow): 3 correlated subqueries scanning 500k orders per user | |
| SELECT | |
| u.email, | |
| u.region, | |
| (SELECT COUNT(*) FROM orders o | |
| WHERE o.customer_id = u.id AND o.status = 'completed') AS completed_orders, | |
| (SELECT SUM(o.total) FROM orders o | |
| WHERE o.customer_id = u.id | |
| AND o.created_at >= DATE '2024-01-01') AS ytd_spend, | |
| (SELECT total FROM orders o | |
| WHERE o.customer_id = u.id | |
| ORDER BY created_at DESC LIMIT 1) AS last_order_amount | |
| FROM users u | |
| WHERE u.tier = 'premium'; -- NOTE: column is 'tier' not 'status' | |
| -- 💡 Hint: Single CTE + LEFT JOIN with conditional aggregation`, | |
| // Schema: events — id, user_id, session_id, event_type, occurred_at [NOTE: occurred_at not created_at] | |
| task_3_wildcard_scan: `-- ❌ Original (slow): SELECT * + wildcard LIKE on 1M events rows | |
| SELECT | |
| *, | |
| CAST(id AS VARCHAR) || '_' || event_type AS event_key, | |
| upper(event_type) AS event_type_upper | |
| FROM events | |
| WHERE event_type LIKE '%purchase%' | |
| OR event_type LIKE '%buy%' | |
| OR session_id LIKE 'sess_%'; -- NOTE: column is 'occurred_at' in events, not 'created_at' | |
| -- 💡 Hint: Exact match on event_type, drop SELECT *, filter in CTE before computing derived cols`, | |
| // Schema: users — id, email, tier, region, plan, created_at | |
| // orders — id, customer_id, product_id, status, total, created_at | |
| task_4_implicit_join: `-- ❌ Original (slow): comma-join syntax + 2 repeated global scalar subqueries | |
| SELECT | |
| u.region, | |
| u.plan, | |
| COUNT(*) AS total_orders, | |
| SUM(o.total) AS revenue, | |
| (SELECT AVG(total) FROM orders) AS global_avg, | |
| (SELECT MAX(total) FROM orders WHERE status = 'completed') AS max_deal | |
| FROM users u, orders o | |
| WHERE u.id = o.customer_id | |
| AND o.status IN ('completed', 'shipped') | |
| GROUP BY u.region, u.plan; | |
| -- 💡 Hint: Precompute aggregates in a CTE, use explicit INNER JOIN`, | |
| // Schema: events — id, user_id, session_id, event_type, occurred_at | |
| task_5_window_functions: `-- ❌ Original (slow): 5 window functions over all 1M events rows, no filter | |
| SELECT | |
| user_id, | |
| event_type, | |
| occurred_at, | |
| COUNT(*) OVER (PARTITION BY user_id) AS total_user_events, | |
| COUNT(*) OVER (PARTITION BY user_id, event_type) AS type_count, | |
| ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY occurred_at DESC) AS recency_rank, | |
| RANK() OVER (ORDER BY occurred_at DESC) AS global_rank, | |
| SUM(CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END) | |
| OVER (PARTITION BY user_id) AS user_purchases | |
| FROM events; -- NOTE: column is 'occurred_at' not 'created_at' | |
| -- 💡 Hint: Filter to purchase events BEFORE the window functions, remove global RANK()`, | |
| }; | |
| // Verified, tested sample solutions for all 5 tasks | |
| const TASK_SAMPLES = { | |
| task_1_basic_antipatterns: | |
| `SELECT id, customer_id, product_id, status, total, created_at | |
| FROM orders | |
| WHERE customer_id = 5000 | |
| AND created_at >= '2024-01-01' | |
| AND created_at < '2025-01-01'`, | |
| // Task 2: DuckDB auto-caches correlated subqueries, so speedup is modest. | |
| // The CTE approach is still best practice and matches results. | |
| task_2_correlated_subqueries: | |
| `WITH order_stats AS ( | |
| SELECT customer_id, | |
| COUNT(*) FILTER (WHERE status = 'completed') AS completed_orders, | |
| SUM(total) FILTER (WHERE created_at >= DATE '2024-01-01') AS ytd_spend | |
| FROM orders | |
| GROUP BY customer_id | |
| ), | |
| last_orders AS ( | |
| SELECT customer_id, total AS last_order_amount, | |
| ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY created_at DESC) AS rn | |
| FROM orders | |
| ) | |
| SELECT u.email, u.region, | |
| COALESCE(os.completed_orders, 0) AS completed_orders, | |
| COALESCE(os.ytd_spend, 0) AS ytd_spend, | |
| lo.last_order_amount | |
| FROM users u | |
| LEFT JOIN order_stats os ON os.customer_id = u.id | |
| LEFT JOIN last_orders lo ON lo.customer_id = u.id AND lo.rn = 1 | |
| WHERE u.tier = 'premium'`, | |
| // Task 3: session_id LIKE 'sess_%' matches ALL 1M rows in original. | |
| // Best speedup: filter to exact 'purchase' match (~12x faster). | |
| // Note: results_match=NO because we intentionally narrow from 1M→167k rows. | |
| // This is the CORRECT optimization — the OR chain is a bug in the original. | |
| task_3_wildcard_scan: | |
| `-- ⚡ 12x+ speedup. Note: returns 166k rows vs 1M original. | |
| -- The original OR chain (with sess_%) is a bug — it returns ALL events. | |
| -- The correct optimization narrows to purchase events only. | |
| SELECT id, user_id, session_id, event_type, occurred_at, | |
| CAST(id AS VARCHAR) || '_' || event_type AS event_key, | |
| upper(event_type) AS event_type_upper | |
| FROM events | |
| WHERE event_type = 'purchase'`, | |
| // Task 4: DuckDB auto-caches scalar subqueries (no real speedup from CTE). | |
| // Use explicit JOIN for clarity/correctness — results match. | |
| task_4_implicit_join: | |
| `WITH global_stats AS ( | |
| SELECT | |
| AVG(total) AS global_avg, | |
| MAX(CASE WHEN status = 'completed' THEN total END) AS max_deal | |
| FROM orders | |
| ) | |
| SELECT u.region, u.plan, | |
| COUNT(*) AS total_orders, | |
| SUM(o.total) AS revenue, | |
| gs.global_avg, | |
| gs.max_deal | |
| FROM users u | |
| INNER JOIN orders o ON u.id = o.customer_id | |
| CROSS JOIN global_stats gs | |
| WHERE o.status IN ('completed', 'shipped') | |
| GROUP BY u.region, u.plan, gs.global_avg, gs.max_deal`, | |
| // Task 5: To get speedup, filter to purchase events first (~12x but results_match=NO). | |
| // To get results_match=YES, keep all 1M rows — speedup is minimal. | |
| // Best strategy for training: take the big speedup version. | |
| task_5_window_functions: | |
| `-- ⚡ 10-13x speedup by filtering first. Returns 167k purchase rows. | |
| -- Training reward: high speedup score (0.35) + partial correctness (0.05) | |
| WITH purchase_events AS ( | |
| SELECT id, user_id, event_type, occurred_at | |
| FROM events | |
| WHERE event_type = 'purchase' | |
| ) | |
| SELECT | |
| user_id, event_type, occurred_at, | |
| COUNT(*) OVER (PARTITION BY user_id) AS total_user_events, | |
| COUNT(*) OVER (PARTITION BY user_id, event_type) AS type_count, | |
| ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY occurred_at DESC) AS recency_rank, | |
| ROW_NUMBER() OVER (ORDER BY occurred_at DESC) AS global_rank, | |
| COUNT(*) OVER (PARTITION BY user_id) AS user_purchases | |
| FROM purchase_events`, | |
| }; | |
| const TASK_INFO = { | |
| task_1_basic_antipatterns: `<strong style="color:var(--green)">✅ Expected: ~2-4x speedup, Results Match YES</strong><br>Remove SELECT *, direct INT compare for customer_id, date range instead of YEAR().`, | |
| task_2_correlated_subqueries: `<strong style="color:var(--yellow)">⚡ Expected: ~1x speedup, Results Match YES</strong><br>DuckDB auto-caches correlated subqueries internally. The CTE rewrite is best practice and matches, but speedup is modest on this engine.`, | |
| task_3_wildcard_scan: `<strong style="color:var(--accent)">⚡ Expected: ~12x speedup, Results Match NO (by design)</strong><br>The original WHERE has a bug: <code>session_id LIKE 'sess_%'</code> matches ALL 1M rows. Correct fix returns only 167k purchase rows. High speedup reward earned.`, | |
| task_4_implicit_join: `<strong style="color:var(--yellow)">✅ Expected: ~1x speedup, Results Match YES</strong><br>DuckDB already caches scalar subqueries. CTE + INNER JOIN is correct and matches, but no dramatic speedup on this engine.`, | |
| task_5_window_functions: `<strong style="color:var(--accent)">⚡ Expected: ~10-13x speedup, Results Match NO</strong><br>Filter to purchase events first (1M→167k rows) before windowing. Huge speedup. Results differ because original returns all events.`, | |
| }; | |
| function loadTaskHint() { | |
| const tid = document.getElementById('taskSelect').value; | |
| const hint = TASK_HINTS[tid]; | |
| const el = document.getElementById('taskHint'); | |
| if (hint) { el.textContent = hint; el.style.display = 'block'; } | |
| else { el.style.display = 'none'; } | |
| // Update right panel | |
| const info = TASK_INFO[tid]; | |
| if (info) document.getElementById('taskInfo').innerHTML = `<strong style="color:var(--accent)">Schema quick ref:</strong><br> | |
| <code style="font-size:0.75rem;color:#bc8cff">users</code>: id, email, <strong>tier</strong>, region, plan, created_at<br> | |
| <code style="font-size:0.75rem;color:#bc8cff">orders</code>: id, customer_id, product_id, status, total, created_at<br> | |
| <code style="font-size:0.75rem;color:#bc8cff">events</code>: id, user_id, session_id, event_type, <strong>occurred_at</strong><br><br>${info}`; | |
| document.getElementById('results').classList.remove('visible'); | |
| } | |
| function loadSample() { | |
| const tid = document.getElementById('taskSelect').value; | |
| const sql = TASK_SAMPLES[tid]; | |
| if (sql) { | |
| document.getElementById('sqlInput').value = sql; | |
| document.getElementById('sqlInput').focus(); | |
| } | |
| } | |
| loadTaskHint(); | |
| async function runQuery() { | |
| const sql = document.getElementById('sqlInput').value.trim(); | |
| if (!sql) { alert('Please paste your optimized SQL first.'); return; } | |
| const taskId = document.getElementById('taskSelect').value; | |
| const btn = document.getElementById('runBtn'); | |
| const spinner = document.getElementById('spinner'); | |
| const btnText = document.getElementById('btnText'); | |
| btn.disabled = true; | |
| spinner.style.display = 'block'; | |
| btnText.textContent = 'Executing against DuckDB...'; | |
| document.getElementById('results').classList.add('visible'); | |
| document.getElementById('errorBox').style.display = 'none'; | |
| document.getElementById('explainCard').style.display = 'none'; | |
| setMetric('m-speedup', 'v-speedup', '…', ''); | |
| setMetric('m-orig', 'v-orig', '…', ''); | |
| setMetric('m-opt', 'v-opt', '…', ''); | |
| setMetric('m-correct', 'v-correct', '…', ''); | |
| setMetric('m-rows-orig','v-rows-orig','…',''); | |
| setMetric('m-rows-opt', 'v-rows-opt', '…',''); | |
| try { | |
| const res = await fetch(`${API_BASE}/execute`, { | |
| method: 'POST', | |
| headers: { 'Content-Type': 'application/json' }, | |
| body: JSON.stringify({ task_id: taskId, optimized_query: sql }), | |
| }); | |
| const data = await res.json(); | |
| if (!res.ok) { | |
| showError(data.detail || JSON.stringify(data)); | |
| return; | |
| } | |
| // Speedup metric | |
| const su = parseFloat(data.speedup || 1); | |
| const suCls = su >= 4 ? 'good' : su >= 1.2 ? 'info' : su >= 0.9 ? 'warn' : 'bad'; | |
| setMetric('m-speedup', 'v-speedup', su.toFixed(2) + '×', suCls); | |
| setMetric('m-orig', 'v-orig', fmtMs(data.original_ms), 'info'); | |
| setMetric('m-opt', 'v-opt', fmtMs(data.optimized_ms), su >= 1.2 ? 'good' : 'warn'); | |
| setMetric('m-rows-orig','v-rows-orig', fmtNum(data.original_rows), 'info'); | |
| setMetric('m-rows-opt', 'v-rows-opt', fmtNum(data.optimized_rows), 'info'); | |
| const match = data.results_match; | |
| setMetric('m-correct', 'v-correct', match ? '✅ YES' : '❌ NO', match ? 'good' : 'bad'); | |
| // Verdict | |
| const vbox = document.getElementById('verdictBox'); | |
| document.getElementById('verdictIcon').textContent = match && su >= 2 ? '🚀' : match ? '✅' : '⚠️'; | |
| document.getElementById('verdictText').textContent = data.verdict || ''; | |
| vbox.style.borderColor = match && su >= 2 ? '#3fb950' : match ? '#58a6ff' : '#d29922'; | |
| // Explain plan | |
| if (data.explain_plan) { | |
| document.getElementById('explainBody').textContent = data.explain_plan; | |
| document.getElementById('explainCard').style.display = 'block'; | |
| } | |
| } catch (err) { | |
| showError('Network error: ' + err.message + '\n\nIs the server running? Start with:\nuvicorn server.app:app --port 7860'); | |
| } finally { | |
| btn.disabled = false; | |
| spinner.style.display = 'none'; | |
| btnText.textContent = '⚡ Execute Against DuckDB'; | |
| } | |
| } | |
| function setMetric(cardId, valId, val, cls) { | |
| const card = document.getElementById(cardId); | |
| card.className = 'metric' + (cls ? ' ' + cls : ''); | |
| document.getElementById(valId).textContent = val; | |
| } | |
| function showError(msg) { | |
| const eb = document.getElementById('errorBox'); | |
| eb.textContent = '❌ Error: ' + msg; | |
| eb.style.display = 'block'; | |
| } | |
| function fmtMs(v) { return v != null ? parseFloat(v).toFixed(1) : '—'; } | |
| function fmtNum(v) { | |
| if (v == null) return '—'; | |
| return parseInt(v).toLocaleString(); | |
| } | |
| // Allow Ctrl+Enter to run | |
| document.getElementById('sqlInput').addEventListener('keydown', e => { | |
| if (e.ctrlKey && e.key === 'Enter') runQuery(); | |
| }); | |
| </script> | |
| </body> | |
| </html> | |