diff --git a/.gitignore b/.gitignore index 216f828..ba9ce5a 100644 --- a/.gitignore +++ b/.gitignore @@ -21,4 +21,6 @@ pnpm-debug.log* .DS_Store # local tools -*.code-workspace \ No newline at end of file +*.code-workspace +.idx/dev.nix +.vscode/settings.json diff --git a/astro.config.mjs b/astro.config.mjs index 64e9407..5a44a06 100644 --- a/astro.config.mjs +++ b/astro.config.mjs @@ -21,7 +21,7 @@ export default defineConfig({ window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); - + gtag('config', 'G-SK2FZXB50K');` } ], @@ -38,7 +38,9 @@ export default defineConfig({ items: [ { label: 'Getting started', link: '/guides/getting-started/' }, { label: 'Minimizing query costs', link: '/guides/minimizing-costs/' }, + { label: 'Guided tour', link: '/guides/guided-tour/' }, { label: 'Release cycle', link: '/guides/release-cycle/' }, + { label: 'Migrate queries to `all` dataset', link: '/guides/migrating-to-all-dataset/' }, ], }, { diff --git a/src/content/docs/guides/bigquery-httparchive-dataset-pinned.png b/src/content/docs/guides/bigquery-httparchive-dataset-pinned.png new file mode 100644 index 0000000..4bba4aa Binary files /dev/null and b/src/content/docs/guides/bigquery-httparchive-dataset-pinned.png differ diff --git a/src/content/docs/guides/bigquery-query-in-a-new-tab.png b/src/content/docs/guides/bigquery-query-in-a-new-tab.png new file mode 100644 index 0000000..92d8909 Binary files /dev/null and b/src/content/docs/guides/bigquery-query-in-a-new-tab.png differ diff --git a/src/content/docs/guides/bigquery-run-sample-query.png b/src/content/docs/guides/bigquery-run-sample-query.png new file mode 100644 index 0000000..56f7412 Binary files /dev/null and b/src/content/docs/guides/bigquery-run-sample-query.png differ diff --git a/src/content/docs/guides/bigquery-summary_pages.png b/src/content/docs/guides/bigquery-summary_pages.png new file mode 100644 index 0000000..f61737b Binary files /dev/null and b/src/content/docs/guides/bigquery-summary_pages.png differ diff --git a/src/content/docs/guides/exploring_summary_pages_tables.png b/src/content/docs/guides/exploring_summary_pages_tables.png new file mode 100644 index 0000000..2031ba2 Binary files /dev/null and b/src/content/docs/guides/exploring_summary_pages_tables.png differ diff --git a/src/content/docs/guides/getting-started.md b/src/content/docs/guides/getting-started.md index fdfe9dd..e8b97ca 100644 --- a/src/content/docs/guides/getting-started.md +++ b/src/content/docs/guides/getting-started.md @@ -3,8 +3,6 @@ title: Getting started accessing the HTTP Archive with BigQuery description: Using HTTP Archive on BigQuery for the first time --- -_This guide was originally published on [GitHub](https://github.com/HTTPArchive/httparchive.org/blob/main/docs/gettingstarted_bigquery.md) on Sep 21, 2018. Some information may be out of date._ - The [HTTP Archive](https://httparchive.org) is an open source project that tracks how the web is built. Historical data is provided to show how the web is constantly evolving, and the project is frequently used for research by the web community, scholars and industry leaders. If you are interested in digging into the HTTP Archive and are not sure where to start, then this guide should help you get started quickly. There are over 1 million pages tracked on desktop and emulated mobile in the most recent HTTP Archive data, and the historical data goes back to 2010. While the HTTP Archive website makes a lot of information available via [curated reports](https://httparchive.org/reports), analyzing the raw data is a powerful way of answering your questions about the web. @@ -13,50 +11,65 @@ All of the data collected by the HTTP Archive is available via [Google BigQuery] This document is an update to [Ilya Grigorik's 2013 introduction](https://www.igvita.com/2013/06/20/http-archive-bigquery-web-performance-answers/), and walks you through everything you need to get started accessing BigQuery and analyzing the data. - ## Setting up BigQuery to Access the HTTP Archive -In order to access the HTTP Archive via BigQuery, you'll need a Google account. To document this process for new visitors, this example uses a new Google account that has never logged into any Google Cloud services. +In order to access the HTTP Archive via BigQuery, you'll need a Google account. To document this process for new visitors, this example uses a new Google account that has never logged into any Google Cloud services. -1. Navigate to the [Google Cloud Projects Page](https://console.cloud.google.com/start) and log in with your Google account if prompted. If this is your first time accessing Google Cloud, you may be prompted to accept the terms of service. Once you are logged in, you'll see a page like this - +1. Navigate to the [Google Cloud Projects Page](https://console.cloud.google.com/start) and log in with your Google account if prompted. If this is your first time accessing Google Cloud, you may be prompted to accept the terms of service. Once you are logged in, you'll see a page like this: - Google Cloud Welcome + ![Google Cloud Welcome](./google-cloud-welcome.png) -2. Click `Select a project` and then "New Project". This takes you to a New Project page. +2. Click **Select a project** and then **New Project**. This takes you to a New Project page. - Google Cloud select project + ![Google Cloud select project](./google-cloud-select-project.png) -3. Give your project a name and then click the `Create` button. +3. Give your project a name and then click the **Create** button. - Create a Project + ![Create a Project](./google-cloud-create-new-project.png) 4. Optional: Enable Billing by clicking on the Billing menu item and adding your billing information. -:::note -BigQuery has a [free tier](https://cloud.google.com/bigquery/pricing#free-tier) that you can use to get started without enabling billing. At the time of this writing, the free tier allows 10GB of storage and 1TB of data processing per month. Google also provides a [$300 credit for new accounts](https://cloud.google.com/free/docs/frequently-asked-questions#free-trial). -::: + :::note + BigQuery has a [free tier](https://cloud.google.com/bigquery/pricing#free-tier) that you can use to get started without enabling billing. At the time of this writing, the free tier allows 10GB of storage and 1TB of data processing per month. Google also provides a [$300 credit for new accounts](https://cloud.google.com/free/docs/frequently-asked-questions#free-trial). + ::: -5. Navigate to the [Big Query console](https://console.cloud.google.com/bigquery) where you should see your project, with no data. +5. Navigate to the [BigQuery console](https://console.cloud.google.com/bigquery) where you should see your project, with no data. -6. In order to add the HTTP Archive tables to your project, click on the "+ Add" button on top of the Explorer sidebar and choose the "Star a project by name" option from the side menu. +6. In order to add the HTTP Archive tables to your project, click on the **+ Add** button on top of the Explorer sidebar and choose the **Star a project by name** option from the side menu. -7. Type in `httparchive` (case-sensitive) and click `STAR`. +7. Type in `httparchive` (case-sensitive) and click **STAR**. 8. You should now see the HTTP Archive data set pinned: - BigQuery HTTPArchive pinned + ![BigQuery HTTPArchive pinned](./bigquery-httparchive-dataset-pinned.png) + +9. Let's run a quick sample query to confirm access is all working. Navigate to the `all` dataset and select the `pages` table: -9. Let's run a quick sample query to confirm access is all working. Navigate to the `summary_pages` tables and select the first one: + ![BigQuery summary_pages tables](./bigquery-summary_pages.png) - BigQuery summary_pages tables +10. Click on the **QUERY** button and select **In a new tab**: -10. Click on the `QUERY` button and select `In a new tab`: + ![BigQuery Query in a new tab](./bigquery-query-in-a-new-tab.png) - BigQuery Query in a new tab +11. Change the query to take a small sample of the table (e.g. `SELECT *`), click the `RUN` button and you should see the results of your query. -11. Change the query to select some columns (e.g. `SELECT *`) and click the `RUN` button and you should see the results of your query. + :::danger + The size of the tables you query are important because BigQuery is billed based on the number of processed data. There is 1TB of processed data included in the free tier, so running a full scan query on one of the larger tables can easily eat up your quota. This is where it becomes important to design queries that process only the data you wish to explore. - BigQuery run a sample query + HTTP Archive collecting metadata from millions of websites each month, an the dataset is _extremely large_—multiple petabytes. + + See the guide on [minimizing query costs](/guides/minimizing-costs/) to learn more. + + Also, take a moment setting up [cost controls](https://cloud.google.com/bigquery/docs/custom-quotas) to be informed of the costs inqurred throughout the month. + ::: + + ```sql + SELECT * + FROM `httparchive.all.pages` TABLESAMPLE SYSTEM (0.00001 PERCENT) + WHERE date = "2024-05-01" + ``` + + ![BigQuery run a sample query](./bigquery-run-sample-query.png) In the next section, we explore the structure of these tables so you can start digging in! @@ -64,153 +77,239 @@ In the next section, we explore the structure of these tables so you can start d So, now you have access! But what do you have access to? -The table below outlines what some of the different grouping of tables includes. You'll find summaries of page views and HTTP requests. There are also JSON encoded HAR files for pages, requests, lighthouse reports and even response bodies! - -HTTP Archive Table Summary +The table below outlines what some of the different tables include. You'll find page views and HTTP requests. There are also JSON encoded HAR files for pages, requests, lighthouse reports and even response bodies! -:::danger -The size of the tables you query are important because BigQuery is billed based on the number of processed data. There is 1TB of processed data included in the free tier, so running a full scan query on one of the larger tables can easily eat up your quota. This is where it becomes important to design queries that process only the data you wish to explore. +Table | Monthly Size | Monthly Rows | History Since +--|--|--|-- +summary_pages.* | ~16 GB | Desktop: ~13M, Mobile: ~16 M | Desktop: Nov 2010, Mobile: May 2011 +summary_requests.* | ~1.9 TB | Desktop: ~1.3B, Mobile: ~1.5B | Desktop: Nov 2010, Mobile: May 2011 +pages.* | ~3.1 TB | Desktop: ~13 M, Mobile: ~16 M | Jan 2016 +requests.* | ~12.5 TB | Desktop: ~1.3 B, Mobile: ~1.5 B | Jan 2016 +response_bodies.* | ~48 TB | Desktop: ~647 M, Mobile: ~780 M | Jan 2016 +all.pages | ~42 TB | Desktop: ~24M, Mobile: ~30M | Mar 2022 +all.requests | ~231 TB | Desktop: ~2.4B, Mobile: ~2.7B | Mar 2022 +lighthouse.* | ~200 GB | Desktop: 12M, Mobile: ~16M | June 2017 -See the guide on [minimizing query costs](/guides/minimizing-costs/) to learn more. +:::note +If table names are mentioned as a wildcard, then the table names all follow the format `yyyy_mm_dd_desktop` and `yyyy_mm_dd_mobile`. +Size and row are rounded counts as of May 2024. ::: -In order to understand what each of these tables contain, you can click on the table name and view the details. For example, if you expand the `summary_pages` dataset and click on the 2018_09_01_desktop (or mobile) table you can see the schema. Clicking `Details` tells you some information about the table, such as its size and the number of rows. Clicking `Preview` shows an example of some data from the table. +In order to understand what each of these tables contain, you can click on the table name and view the details. For example, if you expand the `all` dataset and click on the `pages` table you can see the schema. Clicking **Details** tells you some information about the table, such as its size and the number of rows. Clicking **Preview** shows an example of some data from the table. + +![Table Preview](./exploring_summary_pages_tables.png) + +Some of the types of tables you'll find useful when getting started are described below. + +### HAR Tables + +The HTTP Archive stores detailed information about each page load in [HAR (HTTP Archive) files](https://en.wikipedia.org/wiki/.har). Each HAR file is JSON formatted and contains detailed performance data about a web page. The [specification for this format](https://w3c.github.io/web-performance/specs/HAR/Overview.html) is produced by the Web Performance Working Group of the W3C. The HTTP Archive splits each HAR file into multiple BigQuery tables, which are described below. + +* [`httparchive.all.pages`](/reference/tables/pages/): + + * HAR extract for each page url. + * This table is very large (~938TB as of Jun 2024). + +* [`httparchive.all.requests`](/reference/tables/requests/): -Exploring Tables + * HAR extract for each resource. + * This table is very large (4.97PB as of Jun 2024) -Some of the types of tables you'll find useful when getting started are described below. These table names all follow the format `yyyy_mm_dd_desktop` and `yyyy_mm_dd_mobile`. +* `httparchive.response_bodies.YYYY_MM_DD_CLIENT`: + + * Tables are **OUTDATED**, please use the [`response_body` column](/reference/tables/requests/#response_body) instead. + * HAR extract containing response bodies for each request. + * Table contains a document url, resource url and a JSON-encoded HAR extract containing the first 2MB of each response body. + * Payloads are truncated at 2MB, and there is a column to indicate whether the payload was truncated. + * These tables are extremely large (2.5TB as of Aug 2018). + +* `httparchive.lighthouse.YYYY_MM_DD_CLIENT`: + + * Tables are **OUTDATED**, please use the [`lighthouse` column](/reference/tables/pages/#lighthouse) instead. + * Results from a [Lighthouse](https://developers.google.com/web/tools/lighthouse/) audit of a page. + * Table contains a url, and a JSON-encoded copy of the lighthouse report. + * Lighthouse was intially only run on mobile, but as of May 2021 also runs as part of the desktop crawl. + * These tables are very large (2.3 TB for Mobile only as of May 2021) + +* `httparchive.pages.YYYY_MM_DD_CLIENT`: + + * Tables are **OUTDATED**, please use the [`httparchive.all.pages` tables](/reference/tables/requests/) instead. + * HAR extract for each page url. + * Table contains a url and a JSON-encoded HAR file for the document. + * These tables are large (~13GB as of Aug 2018). + +* `httparchive.requests.YYYY_MM_DD_CLIENT`: + + * Tables are **OUTDATED**, please use the [`httparchive.all.requests` tables](/reference/tables/requests/) instead. + * HAR extract for each resource. + * Table contains a document url, resource url and a JSON-encoded HAR extract for each resource. + * These tables are very large (810GB as of Jun 2024) ### Summary Tables -* `summary_pages` tables: - * Each row contains details about a single page including timings, # of requests, types of requests and sizes. - * Information about the page load such # of domains, redirects, errors, https requests, CDN, etc. - * Summary of different caching parameters. - * Each page URL is associated with a "pageid". +* `httparchive.summary_pages.YYYY_MM_DD_CLIENT`: -* `summary_requests` Tables: - * Every single object loaded by all of the pages. - * Each object has a requestid and a pageid. The pageid can be used to JOIN the corresponding summary_pages table. - * Information about the object, and how it was loaded. - * Contains some response headers for each object. + * Tables are **OUTDATED**, please use the [`summary` column](/reference/tables/pages/#summary) instead. + * Each row contains details about a single page including timings, # of requests, types of requests and sizes. + * Information about the page load such # of domains, redirects, errors, https requests, CDN, etc. + * Summary of different caching parameters. + * Each page URL is associated with a "pageid". -### HAR Tables +* `httparchive.summary_requests.YYYY_MM_DD_CLIENT`: + + * Tables are **OUTDATED**, please use the [`summary` column](/reference/tables/requests/#summary) instead. + * Every single object loaded by all of the pages. + * Each object has a requestid and a pageid. The pageid can be used to JOIN the corresponding summary_pages table. + * Information about the object, and how it was loaded. + * Contains some response headers for each object. -The HTTP Archive stores detailed information about each page load in [HAR (HTTP Archive) files](https://en.wikipedia.org/wiki/.har). Each HAR file is JSON formatted and contains detailed performance data about a web page. The [specification for this format](https://w3c.github.io/web-performance/specs/HAR/Overview.html) is produced by the Web Performance Working Group of the W3C. The HTTP Archive splits each HAR file into multiple BigQuery tables, which are described below. +### Other Tables -* `pages` tables - * HAR extract for each page url. - * Table contains a url and a JSON-encoded HAR file for the document. - * These tables are large (~13GB as of Aug 2018). +* [`httparchive.blink_features.usage`](https://console.cloud.google.com/bigquery?ws=!1m5!1m4!4m3!1shttparchive!2sblink_features!3susage): -* `requests` tables: - * HAR extract for each resource. - * Table contains a document url, resource url and a JSON-encoded HAR extract for each resource. - * These tables are very large (810GB as of Aug 2018) + * Summary information about the [Blink features](https://chromestatus.com/roadmap) detected on each page. + * Table contains the num_urls, the pct_urls and sample urls for each feature. + * This data is also available in the HAR of the `pages` table but is extracted into the `blink_features` tables for easy lookup. + * This table is 944 MB as of May 2024. -* `response_bodies` tables: - * HAR extract containing response bodies for each request. - * Table contains a document url, resource url and a JSON-encoded HAR extract containing the first 2MB of each response body. - * Payloads are truncated at 2MB, and there is a column to indicate whether the payload was truncated. - * These tables are extremely large (2.5TB as of Aug 2018). +* `httparchive.technologies.YYYY_MM_DD_CLIENT`: -* `lighthouse` tables: - * Results from a [Lighthouse](https://developers.google.com/web/tools/lighthouse/) audit of a page. - * Table contains a url, and a JSON-encoded copy of the lighthouse report. - * Lighthouse was intially only run on mobile, but as of May 2021 also runs as part of the desktop crawl. - * These tables are very large (2.3 TB for Mobile only as of May 2021) + * Tables are **OUTDATED**, please use the [`technologies` column](/reference/tables/pages/#technologies) instead. + * Information about the technologies detected on each page (using [Wappalyser rules](https://github.com/HTTPArchive/wappalyzer)). + * Table contains a url and a list of names and categories for technologies detected on the page. + * This data is also available in the HAR of the `pages` table but is extracted into the `technologies` table for easy lookup. + * These tables are small (15 GB as of May 2024). +* `httparchive.blink_features.features`: + + * Tables are **OUTDATED**, please use the [`features` column](/reference/tables/pages/#features) instead. + * Information about the [Blink features](https://chromestatus.com/roadmap) detected on each page. See also the summary `blink_features.usage` table below. + * Table contains a url and Blink feature names detected on the page. + * This data is also available in the HAR of the `pages` table but is extracted into the `blink_features` tables for easy lookup. + * This table is ~300GB per single platform as of May 2024. ## Some Example Queries to Get Started Exploring the Data The [HTTP Archive Discuss section](https://discuss.httparchive.org/) has lots of useful examples and discussion on how to analyze this data. -Now that you are all set up, let's run some queries! Most HTTP Archive users start off examining the summary tables, so we'll start there as well. Below is a simple aggregate query that tells you how many URLs are contained in the latest HTTP Archive data. +Now that you are all set up, let's run some queries! Most HTTP Archive users start off examining the summary tables, so we'll start there as well. Below is a simple aggregate query that tells you how many URLs are contained in the latest HTTP Archive data. ```sql SELECT COUNT(0) total_pages FROM - `httparchive.summary_pages.2018_09_01_desktop` + `httparchive.all.pages` +WHERE + date = "2024-06-01" + AND client = "desktop" + AND is_root_page ``` -![Simple Aggregate Query](https://github.com/HTTPArchive/httparchive.org/raw/main/docs/images/simple_agg_query_example.jpg) +![Simple Aggregate Query](./simple_agg_query_example.png) Perhaps you want to JOIN the pages and requests tables together, and see how many page URLs and request URLs are in this data set. ```sql SELECT - COUNT(distinct pages.url) total_pages, + COUNT(DISTINCT pages.page) total_pages, COUNT(0) total_requests FROM - `httparchive.summary_pages.2018_09_01_desktop` pages + `httparchive.all.pages` pages INNER JOIN - `httparchive.summary_requests.2018_09_01_desktop`requests + `httparchive.all.requests`requests ON - pages.pageid = requests.pageid + pages.page = requests.page +WHERE + pages.date = "2024-06-01" + AND requests.date = "2024-06-01" + AND pages.client = "desktop" + AND requests.client = "desktop" + AND pages.is_root_page + AND requests.is_root_page ``` -When we look at the results of this, you can see how much data was processed during this query. Writing efficient queries limits the number of bytes processed - which is helpful since that's how BigQuery is billed. *Note: There is 1TB free per month* +When we look at the results of this, you can see how much data was processed during this query. Writing efficient queries limits the number of bytes processed - which is helpful since that's how BigQuery is billed. -![Simple JOIN Example](https://github.com/HTTPArchive/httparchive.org/raw/main/docs/images/simple_join_example.jpg) +:::note +There is [1TB free per month](https://cloud.google.com/bigquery/pricing#on_demand_pricing). +::: -If you look closely, you'll notice that this particular query could actually be written without the JOIN. For example, we can count `distinct pageid` from the `summary_requests` table instead of JOINing the `summary_pages` table. If you run this query, you'll notice that the results are the same as the previous query, and the processed bytes are less. +![Simple JOIN Example](./simple_join_example.png) + +If you look closely, you'll notice that this particular query could actually be written without the JOIN. For example, we can count `distinct page` from the `requests` table instead of JOINing the `pages` table. If you run this query, you'll notice that the results are the same as the previous query, and the processed bytes are a bit less. ```sql SELECT - COUNT(distinct pageid) total_pages, + COUNT(DISTINCT page) total_pages, COUNT(0) total_requests FROM - `httparchive.summary_requests.2018_09_01_desktop`requests + `httparchive.all.requests` +WHERE + date = "2024-06-01" + AND client = "desktop" + AND is_root_page ``` -Next let's summarize all of the HTTP requests by mime type, and the number of pages that contain at least one request of that mime type. In the example below, you can see that I added `mimeType` to the SELECT clause, added a GROUP clause and sorted the results by mimeTypes that have the most requests. +Next let's summarize all of the HTTP requests by a type, and the number of pages that contain at least one request of that type. In the example below, you can see that I added `type` to the SELECT clause, added a GROUP clause and sorted the results by types that have the most requests. ```sql SELECT - mimeType, - COUNT(distinct pageid) total_pages, + type, + COUNT(DISTINCT page) total_pages, COUNT(0) total_requests FROM - `httparchive.summary_requests.2018_09_01_desktop`requests + `httparchive.all.requests` +WHERE + date = "2024-06-01" + AND client = "desktop" + AND is_root_page GROUP BY - mimeType + type ORDER BY total_requests DESC ``` Now things are starting to get interesting. -![Simple JOIN Example](https://github.com/HTTPArchive/httparchive.org/raw/main/docs/images/mimeType_summary_example_query.jpg) +![Simple JOIN Example](./type_summary_example_query.png) -So let's try to learn something from this basic example. We know from the first example that there are 1.2 million URLs in the latest HTTP Archive dataset. Let's calculate the percent of pages that have each mimeType. To do this, we'll divide the number of pages by the total pages (using our first query as a subquery). Then we'll use a `ROUND()` function to trim the result to 2 decimal points. +So let's try to learn something from this basic example. We know from the first example that there are 12.7 million URLs in the latest HTTP Archive dataset. Let's calculate the percent of pages that have each resource type. To do this, we'll divide the number of pages by the total pages (using our first query as a subquery). Then we'll use a `ROUND()` function to trim the result to 2 decimal points. ```sql +WITH requests AS ( + SELECT + type, + page, + COUNT(0) OVER () AS requests_total, + COUNT(DISTINCT page) OVER () AS pages_total, + FROM `httparchive.all.requests` + WHERE + date = "2024-06-01" + AND client = "desktop" + AND is_root_page +) + SELECT - mimeType, - COUNT(distinct pageid) total_pages, - COUNT(0) total_requests, - ROUND( - COUNT(distinct pageid) / ( - SELECT - COUNT(0) - FROM - `httparchive.summary_pages.2018_09_01_desktop` - ) - , 2) percent_pages -FROM - `httparchive.summary_requests.2018_09_01_desktop`requests + type, + COUNT(DISTINCT page) AS pages_total, + ANY_VALUE(requests_total) AS requests_total, + ROUND(COUNT(DISTINCT page) / ANY_VALUE(pages_total), 2) AS pages_percent +FROM requests GROUP BY - mimeType + type ORDER BY - total_requests DESC + pages_percent DESC ``` -When analyzing the results from this, you can see the % of websites that use different Content-Types for their JavaScript, you can see that 93% of sites have at least one PNG image, 89% have at least 1 GIF, 48% use JSON, and 3% of sites have MP4 videos on their homepage, etc. +When analyzing the results from this, you can see the % of websites that use different resource types. You can see that: -![Simple JOIN Example](https://github.com/HTTPArchive/httparchive.org/raw/main/docs/images/mimeType_summary_example_query2.jpg) +* 100% of sites have HTML and at least one image, +* 97% have at least 1 script resource, +* 96% load at least 1 CSS style, +* and 87% load fonts on their homepage, etc. -To explore more interactive examples, read the [HTTP Archive Guided Tour](https://github.com/HTTPArchive/httparchive.org/blob/main/docs/guided_tour.md). +![Simple JOIN Example](./type_summary_example_query2.png) -If you want to explore deeper you have everything you need - infrastructure, documentation, community. Enjoy exploring this data and feel free to share your results and ask questions on the [HTTP Archive Discuss section](https://discuss.httparchive.org/). +To explore more interactive examples, read the [Guided Tour](./guided-tour.md). +If you want to explore deeper you have everything you need - infrastructure, documentation, community. Enjoy exploring this data and feel free to share your results and ask questions on the [HTTP Archive Discuss section](https://discuss.httparchive.org/). diff --git a/src/content/docs/guides/google-cloud-create-new-project.png b/src/content/docs/guides/google-cloud-create-new-project.png new file mode 100644 index 0000000..4234840 Binary files /dev/null and b/src/content/docs/guides/google-cloud-create-new-project.png differ diff --git a/src/content/docs/guides/google-cloud-select-project.png b/src/content/docs/guides/google-cloud-select-project.png new file mode 100644 index 0000000..3d276d9 Binary files /dev/null and b/src/content/docs/guides/google-cloud-select-project.png differ diff --git a/src/content/docs/guides/google-cloud-welcome.png b/src/content/docs/guides/google-cloud-welcome.png new file mode 100644 index 0000000..3fcfd16 Binary files /dev/null and b/src/content/docs/guides/google-cloud-welcome.png differ diff --git a/src/content/docs/guides/guided-tour.mdx b/src/content/docs/guides/guided-tour.mdx new file mode 100644 index 0000000..eb8b45f --- /dev/null +++ b/src/content/docs/guides/guided-tour.mdx @@ -0,0 +1,26 @@ +--- +title: Guided Tour +description: HTTP Archive data analysis in BigQuery +--- + + +The HTTP Archive contains a tremendous amount of information that can be used to understand the evolution of the web. And since the raw data is available in Google BigQuery, you can start digging into it with a minimal amount of setup! + +If you are new to BigQuery, then the [Getting Started guide](./getting-started.md) will walk you through the basic setup. That guide ends with a sample query that explores MIME types from the `pages` tables. In this guide, we'll explore more of the tables and build additional queries that you can learn from. The easiest way to get started is by following along, testing some of the queries and learning from them. If you need any help then there is plenty of support available from the community at [https://discuss.httparchive.org](https://discuss.httparchive.org). + +**Prerequisites:** + +- This guide assumes that you've completed the setup from the [Getting Started guide](./getting-started.md). +- You would be safe processing extremely-large tables contained in this dataset if you follow the [minimizing query costs guide](/guides/minimizing-costs/). +- It also assumes some familiarity with SQL. All of the examples provided will be using [Standard SQL](https://cloud.google.com/bigquery/docs/reference/standard-sql/). + +Migration Guides: + +- If you are looking to adapt older HTTP Archive queries, written in [Legacy SQL](https://cloud.google.com/bigquery/docs/reference/legacy-sql), then you may find this [migration guide](https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql) helpful.* +- If you've been working with the deprecated dataset `pages` or `requests`, there is a guide on [migrating your queries to the `all` dataset](/guides/migrating-to-all-dataset/). + +This guide is split into multiple sections, each one focusing on different tables in the HTTP Archive. Each section builds on top of the previous one: + +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) diff --git a/src/content/docs/guides/migrating-to-all-dataset.mdx b/src/content/docs/guides/migrating-to-all-dataset.mdx new file mode 100644 index 0000000..60aac94 --- /dev/null +++ b/src/content/docs/guides/migrating-to-all-dataset.mdx @@ -0,0 +1,327 @@ +--- +title: Migrate queries to `all` dataset +description: Assisting with query migration to the new dataset +--- + +import { Tabs, TabItem } from '@astrojs/starlight/components'; + +New tables have been introduced in the HTTP Archive dataset, which are more efficient and easier to use. The `all` dataset contains all the data from the previous `pages`, `requests`, and other datasets. This guide will help you migrate your queries to the new dataset. + +## Migrating to `all.pages` + +### Page data schemas comparison + +previously | `all.pages` +---|--- +date in a table name | [`date`](/reference/tables/pages/#date) +client as `_TABLE_SUFFIX` | [`client`](/reference/tables/pages/#client) +`url` in `pages.YYYY_MM_DD_client` | [`page`](/reference/tables/pages/#page) +not available | [`is_root_page`](/reference/tables/pages/#is_root_page) +not available | [`root_page`](/reference/tables/pages/#root_page) +not available | [`rank`](/reference/tables/pages/#rank) +`$.testID` within `payload` column in `pages.YYYY_MM_DD_client`, `wptid` column in `summary_pages.YYYY_MM_DD_client` | [`wptid`](/reference/tables/pages/#wptid) +`payload` in `pages.YYYY_MM_DD_client` | [`payload`](/reference/tables/pages/#payload) +`req*`, `resp*` and other in `summary_pages.YYYY_MM_DD_client` | [`summary`](/reference/tables/pages/#summary) +`$.CUSTOM_METRIC_NAME` within `payload` column in `pages.YYYY_MM_DD_client` | [`custom_metrics`](/reference/tables/pages/#custom_metrics) +`report` in `lighthouse.YYYY_MM_DD_client` | [lighthouse](/reference/tables/pages/#lighthouse) +`feature`, `type`, `id` in `blink_features.features` | `feature`, `type`, `id` in [`features`](/reference/tables/pages/#features) +`category`, `app`, `info` in `technologies.YYYY_MM_DD_client` | `categories`, `technology`, `info` in [`technologies`](/reference/tables/pages/#technologies) +not available | [`metadata`](/reference/tables/pages/#metadata) + +### Page query updates + +- Migrate from `blink_features.features` + + + +```sql +SELECT + url, + feature, + type, + id +FROM `httparchive.blink_features.features` +WHERE yyyymmdd = DATE('2024-05-01') + AND client = 'desktop' +``` + + +```sql +SELECT + page, + features.feature, + features.type, + features.id +FROM `httparchive.all.pages`, +UNNEST (features) AS features +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +``` + + + +- Migrate from `lighthouse.YYYY_MM_DD_client` + + + +```sql +SELECT + url, + JSON_QUERY(report, '$.audits.largest-contentful-paint.numericValue') AS LCP, +FROM `httparchive.lighthouse.2024_06_01_desktop` +``` + + +```sql +/* This query will process 17 TB when run. */ +SELECT + page, + JSON_QUERY(lighthouse, '$.audits.largest-contentful-paint.numericValue') AS LCP, +FROM `httparchive.all.pages` +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +``` + + + +- Migrate from `pages.YYYY_MM_DD_client` + + + +```sql +SELECT + url, + _TABLE_SUFFIX AS client, + JSON_QUERY(payload, '$.testID') AS testID, +-- JSON with the results of the custom metrics, + JSON_QUERY(payload, '$._privacy') AS custom_metrics, +FROM `httparchive.pages.2022_06_01_*` +``` + + +```sql +SELECT + page, + client, + wptid, +-- JSON with the results of the custom metrics, + JSON_QUERY(custom_metrics, '$.privacy') AS custom_metrics, +FROM `httparchive.all.pages` +WHERE date = '2022-06-01' + AND is_root_page +``` + + + +- Migrate from `summary_pages.YYYY_MM_DD_client` + + + +```sql +SELECT + numDomains, + COUNT(0) pages, + ROUND(AVG(reqTotal),2) avg_requests, +FROM `httparchive.summary_pages.2024_06_01_desktop` +GROUP BY + numDomains +HAVING + pages > 1000 +ORDER BY + numDomains ASC +``` + + +```sql +/* This query will process 110 GB when run. */ +SELECT + CAST(JSON_VALUE(summary, '$.numDomains') AS INT64) AS numDomains, + COUNT(0) pages, + ROUND(AVG(CAST(JSON_VALUE(summary, '$.reqTotal') AS INT64)),2) as avg_requests, +FROM `httparchive.all.pages` +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +GROUP BY + numDomains +HAVING + pages > 1000 +ORDER BY + numDomains ASC +``` + + + +- Migrate from `technologies.YYYY_MM_DD_client` + + + +```sql +SELECT + url, + category, + app, + info +FROM `httparchive.technologies.2024_06_01_desktop` +``` + + +```sql +/* This query will process 27 GB when run. */ +SELECT + page, + technologies.categories, + technologies.technology, + technologies.info +FROM `httparchive.all.pages`, +UNNEST (technologies) AS technologies +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +``` + + + + +## Migrating to `all.requests` + +### Request data schemas comparison + +previously | `all.requests` +---|--- +date in a table name | [`date`](/reference/tables/requests/#date) +client as `_TABLE_SUFFIX` | [`client`](/reference/tables/requests/#client) +`page` in `requests.YYYY_MM_DD_client` | [`page`](/reference/tables/requests/#page) +not available | [`is_root_page`](/reference/tables/requests/#is_root_page) +not available | [`root_page`](/reference/tables/requests/#root_page) +`url` in `requests.YYYY_MM_DD_client` | [`url`](/reference/tables/requests/#url) +`firstHtml` in `summary_requests.YYYY_MM_DD_client` | [`is_main_document`](/reference/tables/requests/#is_main_document) +`type` in `summary_requests.YYYY_MM_DD_client` | [`type`](/reference/tables/requests/#type) +`$._index` within `payload` in `requests.YYYY_MM_DD_client` | [`index`](/reference/tables/requests/#index) +`payload` column in `requests.YYYY_MM_DD_client` | [`payload`](/reference/tables/requests/#payload) +`req*`, `resp*` and other in `summary_requests.YYYY_MM_DD_client` | [`summary`](/reference/tables/requests/#summary) +`req_*` and `reqOtherHeaders` in `almanac.requests` | [`request_headers`](/reference/tables/requests/#request_headers) +`resp_*` and `respOtherHeaders` in `almanac.requests` | [`response_headers`](/reference/tables/requests/#response_headers) +`body` in `response_bodies.YYYY_MM_DD_client` | [`response_body`](/reference/tables/requests/#response_body) + +### Request query updates + +- Migrate from `almanac.requests` + + + +```sql +SELECT + LOWER(JSON_VALUE(request_headers, '$.name')) AS header_name, + JSON_VALUE(request_headers, '$.value') AS header_value, +FROM `httparchive.almanac.requests`, +UNNEST(JSON_QUERY_ARRAY(request_headers)) AS request_headers +WHERE date = '2024-06-01' + AND client = 'desktop' + AND firstHtml +``` + + +```sql +SELECT + LOWER(request_headers.name) AS header_name, + request_headers.value AS header_value, +FROM `httparchive.all.requests`, +UNNEST(request_headers) AS request_headers +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_main_document + AND is_root_page +``` + + + +- Migrate from `requests.YYYY_MM_DD_client` + + + +```sql +SELECT + page, + url, + JSON_VALUE(payload, '$.response.content.mimeType') AS mimeType, + CAST(JSON_VALUE(payload, '$.response.bodySize') AS INT64) AS respBodySize, +FROM `httparchive.requests.2024_06_01_desktop` +``` + + +```sql +SELECT + page, + url, + JSON_VALUE(summary, '$.mimeType') AS mimeType, + CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64) AS respBodySize, +FROM `httparchive.all.requests` +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +``` + + + +- Migrate from `response_bodies.YYYY_MM_DD_client` + + + + +```sql +SELECT + page, + url, + BYTE_LENGTH(response_body) AS bodySize +FROM `httparchive.response_bodies.2024_06_01_desktop` +``` + + +```sql +/* This query will process 174 TB when run. */ +SELECT + page, + url, + BYTE_LENGTH(response_body) AS bodySize +FROM `httparchive.all.requests` +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +``` + + + +- Migrate from `summary_requests.YYYY_MM_DD_client` + + + + +```sql +SELECT + ROUND(respBodySize/1024/100)*100 AS responseSize100KB, + COUNT(0) requests, +FROM `httparchive.summary_requests.2024_06_01_desktop` +GROUP BY responseSize100KB +HAVING responseSize100KB > 0 +ORDER BY responseSize100KB ASC +``` + + +```sql +/* This query will process 10 TB when run. */ +SELECT + ROUND(CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64)/1024/100)*100 AS responseSize100KB, + COUNT(0) requests, +FROM `httparchive.all.requests` +WHERE date = '2024-06-01' + AND client = 'desktop' + AND is_root_page +GROUP BY responseSize100KB +HAVING responseSize100KB > 0 +ORDER BY responseSize100KB ASC +``` + + diff --git a/src/content/docs/guides/numDomains_requests_graph.png b/src/content/docs/guides/numDomains_requests_graph.png new file mode 100644 index 0000000..49a292b Binary files /dev/null and b/src/content/docs/guides/numDomains_requests_graph.png differ diff --git a/src/content/docs/guides/simple_agg_query_example.png b/src/content/docs/guides/simple_agg_query_example.png new file mode 100644 index 0000000..02f01e3 Binary files /dev/null and b/src/content/docs/guides/simple_agg_query_example.png differ diff --git a/src/content/docs/guides/simple_join_example.png b/src/content/docs/guides/simple_join_example.png new file mode 100644 index 0000000..4a97bd9 Binary files /dev/null and b/src/content/docs/guides/simple_join_example.png differ diff --git a/src/content/docs/guides/sync_async_graph.png b/src/content/docs/guides/sync_async_graph.png new file mode 100644 index 0000000..7dbad41 Binary files /dev/null and b/src/content/docs/guides/sync_async_graph.png differ diff --git a/src/content/docs/guides/type_summary_example_query.png b/src/content/docs/guides/type_summary_example_query.png new file mode 100644 index 0000000..6eba1b3 Binary files /dev/null and b/src/content/docs/guides/type_summary_example_query.png differ diff --git a/src/content/docs/guides/type_summary_example_query2.png b/src/content/docs/guides/type_summary_example_query2.png new file mode 100644 index 0000000..6814788 Binary files /dev/null and b/src/content/docs/guides/type_summary_example_query2.png differ diff --git a/src/content/docs/reference/tables/pages.mdx b/src/content/docs/reference/tables/pages.mdx index 3aa0a5a..b33e690 100644 --- a/src/content/docs/reference/tables/pages.mdx +++ b/src/content/docs/reference/tables/pages.mdx @@ -7,10 +7,40 @@ import { Tabs, TabItem } from '@astrojs/starlight/components'; [`httparchive.all.pages`](https://console.cloud.google.com/bigquery?ws=!1m5!1m4!4m3!1shttparchive!2sall!3spages) is a partitioned and clustered table containing one row per page tested in the HTTP Archive. Pages are tested on a monthly basis and as of April 2022, both the root page and one secondary page are tested. + ## Example queries Here are some common operations you can perform with the `pages` table. +### Count the pages crawled + + + + +```sql +/* This query will process 1 GB when run. */ +SELECT + client, + is_root_page, + count(0) AS pages_total +FROM `httparchive.all.pages` +WHERE date = "2024-05-01" +group by client, is_root_page +``` + + + + +client | is_root_page | pages_total +-- | -- | -- +mobile | false | 13998652 +mobile | true | 16193055 +desktop | true | 12900240 +desktop | false | 11585746 + + + + ### Get the median page weight @@ -45,11 +75,11 @@ client | median_page_weight mobile | 1776291 desktop | 2029751 -The median mobile page weighs 1.78 MB and the median desktop page weighs 2.03 MB. - +The median mobile page weighs 1.78 MB and the median desktop page weighs 2.03 MB. + This query uses the [`APPROX_QUANTILES`](https://cloud.google.com/bigquery/docs/reference/standard-sql/approximate_aggregate_functions#approx_quantiles) function to calculate the median page weight for each client type as of May 2023. The `bytesTotal` property of the `summary` object represents the total number of bytes loaded on the page. This value is stored as a JSON-encoded string, so we use [`JSON_VALUE`](https://cloud.google.com/bigquery/docs/reference/standard-sql/json_functions#json_value) to extract it and [`CAST`](https://cloud.google.com/bigquery/docs/reference/standard-sql/conversion_functions#cast) to convert it to an integer. @@ -58,6 +88,44 @@ We're also using the [`WITH`](https://cloud.google.com/bigquery/docs/reference/s Also note that for demonstration purposes, this query processes a 1% sample of the `httparchive.all.pages` table. This reduces the amount of data processed by the query, which can help reduce costs. But note that the results will be less accurate than if you ran the query on the full table. +### The average number of requests per page + + + + +```sql +/* This query will process 0.6 GB when run. */ +WITH pages_summary AS ( + SELECT + CAST(JSON_VALUE(summary, '$.reqTotal') AS INT64) AS reqTotal, + CAST(JSON_VALUE(summary, '$.numDomains') AS INT64) AS numDomains + FROM `httparchive.all.pages` TABLESAMPLE SYSTEM (1 PERCENT) + WHERE date = "2024-06-01" + AND client = 'mobile' +) + +SELECT + COUNT(0) pages, + ROUND(AVG(reqTotal), 2) avg_requests, + APPROX_QUANTILES(reqTotal, 100)[SAFE_ORDINAL(25)] p25_requests, + APPROX_QUANTILES(reqTotal, 100)[SAFE_ORDINAL(50)] p50_requests, + APPROX_QUANTILES(reqTotal, 100)[SAFE_ORDINAL(75)] p75_requests, + APPROX_QUANTILES(reqTotal, 100)[SAFE_ORDINAL(95)] p95_requests +FROM pages_summary +``` + + + + +pages | avg_requests | p25_requests | p50_requests | p75_requests | p95_requests +-- | -- | -- | -- | -- | -- +306151 | 92.53 | 37 | 65 | 107 | 212 + +The median number of requests per page is 65. The average is in fact skewed by outliers. Also, since the 25th percentile is 37 requests and the 75th percentile is 107 requests, that tells us that 50% of the 300K pages tracked by the HTTP Archive have between 37 and 107 requests. This is also known as the [interquartile range](https://en.wikipedia.org/wiki/Interquartile_range). + + + + ## Schema Field name | Type | Description @@ -69,10 +137,10 @@ Field name | Type | Description [`root_page`](#root_page) | `STRING` | The URL of the root page being tested, the origin followed by `/` [`rank`](#rank) | `INTEGER` | Site popularity rank, from CrUX [`wptid`](#wptid) | `STRING` | ID of the WebPageTest results -[`payload`](#payload) | [`HAR`](/reference/blobs/har/) | JSON-encoded WebPageTest results for the page +[`payload`](#payload) | [`HAR`](/reference/blobs/har/) | JSON-encoded WebPageTest results for the page [`summary`](#summary) | `STRING` | JSON-encoded summarization of the page-level data -[`custom_metrics`](#custom_metrics) | `STRING` | JSON-encoded test results of the custom metrics -[`lighthouse`](#lighthouse) | [`Lighthouse`](/reference/blobs/lighthouse/) | JSON-encoded Lighthouse report +[`custom_metrics`](#custom_metrics) | `STRING` | JSON-encoded test results of the custom metrics +[`lighthouse`](#lighthouse) | [`Lighthouse`](/reference/blobs/lighthouse/) | JSON-encoded Lighthouse report [`features`](#features) | ARRAY<Feature> | Blink features detected at runtime [`technologies`](#technologies) | ARRAY<Technology> | Technologies detected at runtime [`metadata`](#metadata) | `STRING` | Additional metadata about the test diff --git a/src/content/docs/reference/tables/requests.mdx b/src/content/docs/reference/tables/requests.mdx index fec20b9..1741e1c 100644 --- a/src/content/docs/reference/tables/requests.mdx +++ b/src/content/docs/reference/tables/requests.mdx @@ -3,8 +3,158 @@ title: Requests table description: Reference docs for the httparchive.all.requests table --- +import { Tabs, TabItem } from '@astrojs/starlight/components'; + [`httparchive.all.requests`](https://console.cloud.google.com/bigquery?ws=!1m5!1m4!4m3!1shttparchive!2sall!3srequests) is a partitioned and clustered table containing one row per request per page tested in the HTTP Archive. Pages are tested on a monthly basis and as of April 2022, both the root page and one secondary page are tested. +## Example queries + +Here are some common operations you can perform with the `requests` table. + +### Count the pages crawled + + + + + ```sql + /* This query will process 85 GB when run. */ + SELECT + client, + is_root_page, + count(0) AS requests_total + FROM `httparchive.all.requests` + WHERE date = "2024-05-01" + group by client, is_root_page + ``` + + + + + client | is_root_page | requests_total + -- | -- | -- + mobile | true | 1517364094 + desktop | true | 1299394354 + mobile | false | 1216156430 + desktop | false | 1093804725 + + + + +### Size of requests served + +Let's check the size of individual requests served from websites across the entire dataset. To do this, we'll be using the respBodySize summary metric. 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. + + + + + ```sql + /* This query will process 26 GB when run. */ + WITH requests AS ( + SELECT + CEIL(CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64)/1024/100)*100 AS responseSize100KB, + COUNT(0) OVER () AS total_requests + FROM + `httparchive.all.requests` TABLESAMPLE SYSTEM (1 PERCENT) + WHERE + date = '2024-06-01' + AND client = 'desktop' + AND is_root_page + AND CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64) > 0 + ) + + SELECT + responseSize100KB, + COUNT(0) AS requests, + COUNT(0)/ANY_VALUE(total_requests) AS pct_requests + FROM requests + GROUP BY + responseSize100KB + ORDER BY + responseSize100KB ASC + LIMIT 10 + ``` + + + + + responseSize100KB | requests | pct_requests + -- | -- | -- + 100.0 | 10113115 | 0.90864138408777051 + 200.0 | 486257 | 0.043689133714228209 + 300.0 | 188335 | 0.016921490072264605 + 400.0 | 87127 | 0.0078281714260556891 + 500.0 | 54134 | 0.004863822144433972 + 600.0 | 37443 | 0.0033641721017113315 + 700.0 | 26985 | 0.0024245435505883687 + 800.0 | 19817 | 0.0017805143428575023 + 900.0 | 24519 | 0.0022029788147814046 + 1000.0 | 11787 | 0.0010590363102014118 + + + + +We can see that that 91% of requests have a response size less than 100KB. Try repeating this with 10KB bin sizes and you'll be able to see the spread of response sizes with more granularity. + +### Popularity of various image formats + +Let's 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. + + + + + ```sql + /* This query will process 8 GB when run. */ + WITH requests AS ( + SELECT + JSON_VALUE(summary, '$.format') AS format, + page, + COUNT(0) OVER() AS total_requests, + COUNT(DISTINCT page) OVER() AS total_pages + FROM + `httparchive.all.requests` TABLESAMPLE SYSTEM (1 PERCENT) + WHERE + date = '2024-06-01' + AND client = 'desktop' + AND is_root_page + AND type = "image" + ) + + SELECT + format, + COUNT(0) requests, + COUNT(DISTINCT page) pages, + ROUND(COUNT(0) / ANY_VALUE(total_requests), 2) percent_image_requests, + ROUND(COUNT(DISTINCT page) / ANY_VALUE(total_pages), 2) percent_pages + FROM + requests + GROUP BY + format + ORDER BY + requests DESC + ``` + + + + + format | requests | pages | percent_image_requests | percent_pages + -- | -- | -- | -- | -- + jpg | 1644804 | 1310081 | 0.38 | 0.43 + png | 1328825 | 1151809 | 0.31 | 0.38 + gif | 793541 | 495055 | 0.18 | 0.16 + svg | 250130 | 227550 | 0.06 | 0.08 + webp | 223783 | 191184 | 0.05 | 0.06 + ico | 64468 | 64016 | 0.01 | 0.02 + avif | 29226 | 25794 | 0.01 | 0.01 + | | 4405 | 3938 | 0.0 | 0.0 + heic | 395 | 382 | 0.0 | 0.0 + + + + +:::note +It's important to understand the bias in the data when doing this type of analysis. While 1.3 million page views is a very diverse set - 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. There may be cases like this with other type of technologies as well - for example custom web font types that vary based on browser support. +::: + ## Schema Field name | Type | Description @@ -86,4 +236,4 @@ Response headers ### `response_body` -Text-based response body \ No newline at end of file +Text-based response body diff --git a/workbooks/exploring_httparchive-all-pages_tables.ipynb b/workbooks/exploring_httparchive-all-pages_tables.ipynb new file mode 100644 index 0000000..bab7951 --- /dev/null +++ b/workbooks/exploring_httparchive-all-pages_tables.ipynb @@ -0,0 +1 @@ +{"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 \"num_unique_values\": 1,\n \"samples\": [\n \"https://www.google.com/\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_root_page\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 1,\n \"samples\": [\n true\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"root_page\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"https://www.google.com/\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rank\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 1,\n \"samples\": [\n 1000\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"wptid\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"240514_Dx1RL_DMOOB\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"payload\",\n 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https://www.google.com/\\\",\\\"id\\\":\\\"page_1_0_1\\\",\\\"testID\\\":\\\"240514_Dx1RL_DMOOB\\\",\\\"pageTimings\\\":{\\\"onLoad\\\":2372,\\\"onContentLoad\\\":-1,\\\"_startRender\\\":500},\\\"_loadTime\\\":2372,\\\"_docTime\\\":2372,\\\"_fullyLoaded\\\":2517,\\\"_bytesOut\\\":162266,\\\"_bytesOutDoc\\\":159025,\\\"_bytesIn\\\":985082,\\\"_bytesInDoc\\\":984587,\\\"_requests\\\":39,\\\"_requestsFull\\\":39,\\\"_requestsDoc\\\":37,\\\"_responses_200\\\":29,\\\"_responses_404\\\":0,\\\"_responses_other\\\":10,\\\"_result\\\":0,\\\"_testStartOffset\\\":0,\\\"_cached\\\":0,\\\"_optimization_checked\\\":1,\\\"_main_frame\\\":\\\"682737D63193035E165E253039C5C3AC\\\",\\\"_loadEventStart\\\":2303,\\\"_loadEventEnd\\\":2306,\\\"_domContentLoadedEventStart\\\":1062,\\\"_domContentLoadedEventEnd\\\":1062,\\\"_URL\\\":\\\"https://www.google.com/\\\",\\\"_connections\\\":7,\\\"_final_base_page_request\\\":0,\\\"_final_base_page_request_id\\\":\\\"D2B15493C88A89F15F770AB50FB6B847\\\",\\\"_final_url\\\":\\\"https://www.google.com/\\\",\\\"_domInteractive\\\":442,\\\"_firstPaint\\\":473,\\\"_firstContentfulPaint\\\":543,\\\"_firstImagePaint\\\":543,\\\"_firstMeaningfulPaint\\\":543,\\\"_renderBlockingCSS\\\":1,\\\"_renderBlockingJS\\\":0,\\\"_metadata\\\":{\\\"rank\\\":1000,\\\"page_id\\\":22893419,\\\"tested_url\\\":\\\"https://www.google.com/\\\",\\\"layout\\\":\\\"Desktop\\\",\\\"crawl_depth\\\":0,\\\"link_depth\\\":0,\\\"root_page_id\\\":22893419,\\\"root_page_url\\\":\\\"https://www.google.com/\\\",\\\"root_page_test_id\\\":\\\"240514_Dx1RL_DMOOB\\\"},\\\"_TTFB\\\":254,\\\"_basePageSSLTime\\\":0,\\\"_score_cache\\\":87,\\\"_score_cdn\\\":100,\\\"_score_gzip\\\":100,\\\"_score_cookies\\\":-1,\\\"_score_keep-alive\\\":100,\\\"_score_minify\\\":-1,\\\"_score_combine\\\":-1,\\\"_score_compress\\\":100,\\\"_score_etags\\\":-1,\\\"_score_progressive_jpeg\\\":-1,\\\"_gzip_total\\\":902682,\\\"_gzip_savings\\\":0,\\\"_minify_total\\\":-1,\\\"_minify_savings\\\":-1,\\\"_image_total\\\":45379,\\\"_image_savings\\\":0,\\\"_base_page_cdn\\\":\\\"Google\\\",\\\"_cpuTimes\\\":{\\\"ParseHTML\\\":11,\\\"HTMLDocumentParser::FetchQueuedPreloads\\\":1,\\\"EventDispatch\\\":5,\\\"V8.GC_TIME_TO_SAFEPOINT\\\":1,\\\"CommitLoad\\\":0,\\\"EvaluateScript\\\":265,\\\"v8.compile\\\":51,\\\"ResourceFetcher::requestResource\\\":59,\\\"ParseAuthorStyleSheet\\\":0,\\\"FunctionCall\\\":55,\\\"UpdateLayoutTree\\\":7,\\\"Layout\\\":96,\\\"ResourceChangePriority\\\":0,\\\"PrePaint\\\":2,\\\"Paint\\\":2,\\\"Layerize\\\":1,\\\"FireIdleCallback\\\":0,\\\"HitTest\\\":0,\\\"largestContentfulPaint::Candidate\\\":0,\\\"V8.GC_HEAP_EXTERNAL_PROLOGUE\\\":0,\\\"MinorGC\\\":1,\\\"V8.GC_HEAP_PROLOGUE\\\":0,\\\"V8.GC_SCAVENGER\\\":0,\\\"V8.GC_HEAP_PROLOGUE_SAFEPOINT\\\":0,\\\"V8.GC_SCAVENGER_SCAVENGE\\\":0,\\\"V8.GC_SCAVENGER_SCAVENGE_WEAK_GLOBAL_HANDLES_IDENTIFY\\\":0,\\\"V8.GC_SCAVENGER_SCAVENGE_ROOTS\\\":0,\\\"V8.GC_SCAVENGER_SCAVENGE_PARALLEL_PHASE\\\":15,\\\"Parallel 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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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diff --git a/workbooks/exploring_pages_and_requests_tables_joined.ipynb b/workbooks/exploring_pages_and_requests_tables_joined.ipynb new file mode 100644 index 0000000..77e3ba1 --- /dev/null +++ b/workbooks/exploring_pages_and_requests_tables_joined.ipynb @@ -0,0 +1,4896 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "SeTJb51SKs_W" + }, + "outputs": [], + "source": [ + "import altair as alt\n", + "from google.colab import auth\n", + "auth.authenticate_user()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_FrKSq05hrU3" + }, + "source": [ + "In [Part 1](https://colab.research.google.com/github/rviscomi/har.fyi/blob/main/workbooks/exploring_httparchive-all-pages_tables.ipynb) we examined the `pages` tables. Then in [Part 2](https://colab.research.google.com/github/rviscomi/har.fyi/blob/main/workbooks/exploring_httparchive-all-requests_tables.ipynb) we worked with the `requests` tables. Now let's look at how we can combine both the `requests` and `pages` tables to perform a deeper analysis. In the following example, we're going to investigate the following question:\n", + " \"Is image weight more commonly a factor of 1 large image, or many smaller images?\".\n", + "\n", + "Let's start with a simple query against the `pages` table. This is a simple query that just selects the URL, the number of images and the bytes of those images." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488, + "referenced_widgets": [ + "891670ce45ea4607a03236b047f55277", + "f318ea3101ef49c48bdbb177c02ad9df", + "c3427f4f936b45dc9931d36eba7f1ba5", + "5813705e5a8e44a68e2bb350ca26fa4e", + "9c8535f6b1334638b80728d624b23505", + "32a03b8ceddd43c8952beaa7e606b10f", + "04fae5fc31104f5b8a2c001ae3c6f28e", + "4f5b2b6d3f3741c693bb8e4a911943f1", + "c5026dd9365942e0893a3cabfba9e092", + "252a2d2d6142431eadd27212b5539f2a", + "276164f885054385b0febd21cf36165b", + "593387121f85403db1b569f7c6733c68", + "ea5bfea78c2648b1981a26becfe9fd7b", + "9510298597804f1f82f25daad54db6a4", + "fac32e0bed8b4916b604b035032b2c4e", + "97e997d5c4184f41aa971d096720e786", + "cc641e9185fe404584a842255aed4069", + "2c286ddb4ecb47acb036446cefd05abf", + "98534255546447df82fba990ce824959", + "28e00d3b5e1c4723934363d03857e1a2", + "578f68723f434000ac9ee799baa2bd65", + "9d0df9a7aed5499783010de2323e44b8" + ] + }, + "id": "GjXvcNNCgUYE", + "outputId": "a9f243fb-9708-4663-ceee-28fa5269db0b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "891670ce45ea4607a03236b047f55277", + "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": "593387121f85403db1b569f7c6733c68", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading: 0%| |" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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    0https://www.laurel.edu/170143775693
    1https://www.salsayotra.fr/223120086614
    2https://www.traumama.com/65116583274
    3https://gridarchitects.co.uk/349110498520
    4https://studfist.fans/230107665948
    ............
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    \n" + ], + "text/plain": [ + " page image_requests image_bytes\n", + "0 https://www.laurel.edu/ 170 143775693\n", + "1 https://www.salsayotra.fr/ 223 120086614\n", + "2 https://www.traumama.com/ 65 116583274\n", + "3 https://gridarchitects.co.uk/ 349 110498520\n", + "4 https://studfist.fans/ 230 107665948\n", + "... ... ... ...\n", + "12900235 http://www.unam.me/ \n", + "12900236 http://simpro.jayabeton.com/ \n", + "12900237 http://moncrane.mn/ \n", + "12900238 http://iptu.sorocaba.sp.gov.br/ \n", + "12900239 http://www.inas.gov.mz/ \n", + "\n", + "[12900240 rows x 3 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This query will process 25 GB when run.\n", + "%%bigquery --project httparchive\n", + "SELECT\n", + " page,\n", + " CAST(JSON_VALUE(summary, '$.reqImg') AS INT64) AS image_requests,\n", + " CAST(JSON_VALUE(summary, '$.bytesImg') AS INT64) AS image_bytes\n", + "FROM `httparchive.all.pages`\n", + "WHERE date = '2024-05-01'\n", + " AND client='desktop'\n", + " AND is_root_page\n", + "ORDER BY\n", + " image_bytes DESC" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3wTANDmXgWK6" + }, + "source": [ + "In the previous example we didn't use the requests table at all. However if we wanted to, we could do the same analysis by JOINing the request table. In order to do this, I've made the following modifications to the query above:\n", + "\n", + "* Named the tables (i.e., so we can reference \"httparchive.all.pages\" as \"pages\")\n", + "* Reference the tables for some of the columns that exist in both tables (such as page)\n", + "* Since we're focusing on images, we'll include `requests.type=\"image\"` in our WHERE clause.\n", + "* JOIN the tables `ON requests.page = pages.page`\n", + "\n", + "The below query is similar to the previous one, but uses both tables. The unique differentiation here is that `requests` table is much bigger and we are applying sampling to the `requests` table for a faster and cheaper result." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488, + "referenced_widgets": [ + "469da89096554c75be4cb1b07ae15d10", + "30fc7e80d0814aaab0c6b7ed7986bbaa", + "a8b60508000e4c8db52199e077275622", + "59cfb0ebced9458cbd140abcecb8cee2", + "4cc7f46d0e734c71aaa49df574736fc8", + "a6ff7be4bf0f4291be2e9a21b75a0ed1", + "9b75de820009438ab0b93eed95f45283", + "c2f00bca3a0145dbaaed384f3ec91bf5", + "fe0fadb01407403f8d1a2445969df7e5", + "462d2179d200485fab2b8cebde0275bf", + "e1656abcde1d4e98965b526ea63e6dc2", + "a9a9669a49e046bb8e0df392ad86d2d5", + "ccd9107c4b0d45e683d2e4bdafacc365", + "9565bec0c58449358887de1f32104fee", + "caabdb9d6f8f4b2dbd72428c29327b78", + "0add8fd3c108480fb5b992d31be53254", + "95f24d866fe94c5bbdfc4a42e2d4c369", + "ad70c6b217bc430296df91a069ff52f2", + "aece9ec563b34f6baa2ec667315c3d8b", + "76bca57ebb6e4434ad1864ad7887a95e", + "199e42009d2c4e5aa7dad48d8ef4438e", + "cd3b1b27f6af456a9d8e746d0daafb57" + ] + }, + "id": "0GBEhwv9gYEx", + "outputId": "f8ed3c8b-aa9b-40ca-f9a0-b053691c4f22" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "469da89096554c75be4cb1b07ae15d10", + "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": "a9a9669a49e046bb8e0df392ad86d2d5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading: 0%| |" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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    pageimage_requestsimage_bytes
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    1https://hopeshasta.org/168550656
    2https://innoviz.tech/268533708
    3https://www.sassandbelle.co.uk/668387227
    4https://www.radiocolor.ro/368317514
    ............
    10102030https://niagarasushiandramen.com/1<NA>
    10102031https://obituarios.elsalvador.com/1<NA>
    10102032https://www.qcwxjs.com/1<NA>
    10102033https://carispesifikasi.com/1<NA>
    10102034https://www.blackwellrum.com/1<NA>
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If you want to validate this, you can load one of the pages in DevTools or WebPageTest and see for yourself. For example based on the results above, https://princetel.com/ has 1 image totaling 110MB. WebPageTest confirms that this is accurate (although the image weight likely changed slightly between when the HTTP Archive saw the page vs when this manual test was performed." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zh3tBFl92_JN" + }, + "source": [ + "# Content breakdown by mime/type" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "a2dee85becae4f19b63f63cd9b7a0f65", + "32842c8cddfd439e8b787bff8835685a", + "cfaf4fbe2e6d4437ba980687b29041e3", + "80543d1cf2444564a4796689fe394320", + "ade73ed1faf14377a1ea3b01f6075d5f", + "e3f778ea19c04dc9b1a5199c4c48f133", + "7a15837e52634a1aa0877c54a9e2f523", + "8fe4901b81cf447fbab2b42d97744efc", + "18b2bb6dc8af490b8cf79216cba4fc24", + "84040ba0d4d146039b58fa929e4bc4e6", + "657d1cf53f1746d5bc215867b0e2808f", + "4f95d90cf1fb4de690de002d68041d42", + "1932bd5ba326406685c835dcef8da9a2", + "b87223ab4a8740fd9a1c1c08bbf03d53", + "b450d9a04c7448ada9a16003ded7cc93", + "9562a4baee9f4b46805cdcfeb8129050", + "332b1c5e511d4fe38a31177fb564995e", + "25232272eaeb40cf8d44a6dd1593081d", + "bc76eede67b048e8833985b02286ed7d", + "b4e37cd66e004b759c90a40fbf1b38fc", + "75416a638b66422ba3caa1e9ecf175fe", + "bb3f22dbe7774752ab3b82a4709b62c5" + ] + }, + "id": "Ho4ajWJa3k9j", + "outputId": "471b107f-d8b1-47a5-f227-87e3913db1ad" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a2dee85becae4f19b63f63cd9b7a0f65", + "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": "4f95d90cf1fb4de690de002d68041d42", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading: 0%| |" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This query will process 131 GB when run.\n", + "%%bigquery df_requests_type --project httparchive\n", + "SELECT\n", + " type,\n", + " COUNT(0) AS requests,\n", + " SUM(CAST(JSON_VALUE(summary, '$.respBodySize') AS INT64)) AS bytes\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 page='https://princetel.com/'\n", + "GROUP BY\n", + " type\n", + "ORDER BY\n", + " requests DESC" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "E8su2VIxC9QR", + "outputId": "619459e6-34ca-4afa-c698-5a12d3d4c7bb" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"df_requests_type\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"font\",\n \"script\",\n \"image\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"requests\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bytes\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 3,\n \"samples\": [\n 7884\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "df_requests_type" + }, + "text/html": [ + "\n", + "
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    typerequestsbytes
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    \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pie_chart_requests = alt.Chart(df_requests_type).mark_arc().encode(\n", + " theta=alt.Theta(field=\"requests\", type=\"quantitative\", stack=True),\n", + " color=alt.Color(field=\"type\", type=\"nominal\", sort=None),\n", + " order=alt.Order(field=\"requests\", type=\"quantitative\", sort='descending'),\n", + " tooltip=[\"type\", alt.Tooltip(\"requests:Q\", format=\".0\")]\n", + ").properties(\n", + " title=\"Requests by Type\"\n", + ")\n", + "pie_chart_requests + pie_chart_requests.mark_text(\n", + " radius=160\n", + ").encode(\n", + " text=alt.Text(\"requests:Q\", format=\".0\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359 + }, + "id": "JaBGV814GjlY", + "outputId": "07914137-12fb-4d70-9413-478b8048029a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
    \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pie_chart_bytes = alt.Chart(df_requests_type).mark_arc().encode(\n", + " theta=alt.Theta(field=\"bytes\", type=\"quantitative\", stack=True),\n", + " color=alt.Color(field=\"type\", type=\"nominal\", sort=None),\n", + " order=alt.Order(field=\"bytes\", type=\"quantitative\", sort='descending'),\n", + " tooltip=[\"type\", alt.Tooltip(\"bytes:Q\", format=\".1s\")]\n", + ").properties(\n", + " title=\"Bytes by Type\"\n", + ")\n", + "pie_chart_bytes + pie_chart_bytes.mark_text(\n", + " radius=160\n", + ").encode(\n", + " text=alt.Text(\"bytes:Q\", format=\".1s\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P3WbjDmSghdD" + }, + "source": [ + "If this was all the information we needed, then JOINING the `pages` table would have been unecessary. All the requests data can be aggregated using `page` column in `requests` table." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KNYSJpOstPn-" + }, + "source": [ + "Another example we will look into is the requests for JS scripts and page `_TotalBlockingTime` metric within the `payload` column. We are using the `page` column to classify whether the `requests` data is first or third party content.\n", + "\n", + "The query below gets the median Total Blocking Time (TBT) per a number of first and third-party scripts." + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "a4978a2a675b4c8093d5888fe097a0a6", + "03262f609a4843578b35b5e2fc79ab72", + "3773e3f9684943999e2c90c8196f70b5", + "e7fb63602f404184b2bd32905cf87f00", + "f9da6b8919084b618d88d9e85e2361b2", + "2cbaaafb8d10477184f0dcd80b882217", + "759111640996435fb98e53e2f1470e21", + "6605fc8165e14144bd2d8aec22c0d106", + "7a300e4fcd5146d89acf6a1bc34d523f", + "49b124cff22f434db5f5475213676828", + "83451b9b811c478f9bb74c9689918736", + "04bf982fe41c4a0dabf128db3493be7a", + "014b2da94ade42b595e3ccf614397faa", + "4eda685be30b4182bac58fc326db27ab", + "4233496dba034f518b2d25b6b3318168", + "b8d5fc50a65b4f41b6d5610dfca09835", + "fdadab76e6114ba3ba36256b9fde757e", + "0d6ab68b31954907a1bba0b5a5f7e029", + "fd99ad340dfa4f78b79e3695c388e3d8", + "9d46ec11380d486e8cb227dd651999fc", + "a09041530f4046a2b439be0491d6e36e", + "a7e0e23d9f804a2584dcb6023a89942f" + ] + }, + "id": "qeU0sYHHglG4", + "outputId": "450d7ad2-ebf2-4f7e-96e8-44749b840e0b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a4978a2a675b4c8093d5888fe097a0a6", + "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": "04bf982fe41c4a0dabf128db3493be7a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading: 0%| |" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This query will process 47 GB when run.\n", + "%%bigquery df_tbt_scripts --project httparchive\n", + "WITH pages AS (\n", + " SELECT\n", + " page,\n", + " CAST(ROUND(CAST(JSON_VALUE(payload, '$._TotalBlockingTime') AS FLOAT64)/100)*100 AS INT64) AS TBT_bucket\n", + " FROM `httparchive.all.pages` TABLESAMPLE SYSTEM (1 PERCENT)\n", + " WHERE date = '2024-05-01'\n", + " AND client='desktop'\n", + " AND is_root_page\n", + " GROUP BY\n", + " page,\n", + " TBT_bucket\n", + "), requests AS (\n", + " SELECT\n", + " page,\n", + " NET.REG_DOMAIN(url) = NET.REG_DOMAIN(page) AS is_1p_script,\n", + " COUNT(0) AS count\n", + " FROM `httparchive.all.requests` TABLESAMPLE SYSTEM (10 PERCENT)\n", + " WHERE date = '2024-05-01'\n", + " AND client='desktop'\n", + " AND is_root_page\n", + " AND type = \"script\"\n", + " GROUP BY\n", + " page,\n", + " is_1p_script\n", + " HAVING is_1p_script IS NOT NULL\n", + ")\n", + "\n", + "SELECT\n", + " is_1p_script,\n", + " count,\n", + " APPROX_QUANTILES(TBT_bucket, 100)[50] AS TBT_median\n", + "FROM requests\n", + "INNER JOIN pages\n", + "ON requests.page = pages.page\n", + "GROUP BY\n", + " count,\n", + " is_1p_script\n", + "ORDER BY\n", + " count,\n", + " is_1p_script" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "b_-oLuqLV3p5", + "outputId": "6c3ea76c-19fa-4dba-bbea-170bc5dfae41" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"df_tbt_scripts\",\n \"rows\": 127,\n \"fields\": [\n {\n \"column\": \"is_1p_script\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"count\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 77,\n \"samples\": [\n 5,\n 36\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"TBT_median\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 43,\n \"samples\": [\n 2000,\n 4600\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "df_tbt_scripts" + }, + "text/html": [ + "\n", + "
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    \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filter outliers\n", + "df_filtered = df_tbt_scripts[(df_tbt_scripts['TBT_median'] < 20000) & (df_tbt_scripts['count'] < 60)]\n", + "\n", + "base = alt.Chart(df_filtered).mark_circle(opacity=0.5).encode(\n", + " alt.X('count:Q').title('Script Count'),\n", + " alt.Y('TBT_median:Q').axis(format='s').title('Total Blocking Time, ms'),\n", + " alt.Color('is_1p_script:N').title('1st Party Script'),\n", + " tooltip=['TBT_median', 'is_1p_script', 'count']\n", + ").properties(\n", + " title=\"Total Blocking Time (ms) by 1st/3rd Script Count\",\n", + " width=1200,\n", + " height=600\n", + ")\n", + "\n", + "base + base.transform_loess('count', 'TBT_median', groupby=['is_1p_script']).mark_line()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3T0Mu1hmgnC3" + }, + "source": [ + "**Note**: as the query in this guide is sampled the results may misrepresent the true metrics values.\n", + "\n", + "General trend is that 3rd-party scripts tend to have a bigger correllation with the TBT than 1st-party. The TBT tends to increase as the number of 3rd-party scripts increases up to 20 scripts of a particular type per page.\n", + "\n", + "After 20 scripts per page the TBT increases more 3rd-party scripts added to a page." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SgFbeAF_gJHA" + }, + "source": [ + "You can also make a copy of the workbook and experiment with some of your own visualization ideas for the data as well." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "014b2da94ade42b595e3ccf614397faa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": 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