workbooks/exploring_httparchive-all-requests_tables.ipynb

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   "source": [
    "from google.colab import auth\n",
    "auth.authenticate_user()"
   ]
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    "id": "HfqMMAve6r8Q"
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   "source": [
    "GCP_PROJECT = 'httparchive'  # @param {type: \"string\"}"
   ]
  },
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   "source": [
    "The [`requests`](https://har.fyi/reference/tables/requests/) tables contain details about all HTTP requests made by the 24.5 million pages tracked in the archive. This dataset is quite large, as you can see by the aggregate query that counts all rows in the table"
   ]
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       "   requests_total\n",
       "0      2393199079"
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   "source": [
    "# This query will process 38 GB when run.\n",
    "%%bigquery --project {GCP_PROJECT}\n",
    "SELECT COUNT(0) AS requests_total\n",
    "FROM `httparchive.crawl.requests`\n",
    "WHERE date = \"2024-05-01\"\n",
    "  AND client = 'desktop'"
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   "source": [
    "In [Part 1](https://colab.research.google.com/github/HTTPArchive/har.fyi/blob/main/workbooks/exploring_httparchive-all-pages_tables.ipynb) we looked at a sample of what the `pages` table data looked like. The following table shows a sample of a row from the `requests` table. The url is a unique identifier for each request.  The page corresponds to the page column in the `pages` table, and can be used to relate the two datasets. There are also numerous other columns for collecting information from request and response headers, types of objects, and even a partial dump of response body in the `response_body` column."
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class=\\\"a5RLac\\\"><span></span></div></div><style>.MagqMc .ZFiwCf{background-color:#fff;border:1px solid var(--gS5jXb);width:100%}.MagqMc.U48fD{padding:0;margin-top:16px}.MagqMc .Bi9oQd{display:none}.MagqMc{padding:0}.MagqMc:hover .LGwnxb{color:var(--YLNNHc)}.sOmPcf .ZFiwCf{background-color:#fafafa}</style><div class=\\\"Sz7Lee MagqMc U48fD\\\" style=\\\"display:none\\\" aria-label=\\\"See more\\\" role=\\\"button\\\" tabindex=\\\"0\\\"><style>.U48fD{-webkit-tap-highlight-color:transparent;cursor:pointer;display:block;line-height:18px;text-overflow:ellipsis;white-space:nowrap;padding:16px;padding-top:0;margin-top:24px;position:relative}.TQc1id .U48fD{margin-top:16px}.U48fD.df13ud{margin-top:16px}.U48fD.TOQyFc{margin-top:0}.U48fD.p8FEIf{padding-bottom:0}.U48fD.ke7M4{padding-left:0;padding-right:0}.jRKCUd::before{bottom:12px;content:'';left:16px;position:absolute;right:16px;top:-4px}a.jRKCUd:hover{text-decoration:none}</style><style>.ZFiwCf{display:flex;align-items:center;justify-content:center;position:relative;margin:0 auto;font-size:14px;font-family:arial,sans-serif;font-weight:400;width:100%;max-width:300px;height:36px;border-radius:18px;outline:1px solid transparent;background-color:#f1f3f4;}@media (forced-colors:active){.ZFiwCf{border:1px solid transparent}}.TQc1id .ZFiwCf{max-width:unset}.Zjtggb .ZFiwCf{max-width:unset}.ZFiwCf:hover{background-color:#fafafa}.nCFUpc .ZFiwCf{width:100%}.Bi9oQd{background-color:var(--gS5jXb);margin-top:18px;position:absolute;border:0;height:1px;left:0;width:100%}.TQc1id .Bi9oQd{display:none}.kC8B4e .Bi9oQd{display:none}.w2fKdd svg{width:auto}.w2fKdd{color:#5e5e5e}.LGwnxb{overflow:hidden;text-overflow:ellipsis;white-space:nowrap;width:auto;padding-left:0;padding-right:8px;max-width:min(200px,calc(100% - 44px));color:#202124}.LGwnxb:empty{padding-right:0}.LGwnxb span,.LGwnxb div{overflow:hidden;text-overflow:ellipsis;white-space:nowrap;width:auto}</style><hr class=\\\"Bi9oQd\\\" aria-hidden=\\\"true\\\"><div class=\\\"ZFiwCf\\\"><span class=\\\"LGwnxb\\\">See more</span><span class=\\\"w2fKdd z1asCe\\\" aria-hidden=\\\"true\\\" style=\\\"height:20px;line-height:20px;width:20px\\\"><svg focusable=\\\"false\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" viewBox=\\\"0 0 24 24\\\"><path d=\\\"M12 4l-1.41 1.41L16.17 11H4v2h12.17l-5.58 5.59L12 20l8-8z\\\"></path></svg></span></div></div></div><div class=\\\"AQZ9Vd\\\" aria-atomic=\\\"true\\\" role=\\\"button\\\"><style>.JCHpcb:hover,.LvqzR .JCHpcb{color:#1558d6;text-decoration:underline}.JCHpcb{color:#70757a;font:13px arial,sans-serif;cursor:pointer;align-self:center}@media (hover:hover){.sbai{visibility:hidden}.sbhl .sbai{visibility:inherit}}</style><div class=\\\"sbai\\\" role=\\\"presentation\\\">Delete</div></div></div></li><style>#d6ItKb{display:none}.AB2Fdd{display:flex}</style><li class=\\\"AB2Fdd\\\" data-view-type=\\\"9\\\" id=\\\"d6ItKb\\\" role=\\\"presentation\\\"><div class=\\\"eIPGRd\\\"><div class=\\\"ZDHp\\\" id=\\\"fU0xAb\\\" role=\\\"presentation\\\" style=\\\"display:none\\\"><div class=\\\"kZtr1b\\\"><style>.ZDHp{position:relative;margin:20px;display:flex}.DJbVFb,.o6OF0{background:#f8f9fa;border-radius:20px}.o6OF0:hover,.o6OF0.LvqzR{background:#e7e8e9}.o6OF0 .eIPGRd{display:block}@media (forced-colors:none){.o6OF0.sbhl{background:#e7e8e9}}@media (forced-colors:active){.o6OF0.sbhl{background-color:highlight}}.o6OF0 .AQZ9Vd{display:none}.o6OF0 .sbic{display:none}.o6OF0 .pcTkSc{display:none}.o6OF0 .wM6W7d{display:none}.o6OF0 .eIPGRd{max-width:100%;margin:0}.az9Ajc{padding_top:0px}.ZDHp .SHFPkb{margin-bottom:12px}.o6OF0 .SHFPkb{line-height:48px;font-family:Google Sans,arial,sans-serif;font-weight:400;color:var(--YLNNHc);display:-webkit-box;overflow:hidden;-webkit-line-clamp:2;-webkit-box-orient:vertical}.o6OF0 .SHFPkb.ZJ594e{padding-right:58px}.o6OF0 .HrUlUc,.o6OF0 .PnfqLc{font-family:arial,sans-serif;font-weight:400;max-height:72px;color:var(--bbQxAb)}.ZDHp .HrUlUc,.ZDHp .PnfqLc{font-size:18px;line-height:24px}.o6OF0 .bTSf5c{font-family:arial,sans-serif;font-weight:400;color:var(--bbQxAb)}.ZDHp .bTSf5c{line-height:22px;font-size:14px;margin-bottom:6px}.ZDHp .HrUlUc,.ZDHp .PnfqLc{overflow:hidden;display:-webkit-box;-webkit-line-clamp:3;-webkit-box-orient:vertical}.Vlt3wb{font-style:normal;font-family:arial,sans-serif;font-weight:400;font-size:14px;line-height:22px;padding-top:8px;margin-top:12px;color:var(--bbQxAb);border-top:1px solid #dadce0;display:flex;width:100%}.Tnv2td{position:absolute;top:0;right:0}.z76Rnb{padding:6px;width:24px;height:24px;background-color:var(--xhUGwc);color:#5e5e5e;border-radius:9999px;border:1px solid var(--mXZkqc);cursor:pointer}.z76Rnb.LvqzR{color:var(--YLNNHc);background-color:#fafafa}.kZtr1b{display:flex;flex-direction:column;flex-grow:1;min-width:0}.XAFD5c{width:200px;height:200px;background-color:var(--xhUGwc);border-radius:20px;margin-left:20px;flex-shrink:0;background-position:center;background-repeat:no-repeat;background-size:contain}.XAFD5c.iNF0Vd{background-size:136px}.ZDHp .lnnVSe{font-size:18px;flex-grow:1}.ZDHp .HrUlUc,.ZDHp .PnfqLc{display:flex;flex-direction:column}.rnAixd{color:#b3261e}.izxCJf{color:#146c2e}</style><div class=\\\"lnnVSe\\\" aria-atomic=\\\"true\\\" role=\\\"option\\\"><div class=\\\"SHFPkb\\\"></div><div class=\\\"bTSf5c\\\"></div><div class=\\\"PnfqLc\\\"></div><div class=\\\"HrUlUc\\\"></div></div><div class=\\\"Tnv2td\\\" aria-atomic=\\\"true\\\" role=\\\"button\\\" style=\\\"display:none\\\"><span class=\\\"z76Rnb z1asCe JKu1je\\\"><svg focusable=\\\"false\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" viewBox=\\\"0 0 24 24\\\"><path d=\\\"M3 9v6h4l5 5V4L7 9H3zm13.5 3c0-1.77-1.02-3.29-2.5-4.03v8.05c1.48-.73 2.5-2.25 2.5-4.02zM14 3.23v2.06c2.89.86 5 3.54 5 6.71s-2.11 5.85-5 6.71v2.06c4.01-.91 7-4.49 7-8.77s-2.99-7.86-7-8.77z\\\"></path></svg></span></div><div class=\\\"Vlt3wb\\\" style=\\\"display:none\\\"></div></div><span class=\\\"XAFD5c\\\" style=\\\"display:none\\\"></span><div class=\\\"j0GJWd\\\" style=\\\"display:none\\\"><div><img class=\\\"uHGFVd AZNDm\\\" alt=\\\"\\\" style=\\\"display:none\\\"></div><div class=\\\"iQxPRb\\\"><img class=\\\"uHGFVd EOLKOc\\\" alt=\\\"\\\" style=\\\"display:none\\\"><img class=\\\"uHGFVd EOLKOc\\\" alt=\\\"\\\" style=\\\"display:none\\\"></div></div></div></div></li><style>#mitGyb{display:none}.s2Wjec{display:block}.Q82Okf{font-size:16px;font-family:Arial,sans-serif}</style><li data-view-type=\\\"8\\\" class=\\\"sbct PZPZlf\\\" id=\\\"mitGyb\\\" role=\\\"presentation\\\" data-attrid=\\\"AutocompletePrediction\\\" data-entityid=\\\"autocomplete_user_feedback_kp_id\\\"><div class=\\\"eIPGRd hdt0ld\\\"><div class=\\\"sbic\\\"></div><div class=\\\"pcTkSc\\\"><div aria-atomic=\\\"true\\\" class=\\\"lnnVSe\\\" role=\\\"option\\\"><div class=\\\"wM6W7d\\\"><span></span></div><div class=\\\"ClJ9Yb\\\"><span></span></div></div></div><div class=\\\"AQZ9Vd\\\" aria-atomic=\\\"true\\\" role=\\\"button\\\"><div class=\\\"sbai\\\" role=\\\"presentation\\\">Delete</div></div></div></li><div class=\\\"ZDHp\\\" id=\\\"fU0xAb\\\" role=\\\"presentation\\\" style=\\\"display:none\\\"><div class=\\\"kZtr1b\\\"><div class=\\\"lnnVSe\\\" aria-atomic=\\\"true\\\" role=\\\"option\\\"><div class=\\\"SHFPkb\\\"></div><div class=\\\"bTSf5c\\\"></div><div class=\\\"PnfqLc\\\"></div><div class=\\\"HrUlUc\\\"></div></div><div class=\\\"Tnv2td\\\" aria-atomic=\\\"true\\\" role=\\\"button\\\" style=\\\"display:none\\\"><span class=\\\"z76Rnb z1asCe JKu1je\\\"><svg focusable=\\\"false\\\" xmlns=\\\"http://www.w3.org/2000/svg\\\" viewBox=\\\"0 0 24 24\\\"><path d=\\\"M3 9v6h4l5 5V4L7 9H3zm13.5 3c0-1.77-1.02-3.29-2.5-4.03v8.05c1.48-.73 2.5-2.25 2.5-4.02zM14 3.23v2.06c2.89.86 5 3.54 5 6.71s-2.11 5.85-5 6.71v2.06c4.01-.91 7-4.49 7-8.77s-2.99-7.86-7-8.77z\\\"></path></svg></span></div><div class=\\\"Vlt3wb\\\" style=\\\"display:none\\\"></div></div><span class=\\\"XAFD5c\\\" style=\\\"display:none\\\"></span><div class=\\\"j0GJWd\\\" style=\\\"display:none\\\"><div><img class=\\\"uHGFVd AZNDm\\\" alt=\\\"\\\" style=\\\"display:none\\\"></div><div class=\\\"iQxPRb\\\"><img class=\\\"uHGFVd EOLKOc\\\" alt=\\\"\\\" style=\\\"display:none\\\"><img class=\\\"uHGFVd EOLKOc\\\" alt=\\\"\\\" style=\\\"display:none\\\"></div></div></div><style>#TN4rFf{display:none}.IDVnvc{display:inline-block;overflow:hidden;max-width:223px;border-radius:16px;height:178px;margin:-2px 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data-entityid=\\\"autocomplete_user_feedback_kp_id\\\"><div class=\\\"cRV9hb\\\"><div class=\\\"aVbWac\\\"><div class=\\\"sbic\\\"></div></div><div class=\\\"pcTkSc\\\" role=\\\"presentation\\\"><div class=\\\"lnnVSe\\\" aria-atomic=\\\"true\\\" role=\\\"option\\\"><div class=\\\"wM6W7d\\\"><span></span></div><div class=\\\"ClJ9Yb\\\"><span></span></div></div></div></div></li><div jsname=\\\"VlcLAe\\\" class=\\\"lJ9FBc\\\"> <style>.lJ9FBc{height:70px}.lJ9FBc input[type=\\\"submit\\\"],.gbqfba{background-color:#f8f9fa;border:1px solid #f8f9fa;border-radius:4px;color:#3c4043;font-family:arial,sans-serif;font-size:14px;margin:11px 4px;padding:0 16px;line-height:27px;height:36px;min-width:54px;text-align:center;cursor:pointer;user-select:none}.lJ9FBc input[type=\\\"submit\\\"]:hover{box-shadow:0 1px 1px rgba(0,0,0,.1);background-color:#f8f9fa;border:1px solid #dadce0;color:#202124}.lJ9FBc input[type=\\\"submit\\\"]:focus{border:1px solid #4285f4;outline:none}</style> <center> <input class=\\\"gNO89b\\\" value=\\\"Google Search\\\" aria-label=\\\"Google Search\\\" name=\\\"btnK\\\" role=\\\"button\\\" tabindex=\\\"0\\\" type=\\\"submit\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQ4dUDCA0\\\"> <input class=\\\"RNmpXc\\\" value=\\\"I'm Feeling Lucky\\\" aria-label=\\\"I'm Feeling Lucky\\\" name=\\\"btnI\\\" type=\\\"submit\\\" jsaction=\\\"trigger.kWlxhc\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQ19QECA4\\\">  </center> </div></div><div jsname=\\\"JUypV\\\"><style>.WzNHm{font-size:11px;font-style:italic;margin-top:-16px;position:absolute;right:20px;color:var(--IXoxUe)}</style><div class=\\\"WzNHm mWcf0e\\\" jscontroller=\\\"gSZvdb\\\" data-dccl=\\\"false\\\" role=\\\"button\\\" tabindex=\\\"0\\\" jsdata=\\\"vST7rb;_;AQ7+OI\\\" jsaction=\\\"i5KCU;kVBCVd:yM1YJe\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQ6scECA8\\\"><div class=\\\"VfL2Y LRZwuc\\\">Report inappropriate predictions</div></div></div></div><div jsname=\\\"mvaK7d\\\" class=\\\"M8H8pb\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQ4d8ICBA\\\"></div><div class=\\\"FPdoLc lJ9FBc\\\">  <center> <input class=\\\"gNO89b\\\" value=\\\"Google Search\\\" aria-label=\\\"Google Search\\\" name=\\\"btnK\\\" role=\\\"button\\\" tabindex=\\\"0\\\" type=\\\"submit\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQ4dUDCBE\\\">  <input id=\\\"gbqfbb\\\" value=\\\"I'm Feeling Lucky\\\" aria-label=\\\"I'm Feeling Lucky\\\" name=\\\"btnI\\\" role=\\\"button\\\" tabindex=\\\"0\\\" type=\\\"submit\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQnRsIEg\\\"> </center> </div></div> <div style=\\\"background:url(/images/searchbox/desktop_searchbox_sprites318_hr.webp)\\\"> </div> <script nonce=\\\"9wxr-CpHUx-3aShmmrmtKQ\\\">(function(){\\nvar a=this||self;var c=document.querySelector(\\\"form\\\");if(c){var d=function(b){\\\"Enter\\\"!==b.key||b.shiftKey||(b.preventDefault(),c.submit&&c.submit())};c.addEventListener(\\\"keydown\\\",d);a.sbmlhf=d};}).call(this);</script> </div> <div id=\\\"tophf\\\"><input name=\\\"sca_esv\\\" value=\\\"bce7959204881098\\\" type=\\\"hidden\\\"><input name=\\\"source\\\" type=\\\"hidden\\\" value=\\\"hp\\\"><input value=\\\"g9pFZorXCdaKwbkPg_OmiA4\\\" name=\\\"ei\\\" type=\\\"hidden\\\"><input value=\\\"AL9hbdgAAAAAZkXok50osAfECyonptn4vXG6ZlbILa55\\\" name=\\\"iflsig\\\" type=\\\"hidden\\\"></div></form></div><div class=\\\"o3j99 qarstb\\\"><style>.vcVZ7d{text-align:center}</style><div jscontroller=\\\"ms4mZb\\\" data-jiis=\\\"up\\\" data-async-type=\\\"hpba\\\" id=\\\"a3JU5b\\\" class=\\\"yp\\\" jsaction=\\\"rcuQ6b:npT2md\\\" data-ved=\\\"0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQj-0KCBM\\\"></div></div><div jscontroller=\\\"B2qlPe\\\" jsname=\\\"cgsKlb\\\" jsaction=\\\"rcuQ6b:npT2md\\\"><style>.ceE3R{opacity:0.06;pointer-events:none}</style></div><div class=\\\"o3j99 c93Gbe\\\" role=\\\"contentinfo\\\"><style>.c93Gbe{background:#f2f2f2}.uU7dJb{padding:15px 30px;border-bottom:1px solid var(--gS5jXb);font-size:15px;color:var(--YLNNHc);display:flex;flex-wrap:wrap;justify-content:space-between}.Wymece{justify-content:flex-end}.SSwjIe{padding:0 20px}.KxwPGc{display:flex;flex-wrap:wrap;justify-content:space-between}@media only screen and (max-width:1200px){.KxwPGc{justify-content:space-evenly}}.pHiOh{display:block;padding:15px;white-space:nowrap}.pHiOh,a.pHiOh{color:var(--YLNNHc)}</style><div jscontroller=\\\"NzU6V\\\" class=\\\"KxwPGc SSwjIe\\\" data-sfe=\\\"false\\\" data-sfsw=\\\"1200\\\" jsaction=\\\"rcuQ6b:npT2md\\\"><div class=\\\"KxwPGc AghGtd\\\"><a class=\\\"pHiOh\\\" href=\\\"https://about.google/?utm_source=google-ZZ&amp;utm_medium=referral&amp;utm_campaign=hp-footer&amp;fg=1\\\" ping=\\\"/url?sa=t&amp;rct=j&amp;source=webhp&amp;url=https://about.google/%3Futm_source%3Dgoogle-ZZ%26utm_medium%3Dreferral%26utm_campaign%3Dhp-footer%26fg%3D1&amp;ved=0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQkNQCCBQ&amp;bl=27x2&amp;opi=89978449\\\">About</a><a class=\\\"pHiOh\\\" href=\\\"https://www.google.com.gh/intl/en_zz/ads/?subid=ww-ww-et-g-awa-a-g_hpafoot1_1!o2&amp;utm_source=google.com&amp;utm_medium=referral&amp;utm_campaign=google_hpafooter&amp;fg=1\\\" ping=\\\"/url?sa=t&amp;rct=j&amp;source=webhp&amp;url=https://www.google.com.gh/intl/en_zz/ads/%3Fsubid%3Dww-ww-et-g-awa-a-g_hpafoot1_1!o2%26utm_source%3Dgoogle.com%26utm_medium%3Dreferral%26utm_campaign%3Dgoogle_hpafooter%26fg%3D1&amp;ved=0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQkdQCCBU&amp;bl=27x2&amp;opi=89978449\\\">Advertising</a><a class=\\\"pHiOh\\\" href=\\\"https://www.google.com.gh/services/?subid=ww-ww-et-g-awa-a-g_hpbfoot1_1!o2&amp;utm_source=google.com&amp;utm_medium=referral&amp;utm_campaign=google_hpbfooter&amp;fg=1\\\" ping=\\\"/url?sa=t&amp;rct=j&amp;source=webhp&amp;url=https://www.google.com.gh/services/%3Fsubid%3Dww-ww-et-g-awa-a-g_hpbfoot1_1!o2%26utm_source%3Dgoogle.com%26utm_medium%3Dreferral%26utm_campaign%3Dgoogle_hpbfooter%26fg%3D1&amp;ved=0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQktQCCBY&amp;bl=27x2&amp;opi=89978449\\\">Business</a><a class=\\\"pHiOh\\\" href=\\\"https://google.com/search/howsearchworks/?fg=1\\\"> How Search works </a></div><div class=\\\"KxwPGc iTjxkf\\\"><a class=\\\"pHiOh\\\" href=\\\"https://policies.google.com/privacy?hl=en&amp;fg=1\\\" ping=\\\"/url?sa=t&amp;rct=j&amp;source=webhp&amp;url=https://policies.google.com/privacy%3Fhl%3Den%26fg%3D1&amp;ved=0ahUKEwjKpO-L9pGGAxVWRTABHYO5CeEQ8awCCBc&amp;bl=27x2&amp;opi=89978449\\\">Privacy</a><a class=\\\"pHiOh\\\" href=\\\"https://policies.google.com/terms?hl=en&amp;fg=1\\\" 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       "      <td>[{'name': 'accept-ch', 'value': 'Sec-CH-UA-Pla...</td>\n",
       "      <td>&lt;!doctype html&gt;&lt;html itemscope=\"\" itemtype=\"ht...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-994c93e6-f6b5-4777-92b6-fd7ffecfc41a')\"\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    .colab-df-buttons div {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-994c93e6-f6b5-4777-92b6-fd7ffecfc41a button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-994c93e6-f6b5-4777-92b6-fd7ffecfc41a');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "         date   client                        page  is_root_page  \\\n",
       "0  2024-05-01  desktop  https://www.google.com.gh/          True   \n",
       "\n",
       "                    root_page                         url  is_main_document  \\\n",
       "0  https://www.google.com.gh/  https://www.google.com.gh/              True   \n",
       "\n",
       "   type  index                                            payload  \\\n",
       "0  html      1  {\"pageref\":\"page_1_0_1\",\"_run\":1,\"_cached\":0,\"...   \n",
       "\n",
       "                                             summary  \\\n",
       "0  {\"requestid\": 97122220018499585, \"pageid\": 226...   \n",
       "\n",
       "                                     request_headers  \\\n",
       "0  [{'name': 'accept', 'value': 'text/html,applic...   \n",
       "\n",
       "                                    response_headers  \\\n",
       "0  [{'name': 'accept-ch', 'value': 'Sec-CH-UA-Pla...   \n",
       "\n",
       "                                       response_body  \n",
       "0  <!doctype html><html itemscope=\"\" itemtype=\"ht...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This query will process 10 GB when run.\n",
    "%%bigquery --project {GCP_PROJECT}\n",
    "SELECT *\n",
    "FROM `httparchive.crawl.requests` TABLESAMPLE SYSTEM (5 PERCENT)\n",
    "WHERE date = \"2024-05-01\"\n",
    "  AND client = 'desktop'\n",
    "  AND is_root_page\n",
    "  AND is_main_document\n",
    "  AND CONTAINS_SUBSTR(page, 'www.google.com')\n",
    "LIMIT 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wY3AUA6LRD3T"
   },
   "source": [
    "Since each page tracked by the HTTP Archive has a unique page URL value, we can also summarize these results by the number of distinct pages.  For example, the following query tells us that there are 2,393,199,079 requests in this dataset and that they're loaded from 24,485,975 pages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "height": 145,
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      "e69eb625017341cfb43c1b6e307e8ee1",
      "2eb184ecc6df40468d10f820f9efb67d",
      "fe34fed3392349949b1779a98edbd249",
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    "id": "qms1stnMe76Y",
    "outputId": "626cfada-b30e-4545-e035-aaf76e293cc4"
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   "outputs": [
    {
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       "Query is running:   0%|          |"
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       "Downloading:   0%|          |"
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       "summary": "{\n  \"name\": \"get_ipython()\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"requests\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2393199079\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pages\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          24485975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
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       "\n",
       "  <div id=\"df-5b73991c-6820-440a-a3bb-07dc3a846928\" class=\"colab-df-container\">\n",
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       "      <th></th>\n",
       "      <th>requests</th>\n",
       "      <th>pages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
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       "\n",
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       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-5b73991c-6820-440a-a3bb-07dc3a846928');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "     requests     pages\n",
       "0  2393199079  24485975"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This query will process 137 GB when run.\n",
    "%%bigquery --project {GCP_PROJECT}\n",
    "SELECT\n",
    "  COUNT(0) requests,\n",
    "  COUNT(DISTINCT page) pages\n",
    "FROM `httparchive.crawl.requests`\n",
    "WHERE date = \"2024-05-01\"\n",
    "  AND client = 'desktop'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6JMM1rJ8e-DG"
   },
   "source": [
    "We already knew the number of pages from previous queries, but now lets add a dimension to the query to explore this table some more. The following query adds the 'type' column, which indicates the type of resource loaded (ie, script, image, css, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    "outputId": "c2236403-a1a1-4f43-e29d-9ea84ec108fc"
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    {
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       "\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>image</td>\n",
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       "      <td>24146223</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>css</td>\n",
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       "      <td>23259825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>other</td>\n",
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       "      <td>11901742</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>7</th>\n",
       "      <td>video</td>\n",
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       "      <td>1407697</td>\n",
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       "      <th>8</th>\n",
       "      <td>xml</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>audio</td>\n",
       "      <td>1177141</td>\n",
       "      <td>518769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>wasm</td>\n",
       "      <td>142421</td>\n",
       "      <td>51063</td>\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-4b4e403c-2de1-41e7-a7c2-27ac3ad62477 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "      async function convertToInteractive(key) {\n",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
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       "  </button>\n",
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       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --disabled-fill-color: #AAA;\n",
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       "\n",
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       "  .colab-df-spinner {\n",
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       "  @keyframes spin {\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
       "      let quickchartButtonEl =\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      type   requests     pages\n",
       "0   script  813565233  23556030\n",
       "1    image  747018774  24146223\n",
       "2      css  278463773  23259825\n",
       "3    other  194533696  11901742\n",
       "4     html  185868729  24145122\n",
       "5     font  128104348  21224863\n",
       "6     text   64065722  14736086\n",
       "7    video    7191779   1407697\n",
       "8      xml    2714133    747170\n",
       "9    audio    1177141    518769\n",
       "10    wasm     142421     51063"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This query will process 155 GB when run.\n",
    "%%bigquery --project {GCP_PROJECT}\n",
    "SELECT\n",
    "  type,\n",
    "  COUNT(0) requests,\n",
    "  COUNT(DISTINCT page) pages\n",
    "FROM `httparchive.crawl.requests`\n",
    "WHERE date = '2024-05-01'\n",
    "    AND client = 'desktop'\n",
    "GROUP BY type\n",
    "ORDER BY requests DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PKXlevg9fDqA"
   },
   "source": [
    "When we look at this data we can see counts of requests and pages for each content type. But what if we want the percentage of total? You could divide by the numbers that we know. But a more repeatable way of doing this would be to include a subquery. In the following query you'll notice that the number of requests per type is divided by the total number of requests. And likewise the same is done for pages. As we seen in Part 1, we can use the `ROUND()` function to trim the result to 2 decimal points for readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "c636d88bd0f44e42b8d93df1bc9c3f1f",
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      "a2cf84f40d7d4a66957449c78dbfa886",
      "b98d2b861b8f41ed8bc5aebd16aefa30",
      "98c4fa4f77e14436ab7bf540b7535745",
      "247f3cba522248efa8b3c02f273115cc",
      "72ea553d38324817931f62386369e0b3",
      "40c6d92fcdf644e08524118d4be803ce",
      "bd79588291e84b43874a8c876e9e2375",
      "754b568b48f24dcc8371064444cd9c6d",
      "16e8f5f8073a422c8ea758411256b4e0",
      "86a08cddcce2496c98f704ff510e1662",
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     ]
    },
    "id": "HyaE62NTfGe9",
    "outputId": "29a7d0ae-69de-4792-8020-46b3e5306068"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c636d88bd0f44e42b8d93df1bc9c3f1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16e8f5f8073a422c8ea758411256b4e0",
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      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This query will process 153 GB when run.\n",
    "%%bigquery requests_type_df --project {GCP_PROJECT}\n",
    "WITH requests AS (\n",
    "  SELECT\n",
    "    type,\n",
    "    page,\n",
    "    COUNT(0) OVER() AS total_requests,\n",
    "    COUNT(DISTINCT page) OVER() AS total_pages\n",
    "  FROM `httparchive.crawl.requests`\n",
    "  WHERE date = '2024-05-01'\n",
    "    AND client = 'desktop'\n",
    ")\n",
    "\n",
    "SELECT\n",
    "  type,\n",
    "  COUNT(0) requests,\n",
    "  COUNT(DISTINCT page) pages,\n",
    "  ROUND(COUNT(0) / ANY_VALUE(total_requests), 2) percent_requests,\n",
    "  ROUND(COUNT(DISTINCT page) / ANY_VALUE(total_pages), 2) percent_pages\n",
    "FROM requests\n",
    "GROUP BY type\n",
    "ORDER BY requests DESC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "30c8TCmWmQ9w",
    "outputId": "f735c7ad-bb37-4481-ed1c-60e0ef05619c"
   },
   "outputs": [
    {
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       "type": "dataframe",
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       "      <th>requests</th>\n",
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       "      <td>0.07</td>\n",
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       "      <td>59759</td>\n",
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       "      width: 32px;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      fill: #174EA6;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "\n",
       "    <script>\n",
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       "        document.querySelector('#df-8992d530-cc01-4c29-906f-968db05dd914 button.colab-df-convert');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-8992d530-cc01-4c29-906f-968db05dd914');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        element.appendChild(docLink);\n",
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       "\n",
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       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "      --hover-bg-color: #E2EBFA;\n",
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       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
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       "      --hover-bg-color: #434B5C;\n",
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       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
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       "</div>\n",
       "\n",
       "  <div id=\"id_b4a69b9b-4deb-437a-a68a-4d4275d0e0c5\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
       "        background-color: #E8F0FE;\n",
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       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
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       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
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       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "      [theme=dark] .colab-df-generate {\n",
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       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
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       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('requests_type_df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_b4a69b9b-4deb-437a-a68a-4d4275d0e0c5 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('requests_type_df');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      type   requests     pages  percent_requests  percent_pages\n",
       "0   script  817636850  23885146              0.34           0.98\n",
       "1    image  744181758  24465443              0.31           1.00\n",
       "2      css  279208078  23575222              0.12           0.96\n",
       "3    other  187424669  12001071              0.08           0.49\n",
       "4     html  162335275  24462613              0.07           1.00\n",
       "5     font  128926306  21491621              0.05           0.88\n",
       "6     text   62780147  14798581              0.03           0.60\n",
       "7    video    6874793   1377851              0.00           0.06\n",
       "8      xml    2504041    772155              0.00           0.03\n",
       "9    audio    1184613    504820              0.00           0.02\n",
       "10    wasm     142549     59759              0.00           0.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requests_type_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P3suKO-2fMrE"
   },
   "source": [
    "Graphing this we can see both the distribution of content types across all requests in the archive as well as the popularity of certain types of content on each site.  For example, 100% of sites contained images and HTML. 97% contain JavaScript and 96% - CSS. 88% contain custom webfonts and 6% contain video files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "id": "8eHylgTVmF0J",
    "outputId": "9cdbd01a-a811-42ec-bf25-594d295890ba"
   },
   "outputs": [
    {
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"arc\", \"encoding\": {\"color\": {\"field\": \"type\", \"sort\": null, \"type\": \"nominal\"}, \"order\": {\"field\": \"percent_requests\", \"sort\": \"descending\", \"type\": \"quantitative\"}, \"theta\": {\"field\": \"percent_requests\", \"stack\": true, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"type\", \"type\": \"nominal\"}, {\"field\": \"percent_requests\", \"format\": \".0%\", \"type\": \"quantitative\"}]}, \"title\": \"Requests by Type\"}, {\"mark\": {\"type\": \"text\", \"radius\": 170}, \"encoding\": {\"color\": {\"field\": \"type\", \"sort\": null, \"type\": \"nominal\"}, \"order\": {\"field\": \"percent_requests\", \"sort\": \"descending\", \"type\": \"quantitative\"}, \"text\": {\"field\": \"percent_requests\", \"format\": \".0%\", \"type\": \"quantitative\"}, \"theta\": {\"field\": \"percent_requests\", \"stack\": true, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"type\", \"type\": \"nominal\"}, {\"field\": \"percent_requests\", \"format\": \".0%\", \"type\": \"quantitative\"}]}, \"title\": \"Requests by Type\"}], \"data\": {\"name\": \"data-e2e4fff7321aebe7e2b1e0b1b024a4e6\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-e2e4fff7321aebe7e2b1e0b1b024a4e6\": [{\"type\": \"script\", \"requests\": 817636850, \"pages\": 23885146, \"percent_requests\": 0.34, \"percent_pages\": 0.98}, {\"type\": \"image\", \"requests\": 744181758, \"pages\": 24465443, \"percent_requests\": 0.31, \"percent_pages\": 1.0}, {\"type\": \"css\", \"requests\": 279208078, \"pages\": 23575222, \"percent_requests\": 0.12, \"percent_pages\": 0.96}, {\"type\": \"other\", \"requests\": 187424669, \"pages\": 12001071, \"percent_requests\": 0.08, \"percent_pages\": 0.49}, {\"type\": \"html\", \"requests\": 162335275, \"pages\": 24462613, \"percent_requests\": 0.07, \"percent_pages\": 1.0}, {\"type\": \"font\", \"requests\": 128926306, \"pages\": 21491621, \"percent_requests\": 0.05, \"percent_pages\": 0.88}, {\"type\": \"text\", \"requests\": 62780147, \"pages\": 14798581, \"percent_requests\": 0.03, \"percent_pages\": 0.6}, {\"type\": \"video\", \"requests\": 6874793, \"pages\": 1377851, \"percent_requests\": 0.0, \"percent_pages\": 0.06}, {\"type\": \"xml\", \"requests\": 2504041, \"pages\": 772155, \"percent_requests\": 0.0, \"percent_pages\": 0.03}, {\"type\": \"audio\", \"requests\": 1184613, \"pages\": 504820, \"percent_requests\": 0.0, \"percent_pages\": 0.02}, {\"type\": \"wasm\", \"requests\": 142549, \"pages\": 59759, \"percent_requests\": 0.0, \"percent_pages\": 0.0}]}}, {\"mode\": \"vega-lite\"});\n",
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       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "\n",
    "pie_chart_requests = alt.Chart(requests_type_df).mark_arc().encode(\n",
    "    theta=alt.Theta(field=\"percent_requests\", type=\"quantitative\", stack=True),\n",
    "    color=alt.Color(field=\"type\", type=\"nominal\", sort=None),\n",
    "    order=alt.Order(field=\"percent_requests\", type=\"quantitative\",sort='descending'),\n",
    "    tooltip=[\"type\", alt.Tooltip(\"percent_requests:Q\", format=\".0%\")]\n",
    ").properties(\n",
    "    title=\"Requests by Type\"\n",
    ")\n",
    "pie_chart_requests + pie_chart_requests.mark_text(\n",
    "    radius=170\n",
    ").encode(\n",
    "    text=alt.Text(\"percent_requests:Q\", format=\".0%\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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     "height": 414
    },
    "id": "Kf8aU76Qm_ym",
    "outputId": "5cfdbd66-8995-4b72-c6ae-9bcb268061bd"
   },
   "outputs": [
    {
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       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"type\", \"sort\": null}, \"tooltip\": [{\"field\": \"type\", \"type\": \"nominal\"}, {\"field\": \"percent_pages\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"type\", \"sort\": null, \"title\": \"Type\", \"type\": \"nominal\"}, \"y\": {\"field\": \"percent_pages\", \"title\": \"Percent Pages\", \"type\": \"quantitative\"}}, \"title\": \"Percent Pages by Type\"}, {\"mark\": {\"type\": \"text\", \"dy\": -10}, \"encoding\": {\"color\": {\"field\": \"type\", \"sort\": null}, \"text\": {\"field\": \"percent_pages\", \"format\": \".0%\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"type\", \"type\": \"nominal\"}, {\"field\": \"percent_pages\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"type\", \"sort\": null, \"title\": \"Type\", \"type\": \"nominal\"}, \"y\": {\"field\": \"percent_pages\", \"title\": \"Percent Pages\", \"type\": \"quantitative\"}}, \"title\": \"Percent Pages by Type\"}], \"data\": {\"name\": \"data-e2e4fff7321aebe7e2b1e0b1b024a4e6\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-e2e4fff7321aebe7e2b1e0b1b024a4e6\": [{\"type\": \"script\", \"requests\": 817636850, \"pages\": 23885146, \"percent_requests\": 0.34, \"percent_pages\": 0.98}, {\"type\": \"image\", \"requests\": 744181758, \"pages\": 24465443, \"percent_requests\": 0.31, \"percent_pages\": 1.0}, {\"type\": \"css\", \"requests\": 279208078, \"pages\": 23575222, \"percent_requests\": 0.12, \"percent_pages\": 0.96}, {\"type\": \"other\", \"requests\": 187424669, \"pages\": 12001071, \"percent_requests\": 0.08, \"percent_pages\": 0.49}, {\"type\": \"html\", \"requests\": 162335275, \"pages\": 24462613, \"percent_requests\": 0.07, \"percent_pages\": 1.0}, {\"type\": \"font\", \"requests\": 128926306, \"pages\": 21491621, \"percent_requests\": 0.05, \"percent_pages\": 0.88}, {\"type\": \"text\", \"requests\": 62780147, \"pages\": 14798581, \"percent_requests\": 0.03, \"percent_pages\": 0.6}, {\"type\": \"video\", \"requests\": 6874793, \"pages\": 1377851, \"percent_requests\": 0.0, \"percent_pages\": 0.06}, {\"type\": \"xml\", \"requests\": 2504041, \"pages\": 772155, \"percent_requests\": 0.0, \"percent_pages\": 0.03}, {\"type\": \"audio\", \"requests\": 1184613, \"pages\": 504820, \"percent_requests\": 0.0, \"percent_pages\": 0.02}, {\"type\": \"wasm\", \"requests\": 142549, \"pages\": 59759, \"percent_requests\": 0.0, \"percent_pages\": 0.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_chart_pages = alt.Chart(requests_type_df).mark_bar().encode(\n",
    "    x=alt.X(\"type\", title=\"Type\", sort=None),\n",
    "    y=alt.Y(\"percent_pages\", title=\"Percent Pages\"),\n",
    "    color=alt.Color(field=\"type\", sort=None),\n",
    "    tooltip=[\"type\", \"percent_pages\"]\n",
    ").properties(\n",
    "    title=\"Percent Pages by Type\"\n",
    ")\n",
    "bar_chart_pages + bar_chart_pages.mark_text(\n",
    "    dy=-10,\n",
    ").encode(\n",
    "    text=alt.Text(\"percent_pages:Q\", format=\".0%\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XvgAaUVsmFMh"
   },
   "source": [
    "Let's say we want to extend this query some more and look at the formats of each type. Now we have a query that is summarizing the % of requests and pages for each file type and format.\n",
    "\n",
    "The summary columns stores quite a lot of data and we would need to analyze ~10TB of data. So let's look at the root pages only and apply sampling to the data scanned by the query using [`TABLESAMPLE`](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#tablesample_operator) operator to optimize the cost of the query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "1b348118d0664b5a83932bee399e3a2c",
      "929e372de1fe4bddb11d1f3871ff5ec4",
      "da4ebaee8669470787858afdec80ee36",
      "4b2c63265cd34c47b318a22744328885",
      "e30398b1656341478a4f231ee01373bc",
      "31bc31ad61584273b76f25b80a9f4c50",
      "930c60e33af94661866d0488a70e46a6",
      "3542dedcc6b9417bad6bf343f5bd81a5",
      "6721d1a376b54678adb9b801c2c48d05",
      "7609ac7fa8e644e9bbebc5233899d1d8",
      "87ba0b6cb6bb4d539fe6abcdf0219969",
      "5cdf3c2c07d9467999680af82b25b53b",
      "3c7e9a86cbd648c69a64c1c4763a9bd5",
      "fdb88a71c2bf4903ab876bb35fd350e7",
      "12baaf1c02fb46fb8ad874948894b321",
      "65129042388a482cadc1e8f779ab802f",
      "f9ffce03077b48bf97680b420c638fd3",
      "abf6bc17abd64f5fb6b6a8d2d5e5efae",
      "aa9e17f6129a476cbf89c50d3496c5a2",
      "7335434ed8574c3ea009f0d6b6e4b04e",
      "a41fdd0e62f44ebd98f6b22b01509946",
      "e5d547f7043e473eb79849c6039bda46"
     ]
    },
    "id": "uRQPMFPdfPgz",
    "outputId": "30f321cb-e6a5-4077-c403-0cf5f79e67ac"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b348118d0664b5a83932bee399e3a2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cdf3c2c07d9467999680af82b25b53b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"get_ipython()\",\n  \"rows\": 34,\n  \"fields\": [\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"xml\",\n          \"image\",\n          \"font\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"format\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"heic\",\n          \"flash\",\n          \"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"requests\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          110612,\n          52266,\n          1505\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pages\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          91400,\n          22948,\n          871\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_requests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06291564111262947,\n        \"min\": 0.0,\n        \"max\": 0.32,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.13,\n          0.06,\n          0.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_pages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1559537387414966,\n        \"min\": 0.0,\n        \"max\": 0.69,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.01,\n          0.06,\n          0.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-7fc768cb-0259-4470-91be-95e09841951e\" class=\"colab-df-container\">\n",
       "    <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>type</th>\n",
       "      <th>format</th>\n",
       "      <th>requests</th>\n",
       "      <th>pages</th>\n",
       "      <th>percent_requests</th>\n",
       "      <th>percent_pages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>script</td>\n",
       "      <td></td>\n",
       "      <td>21085473</td>\n",
       "      <td>8030900</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>image</td>\n",
       "      <td>jpg</td>\n",
       "      <td>8696102</td>\n",
       "      <td>4322825</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>image</td>\n",
       "      <td>png</td>\n",
       "      <td>6928635</td>\n",
       "      <td>4122297</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>css</td>\n",
       "      <td></td>\n",
       "      <td>6775517</td>\n",
       "      <td>4371876</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other</td>\n",
       "      <td></td>\n",
       "      <td>4447765</td>\n",
       "      <td>1466010</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>html</td>\n",
       "      <td></td>\n",
       "      <td>4139296</td>\n",
       "      <td>2255120</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>image</td>\n",
       "      <td>gif</td>\n",
       "      <td>4022545</td>\n",
       "      <td>1539581</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>font</td>\n",
       "      <td></td>\n",
       "      <td>3812048</td>\n",
       "      <td>2903219</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>text</td>\n",
       "      <td></td>\n",
       "      <td>1406090</td>\n",
       "      <td>1120520</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>image</td>\n",
       "      <td>svg</td>\n",
       "      <td>1316180</td>\n",
       "      <td>942875</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>image</td>\n",
       "      <td>webp</td>\n",
       "      <td>1091543</td>\n",
       "      <td>653911</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>image</td>\n",
       "      <td>ico</td>\n",
       "      <td>285567</td>\n",
       "      <td>281780</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>video</td>\n",
       "      <td>mp4</td>\n",
       "      <td>202543</td>\n",
       "      <td>125799</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>image</td>\n",
       "      <td>None</td>\n",
       "      <td>201812</td>\n",
       "      <td>155606</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>image</td>\n",
       "      <td>avif</td>\n",
       "      <td>137423</td>\n",
       "      <td>84078</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>html</td>\n",
       "      <td>None</td>\n",
       "      <td>110612</td>\n",
       "      <td>91400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>xml</td>\n",
       "      <td></td>\n",
       "      <td>103278</td>\n",
       "      <td>45869</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>script</td>\n",
       "      <td>None</td>\n",
       "      <td>89199</td>\n",
       "      <td>78347</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>other</td>\n",
       "      <td>None</td>\n",
       "      <td>59858</td>\n",
       "      <td>39318</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>video</td>\n",
       "      <td></td>\n",
       "      <td>52266</td>\n",
       "      <td>22948</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>audio</td>\n",
       "      <td></td>\n",
       "      <td>42861</td>\n",
       "      <td>21862</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>xml</td>\n",
       "      <td>None</td>\n",
       "      <td>33215</td>\n",
       "      <td>23034</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>image</td>\n",
       "      <td></td>\n",
       "      <td>24148</td>\n",
       "      <td>18279</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>font</td>\n",
       "      <td>None</td>\n",
       "      <td>21220</td>\n",
       "      <td>19105</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>css</td>\n",
       "      <td>None</td>\n",
       "      <td>19725</td>\n",
       "      <td>17801</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>text</td>\n",
       "      <td>None</td>\n",
       "      <td>15469</td>\n",
       "      <td>13509</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>image</td>\n",
       "      <td>heic</td>\n",
       "      <td>2365</td>\n",
       "      <td>1932</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>video</td>\n",
       "      <td>None</td>\n",
       "      <td>1505</td>\n",
       "      <td>871</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>video</td>\n",
       "      <td>flv</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>video</td>\n",
       "      <td>flash</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>video</td>\n",
       "      <td>swf</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>image</td>\n",
       "      <td>heif</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>audio</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>image</td>\n",
       "      <td>jxl</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fc768cb-0259-4470-91be-95e09841951e')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
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       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-7fc768cb-0259-4470-91be-95e09841951e button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-7fc768cb-0259-4470-91be-95e09841951e');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
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       "\n",
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       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
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       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
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       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
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       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
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       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
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       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      type format  requests    pages  percent_requests  percent_pages\n",
       "0   script         21085473  8030900              0.32           0.69\n",
       "1    image    jpg   8696102  4322825              0.13           0.37\n",
       "2    image    png   6928635  4122297              0.11           0.35\n",
       "3      css          6775517  4371876              0.10           0.38\n",
       "4    other          4447765  1466010              0.07           0.13\n",
       "5     html          4139296  2255120              0.06           0.19\n",
       "6    image    gif   4022545  1539581              0.06           0.13\n",
       "7     font          3812048  2903219              0.06           0.25\n",
       "8     text          1406090  1120520              0.02           0.10\n",
       "9    image    svg   1316180   942875              0.02           0.08\n",
       "10   image   webp   1091543   653911              0.02           0.06\n",
       "11   image    ico    285567   281780              0.00           0.02\n",
       "12   video    mp4    202543   125799              0.00           0.01\n",
       "13   image   None    201812   155606              0.00           0.01\n",
       "14   image   avif    137423    84078              0.00           0.01\n",
       "15    html   None    110612    91400              0.00           0.01\n",
       "16     xml           103278    45869              0.00           0.00\n",
       "17  script   None     89199    78347              0.00           0.01\n",
       "18   other   None     59858    39318              0.00           0.00\n",
       "19   video            52266    22948              0.00           0.00\n",
       "20   audio            42861    21862              0.00           0.00\n",
       "21     xml   None     33215    23034              0.00           0.00\n",
       "22   image            24148    18279              0.00           0.00\n",
       "23    font   None     21220    19105              0.00           0.00\n",
       "24     css   None     19725    17801              0.00           0.00\n",
       "25    text   None     15469    13509              0.00           0.00\n",
       "26   image   heic      2365     1932              0.00           0.00\n",
       "27   video   None      1505      871              0.00           0.00\n",
       "28   video    flv        38       26              0.00           0.00\n",
       "29   video  flash        37       37              0.00           0.00\n",
       "30   video    swf        11       11              0.00           0.00\n",
       "31   image   heif         7        6              0.00           0.00\n",
       "32   audio   None         4        3              0.00           0.00\n",
       "33   image    jxl         1        1              0.00           0.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This query will process 129 GB when run.\n",
    "%%bigquery --project {GCP_PROJECT}\n",
    "WITH requests AS (\n",
    "  SELECT\n",
    "    type,\n",
    "    STRING(summary.format) AS format,\n",
    "    page,\n",
    "    COUNT(0) OVER() AS total_requests,\n",
    "    COUNT(DISTINCT page) OVER() AS total_pages\n",
    "  FROM `httparchive.crawl.requests` TABLESAMPLE SYSTEM (5 PERCENT)\n",
    "  WHERE date = '2024-05-01'\n",
    "    AND client = 'desktop'\n",
    "    AND is_root_page\n",
    ")\n",
    "\n",
    "SELECT\n",
    "  type,\n",
    "  format,\n",
    "  COUNT(0) requests,\n",
    "  COUNT(DISTINCT page) pages,\n",
    "  ROUND(COUNT(0) / ANY_VALUE(total_requests), 2) percent_requests,\n",
    "  ROUND(COUNT(DISTINCT page) / ANY_VALUE(total_pages), 2) percent_pages\n",
    "FROM requests\n",
    "GROUP BY\n",
    "  type,\n",
    "  format\n",
    "ORDER BY requests DESC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OYGSiLhhfQxy"
   },
   "source": [
    "Using a WHERE clause we can filter out all of the non-Image content and examine the popularity of various image formats. For example, how often is \"jpg\", \"gif\", \"webp\", etc used.\n",
    "\n",
    "Filtering by `type` column we are leveraging table's clustering to optimize the data scanned by the query even further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "bee716f2c8114b8f833aff68d931b75e",
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      "772c6f229e5d43399633ab36394514ec",
      "2e4fe2d3a68b46c9981db237599c2bd6",
      "af385fd5dafa4ad88cdfddf162508aa8",
      "de755bb356874567b231e6286049b2df",
      "a9848418eec64a92b6fc2560dba1ce84",
      "16e8cc44f2654748b5badb2bc04aeb6a",
      "e0eed4c61bce48dab8903675af8da0f3",
      "4db6fbc2b6e44e11a9f45cc286e306c4",
      "7518f64a50184d54a1dc78864a624c43",
      "38b137fae3b94bbb9b1fc23a61a32322",
      "865c9c1a7b194430baff53db88cd53b2",
      "13d1d76908804346befa6fec8e3eeb64",
      "cd3ccfba6d104c2ba347291454d8ed69",
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      "aec648dca2024bbeb423cd2e68ac6410",
      "fe2a38848d9b4a20910c6f3859c914b7"
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    },
    "id": "9dhWIwI8fTjH",
    "outputId": "2cf5ad42-2b9f-4c00-92cd-e4132b0aa242"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bee716f2c8114b8f833aff68d931b75e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0eed4c61bce48dab8903675af8da0f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This query will process 41 GB when run.\n",
    "%%bigquery requests_image_df --project {GCP_PROJECT}\n",
    "WITH requests AS (\n",
    "  SELECT\n",
    "    STRING(summary.format) AS format,\n",
    "    page,\n",
    "    COUNT(0) OVER() AS total_requests,\n",
    "    COUNT(DISTINCT page) OVER() AS total_pages\n",
    "  FROM `httparchive.crawl.requests` TABLESAMPLE SYSTEM (5 PERCENT)\n",
    "  WHERE date = '2024-05-01'\n",
    "    AND client = 'desktop'\n",
    "    AND is_root_page\n",
    "    AND type = 'image'\n",
    ")\n",
    "\n",
    "SELECT\n",
    "  format,\n",
    "  COUNT(0) requests,\n",
    "  COUNT(DISTINCT page) pages,\n",
    "  ROUND(COUNT(0) / ANY_VALUE(total_requests), 2) percent_requests,\n",
    "  ROUND(COUNT(DISTINCT page) / ANY_VALUE(total_pages), 2) percent_pages\n",
    "FROM requests\n",
    "GROUP BY format\n",
    "ORDER BY requests DESC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "lEynOaPZ78FM",
    "outputId": "e23275bb-5d68-4a49-a16e-796f17f60091"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"requests_image_df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"format\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"heic\",\n          \"png\",\n          \"ico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"requests\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          272608,\n          8290474,\n          2156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pages\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          269039,\n          4189907,\n          1783\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_requests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13658829977843506,\n        \"min\": 0.0,\n        \"max\": 0.38,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.38,\n          0.31,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_pages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19759922708718913,\n        \"min\": 0.0,\n        \"max\": 0.53,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.01,\n          0.5,\n          0.03\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "requests_image_df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-6b078901-c2af-4311-9f2b-5d347cebd25d\" class=\"colab-df-container\">\n",
       "    <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>format</th>\n",
       "      <th>requests</th>\n",
       "      <th>pages</th>\n",
       "      <th>percent_requests</th>\n",
       "      <th>percent_pages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jpg</td>\n",
       "      <td>8290474</td>\n",
       "      <td>4189907</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>png</td>\n",
       "      <td>6604473</td>\n",
       "      <td>3984064</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gif</td>\n",
       "      <td>3836964</td>\n",
       "      <td>1487233</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>svg</td>\n",
       "      <td>1255002</td>\n",
       "      <td>906022</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>webp</td>\n",
       "      <td>1040312</td>\n",
       "      <td>630692</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ico</td>\n",
       "      <td>272608</td>\n",
       "      <td>269039</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>191874</td>\n",
       "      <td>148218</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>avif</td>\n",
       "      <td>131101</td>\n",
       "      <td>81393</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>23113</td>\n",
       "      <td>17699</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
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       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('requests_image_df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
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       "\n",
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       "  </svg>\n",
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       "    <script>\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('requests_image_df');\n",
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       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   format  requests    pages  percent_requests  percent_pages\n",
       "0     jpg   8290474  4189907              0.38           0.53\n",
       "1     png   6604473  3984064              0.31           0.50\n",
       "2     gif   3836964  1487233              0.18           0.19\n",
       "3     svg   1255002   906022              0.06           0.11\n",
       "4    webp   1040312   630692              0.05           0.08\n",
       "5     ico    272608   269039              0.01           0.03\n",
       "6    None    191874   148218              0.01           0.02\n",
       "7    avif    131101    81393              0.01           0.01\n",
       "8             23113    17699              0.00           0.00\n",
       "9    heic      2156     1783              0.00           0.00\n",
       "10   heif         2        2              0.00           0.00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requests_image_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SUGpb6bUfV6c"
   },
   "source": [
    "**Note**: It's important to understand the bias in the data when we are doing this type of analysis. While we do have a very diverse set of 1.3 million page views - the technology used to parse these pages is Chrome browsers (both Desktop and Emulated mobile). Because of this, some formats may be under-represented - since Chrome supports webp but not jpeg-xr or jpeg2000.  You may find cases like this with other type of technologies as well - for example custom web font types that vary based on browser support.\n",
    "\n",
    "Let's graph the results now to see what types of images are being served to Chrome browsers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 397
    },
    "id": "3MOvKaUJfaiR",
    "outputId": "23c61003-e5ab-4df6-d7e0-0c4211017e99"
   },
   "outputs": [
    {
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       "alt.LayerChart(...)"
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     },
     "execution_count": 18,
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   "source": [
    "pie_chart_image_formats = alt.Chart(requests_image_df).mark_arc().encode(\n",
    "    theta=alt.Theta(field=\"percent_requests\", type=\"quantitative\", stack=True),\n",
    "    color=alt.Color(field=\"format\", sort=None),\n",
    "    order=alt.Order(field=\"percent_requests\", type=\"quantitative\",sort='descending'),\n",
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    ").properties(\n",
    "    title=\"Distribution of image formats\"\n",
    ")\n",
    "pie_chart_image_formats + pie_chart_image_formats.mark_text(\n",
    "    radius=170\n",
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    "    text=alt.Text(\"percent_requests:Q\", format=\".0%\")\n",
    ")"
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  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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     "height": 400
    },
    "id": "T9fwfkGY8R8h",
    "outputId": "7fb5aeab-edc4-4874-8127-31ae625b3c19"
   },
   "outputs": [
    {
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       "      \"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",
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       "          s.async = true;\n",
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       "          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",
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       "      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",
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       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"format\", \"sort\": null}, \"tooltip\": [{\"field\": \"format\", \"type\": \"nominal\"}, {\"field\": \"percent_pages\", \"format\": \".0%\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"format\", \"sort\": null, \"title\": \"Type\", \"type\": \"nominal\"}, \"y\": {\"field\": \"percent_pages\", \"sort\": null, \"title\": \"Percent Pages\", \"type\": \"quantitative\"}}, \"title\": \"Percent Pages by Type\"}, {\"mark\": {\"type\": \"text\", \"dy\": -10}, \"encoding\": {\"color\": {\"field\": \"format\", \"sort\": null}, \"text\": {\"field\": \"percent_pages\", \"format\": \".0%\", \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"format\", \"type\": \"nominal\"}, {\"field\": \"percent_pages\", \"format\": \".0%\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"format\", \"sort\": null, \"title\": \"Type\", \"type\": \"nominal\"}, \"y\": {\"field\": \"percent_pages\", \"sort\": null, \"title\": \"Percent Pages\", \"type\": \"quantitative\"}}, \"title\": \"Percent Pages by Type\"}], \"data\": {\"name\": \"data-83f0f57fcafc23d0ed72da8f1a1f0f0a\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-83f0f57fcafc23d0ed72da8f1a1f0f0a\": [{\"format\": \"jpg\", \"requests\": 8290474, \"pages\": 4189907, \"percent_requests\": 0.38, \"percent_pages\": 0.53}, {\"format\": \"png\", \"requests\": 6604473, \"pages\": 3984064, \"percent_requests\": 0.31, \"percent_pages\": 0.5}, {\"format\": \"gif\", \"requests\": 3836964, \"pages\": 1487233, \"percent_requests\": 0.18, \"percent_pages\": 0.19}, {\"format\": \"svg\", \"requests\": 1255002, \"pages\": 906022, \"percent_requests\": 0.06, \"percent_pages\": 0.11}, {\"format\": \"webp\", \"requests\": 1040312, \"pages\": 630692, \"percent_requests\": 0.05, \"percent_pages\": 0.08}, {\"format\": \"ico\", \"requests\": 272608, \"pages\": 269039, \"percent_requests\": 0.01, \"percent_pages\": 0.03}, {\"format\": null, \"requests\": 191874, \"pages\": 148218, \"percent_requests\": 0.01, \"percent_pages\": 0.02}, {\"format\": \"avif\", \"requests\": 131101, \"pages\": 81393, \"percent_requests\": 0.01, \"percent_pages\": 0.01}, {\"format\": \"\", \"requests\": 23113, \"pages\": 17699, \"percent_requests\": 0.0, \"percent_pages\": 0.0}, {\"format\": \"heic\", \"requests\": 2156, \"pages\": 1783, \"percent_requests\": 0.0, \"percent_pages\": 0.0}, {\"format\": \"heif\", \"requests\": 2, \"pages\": 2, \"percent_requests\": 0.0, \"percent_pages\": 0.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_chart_image_pages = alt.Chart(requests_image_df).mark_bar().encode(\n",
    "    x=alt.X(\"format\", title=\"Type\", sort=None),\n",
    "    y=alt.Y(\"percent_pages\", title=\"Percent Pages\", sort=None),\n",
    "    color=alt.Color(field=\"format\", sort=None),\n",
    "    tooltip=[\"format\", alt.Tooltip(\"percent_pages:Q\", format=\".0%\")]\n",
    ").properties(\n",
    "    title=\"Percent Pages by Type\"\n",
    ")\n",
    "bar_chart_image_pages + bar_chart_image_pages.mark_text(\n",
    "    dy=-10,\n",
    ").encode(\n",
    "    text=alt.Text(\"percent_pages:Q\", format=\".0%\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xn_CsHD9fZ1f"
   },
   "source": [
    "\n",
    "Let's explore a simple histogram of the requests dataset by looking at the distribution of response sizes. Histograms are useful for representing the distribution of data, by organizing a range of values into \"bins\" (or buckets), and then counting the number of values that fall into each of the bins. If you are not familiar with this type of visualization, then you can [read more about histograms here](https://en.wikipedia.org/wiki/Histogram).\n",
    "\n",
    "In the example below, we'll be using a histogram to visualize the size of individual requests served from websites across the entire dataset. To do this, we'll be using the `respBodySize` metric from `summary` column. This metric represents the size of the response payload in bytes. Since 1 byte is very granular, we'll divide by 1024 to get to 1 KB and then by 100 so that we are looking at this data with bin sizes of 100KB. We'll also wrap this in a `CEIL()` function to remove the decimal points and then multiply the result by 100. Using this technique, 1234567 bytes would be rounded to a bin of 1300 KB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
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    },
    "id": "if5TNXaJfgoo",
    "outputId": "b3858dc2-8a09-416c-a950-fcc2a12a9f95"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "210379ad5a974e8d8a4e2014b114790d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Query is running:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "Downloading:   0%|          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This query will process 125 GB when run.\n",
    "%%bigquery response_size_df --project {GCP_PROJECT}\n",
    "WITH requests AS (\n",
    "  SELECT\n",
    "    CEIL(INT64(summary.respBodySize)/1024/100)*100 AS responseSize100KB,\n",
    "    COUNT(0) OVER () AS total_requests\n",
    "  FROM `httparchive.crawl.requests` TABLESAMPLE SYSTEM (5 PERCENT)\n",
    "  WHERE date = '2024-05-01'\n",
    "    AND client = 'desktop'\n",
    "    AND is_root_page\n",
    "    AND INT64(summary.respBodySize) > 0\n",
    ")\n",
    "\n",
    "SELECT\n",
    "  responseSize100KB,\n",
    "  COUNT(0) AS requests,\n",
    "  COUNT(0)/ANY_VALUE(total_requests) AS pct_requests\n",
    "FROM requests\n",
    "GROUP BY responseSize100KB\n",
    "ORDER BY responseSize100KB ASC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
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     "height": 424
    },
    "id": "Bjm4Xba3GWkD",
    "outputId": "bf8b1e87-978f-4d71-8188-13972ba0504d"
   },
   "outputs": [
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      "text/plain": [
       "     responseSize100KB  requests  pct_requests\n",
       "0                100.0  50125121  9.134219e-01\n",
       "1                200.0   2343976  4.271389e-02\n",
       "2                300.0    818513  1.491563e-02\n",
       "3                400.0    405782  7.394499e-03\n",
       "4                500.0    252538  4.601959e-03\n",
       "..                 ...       ...           ...\n",
       "555            67000.0         1  1.822284e-08\n",
       "556            67300.0         1  1.822284e-08\n",
       "557            67700.0         1  1.822284e-08\n",
       "558            67800.0         1  1.822284e-08\n",
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       "\n",
       "[560 rows x 3 columns]"
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   "source": [
    "When we analyze this data we can see that that 91% of requests have a response size less than 100KB. Try repeating this with 10KB bin sizes and you'll be able to see the spread of response sizes with more granularity."
   ]
  },
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    "You can find many examples of working with the `requests` table in the [HTTP Archive discussion forums](https://discuss.httparchive.org/).\n",
    "\n",
    "You can also make a copy of the workbook and experiment with some of your own visualization ideas for the data as well.\n",
    "\n",
    "In [Part 3](https://colab.research.google.com/github/HTTPArchive/har.fyi/blob/main/workbooks/exploring_pages_and_requests_tables_joined.ipynb) we'll look at how you can use SQL JOINs to analyze both the `pages` and `requests` datasets.\n"
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