From 849cfde9cb807808641aedab05351c6b3d14b6e3 Mon Sep 17 00:00:00 2001 From: Max Ostapenko <1611259+max-ostapenko@users.noreply.github.com> Date: Sun, 21 Jul 2024 16:44:33 +0200 Subject: [PATCH] Query costs and estimates clarified (#11) * add query estimate note * GCP_PROJECT variable * note update * description fix * source json formatted --- src/content/docs/guides/guided-tour.mdx | 6 + src/content/docs/guides/minimizing-costs.md | 4 + ...ploring_httparchive-all-pages_tables.ipynb | 8579 ++++++++++++++- ...ring_httparchive-all-requests_tables.ipynb | 9413 ++++++++++++++++- ...ing_pages_and_requests_tables_joined.ipynb | 17 +- 5 files changed, 18013 insertions(+), 6 deletions(-) diff --git a/src/content/docs/guides/guided-tour.mdx b/src/content/docs/guides/guided-tour.mdx index 0e00267..e588d7f 100644 --- a/src/content/docs/guides/guided-tour.mdx +++ b/src/content/docs/guides/guided-tour.mdx @@ -24,3 +24,9 @@ This guide is split into multiple sections, each one focusing on different table 1. [Exploring the `httparchive.all.pages` tables](https://colab.research.google.com/github/rviscomi/har.fyi/blob/main/workbooks/exploring_httparchive-all-pages_tables.ipynb) 2. [Exploring the `httparchive.all.requests` tables](https://colab.research.google.com/github/rviscomi/har.fyi/blob/main/workbooks/exploring_httparchive-all-requests_tables.ipynb) 3. [JOINing `pages` and `requests` tables](https://colab.research.google.com/github/rviscomi/har.fyi/blob/main/workbooks/exploring_pages_and_requests_tables_joined.ipynb) + +:::caution +HTTP Archive uses clustered tables. BigQuery [doesn't guarantee](https://cloud.google.com/bigquery/docs/clustered-tables#clustered_table_pricing:~:text=BigQuery%20might%20not%20be%20able%20to%20accurately%20estimate%20the%20bytes%20to%20be%20processed) accuracy of estimations for bytes to be processed when querying clustered tables. For your information the actual bytes processed amount is provided in a comment for each query. + +Please also read [Minimizing query costs](../minimizing-costs/) for more details on the topic. +::: diff --git a/src/content/docs/guides/minimizing-costs.md b/src/content/docs/guides/minimizing-costs.md index fe41c12..0fae37a 100644 --- a/src/content/docs/guides/minimizing-costs.md +++ b/src/content/docs/guides/minimizing-costs.md @@ -14,6 +14,10 @@ Table | Partitioned by | Clustered by For example, the `httparchive.all.pages` table is [partitioned](https://cloud.google.com/bigquery/docs/partitioned-tables) by `date` and [clustered](https://cloud.google.com/bigquery/docs/clustered-tables) by the `client`, `is_root_page`, and `rank` columns, which means that queries that filter on these columns will be much faster and cheaper than queries that don't. +:::caution +BigQuery [doesn't guarantee](https://cloud.google.com/bigquery/docs/clustered-tables#clustered_table_pricing:~:text=BigQuery%20might%20not%20be%20able%20to%20accurately%20estimate%20the%20bytes%20to%20be%20processed) accuracy of estimations for 'Bytes processed' when querying clustered tables ([Issue Link](https://issuetracker.google.com/issues/176795805)). The actual data volume may be smaller than the amount provided in the estimate. +::: + Legacy tables like `httparchive.pages.2023_05_01_desktop`, however, do not take advantage of these optimizations and always incur the full cost of scanning the entire table. :::tip diff --git a/workbooks/exploring_httparchive-all-pages_tables.ipynb b/workbooks/exploring_httparchive-all-pages_tables.ipynb index bab7951..8f8e4a5 100644 --- a/workbooks/exploring_httparchive-all-pages_tables.ipynb +++ b/workbooks/exploring_httparchive-all-pages_tables.ipynb @@ -1 +1,8578 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"KcAZ2RHCg_Ze"},"source":["# Before you begin\n","\n","1. Use the [Cloud Resource Manager](https://console.cloud.google.com/cloud-resource-manager) to Create a Cloud Platform project if you do not already have one.\n","2. [Enable billing](https://support.google.com/cloud/answer/6293499#enable-billing) for the project.\n","3. [Enable BigQuery](https://console.cloud.google.com/flows/enableapi?apiid=bigquery) APIs for the project.\n","4. Provide your credentials to the runtime"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":11667,"status":"ok","timestamp":1719068573829,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"SeTJb51SKs_W"},"outputs":[],"source":["from google.colab import auth\n","auth.authenticate_user()"]},{"cell_type":"markdown","metadata":{"id":"OHCBQv4uTCPc"},"source":["The [`pages`](https://har.fyi/reference/tables/pages/) table contains details about each page tracked in the archive. It includes timings, number of requests, types of requests and byte sizes. You can see the table schema by [selecting the table in BigQuery UI](https://console.cloud.google.com/bigquery?ws=!1m5!1m4!4m3!1shttparchive!2sall!3spages) or reading the [schema reference](https://har.fyi/reference/tables/pages/#schema). You can also preview the contents by clicking on the **Preview** button."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["25c70abd54e04053a9957c5263ae6544","008518563ea448ba9b00318190496fd9","2a16e4bbcec04c2c88b5bbd761c0e206","d2f7ce0c783846e6884ac5fb57afbfcf","56e1776f9c504c82b2534a3a6980ef09","0204bf22ede8494ca311ffb940fe2f57","45d8b06fd8d94ce2939111e3137a9673","8062fe9b0c6841e893244abb18974600","2646c6a73c8f4a6e94fd43127a6d8965","273f8b551a9c461cab911c430ef671bb","21a7ae3a9be647a1bb573aff3ab2774e","94375ae3c32e40e2b145d815e17fa370","6e08fe2ec5994d55bd11d9254ee8b86d","b2883ac3a1c943fd825663e4fd9aaa00","b715fc46614641bfa61fa70f5824aac3","920db2a75ed84b3180ce7223575e8a58","f4c7b8f31f054711a8ef75bb40518e82","f6d918d23a054f96a05b6ed767b9e9c9","e3044d1360e847409bff317b4a6ba9fa","ed4d759032de415599b121c14ec7eef3","5ef29256d9ec47dfaa1e7c1e5061b4ea","244055dba4c64e568d96fe9a96452396"]},"executionInfo":{"elapsed":4361,"status":"ok","timestamp":1719068824099,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"DMxOKQ3vJplj","outputId":"993088b9-0b07-47f4-bc04-7844a0fd367f"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"25c70abd54e04053a9957c5263ae6544","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":"94375ae3c32e40e2b145d815e17fa370","version_major":2,"version_minor":0},"text/plain":["Downloading: 0%| |"]},"metadata":{},"output_type":"display_data"}],"source":["# This query will process 898 MB when run.\n","%%bigquery df_preview --project httparchive\n","SELECT *\n","FROM `httparchive.all.pages`\n","WHERE date = '2024-05-01'\n"," AND client='desktop'\n"," AND is_root_page\n"," AND rank = 1000\n"," AND page = 'https://www.google.com/'"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":6817},"executionInfo":{"elapsed":1824,"status":"ok","timestamp":1719068840615,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"W8k33kn4fKaV","outputId":"86aa41c4-b322-4842-c299-15529e0f4ba7"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"df_preview\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"dbdate\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"2024-05-01\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"client\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"desktop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"page\",\n \"properties\": {\n \"dtype\": \"string\",\n 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[Learn more about this errors in console diagnostic audit](https://developer.chrome.com/docs/lighthouse/best-practices/errors-in-console/)\\\",\\\"score\\\":0,\\\"scoreDisplayMode\\\":\\\"binary\\\",\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[{\\\"key\\\":\\\"sourceLocation\\\",\\\"valueType\\\":\\\"source-location\\\",\\\"label\\\":\\\"Source\\\"},{\\\"key\\\":\\\"description\\\",\\\"valueType\\\":\\\"code\\\",\\\"label\\\":\\\"Description\\\"}],\\\"items\\\":[{\\\"source\\\":\\\"violation\\\",\\\"description\\\":\\\"Permissions policy violation: unload is not allowed in this document.\\\",\\\"sourceLocation\\\":{\\\"type\\\":\\\"source-location\\\",\\\"url\\\":\\\"https://www.google.com/xjs/_/js/k=xjs.hd.en.X1agvzNiTt4.O/am=AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAAAAAIARQAAAAJQAAIAAAAAAAAABBAAAEABCA9wBACCAAgQGAAQCCADyUCQAETAAAAAABAIEABAEAQAAAAIACAAAAAAAAAAAAABxAAAIAAAAAAAAAAAAAAHQAAAIAgMAAQgAACAAAAEAeAIIDYJCCAAAAAAAAAAAAAAAIQIJgLkhAQQAEAAAAAAAAAAAAAABISScWxg/d=1/ed=1/dg=2/br=1/rs=ACT90oF25aoatTXYJh4RRs9y-CJ7NVohdQ/ee=ALeJib:B8gLwd;AfeaP:TkrAjf;Afksuc:wMx0R;BMxAGc:E5bFse;BgS6mb:fidj5d;BjwMce:cXX2Wb;CxXAWb:YyRLvc;DM55c:imLrKe;DULqB:RKfG5c;Dkk6ge:wJqrrd;DpcR3d:zL72xf;EABSZ:MXZt9d;ESrPQc:mNTJvc;EVNhjf:pw70Gc;EmZ2Bf:zr1jrb;EnlcNd:WeHg4;Erl4fe:FloWmf,FloWmf;F9mqte:UoRcbe;Fmv9Nc:O1Tzwc;G0KhTb:LIaoZ;G6wU6e:hezEbd;GleZL:J1A7Od;HMDDWe:G8QUdb;HqeXPd:cmbnH;IBADCc:RYquRb;IoGlCf:b5lhvb;IsdWVc:qzxzOb;JXS8fb:Qj0suc;JbMT3:M25sS;JsbNhc:Xd8iUd;KOxcK:OZqGte;KQzWid:ZMKkN;KcokUb:KiuZBf;KpRAue:Tia57b;LBgRLc:SdcwHb,XVMNvd;LEikZe:byfTOb,lsjVmc;LsNahb:ucGLNb;Me32dd:MEeYgc;NPKaK:SdcwHb;NSEoX:lazG7b;Np8Qkd:Dpx6qc;Nyt6ic:jn2sGd;OgagBe:cNTe0;Oj465e:KG2eXe,KG2eXe;OohIYe:mpEAQb;Pjplud:EEDORb,PoEs9b;Q1Ow7b:x5CSu;Q6C5kf:pfdZCe;QGR0gd:Mlhmy;R2kc8b:ALJqWb;R4IIIb:QWfeKf;R9Ulx:CR7Ufe;RDNBlf:zPRCJb;SLtqO:Kh1xYe;SMDL4c:fTfGO,fTfGO;SNUn3:ZwDk9d,x8cHvb;ShpF6e:N0pvGc;TxfV6d:YORN0b;U96pRd:FsR04;UDrY1c:eps46d;UVmjEd:EesRsb;UyG7Kb:wQd0G;V2HTTe:RolTY;VGRfx:VFqbr;VN6jIc:ddQyuf;VOcgDe:YquhTb;VsAqSb:PGf2Re;VxQ32b:k0XsBb;WCEKNd:I46Hvd;WDGyFe:jcVOxd;Wfmdue:g3MJlb;XUezZ:sa7lqb;YV5bee:IvPZ6d;YkQtAf:rx8ur;ZMvdv:PHFPjb;ZWEUA:afR4Cf;a56pNe:JEfCwb;aAJE9c:WHW6Ef;aCJ9tf:qKftvc;aZ61od:arTwJ;af0EJf:ghinId;bDXwRe:UsyOtc;bFZ6gf:RsDQqe;bcPXSc:gSZLJb;cEt90b:ws9Tlc;cFTWae:gT8qnd;coJ8e:KvoW8;dIoSBb:ZgGg9b;dLlj2:Qqt3Gf;daB6be:lMxGPd;dtl0hd:lLQWFe;eBAeSb:Ck63tb;eBZ5Nd:VruDBd;eHDfl:ofjVkb;eO3lse:nFClrf;fWLTFc:TVBJbf;g8nkx:U4MzKc;gaub4:TN6bMe;gtVSi:ekUOYd;h3MYod:cEt90b;hK67qb:QWEO5b;heHB1:sFczq;hjRo6e:F62sG;hsLsYc:Vl118;iFQyKf:QIhFr,vfuNJf;imqimf:jKGL2e;io8t5d:sgY6Zb;jY0zg:Q6tNgc;k2Qxcb:XY51pe;kCQyJ:ueyPK;kMFpHd:OTA3Ae;kbAm9d:MkHyGd;lkq0A:JyBE3e;nAFL3:NTMZac,s39S4;oGtAuc:sOXFj;oSUNyd:fTfGO,fTfGO;oUlnpc:RagDlc;okUaUd:wItadb;p2tIDb:tp1Cx;pKJiXd:VCenhc;pNsl2d:j9Yuyc;pXdRYb:JKoKVe;pj82le:mg5CW;qZx2Fc:j0xrE;qaS3gd:yiLg6e;qavrXe:zQzcXe;qddgKe:d7YSfd,x4FYXe;rQSrae:C6D5Fc;sP4Vbe:VwDzFe;sTsDMc:kHVSUb;tH4IIe:Ymry6;tosKvd:ZCqP3;trZL0b:qY8PFe;uY49fb:COQbmf;uknmt:GkPrzb;uuQkY:u2V3ud;vGrMZ:lPJJ0c;vfVwPd:lcrkwe;w3bZCb:ZPGaIb;w4rSdf:XKiZ9;w9w86d:dt4g2b;wQlYve:aLUfP;wR5FRb:O1Gjze,TtcOte;wV5Pjc:L8KGxe;whEZac:F4AmNb;xBbsrc:NEW1Qc;yGxLoc:FmAr0c;yxTchf:KUM7Z;z97YGf:oug9te;zOsCQe:Ko78Df;zaIgPb:Qtpxbd/m=cdos,hsm,jsa,mb4ZUb,d,csi,cEt90b,SNUn3,qddgKe,sTsDMc,dtl0hd,eHDfl\\\",\\\"urlProvider\\\":\\\"network\\\",\\\"line\\\":427,\\\"column\\\":246}}]}},\\\"server-response-time\\\":{\\\"id\\\":\\\"server-response-time\\\",\\\"title\\\":\\\"Initial server response time was short\\\",\\\"description\\\":\\\"Keep the server response time for the main document short because all other requests depend on it. [Learn more about the Time to First Byte metric](https://developer.chrome.com/docs/lighthouse/performance/time-to-first-byte/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"informative\\\",\\\"numericValue\\\":141.021,\\\"numericUnit\\\":\\\"millisecond\\\",\\\"displayValue\\\":\\\"Root document took 140\\u00a0ms\\\",\\\"metricSavings\\\":{\\\"FCP\\\":50,\\\"LCP\\\":50},\\\"details\\\":{\\\"type\\\":\\\"opportunity\\\",\\\"headings\\\":[{\\\"key\\\":\\\"url\\\",\\\"valueType\\\":\\\"url\\\",\\\"label\\\":\\\"URL\\\"},{\\\"key\\\":\\\"responseTime\\\",\\\"valueType\\\":\\\"timespanMs\\\",\\\"label\\\":\\\"Time Spent\\\"}],\\\"items\\\":[{\\\"url\\\":\\\"https://www.google.com/\\\",\\\"responseTime\\\":141.021}],\\\"overallSavingsMs\\\":41.02099999999999},\\\"guidanceLevel\\\":1},\\\"interactive\\\":{\\\"id\\\":\\\"interactive\\\",\\\"title\\\":\\\"Time to Interactive\\\",\\\"description\\\":\\\"Time to Interactive is the amount of time it takes for the page to become fully interactive. [Learn more about the Time to Interactive metric](https://developer.chrome.com/docs/lighthouse/performance/interactive/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"numeric\\\",\\\"numericValue\\\":1114.671,\\\"numericUnit\\\":\\\"millisecond\\\",\\\"displayValue\\\":\\\"1.1\\u00a0s\\\"},\\\"user-timings\\\":{\\\"id\\\":\\\"user-timings\\\",\\\"title\\\":\\\"User Timing marks and measures\\\",\\\"description\\\":\\\"Consider instrumenting your app with the User Timing API to measure your app's real-world performance during key user experiences. [Learn more about User Timing marks](https://developer.chrome.com/docs/lighthouse/performance/user-timings/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"informative\\\",\\\"displayValue\\\":\\\"10 user timings\\\",\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[{\\\"key\\\":\\\"name\\\",\\\"valueType\\\":\\\"text\\\",\\\"label\\\":\\\"Name\\\"},{\\\"key\\\":\\\"timingType\\\",\\\"valueType\\\":\\\"text\\\",\\\"label\\\":\\\"Type\\\"},{\\\"key\\\":\\\"startTime\\\",\\\"valueType\\\":\\\"ms\\\",\\\"granularity\\\":0.01,\\\"label\\\":\\\"Start Time\\\"},{\\\"key\\\":\\\"duration\\\",\\\"valueType\\\":\\\"ms\\\",\\\"granularity\\\":0.01,\\\"label\\\":\\\"Duration\\\"}],\\\"items\\\":[{\\\"name\\\":\\\"fcbyXe\\\",\\\"startTime\\\":703.942,\\\"duration\\\":28.957,\\\"timingType\\\":\\\"Measure\\\"},{\\\"name\\\":\\\"kDcP9b\\\",\\\"startTime\\\":706.924,\\\"duration\\\":24.375,\\\"timingType\\\":\\\"Measure\\\"},{\\\"name\\\":\\\"fcbyXe\\\",\\\"startTime\\\":767.841,\\\"duration\\\":0.358,\\\"timingType\\\":\\\"Measure\\\"},{\\\"name\\\":\\\"fcbyXe\\\",\\\"startTime\\\":1182.676,\\\"duration\\\":0.324,\\\"timingType\\\":\\\"Measure\\\"},{\\\"name\\\":\\\"SearchAFTStart\\\",\\\"startTime\\\":282,\\\"timingType\\\":\\\"Mark\\\"},{\\\"name\\\":\\\"trigger:SearchAFTEnd\\\",\\\"startTime\\\":505,\\\"timingType\\\":\\\"Mark\\\"},{\\\"name\\\":\\\"triggerRender_0\\\",\\\"startTime\\\":703.942,\\\"timingType\\\":\\\"Mark\\\"},{\\\"name\\\":\\\"O7jPNb\\\",\\\"startTime\\\":706.924,\\\"timingType\\\":\\\"Mark\\\"},{\\\"name\\\":\\\"triggerRender_1\\\",\\\"startTime\\\":767.841,\\\"timingType\\\":\\\"Mark\\\"},{\\\"name\\\":\\\"triggerRender_2\\\",\\\"startTime\\\":1182.676,\\\"timingType\\\":\\\"Mark\\\"}]},\\\"guidanceLevel\\\":2},\\\"critical-request-chains\\\":{\\\"id\\\":\\\"critical-request-chains\\\",\\\"title\\\":\\\"Avoid chaining critical requests\\\",\\\"description\\\":\\\"The Critical Request Chains below show you what resources are loaded with a high priority. Consider reducing the length of chains, reducing the download size of resources, or deferring the download of unnecessary resources to improve page load. [Learn how to avoid chaining critical requests](https://developer.chrome.com/docs/lighthouse/performance/critical-request-chains/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"informative\\\",\\\"displayValue\\\":\\\"2 chains found\\\",\\\"details\\\":{\\\"type\\\":\\\"criticalrequestchain\\\",\\\"chains\\\":{\\\"94E918B5417340A96230EA63AC399CF8\\\":{\\\"request\\\":{\\\"url\\\":\\\"https://www.google.com/\\\",\\\"startTime\\\":73755.313002,\\\"endTime\\\":73755.533201,\\\"responseReceivedTime\\\":73755.456297,\\\"transferSize\\\":55952},\\\"children\\\":{\\\"498041.2\\\":{\\\"request\\\":{\\\"url\\\":\\\"https://www.google.com/xjs/_/ss/k=xjs.hd.yW324tanFjY.L.B1.O/am=AKYAAAAAAADAAAAAAAAAAAAAAAAAAIAAAAgAAAAAAAWAQDgAAcCGAAAAAAEAAAAAAAAEAAAA4wAAAIAAgQCAAUCAAAAAAAiEAEAABBABJIEgAwEARDDCAKQCAAMAAAACSAAAAABAAAEIDIgACHgIAQAADHRAAAkAAMAAQgAAAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAIAAAABAAQAAAAAAAAAAAAAAAAAAAQ/d=1/ed=1/br=1/rs=ACT90oGsNS-Aw40wHxbiNjKwCEWc-sPAPg/m=cdos,hsm,jsa,mb4ZUb,d,csi,cEt90b,SNUn3,qddgKe,sTsDMc,dtl0hd,eHDfl\\\",\\\"startTime\\\":73755.492207,\\\"endTime\\\":73755.544688,\\\"responseReceivedTime\\\":73755.540241,\\\"transferSize\\\":1108}},\\\"498041.10\\\":{\\\"request\\\":{\\\"url\\\":\\\"https://www.gstatic.com/og/_/ss/k=og.qtm.RS0dNtaZmo0.L.W.O/m=qcwid/excm=qaaw,qadd,qaid,qein,qhaw,qhba,qhbr,qhch,qhga,qhid,qhin/d=1/ed=1/ct=zgms/rs=AA2YrTuhe2hCYlalU7rKCW-qT_-zMhVRaw\\\",\\\"startTime\\\":73755.626312,\\\"endTime\\\":73755.884866,\\\"responseReceivedTime\\\":73755.88408399999,\\\"transferSize\\\":1149}}}}},\\\"longestChain\\\":{\\\"duration\\\":571.8640000075102,\\\"length\\\":2,\\\"transferSize\\\":1149}},\\\"guidanceLevel\\\":1},\\\"redirects\\\":{\\\"id\\\":\\\"redirects\\\",\\\"title\\\":\\\"Avoid multiple page redirects\\\",\\\"description\\\":\\\"Redirects introduce additional delays before the page can be loaded. [Learn how to avoid page redirects](https://developer.chrome.com/docs/lighthouse/performance/redirects/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"metricSavings\\\",\\\"numericValue\\\":0,\\\"numericUnit\\\":\\\"millisecond\\\",\\\"displayValue\\\":\\\"\\\",\\\"metricSavings\\\":{\\\"LCP\\\":0,\\\"FCP\\\":0},\\\"details\\\":{\\\"type\\\":\\\"opportunity\\\",\\\"headings\\\":[],\\\"items\\\":[],\\\"overallSavingsMs\\\":0},\\\"guidanceLevel\\\":2},\\\"image-aspect-ratio\\\":{\\\"id\\\":\\\"image-aspect-ratio\\\",\\\"title\\\":\\\"Displays images with correct aspect ratio\\\",\\\"description\\\":\\\"Image display dimensions should match natural aspect ratio. [Learn more about image aspect ratio](https://developer.chrome.com/docs/lighthouse/best-practices/image-aspect-ratio/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"binary\\\",\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[],\\\"items\\\":[]}},\\\"image-size-responsive\\\":{\\\"id\\\":\\\"image-size-responsive\\\",\\\"title\\\":\\\"Serves images with appropriate resolution\\\",\\\"description\\\":\\\"Image natural dimensions should be proportional to the display size and the pixel ratio to maximize image clarity. [Learn how to provide responsive images](https://web.dev/articles/serve-responsive-images).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"binary\\\",\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[],\\\"items\\\":[]}},\\\"deprecations\\\":{\\\"id\\\":\\\"deprecations\\\",\\\"title\\\":\\\"Avoids deprecated APIs\\\",\\\"description\\\":\\\"Deprecated APIs will eventually be removed from the browser. [Learn more about deprecated APIs](https://developer.chrome.com/docs/lighthouse/best-practices/deprecations/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"binary\\\",\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[],\\\"items\\\":[]}},\\\"third-party-cookies\\\":{\\\"id\\\":\\\"third-party-cookies\\\",\\\"title\\\":\\\"Avoids third-party cookies\\\",\\\"description\\\":\\\"Support for third-party cookies will be removed in a future version of Chrome. [Learn more about phasing out third-party cookies](https://developer.chrome.com/en/docs/privacy-sandbox/third-party-cookie-phase-out/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"binary\\\",\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[],\\\"items\\\":[]}},\\\"mainthread-work-breakdown\\\":{\\\"id\\\":\\\"mainthread-work-breakdown\\\",\\\"title\\\":\\\"Minimizes main-thread work\\\",\\\"description\\\":\\\"Consider reducing the time spent parsing, compiling and executing JS. You may find delivering smaller JS payloads helps with this. [Learn how to minimize main-thread work](https://developer.chrome.com/docs/lighthouse/performance/mainthread-work-breakdown/)\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"informative\\\",\\\"numericValue\\\":665.2169999999993,\\\"numericUnit\\\":\\\"millisecond\\\",\\\"displayValue\\\":\\\"0.7\\u00a0s\\\",\\\"metricSavings\\\":{\\\"TBT\\\":50},\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[{\\\"key\\\":\\\"groupLabel\\\",\\\"valueType\\\":\\\"text\\\",\\\"label\\\":\\\"Category\\\"},{\\\"key\\\":\\\"duration\\\",\\\"valueType\\\":\\\"ms\\\",\\\"granularity\\\":1,\\\"label\\\":\\\"Time Spent\\\"}],\\\"items\\\":[{\\\"group\\\":\\\"scriptEvaluation\\\",\\\"groupLabel\\\":\\\"Script Evaluation\\\",\\\"duration\\\":283.60299999999967},{\\\"group\\\":\\\"other\\\",\\\"groupLabel\\\":\\\"Other\\\",\\\"duration\\\":255.82699999999946},{\\\"group\\\":\\\"styleLayout\\\",\\\"groupLabel\\\":\\\"Style & Layout\\\",\\\"duration\\\":45.224000000000004},{\\\"group\\\":\\\"scriptParseCompile\\\",\\\"groupLabel\\\":\\\"Script Parsing & Compilation\\\",\\\"duration\\\":33.52},{\\\"group\\\":\\\"garbageCollection\\\",\\\"groupLabel\\\":\\\"Garbage Collection\\\",\\\"duration\\\":25.96},{\\\"group\\\":\\\"parseHTML\\\",\\\"groupLabel\\\":\\\"Parse HTML & CSS\\\",\\\"duration\\\":17.401999999999994},{\\\"group\\\":\\\"paintCompositeRender\\\",\\\"groupLabel\\\":\\\"Rendering\\\",\\\"duration\\\":3.680999999999995}],\\\"sortedBy\\\":[\\\"duration\\\"]},\\\"guidanceLevel\\\":1},\\\"bootup-time\\\":{\\\"id\\\":\\\"bootup-time\\\",\\\"title\\\":\\\"JavaScript execution time\\\",\\\"description\\\":\\\"Consider reducing the time spent parsing, compiling, and executing JS. You may find delivering smaller JS payloads helps with this. [Learn how to reduce Javascript execution time](https://developer.chrome.com/docs/lighthouse/performance/bootup-time/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"informative\\\",\\\"numericValue\\\":256.09500000000014,\\\"numericUnit\\\":\\\"millisecond\\\",\\\"displayValue\\\":\\\"0.3\\u00a0s\\\",\\\"metricSavings\\\":{\\\"TBT\\\":50},\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[{\\\"key\\\":\\\"url\\\",\\\"valueType\\\":\\\"url\\\",\\\"label\\\":\\\"URL\\\"},{\\\"key\\\":\\\"total\\\",\\\"granularity\\\":1,\\\"valueType\\\":\\\"ms\\\",\\\"label\\\":\\\"Total CPU Time\\\"},{\\\"key\\\":\\\"scripting\\\",\\\"granularity\\\":1,\\\"valueType\\\":\\\"ms\\\",\\\"label\\\":\\\"Script Evaluation\\\"},{\\\"key\\\":\\\"scriptParseCompile\\\",\\\"granularity\\\":1,\\\"valueType\\\":\\\"ms\\\",\\\"label\\\":\\\"Script 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[Learn how to preconnect to required origins](https://developer.chrome.com/docs/lighthouse/performance/uses-rel-preconnect/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"metricSavings\\\",\\\"numericValue\\\":0,\\\"numericUnit\\\":\\\"millisecond\\\",\\\"displayValue\\\":\\\"\\\",\\\"warnings\\\":[],\\\"metricSavings\\\":{\\\"LCP\\\":0,\\\"FCP\\\":0},\\\"details\\\":{\\\"type\\\":\\\"opportunity\\\",\\\"headings\\\":[],\\\"items\\\":[],\\\"overallSavingsMs\\\":0,\\\"sortedBy\\\":[\\\"wastedMs\\\"]},\\\"guidanceLevel\\\":3},\\\"font-display\\\":{\\\"id\\\":\\\"font-display\\\",\\\"title\\\":\\\"All text remains visible during webfont loads\\\",\\\"description\\\":\\\"Leverage the `font-display` CSS feature to ensure text is user-visible while webfonts are loading. [Learn more about `font-display`](https://developer.chrome.com/docs/lighthouse/performance/font-display/).\\\",\\\"score\\\":1,\\\"scoreDisplayMode\\\":\\\"metricSavings\\\",\\\"warnings\\\":[],\\\"details\\\":{\\\"type\\\":\\\"table\\\",\\\"headings\\\":[],\\\"items\\\":[]},\\\"guidanceLevel\\\":3},\\\"diagnostics\\\":{\\\"id\\\":\\\"diagnostics\\\",\\\"title\\\":\\\"Diagnostics\\\",\\\"description\\\":\\\"Collection of useful page 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    \n"," \n"],"text/plain":[" requests pages\n","0 2393199079 24485975"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# This query will process 136 GB when run.\n","%%bigquery --project httparchive\n","SELECT\n"," COUNT(0) requests,\n"," COUNT(DISTINCT page) pages\n","FROM `httparchive.all.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,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":458,"referenced_widgets":["930c9481c4d64d3d861977e112233afa","b596d83fc4214afc834a833c7f6404f3","7eed69dcbfa8440c86606e3f5053770a","e9492200c9ce4e3c9e972cc5f682b695","224410d6e2f240c1b2e0492049dd6c04","db9dbc37535e4fe0804ffa5b175f65d4","3a6bca9276e0403394c31e0cbf40d841","019b861f5a9a49dcb9d63b1b076d70e5","ca9534bdda54496393f92b313b96f50a","4442cd19fbe640bb908e714028e1a2aa","ee3ce490be3340c6abc3f73b70091af8","33a4f7f8f8ed42b88fac6cf1eff7adbb","f0e1952324044f73be0055d1a77975fe","f985f0f07f4946588c22a1419bd78732","84cae140493c4b3ca526bee29bfe101b","14c923624d934f1c82db5bd867f5a32b","10aa9eacf3a543499b0e72ed034e5dfd","f2334df789be4924a5bfb744399805fe","a9e2dbb7e382457991b63c2120eda9e8","eabb7ce20a604721909ef00bcac3f724","a62d183b588b4e338ccc7701cf7a6a04","75ea379d768e432cbbadd615ac12e595"]},"executionInfo":{"elapsed":56184,"status":"ok","timestamp":1719070093923,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"koKF_VyLfBG7","outputId":"d85c9b7f-d403-4148-e4a3-c82baf1631df"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"930c9481c4d64d3d861977e112233afa","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":"33a4f7f8f8ed42b88fac6cf1eff7adbb","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\": 11,\n \"fields\": [\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"font\",\n \"script\",\n \"audio\"\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 128104348,\n 813565233,\n 1177141\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 21224863,\n 23556030,\n 518769\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe"},"text/html":["\n","
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    typerequestspages
    0script81356523323556030
    1image74701877424146223
    2css27846377323259825
    3other19453369611901742
    4html18586872924145122
    5font12810434821224863
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    \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 154 GB when run.\n","%%bigquery --project httparchive\n","SELECT\n"," type,\n"," COUNT(0) requests,\n"," COUNT(DISTINCT page) pages\n","FROM `httparchive.all.requests`\n","WHERE date = '2024-06-01'\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":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["193b66f1eca741e699658ac6abaf648f","72744e68835947e6b237e2e2a3decf9c","4285bddd87174ad48c62141d08132b4a","285328c168734536acd26e98991c8a63","44c1ec80d48e4b928d814068cbcd2863","4e28fb249a1847238938b9cad4697c6d","576d921553764a99bb4e47a3d29a1f56","1166d398c1664f1d9abb8463f7981655","9c9fd148f35447608359fee256b1cbfd","f56a8a2484d04257beb8d9624f381b70","d90784b265f84491902cd3be7f4b99ae","319adafc2c9144d68b6d401e122834e1","0186695bc85c4e0687dadce9933ad151","10447c10adf24c73b8fecd857f657227","bc2776bd1a9d472cb80761ec521d6f5b","26aee6ec09104d76b90331d043d592cb","25f77534f5d0450da7b847a4c5eb1a1f","0e578e40ec76433b851f95bafb714cec","0f95db5c06c149d8b0f2173e288240b2","3cccc2f8cec44c35a545566dcf270957","b3808e4d78a44f0e9a53f10704697e90","426ed755de064058b609c481ad326778"]},"executionInfo":{"elapsed":3350,"status":"ok","timestamp":1719070594121,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"HyaE62NTfGe9","outputId":"2281d9c1-b0df-4630-afb4-8f865a6d68dc"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"193b66f1eca741e699658ac6abaf648f","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":"319adafc2c9144d68b6d401e122834e1","version_major":2,"version_minor":0},"text/plain":["Downloading: 0%| |"]},"metadata":{},"output_type":"display_data"}],"source":["# This query will process 154 GB when run.\n","%%bigquery requests_type_df --project httparchive\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.all.requests`\n"," WHERE date = '2024-06-01'\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":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"executionInfo":{"elapsed":275,"status":"ok","timestamp":1719070696558,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"30c8TCmWmQ9w","outputId":"c6654355-bedd-432c-ce88-e8d674b3a1df"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"requests_type_df\",\n \"rows\": 11,\n \"fields\": [\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"font\",\n \"script\",\n \"audio\"\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 128104348,\n 813565233,\n 1177141\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 21224863,\n 23556030,\n 518769\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_requests\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12210278829367865,\n \"min\": 0.0,\n \"max\": 0.34,\n \"num_unique_values\": 7,\n \"samples\": [\n 0.34,\n 0.31,\n 0.03\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_pages\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.44265315788077036,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.02,\n 1.0,\n 0.61\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe","variable_name":"requests_type_df"},"text/html":["\n","
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    typerequestspagespercent_requestspercent_pages
    0script813565233235560300.340.97
    1image747018774241462230.311.00
    2css278463773232598250.110.96
    3other194533696119017420.080.49
    4html185868729241451220.081.00
    5font128104348212248630.050.88
    6text64065722147360860.030.61
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    10wasm142421510630.000.00
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    \n"],"text/plain":[" type requests pages percent_requests percent_pages\n","0 script 813565233 23556030 0.34 0.97\n","1 image 747018774 24146223 0.31 1.00\n","2 css 278463773 23259825 0.11 0.96\n","3 other 194533696 11901742 0.08 0.49\n","4 html 185868729 24145122 0.08 1.00\n","5 font 128104348 21224863 0.05 0.88\n","6 text 64065722 14736086 0.03 0.61\n","7 video 7191779 1407697 0.00 0.06\n","8 xml 2714133 747170 0.00 0.03\n","9 audio 1177141 518769 0.00 0.02\n","10 wasm 142421 51063 0.00 0.00"]},"execution_count":13,"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":177,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399},"executionInfo":{"elapsed":298,"status":"ok","timestamp":1719078675756,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"8eHylgTVmF0J","outputId":"14145b5e-8d1a-4282-b4f8-fe3cc73a8627"},"outputs":[{"data":{"text/html":["\n","
    \n",""],"text/plain":["alt.LayerChart(...)"]},"execution_count":177,"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":182,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":414},"executionInfo":{"elapsed":294,"status":"ok","timestamp":1719078803423,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"Kf8aU76Qm_ym","outputId":"c0ffb2c0-71da-43c8-955f-210efbd68611"},"outputs":[{"data":{"text/html":["\n","
    \n",""],"text/plain":["alt.LayerChart(...)"]},"execution_count":182,"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":75,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":866,"referenced_widgets":["fbf43300d0e34bc9a2d9d08868c8619e","b998b84c8fe344ffb0cbce35dcdd8d94","8d40e645ae844d1ca3ae2116bbb1f16c","8883a38ab4634285aa2bd180d13ae58e","4bd93e4d685145d898b591d1dc7d5822","2bbea2bcd9eb467788a13e84c52d757b","588bc2ce3d6047f9969ef315a56b0261","0d68b90a92b24d44b1b606eb6a378fa3","092a6ee540874fcbaccda206657b6118","c0003c360865446da499df0019cecd00","037517da35804422ae4c8bbaf820a824","a88f6b4843304133b8522f8a9f7906e7","697add1e80954140a99ee668d0719ff0","4bacede4594044b3a32dafa195f9455e","82c8da0a4a334499bd4ec6fc1cdb9711","1196079bd7e24f18b16830cedbc5ebad","67a07fe41e774294bdb0df5cae9581dd","c0be1cba4bc249c89add5539cef59d60","475a3306cb6b40e2bc9f8d7e05718a7e","74fb4bf87ee44da0a721fd71de4f2ace","26599b464d4d404b9543e7a9c5d7c250","6db951ebc6ab4b86863ea48b280490c5"]},"executionInfo":{"elapsed":31464,"status":"ok","timestamp":1719073004910,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"uRQPMFPdfPgz","outputId":"42a7da64-e2d5-47d9-cd62-9592697f2a8f"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fbf43300d0e34bc9a2d9d08868c8619e","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":"a88f6b4843304133b8522f8a9f7906e7","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\": 24,\n \"fields\": [\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [\n \"video\",\n \"image\",\n \"font\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"format\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 14,\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\": 24,\n \"samples\": [\n 1956268,\n 21277,\n 21141126\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pages\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 24,\n \"samples\": [\n 1516355,\n 16238,\n 8068108\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_requests\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07170667908979579,\n \"min\": 0.0,\n \"max\": 0.32,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.02,\n 0.12,\n 0.07\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_pages\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.17816648703235208,\n \"min\": 0.0,\n \"max\": 0.7,\n \"num_unique_values\": 13,\n \"samples\": [\n 0.01,\n 0.06,\n 0.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe"},"text/html":["\n","
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    typeformatrequestspagespercent_requestspercent_pages
    0script2114112680681080.320.70
    1imagejpg800214341273090.120.36
    2css724245346416360.110.40
    3imagepng647392139714160.100.35
    4other518846016069180.080.14
    5html466976424636350.070.21
    6imagegif383735114803180.060.13
    7font369042428688670.060.25
    8text195626815163550.030.13
    9imagesvg12161518898470.020.08
    10imagewebp10915116697070.020.06
    11imageico3162513125390.000.03
    12videomp41698291275370.000.01
    13imageavif142923943140.000.01
    14xml108975623290.000.01
    15video41888243260.000.00
    16image21277162380.000.00
    17imageheic209517410.000.00
    18htmlNone46460.000.00
    19videoflv28230.000.00
    20videoflash24240.000.00
    21videoswf660.000.00
    22imageheif330.000.00
    23imageNone110.000.00
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    \n"],"text/plain":[" type format requests pages percent_requests percent_pages\n","0 script 21141126 8068108 0.32 0.70\n","1 image jpg 8002143 4127309 0.12 0.36\n","2 css 7242453 4641636 0.11 0.40\n","3 image png 6473921 3971416 0.10 0.35\n","4 other 5188460 1606918 0.08 0.14\n","5 html 4669764 2463635 0.07 0.21\n","6 image gif 3837351 1480318 0.06 0.13\n","7 font 3690424 2868867 0.06 0.25\n","8 text 1956268 1516355 0.03 0.13\n","9 image svg 1216151 889847 0.02 0.08\n","10 image webp 1091511 669707 0.02 0.06\n","11 image ico 316251 312539 0.00 0.03\n","12 video mp4 169829 127537 0.00 0.01\n","13 image avif 142923 94314 0.00 0.01\n","14 xml 108975 62329 0.00 0.01\n","15 video 41888 24326 0.00 0.00\n","16 image 21277 16238 0.00 0.00\n","17 image heic 2095 1741 0.00 0.00\n","18 html None 46 46 0.00 0.00\n","19 video flv 28 23 0.00 0.00\n","20 video flash 24 24 0.00 0.00\n","21 video swf 6 6 0.00 0.00\n","22 image heif 3 3 0.00 0.00\n","23 image None 1 1 0.00 0.00"]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["# This query will process 131 GB when run.\n","%%bigquery --project httparchive\n","WITH requests AS (\n"," SELECT\n"," type,\n"," JSON_VALUE(summary, '$.format') AS format,\n"," page,\n"," COUNT(0) OVER() AS total_requests,\n"," COUNT(DISTINCT page) OVER() AS total_pages\n"," FROM `httparchive.all.requests` TABLESAMPLE SYSTEM (5 PERCENT)\n"," WHERE date = '2024-06-01'\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. Optimising our query by using cluster columns we may skip applying sampling here."]},{"cell_type":"code","execution_count":77,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["16d9263d45b340f0a7a92583da89e43e","52c46fee4f254c918ad6c65a716c03bd","f509db4e53624faea08a80be1ebba0da","baedbe8751ef43c3a5f0ac43fffcf255","157ec81aa0764a1c8465d4e8e5e46343","08e8963ee5ce45cf9d05cc78ae78adea","c794429785d34be592081cf2bcad7ded","e2ba7876fec34694bb30766748e00173","cdce8a21a53f497694bf49fe111684d2","de7fbac009d24b56b97f88385cab3bae","3b1fba01b8a1421480e544e3a2b6c9cb","06c9371eb8a246a2acc9fa07768f9759","49e032edc4d94d7aa35a3251fa2b6f54","035f5107815e43348609347c7f3b44ee","dcff1c0454574a6992fab3b5b4e6f9bd","24dd022c67fe4afb9275626c10b1a6e5","7b890ee971404904b8dff58052d8bda6","67ac5460c90c4f65848ff17bbfa486b0","83ff34bff4bb4430aebc26f8d9ac3086","fe60d2cd2bb9456a90e38c9f7d03b30a","c887f6d4e3144b28b8a5c4c13a69ca9d","b50d0aa59a734f3fbccc97fec03a87ad"]},"executionInfo":{"elapsed":19912,"status":"ok","timestamp":1719076185573,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"9dhWIwI8fTjH","outputId":"b2b9a98b-e738-4d86-fbd3-b2080ae0176c"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"16d9263d45b340f0a7a92583da89e43e","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":"06c9371eb8a246a2acc9fa07768f9759","version_major":2,"version_minor":0},"text/plain":["Downloading: 0%| |"]},"metadata":{},"output_type":"display_data"}],"source":["# This query will process 832 GB when run.\n","%%bigquery requests_image_df --project httparchive\n","WITH requests AS (\n"," SELECT\n"," JSON_VALUE(summary, '$.format') AS format,\n"," page,\n"," COUNT(0) OVER() AS total_requests,\n"," COUNT(DISTINCT page) OVER() AS total_pages\n"," FROM `httparchive.all.requests`\n"," WHERE date = '2024-06-01'\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":78,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"executionInfo":{"elapsed":303,"status":"ok","timestamp":1719076378617,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"lEynOaPZ78FM","outputId":"bd16ff7a-62b4-4302-b86e-d25dda984444"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"requests_image_df\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"format\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"ico\",\n \"jpg\",\n \"heif\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"requests\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 12,\n \"samples\": [\n 15,\n 52,\n 164174651\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pages\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 12,\n \"samples\": [\n 5,\n 32,\n 10620609\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_requests\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.13343935178971983,\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\": 0.3447034298428935,\n \"min\": 0.0,\n \"max\": 0.93,\n \"num_unique_values\": 9,\n \"samples\": [\n 0.02,\n 0.93,\n 0.48\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe","variable_name":"requests_image_df"},"text/html":["\n","
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    formatrequestspagespercent_requestspercent_pages
    0jpg164174651106206090.380.83
    1png132785707117836490.310.93
    2gif7879662472761840.180.57
    3svg2494985948211150.060.38
    4webp2239471826905270.050.21
    5ico648160761518600.010.48
    6avif29310983745950.010.03
    74397331946250.000.02
    8heic44171117680.000.00
    9heif52320.000.00
    10None1550.000.00
    11jxl640.000.00
    \n","
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    \n","\n","\n","
    \n"," \n","\n","\n","\n"," \n","
    \n","\n","
    \n"," \n"," \n"," \n","
    \n","\n","
    \n","
    \n"],"text/plain":[" format requests pages percent_requests percent_pages\n","0 jpg 164174651 10620609 0.38 0.83\n","1 png 132785707 11783649 0.31 0.93\n","2 gif 78796624 7276184 0.18 0.57\n","3 svg 24949859 4821115 0.06 0.38\n","4 webp 22394718 2690527 0.05 0.21\n","5 ico 6481607 6151860 0.01 0.48\n","6 avif 2931098 374595 0.01 0.03\n","7 439733 194625 0.00 0.02\n","8 heic 44171 11768 0.00 0.00\n","9 heif 52 32 0.00 0.00\n","10 None 15 5 0.00 0.00\n","11 jxl 6 4 0.00 0.00"]},"execution_count":78,"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":184,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":395},"executionInfo":{"elapsed":349,"status":"ok","timestamp":1719078845237,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"3MOvKaUJfaiR","outputId":"7d45d50b-c966-4520-9781-e043426659dc"},"outputs":[{"data":{"text/html":["\n","
    \n",""],"text/plain":["alt.LayerChart(...)"]},"execution_count":184,"metadata":{},"output_type":"execute_result"}],"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"," tooltip=[\"format\", alt.Tooltip(\"percent_requests:Q\", format=\".0%\")]\n",").properties(\n"," title=\"Distribution of image formats\"\n",")\n","pie_chart_image_formats + pie_chart_image_formats.mark_text(\n"," radius=170\n",").encode(\n"," text=alt.Text(\"percent_requests:Q\", format=\".0%\")\n",")"]},{"cell_type":"code","execution_count":185,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399},"executionInfo":{"elapsed":315,"status":"ok","timestamp":1719078861795,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"T9fwfkGY8R8h","outputId":"b4e53737-e909-4bec-f92c-8eb9f5f71d20"},"outputs":[{"data":{"text/html":["\n","
    \n",""],"text/plain":["alt.LayerChart(...)"]},"execution_count":185,"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 `ROUND()` 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 1200 KB."]},{"cell_type":"code","execution_count":186,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["79930fa7bb1342159effecdae9f2ef17","cd53cd6f00364a16a55bd9a02eb33820","f2442cf5f5144502a2987c11d63fcb9e","a3b8c8b0936d42b8a0af9bc7318bfcee","a569d421a04f4ae59a149506ea57e3bb","c959667c995c4b4f936f01e4e985a1fe","4cc21c51f3844ad995189edbe3c51f46","3a050153b8ec464f9f5254a481ea2ee5","fe2978aecf4c44ab9676f3e06ba3c7bf","acdf9c943df349f99fb6ca3a67275a7c","628fd80d6db74c8591b6a8abf93e17d3","5b84bb2e0fce4a02b7cc3844259b3374","544825bdef3147da80356aa215fbc982","4fa4b4c65b974f618d684fb60925e9dd","7c4a09318b044c9ea227664136b7216a","98562e5923fd4ff78c674c2f6a54458c","c9318bccf7504e289a533fbbc9793dd5","74ecdcf8f37744719e4ea4e141bf4ce9","1113575021184ad09ef2b05fb0a9eb07","adb99c8f7dbb475c8c6918a66edac76c","61ae9b1549b14ccd9db5fb43e7fde809","3ed1a2cac97a491897d0988530c85dfb"]},"executionInfo":{"elapsed":27446,"status":"ok","timestamp":1719079339313,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"if5TNXaJfgoo","outputId":"a939244f-84e7-4b19-a49e-1742fed178a3"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"79930fa7bb1342159effecdae9f2ef17","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":"5b84bb2e0fce4a02b7cc3844259b3374","version_major":2,"version_minor":0},"text/plain":["Downloading: 0%| |"]},"metadata":{},"output_type":"display_data"}],"source":["# This query will process 125 GB when run.\n","%%bigquery response_size_df --project httparchive\n","WITH requests AS (\n"," SELECT\n"," ROUND(CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64)/1024/100)*100 AS responseSize100KB,\n"," COUNT(0) OVER () AS total_requests\n"," FROM `httparchive.all.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"," responseSize100KB,\n"," COUNT(0) AS requests,\n"," COUNT(0)/ANY_VALUE(total_requests) AS pct_requests\n","FROM requests\n","GROUP BY responseSize100KB\n","HAVING responseSize100KB>0\n","ORDER BY responseSize100KB ASC"]},{"cell_type":"code","execution_count":187,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":473,"status":"ok","timestamp":1719079366891,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"Bjm4Xba3GWkD","outputId":"fe2cb0d9-c289-4a43-cc68-01ea90f08302"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n \"name\": \"response_size_df\",\n \"rows\": 580,\n \"fields\": [\n {\n \"column\": \"responseSize100KB\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18413.255227637972,\n \"min\": 100.0,\n \"max\": 67800.0,\n \"num_unique_values\": 580,\n \"samples\": [\n 66500.0,\n 27800.0,\n 10500.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"requests\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 208,\n \"samples\": [\n 48,\n 20655,\n 491\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pct_requests\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.003970850388510014,\n \"min\": 1.5558038445624383e-08,\n \"max\": 0.0929266856220621,\n \"num_unique_values\": 208,\n \"samples\": [\n 7.467858453899704e-07,\n 0.00032135128409437166,\n 7.638996876801573e-06\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}","type":"dataframe","variable_name":"response_size_df"},"text/html":["\n","
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    responseSize100KBrequestspct_requests
    0100.059729059.292669e-02
    1200.012969892.017860e-02
    2300.05478338.523207e-03
    3400.03180904.948856e-03
    4500.02062483.208814e-03
    ............
    57566500.023.111608e-08
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    580 rows × 3 columns

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    \n"],"text/plain":[" responseSize100KB requests pct_requests\n","0 100.0 5972905 9.292669e-02\n","1 200.0 1296989 2.017860e-02\n","2 300.0 547833 8.523207e-03\n","3 400.0 318090 4.948856e-03\n","4 500.0 206248 3.208814e-03\n",".. ... ... ...\n","575 66500.0 2 3.111608e-08\n","576 66700.0 1 1.555804e-08\n","577 66900.0 1 1.555804e-08\n","578 67600.0 1 1.555804e-08\n","579 67800.0 1 1.555804e-08\n","\n","[580 rows x 3 columns]"]},"execution_count":187,"metadata":{},"output_type":"execute_result"}],"source":["response_size_df"]},{"cell_type":"markdown","metadata":{"id":"R1_EmIoKfw8n"},"source":["When we analyze this data we can see that that 9% 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."]},{"cell_type":"code","execution_count":193,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":268},"executionInfo":{"elapsed":301,"status":"ok","timestamp":1719079812091,"user":{"displayName":"Max Ostapenko","userId":"15524868757665302379"},"user_tz":-120},"id":"4bFTTRx9H3gE","outputId":"ab8be017-0527-457a-fde0-208e017a91b6"},"outputs":[{"data":{"text/html":["\n","
    \n",""],"text/plain":["alt.Chart(...)"]},"execution_count":193,"metadata":{},"output_type":"execute_result"}],"source":["# Plotting 10 first rows\n","alt.Chart(response_size_df.head(10)).mark_point().encode(\n"," x=alt.X(\"responseSize100KB\", title=\"Response Size, 100KB bins\"),\n"," y=alt.Y(\"pct_requests\", title=\"Percent Requests\"),\n"," tooltip=['responseSize100KB', alt.Tooltip(\"pct_requests:Q\", format=\".0%\")]\n",").properties(\n"," width=1200,\n"," height=200\n",")"]},{"cell_type":"markdown","metadata":{"id":"A-vA8ALMgEQI"},"source":["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/rviscomi/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"]}],"metadata":{"colab":{"provenance":[{"file_id":"1Djg34nDFGRnNA9J2AiVG2RUzDojBUJOa","timestamp":1719016481664},{"file_id":"/v2/external/notebooks/bigquery.ipynb","timestamp":1719012850283}]},"kernelspec":{"display_name":"Python 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This dataset is quite large, as you can see by the aggregate query that counts all rows in the table" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "7b9970adfc8b467abae929a9e60df9b6", + "bed750dded6e4a3ea7c68739c7492a88", + "b05f788e6d1245b88a3bc644431e1b86", + "c711a92264ae43d7b483478e09959ac4", + "c388a75817214d478bd91b11700b8933", + "fb792dd762c24c8087b03283297919e6", + "da69b6bfcc354dab850ed1e7315c1ea5", + "5115195d57564559a70b1b18e5c33206", + "39e33bd843674d5b8c92512ee3fdc405", + "c4be651cc9134bf798adea4b86ed0cf8", + "6a07dd02a01840e98e185c236cafc6ca", + "1c2f1672573a476ba3749f30e7984456", + "c017d96ab4054bd2807187bf7187cf39", + "81e48b0a267e4cdf8878ce5917e3091a", + "974f8ec174a546acbfd79b45bf5f6200", + "7549de7e8a044d0695e8e4fdabfa49c5", + "12b58872a86d4988bfa42f47ecf871dc", + "f5a5f8f5a0e148aeadd13c277f9b92e3", + "155b79ac5f4543d38d0d6aa6bf601acc", + "8f23d945ea154df48c4295ce23dd7105", + "eef2d10edf9447788468f5003e74ac4e", + "a455834a03f0413dbdcc91843f3cc7f7" + ] + }, + "id": "e_uYeb-Iezgo", + "outputId": "c4f478d8-a8a7-4053-d8eb-0f52a8fda7c6" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "7b9970adfc8b467abae929a9e60df9b6" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "1c2f1672573a476ba3749f30e7984456" + } + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " requests_total\n", + "0 2393199079" + ], + "text/html": [ + "\n", + "
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    requests_total
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    \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"get_ipython()\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"requests_total\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 1,\n \"samples\": [\n 2393199079\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "# This query will process 38 GB when run.\n", + "%%bigquery --project {GCP_PROJECT}\n", + "SELECT COUNT(0) AS requests_total\n", + "FROM `httparchive.all.requests`\n", + "WHERE date = \"2024-05-01\"\n", + " AND client = 'desktop'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fJMoFIR_e4RC" + }, + "source": [ + "In [Part 1](https://colab.research.google.com/github/rviscomi/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." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 217, + "referenced_widgets": [ + "0c0eec77f49142c3a73757f4f2099df7", + "2c6e5fa0ba0545249c2bdf97251ff2f9", + "0b830e04edc749ea8c99b09f557b0f32", + "5277c81c9b604888a6c6bae719d0b21e", + "e741608d240b4ab488ba7b815a510b44", + "c6a1814737b54dbcb17578da64361817", + "3e088542ee8f4923840e811b8e1bc151", + "d79397ea571f47cfa52516d1eaf2d9e1", + "23ba083852e243fdb609a8db852be080", + "05acbc43be494212b2dc7e1f560f1da1", + "8c3b189158984da69b2a73fbb80fe1ec", + "4fef5080ee7f460b8c012a1b8cce6b3b", + "7dce49ea38504712af48f48e087054f1", + "e177fa73a53c46baa5dc3ac67f62addd", + "6a6d3eb53bf346a79bc89f5d85b6156a", + "9abb21fb1e614cc3bbb5fc0a70d12d78", + "2775f88b01da48a78a394ea091515c66", + "711a772d46e343f0bab596e41398c176", + "975bcdba74f14a4797fc50d8189487a4", + "7b12c0ba16a3485f858e4601978ca230", + "c7453e7292c44d5a8e09acd0266dd52d", + "018fb4f7b06b41bdb766b9c2ef1f35be" + ] + }, + "id": "a4svvdb1RDXg", + "outputId": "44d24ed4-9255-4794-f88e-6de02f3c50c4" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "0c0eec77f49142c3a73757f4f2099df7" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4fef5080ee7f460b8c012a1b8cce6b3b" + } + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "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 \n", + "
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      Dark theme: Off
      Google apps
      \n", + " \n", + "\n", + "\n", + " \n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "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}" + } + }, + "metadata": {}, + "execution_count": 8 + } + ], + "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.all.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, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458, + "referenced_widgets": [ + "b81a59d22a4b463f91c643b83101ac91", + "e1d6b0f20e7c4927bc12f713f66af7ed", + "8d2872d4672441fdb56d56ac356f233f", + "626b16557d324192827040d52cd8af8c", + "722288e29a2c4fc7af2af5abff7a6c95", + "b5d2f46b570241d893fa06f8cb5653c2", + "fcf9542f8738486a9fa68de5c92eee6c", + "fef097b977de4914bfecbf5e01efca76", + "6c56bffd052a43f0894a278059d81d41", + "1ebe3a05981a4883a6486937c2f67641", + "818e130806b6471996e92dd9ee0fbd1f", + "23a689fbfe664bda967ec827df983a98", + "d438be0c560c4be3ae97f7ac2be66db1", + "5d313adea9d6494f9f19492d961fc7df", + "dd1c8444ecd7453683f7cd3b1d7b827f", + "fe6ff28f72ea4a2892d765cfa7fd7a78", + "9b28fd630f9a42569daa50b8e771dede", + "3cd12777d47c4c318bac3faf822eddfa", + "30cafd3986334741ab53a9c02c566954", + "ad6a31602360469aa0e9a750eb538152", + "21d4d9faa7304604938a620f22dadddd", + "e8255dbf9e40458ab3fb0104f1eff5c3" + ] + }, + "id": "koKF_VyLfBG7", + "outputId": "c2236403-a1a1-4f43-e29d-9ea84ec108fc" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b81a59d22a4b463f91c643b83101ac91" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "23a689fbfe664bda967ec827df983a98" + } + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "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" + ], + "text/html": [ + "\n", + "
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      typerequestspages
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      \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"get_ipython()\",\n \"rows\": 11,\n \"fields\": [\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"font\",\n \"script\",\n \"audio\"\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 128104348,\n 813565233,\n 1177141\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 21224863,\n 23556030,\n 518769\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 9 + } + ], + "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.all.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", + "a1375461f2024c8ca3d34092ad7d1bff", + "4f6978590e9b43d88d8e44322d06b30f", + "a2cf84f40d7d4a66957449c78dbfa886", + "b98d2b861b8f41ed8bc5aebd16aefa30", + "98c4fa4f77e14436ab7bf540b7535745", + "247f3cba522248efa8b3c02f273115cc", + "72ea553d38324817931f62386369e0b3", + "40c6d92fcdf644e08524118d4be803ce", + "bd79588291e84b43874a8c876e9e2375", + "754b568b48f24dcc8371064444cd9c6d", + "16e8f5f8073a422c8ea758411256b4e0", + "86a08cddcce2496c98f704ff510e1662", + "2bfdadf2db3e450fbcb7c9fa0da75e89", + "789dc96f3a774151bf77c6cd7de82ef1", + "49cdc874343048e59c9fc63d74a11671", + "aed19a77ec0147c18bee3038ac714b88", + "61a56381161246d99df0c5fa61dc4bfc", + "6b374ee8e5164ab4bde4540c337c68ec", + "78391a9a45dc48fbbb7f556858fd0c6e", + "13798013579345c98f9edf79f0c55005", + "c47ed81c7ae944f7a8b1166a7b164bf7" + ] + }, + "id": "HyaE62NTfGe9", + "outputId": "29a7d0ae-69de-4792-8020-46b3e5306068" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c636d88bd0f44e42b8d93df1bc9c3f1f" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "16e8f5f8073a422c8ea758411256b4e0" + } + }, + "metadata": {} + } + ], + "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.all.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": [ + { + "output_type": "execute_result", + "data": { + "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" + ], + "text/html": [ + "\n", + "
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      typerequestspagespercent_requestspercent_pages
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      \n", + "
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      \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "requests_type_df", + "summary": "{\n \"name\": \"requests_type_df\",\n \"rows\": 11,\n \"fields\": [\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"font\",\n \"script\",\n \"audio\"\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 128926306,\n 817636850,\n 1184613\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 21491621,\n 23885146,\n 504820\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_requests\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12242994286158476,\n \"min\": 0.0,\n \"max\": 0.34,\n \"num_unique_values\": 8,\n \"samples\": [\n 0.31,\n 0.05,\n 0.34\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"percent_pages\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4434882390569317,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 10,\n \"samples\": [\n 0.02,\n 1.0,\n 0.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 12 + } + ], + "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": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
      \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ], + "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, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 414 + }, + "id": "Kf8aU76Qm_ym", + "outputId": "5cfdbd66-8995-4b72-c6ae-9bcb268061bd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
      \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "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": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "1b348118d0664b5a83932bee399e3a2c" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "5cdf3c2c07d9467999680af82b25b53b" + } + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "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" + ], + "text/html": [ + "\n", + "
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      typeformatrequestspagespercent_requestspercent_pages
      0script2108547380309000.320.69
      1imagejpg869610243228250.130.37
      2imagepng692863541222970.110.35
      3css677551743718760.100.38
      4other444776514660100.070.13
      5html413929622551200.060.19
      6imagegif402254515395810.060.13
      7font381204829032190.060.25
      8text140609011205200.020.10
      9imagesvg13161809428750.020.08
      10imagewebp10915436539110.020.06
      11imageico2855672817800.000.02
      12videomp42025431257990.000.01
      13imageNone2018121556060.000.01
      14imageavif137423840780.000.01
      15htmlNone110612914000.000.01
      16xml103278458690.000.00
      17scriptNone89199783470.000.01
      18otherNone59858393180.000.00
      19video52266229480.000.00
      20audio42861218620.000.00
      21xmlNone33215230340.000.00
      22image24148182790.000.00
      23fontNone21220191050.000.00
      24cssNone19725178010.000.00
      25textNone15469135090.000.00
      26imageheic236519320.000.00
      27videoNone15058710.000.00
      28videoflv38260.000.00
      29videoflash37370.000.00
      30videoswf11110.000.00
      31imageheif760.000.00
      32audioNone430.000.00
      33imagejxl110.000.00
      \n", + "
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      \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "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}" + } + }, + "metadata": {}, + "execution_count": 15 + } + ], + "source": [ + "# This query will process 129 GB when run.\n", + "%%bigquery --project {GCP_PROJECT}\n", + "WITH requests AS (\n", + " SELECT\n", + " type,\n", + " JSON_VALUE(summary, '$.format') AS format,\n", + " page,\n", + " COUNT(0) OVER() AS total_requests,\n", + " COUNT(DISTINCT page) OVER() AS total_pages\n", + " FROM `httparchive.all.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", + "496577d31b794fbe989e563cd036d920", + "c87b10790aab44dbabf75eeea66d7f36", + "7303539d84344e308538b667522a7b27", + "c805453255404b90b8a5f4a001646417", + "772c6f229e5d43399633ab36394514ec", + "2e4fe2d3a68b46c9981db237599c2bd6", + "af385fd5dafa4ad88cdfddf162508aa8", + "de755bb356874567b231e6286049b2df", + "a9848418eec64a92b6fc2560dba1ce84", + "16e8cc44f2654748b5badb2bc04aeb6a", + "e0eed4c61bce48dab8903675af8da0f3", + "4db6fbc2b6e44e11a9f45cc286e306c4", + "7518f64a50184d54a1dc78864a624c43", + "38b137fae3b94bbb9b1fc23a61a32322", + "865c9c1a7b194430baff53db88cd53b2", + "13d1d76908804346befa6fec8e3eeb64", + "cd3ccfba6d104c2ba347291454d8ed69", + "910152c1f6b94e24a0861967a58c1cb5", + "52dd5191bf884830928af927c8888d2c", + "aec648dca2024bbeb423cd2e68ac6410", + "fe2a38848d9b4a20910c6f3859c914b7" + ] + }, + "id": "9dhWIwI8fTjH", + "outputId": "2cf5ad42-2b9f-4c00-92cd-e4132b0aa242" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "bee716f2c8114b8f833aff68d931b75e" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "e0eed4c61bce48dab8903675af8da0f3" + } + }, + "metadata": {} + } + ], + "source": [ + "# This query will process 41 GB when run.\n", + "%%bigquery requests_image_df --project {GCP_PROJECT}\n", + "WITH requests AS (\n", + " SELECT\n", + " JSON_VALUE(summary, '$.format') AS format,\n", + " page,\n", + " COUNT(0) OVER() AS total_requests,\n", + " COUNT(DISTINCT page) OVER() AS total_pages\n", + " FROM `httparchive.all.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": [ + { + "output_type": "execute_result", + "data": { + "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" + ], + "text/html": [ + "\n", + "
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      \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "requests_image_df", + "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}" + } + }, + "metadata": {}, + "execution_count": 17 + } + ], + "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": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
      \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "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", + " tooltip=[\"format\", alt.Tooltip(\"percent_requests:Q\", format=\".0%\")]\n", + ").properties(\n", + " title=\"Distribution of image formats\"\n", + ")\n", + "pie_chart_image_formats + pie_chart_image_formats.mark_text(\n", + " radius=170\n", + ").encode(\n", + " text=alt.Text(\"percent_requests:Q\", format=\".0%\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 400 + }, + "id": "T9fwfkGY8R8h", + "outputId": "7fb5aeab-edc4-4874-8127-31ae625b3c19" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
      \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "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": [ + "210379ad5a974e8d8a4e2014b114790d", + "959d9078ce0b4097a042c173eed6aa5b", + "7179474d80d4413890dee4735fa728e7", + "f65b2163c5e54f499bec454f87c2f94f", + "e50354259daf4b74a4c76ab2b2decf1f", + "1760bddf24794fa1be13554319d0134e", + "1f1446d0f9e54c9eac09d4ce4b919d81", + "79c58f4f0ce94ef5b74d226b716baf4f", + "1b04c4b57d324d0eae7ccab53640b290", + "6e5857b3841c4a5e9393a7e144954248", + "26f684f316d241d39f1eb367a82b58fd", + "4b6b64dc32cd4ed6975924da7270bccf", + "06e5e1b6c9314c9b915df4b636cc0db5", + "0cd3c4b54ab54c94a7dc1b412b5f1abe", + "f47649d65ab3492481aa60be84ed4171", + "b7b88a0c6f8f4a2c802d43e6a5b95cad", + "1fb07661602440a09d83f2f7d55531d9", + "d3414550c528488db639f0ef34780206", + "98fce80508d647fbaafc93993834bf0e", + "ad94b7fef0da470aa297f2c1a4ab16a1", + "74163023d2344462aafc051492372730", + "6eb928d33b2e4717b60f0392cf83fd35" + ] + }, + "id": "if5TNXaJfgoo", + "outputId": "b3858dc2-8a09-416c-a950-fcc2a12a9f95" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Query is running: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "210379ad5a974e8d8a4e2014b114790d" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| |" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4b6b64dc32cd4ed6975924da7270bccf" + } + }, + "metadata": {} + } + ], + "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(CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64)/1024/100)*100 AS responseSize100KB,\n", + " COUNT(0) OVER () AS total_requests\n", + " FROM `httparchive.all.requests` TABLESAMPLE SYSTEM (5 PERCENT)\n", + " WHERE date = '2024-05-01'\n", + " AND client = 'desktop'\n", + " AND is_root_page\n", + " AND CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64) > 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": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "Bjm4Xba3GWkD", + "outputId": "bf8b1e87-978f-4d71-8188-13972ba0504d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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", + "559 68400.0 1 1.822284e-08\n", + "\n", + "[560 rows x 3 columns]" + ], + "text/html": [ + "\n", + "
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      \n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "# Plotting 10 first rows\n", + "alt.Chart(response_size_df.head(10)).mark_point().encode(\n", + " x=alt.X(\"responseSize100KB\", title=\"Response Size, 100KB bins\"),\n", + " y=alt.Y(\"pct_requests\", title=\"Percent Requests\"),\n", + " tooltip=['responseSize100KB', alt.Tooltip(\"pct_requests:Q\", format=\".0%\")]\n", + ").properties(\n", + " width=1200,\n", + " height=200\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A-vA8ALMgEQI" + }, + "source": [ + "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/rviscomi/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" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "7b9970adfc8b467abae929a9e60df9b6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bed750dded6e4a3ea7c68739c7492a88", + "IPY_MODEL_b05f788e6d1245b88a3bc644431e1b86", + 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"auth.authenticate_user()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "GCP_PROJECT = 'httparchive' # @param {type: \"string\"}" + ] + }, { "cell_type": "markdown", "metadata": { @@ -425,7 +434,7 @@ ], "source": [ "# This query will process 25 GB when run.\n", - "%%bigquery --project httparchive\n", + "%%bigquery --project {GCP_PROJECT}\n", "SELECT\n", " page,\n", " CAST(JSON_VALUE(summary, '$.reqImg') AS INT64) AS image_requests,\n", @@ -854,7 +863,7 @@ ], "source": [ "# This query will process 80 GB when run.\n", - "%%bigquery --project httparchive\n", + "%%bigquery --project {GCP_PROJECT}\n", "WITH pages AS (\n", " SELECT\n", " page\n", @@ -972,7 +981,7 @@ ], "source": [ "# This query will process 131 GB when run.\n", - "%%bigquery df_requests_type --project httparchive\n", + "%%bigquery df_requests_type --project {GCP_PROJECT}\n", "SELECT\n", " type,\n", " COUNT(0) AS requests,\n", @@ -1560,7 +1569,7 @@ ], "source": [ "# This query will process 47 GB when run.\n", - "%%bigquery df_tbt_scripts --project httparchive\n", + "%%bigquery df_tbt_scripts --project {GCP_PROJECT}\n", "WITH pages AS (\n", " SELECT\n", " page,\n",