MaybeDont 0.1.1

Duplicate detector

maybedont.DupePredictor collects statistics about page URLs and contents, and is able to predict if the new URL will bring any new content.

First, initialize a DupePredictor:

from maybedont import DupePredictor
dp = DupePredictor(
    texts_sample=[page_1, page_2, page_3],
    jaccard_threshold=0.9)  # default value

texts_sample is a list of page contents. It can be ommited, but it is recommended to provide it: it is used to learn which parts of the page are common for a lot of site’s pages, and excludes this parts from duplicate comparison. This helps with pages where the content is small relative to the site chrome (footer, header, etc.): without removing chrome all such pages would be considered duplicates, as only a tiny fraction of the content changes.

Next, we can update DupePredictor model with downloaded pages:

dp.update_model(url_4, text_4)
dp.update_model(url_5, text_5)

After a while, DupePredictor will learn which arguments in URLs are important, and which can be safely ignored. DupePredictor.get_dupe_prob returns the probability of url being a duplicate of some content that has already been seem:

dp.get_dupe_prob(url_6)

Runtime overhead should be not too large: on a crawl with < 100k pages, expected time to update the model is 1-5 ms, and below 1 ms to get the probability. All visited urls and hashes of content are stored in memory, along with some indexing structures.

Install

pip install MaybeDont

Spider middleware

If you have a Scrapy spider, or are looking for an inspiration for a spider middleware, check out maybedont.scrapy_middleware.AvoidDupContentMiddleware. First, it collects an queue of documents to know better which page elements are common on the site, in order to exclude them from content comparison. After that it builds it’s DupePredictor, updates it with crawled pages (only textual pages are taken into account), and starts dropping requests for duplicate content once it gets confident enough. Not all requests for duplicates are dropped: with a small probability (currenty 5%) requests are carried anyway. This makes duplicate detection more robust against changes in site URL or content structure as the crawl progresses.

To enable the middleware, the following settings are required:

AVOID_DUP_CONTENT_ENABLED = True
DOWNLOADER_MIDDLEWARES['maybedont.scrapy_middleware.AvoidDupContentMiddleware'] = 200

Middleware is only applied to requests with avoid_dup_content in request.meta.

Optional settings:

• AVOID_DUP_CONTENT_THRESHOLD = 0.98 - minimal probability when requests are skipped.

• AVOID_DUP_CONTENT_EXPLORATION = 0.05 - probability of still making a request that should be dropped

• AVOID_DUP_CONTENT_INITIAL_QUEUE_LIMIT = 300 - number of pages that should be downloaded before DupePredictor is initialized

How it works

Duplicate detection is based on MinHashLSH from the datasketch library. Text 4-shingles of words are used for hashing, not spanning line breaks in the extracted text.

Several hypotheses about duplicates are tested:

1. All URLs with a given URL path are the same (have the same content), regardless of query parameters;

2. All URLs which only differ in a given URL query parameter are the same (e.g. session tokens can be detected this way);

3. All URLs which have a given path and only differ in a given URL query parameter are the same;

4. All URLs which have a given path and query string and only differ in a single given query parameter are the same;

5. URLs are the same if they have same path and only differ in that some of them have a given param=value query argument added;

6. URLs are the same if they have a given path and only differ in a given param=value query argument;

Bernoulli distribution is fit for each hypothesis.

License

License is MIT