In theory, the current 7-kilometre line constitutes phase 1 of the development project. After a long preparatory phase the actual construction of the tunnels and stations commenced inand the line was eventually opened to the public on 28 March The basic concept of the development project was the quality improvement of urban public transport that would serve as the centrepiece of the urban development of Budapest. The development of the public transport system serves the multiple purposes of improving the operation and financial situation, as well as the quality of life and the environment of this metropolis.
Pattern allows you to define phrase patterns and extract the text matching a specific placeholders. I packaged it with a straightforward GUI and presented the demo as a big data driven family feud. The app would then display the results as a world cloud as follows. I wondered how much it would cost me to try and reproduce this demo nowadays.
Exalead is a company with hundreds of servers to back this search engine. I happen to develop a search engine library in Rust called tantivy.
Indexing common-crawl would be a great way to test it, and a cool way to slap a well-deserved sarcastic webscale label on it. Let me explain how I did it. Common Crawl Common Crawl is one of my favorite open datasets. It consists in 3. Of course, 3 billions is far from exhaustive.
The web contains hundreds of trillions of webpages, and most of it is unindexed. It would be interesting to compare this figure to recent search engines to give us some frame of reference.
Unfortunately Google and Bing are very secretive about the number of web pages they index. Nothing to sneeze as really. The Common Crawl website lists example projects. That kind of dataset is typically useful to mine for facts or linguistics.
It can be helpful to train train a language model for instance, or try to create a list of companies in a specific industry for instance. Since it sits conveniently on Amazon S3, it is possible to grep through it with EC2 instances for the price of a sandwich.
As far as I know, nobody actually indexed Common Crawl so far. A opensource project called Common Search had the ambitious plan to make a public search engine out of it using elasticsearch. It seems inactive today unfortunately.
I would assume it lacked financial support to cover server costs. That kind of project would require a bare minimum of 40 server relatively high spec servers. Since I focus on the documents containing English text, we can bring the 3.
We can shard our index into 80 shards including 1, WET files each. To reproduce the family Feud demo, we will need to access the original text of the matched documents. After We typically get an inverse compression rate of 0.
We should therefore expect our index, including the stored data, to be roughly equal to 17TB as well. Indexing cost should not be an issue.
Tantivy is already quite fast at indexing. Indexing wikipedia 8GB even with stemming enabled and including stored data typically takes around 10mn on my recently acquired Dell XPS 13 laptop.
We might want larger segments for Common-crawl, so maybe we should take a large margin and consider that a cheap t2. The problem is extremely easy to distribute over 80 instances, each of them in charge of WET files for instance. The whole operation should cost us less than 50 bucks. Not bad… But where do we store this 17B index?
Should we upload all of these shards to S3.
Then when we eventually want to query it, start many instances, have them download their respective set of shards and start up a search engine instance? Interestingly, search engines are designed so that an individual query actually requires as litte IO as possible. My initial plan was therefore to leave the index on Amazon S3, and query the data directly from there.
Tantivy abstracts file accesses via a Directory trait. Maybe it would be a good solution to have some kind of S3 directory that downloads specific slices of files while queries are being run? How would that go?Log into Facebook to start sharing and connecting with your friends, family, and people you know.
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