2.IngestCost.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>queryId</th>\n",
       "      <th>warehouseId</th>\n",
       "      <th>databaseId</th>\n",
       "      <th>createdTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>durationTotal</th>\n",
       "      <th>durationExec</th>\n",
       "      <th>durationControlPlane</th>\n",
       "      <th>durationCompiling</th>\n",
       "      <th>...</th>\n",
       "      <th>profDmlRso</th>\n",
       "      <th>profHjRso</th>\n",
       "      <th>profBufRso</th>\n",
       "      <th>profFlatRso</th>\n",
       "      <th>profBloomRso</th>\n",
       "      <th>profAggRso</th>\n",
       "      <th>profBandRso</th>\n",
       "      <th>profPercentileRso</th>\n",
       "      <th>profUdtfRso</th>\n",
       "      <th>memoryUsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>7891774171123969424</td>\n",
       "      <td>7097937327349925659</td>\n",
       "      <td>2018-03-02 13:13:50.913</td>\n",
       "      <td>2018-03-02 13:14:31.815</td>\n",
       "      <td>40902</td>\n",
       "      <td>7986</td>\n",
       "      <td>733</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>216</td>\n",
       "      <td>8241806116388669556</td>\n",
       "      <td>7891774171123969424</td>\n",
       "      <td>7097937327349925659</td>\n",
       "      <td>2018-03-02 13:32:48.609</td>\n",
       "      <td>2018-03-02 13:33:26.715</td>\n",
       "      <td>38106</td>\n",
       "      <td>4881</td>\n",
       "      <td>828</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>268</td>\n",
       "      <td>1096864241800489343</td>\n",
       "      <td>7891774171123969424</td>\n",
       "      <td>7097937327349925659</td>\n",
       "      <td>2018-03-02 14:34:07.451</td>\n",
       "      <td>2018-03-02 14:34:44.425</td>\n",
       "      <td>36974</td>\n",
       "      <td>1677</td>\n",
       "      <td>222</td>\n",
       "      <td>1969</td>\n",
       "      <td>...</td>\n",
       "      <td>3007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>7054370592006144647</td>\n",
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       "      <td>7097937327349925659</td>\n",
       "      <td>2018-03-02 13:34:29.365</td>\n",
       "      <td>2018-03-02 13:35:36.926</td>\n",
       "      <td>67561</td>\n",
       "      <td>2324</td>\n",
       "      <td>680</td>\n",
       "      <td>1854</td>\n",
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       "      <td>3998</td>\n",
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       "      <td>777382398</td>\n",
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       "      <th>4</th>\n",
       "      <td>416</td>\n",
       "      <td>5643653041788828328</td>\n",
       "      <td>7891774171123969424</td>\n",
       "      <td>7097937327349925659</td>\n",
       "      <td>2018-03-02 13:41:15.618</td>\n",
       "      <td>2018-03-02 13:41:44.962</td>\n",
       "      <td>29344</td>\n",
       "      <td>2420</td>\n",
       "      <td>682</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>2796</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
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       "      <td>2018-03-05 17:31:32.798</td>\n",
       "      <td>2018-03-05 17:32:09.559</td>\n",
       "      <td>36761</td>\n",
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       "      <td>2018-03-05 17:33:25.252</td>\n",
       "      <td>2018-03-05 17:33:28.347</td>\n",
       "      <td>3095</td>\n",
       "      <td>1620</td>\n",
       "      <td>521</td>\n",
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       "      <td>2018-03-05 16:30:22.250</td>\n",
       "      <td>2018-03-05 16:30:24.366</td>\n",
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       "      <td>2018-03-05 16:31:07.721</td>\n",
       "      <td>2018-03-05 16:31:13.563</td>\n",
       "      <td>5842</td>\n",
       "      <td>179</td>\n",
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       "<p>692148 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index              queryId          warehouseId  \\\n",
       "0             96  3573560888585338320  7891774171123969424   \n",
       "1            216  8241806116388669556  7891774171123969424   \n",
       "2            268  1096864241800489343  7891774171123969424   \n",
       "3            376  7054370592006144647  7891774171123969424   \n",
       "4            416  5643653041788828328  7891774171123969424   \n",
       "...          ...                  ...                  ...   \n",
       "692143  34594109  5620861797097593870   518617277384270687   \n",
       "692144  34594163  1221273439061198724   518617277384270687   \n",
       "692145  34594179  5344795702022901932  9217905476676616273   \n",
       "692146  34594190  2097026435791425528  9217905476676616273   \n",
       "692147  34594192  6890375982100765014   518617277384270687   \n",
       "\n",
       "                 databaseId             createdTime                 endTime  \\\n",
       "0       7097937327349925659 2018-03-02 13:13:50.913 2018-03-02 13:14:31.815   \n",
       "1       7097937327349925659 2018-03-02 13:32:48.609 2018-03-02 13:33:26.715   \n",
       "2       7097937327349925659 2018-03-02 14:34:07.451 2018-03-02 14:34:44.425   \n",
       "3       7097937327349925659 2018-03-02 13:34:29.365 2018-03-02 13:35:36.926   \n",
       "4       7097937327349925659 2018-03-02 13:41:15.618 2018-03-02 13:41:44.962   \n",
       "...                     ...                     ...                     ...   \n",
       "692143  6399611409063837993 2018-03-05 17:31:32.798 2018-03-05 17:32:09.559   \n",
       "692144  6399611409063837993 2018-03-05 17:33:25.252 2018-03-05 17:33:28.347   \n",
       "692145  6399611409063837993 2018-03-05 16:31:18.700 2018-03-05 16:31:24.990   \n",
       "692146  6399611409063837993 2018-03-05 16:30:22.250 2018-03-05 16:30:24.366   \n",
       "692147  6399611409063837993 2018-03-05 16:31:07.721 2018-03-05 16:31:13.563   \n",
       "\n",
       "        durationTotal  durationExec  durationControlPlane  durationCompiling  \\\n",
       "0               40902          7986                   733                 76   \n",
       "1               38106          4881                   828                 77   \n",
       "2               36974          1677                   222               1969   \n",
       "3               67561          2324                   680               1854   \n",
       "4               29344          2420                   682                 88   \n",
       "...               ...           ...                   ...                ...   \n",
       "692143          36761         17776                  1017               2648   \n",
       "692144           3095          1620                   521                531   \n",
       "692145           5399          3017                     5               2331   \n",
       "692146           2341          1082                     4               1222   \n",
       "692147           5842           179                   185                 75   \n",
       "\n",
       "        ...  profDmlRso  profHjRso  profBufRso  profFlatRso  profBloomRso  \\\n",
       "0       ...       22536          0           0            0             0   \n",
       "1       ...       11997          0           0            0             0   \n",
       "2       ...        3007          0           0            0             0   \n",
       "3       ...        3998          0           0            0             0   \n",
       "4       ...        2796          0           0            0             0   \n",
       "...     ...         ...        ...         ...          ...           ...   \n",
       "692143  ...        4601      69011           0            0         13856   \n",
       "692144  ...         545          0           0            0             0   \n",
       "692145  ...           0       4926           0            0             0   \n",
       "692146  ...           0         99           0            0            24   \n",
       "692147  ...          35          0           0            0             0   \n",
       "\n",
       "        profAggRso  profBandRso  profPercentileRso  profUdtfRso  memoryUsed  \n",
       "0                0            0                  0            0  2144017808  \n",
       "1                0            0                  0            0  1400891664  \n",
       "2                0            0                  0            0   789206816  \n",
       "3                0            0                  0            0   777382398  \n",
       "4               99            0                  0            0   529882272  \n",
       "...            ...          ...                ...          ...         ...  \n",
       "692143          33            0                  0            0  2687622704  \n",
       "692144           0            0                  0            0   166701408  \n",
       "692145         419            0                  0            0   747001024  \n",
       "692146          99            0                  0            0   509438896  \n",
       "692147           0            0                  0            0    27422752  \n",
       "\n",
       "[692148 rows x 93 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "import duckdb\n",
    "\n",
    "sf_all = duckdb.query(\"select * from 'snowset/part.*.parquet' using sample 1 percent (bernoulli)\").df()\n",
    "\n",
    "sf_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<small>shape: (53, 53)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>persistentReadBytesS3</th><th>persistentReadBytesCache</th><th>persistentWriteBytesCache</th><th>persistentWriteBytesS3</th><th>intDataWriteBytesLocalSSD</th><th>intDataReadBytesLocalSSD</th><th>intDataWriteBytesS3</th><th>intDataReadBytesS3</th><th>ioRemoteExternalReadBytes</th><th>intDataNetReceivedBytes</th><th>intDataNetSentBytes</th><th>producedRows0</th><th>producedRows1</th><th>returnedRows0</th><th>returnedRows1</th><th>remoteSeqScanFileOps</th><th>localSeqScanFileOps</th><th>localWriteFileOps</th><th>remoteWriteFileOps</th><th>filesCreated</th><th>profPersistentReadCache</th><th>profPersistentWriteCache</th><th>profPersistentReadS3</th><th>profPersistentWriteS3</th><th>profIntDataReadLocalSSD</th><th>profIntDataWriteLocalSSD</th><th>profIntDataReadS3</th><th>profIntDataWriteS3</th><th>profRemoteExtRead</th><th>profRemoteExtWrite</th><th>profResWriteS3</th><th>profFsMeta</th><th>profDataExchangeNet</th><th>profDataExchangeMsg</th><th>profControlPlaneMsg</th><th>profOs</th><th>profMutex</th><th>profSetup</th><th>profSetupMesh</th><th>profTeardown</th><th>profScanRso</th><th>profXtScanRso</th><th>profProjRso</th><th>profSortRso</th><th>profFilterRso</th><th>profResRso</th><th>profDmlRso</th><th>profHjRso</th><th>profBufRso</th><th>profFlatRso</th><th>profBloomRso</th><th>profAggRso</th><th>profBandRso</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>1.0</td><td>0.026273</td><td>0.365169</td><td>-0.017117</td><td>0.002858</td><td>0.003445</td><td>0.039466</td><td>0.039419</td><td>-0.166483</td><td>0.474575</td><td>0.474576</td><td>-0.033491</td><td>-0.087929</td><td>0.045615</td><td>-0.045806</td><td>-0.165578</td><td>0.003144</td><td>0.380272</td><td>0.021056</td><td>-0.017257</td><td>0.142693</td><td>0.275176</td><td>0.85553</td><td>0.034608</td><td>0.046463</td><td>0.051136</td><td>0.033402</td><td>0.039452</td><td>-0.154662</td><td>0.045862</td><td>0.086164</td><td>0.326694</td><td>0.283018</td><td>0.121478</td><td>0.112681</td><td>0.085665</td><td>0.199773</td><td>0.159041</td><td>0.205108</td><td>0.069498</td><td>0.513029</td><td>-0.165154</td><td>0.214113</td><td>0.118524</td><td>0.167668</td><td>0.028796</td><td>-0.013135</td><td>0.09444</td><td>0.064074</td><td>0.050648</td><td>0.068966</td><td>0.166336</td><td>0.001938</td></tr><tr><td>0.026273</td><td>1.0</td><td>-0.142457</td><td>-0.22985</td><td>0.021171</td><td>0.023011</td><td>0.013938</td><td>0.013921</td><td>-0.447734</td><td>0.14095</td><td>0.14095</td><td>0.08769</td><td>0.048216</td><td>0.27432</td><td>-0.274389</td><td>-0.447212</td><td>0.02293</td><td>-0.137161</td><td>-0.215541</td><td>-0.229894</td><td>0.241101</td><td>0.090544</td><td>0.085702</td><td>-0.127981</td><td>0.03983</td><td>0.035156</td><td>0.010642</td><td>0.013924</td><td>-0.412613</td><td>0.0134</td><td>0.05648</td><td>0.142169</td><td>0.173176</td><td>0.185477</td><td>-0.044599</td><td>0.069926</td><td>0.259751</td><td>0.173253</td><td>0.108209</td><td>0.057839</td><td>0.331773</td><td>-0.424987</td><td>0.153115</td><td>0.144792</td><td>0.225325</td><td>-0.005369</td><td>-0.227234</td><td>0.230381</td><td>0.116228</td><td>0.027466</td><td>0.204869</td><td>0.189323</td><td>0.014423</td></tr><tr><td>0.365169</td><td>-0.142457</td><td>1.0</td><td>0.688848</td><td>0.075317</td><td>0.07507</td><td>0.019014</td><td>0.019021</td><td>0.224537</td><td>-0.047464</td><td>-0.047464</td><td>-0.406768</td><td>-0.310173</td><td>-0.534147</td><td>0.534474</td><td>0.224712</td><td>0.075071</td><td>0.968458</td><td>0.682521</td><td>0.688766</td><td>-0.08586</td><td>0.109668</td><td>0.314879</td><td>0.5484</td><td>0.051484</td><td>0.051689</td><td>0.016418</td><td>0.019006</td><td>0.243563</td><td>-0.024497</td><td>-0.073693</td><td>0.127534</td><td>0.023091</td><td>0.040546</td><td>0.248637</td><td>0.009481</td><td>-0.127855</td><td>-0.115825</td><td>-0.005529</td><td>-0.019317</td><td>-0.124306</td><td>0.229607</td><td>0.014851</td><td>0.096254</td><td>-0.254939</td><td>0.011612</td><td>0.667802</td><td>-0.218726</td><td>0.101415</td><td>0.00182</td><td>-0.183792</td><td>-0.120665</td><td>-0.01187</td></tr><tr><td>-0.017117</td><td>-0.22985</td><td>0.688848</td><td>1.0</td><td>0.092947</td><td>0.092697</td><td>0.025536</td><td>0.02563</td><td>0.359726</td><td>-0.264052</td><td>-0.264051</td><td>-0.520914</td><td>-0.333741</td><td>-0.784086</td><td>0.784927</td><td>0.359849</td><td>0.092612</td><td>0.671699</td><td>0.925031</td><td>0.99985</td><td>-0.228843</td><td>-0.03982</td><td>-0.04216</td><td>0.795872</td><td>0.049373</td><td>0.048359</td><td>0.021719</td><td>0.025544</td><td>0.369321</td><td>-0.117258</td><td>-0.250951</td><td>-0.01644</td><td>-0.097233</td><td>0.028437</td><td>0.338679</td><td>-0.040875</td><td>-0.242804</td><td>-0.193743</td><td>-0.118247</td><td>-0.06469</td><td>-0.303889</td><td>0.361919</td><td>-0.052095</td><td>0.129195</td><td>-0.469564</td><td>-0.028814</td><td>0.956821</td><td>-0.266324</td><td>0.128922</td><td>-0.032633</td><td>-0.229939</td><td>-0.303331</td><td>-0.011319</td></tr><tr><td>0.002858</td><td>0.021171</td><td>0.075317</td><td>0.092947</td><td>1.0</td><td>0.996383</td><td>0.203698</td><td>0.203523</td><td>0.167525</td><td>0.030428</td><td>0.030428</td><td>-0.051765</td><td>-0.0471</td><td>-0.081909</td><td>0.081899</td><td>0.171444</td><td>0.99569</td><td>0.09464</td><td>0.101712</td><td>0.092399</td><td>0.021432</td><td>0.104174</td><td>0.028545</td><td>0.128478</td><td>0.614861</td><td>0.633616</td><td>0.176421</td><td>0.203602</td><td>0.16729</td><td>0.04361</td><td>-0.005608</td><td>0.292794</td><td>0.190738</td><td>0.286155</td><td>0.16716</td><td>0.106266</td><td>0.160659</td><td>0.022169</td><td>0.026675</td><td>0.017468</td><td>0.067192</td><td>0.173733</td><td>0.199357</td><td>0.40034</td><td>-0.037568</td><td>0.000962</td><td>0.103243</td><td>-0.009439</td><td>0.581306</td><td>0.014768</td><td>-0.022401</td><td>0.008753</td><td>0.094309</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>0.064074</td><td>0.116228</td><td>0.101415</td><td>0.128922</td><td>0.581306</td><td>0.583554</td><td>0.136336</td><td>0.136443</td><td>0.225199</td><td>0.063181</td><td>0.063182</td><td>-0.07336</td><td>-0.068801</td><td>-0.119697</td><td>0.119762</td><td>0.227028</td><td>0.583193</td><td>0.113619</td><td>0.13706</td><td>0.12881</td><td>-0.005298</td><td>0.085343</td><td>0.104415</td><td>0.179283</td><td>0.346401</td><td>0.357957</td><td>0.11575</td><td>0.136291</td><td>0.229605</td><td>0.04283</td><td>-0.010375</td><td>0.323627</td><td>0.256281</td><td>0.377987</td><td>0.251208</td><td>0.079796</td><td>0.258794</td><td>0.098252</td><td>0.11865</td><td>0.043624</td><td>0.173794</td><td>0.241895</td><td>0.28883</td><td>0.606126</td><td>-0.075404</td><td>-0.000399</td><td>0.140861</td><td>-0.043206</td><td>1.0</td><td>0.006491</td><td>-0.0531</td><td>0.017206</td><td>0.071011</td></tr><tr><td>0.050648</td><td>0.027466</td><td>0.00182</td><td>-0.032633</td><td>0.014768</td><td>0.01478</td><td>0.006927</td><td>0.00693</td><td>-0.037119</td><td>0.039823</td><td>0.039823</td><td>0.014935</td><td>-0.0068</td><td>0.026298</td><td>-0.026277</td><td>-0.036182</td><td>0.01479</td><td>0.004066</td><td>-0.025047</td><td>-0.032662</td><td>0.056687</td><td>0.040873</td><td>0.048468</td><td>-0.017418</td><td>0.025284</td><td>0.025622</td><td>0.007759</td><td>0.006879</td><td>-0.040841</td><td>0.007126</td><td>0.021001</td><td>0.045837</td><td>0.071484</td><td>0.041434</td><td>0.015553</td><td>0.05509</td><td>0.070298</td><td>0.06967</td><td>0.050603</td><td>0.027201</td><td>0.048216</td><td>-0.039223</td><td>0.101453</td><td>0.005722</td><td>0.087152</td><td>-0.000682</td><td>-0.030281</td><td>0.086547</td><td>0.006491</td><td>1.0</td><td>0.067395</td><td>0.078344</td><td>-0.001577</td></tr><tr><td>0.068966</td><td>0.204869</td><td>-0.183792</td><td>-0.229939</td><td>-0.022401</td><td>-0.022242</td><td>-0.009724</td><td>-0.009835</td><td>-0.223783</td><td>0.053229</td><td>0.053229</td><td>0.103488</td><td>0.115746</td><td>0.203498</td><td>-0.203902</td><td>-0.223423</td><td>-0.022315</td><td>-0.177006</td><td>-0.212961</td><td>-0.230198</td><td>0.378397</td><td>0.200136</td><td>0.089681</td><td>-0.160204</td><td>0.020595</td><td>0.021701</td><td>-0.007192</td><td>-0.009753</td><td>-0.216001</td><td>0.047271</td><td>0.047493</td><td>0.175188</td><td>0.222458</td><td>0.236622</td><td>-0.023274</td><td>0.135247</td><td>0.347119</td><td>0.297815</td><td>0.097808</td><td>0.084778</td><td>0.277678</td><td>-0.221257</td><td>0.296193</td><td>0.074223</td><td>0.359543</td><td>-0.012285</td><td>-0.220013</td><td>0.593159</td><td>-0.0531</td><td>0.067395</td><td>1.0</td><td>0.262626</td><td>0.004091</td></tr><tr><td>0.166336</td><td>0.189323</td><td>-0.120665</td><td>-0.303331</td><td>0.008753</td><td>0.008967</td><td>0.006651</td><td>0.006545</td><td>-0.195628</td><td>0.251071</td><td>0.251071</td><td>0.000675</td><td>0.068487</td><td>0.377778</td><td>-0.378191</td><td>-0.195149</td><td>0.00888</td><td>-0.106031</td><td>-0.236545</td><td>-0.303571</td><td>0.280134</td><td>0.167268</td><td>0.184958</td><td>-0.219851</td><td>0.044803</td><td>0.047778</td><td>0.006772</td><td>0.006616</td><td>-0.189502</td><td>0.057998</td><td>0.188523</td><td>0.194556</td><td>0.330492</td><td>0.278078</td><td>-0.027461</td><td>0.185181</td><td>0.357956</td><td>0.296883</td><td>0.208622</td><td>0.103861</td><td>0.239581</td><td>-0.19448</td><td>0.322356</td><td>0.069361</td><td>0.412658</td><td>0.013218</td><td>-0.289844</td><td>0.32252</td><td>0.017206</td><td>0.078344</td><td>0.262626</td><td>1.0</td><td>0.007262</td></tr><tr><td>0.001938</td><td>0.014423</td><td>-0.01187</td><td>-0.011319</td><td>0.094309</td><td>0.094561</td><td>0.000841</td><td>0.000766</td><td>-0.015731</td><td>0.010392</td><td>0.010392</td><td>0.004713</td><td>-0.006175</td><td>0.027649</td><td>-0.027647</td><td>-0.015566</td><td>0.094605</td><td>0.00962</td><td>-0.011533</td><td>-0.011423</td><td>0.021413</td><td>0.003545</td><td>0.000615</td><td>-0.009584</td><td>0.082329</td><td>0.089529</td><td>0.001126</td><td>0.00083</td><td>-0.015158</td><td>0.001713</td><td>-0.000127</td><td>0.028046</td><td>0.041565</td><td>0.032796</td><td>-0.005274</td><td>0.012536</td><td>0.03121</td><td>0.002971</td><td>-0.000395</td><td>0.00247</td><td>0.019185</td><td>-0.015538</td><td>0.019006</td><td>0.003486</td><td>-0.000418</td><td>-0.000699</td><td>-0.012438</td><td>0.053786</td><td>0.071011</td><td>-0.001577</td><td>0.004091</td><td>0.007262</td><td>1.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (53, 53)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ persisten ┆ persisten ┆ persisten ┆ persisten ┆ … ┆ profFlatR ┆ profBloom ┆ profAggRs ┆ profBand │\n",
       "│ tReadByte ┆ tReadByte ┆ tWriteByt ┆ tWriteByt ┆   ┆ so        ┆ Rso       ┆ o         ┆ Rso      │\n",
       "│ sS3       ┆ sCache    ┆ esCache   ┆ esS3      ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
       "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
       "│ f64       ┆ f64       ┆ f64       ┆ f64       ┆   ┆           ┆           ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ 1.0       ┆ 0.026273  ┆ 0.365169  ┆ -0.017117 ┆ … ┆ 0.050648  ┆ 0.068966  ┆ 0.166336  ┆ 0.001938 │\n",
       "│ 0.026273  ┆ 1.0       ┆ -0.142457 ┆ -0.22985  ┆ … ┆ 0.027466  ┆ 0.204869  ┆ 0.189323  ┆ 0.014423 │\n",
       "│ 0.365169  ┆ -0.142457 ┆ 1.0       ┆ 0.688848  ┆ … ┆ 0.00182   ┆ -0.183792 ┆ -0.120665 ┆ -0.01187 │\n",
       "│ -0.017117 ┆ -0.22985  ┆ 0.688848  ┆ 1.0       ┆ … ┆ -0.032633 ┆ -0.229939 ┆ -0.303331 ┆ -0.01131 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 9        │\n",
       "│ 0.002858  ┆ 0.021171  ┆ 0.075317  ┆ 0.092947  ┆ … ┆ 0.014768  ┆ -0.022401 ┆ 0.008753  ┆ 0.094309 │\n",
       "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
       "│ 0.064074  ┆ 0.116228  ┆ 0.101415  ┆ 0.128922  ┆ … ┆ 0.006491  ┆ -0.0531   ┆ 0.017206  ┆ 0.071011 │\n",
       "│ 0.050648  ┆ 0.027466  ┆ 0.00182   ┆ -0.032633 ┆ … ┆ 1.0       ┆ 0.067395  ┆ 0.078344  ┆ -0.00157 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
       "│ 0.068966  ┆ 0.204869  ┆ -0.183792 ┆ -0.229939 ┆ … ┆ 0.067395  ┆ 1.0       ┆ 0.262626  ┆ 0.004091 │\n",
       "│ 0.166336  ┆ 0.189323  ┆ -0.120665 ┆ -0.303331 ┆ … ┆ 0.078344  ┆ 0.262626  ┆ 1.0       ┆ 0.007262 │\n",
       "│ 0.001938  ┆ 0.014423  ┆ -0.01187  ┆ -0.011319 ┆ … ┆ -0.001577 ┆ 0.004091  ┆ 0.007262  ┆ 1.0      │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "select\n",
    "\n",
    "persistentReadBytesS3 > 0 as persistentReadBytesS3,\n",
    "persistentReadBytesCache > 0 as persistentReadBytesCache,\n",
    "persistentWriteBytesCache > 0 as persistentWriteBytesCache,\n",
    "persistentWriteBytesS3 > 0 as persistentWriteBytesS3,\n",
    "intDataWriteBytesLocalSSD > 0 as intDataWriteBytesLocalSSD,\n",
    "intDataReadBytesLocalSSD > 0 as intDataReadBytesLocalSSD,\n",
    "intDataWriteBytesS3 > 0 as intDataWriteBytesS3,\n",
    "intDataReadBytesS3 > 0 as intDataReadBytesS3,\n",
    "ioRemoteExternalReadBytes > 0 as ioRemoteExternalReadBytes,\n",
    "intDataNetReceivedBytes > 0 as intDataNetReceivedBytes,\n",
    "intDataNetSentBytes > 0 as intDataNetSentBytes,\n",
    "\n",
    "producedRows == 0 as producedRows0,\n",
    "producedRows == 1 as producedRows1,\n",
    "\n",
    "returnedRows == 0 as returnedRows0,\n",
    "returnedRows == 1 as returnedRows1,\n",
    "\n",
    "remoteSeqScanFileOps > 0 as remoteSeqScanFileOps,\n",
    "localSeqScanFileOps > 0 as localSeqScanFileOps,\n",
    "localWriteFileOps > 0 as localWriteFileOps,\n",
    "remoteWriteFileOps > 0 as remoteWriteFileOps,\n",
    "filesCreated > 0 as filesCreated,\n",
    "profPersistentReadCache > 0 as profPersistentReadCache,\n",
    "profPersistentWriteCache > 0 as profPersistentWriteCache,\n",
    "profPersistentReadS3 > 0 as profPersistentReadS3,\n",
    "profPersistentWriteS3 > 0 as profPersistentWriteS3,\n",
    "profIntDataReadLocalSSD > 0 as profIntDataReadLocalSSD,\n",
    "profIntDataWriteLocalSSD > 0 as profIntDataWriteLocalSSD,\n",
    "profIntDataReadS3 > 0 as profIntDataReadS3,\n",
    "profIntDataWriteS3 > 0 as profIntDataWriteS3,\n",
    "profRemoteExtRead > 0 as profRemoteExtRead,\n",
    "profRemoteExtWrite > 0 as profRemoteExtWrite,\n",
    "profResWriteS3 > 0 as profResWriteS3,\n",
    "profFsMeta > 0 as profFsMeta,\n",
    "profDataExchangeNet > 0 as profDataExchangeNet,\n",
    "profDataExchangeMsg > 0 as profDataExchangeMsg,\n",
    "profControlPlaneMsg > 0 as profControlPlaneMsg,\n",
    "profOs > 0 as profOs,\n",
    "profMutex > 0 as profMutex,\n",
    "profSetup > 0 as profSetup,\n",
    "profSetupMesh > 0 as profSetupMesh,\n",
    "profTeardown > 0 as profTeardown,\n",
    "profScanRso > 0 as profScanRso,\n",
    "profXtScanRso > 0 as profXtScanRso,\n",
    "profProjRso > 0 as profProjRso,\n",
    "profSortRso > 0 as profSortRso,\n",
    "profFilterRso > 0 as profFilterRso,\n",
    "profResRso > 0 as profResRso,\n",
    "profDmlRso > 0 as profDmlRso,\n",
    "profHjRso > 0 as profHjRso,\n",
    "profBufRso > 0 as profBufRso,\n",
    "profFlatRso > 0 as profFlatRso,\n",
    "profBloomRso > 0 as profBloomRso,\n",
    "profAggRso > 0 as profAggRso,\n",
    "profBandRso > 0 as profBandRso,\n",
    "\n",
    "from 'snowset/part.*.parquet'\n",
    "\"\"\"\n",
    "sf_features = duckdb.query(query).pl()\n",
    "sf_corr = sf_features.corr()\n",
    "sf_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.cluster.hierarchy as sch\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr_matrix = sf_corr.to_numpy()\n",
    "\n",
    "# Clean up numerical error\n",
    "for i in range(len(corr_matrix)):\n",
    "    corr_matrix[i, i] = 1\n",
    "corr_matrix = (corr_matrix + corr_matrix.T) / 2\n",
    "\n",
    "# Create the distance matrix: 1 - |correlation|\n",
    "dist_matrix = 1 - np.abs(corr_matrix)\n",
    "\n",
    "# Perform hierarchical clustering on the symmetric distance matrix\n",
    "linkage = sch.linkage(sch.distance.squareform(dist_matrix), method='average')\n",
    "\n",
    "# Get the order of the features after clustering\n",
    "ordered_indices = sch.dendrogram(linkage, no_plot=True)['leaves']\n",
    "\n",
    "# Reorder the matrix using the ordered indices\n",
    "corr_matrix = corr_matrix[np.ix_(ordered_indices, ordered_indices)]\n",
    "\n",
    "# Step 4: Plot the reordered correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', vmin=-1, vmax=1, yticklabels=[sf_corr.columns[i] for i in ordered_indices])\n",
    "plt.title('Reordered Correlation Matrix with Hierarchical Clustering')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-6cd9e9b11be34753be2a752bb3121c7b.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-6cd9e9b11be34753be2a752bb3121c7b.vega-embed details,\n",
       "  #altair-viz-6cd9e9b11be34753be2a752bb3121c7b.vega-embed details summary {\n",
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       "<div id=\"altair-viz-6cd9e9b11be34753be2a752bb3121c7b\"></div>\n",
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       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
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       "      outputDiv = document.getElementById(\"altair-viz-6cd9e9b11be34753be2a752bb3121c7b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
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       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
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       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
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       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
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      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt \n",
    "\n",
    "sf_costs = duckdb.query(\n",
    "\"\"\"\n",
    "select \n",
    "    case\n",
    "        when profRemoteExtRead > 0 then 'Ingest'\n",
    "        when profRemoteExtWrite > 0 then 'Export'\n",
    "        when persistentReadBytesS3 + persistentReadBytesCache == 0 and persistentWriteBytesS3 > 0 then 'Ingest'\n",
    "        when persistentReadBytesS3 + persistentReadBytesCache > 0 and persistentWriteBytesS3 > 0 then 'Transformation'\n",
    "        when persistentReadBytesS3 + persistentReadBytesCache > 0 and persistentWriteBytesS3 == 0 then 'Read'\n",
    "        else 'Other' end as \"Query Type\",\n",
    "    sum(durationExec * warehouseSize) as \"Cost\",\n",
    "from 'snowset/part.*.parquet'\n",
    "group by all\n",
    "\"\"\"\n",
    ").df()\n",
    "sf_costs['Cost'] = sf_costs['Cost'] / sf_costs['Cost'].sum()\n",
    "\n",
    "alt.Chart(sf_costs).mark_bar(color='blue').encode(\n",
    "    x='Cost', \n",
    "    y=alt.Y('Query Type', sort=[\"Ingest\", \"Transformation\", \"Read\", \"Export\", \"Other\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>execution_duration_ms</th>\n",
       "      <th>feature_fingerprint</th>\n",
       "      <th>...</th>\n",
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       "      <th>num_external_tables_accessed</th>\n",
       "      <th>num_system_tables_accessed</th>\n",
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       "      <th>write_table_ids</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>1710408</td>\n",
       "      <td>2024-05-29 23:38:52.357817</td>\n",
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       "      <td>186850,2</td>\n",
       "      <td>None</td>\n",
       "      <td>219.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4329214</th>\n",
       "      <td>99</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>993135</td>\n",
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       "      <td>1</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4329215 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         instance_id  cluster_size  user_id  database_id  query_id  \\\n",
       "0                  0           NaN        2            0   4338982   \n",
       "1                  0           NaN        2            0   3200032   \n",
       "2                  0           NaN        2            0   2927869   \n",
       "3                  0           NaN        2            0   3132067   \n",
       "4                  0           NaN       10            0   3346993   \n",
       "...              ...           ...      ...          ...       ...   \n",
       "4329210           99           4.0        0            0   1112430   \n",
       "4329211           99           4.0        0            0   1792593   \n",
       "4329212           99           4.0        0            0   1683808   \n",
       "4329213           99           4.0        0            0   1710408   \n",
       "4329214           99           4.0        0            0    993135   \n",
       "\n",
       "                 arrival_timestamp  compile_duration_ms  queue_duration_ms  \\\n",
       "0       2024-03-01 00:01:25.248305                 80.0                  0   \n",
       "1       2024-03-01 00:03:05.040435               1368.0                  0   \n",
       "2       2024-03-01 00:10:56.886588                834.0                  0   \n",
       "3       2024-03-01 00:11:17.734212                896.0                  0   \n",
       "4       2024-03-01 00:11:53.995976                  0.0                  0   \n",
       "...                            ...                  ...                ...   \n",
       "4329210 2024-05-29 23:16:11.923376                  9.0                  0   \n",
       "4329211 2024-05-29 23:30:11.747780                 70.0                  0   \n",
       "4329212 2024-05-29 23:33:36.123356                  NaN                  0   \n",
       "4329213 2024-05-29 23:38:52.357817                 12.0                  0   \n",
       "4329214 2024-05-29 23:48:47.573603                  1.0                  0   \n",
       "\n",
       "         execution_duration_ms  \\\n",
       "0                           82   \n",
       "1                         1580   \n",
       "2                          839   \n",
       "3                          944   \n",
       "4                          464   \n",
       "...                        ...   \n",
       "4329210                    320   \n",
       "4329211                     72   \n",
       "4329212                      2   \n",
       "4329213                     21   \n",
       "4329214                     10   \n",
       "\n",
       "                                       feature_fingerprint  ...  \\\n",
       "0        c7eb643698bc57d7571700717413c12dc5f533291e1312...  ...   \n",
       "1        58dba2ec912c82b5f28cea09b165b0b88e53c413351b6b...  ...   \n",
       "2                                                     None  ...   \n",
       "3                                                     None  ...   \n",
       "4        6325e1482968eae895cc640d7c9f29d0d4a26132c80042...  ...   \n",
       "...                                                    ...  ...   \n",
       "4329210  ebb731afb39e004dfa53854adfc2c029d374051f2eb9c1...  ...   \n",
       "4329211  49a66d7b66a6a2b17f9b8a2d2a4a10e53b0efe6ba8e88f...  ...   \n",
       "4329212                                               None  ...   \n",
       "4329213  43fca7b243173b30e3234dd3582cf6888a6dca8a464d3b...  ...   \n",
       "4329214  c4d4f484ba984732d65f3995c66ea3164ce170f809d5bd...  ...   \n",
       "\n",
       "         num_permanent_tables_accessed  num_external_tables_accessed  \\\n",
       "0                                  1.0                           0.0   \n",
       "1                                  1.0                           1.0   \n",
       "2                                  1.0                           0.0   \n",
       "3                                  1.0                           0.0   \n",
       "4                                  0.0                           1.0   \n",
       "...                                ...                           ...   \n",
       "4329210                            1.0                           0.0   \n",
       "4329211                            0.0                           0.0   \n",
       "4329212                            1.0                           0.0   \n",
       "4329213                            1.0                           0.0   \n",
       "4329214                            1.0                           0.0   \n",
       "\n",
       "         num_system_tables_accessed read_table_ids  write_table_ids  \\\n",
       "0                               0.0             12               12   \n",
       "1                               0.0           None               12   \n",
       "2                               0.0         150804             None   \n",
       "3                               0.0         171990             None   \n",
       "4                               0.0           None             None   \n",
       "...                             ...            ...              ...   \n",
       "4329210                         0.0       127684,5             None   \n",
       "4329211                         0.0           None           109431   \n",
       "4329212                         0.0           None             None   \n",
       "4329213                         0.0       186850,2             None   \n",
       "4329214                         0.0              1             None   \n",
       "\n",
       "         mbytes_scanned  mbytes_spilled num_joins num_scans  num_aggregations  \n",
       "0                 123.0             0.0         0         1                 2  \n",
       "1                  26.0             0.0         0         0                 2  \n",
       "2                1944.0             0.0         0         1                 2  \n",
       "3                1961.0             0.0         0         1                 2  \n",
       "4                   0.0             0.0         0         0                 0  \n",
       "...                 ...             ...       ...       ...               ...  \n",
       "4329210          1037.0             0.0         1         2                 0  \n",
       "4329211             0.0             0.0         0         0                 2  \n",
       "4329212             NaN             NaN         0         0                 0  \n",
       "4329213           219.0             0.0         1         2                 0  \n",
       "4329214            10.0             0.0         0         1                 0  \n",
       "\n",
       "[4329215 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "redshift_sample = duckdb.query(\"select * from 'redset/provisioned/parts/*.parquet' using sample 1 percent (bernoulli)\").df()\n",
    "redshift_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query_type</th>\n",
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       "      <td>0.286966</td>\n",
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       "      <td>0.165365</td>\n",
       "      <td>0.167070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>copy</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100758</td>\n",
       "      <td>0.100914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>delete</td>\n",
       "      <td>0.029675</td>\n",
       "      <td>0.029658</td>\n",
       "      <td>0.029676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>update</td>\n",
       "      <td>0.029285</td>\n",
       "      <td>0.029216</td>\n",
       "      <td>0.029288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>analyze</td>\n",
       "      <td>0.015770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>vacuum</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.013629</td>\n",
       "      <td>0.014166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>0.001236</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0.002673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  query_type     reads    writes      cost\n",
       "0       ctas  0.270622  0.278656  0.286966\n",
       "1     select  0.147961  0.000000  0.180584\n",
       "2     unload  0.153965  0.157082  0.169176\n",
       "3     insert  0.150272  0.165365  0.167070\n",
       "4       copy  0.000000  0.100758  0.100914\n",
       "5     delete  0.029675  0.029658  0.029676\n",
       "6     update  0.029285  0.029216  0.029288\n",
       "7    analyze  0.015770  0.000000  0.019488\n",
       "8     vacuum  0.000229  0.013629  0.014166\n",
       "9      other  0.001236  0.001173  0.002673"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_sum = duckdb.query(\n",
    "\"\"\"\n",
    "select \n",
    "    query_type, \n",
    "    sum(if(read_table_ids is not null, execution_duration_ms * cluster_size, 0)) as reads,\n",
    "    sum(if(write_table_ids is not null, execution_duration_ms * cluster_size, 0)) as writes,\n",
    "    sum(execution_duration_ms * cluster_size) as cost\n",
    "from 'redset/provisioned/parts/*.parquet'\n",
    "group by all\n",
    "order by cost desc\n",
    "\"\"\"\n",
    ").df()\n",
    "rs_sum['reads'] = rs_sum['reads'] / rs_sum['cost'].sum()\n",
    "rs_sum['writes'] = rs_sum['writes'] / rs_sum['cost'].sum()\n",
    "rs_sum['cost'] = rs_sum['cost'] / rs_sum['cost'].sum()\n",
    "rs_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-9d559e12fc8f4228ac03d94b319e8a6b.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-9d559e12fc8f4228ac03d94b319e8a6b.vega-embed details,\n",
       "  #altair-viz-9d559e12fc8f4228ac03d94b319e8a6b.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-9d559e12fc8f4228ac03d94b319e8a6b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-9d559e12fc8f4228ac03d94b319e8a6b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-9d559e12fc8f4228ac03d94b319e8a6b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-2f944770e0820b4e4c32decdd25fe90a\"}, \"mark\": {\"type\": \"bar\", \"color\": \"red\"}, \"encoding\": {\"x\": {\"field\": \"Cost\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Query Type\", \"sort\": [\"Ingest\", \"Transformation\", \"Read\", \"Export\", \"Other\"], \"type\": \"nominal\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.17.0.json\", \"datasets\": {\"data-2f944770e0820b4e4c32decdd25fe90a\": [{\"Query Type\": \"Export\", \"Cost\": 0.1691760695789889}, {\"Query Type\": \"Other\", \"Cost\": 0.036327413780600816}, {\"Query Type\": \"Read\", \"Cost\": 0.1805836019868359}, {\"Query Type\": \"Transformation\", \"Cost\": 0.286965552512138}, {\"Query Type\": \"Ingest\", \"Cost\": 0.3269473621414364}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "redshift_costs = duckdb.query(\n",
    "\"\"\"\n",
    "select \n",
    "    case when query_type in ('insert', 'copy', 'delete', 'update') then 'Ingest'\n",
    "         when query_type in ('ctas') then 'Transformation'\n",
    "         when query_type in ('select') then 'Read'\n",
    "         when query_type in ('unload') then 'Export'\n",
    "         when query_type in ('analyze', 'vacuum', 'other') then 'Other'\n",
    "         else error(query_type) end as \"Query Type\",\n",
    "    sum(execution_duration_ms * cluster_size) as \"Cost\"\n",
    "from 'redset/provisioned/parts/*.parquet'\n",
    "where cluster_size is not null\n",
    "group by all\n",
    "\"\"\"\n",
    ").df()\n",
    "redshift_costs['Cost'] = redshift_costs['Cost'] / redshift_costs['Cost'].sum()\n",
    "\n",
    "alt.Chart(redshift_costs).mark_bar(color='red').encode(\n",
    "    x='Cost', \n",
    "    y=alt.Y('Query Type', sort=[\"Ingest\", \"Transformation\", \"Read\", \"Export\", \"Other\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-f9c9919211e8406aba206a9bff502687.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-f9c9919211e8406aba206a9bff502687.vega-embed details,\n",
       "  #altair-viz-f9c9919211e8406aba206a9bff502687.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-f9c9919211e8406aba206a9bff502687\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-f9c9919211e8406aba206a9bff502687\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-f9c9919211e8406aba206a9bff502687\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-55d3b661dc96d5dfb7136b94a2018760\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"color\": {\"field\": \"Warehouse\", \"scale\": {\"scheme\": \"paired\"}, \"type\": \"nominal\"}, \"x\": {\"field\": \"Cost\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Query Type\", \"sort\": [\"Ingest\", \"Transformation\", \"Read\", \"Export\", \"Other\"], \"type\": \"nominal\"}, \"yOffset\": {\"field\": \"Warehouse\", \"type\": \"nominal\"}}, \"height\": 200, \"width\": 200, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.17.0.json\", \"datasets\": {\"data-55d3b661dc96d5dfb7136b94a2018760\": [{\"Warehouse\": \"Snowflake\", \"Query Type\": \"Read\", \"Cost\": 0.23098777336467546}, {\"Warehouse\": \"Snowflake\", \"Query Type\": \"Transformation\", \"Cost\": 0.5440187799190942}, {\"Warehouse\": \"Snowflake\", \"Query Type\": \"Export\", \"Cost\": 0.025564577133790956}, {\"Warehouse\": \"Snowflake\", \"Query Type\": \"Ingest\", \"Cost\": 0.1975240032067743}, {\"Warehouse\": \"Snowflake\", \"Query Type\": \"Other\", \"Cost\": 0.0019048663756651187}, {\"Warehouse\": \"Redshift\", \"Query Type\": \"Export\", \"Cost\": 0.1691760695789889}, {\"Warehouse\": \"Redshift\", \"Query Type\": \"Other\", \"Cost\": 0.036327413780600816}, {\"Warehouse\": \"Redshift\", \"Query Type\": \"Read\", \"Cost\": 0.1805836019868359}, {\"Warehouse\": \"Redshift\", \"Query Type\": \"Transformation\", \"Cost\": 0.286965552512138}, {\"Warehouse\": \"Redshift\", \"Query Type\": \"Ingest\", \"Cost\": 0.3269473621414364}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_costs = duckdb.query(\n",
    "\"\"\"\n",
    "select 'Snowflake' as \"Warehouse\", *\n",
    "from sf_costs\n",
    "union all\n",
    "select 'Redshift' as \"Warehouse\", *\n",
    "from redshift_costs\n",
    "\"\"\"\n",
    ").df()\n",
    "\n",
    "alt.Chart(combined_costs).mark_bar().encode(\n",
    "    color=alt.Color('Warehouse').scale(scheme='paired'),\n",
    "    yOffset='Warehouse',\n",
    "    x='Cost', \n",
    "    y=alt.Y('Query Type', sort=[\"Ingest\", \"Transformation\", \"Read\", \"Export\", \"Other\"])\n",
    ").properties(\n",
    "    width=200,\n",
    "    height=200,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-f3282d88df6a4296ab07b579f58ad7d0.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-f3282d88df6a4296ab07b579f58ad7d0.vega-embed details,\n",
       "  #altair-viz-f3282d88df6a4296ab07b579f58ad7d0.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-f3282d88df6a4296ab07b579f58ad7d0\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-f3282d88df6a4296ab07b579f58ad7d0\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-f3282d88df6a4296ab07b579f58ad7d0\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-8b6981af2edf03a6098243ce3cbdd482\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"x\": {\"field\": \"count\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"vCPU\", \"type\": \"nominal\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.17.0.json\", \"datasets\": {\"data-8b6981af2edf03a6098243ce3cbdd482\": [{\"vCPU\": 8, \"count\": 221415}, {\"vCPU\": 8, \"count\": 814994}, {\"vCPU\": 8, \"count\": 1950442}, {\"vCPU\": 8, \"count\": 12266290}, {\"vCPU\": 16, \"count\": 276697}, {\"vCPU\": 16, \"count\": 402603}, {\"vCPU\": 16, \"count\": 1615171}, {\"vCPU\": 16, \"count\": 8207010}, {\"vCPU\": 32, \"count\": 134293}, {\"vCPU\": 32, \"count\": 282311}, {\"vCPU\": 32, \"count\": 551435}, {\"vCPU\": 32, \"count\": 4550876}, {\"vCPU\": 64, \"count\": 95415}, {\"vCPU\": 64, \"count\": 186360}, {\"vCPU\": 64, \"count\": 915366}, {\"vCPU\": 64, \"count\": 5477515}, {\"vCPU\": 128, \"count\": 613}, {\"vCPU\": 128, \"count\": 45657}, {\"vCPU\": 128, \"count\": 122953}, {\"vCPU\": 128, \"count\": 7423995}, {\"vCPU\": 256, \"count\": 268}, {\"vCPU\": 256, \"count\": 78875}, {\"vCPU\": 256, \"count\": 443620}, {\"vCPU\": 512, \"count\": 310}, {\"vCPU\": 512, \"count\": 395439}, {\"vCPU\": 1024, \"count\": 10220}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import duckdb\n",
    "import altair as alt\n",
    "\n",
    "sf_sizes = duckdb.query(\n",
    "\"\"\"\n",
    "select warehouseSize * perServerCores as vCPU, count(*) as count \n",
    "from 'snowset/part.*.parquet' \n",
    "group by all \n",
    "having vCPU >= 8 \n",
    "order by all\"\"\"\n",
    ").df()\n",
    "\n",
    "alt.Chart(sf_sizes).mark_bar().encode(x='count:Q', y='vCPU:N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-96078087e26646439524e07726df915e.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-96078087e26646439524e07726df915e.vega-embed details,\n",
       "  #altair-viz-96078087e26646439524e07726df915e.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-96078087e26646439524e07726df915e\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-96078087e26646439524e07726df915e\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-96078087e26646439524e07726df915e\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-440c30259d45e74531cab3732fa3cfa2\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"x\": {\"field\": \"mb_scanned\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"p\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.17.0.json\", \"datasets\": {\"data-440c30259d45e74531cab3732fa3cfa2\": [{\"log_mb_scanned\": 0.0, \"mb_scanned\": 1.0, \"count\": 704823, \"p\": 0.04101435525419342}, {\"log_mb_scanned\": 1.0, \"mb_scanned\": 2.0, \"count\": 991426, \"p\": 0.05769207045207657}, {\"log_mb_scanned\": 2.0, \"mb_scanned\": 4.0, \"count\": 1321491, \"p\": 0.07689888289573313}, {\"log_mb_scanned\": 3.0, \"mb_scanned\": 8.0, \"count\": 1706930, \"p\": 0.09932796377819732}, {\"log_mb_scanned\": 4.0, \"mb_scanned\": 16.0, \"count\": 2015200, \"p\": 0.11726650337496163}, {\"log_mb_scanned\": 5.0, \"mb_scanned\": 32.0, \"count\": 1593707, \"p\": 0.09273940417536719}, {\"log_mb_scanned\": 6.0, \"mb_scanned\": 64.0, \"count\": 1687045, \"p\": 0.09817083574147088}, {\"log_mb_scanned\": 7.0, \"mb_scanned\": 128.0, \"count\": 1612994, \"p\": 0.09386173399404171}, {\"log_mb_scanned\": 8.0, \"mb_scanned\": 256.0, \"count\": 2278338, \"p\": 0.13257876675580751}, {\"log_mb_scanned\": 9.0, \"mb_scanned\": 512.0, \"count\": 1440453, \"p\": 0.08382140064806153}, {\"log_mb_scanned\": 10.0, \"mb_scanned\": 1024.0, \"count\": 729068, \"p\": 0.042425196051298394}, {\"log_mb_scanned\": 11.0, \"mb_scanned\": 2048.0, \"count\": 420833, \"p\": 0.02448869313953713}, {\"log_mb_scanned\": 12.0, \"mb_scanned\": 4096.0, \"count\": 233228, \"p\": 0.013571770568249082}, {\"log_mb_scanned\": 13.0, \"mb_scanned\": 8192.0, \"count\": 200350, \"p\": 0.011658566867394582}, {\"log_mb_scanned\": 14.0, \"mb_scanned\": 16384.0, \"count\": 103173, \"p\": 0.00600374005195758}, {\"log_mb_scanned\": 15.0, \"mb_scanned\": 32768.0, \"count\": 71722, \"p\": 0.004173574908226974}, {\"log_mb_scanned\": 16.0, \"mb_scanned\": 65536.0, \"count\": 37511, \"p\": 0.0021828026042567416}, {\"log_mb_scanned\": 17.0, \"mb_scanned\": 131072.0, \"count\": 20538, \"p\": 0.0011951267597831291}, {\"log_mb_scanned\": 18.0, \"mb_scanned\": 262144.0, \"count\": 8291, \"p\": 0.0004824615817198327}, {\"log_mb_scanned\": 19.0, \"mb_scanned\": 524288.0, \"count\": 4977, \"p\": 0.000289616607432108}, {\"log_mb_scanned\": 20.0, \"mb_scanned\": 1048576.0, \"count\": 1789, \"p\": 0.00010410369915532272}, {\"log_mb_scanned\": 21.0, \"mb_scanned\": 2097152.0, \"count\": 706, \"p\": 4.1082846061295605e-05}, {\"log_mb_scanned\": 22.0, \"mb_scanned\": 4194304.0, \"count\": 103, \"p\": 5.99367300894256e-06}, {\"log_mb_scanned\": 23.0, \"mb_scanned\": 8388608.0, \"count\": 65, \"p\": 3.782415005643363e-06}, {\"log_mb_scanned\": 24.0, \"mb_scanned\": 16777216.0, \"count\": 21, \"p\": 1.2220110018232403e-06}, {\"log_mb_scanned\": 25.0, \"mb_scanned\": 33554432.0, \"count\": 5, \"p\": 2.9095500043410484e-07}, {\"log_mb_scanned\": 26.0, \"mb_scanned\": 67108864.0, \"count\": 1, \"p\": 5.819100008682097e-08}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_scanned = duckdb.query(\n",
    "\"\"\"\n",
    "select floor(log2((persistentReadBytesS3 + persistentReadBytesCache) / 1024 / 1024)) as log_mb_scanned, 2**log_mb_scanned as mb_scanned, count(*) as count \n",
    "from 'snowset/part.*.parquet' \n",
    "where persistentWriteBytesS3 = 0\n",
    "and profRemoteExtRead == 0\n",
    "and profRemoteExtWrite == 0\n",
    "and persistentReadBytesS3 + persistentReadBytesCache >= 1024*1024 \n",
    "group by all \n",
    "order by all\n",
    "\"\"\"\n",
    ").df()\n",
    "sf_scanned['p'] = sf_scanned['count'] / sf_scanned['count'].sum()\n",
    "\n",
    "alt.Chart(sf_scanned).mark_bar().encode(x='mb_scanned:O', y='p:Q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-fecf4416b13d40248bddf75a11178542.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-fecf4416b13d40248bddf75a11178542.vega-embed details,\n",
       "  #altair-viz-fecf4416b13d40248bddf75a11178542.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-fecf4416b13d40248bddf75a11178542\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-fecf4416b13d40248bddf75a11178542\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-fecf4416b13d40248bddf75a11178542\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-86b33f0c924ad45ce7772f2313344e8c\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"x\": {\"field\": \"mb_scanned\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"p\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.17.0.json\", \"datasets\": {\"data-86b33f0c924ad45ce7772f2313344e8c\": [{\"log_mb_scanned\": 0.0, \"mb_scanned\": 1.0, \"count\": 911749, \"p\": 0.01440907219886302}, {\"log_mb_scanned\": 1.0, \"mb_scanned\": 2.0, \"count\": 1611603, \"p\": 0.02546940438969962}, {\"log_mb_scanned\": 2.0, \"mb_scanned\": 4.0, \"count\": 2630750, \"p\": 0.04157576996208265}, {\"log_mb_scanned\": 3.0, \"mb_scanned\": 8.0, \"count\": 3158503, \"p\": 0.04991625739904891}, {\"log_mb_scanned\": 4.0, \"mb_scanned\": 16.0, \"count\": 5664773, \"p\": 0.08952477397526058}, {\"log_mb_scanned\": 5.0, \"mb_scanned\": 32.0, \"count\": 10134525, \"p\": 0.16016370999713983}, {\"log_mb_scanned\": 6.0, \"mb_scanned\": 64.0, \"count\": 8941911, \"p\": 0.14131591172001004}, {\"log_mb_scanned\": 7.0, \"mb_scanned\": 128.0, \"count\": 8548584, \"p\": 0.1350998619730268}, {\"log_mb_scanned\": 8.0, \"mb_scanned\": 256.0, \"count\": 6576295, \"p\": 0.10393025871815804}, {\"log_mb_scanned\": 9.0, \"mb_scanned\": 512.0, \"count\": 7552248, \"p\": 0.11935399621575549}, {\"log_mb_scanned\": 10.0, \"mb_scanned\": 1024.0, \"count\": 3554832, \"p\": 0.056179750065893826}, {\"log_mb_scanned\": 11.0, \"mb_scanned\": 2048.0, \"count\": 2089861, \"p\": 0.03302768419223719}, {\"log_mb_scanned\": 12.0, \"mb_scanned\": 4096.0, \"count\": 868671, \"p\": 0.01372827736148714}, {\"log_mb_scanned\": 13.0, \"mb_scanned\": 8192.0, \"count\": 402218, \"p\": 0.006356561072929376}, {\"log_mb_scanned\": 14.0, \"mb_scanned\": 16384.0, \"count\": 279503, \"p\": 0.004417201342473433}, {\"log_mb_scanned\": 15.0, \"mb_scanned\": 32768.0, \"count\": 142051, \"p\": 0.002244941442130116}, {\"log_mb_scanned\": 16.0, \"mb_scanned\": 65536.0, \"count\": 116852, \"p\": 0.0018467022224115865}, {\"log_mb_scanned\": 17.0, \"mb_scanned\": 131072.0, \"count\": 21600, \"p\": 0.00034136144870511645}, {\"log_mb_scanned\": 18.0, \"mb_scanned\": 262144.0, \"count\": 30744, \"p\": 0.0004858711286569491}, {\"log_mb_scanned\": 19.0, \"mb_scanned\": 524288.0, \"count\": 22701, \"p\": 0.00035876140032661336}, {\"log_mb_scanned\": 20.0, \"mb_scanned\": 1048576.0, \"count\": 12417, \"p\": 0.0001962354216931218}, {\"log_mb_scanned\": 21.0, \"mb_scanned\": 2097152.0, \"count\": 2055, \"p\": 3.247674893930622e-05}, {\"log_mb_scanned\": 22.0, \"mb_scanned\": 4194304.0, \"count\": 1155, \"p\": 1.82533552432597e-05}, {\"log_mb_scanned\": 23.0, \"mb_scanned\": 8388608.0, \"count\": 367, \"p\": 5.799983873832303e-06}, {\"log_mb_scanned\": 24.0, \"mb_scanned\": 16777216.0, \"count\": 63, \"p\": 9.956375587232564e-07}, {\"log_mb_scanned\": 25.0, \"mb_scanned\": 33554432.0, \"count\": 6, \"p\": 9.482262464031012e-08}, {\"log_mb_scanned\": 27.0, \"mb_scanned\": 134217728.0, \"count\": 1, \"p\": 1.580377077338502e-08}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_scanned = duckdb.query(\n",
    "\"\"\"\n",
    "select floor(log2(mbytes_scanned)) as log_mb_scanned, 2**log_mb_scanned as mb_scanned, count(*) as count \n",
    "from 'redset/provisioned/parts/*.parquet' \n",
    "where query_type = 'select' \n",
    "and mbytes_scanned >= 1\n",
    "and num_permanent_tables_accessed > 0\n",
    "group by all \n",
    "order by all\n",
    "\"\"\"\n",
    ").df()\n",
    "rs_scanned['p'] = rs_scanned['count'] / rs_scanned['count'].sum()\n",
    "\n",
    "alt.Chart(rs_scanned).mark_bar().encode(x='mb_scanned:O', y='p:Q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-69d442e7f7114d95bd78504fccdd1228.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-69d442e7f7114d95bd78504fccdd1228.vega-embed details,\n",
       "  #altair-viz-69d442e7f7114d95bd78504fccdd1228.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-69d442e7f7114d95bd78504fccdd1228\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-69d442e7f7114d95bd78504fccdd1228\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-69d442e7f7114d95bd78504fccdd1228\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-71353c2d8b36d485f76145748e76454f\"}, \"mark\": {\"type\": \"bar\"}, \"encoding\": {\"color\": {\"field\": \"warehouse\", \"scale\": {\"scheme\": \"paired\"}, \"title\": \"System\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"labelExpr\": \"datum.value < 1024 ? datum.value + \\\" MB\\\" : datum.value < 1024*1024 ? datum.value/1024 + \\\" GB\\\" : \\\"1 TB+\\\"\"}, \"field\": \"mb_scanned\", \"title\": \"Data Scanned\", \"type\": \"ordinal\"}, \"xOffset\": {\"field\": \"warehouse\", \"type\": \"nominal\"}, \"y\": {\"field\": \"p\", \"title\": \"Query Fraction\", \"type\": \"quantitative\"}}, \"height\": 200, \"width\": 400, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.17.0.json\", \"datasets\": {\"data-71353c2d8b36d485f76145748e76454f\": [{\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 0.0, \"count\": 704823, \"p\": 0.04101435525419342, \"mb_scanned\": 1.0, \"label\": \"1 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 1.0, \"count\": 991426, \"p\": 0.05769207045207657, \"mb_scanned\": 2.0, \"label\": \"2 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 2.0, \"count\": 1321491, \"p\": 0.07689888289573313, \"mb_scanned\": 4.0, \"label\": \"4 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 3.0, \"count\": 1706930, \"p\": 0.09932796377819732, \"mb_scanned\": 8.0, \"label\": \"8 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 4.0, \"count\": 2015200, \"p\": 0.11726650337496163, \"mb_scanned\": 16.0, \"label\": \"16 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 5.0, \"count\": 1593707, \"p\": 0.09273940417536719, \"mb_scanned\": 32.0, \"label\": \"32 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 6.0, \"count\": 1687045, \"p\": 0.09817083574147088, \"mb_scanned\": 64.0, \"label\": \"64 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 7.0, \"count\": 1612994, \"p\": 0.09386173399404171, \"mb_scanned\": 128.0, \"label\": \"128 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 8.0, \"count\": 2278338, \"p\": 0.13257876675580751, \"mb_scanned\": 256.0, \"label\": \"256 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 9.0, \"count\": 1440453, \"p\": 0.08382140064806153, \"mb_scanned\": 512.0, \"label\": \"512 MB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 10.0, \"count\": 729068, \"p\": 0.042425196051298394, \"mb_scanned\": 1024.0, \"label\": \"1 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 11.0, \"count\": 420833, \"p\": 0.02448869313953713, \"mb_scanned\": 2048.0, \"label\": \"2 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 12.0, \"count\": 233228, \"p\": 0.013571770568249082, \"mb_scanned\": 4096.0, \"label\": \"4 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 13.0, \"count\": 200350, \"p\": 0.011658566867394582, \"mb_scanned\": 8192.0, \"label\": \"8 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 14.0, \"count\": 103173, \"p\": 0.00600374005195758, \"mb_scanned\": 16384.0, \"label\": \"16 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 15.0, \"count\": 71722, \"p\": 0.004173574908226974, \"mb_scanned\": 32768.0, \"label\": \"32 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 16.0, \"count\": 37511, \"p\": 0.0021828026042567416, \"mb_scanned\": 65536.0, \"label\": \"64 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 17.0, \"count\": 20538, \"p\": 0.0011951267597831291, \"mb_scanned\": 131072.0, \"label\": \"128 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 18.0, \"count\": 8291, \"p\": 0.0004824615817198327, \"mb_scanned\": 262144.0, \"label\": \"256 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 19.0, \"count\": 4977, \"p\": 0.000289616607432108, \"mb_scanned\": 524288.0, \"label\": \"512 GB\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 20.0, \"count\": 1789, \"p\": 0.00010410369915532272, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 21.0, \"count\": 706, \"p\": 4.1082846061295605e-05, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 22.0, \"count\": 103, \"p\": 5.99367300894256e-06, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 23.0, \"count\": 65, \"p\": 3.782415005643363e-06, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 24.0, \"count\": 21, \"p\": 1.2220110018232403e-06, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 25.0, \"count\": 5, \"p\": 2.9095500043410484e-07, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Snowflake\", \"log_mb_scanned\": 26.0, \"count\": 1, \"p\": 5.819100008682097e-08, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 0.0, \"count\": 911749, \"p\": 0.01440907219886302, \"mb_scanned\": 1.0, \"label\": \"1 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 1.0, \"count\": 1611603, \"p\": 0.02546940438969962, \"mb_scanned\": 2.0, \"label\": \"2 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 2.0, \"count\": 2630750, \"p\": 0.04157576996208265, \"mb_scanned\": 4.0, \"label\": \"4 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 3.0, \"count\": 3158503, \"p\": 0.04991625739904891, \"mb_scanned\": 8.0, \"label\": \"8 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 4.0, \"count\": 5664773, \"p\": 0.08952477397526058, \"mb_scanned\": 16.0, \"label\": \"16 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 5.0, \"count\": 10134525, \"p\": 0.16016370999713983, \"mb_scanned\": 32.0, \"label\": \"32 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 6.0, \"count\": 8941911, \"p\": 0.14131591172001004, \"mb_scanned\": 64.0, \"label\": \"64 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 7.0, \"count\": 8548584, \"p\": 0.1350998619730268, \"mb_scanned\": 128.0, \"label\": \"128 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 8.0, \"count\": 6576295, \"p\": 0.10393025871815804, \"mb_scanned\": 256.0, \"label\": \"256 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 9.0, \"count\": 7552248, \"p\": 0.11935399621575549, \"mb_scanned\": 512.0, \"label\": \"512 MB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 10.0, \"count\": 3554832, \"p\": 0.056179750065893826, \"mb_scanned\": 1024.0, \"label\": \"1 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 11.0, \"count\": 2089861, \"p\": 0.03302768419223719, \"mb_scanned\": 2048.0, \"label\": \"2 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 12.0, \"count\": 868671, \"p\": 0.01372827736148714, \"mb_scanned\": 4096.0, \"label\": \"4 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 13.0, \"count\": 402218, \"p\": 0.006356561072929376, \"mb_scanned\": 8192.0, \"label\": \"8 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 14.0, \"count\": 279503, \"p\": 0.004417201342473433, \"mb_scanned\": 16384.0, \"label\": \"16 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 15.0, \"count\": 142051, \"p\": 0.002244941442130116, \"mb_scanned\": 32768.0, \"label\": \"32 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 16.0, \"count\": 116852, \"p\": 0.0018467022224115865, \"mb_scanned\": 65536.0, \"label\": \"64 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 17.0, \"count\": 21600, \"p\": 0.00034136144870511645, \"mb_scanned\": 131072.0, \"label\": \"128 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 18.0, \"count\": 30744, \"p\": 0.0004858711286569491, \"mb_scanned\": 262144.0, \"label\": \"256 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 19.0, \"count\": 22701, \"p\": 0.00035876140032661336, \"mb_scanned\": 524288.0, \"label\": \"512 GB\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 20.0, \"count\": 12417, \"p\": 0.0001962354216931218, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 21.0, \"count\": 2055, \"p\": 3.247674893930622e-05, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 22.0, \"count\": 1155, \"p\": 1.82533552432597e-05, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 23.0, \"count\": 367, \"p\": 5.799983873832303e-06, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 24.0, \"count\": 63, \"p\": 9.956375587232564e-07, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 25.0, \"count\": 6, \"p\": 9.482262464031012e-08, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}, {\"warehouse\": \"Redshift\", \"log_mb_scanned\": 27.0, \"count\": 1, \"p\": 1.580377077338502e-08, \"mb_scanned\": 1048576.0, \"label\": \"1 TB+\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_scanned = duckdb.query(\n",
    "\"\"\"\n",
    "with combined as (\n",
    "    select 'Snowflake' as warehouse, * from sf_scanned\n",
    "    union all\n",
    "    select 'Redshift' as warehouse, * from rs_scanned\n",
    ")\n",
    "select \n",
    "    * exclude (mb_scanned), \n",
    "    least(mb_scanned, 2**20) as mb_scanned,\n",
    "    case when mb_scanned < 1024 then format('{:d} MB', mb_scanned::int)\n",
    "        when mb_scanned < 1024*1024 then format('{:d} GB', (mb_scanned/1024)::int)\n",
    "        else '1 TB+' end as label\n",
    "from combined\n",
    "\"\"\"\n",
    ").df()\n",
    "labels = {2**i: f\"{2**i} MB\" for i in range(0, 10)} | {2**i: f\"{2**(i-10)} GB\" for i in range(10,20)} | {2**20: f\"1 TB+\"}\n",
    "\n",
    "alt.Chart(combined_scanned).mark_bar().encode(\n",
    "    color=alt.Color('warehouse').scale(scheme='paired').title('System'),\n",
    "    xOffset='warehouse',\n",
    "    x=alt.X('mb_scanned:O').title('Data Scanned').axis(labelExpr='datum.value < 1024 ? datum.value + \" MB\" : datum.value < 1024*1024 ? datum.value/1024 + \" GB\" : \"1 TB+\"'), \n",
    "    y=alt.Y('p:Q').title('Query Fraction')\n",
    ").properties(\n",
    "    width=400,\n",
    "    height=200,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>warehouse</th>\n",
       "      <th>q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Redshift</td>\n",
       "      <td>[108.0, 303759.0009999871]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Snowflake</td>\n",
       "      <td>[70.6396484375, 253167.99369376266]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   warehouse                                    q\n",
       "0   Redshift           [108.0, 303759.0009999871]\n",
       "1  Snowflake  [70.6396484375, 253167.99369376266]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.query(\n",
    "\"\"\"\n",
    "select 'Redshift' as warehouse, quantile_cont(mbytes_scanned, [0.5, 0.999]) as q\n",
    "from 'redset/provisioned/parts/*.parquet' \n",
    "where query_type = 'select' \n",
    "and mbytes_scanned >= 1\n",
    "and num_permanent_tables_accessed > 0\n",
    "\n",
    "union all\n",
    "\n",
    "select 'Snowflake' as warehouse, quantile_cont((persistentReadBytesS3 + persistentReadBytesCache) / 1024 / 1024, [0.5, 0.999]) as q\n",
    "from 'snowset/part.*.parquet' \n",
    "where persistentWriteBytesS3 = 0\n",
    "and profRemoteExtRead == 0\n",
    "and profRemoteExtWrite == 0\n",
    "and persistentReadBytesS3 + persistentReadBytesCache >= 1024*1024 \n",
    "\"\"\"\n",
    ").df()"
   ]
  }
 ],
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