We should expect 100ms of latency for each read. The api allows to ask for several ranges at once, but since we have no idea where the subsequent jumps will be, all of these reads will end up being sequential. Looking up a writing single keyword in our dictionary may end up taking close to a second. Fortunately tantivy has an undocumented alternative dictionary format that should help us here. Another problem is that files are accessed via a readOnlysource struct. Currently, the only real directory relies on Mmap, so throughout the code, tantivy relies heavily on the os paging data for us, and liberally request for huge slices of data. We will therefore also need to go through all lines of code that access data, and only request the amount of data that is needed. Alternatively we could try and hack a solution around libsigsegv, but really this sounds dangerous, and might not be worth the artistic points. Well, overall this sounds like a quite a bit of work, but which may result in valuable features for tantivy.
Thats sounds extremely expensive, and would essay require a very high start up time. 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? The default dictionary in tantivy is based on a finite state transduce implementation : the excellent fst crate. This is not ideal here, as accessing a key requires quite a few random accesses. When hitting S3, the cost of random accesses is magnified.
We might want larger segments for Common-crawl, so maybe we should take a large margin and consider that a cheap dium (2 vcpu) instance can index index 1gb of text in 3mn? Our 17TB would require an overall 875 hours to index on instances that cost.05. The problem is extremely easy to distribute over 80 instances, each of them in charge of 1000 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. 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?
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Common Crawl conveniently distributes so-called wet files that contains the text extracted from the html markup of the page. The data is split into 80,000 wet files of roughly 115mb each, amounting overall to 9tb gzipped data, and somewhere albert around 17tb uncompressed. We can shard our index into 80 shards including 1,000 wet files each. To reproduce the family feud demo, we will need to access the original text of the matched documents. For convenience, tantivy makes this possible by defining our fields as stored in our schema. Tantivys docstore compresses the data using LZ4 compression. After we typically get an inverse compression rate.6 on natural language (by which I mean you compressed file is 60 the size of your original data).
The inverted index on the other hand, with positions, takes around 40 of the size of the uncompressed text. 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.
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. As far as i know, all of these projects are batching Common Crawls data. 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 the data is conveniently sitting on Amazon S3 as part. Amazons public dataset program, i naturally first considered indexing everything on EC2. Lets see how much that would have cost. Since i focus on the documents containing English text, we can bring the.2 billions documents down to roughly.15 billions.
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Of course, 3 billions is far from exhaustive. The japanese 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 best some frame of reference. Unfortunately google and Bing are very secretive about the number of web pages they index. We have some figure about the past: In 2000, google reached its first billion indexed web pages. In 2012, yandex -the leading russian search engine- grew from 4 billions to tens of billions web pages. 3 billions pages indexed might have been enough to compete in the global search engine market in 2002. Nothing to sneeze as really. The common Crawl website lists example projects.
Obviously Im on a tighter parts budget. 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. Well so far, i indexed a bit more than 25 of it, and indexing it entirely should cost me less than 400. Let me explain how I did. If you are impatient, just scroll down, youll be able to see colorful pictures, i promise. Common Crawl is one of my favorite open datasets. It consists.2 billions pages crawled from the web.
I was working at Exalead, i had the chance to have access to a 16 billions pages search engine to play with. During a hackathon, i plugged together Exaleads search engine with a nifty python package called pattern, and a word cloud generator. 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. To answer a question like, which adjectives are stereotypically associated with French people?, one would simply enter, french people are adjective the app would run the phrase query "French people are" on the search engine, stream the results to a short python program that would. 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.
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