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Treasure Studio: SQL Query on Sample Datasets
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chat.json
{
"id": "chat-1770663468168",
"title": "SQL Query on Sample Datasets",
"messages": [
{
"id": "msg-1770665030205-o6rspu0wt",
"role": "user",
"content": "run a long single line SQL with tdx using sample_datasets as a demo",
"timestamp": "2026-02-09T19:23:50.205Z"
},
{
"id": "msg-1770665049009-zwau6tva2",
"role": "assistant",
"content": "I'll run a SQL query using tdx against the sample_datasets database. Let me execute a query that demonstrates some interesting data.The query executed successfully against the `sample_datasets` database. It retrieved January 2014 stock data for major tech companies (AAPL, GOOGL, MSFT, AMZN) from the `nasdaq` table.\n\n**Query highlights:**\n- Used `TD_TIME_RANGE(time, '2014-01-01', '2014-01-31')` for efficient time-based filtering with partition pruning\n- Filtered for 4 major tech stocks\n- Returned open, high, low, close prices and volume\n- Processed 528.4K rows in 2.5 seconds\n\nThe `-d sample_datasets` flag sets the database context, allowing you to reference tables directly without the `database.table` syntax.",
"timestamp": "2026-02-09T19:24:09.009Z",
"segments": [
{
"type": "thinking",
"content": "The user wants to run a SQL query using tdx CLI against the sample_datasets database. Let me first invoke the tdx-basic skill to get the proper syntax and then run a query."
},
{
"type": "content",
"content": "I'll run a SQL query using tdx against the sample_datasets database. Let me execute a query that demonstrates some interesting data."
},
{
"type": "tool_call",
"toolCall": {
"id": "tooluse_6FOKVDKs22wFZWpZrBwpfV",
"name": "Bash",
"arguments": {
"command": "tdx query -d sample_datasets \"SELECT time, symbol, open, high, low, close, volume FROM nasdaq WHERE symbol IN ('AAPL', 'GOOGL', 'MSFT', 'AMZN') AND TD_TIME_RANGE(time, '2014-01-01', '2014-01-31') ORDER BY time DESC LIMIT 20\"",
"description": "Run SQL query on sample_datasets with tdx"
},
"result": "┌────────────┬────────┬─────────┬─────────┬─────────┬─────────┬───────────┐\n│ time │ symbol │ open │ high │ low │ close │ volume │\n│ long │ string │ double │ double │ double │ double │ long │\n├────────────┼────────┼─────────┼─────────┼─────────┼─────────┼───────────┤\n│ 1391097600 │ AAPL │ 71.791 │ 72.357 │ 70.957 │ 71.397 │ 169762789 │\n│ 1391097600 │ AMZN │ 393.77 │ 406.25 │ 387.7 │ 403.01 │ 10900116 │\n│ 1391097600 │ GOOGL │ 573.072 │ 576.821 │ 564.189 │ 568.265 │ 5141107 │\n│ 1391097600 │ MSFT │ 36.79 │ 36.88 │ 36.23 │ 36.86 │ 35111959 │\n│ 1391011200 │ GOOGL │ 560.12 │ 561.44 │ 550.26 │ 554.01 │ 2411641 │\n│ 1391011200 │ MSFT │ 35.98 │ 36.88 │ 35.9 │ 36.66 │ 52790351 │\n│ 1391011200 │ AAPL │ 71.99 │ 72.48 │ 71.23 │ 71.54 │ 125942796 │\n│ 1391011200 │ AMZN │ 392.16 │ 392.85 │ 383.24 │ 384.2 │ 3399294 │\n│ 1390924800 │ MSFT │ 36.12 │ 36.39 │ 35.75 │ 36.27 │ 36225755 │\n│ 1390924800 │ AMZN │ 387.4 │ 394.74 │ 387.12 │ 394.43 │ 2901611 │\n│ 1390924800 │ AAPL │ 72.68 │ 73.57 │ 71.72 │ 72.36 │ 266833581 │\n│ 1390924800 │ GOOGL │ 555.715 │ 563.437 │ 555.525 │ 562.067 │ 2217723 │\n│ 1390838400 │ AAPL │ 78.58 │ 79.26 │ 77.96 │ 78.64 │ 144219152 │\n│ 1390838400 │ MSFT │ 36.87 │ 36.89 │ 35.98 │ 36.03 │ 44485767 │\n│ 1390838400 │ GOOGL │ 563.61 │ 563.81 │ 541.68 │ 551.17 │ 4362978 │\n│ 1390838400 │ AMZN │ 390.5 │ 394.1 │ 380.49 │ 386.28 │ 3944752 │\n│ 1390579200 │ AMZN │ 398.16 │ 400.2 │ 387.27 │ 387.6 │ 4513038 │\n│ 1390579200 │ GOOGL │ 576.081 │ 577.349 │ 562.061 │ 562.477 │ 3926347 │\n│ 1390579200 │ MSFT │ 37.45 │ 37.55 │ 36.53 │ 36.805 │ 77051806 │\n│ 1390579200 │ AAPL │ 79.14 │ 79.37 │ 77.82 │ 78.01 │ 108384437 │\n├────────────┴────────┴─────────┴─────────┴─────────┴─────────┴───────────┤\n│ 20 rows │\n└─────────────────────────────────────────────────────────────────────────┘\n- Executing query...\n✔ Query completed: Processed 528.4K rows in 2.5s [Job ID: 2966917632]",
"status": "completed"
}
},
{
"type": "thinking",
"content": "The query ran successfully. It retrieved stock data for major tech companies (AAPL, GOOGL, MSFT, AMZN) from January 2014 in the nasdaq table within sample_datasets. The results show trading data including open, high, low, close prices and volume."
},
{
"type": "content",
"content": "The query executed successfully against the `sample_datasets` database. It retrieved January 2014 stock data for major tech companies (AAPL, GOOGL, MSFT, AMZN) from the `nasdaq` table.\n\n**Query highlights:**\n- Used `TD_TIME_RANGE(time, '2014-01-01', '2014-01-31')` for efficient time-based filtering with partition pruning\n- Filtered for 4 major tech stocks\n- Returned open, high, low, close prices and volume\n- Processed 528.4K rows in 2.5 seconds\n\nThe `-d sample_datasets` flag sets the database context, allowing you to reference tables directly without the `database.table` syntax."
}
],
"toolCalls": [
{
"id": "tooluse_6FOKVDKs22wFZWpZrBwpfV",
"name": "Bash",
"arguments": {
"command": "tdx query -d sample_datasets \"SELECT time, symbol, open, high, low, close, volume FROM nasdaq WHERE symbol IN ('AAPL', 'GOOGL', 'MSFT', 'AMZN') AND TD_TIME_RANGE(time, '2014-01-01', '2014-01-31') ORDER BY time DESC LIMIT 20\"",
"description": "Run SQL query on sample_datasets with tdx"
},
"result": "┌────────────┬────────┬─────────┬─────────┬─────────┬─────────┬───────────┐\n│ time │ symbol │ open │ high │ low │ close │ volume │\n│ long │ string │ double │ double │ double │ double │ long │\n├────────────┼────────┼─────────┼─────────┼─────────┼─────────┼───────────┤\n│ 1391097600 │ AAPL │ 71.791 │ 72.357 │ 70.957 │ 71.397 │ 169762789 │\n│ 1391097600 │ AMZN │ 393.77 │ 406.25 │ 387.7 │ 403.01 │ 10900116 │\n│ 1391097600 │ GOOGL │ 573.072 │ 576.821 │ 564.189 │ 568.265 │ 5141107 │\n│ 1391097600 │ MSFT │ 36.79 │ 36.88 │ 36.23 │ 36.86 │ 35111959 │\n│ 1391011200 │ GOOGL │ 560.12 │ 561.44 │ 550.26 │ 554.01 │ 2411641 │\n│ 1391011200 │ MSFT │ 35.98 │ 36.88 │ 35.9 │ 36.66 │ 52790351 │\n│ 1391011200 │ AAPL │ 71.99 │ 72.48 │ 71.23 │ 71.54 │ 125942796 │\n│ 1391011200 │ AMZN │ 392.16 │ 392.85 │ 383.24 │ 384.2 │ 3399294 │\n│ 1390924800 │ MSFT │ 36.12 │ 36.39 │ 35.75 │ 36.27 │ 36225755 │\n│ 1390924800 │ AMZN │ 387.4 │ 394.74 │ 387.12 │ 394.43 │ 2901611 │\n│ 1390924800 │ AAPL │ 72.68 │ 73.57 │ 71.72 │ 72.36 │ 266833581 │\n│ 1390924800 │ GOOGL │ 555.715 │ 563.437 │ 555.525 │ 562.067 │ 2217723 │\n│ 1390838400 │ AAPL │ 78.58 │ 79.26 │ 77.96 │ 78.64 │ 144219152 │\n│ 1390838400 │ MSFT │ 36.87 │ 36.89 │ 35.98 │ 36.03 │ 44485767 │\n│ 1390838400 │ GOOGL │ 563.61 │ 563.81 │ 541.68 │ 551.17 │ 4362978 │\n│ 1390838400 │ AMZN │ 390.5 │ 394.1 │ 380.49 │ 386.28 │ 3944752 │\n│ 1390579200 │ AMZN │ 398.16 │ 400.2 │ 387.27 │ 387.6 │ 4513038 │\n│ 1390579200 │ GOOGL │ 576.081 │ 577.349 │ 562.061 │ 562.477 │ 3926347 │\n│ 1390579200 │ MSFT │ 37.45 │ 37.55 │ 36.53 │ 36.805 │ 77051806 │\n│ 1390579200 │ AAPL │ 79.14 │ 79.37 │ 77.82 │ 78.01 │ 108384437 │\n├────────────┴────────┴─────────┴─────────┴─────────┴─────────┴───────────┤\n│ 20 rows │\n└─────────────────────────────────────────────────────────────────────────┘\n- Executing query...\n✔ Query completed: Processed 528.4K rows in 2.5s [Job ID: 2966917632]",
"status": "completed"
}
]
},
{
"id": "msg-1770665334307-8qqmvhq1r",
"role": "user",
"content": "SELECT method, COUNT(*) as cnt FROM sample_datasets.www_access GROUP BY method ORDER BY cnt DESC",
"timestamp": "2026-02-09T19:28:54.307Z"
},
{
"id": "msg-1770665345550-t9o3o8nci",
"role": "assistant",
"content": "The query shows HTTP method distribution in the `www_access` table:\n\n| Method | Count |\n|--------|-------|\n| GET | 4,624 |\n| POST | 376 |\n\nAbout 92% of requests are GET and 8% are POST. The table contains 5,000 total web access log records.",
"timestamp": "2026-02-09T19:29:05.550Z",
"segments": [
{
"type": "thinking",
"content": "The user wants me to run this SQL query using tdx. It's a simple aggregation query on the www_access table in sample_datasets."
},
{
"type": "tool_call",
"toolCall": {
"id": "tooluse_PToqeHN7SJKdCMVzB4MUGt",
"name": "Bash",
"arguments": {
"command": "tdx query \"SELECT method, COUNT(*) as cnt FROM sample_datasets.www_access GROUP BY method ORDER BY cnt DESC\"",
"description": "Run SQL query to count HTTP methods in www_access"
},
"result": "┌────────┬──────┐\n│ method │ cnt │\n│ string │ long │\n├────────┼──────┤\n│ GET │ 4624 │\n│ POST │ 376 │\n├────────┴──────┤\n│ 2 rows │\n└───────────────┘\n- Executing query...\n✔ Query completed: Processed 5.00K rows in 0.5s [Job ID: 2966923669]",
"status": "completed"
}
},
{
"type": "thinking",
"content": "The query ran successfully. It shows that in the www_access table, there are 4624 GET requests and 376 POST requests. Simple and clean results."
},
{
"type": "content",
"content": "The query shows HTTP method distribution in the `www_access` table:\n\n| Method | Count |\n|--------|-------|\n| GET | 4,624 |\n| POST | 376 |\n\nAbout 92% of requests are GET and 8% are POST. The table contains 5,000 total web access log records."
}
],
"toolCalls": [
{
"id": "tooluse_PToqeHN7SJKdCMVzB4MUGt",
"name": "Bash",
"arguments": {
"command": "tdx query \"SELECT method, COUNT(*) as cnt FROM sample_datasets.www_access GROUP BY method ORDER BY cnt DESC\"",
"description": "Run SQL query to count HTTP methods in www_access"
},
"result": "┌────────┬──────┐\n│ method │ cnt │\n│ string │ long │\n├────────┼──────┤\n│ GET │ 4624 │\n│ POST │ 376 │\n├────────┴──────┤\n│ 2 rows │\n└───────────────┘\n- Executing query...\n✔ Query completed: Processed 5.00K rows in 0.5s [Job ID: 2966923669]",
"status": "completed"
}
]
},
{
"id": "msg-1770666350330-z1cy43xn3",
"role": "user",
"content": "Run a very long SQL just for testing purpose",
"timestamp": "2026-02-09T19:45:50.330Z"
},
{
"id": "msg-1770666372404-ylx9vbqtz",
"role": "assistant",
"content": "The long SQL query executed successfully. Here's what it did:\n\n**Query Structure (3 CTEs + final aggregation):**\n1. **`daily_stats`** - Calculated daily averages, highs, lows, and ranges per symbol\n2. **`ranked_data`** - Added window functions: `ROW_NUMBER`, `LAG`, `LEAD` for ranking and previous/next close prices\n3. **`performance`** - Computed daily return percentages and volatility categories\n4. **Final SELECT** - Aggregated everything into summary statistics per symbol\n\n**Key Insights from Q1 2014:**\n\n| Stock | Avg Volume | Avg Daily Return | Volatility |\n|-------|-----------|------------------|------------|\n| AAPL | 81.4M | -0.04% | All low volatility |\n| FB | 60.4M | +0.20% | Mixed (27 medium days) |\n| TSLA | 9.5M | +0.66% | 53 high volatility days |\n| AMZN | 3.9M | -0.25% | 56 high volatility days |\n| NFLX | 3.0M | +0.02% | 58 high volatility days |\n\nTesla had the highest average daily return (+0.66%) but also extreme volatility. Apple had the highest trading volume but slight negative returns that quarter.",
"timestamp": "2026-02-09T19:46:12.404Z",
"segments": [
{
"type": "thinking",
"content": "The user wants me to run a very long SQL query for testing purposes. I'll create a complex query with multiple CTEs, joins, window functions, and aggregations to demonstrate a long single-line SQL query using the sample_datasets database."
},
{
"type": "tool_call",
"toolCall": {
"id": "tooluse_Quvb0tTw0GSyezwjJG4Spd",
"name": "Bash",
"arguments": {
"command": "tdx query \"WITH daily_stats AS (SELECT symbol, TD_TIME_FORMAT(time, 'yyyy-MM-dd') as trade_date, AVG(close) as avg_close, AVG(volume) as avg_volume, MAX(high) as daily_high, MIN(low) as daily_low, MAX(high) - MIN(low) as daily_range FROM sample_datasets.nasdaq WHERE TD_TIME_RANGE(time, '2014-01-01', '2014-03-31') AND symbol IN ('AAPL', 'GOOGL', 'MSFT', 'AMZN', 'FB', 'NFLX', 'TSLA', 'ORCL', 'IBM', 'INTC') GROUP BY symbol, TD_TIME_FORMAT(time, 'yyyy-MM-dd')), ranked_data AS (SELECT symbol, trade_date, avg_close, avg_volume, daily_high, daily_low, daily_range, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY avg_volume DESC) as volume_rank, LAG(avg_close) OVER (PARTITION BY symbol ORDER BY trade_date) as prev_close, LEAD(avg_close) OVER (PARTITION BY symbol ORDER BY trade_date) as next_close FROM daily_stats), performance AS (SELECT symbol, trade_date, avg_close, avg_volume, daily_high, daily_low, daily_range, volume_rank, prev_close, next_close, CASE WHEN prev_close IS NOT NULL THEN ROUND((avg_close - prev_close) / prev_close * 100, 2) ELSE NULL END as daily_return_pct, CASE WHEN daily_range > 5 THEN 'HIGH_VOLATILITY' WHEN daily_range > 2 THEN 'MEDIUM_VOLATILITY' ELSE 'LOW_VOLATILITY' END as volatility_category FROM ranked_data) SELECT symbol, COUNT(*) as trading_days, ROUND(AVG(avg_close), 2) as mean_close_price, ROUND(MIN(daily_low), 2) as period_low, ROUND(MAX(daily_high), 2) as period_high, ROUND(AVG(avg_volume), 0) as mean_volume, ROUND(AVG(daily_return_pct), 4) as avg_daily_return, SUM(CASE WHEN volatility_category = 'HIGH_VOLATILITY' THEN 1 ELSE 0 END) as high_vol_days, SUM(CASE WHEN volatility_category = 'MEDIUM_VOLATILITY' THEN 1 ELSE 0 END) as med_vol_days, SUM(CASE WHEN volatility_category = 'LOW_VOLATILITY' THEN 1 ELSE 0 END) as low_vol_days FROM performance GROUP BY symbol ORDER BY mean_volume DESC LIMIT 10\"",
"description": "Run complex multi-CTE SQL query for stock analysis"
},
"result": "┌────────┬──────────────┬──────────────────┬────────────┬─────────────┬─────────────┬──────────────────┬───────────────┬──────────────┬──────────────┐\n│ symbol │ trading_days │ mean_close_price │ period_low │ period_high │ mean_volume │ avg_daily_return │ high_vol_days │ med_vol_days │ low_vol_days │\n│ string │ long │ double │ double │ double │ double │ double │ long │ long │ long │\n├────────┼──────────────┼──────────────────┼────────────┼─────────────┼─────────────┼──────────────────┼───────────────┼──────────────┼──────────────┤\n│ AAPL │ 60 │ 76.08 │ 70.51 │ 80.03 │ 81448897 │ -0.0392 │ 0 │ 0 │ 60 │\n│ FB │ 60 │ 63.42 │ 51.85 │ 72.59 │ 60363730 │ 0.2002 │ 0 │ 27 │ 33 │\n│ MSFT │ 60 │ 37.48 │ 34.63 │ 40.99 │ 39699734 │ 0.1481 │ 0 │ 0 │ 60 │\n│ INTC │ 60 │ 24.95 │ 23.5 │ 27.12 │ 31818523 │ -0.0056 │ 0 │ 0 │ 60 │\n│ TSLA │ 60 │ 200.79 │ 136.67 │ 265 │ 9498450 │ 0.6607 │ 53 │ 7 │ 0 │\n│ AMZN │ 60 │ 371.72 │ 330.88 │ 408.06 │ 3928478 │ -0.252 │ 56 │ 4 │ 0 │\n│ NFLX │ 60 │ 401.64 │ 319.07 │ 458 │ 3037457 │ 0.0231 │ 58 │ 2 │ 0 │\n│ GOOGL │ 60 │ 585.54 │ 541.68 │ 615.05 │ 2285740 │ 0.02 │ 50 │ 10 │ 0 │\n├────────┴──────────────┴──────────────────┴────────────┴─────────────┴─────────────┴──────────────────┴───────────────┴──────────────┴──────────────┤\n│ 8 rows │\n└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n- Executing query...\n✔ Query completed: Processed 528.4K rows in 1.1s [Job ID: 2966941339]",
"status": "completed"
}
},
{
"type": "thinking",
"content": "The complex query ran successfully. It shows a comprehensive stock analysis for Q1 2014 with multiple CTEs analyzing daily statistics, rankings, and volatility classifications."
},
{
"type": "content",
"content": "The long SQL query executed successfully. Here's what it did:\n\n**Query Structure (3 CTEs + final aggregation):**\n1. **`daily_stats`** - Calculated daily averages, highs, lows, and ranges per symbol\n2. **`ranked_data`** - Added window functions: `ROW_NUMBER`, `LAG`, `LEAD` for ranking and previous/next close prices\n3. **`performance`** - Computed daily return percentages and volatility categories\n4. **Final SELECT** - Aggregated everything into summary statistics per symbol\n\n**Key Insights from Q1 2014:**\n\n| Stock | Avg Volume | Avg Daily Return | Volatility |\n|-------|-----------|------------------|------------|\n| AAPL | 81.4M | -0.04% | All low volatility |\n| FB | 60.4M | +0.20% | Mixed (27 medium days) |\n| TSLA | 9.5M | +0.66% | 53 high volatility days |\n| AMZN | 3.9M | -0.25% | 56 high volatility days |\n| NFLX | 3.0M | +0.02% | 58 high volatility days |\n\nTesla had the highest average daily return (+0.66%) but also extreme volatility. Apple had the highest trading volume but slight negative returns that quarter."
}
],
"toolCalls": [
{
"id": "tooluse_Quvb0tTw0GSyezwjJG4Spd",
"name": "Bash",
"arguments": {
"command": "tdx query \"WITH daily_stats AS (SELECT symbol, TD_TIME_FORMAT(time, 'yyyy-MM-dd') as trade_date, AVG(close) as avg_close, AVG(volume) as avg_volume, MAX(high) as daily_high, MIN(low) as daily_low, MAX(high) - MIN(low) as daily_range FROM sample_datasets.nasdaq WHERE TD_TIME_RANGE(time, '2014-01-01', '2014-03-31') AND symbol IN ('AAPL', 'GOOGL', 'MSFT', 'AMZN', 'FB', 'NFLX', 'TSLA', 'ORCL', 'IBM', 'INTC') GROUP BY symbol, TD_TIME_FORMAT(time, 'yyyy-MM-dd')), ranked_data AS (SELECT symbol, trade_date, avg_close, avg_volume, daily_high, daily_low, daily_range, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY avg_volume DESC) as volume_rank, LAG(avg_close) OVER (PARTITION BY symbol ORDER BY trade_date) as prev_close, LEAD(avg_close) OVER (PARTITION BY symbol ORDER BY trade_date) as next_close FROM daily_stats), performance AS (SELECT symbol, trade_date, avg_close, avg_volume, daily_high, daily_low, daily_range, volume_rank, prev_close, next_close, CASE WHEN prev_close IS NOT NULL THEN ROUND((avg_close - prev_close) / prev_close * 100, 2) ELSE NULL END as daily_return_pct, CASE WHEN daily_range > 5 THEN 'HIGH_VOLATILITY' WHEN daily_range > 2 THEN 'MEDIUM_VOLATILITY' ELSE 'LOW_VOLATILITY' END as volatility_category FROM ranked_data) SELECT symbol, COUNT(*) as trading_days, ROUND(AVG(avg_close), 2) as mean_close_price, ROUND(MIN(daily_low), 2) as period_low, ROUND(MAX(daily_high), 2) as period_high, ROUND(AVG(avg_volume), 0) as mean_volume, ROUND(AVG(daily_return_pct), 4) as avg_daily_return, SUM(CASE WHEN volatility_category = 'HIGH_VOLATILITY' THEN 1 ELSE 0 END) as high_vol_days, SUM(CASE WHEN volatility_category = 'MEDIUM_VOLATILITY' THEN 1 ELSE 0 END) as med_vol_days, SUM(CASE WHEN volatility_category = 'LOW_VOLATILITY' THEN 1 ELSE 0 END) as low_vol_days FROM performance GROUP BY symbol ORDER BY mean_volume DESC LIMIT 10\"",
"description": "Run complex multi-CTE SQL query for stock analysis"
},
"result": "┌────────┬──────────────┬──────────────────┬────────────┬─────────────┬─────────────┬──────────────────┬───────────────┬──────────────┬──────────────┐\n│ symbol │ trading_days │ mean_close_price │ period_low │ period_high │ mean_volume │ avg_daily_return │ high_vol_days │ med_vol_days │ low_vol_days │\n│ string │ long │ double │ double │ double │ double │ double │ long │ long │ long │\n├────────┼──────────────┼──────────────────┼────────────┼─────────────┼─────────────┼──────────────────┼───────────────┼──────────────┼──────────────┤\n│ AAPL │ 60 │ 76.08 │ 70.51 │ 80.03 │ 81448897 │ -0.0392 │ 0 │ 0 │ 60 │\n│ FB │ 60 │ 63.42 │ 51.85 │ 72.59 │ 60363730 │ 0.2002 │ 0 │ 27 │ 33 │\n│ MSFT │ 60 │ 37.48 │ 34.63 │ 40.99 │ 39699734 │ 0.1481 │ 0 │ 0 │ 60 │\n│ INTC │ 60 │ 24.95 │ 23.5 │ 27.12 │ 31818523 │ -0.0056 │ 0 │ 0 │ 60 │\n│ TSLA │ 60 │ 200.79 │ 136.67 │ 265 │ 9498450 │ 0.6607 │ 53 │ 7 │ 0 │\n│ AMZN │ 60 │ 371.72 │ 330.88 │ 408.06 │ 3928478 │ -0.252 │ 56 │ 4 │ 0 │\n│ NFLX │ 60 │ 401.64 │ 319.07 │ 458 │ 3037457 │ 0.0231 │ 58 │ 2 │ 0 │\n│ GOOGL │ 60 │ 585.54 │ 541.68 │ 615.05 │ 2285740 │ 0.02 │ 50 │ 10 │ 0 │\n├────────┴──────────────┴──────────────────┴────────────┴─────────────┴─────────────┴──────────────────┴───────────────┴──────────────┴──────────────┤\n│ 8 rows │\n└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘\n- Executing query...\n✔ Query completed: Processed 528.4K rows in 1.1s [Job ID: 2966941339]",
"status": "completed"
}
]
}
],
"createdAt": "2026-02-09T19:23:50.206Z",
"updatedAt": "2026-02-09T19:46:12.405Z"
}
Raw
chat.md

SQL Query on Sample Datasets

Shared from Treasure Studio

User — Feb 9, 2026, 11:23 AM

run a long single line SQL with tdx using sample_datasets as a demo

Assistant — Feb 9, 2026, 11:24 AM

Thinking

The user wants to run a SQL query using tdx CLI against the sample_datasets database. Let me first invoke the tdx-basic skill to get the proper syntax and then run a query.

I'll run a SQL query using tdx against the sample_datasets database. Let me execute a query that demonstrates some interesting data.

Tool: Bash

Result: ┌────────────┬────────┬─────────┬─────────┬─────────┬─────────┬───────────┐
│    time    │ symbol │  open   │  high   │   low   │  close  │  volume   │
│    long    │ string │ double  │ double  │ double  │ double  │   long    │
├────────────┼────────┼─────────┼─────────┼─────────┼─────────┼───────────┤
│ 1391097600 │ AAPL   │  71.791 │  72.357 │  70.957 │  71.397 │ 169762789 │
│ 1391097600 │ AMZN   │  393.77 │  406.25 │   387.7 │  403.01 │  10900116 │
│ 1391097600 │ GOOGL  │ 573.072 │ 576.821 │
Thinking

The query ran successfully. It retrieved stock data for major tech companies (AAPL, GOOGL, MSFT, AMZN) from January 2014 in the nasdaq table within sample_datasets. The results show trading data including open, high, low, close prices and volume.

The query executed successfully against the sample_datasets database. It retrieved January 2014 stock data for major tech companies (AAPL, GOOGL, MSFT, AMZN) from the nasdaq table.

Query highlights:

  • Used TD_TIME_RANGE(time, '2014-01-01', '2014-01-31') for efficient time-based filtering with partition pruning
  • Filtered for 4 major tech stocks
  • Returned open, high, low, close prices and volume
  • Processed 528.4K rows in 2.5 seconds

The -d sample_datasets flag sets the database context, allowing you to reference tables directly without the database.table syntax.

User — Feb 9, 2026, 11:28 AM

SELECT method, COUNT(*) as cnt FROM sample_datasets.www_access GROUP BY method ORDER BY cnt DESC

Assistant — Feb 9, 2026, 11:29 AM

Thinking

The user wants me to run this SQL query using tdx. It's a simple aggregation query on the www_access table in sample_datasets.

Tool: Bash

Result: ┌────────┬──────┐
│ method │ cnt  │
│ string │ long │
├────────┼──────┤
│ GET    │ 4624 │
│ POST   │  376 │
├────────┴──────┤
│ 2 rows        │
└───────────────┘
- Executing query...
✔ Query completed: Processed 5.00K rows in 0.5s [Job ID: 2966923669]
Thinking

The query ran successfully. It shows that in the www_access table, there are 4624 GET requests and 376 POST requests. Simple and clean results.

The query shows HTTP method distribution in the www_access table:

Method Count
GET 4,624
POST 376

About 92% of requests are GET and 8% are POST. The table contains 5,000 total web access log records.

User — Feb 9, 2026, 11:45 AM

Run a very long SQL just for testing purpose

Assistant — Feb 9, 2026, 11:46 AM

Thinking

The user wants me to run a very long SQL query for testing purposes. I'll create a complex query with multiple CTEs, joins, window functions, and aggregations to demonstrate a long single-line SQL query using the sample_datasets database.

Tool: Bash

Result: ┌────────┬──────────────┬──────────────────┬────────────┬─────────────┬─────────────┬──────────────────┬───────────────┬──────────────┬──────────────┐
│ symbol │ trading_days │ mean_close_price │ period_low │ period_high │ mean_volume │ avg_daily_return │ high_vol_days │ med_vol_days │ low_vol_days │
│ string │     long     │      double      │   double   │   double    │   double    │      double      │     long      │     long     │     long     │
├────────┼──────────────┼──────────────────┼───
Thinking

The complex query ran successfully. It shows a comprehensive stock analysis for Q1 2014 with multiple CTEs analyzing daily statistics, rankings, and volatility classifications.

The long SQL query executed successfully. Here's what it did:

Query Structure (3 CTEs + final aggregation):

  1. daily_stats - Calculated daily averages, highs, lows, and ranges per symbol
  2. ranked_data - Added window functions: ROW_NUMBER, LAG, LEAD for ranking and previous/next close prices
  3. performance - Computed daily return percentages and volatility categories
  4. Final SELECT - Aggregated everything into summary statistics per symbol

Key Insights from Q1 2014:

Stock Avg Volume Avg Daily Return Volatility
AAPL 81.4M -0.04% All low volatility
FB 60.4M +0.20% Mixed (27 medium days)
TSLA 9.5M +0.66% 53 high volatility days
AMZN 3.9M -0.25% 56 high volatility days
NFLX 3.0M +0.02% 58 high volatility days

Tesla had the highest average daily return (+0.66%) but also extreme volatility. Apple had the highest trading volume but slight negative returns that quarter.

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