Mwmbl: No ads, no tracking, no cruft, no profit
Mwmbl is a non-profit, ad-free, free-libre and free-lunch search
engine with a focus on useability and speed. At the moment it is
little more than an idea together with a proof of concept
the web front-end and search technology on a very small index. A
crawler is still to be implemented.
Our vision is a community working to provide top quality search
particularly for hackers, funded purely by donations.
Why a non-profit search engine?
The motives of ad-funded search engine are at odds with providing an
optimal user experience. These sites are optimised for ad revenue,
with user experience taking second place. This means that pages are
loaded with ads which are often not clearly distinguished from search
results. Also, eitland on Hacker News
Thinking about it it seems logical that for a search engine that
practically speaking has monopoly both on users and as mattgb points
out – [to some] degree also on indexing – serving the correct answer
first is just dumb: if they can keep me going between their search
results and tech blogs with their ads embedded one, two or five
times extra that means one, two or five times more ad impressions.
But what about…?
The space of alternative search engines has expanded rapidly in recent
years. Here’s a very incomplete list of some that have interested me:
- YaCy – an open source distributed search engine
- search.marginalia.nu – a search
engine favouring text-heavy websites
- Gigablast – a privacy-focused search
engine whose owner makes money by selling the technology to third
Of these, YaCy is the closest in spirit to the idea of a non-profit
search engine. The index is distributed across a peer-to-peer
network. Unfortunately this design decision makes search very slow.
Marginalia Search is fantastic, but it is more of a personal project
than an open source community.
All other search engines that I’ve come across are for-profit. Please
let me know if I’ve missed one!
Designing for non-profit
To be a good search engine, we need to store many items, but the cost
of running the engine is at least proportional to the number of items
stored. Our main consideration is thus to reduce the cost per item
The design is founded on the observation that most items rank for a
small set of terms. In the extreme version of this, where each item
ranks for a single term, the usual inverted index design is grossly
inefficient, since we have to store each term at least twice: once in
the index and once in the item data itself.
Our design is a giant hash map. We have a single store consisting of a
fixed number N of pages. Each page is of a fixed size (currently 4096
bytes to match a page of memory), and consists of a compressed list of
items. Given a term for which we want an item to rank, we compute a
hash of the term, a value between 0 and N – 1. The item is then stored
in the corresponding page.
To retrieve pages, we simply compute the hash of the terms in the user
query and load the corresponding pages, filter the items to those
containing the term and rank the items. Since each page is small, this
can be done very quickly.
Because we compress the list of items, we can rank for more than a
single term and maintain an index smaller than the inverted index
design. Well, that’s the theory. This idea has yet to be tested out on
a large scale.
Our current index is a small sample of the excellent Common Crawl,
restricted to English content and domains which score highly on
average in Hacker News submissions. It is likely for a variety of
reasons that we will want to go beyond Common Crawl data at some
point, so building a crawler becomes inevitable. We plan to start work
on a distributed crawler, probably implemented as a browser extension
that can be installed by volunteers.
How to contribute
There are lots of ways to help:
- Give feedback/suggestions
- Volunteer to test out the distributed crawler when it’s ready
- Help out with development of the engine itself
- Donate some money towards hosting costs and/or founding an official
If you would like to help in any of these or other ways, thank you!
Please join our Matrix chat
server or email the main
author (email address is in the git commit history).