diff --git a/content/academy/advanced_web_scraping.md b/content/academy/advanced_web_scraping.md index fd08972cf..0d3b9cfe8 100644 --- a/content/academy/advanced_web_scraping.md +++ b/content/academy/advanced_web_scraping.md @@ -1,6 +1,6 @@ --- title: Advanced web scraping -description: Take your scrapers to the next level by learning various advanced concepts and techniques that will help you build highly scalable and reliable crawlers. +description: Take your scrapers to production-ready level by learning various advanced concepts and techniques that will help you build highly scalable and reliable crawlers. menuWeight: 6 category: web scraping & automation paths: @@ -9,11 +9,21 @@ paths: # Advanced web scraping -In this course, we'll be tackling some of the most challenging and advanced web-scraping cases, such as mobile-app scraping, scraping sites with limited pagination, and handling large-scale cases where millions of items are scraped. Are **you** ready to take your scrapers to the next level? +In [**Web scraping for beginners**]({{@link web_scraping_for_beginners.md}}) course, we have learned the nesesary basics required to create a scraper. In the following courses, we enhanced our scraping toolbox by scraping APIs, using browsers, scraping dynamic websites, understanding website anti-scraping protection and making our code more maintainable by moving into Typescript. -If you've managed to follow along with all of the courses prior to this one, then you're more than ready to take these upcoming lessons on 😎 +In this course, we will take all of that knowledge, add a few more advanced concepts and apply them to learn how to build a production-ready web scraper. + +## [](#what-does-production-ready-mean) What does production-ready mean? + +Of course, there is no single world-wide definition of what production-ready system is. Different companies and use-cases will place different priorities on the project. But in general, a production-ready system is stable, reliable, scalable, performant, observable and maintainable. - +The following sections will cover the core concepts that will ensure that your scraper is production-ready: +- Advanced crawling section will cover how to ensure we find all pages or products on the website. +- The advanced data extraction will cover how to efficiently extact data from particular page or API. + +Both of these section will include guides for monitoring, performance, anti-scraping protections and debugging. + +If you've managed to follow along with all of the courses prior to this one, then you're more than ready to take these upcoming lessons on 😎 ## [](#first-up) First up diff --git a/content/academy/advanced_web_scraping/crawling/crawling-sitemaps.md b/content/academy/advanced_web_scraping/crawling/crawling-sitemaps.md new file mode 100644 index 000000000..9fb9f79fb --- /dev/null +++ b/content/academy/advanced_web_scraping/crawling/crawling-sitemaps.md @@ -0,0 +1,59 @@ +--- +title: Crawling sitemaps +description: Learn how to extract all of a website's listings even if they limit the number of results pages. See code examples for setting up your scraper. +menuWeight: 2 +paths: +- advanced-web-scraping/crawling/crawling-sitemaps +--- + +In the previous lesson, we learned what is the utility (and dangers) of crawling sitemaps. In this lesson, we will go in-depth to how to crawl sitemaps. + +We will look at the following topics: +- How to find sitemap URLs +- How to set up HTTP requests to download sitemaps +- How to parse URLs from sitemaps + +## [](#how-to-find-sitemap-urls) How to find sitemap URLs +Sitemaps are commonly restricted to contain max 50k URLs so usually there will be a whole list of them. There can be a master sitemap containing URLs of all other sitemaps or the sitemaps might simply be indexed in robots.txt and/or having auto-incremented URLs like `/sitemap1.xml`, `/sitemap2.xml`, etc. + +### [](#google) Google +You can try your luck on Google by searching for `site:example.com sitemap.xml` or `site:example.com sitemap.xml.gz` and see if you get any results. If you do, you can try to download the sitemap and see if it contains any useful URLs. This success of this approach depends if the website tells Google to index the sitemap file itself which is rather uncommon. + +### [](#robots-txt) robots.txt +If the website has a robots.txt file, it often contains sitemap URLs. The sitemap URLs are usually listed under `Sitemap:` directive. + +### [](#common-url-paths) Common URL paths +You can try to iterate over a common URL paths like: +``` +/sitemap.xml +/product_index.xml +/product_template.xml +/sitemap_index.xml +/sitemaps/sitemap_index.xml +/sitemap/product_index.xml +/media/sitemap.xml +/media/sitemap/sitemap.xml +/media/sitemap/index.xml +``` + +Make also sure you test the list with `.gz`, `.tar.gz` and `.tgz` extensions and by capitalizing the words (e.g. `/Sitemap_index.xml.tar.gz`). + +Some websites also provide an HTML version, to help indexing bots find new content. Those include: + +``` +/sitemap +/category-sitemap +/sitemap.html +/sitemap_index +``` + +Apify provides the [Sitemap Sniffer actor](https://apify.com/vaclavrut/sitemap-sniffer) (open-source code), that scans the URL variations automatically for you, so that you don't have to check manually. + +## [](#how-to-set-up-http-requests-to-download-sitemaps) How to set up HTTP requests to download sitemaps +For most sitemaps, you can do a simple HTTP request and parse the downloaded XML text with Cheerio (or just use `CheerioCrawler`). Some sitemaps ar compressed and have to be streamed and decompressed. [This article]({{@link node-js/parsing_compressed_sitemaps}} describes step by step guide how to handle that. + +## [](#how-to-parse-urls-from-sitemaps) How to parse URLs from sitemaps +The easiest part is to parse the actual URLs from the sitemap. The URLs are usually listed under `` tags. You can use Cheerio to parse the XML text and extract the URLs. Just be careful that the sitemap might contain other URLs that you don't want to crawl (e.g. /about, /contact or various special category sections). [This article]({{@link node-js/node-js/scraping-from-sitemaps}} provides code examples for parsing sitemaps. + +## [](#next) Next up +That's all we need to know about sitemaps for now. Let's dive into a much more interesting topic - search, filters and pagination. \ No newline at end of file diff --git a/content/academy/advanced_web_scraping/scraping_paginated_sites.md b/content/academy/advanced_web_scraping/crawling/crawling-with-search-i.md similarity index 96% rename from content/academy/advanced_web_scraping/scraping_paginated_sites.md rename to content/academy/advanced_web_scraping/crawling/crawling-with-search-i.md index 68c011729..7bbf56896 100644 --- a/content/academy/advanced_web_scraping/scraping_paginated_sites.md +++ b/content/academy/advanced_web_scraping/crawling/crawling-with-search-i.md @@ -1,12 +1,14 @@ --- -title: Scraping paginated sites +title: Crawling with search I description: Learn how to extract all of a website's listings even if they limit the number of results pages. See code examples for setting up your scraper. -menuWeight: 1 +menuWeight: 3 paths: -- advanced-web-scraping/scraping-paginated-sites +- advanced-web-scraping/crawling/crawling-with-search-i --- -# Scraping websites with limited pagination +# Scraping websites with search I + +In this lesson, we will start with a simpler example of scraping HTML based websites with limited pagination. Limited pagination is a common practice on e-commerce sites and is becoming more popular over time. It makes sense: a real user will never want to look through more than 200 pages of results – only bots love unlimited pagination. Fortunately, there are ways to overcome this limit while keeping our code clean and generic. @@ -18,7 +20,7 @@ Limited pagination is a common practice on e-commerce sites and is becoming more Websites usually limit the pagination of a single (sub)category to somewhere between 1,000 to 20,000 listings. The site might have over a million listings in total. Without a proven algorithm, it will be very manual and almost impossible to scrape all listings. -We will first look at a couple ideas that don't work so well and then present the [final robust solution](#using-filter-ranges). +We will first look at a couple ideas that might cross our mind but don't work so well and then present the [most robust solution](#using-filter-ranges). ### [](#going-deeper-into-subcategories) Going deeper into subcategories @@ -278,9 +280,10 @@ for (const filter of newFilters) { await crawler.addRequests(requestsToEnqueue); ``` +Check out the [full code example](https://github.com/metalwarrior665/apify-utils/tree/master/examples/crawler-with-filters). + ## [](#summary) Summary -And that's it. We have an elegant and simple solution for a complicated problem. In a real project, you would want to make this a bit more robust and [save analytics data]({{@link expert_scraping_with_apify/saving_useful_stats.md}}). This will let you know what filters you went through and how many products each of them had. +And that's it. We have an elegant and simple solution for a complicated problem. In the next lesson, we will explore how to refine this algorithm and apply it to bigger use-cases like scraping APIs. -Check out the [full code example](https://github.com/metalwarrior665/apify-utils/tree/master/examples/crawler-with-filters). diff --git a/content/academy/advanced_web_scraping/crawling/crawling-with-search-ii.md b/content/academy/advanced_web_scraping/crawling/crawling-with-search-ii.md new file mode 100644 index 000000000..3a9999718 --- /dev/null +++ b/content/academy/advanced_web_scraping/crawling/crawling-with-search-ii.md @@ -0,0 +1,340 @@ +--- +title: Crawling with search II +description: A more complete solution the crawling with search problem. +menuWeight: 3 +paths: +- advanced-web-scraping/crawling/crawling-with-search-ii +--- + +# Crawling websites with search II + +## [](#price-range-splitting-might-not-be-enough) Price range splitting might not be enough +In the previous lesson, we implemented relatively simple price range splitting algorithm that works good enough for most websites. However, it has a few shortcomings: +- **Prices tend to aggregate around specific values** (like $100 or $9.99) - This might cause that we end up splitting up to a point where the range contains just a single value (e.g. $9.99-$9.99) and we are still over the pagination limit. +- **Some products might not have any price** - Typically products that are out of stock. On some sites, they are not included in the price range even if it starts with 0. + +## [](#api-examples) API Examples +To solve a case where price splitting is not enough, we can split over a different filter like `size` or `brand`. That might get us to the finish line but the more sites like these we encounter, the more sense it makes to implement a more general solution. Fortunately, the typical API responses from these searches are very similar and can guide us in the implementation. + +### [](#response-examples) Response examples +In this lesson, we will be working with JSON APIs which is the most common case for implementing search. If the website uses purely server-rendered HTML (like Amazon), you can still use this framework but you might miss some of the details and will need to convert the HTML into data structures. + +Typical API responses from search queries looks like this (the JSON is heavily truncated, there are many more options): +```jsonc +{ + "response": { + "result_sources": { + "token_match": { + "count": 275655 + } + }, + "results": [ + // Product data we are extracting + ], + "sort_optipons" [ + { + "sort_by": "release_date", + "display_name": "Upcoming Releases", + "sort_order": "ascending" + }, + { + "sort_by": "lowest_price_cents", + "display_name": "Price (Low - High)", + "sort_order": "ascending" + } + ], + "facets": [ + { + "display_name": "Brand", + "name": "brand", + "type": "multiple", + "options": [ + { + "count": 216, + "display_name": "424", + "value": "424" + }, + { + "status": "", + "count": 125, + "display_name": "032C", + "value": "032c" + } + ] + }, + { + "display_name": "Price", + "name": "lowest_price_cents", + "type": "range", + "min": 0, + "max": 11302500 + }, + // Imagine other facets like size, consdition, color, release year, shipping price + ] + } +} +``` + +Let's break it down: +- `response.result_sources.token_match.count` - This is the total number of products that match the search query. We can use it to determine if we are under the limit and can start paginating or need to refine the search further +- `response.results` - This is the list of products that we are extracting. It is usually paginated and we need to iterate over all pages to get all the products. We don't really care about the results in this lesson, paginating them is trivial +- `response.sort_options` - This is the list of sorting options that the website provides. We don't need to use sorting but it can for example enable us to double the limit for price range filter (we can sort from cheapest and then from most expensive and get twice as many products) +- `response.facets` - This is the list of filters that the website provides. The `count` property of the options is usually tied to the current search query so as you refine the search, these numbers will lower as well. We can use them to further refine the search. In this example, we can use the `brand` filter to split the search into multiple smaller searches. We can also use the `lowest_price_cents` filter to split the price range into smaller ranges. Facets are key to our algorithm and we will explore them in more detail in the next section. + +Another response example might look like: +```json +{ + "data": { + "browse": { + "results": { + "edges": [ + // products + ], + "pageInfo": { + "limit": 40, + "page": 1, + "total": 76903 + } + } + } + } +} +``` + +In this case, the response doesn't contain sort options or facets. We will have to extract those manually or programmatically from the page. + +### [](#request-examples) Request examples +It is useful to also look into the requests that triggered these responses. + +For the first response, the request was GET and all parameters were contained in the URL: +https://website.com/search?key=key_fsgbjdbvGTECO5d8&page=1&num_results_per_page=24&filters[Blowest_price_cents]=931000-10286000&filters[size_converted]=us_sneakers_men_4&sort_order=descending + +Let's break down the parameters: +- `key` - Is API key required for the request. It might be hardcoded and provided in the initial HTML (sometimes it gets more complicated but that is not the topic for this lesson) +- `page` - The page number. For refining the search, we want to keep this on 1 so we always get results. +- `num_results_per_page` - The number of results per page. There is usually a limit to it but what we care more about is the total limit for each search which is often not displayed and we have to find that value by trial and error. +- `filters` - Filters here use a custom URL format of `filters[facet_name]=facet_value`. The value can be a single string or a number range depending on the type of the facet. +- `sort_order` - Making sure we use the same sorting order for the whole search helps with debugging. + +The second API was using a POST request with the parameters in the body. This is using [Graph QL]({{@link api_scraping/graphql_scraping.md}}). +```json +{ + "operationName": "Browse", + "query": "query long-graphql-query...", + "variables":{ + "category": "sneakers", + "filters": [ + { + "id": "browseVerticals", + "selectedValues": [ + "sneakers" + ] + }, + { + "id": "shoeSize", + "selectedValues": [ + "4" + ] + } + ], + "filtersVersion": 4, + "sort": { + "id": "featured", + "order": "DESC" + }, + "page": { + "index": 1, + "limit": 40 + }, + "currency": "USD" + } +} +``` + +## [](#the-framework) The framework +> We are working on a TypeScript library that will provide this framework and also auto-implement it for popular search APIs like Algolia. + +Let's be a bit more formal here and define the goals for our framework: +- We want to extract all products from a website (or its category). If it is not possible, we want to extract as much as possible. +- We want to be as efficient as possible. That means extracting all products with minimum number of requests. +- We want to know if we succeeded or not and if not, why. The framework needs to provide us with enough information to debug the issue. + +### [](#facets-vs-filters) Facets vs filters +There probably isn't a single global definition of what a facet is and what a filter is. In this framework, we will use the following definitions: +**Facet** - A lit of options (or range) that you can apply to get a specific filter. You get facets from the API response. +**Filter** - A chosen option from the facet that you can use to refine the search. You set filters in the API request. + + +### [](#known-vs-unknown) Known vs unknown facets +The first thing we need to do is to figure is if the website provides the counts for options of the facets. + +### [](#categorizing-facets) Categorizing facets +As we explained in the previous section, facets are key to our algorithm. To choose the correct facet, we need to understand how to rank them. There are 2 main types: + +#### [](#range-facets) Range facets +Range facets like price, weight, area, etc. are defined by two numerical values. Ranges are typically recursively split into two equal sub-ranges until both min and max values equal. Some sites will allow defining open ended ranges where we can only define the lower or upper bound but some sites do not allow that. We also have to be mindful about if the bounds are inclusive or exclusive. + +Range facets advantage is that they can be recursively split by half which has a nice property of logarithmic complexity. The disadvantage is that we don't get the exact number of products in each sub-range upfront so some ranges are actually not complete facets. + +#### [](#multichoice-facets) Multichoice facets +Multichoice facets are defined by a list of options. Each option has a value and a count (if provided by the website). The count is usually tied to the current search query and will lower as we refine the search further but that might not be the case for facets provided in the request. + +Multichoice facets advantage is that we can get the exact number of products in each sub-facet upfront. That makes it easy to create accurate automatic solutions because we know if the facet is complete or not and how even is its option distribution. The disadvantage is that they usually offer only small amount of options and thus we have to combine many of them to achieve a broad coverage. They also tend to be incompete. + +### [](#ranking-facets) Ranking facets +A good facet should be: +- **Complete** - A sum of all option counts should equal to the total number of products for this request. If it is lower or higher, we cannot ensure that we are getting all the products. (We can still use it for best-effort scraping) +- **Evenly distributed** - The enable deep splitting of the search, it should be relatively evenly distributed. This applies both to range and multichoice facets but we can only calculate it for multichoice facets (for range ones we have to set their rank manually). + +#### [](#complete-vs-incomplete-vs-unknown) Complete vs incomplete vs unknown +**Complete facets** are the easiest to work with. If we can complete the scrape with just them, we can create a very efficient algorithm that will get all the products with minimum number of requests and reliably. + +If unfortuantely, we run out of splits over complete facets, we will have to finish with **incomplete facets**. Their part in the algorithm depends a lot on our business use-case. Since we cannot create a predictable paths for incomplete facets, we will have to create a lot of different filter combinations and hope that we will eventually get all the products. We have to put hard-stops on the number of requests we make and the number of products we get. If we don't, we might end up with an almost infinite loop. That being said, we can still rank incomplete facets and find a lot of products with them. + +Unknown facets are those where we don't count of the options upfront. We can either just enqueue all combinations (with some limits) or manually rank them as needed. There is also an option to gather these counts partially as we scrape to identify the most promising facets. + +## [](#the-manual-solution) The manual solution +We don't necessarily need a refined algorithm to properly traverse the whole website. The obvious way to start is to just manually check the facets distribution on the website and figure out the best path. It might be something like splitting over `price`, then `size` and then `brand`. At that point, we might be confident no final combination will end up over the limit. + +The disadvantage of this approach is that it is vulnerable to changes on the website. As products are added and removed, the distribution of the facets will change and we might end up with a suboptimal solution. If the names of the facets change, our solution will break completely. Fortunately, this doesn't happen very often but we need to be sure to have a good monitoring system to catch this (more on monitoring in later lessons). + + +## [](#the-algorithm) The algorithm +We are finally getting to the meat of the lesson. The output of the algorithm is to give us the best facet every time we call it (and provide it the currently used filters) and tell us if we are ensuring all products are extracted or not. The implementation will have to be adjusted for each website because they will give us different amount of information. + +We sort the facets by their rank which is calculated as follows: +- Complete facets are ranked higher than range ones and incomplete ones +- Evenly distributed facets are ranked higher than unevenly distributed ones. Range facets are by default somewhere in the middle but we can override their rank manually. +- Facets with only one option are discarded completely. + +The algorithm is as follows: +1. Fist we start with complete multichoice facets ranked by their distribution. +2. If we run out of complete facets, we start with range facets. We don't know if their are complete upfront so we enqueue them and pass expected total results. If we get less than expected, we know that the facet is incomplete and and re-enqueue the previous request with this extra information to find the next best facet. +3. If we run out of range facets, we start with incomplete multichoice facets ranked by their distribution. We iterate over them until we get all products or we run into hard request limits. + +### [](#code) Code +We will write quite a bit of code in the next section so please make yourself comfortable. We will use the reponse format from the first [response example](#response-examples) that includes pretty rich information about the facets, mainly their type and count. If this information is not available on target website, the solution will never be this much automatic and will require manual ordering of the facets. + +The below implementation is by no means optimal and might be simply too complicated for your needs but the goal is to present several ideas and also prepare you for common problems you might encounter. + +#### [](#types) Types +If you use TypeScript, it is useful to define the Facet type for type check and code completion: +```typescript +interface FacetOption { + count: number, + display_name: string, + value: string +} + +interface MultichoiceFacet { + name: string, + display_name: string, + type: 'multiple', + options: FacetOption[], +} + +interface RangeFacet { + name: string, + display_name: string, + type: 'range', + min: number, + max: number, +} + +type Facet = MultichoiceFacet | RangeFacet; +``` + +#### [](#utilities) utilities +First we create utility functions +```typescript +// Facets that have no or only one option are useless +const isUsableFacet = (facet: Facet) => { + if (facet.type === 'range') { + return true; + } + return facet.options.length > 1; +} + +const isFacetComplete = (facet: Facet, total: number): boolean => { + if (facet.type === 'range') { + return true; + } + const sumOfOptions = facet.options.reduce((sum, option) => sum + option.count, 0); + return sumOfOptions === total; +}; + +// Returns number between 0 and 1. Lower is better +const facetDistributionRank = (facet: Facet): number => { + // We will give range facets a middle value. They are good but not necessarily the most efficient. + if (facet.type === 'range') { + return 0.5; + } + + const { options } = facet; + const total = options.reduce((sum, option) => sum + option.count, 0); + // Mean average count for single option + const average = total / options.length; + const sumOfDifferences = options.reduce((sum, option) => sum + Math.abs(option.count - average), 0); + // We divide it the differences by total so the result is not skewed by the total number of options + // and lands between 0 and 1 + return sumOfDifferences / total; +}; +``` + +### [](#url-parser) URL parser +We will also need a function to parse the facets from the current URL so we don't repeat ourselves. Let's do this properly and create a class for manipulating the URL to get and set facets. We are still following the 1st response and request examples +```typescript +// In a separate file +type RangeFilter = { + name: string, + min: number; + max: number; +} + +type MultiChoiceFilter = { + name: string; + value: string; +} + +type Filter = RangeFilter | MultiChoiceFilter; + +export class UrlParser extends URL { + getFilters(): Filter[] { + const filters = []; + for (const [key, value] of this.searchParams) { + const match = key.match(/filters=\[(.+)\]$/); + if (match) { + const name = match[1]; + const rangeMatch = value.match(/(\d+)-(\d+)/); + if (rangeMatch) { + filters.push({ + name, + min: Number(rangeMatch[1]), + max: Number(rangeMatch[2]), + }); + } else { + filters.push({ + name, + value, + }); + } + } + } + return filters; + } +} +``` + +There are many ways we can structure the code that will choose the best facet but let's use simple procedural code for now: +```typescript +// We +const currentlyUsedFacets +``` + +Now we have all we need to process the response: +```typescript +const OVERRIDE_RANK = { + // We want to skip the shipping price facet because it is not evenly distributed + shipping_price: 0.9, +} +``` \ No newline at end of file diff --git a/content/academy/advanced_web_scraping/crawling/sitemaps-vs-search.md b/content/academy/advanced_web_scraping/crawling/sitemaps-vs-search.md new file mode 100644 index 000000000..803e09ed0 --- /dev/null +++ b/content/academy/advanced_web_scraping/crawling/sitemaps-vs-search.md @@ -0,0 +1,55 @@ +--- +title: Sitemaps vs search +description: Learn how to extract all of a website's listings even if they limit the number of results pages. See code examples for setting up your scraper. +menuWeight: 1 +paths: +- advanced-web-scraping/crawling/sitemaps-vs-search +--- + +The core crawling problem comes to down to ensuring that we reliably find all detail pages on the target website or its categories. This is trivial for small sites. We just open the home page or category pages and paginate to the end like we did in the Web Scraping for Beginners cource. + +Unfortunately, **most modern websites restrict pagination** only to somewhere between 1 and 10 thousands products. Solving this problem might seem relatively straightforward at first but there are multiple hurdless that we will explore in this lesson. + +There are two main approaches to solving this problem: +- Extracting the list of all pages from the website's **sitemap**. +- Using **categories, search and filters** to ensure we get under the pagination limit. + +Both of these approaches have their pros and cons so the best solution is to **use both and combine the results**. Here we will learn why. + +## Pros and cons of sitemaps +Sitemap is usally a simple XML file that contains a list of all pages on the website. They are created and maintained mainly for search engines like Google to help ensure that the website gets fully indexed there. They are commonly located at URLs like `https://example.com/sitemap.xml` or `https://example.com/sitemap.xml.gz`. We will get to working with sitemaps in the next lesson. + +### Pros +- **Quick to setup** - The logic to find all sitemaps and extract all URLs is usually simple and can be done in a few lines of code. +- **Fast to run** - You only need to run a single request for each sitemap which rarely is more than 100. This means you can get the URLs in a matter of seconds. +- **Usually complete** - Websites have incentive to keep their sitemaps up to date as they are used by search engines. This means that they usually contain all pages on the website. + +### Cons +- **Does not directly reflect the website** - There is no way you can ensure that all pages on the website are in the sitemap. The sitemap also can contain pages that were already removed and will return 404s. This is a major downside of sitemaps which prevents us from using them as the only source of URLs. +- **Updated in intervals** - Sitemaps are usually not updated in real-time. This means that you might miss some pages if you scrape them too soon after they were added to the website. Common update intervals are 1 day or 1 week. +- **Hard to find or unavailable** - Sitemaps are not always trivial to locate. They can be deployed on a CDN with unpredictable URLs. Sometimes they are not available at all. +- **Streamed, compressed and archived** - Sitemaps are often streamed and archived with .tgz extensions and compressed with gzip. This means that you cannot use default HTTP client settings and must handle these cases with extra code. Fortunately, we will get to this in the next lesson. + +## Pros and cons of categories, search and filters +This approach means traversing the website like a normal user do by going through categories, setting up different filters, ranges and sorting options. The goal is to traverse it is a way that ensures we covered all categories/ranges where products can be located and for each of those we stayed under the pagination limit. + +The pros and cons of this approach are pretty much the opposite of the sitemaps approach. + +### Pros +- **Directly reflects the website** - With most scraping use-cases, we want to analyze the website as the regular users see it. By going through the intended user flow, we ensure that we are getting the same pages as the users. +- **Updated in real-time** - The website is updated in real-time so we can be sure that we are getting all pages. +- **Often contain detailed data** - While sitemaps are usually just a list of URLs, categories, search and filters often contain additional data like product names, prices, categories, etc. This means that we can sometimes get all the data we need without going to the detail pages. + +### Cons +- **Complex to setup** - The logic to traverse the website is usually more complex and can take a lot of time to get right. We will get to this in the next lessons. +- **Slow to run** - The traversing can require a lot of requests. Some filters or categories will have products we already found. +- **Not always complete** - Sometimes the combination of filters and categories will not allow us to ensure we got all products. This is especially painful for sites where we don't know the exact number of products we are looknig for. The framework we will build in the next lessons will help us with this. + +## Do we know how many products there are? +Fortunately, most websites list a total number of details pages somewhere. It might be displayed on the home page or search results or be provided in the API response. We just need to make sure that this number really represent the whole site or category we are looking to scrape. By knowing the total number of products, we can tell if our approach to scrape all succeeded or if we still need to refine it. + +Unfortunately, some sites like Amazon do not provide exact numbers. In this case, we have to work with what they give us to and out even more effort into making our scraping logic accurate. We will tackle this in the next lessons as well. + +## [](#next) Next up + +First we will look into the easier approach, the [sitemap crawling]({{@link crawling-sitemaps.md}}). Then we will go through all intricacies of the category, search and filter crawling and build up a generic framework that we can use on any website. At last, we will combine the results of both approaches and set up monitoring and persistence to ensure we can run this regularly without any manual controls. diff --git a/content/academy/node_js/parsing_compressed_sitemaps.md b/content/academy/node_js/parsing_compressed_sitemaps.md new file mode 100644 index 000000000..58852921d --- /dev/null +++ b/content/academy/node_js/parsing_compressed_sitemaps.md @@ -0,0 +1,221 @@ +--- +title: How to parse compressed sitemaps +description: Some sitemaps.xml files are compressed and offered only as a stream. Learn how to configure your HTTP client to download and parse these. +menuWeight: 14.7 +paths: + - node-js/parsing_compressed_sitemaps +--- + +# [](#how-to-parse-compressed-sitemaps-with-streams) How to parse compressed sitemaps with streams + +## [](#simple-sitemaps) Simple sitemaps +Most sitemaps can be downloaded using deault HTTP client settings. We can use `got-scraping` or `CheerioCrawler` to process these sitemaps easily. + +```typescript +import { gotScraping } from 'got-scraping'; +import cheerio from 'cheerio'; + +// Make sure to use proxies in real projects +const { body } = await gotScraping('https://stockx.com/sitemap/sitemap-0.xml'); +const $ = cheerio.load(body); +console.log(`Found ${$('loc').length} URLs in the sitemap.`); +``` + +```typescript +import { CheerioCrawler } from 'crawlee'; + +const crawler = new CheerioCrawler({ + requestHandler: async ({ $ }) => { + console.log(`Found ${$('loc').length} URLs in the sitemap.`); + }, +}); + +await crawler.run('https://stockx.com/sitemap/sitemap-0.xml'); +``` + +## [](#compressed-sitemaps) Compressed sitemaps +Unfortunately, the above code will not work for some sitemaps. We will need to create some scaffoliding code to decompress these sitemaps on the fly with streams. The code below will be fairly complicated but you can just copy paste it and use it in your projects. + +Before we write any logic, let's add some imports. Don't forget to install `cheerio` and `got-scraping`. Let's create a separate file `gzip-stream.ts` so we have this logic nicely separated. We will use TypeScript for better clarity but you can use plain JavaScript as well, just remove the type annotations. + +```typescript +// stream a zlib are default node.js modules +import { Readable, PassThrough, Writable, pipeline } from 'stream'; +import { createUnzip } from 'zlib'; + +import cheerio from 'cheerio'; +import type { CheerioAPI } from 'cheerio'; +import { gotScraping } from 'got-scraping'; +``` + +First, we convert our `gotScraping` HTTP client into a stream and assign a status code to it so we can throw and retry it later. + +```typescript +const requestStream = async ({ url, headers = {}, proxyUrl } + : { url: string, headers?: Dictionary, proxyUrl?: string}) => { + const stream: any = await gotScraping({ + url, + proxyUrl, + retry: { + limit: 0, + }, + timeout: { + response: 80000, + request: 87000, + }, + isStream: true, + headers: { + ...headers, + }, + useHeaderGenerator: false, + } as any); + + if (!stream) { + throw new Error('Empty stream'); + } + + stream.statusCode = await new Promise((res, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Timeout 90s waiting for response code')); + }, 90000); + + stream.on('response', (response: any) => { + clearTimeout(timeout); + res(response.statusCode); + }); + + stream.on('error', (e: any) => { + clearTimeout(timeout); + reject(e); + }); + }); + + return stream; +}; +``` + +Now we define two functions that will add handling timeouts and other things to the stream. + +```typescript +const passthroughAbortStream = (timeoutMillis: number) => { + let lastActivity = Date.now(); + + const passthrough = new PassThrough({ + autoDestroy: true, + emitClose: true, + highWaterMark: 4096, + transform(chunk, _encoding, callback) { + lastActivity = Date.now(); + callback(null, chunk); + }, + }); + + const interval = setInterval(() => { + if (Date.now() - lastActivity >= timeoutMillis) { + clear(); + passthrough.destroy(new Error(`Aborted slow/stuck stream after ${timeoutMillis}ms`)); + } + }, 1000); + + const clear = () => clearInterval(interval); + + passthrough + .once('close', clear) + .once('end', clear) + .once('error', clear); + + return passthrough; +}; + +const processStream = (transform: (buffer: Buffer) => any) => { + /** + * @param {(val: T) => void} resolve + * @param {(val: Error) => void} reject + */ + return (resolve: any, reject: any) => { + const buffers: Buffer[] = []; + const writable = new Writable({ + autoDestroy: true, + emitClose: true, + highWaterMark: 4096, + write(chunk, _encoding, callback) { + buffers.push(chunk); + callback(); + }, + objectMode: false, + }); + + let finished = false; + /** + * + * @param {(err: Error | null) => void} callback + * @returns {(err: Error | null) => void} + */ + const cleanup = (callback: any) => (err: any) => { + if (!finished) { + finished = true; + try { + writable.destroy(); + writable.end(); + } catch (e) {} + callback(err instanceof Error ? err : null); + } + }; + + const finish = () => { + const concated = Buffer.concat(buffers); + if (concated.length === 0) { + return reject(new Error('Empty buffer')); + } + + try { + resolve(transform(concated)); + } catch (e) { + reject(e); + } + }; + + writable.on('close', cleanup(finish)); + writable.on('end', cleanup(finish)); + writable.on('error', cleanup(reject)); + + return writable; + }; +}; + +const cheerioStream = processStream((buffer) => { + return cheerio.load(buffer); +}); +``` + +In the end we define the actual function that we will use and export it. It will also allow us to optionally pass in proxy URL. +```typescript +export const getGzipSitemap = async (url: string, proxyUrl?: string): Promise => { + const stream = await requestStream({ url, proxyUrl }); + return new Promise((resolve, reject) => { + pipeline( + stream, + passthroughAbortStream(15000), + createUnzip(), + cheerioStream(resolve, reject), + (err) => { + if (err) { + reject(err); + } + }, + ); + }); +}; +``` + +Now we can call it in our scraping code to get the cheerio `$` handler and parse the sitemap. + +```typescript +import { getGzipSitemap } from './gzip-stream.js'; + +// Some other scraping code... +// ... + +const $ = await getGzipSitemap('https://d15790c7fypqrz.cloudfront.net/sitemaps/product_template.xml.gz'); +console.log(`We found ${$('loc').length} URLs in the sitemap`); +``` \ No newline at end of file diff --git a/content/academy/node_js/scraping_from_sitemaps.md b/content/academy/node_js/scraping_from_sitemaps.md index 378136bf8..f6f0e72eb 100644 --- a/content/academy/node_js/scraping_from_sitemaps.md +++ b/content/academy/node_js/scraping_from_sitemaps.md @@ -1,7 +1,7 @@ --- title: How to scrape from sitemaps description: The sitemap.xml file is a jackpot for every web scraper developer. Take advantage of this and learn an easier way to extract data from websites using Crawlee. -menuWeight: 14.7 +menuWeight: 14.8 paths: - node-js/scraping-from-sitemaps --- diff --git a/content/academy/node_js/scraping_shadow_doms.md b/content/academy/node_js/scraping_shadow_doms.md index dbb8c528f..81a84afbe 100644 --- a/content/academy/node_js/scraping_shadow_doms.md +++ b/content/academy/node_js/scraping_shadow_doms.md @@ -1,7 +1,7 @@ --- title: How to scrape sites with a shadow DOM description: The shadow DOM enables the isolation of web components, but causes problems for those building web scrapers. Here's an easy workaround. -menuWeight: 14.8 +menuWeight: 14.9 paths: - node-js/scraping-shadow-doms --- diff --git a/examples/academy/advanced-crawling/crawling-with-search/.dockerignore b/examples/academy/advanced-crawling/crawling-with-search/.dockerignore new file mode 100644 index 000000000..ceb85b1ce --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/.dockerignore @@ -0,0 +1,10 @@ +# configurations +.idea + +# crawlee and apify storage folders +apify_storage +crawlee_storage +storage + +# installed files +node_modules diff --git a/examples/academy/advanced-crawling/crawling-with-search/.gitignore b/examples/academy/advanced-crawling/crawling-with-search/.gitignore new file mode 100644 index 000000000..28ce0839b --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/.gitignore @@ -0,0 +1,11 @@ +# This file tells Git which files shouldn't be added to source control + +.idea +dist +node_modules +apify_storage +crawlee_storage +storage +# Not relevant for this example +package-lock.json +apify.json \ No newline at end of file diff --git a/examples/academy/advanced-crawling/crawling-with-search/Dockerfile b/examples/academy/advanced-crawling/crawling-with-search/Dockerfile new file mode 100644 index 000000000..43815f97d --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/Dockerfile @@ -0,0 +1,50 @@ +# Specify the base Docker image. You can read more about +# the available images at https://crawlee.dev/docs/guides/docker-images +# You can also use any other image from Docker Hub. +FROM apify/actor-node:16 AS builder + +# Copy just package.json and package-lock.json +# to speed up the build using Docker layer cache. +COPY package*.json ./ + +# Install all dependencies. Don't audit to speed up the installation. +RUN npm install --include=dev --audit=false + +# Next, copy the source files using the user set +# in the base image. +COPY . ./ + +# Install all dependencies and build the project. +# Don't audit to speed up the installation. +RUN npm run build + +# Create final image +FROM apify/actor-node:16 + +# Copy only built JS files from builder image +COPY --from=builder /usr/src/app/dist ./dist + +# Copy just package.json and package-lock.json +# to speed up the build using Docker layer cache. +COPY package*.json ./ + +# Install NPM packages, skip optional and development dependencies to +# keep the image small. Avoid logging too much and print the dependency +# tree for debugging +RUN npm --quiet set progress=false \ + && npm install --omit=dev --omit=optional \ + && echo "Installed NPM packages:" \ + && (npm list --omit=dev --all || true) \ + && echo "Node.js version:" \ + && node --version \ + && echo "NPM version:" \ + && npm --version + +# Next, copy the remaining files and directories with the source code. +# Since we do this after NPM install, quick build will be really fast +# for most source file changes. +COPY . ./ + + +# Run the image. +CMD npm run start:prod --silent diff --git a/examples/academy/advanced-crawling/crawling-with-search/README.md b/examples/academy/advanced-crawling/crawling-with-search/README.md new file mode 100644 index 000000000..576034706 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/README.md @@ -0,0 +1,27 @@ +# Crawlee + CheerioCrawler + TypeScript project + +This template is a production ready boilerplate for developing with `CheerioCrawler`. Use this to bootstrap your projects using the most up-to-date code. + +If you're looking for examples or want to learn more visit: + +- [Tutorial](https://crawlee.dev/docs/guides/cheerio-crawler-guide) +- [Documentation](https://crawlee.dev/api/cheerio-crawler/class/CheerioCrawler) +- [Examples](https://crawlee.dev/docs/examples/cheerio-crawler) + +## Writing a README + +See our tutorial on [writing READMEs for your actors](https://help.apify.com/en/articles/2912548-how-to-write-great-readme-for-your-actors) if you need more inspiration. + +### Table of contents + +If your README requires a table of contents, use the template below and make sure to keep the `` and `` markers. + + +- Introduction +- Use Cases + - Case 1 + - Case 2 +- Input +- Output +- Miscellaneous + diff --git a/examples/academy/advanced-crawling/crawling-with-search/package.json b/examples/academy/advanced-crawling/crawling-with-search/package.json new file mode 100644 index 000000000..22ecad4f5 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/package.json @@ -0,0 +1,24 @@ +{ + "name": "crawling-with-search", + "version": "0.0.1", + "type": "module", + "description": "This is an example of a Crawlee project.", + "dependencies": { + "crawlee": "^3.0.0" + }, + "devDependencies": { + "@apify/tsconfig": "^0.1.0", + "@types/node": "^18.11.9", + "ts-node": "^10.8.0", + "typescript": "^4.7.4" + }, + "scripts": { + "start": "npm run start:dev", + "start:prod": "node dist/main.js", + "start:dev": "ts-node-esm -T src/main.ts", + "build": "tsc", + "test": "echo \"Error: oops, the actor has no tests yet, sad!\" && exit 1" + }, + "author": "It's not you it's me", + "license": "ISC" +} diff --git a/examples/academy/advanced-crawling/crawling-with-search/src/consts.ts b/examples/academy/advanced-crawling/crawling-with-search/src/consts.ts new file mode 100644 index 000000000..4dd6ab791 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/src/consts.ts @@ -0,0 +1,16 @@ +// We will run on a real website to see the code in action +export const BASE_URL = `https://ac.cnstrc.com/browse/group_id/all?c=ciojs-client-2.29.12&key=key_XT7bjdbvjgECO5d8&i=68df40a5-a147-4a20-86a5-e6565066b9d3&s=14&page=1&num_results_per_page=24&_dt=${Date.now()}`; + +export const LABELS = { + SEARCH: 'SEARCH', + PAGINATION: 'PAGINATION', +} + +export const OVERRIDE_RANK = { + // We want to skip the shipping price facet because it is not evenly distributed + shipping_price: 0.9, +} + +export const MIN_RANGE_FILTER_MIN = 0; +// This will depend on the targt site +export const MAX_RANGE_FILTER_MAX = 1_000_000_000; diff --git a/examples/academy/advanced-crawling/crawling-with-search/src/facets.ts b/examples/academy/advanced-crawling/crawling-with-search/src/facets.ts new file mode 100644 index 000000000..c1d4bed57 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/src/facets.ts @@ -0,0 +1,183 @@ +import { MIN_RANGE_FILTER_MIN, MAX_RANGE_FILTER_MAX } from './consts.js'; + +// E.g. filters[lowest_price_cents]=0-10000000 +type RangeFilter = { + name: string, + min: number; + max: number; +} + +// E.g. filters[web_groups]=apparel +type MultiChoiceFilter = { + name: string; + value: string; +} + +export type Filter = RangeFilter | MultiChoiceFilter; + +interface FacetOption { + count: number, + display_name: string, + value: string +} + +export interface MultichoiceFacet { + name: string, + display_name: string, + type: 'multiple', + options: FacetOption[], +} + +interface RangeFacet { + name: string, + display_name: string, + type: 'range', + min?: number, + max?: number, +} + +export type Facet = MultichoiceFacet | RangeFacet; + +interface FilterCache { + [name: string]: { + [value: string]: number; + } +} + +// Facets that have no or only one option are useless +const isUsableFacet = (facet: Facet) => { + if (facet.type === 'range') { + return true; + } + return facet.options.length > 1; +} + +const facetDifferenceFromComplete = (facet: Facet, total: number): number => { + if (facet.type === 'range') { + return 0; + } + const sumOfOptions = facet.options.reduce((sum, option) => sum + option.count, 0); + console.log(`Facet ${facet.name} has ${sumOfOptions} options out of ${total}, difference is ${total - sumOfOptions}`); + return total - sumOfOptions; +}; + +// Returns number between 0 and 1. Lower is better +export const facetDistributionRank = (facet: Facet): number => { + // We will give range facets a middle value. They are good but not necessarily the most efficient. + if (facet.type === 'range') { + return 2; + } + + const { options } = facet; + const sumOfCounts = options.reduce((sum, option) => sum + option.count, 0); + // Mean average count for single option + const average = sumOfCounts / options.length; + const highest = options.reduce((highest, option) => Math.max(highest, option.count), 0); + const sumOfDifferencesHighest = options.reduce((sum, option) => sum + highest / option.count, 0); + const sumOfDifferencesAverage = options.reduce((sum, option) => sum + Math.abs(option.count - average), 0); + const highestAndAverageCompromise = (sumOfDifferencesHighest + sumOfDifferencesAverage) / 2; + // We divide it the differences by total so the result is not skewed by the total number of options + // and lands between 0 and 1 + const rank = (highestAndAverageCompromise / sumOfCounts) / 2; + console.log(`Sum of counts: ${sumOfCounts},sumOfDifferencesHighest: ${sumOfDifferencesHighest}, sumOfDifferencesAverage: ${sumOfDifferencesAverage}, highestAndAverageCompromise ${highestAndAverageCompromise}`); + return rank; +}; + +// We assume no open-ended ranges but can easily be extended +export const splitRangeFilter = (rangeFilter: RangeFilter): [RangeFilter, RangeFilter] => { + const { name, min, max } = rangeFilter; + + const middle = min + Math.floor((max - min) / 2); + + const filterMin = { + name, + min, + max: Math.max(middle, min), + }; + const filterMax = { + name, + min: Math.min(middle + 1, max), + max, + }; + return [filterMin, filterMax]; +}; + +interface PickBestFilterInput { + currentFilters: Filter[], + facets: Facet[], + filterCache: FilterCache, + total: number, + overrideRank?: Record +} + +// We return also some metadata so we can react to not complete facets +// It is up to the caller to decide what to do with it +interface PickBestFiltersResult { + filters: Filter[], + rank?: number, + differenceFromComplete?: number, +} + +export const pickBestFilters = ({ + facets, + total, + currentFilters, + filterCache, + overrideRank, +}: PickBestFilterInput): PickBestFiltersResult=> { + // First we check if we have an active range filter, if yes we continue splitting it + const activeRangeFilters = currentFilters.filter((filter): filter is RangeFilter => { + return 'min' in filter && filter.min !== filter.max; + }); + + if (activeRangeFilters.length > 0) { + return { + filters: splitRangeFilter(activeRangeFilters[0]), + }; + } + + const validFacets = facets.filter(isUsableFacet); + + if (validFacets.length === 0) { + return { + filters: [], + }; + } + + // If we don't have active range filter, we will find the best facet + const facetsWithRanks = validFacets.map((facet) => ({ + facet, + rank: overrideRank?.[facet.name] ?? facetDistributionRank(facet), + differenceFromComplete: facetDifferenceFromComplete(facet, total), + })); + + // We always prioritize complete facets + facetsWithRanks.sort((a, b) => { + if (a.differenceFromComplete !== b.differenceFromComplete) { + return a.differenceFromComplete - b.differenceFromComplete; + } + return a.rank - b.rank; + }); + + const bestFacet = facetsWithRanks[0]; + if (bestFacet.facet.type === 'range') { + return { + filters: [{ + name: bestFacet.facet.name, + min: bestFacet.facet.min ?? MIN_RANGE_FILTER_MIN, + max: bestFacet.facet.max ?? MAX_RANGE_FILTER_MAX, + }], + rank: bestFacet.rank, + differenceFromComplete: bestFacet.differenceFromComplete, + }; + } else { + return { + filters: bestFacet.facet.options.map((option) => ({ + name: bestFacet.facet.name, + value: option.value, + })), + rank: bestFacet.rank, + differenceFromComplete: bestFacet.differenceFromComplete, + }; + } +} \ No newline at end of file diff --git a/examples/academy/advanced-crawling/crawling-with-search/src/main.ts b/examples/academy/advanced-crawling/crawling-with-search/src/main.ts new file mode 100644 index 000000000..43d139e3e --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/src/main.ts @@ -0,0 +1,36 @@ +import { CheerioCrawler, ProxyConfiguration, log } from 'crawlee'; +import { router } from './routes.js'; +import { LABELS, BASE_URL } from './consts.js'; +import { getProxyUrl } from './utils.js'; + +interface CrawlerState { + export interface FilterState { + filterCache: FilterCache; + fullTotal: number; + } + +} + +// TODO: +// 1. Range filters going to trap +// 2. Most are incomplete + +const crawler = new CheerioCrawler({ + // Adjust the proxy settings to your needs + proxyConfiguration: new ProxyConfiguration({ proxyUrls: [getProxyUrl()] }), + requestHandler: router, +}); + +const slugsObj = await crawler.useState>({}); +const logInterval = setInterval(() => { + log.info(`Unique URLs: ${Object.keys(slugsObj).length}`); +}, 60_000); + +// We start with top level URL that will return us all facets in full +await crawler.run([{ + url: BASE_URL, + label: LABELS.SEARCH, + userData: { isFirstPage: true, }, +}]); + +clearInterval(logInterval); diff --git a/examples/academy/advanced-crawling/crawling-with-search/src/routes.ts b/examples/academy/advanced-crawling/crawling-with-search/src/routes.ts new file mode 100644 index 000000000..f60050be6 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/src/routes.ts @@ -0,0 +1,110 @@ +import { createCheerioRouter } from 'crawlee'; +import type { CheerioCrawler, RequestOptions } from 'crawlee'; + +import { UrlParser } from './url-parser.js'; +import { filtersToLogPrefix } from './utils.js'; +import { LABELS, OVERRIDE_RANK } from './consts.js'; +import { pickBestFilters } from './facets.js'; +import type { Facet, FilterState } from './facets.js'; + +export const router = createCheerioRouter(); + +interface Response { + facets: Facet[], + results: any[], + sort_options: any[], + total_num_results: number, +} + +const enqueuePagination = async (request: RequestOptions, crawler: CheerioCrawler) => { + await crawler.addRequests([{ + url: request.url, + // We need to update the uniqueKey because we are enqueueing the same URL as we have + uniqueKey: `${request.url}-${LABELS.PAGINATION}`, + label: LABELS.PAGINATION, + }]); +} + +// In real-world project, you would want to add extra error handling +router.addHandler(LABELS.SEARCH, async ({ log, request, json, crawler }) => { + const { isFirstPage } = request.userData as { isFirstPage: boolean }; + + // We always parse the current URL to get the current filters + const parsedUrl = new UrlParser(request.url); + const currentFilters = parsedUrl.getFilters(); + + // We collect all important information from the response + const total: number = json.response.result_sources.token_match.count; + const { facets, results, sort_options, total_num_results } = json.response as Response; + + // We construct a log prefix that will connect all log messages for this request + const logPrefix = `${filtersToLogPrefix(currentFilters)}: total: ${total}, max per search: ${total_num_results}, facets: ${facets.length}`; + + // total_num_results is the maximum number of results per single search, that is our pagination limit + // Most websites will not share this number so you might need to find it manually + if (total <= total_num_results) { + // We don't need to refine the search any further and can start paginating + // It would be more efficient to parse the first page of products right here and enqueue 2nd page + log.info(`${logPrefix} No more refinements needed, enqueuing pagination - ${request.url}`); + await enqueuePagination(request, crawler); + return; + } + + const state = await crawler.useState({}); + + if (isFirstPage) { + state.fullTotal = total; + } + + if (!state.filterCache) { + state.filterCache = {}; + } + + state.filterCache + + const { filters, rank, differenceFromComplete } = pickBestFilters({ + facets, + total, + currentFilters, + filterCache: state.filterCache, + overrideRank: OVERRIDE_RANK + }); + if (differenceFromComplete !== 0) { + // At this point, we know we cannot we sure we are able to scrape all products + // because the best filter we could find is not complete + // We could also decide to stop the crawling here (or stop on bad ranking) + log.warning(`${logPrefix} Best filter is not complete, rank: ${rank}, enqueuing pagination`); + } + + if (filters.length === 0) { + log.warning(`${logPrefix} No more usable filters, enqueuing pagination`); + await enqueuePagination(request, crawler); + return; + } + + log.info(`${logPrefix} Enqueuing new filter ${filters[0].name} with ${filters.length} requests with rank: ${rank}`); + + const requestsToEnqueue = filters.map((filter) => { + console.log('filters'); + console.dir(filter); + const newParsedUrl = new UrlParser(request.url); + newParsedUrl.setFilter(filter); + return { + url: newParsedUrl.toString(), + label: LABELS.SEARCH, + userData: { referrerUrl: request.url, referrerTotal: total }, + } as RequestOptions; + }); + + await crawler.addRequests(requestsToEnqueue, { forefront: true }); +}); + +router.addHandler(LABELS.PAGINATION, async ({ crawler, json }) => { + // Here you can parse the data and store them or enqueue the details + // For sake of this tutorial, we will store the unique URLs so we can test how many we scraped + const slugsObj = await crawler.useState>({}); + json.response.results.forEach((result: any) => { + const slug = `${result.data.product_type}/${result.data.slug}`; + slugsObj[slug] = true; + }); +}); diff --git a/examples/academy/advanced-crawling/crawling-with-search/src/url-parser.ts b/examples/academy/advanced-crawling/crawling-with-search/src/url-parser.ts new file mode 100644 index 000000000..17a15668a --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/src/url-parser.ts @@ -0,0 +1,41 @@ +import type { Filter } from "./facets"; + +export class UrlParser extends URL { + getFilters(): Filter[] { + const filters = []; + for (const [key, value] of this.searchParams) { + const match = key.match(/filters\[(.+)\]$/); + if (match) { + const name = match[1]; + const rangeMatch = value.match(/(\d+)-(\d+)/); + if (rangeMatch) { + filters.push({ + name, + min: Number(rangeMatch[1]), + max: Number(rangeMatch[2]), + }); + } else { + filters.push({ + name, + value, + }); + } + } + } + return filters; + } + + setFilter(filter: Filter) { + if ('min' in filter) { + this.searchParams.set(`filters[${filter.name}]`, `${filter.min}-${filter.max}`); + } else { + this.searchParams.set(`filters[${filter.name}]`, filter.value); + } + } + + setFilters(filters: Filter[]) { + for (const filter of filters) { + this.setFilter(filter); + } + } +} \ No newline at end of file diff --git a/examples/academy/advanced-crawling/crawling-with-search/src/utils.ts b/examples/academy/advanced-crawling/crawling-with-search/src/utils.ts new file mode 100644 index 000000000..d06d16943 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/src/utils.ts @@ -0,0 +1,18 @@ +import type { Filter } from './facets.js'; + +export const filtersToLogPrefix = (filters: Filter[]): string => { + return filters.map((filter) => { + if ('min' in filter) { + return `[${filter.name}=${filter.min}-${filter.max}]`; + } + return `[${filter.name}=${filter.value}]`; + }).join(''); +}; + +// Re-configure this function as you need +// Apify proxy is good for testing if you have access to it +export const getProxyUrl = () => { + const username = 'groups-RESIDENTIAL'; + const password = process.env.APIFY_PROXY_PASSWORD; + return `http://${username}:${password}@proxy.apify.com:8000`; +} \ No newline at end of file diff --git a/examples/academy/advanced-crawling/crawling-with-search/test/mock-facet.ts b/examples/academy/advanced-crawling/crawling-with-search/test/mock-facet.ts new file mode 100644 index 000000000..1f39d9d96 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/test/mock-facet.ts @@ -0,0 +1,39 @@ +import { readFileSync } from 'fs'; + +import type { MultichoiceFacet, Facet } from '../src/facets.js'; + + +const json = JSON.parse( + readFileSync('./test/real-response.json').toString() +); + +const createMock = (loopCounts: number[]) => { + const mockFacet: MultichoiceFacet = { + name: 'dummy', + display_name: 'Dummy', + type: 'multiple', + options: [], + }; + + for (let i = 0; i < loopCounts.length; i++) { + mockFacet.options.push({ + value: `option${i}`, + display_name: `Option ${i}`, + count: loopCounts[i], + }); + + } + + return mockFacet; +}; + +/* +for (let i = 0; i < 5; i++) { + mockFacet.options.push({ count: 1000, display_name: '2', value: `${i}` }); +} +*/ + +export const getMockFacets = () => { + return json.response.facets; +} + diff --git a/examples/academy/advanced-crawling/crawling-with-search/test/rank.ts b/examples/academy/advanced-crawling/crawling-with-search/test/rank.ts new file mode 100644 index 000000000..30dc2ea7d --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/test/rank.ts @@ -0,0 +1,29 @@ +import { facetDistributionRank, pickBestFilters } from '../src/facets.js'; +import { getMockFacets } from './mock-facet.js'; +import type { Filter } from '../src/facets.js'; + +// Rules of ranking: +// 1. Higher values are generally better, lot of 1s are bad +// 2. Values close to average are better +// 3. Values close to highest are better + +/* +for (const facet of getMockFacets()) { + console.log(facet.name, facetDistributionRank(facet)); +} +*/ + +let totalFilters: Filter[] = []; +for (;;) { + const { filters, rank, differenceFromComplete } = pickBestFilters({ + facets: getMockFacets(), + currentFilters: totalFilters, + filterCache: {}, + total: 275655, + }); + if (filters.length === 0) { + break; + } + console.log(` Enqueuing new filter ${filters[0].name} with ${filters.length} requests with rank: ${rank}, difference: ${differenceFromComplete}`); + filters.push(...filters); +} \ No newline at end of file diff --git a/examples/academy/advanced-crawling/crawling-with-search/test/real-response.json b/examples/academy/advanced-crawling/crawling-with-search/test/real-response.json new file mode 100644 index 000000000..4d0f53c90 --- /dev/null +++ b/examples/academy/advanced-crawling/crawling-with-search/test/real-response.json @@ -0,0 +1,16711 @@ +{ + "response": { + "result_sources": { + "token_match": { + "count": 275655 + }, + "embeddings_match": { + "count": 0 + } + }, + "facets": [ + { + "display_name": "Brand", + "name": "brand", + "type": "multiple", + "options": [ + { + "status": "", + "count": 216, + "display_name": "424", + "value": "424", + "data": { + "year": "2010", + "mobile_display": true + } + }, + { + "status": "", + "count": 125, + "display_name": "032C", + "value": "032c", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 290, + "display_name": "1017 ALYX 9SM", + "value": "1017 alyx 9sm", + "data": { + "year": "2015", + "location": "New York, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "11 by Boris Bidjan Saberi", + "value": "11 by boris bidjan saberi", + "data": { + "year": "2013", + "location": "Barcelona, ES", + "mobile_display": false + } + }, + { + "status": "", + "count": 19, + "display_name": "2 Moncler", + "value": "2 moncler", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 5, + "display_name": "361 Degrees", + "value": "361 degrees", + "data": { + "year": "2003", + "mobile_display": true + } + }, + { + "status": "", + "count": 12, + "display_name": "3.PARADIS", + "value": "3paradis", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 26, + "display_name": "5 Moncler", + "value": "5 moncler", + "data": { + "year": "1952", + "mobile_display": false + } + }, + { + "status": "", + "count": 274, + "display_name": "A-Cold-Wall*", + "value": "a cold wall", + "data": { + "year": "2015", + "location": "London, UK", + "mobile_display": true + } + }, + { + "status": "", + "count": 414, + "display_name": "Acne Studios", + "value": "acne studios", + "data": { + "year": "1996", + "location": "Stockholm, SE", + "mobile_display": true + } + }, + { + "status": "", + "count": 60, + "display_name": "Acronym", + "value": "acronym", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 114, + "display_name": "Ader Error", + "value": "ader error", + "data": { + "year": "2014", + "location": "Seoul, KR", + "mobile_display": true + } + }, + { + "status": "", + "count": 33473, + "display_name": "adidas", + "value": "adidas", + "data": { + "year": "1949", + "location": "Herzogenaurach, DE", + "mobile_display": true + } + }, + { + "status": "", + "count": 63, + "display_name": "Advisory Board Crystals", + "value": "advisory board crystals", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 3, + "display_name": "ADYAR", + "value": "adyar", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 31, + "display_name": "Afield Out", + "value": "afield out", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 27, + "display_name": "Agolde", + "value": "agolde", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 9, + "display_name": "AI Studios", + "value": "ai studios", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 295, + "display_name": "Aimé Leon Dore", + "value": "aime leon dore", + "data": { + "year": "2014", + "location": "New York, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 13590, + "display_name": "Air Jordan", + "value": "air jordan", + "data": { + "year": "1984", + "location": "Chicago, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Akai", + "value": "akai", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 1, + "display_name": "Akila", + "value": "akila", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 1, + "display_name": "Akira", + "value": "akira", + "data": { + "year": "2002", + "mobile_display": false + } + }, + { + "status": "", + "count": 1385, + "display_name": "Alexander McQueen", + "value": "alexander mcqueen", + "data": { + "year": "1992", + "location": "London, UK", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Alexander Wang", + "value": "alexander wang", + "data": { + "year": "2007", + "mobile_display": false + } + }, + { + "status": "", + "count": 209, + "display_name": "Alife", + "value": "alife", + "data": { + "year": "1999", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Alighieri", + "value": "alighieri", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 368, + "display_name": "Ambush", + "value": "ambush", + "data": { + "year": "2008", + "location": "Tokyo, JP", + "mobile_display": true + } + }, + { + "status": "", + "count": 202, + "display_name": "Ami", + "value": "ami", + "data": { + "year": "2011", + "location": "Paris, FR", + "mobile_display": true + } + }, + { + "status": "", + "count": 1723, + "display_name": "Amiri", + "value": "amiri", + "data": { + "year": "2014", + "location": "Los Angeles, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 14, + "display_name": "And Wander", + "value": "and wander", + "data": { + "year": "2011", + "location": "Tokyo, JP", + "mobile_display": true + } + }, + { + "status": "", + "count": 22, + "display_name": "AND1", + "value": "and1", + "data": { + "year": "1993", + "mobile_display": true + } + }, + { + "status": "", + "count": 11, + "display_name": "Andersson Bell", + "value": "andersson bell", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Andrej Urem", + "value": "andrej urem", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 2, + "display_name": "Angel Chen", + "value": "angel chen", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 2, + "display_name": "Ann Demeulemeester", + "value": "ann demeulemeester", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 4, + "display_name": "Anonymous Club", + "value": "anonymous club", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 90, + "display_name": "Anta", + "value": "anta", + "data": { + "year": "1990", + "mobile_display": true + } + }, + { + "status": "", + "count": 915, + "display_name": "Anti Social Social Club", + "value": "anti social social club", + "data": { + "year": "2015", + "location": "Los Angeles, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 155, + "display_name": "a.p.c.", + "value": "apc", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 3, + "display_name": "AREA", + "value": "area", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 211, + "display_name": "Aries", + "value": "aries", + "data": { + "year": "2012", + "location": "London, UK", + "mobile_display": true + } + }, + { + "status": "", + "count": 6, + "display_name": "Art", + "value": "art", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 7954, + "display_name": "ASICS", + "value": "asics", + "data": { + "year": "1977", + "location": "Kobe, JP", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Attico", + "value": "attico", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 97, + "display_name": "Awake NY", + "value": "awake ny", + "data": { + "year": "2012", + "location": "New York, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 6, + "display_name": "Babylon LA", + "value": "babylon la", + "data": { + "year": "2015", + "location": "Los Angeles, US", + "mobile_display": false + } + }, + { + "status": "", + "count": 3046, + "display_name": "Balenciaga", + "value": "balenciaga", + "data": { + "year": "1919", + "location": "Paris, FR", + "mobile_display": true + } + }, + { + "status": "", + "count": 246, + "display_name": "Balmain", + "value": "balmain", + "data": { + "year": "1945", + "location": "Paris, FR", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Bandai", + "value": "bandai", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 24, + "display_name": "Bao Bao Issey Miyake", + "value": "bao bao issey miyake", + "data": { + "year": "1971", + "mobile_display": true + } + }, + { + "status": "", + "count": 3033, + "display_name": "BAPE", + "value": "bape", + "data": { + "year": "1993", + "location": "Tokyo, JP", + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Bernhard Willhelm", + "value": "bernhard willhelm", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 1, + "display_name": "Better™ Gift Shop", + "value": "better gift shop", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 13, + "display_name": "Bianca Chandôn", + "value": "bianca chandon", + "data": { + "year": "2014", + "mobile_display": false + } + }, + { + "status": "", + "count": 5, + "display_name": "Bicycle", + "value": "bicycle", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 22, + "display_name": "Big Baller Brand", + "value": "big baller brand", + "data": { + "year": "2016", + "mobile_display": true + } + }, + { + "status": "", + "count": 1718, + "display_name": "Billionaire Boys Club", + "value": "billionaire boys club", + "data": { + "year": "2005", + "location": "New York, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 4, + "display_name": "Binu Binu", + "value": "binu binu", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 10, + "display_name": "Birkenstock", + "value": "birkenstock", + "data": { + "year": "1774", + "mobile_display": true + } + }, + { + "status": "", + "count": 25, + "display_name": "BLUEMARBLE", + "value": "bluemarble", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 39, + "display_name": "Bode", + "value": "bode", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 46, + "display_name": "Books", + "value": "books", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 1, + "display_name": "Boris Bidjan Saberi", + "value": "boris bidjan saberi", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 88, + "display_name": "Born x Raised", + "value": "born x raised", + "data": { + "year": "2011", + "location": "Los Angeles, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 950, + "display_name": "Bottega Veneta", + "value": "bottega veneta", + "data": { + "year": "1966", + "location": "Vicenza, IT", + "mobile_display": true + } + }, + { + "status": "", + "count": 724, + "display_name": "Brain Dead", + "value": "brain dead", + "data": { + "year": "2014", + "location": "Los Angeles, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 34, + "display_name": "Brandblack", + "value": "brandblack", + "data": { + "year": "2014", + "mobile_display": true + } + }, + { + "status": "", + "count": 42, + "display_name": "bricks & wood", + "value": "bricks wood", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 717, + "display_name": "Brooks", + "value": "brooks", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 690, + "display_name": "Burberry", + "value": "burberry", + "data": { + "year": "1856", + "location": "London, UK", + "mobile_display": true + } + }, + { + "status": "", + "count": 89, + "display_name": "Buscemi", + "value": "buscemi", + "data": { + "year": "2013", + "mobile_display": true + } + }, + { + "status": "", + "count": 75, + "display_name": "C2H4", + "value": "c2h4", + "data": { + "year": "2014", + "location": "Los Angeles, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 449, + "display_name": "Cactus Jack by Travis Scott", + "value": "cactus jack by travis scott", + "data": { + "year": "2017", + "location": "US", + "mobile_display": true + } + }, + { + "status": "", + "count": 105, + "display_name": "Cactus Plant Flea Market", + "value": "cactus plant flea market", + "data": { + "year": "2015", + "location": "London, UK", + "mobile_display": true + } + }, + { + "status": "", + "count": 18, + "display_name": "Calvin Klein", + "value": "calvin klein", + "data": { + "year": "1968", + "location": "New York, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 13, + "display_name": "Camp High", + "value": "camp high", + "data": { + "mobile_display": true + } + }, + { + "status": "", + "count": 1, + "display_name": "Canal Cafeteria", + "value": "canal cafeteria", + "data": { + "mobile_display": false + } + }, + { + "status": "", + "count": 644, + "display_name": "Carhartt WIP", + "value": "carhartt wip", + "data": { + "year": "1889", + "location": "Detroit, US", + "mobile_display": true + } + }, + { + "status": "", + "count": 2, + "display_name": 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+ } + }, + { + "feature_name": "query_items", + "display_name": "Learn To Rank", + "enabled": true, + "variant": { + "name": "goat_weighted_ctr_w_atc_purchases_diff_periods", + "display_name": "" + } + } + ] + }, + "result_id": "67cad970-69d6-46a3-aed5-348b90564005", + "request": { + "page": 1, + "num_results_per_page": 24, + "fmt_options": { + "hidden_fields": [ + "gp_lowest_price_cents_4", + "gp_instant_ship_lowest_price_cents_4" + ], + "hidden_facets": [ + "gp_lowest_price_cents_4", + "gp_instant_ship_lowest_price_cents_4" + ], + "groups_start": "current", + "groups_max_depth": 1 + }, + "browse_filter_name": "group_id", + "browse_filter_value": "all", + "term": "", + "sort_by": "relevance", + "sort_order": "descending", + "section": "Products", + "features": { + "query_items": true, + "auto_generated_refined_query_rules": true, + "manual_searchandizing": true, + "personalization": true, + "filter_items": true + }, + "feature_variants": { + "query_items": 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