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  • Using the internet archive to crawl a website

    Python Cache Crawling

    If a website is offline or restricts how quickly it can be crawled then downloading from someone else’s cache can be necessary. In previous posts I discussed using Google Translate and Google Cache to help crawl a website. Another useful source is the Wayback Machine at archive.org, which has been crawling and caching webpages since 1998.

  • Caching data efficiently

    Python Cache Sqlite

    When crawling websites I usually cache all HTML on disk to avoid having to re-download later. I wrote the pdict module to automate this process. Here is an example:

  • Using Google Translate to crawl a website

    Google Crawling Cache

    I wrote previously about using Google Cache to crawl a website. Sometimes, for whatever reason, Google Cache does not include a webpage so it is helpful to have backup options.

    One option is using Google Translate, which let’s you translate a webpage into another language. If the source language is selected as something you know it is not (eg Dutch) then no translation will take place and you will just get back the original content:

  • Using Google Cache to crawl a website

    Google Cache Crawling

    Occasionally I come across a website that blocks your IP after only a few requests. If the website contains a lot of data then downloading it quickly would take an expensive amount of proxies.

    Fortunately there is an alternative - Google.

    If a website doesn’t exist in Google’s search results then for most people it doesn’t exist at all. Websites want visitors so will usually be happy for Google to crawl their content. This meansGoogle has likely already downloaded all the web pages we want. And after downloading Google makes much of the content available through their cache.

  • Caching crawled webpages

    Python Cache

    When crawling large websites I store the HTML in a local cache so if I need to rescrape the website later I can load the webpages quickly and avoid extra load on their website server. This is often necessary when a client realizes they require additional features included in the scraped output.

    I built the pdict library to manage my cache. Pdict provides a dictionary like interface but stores the data in a sqlite database on disk rather than in memory. All data is automatically compressed (using zlib) before writing and decompressed after reading. Both zlib and sqlite3 come builtin with Python (2.5+) so there are no external dependencies.

    Here is some example usage of pdict: