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Reel Fun Dare to Win it! 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.
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. 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? The default dictionary in tantivy is based on a finite state transduce implementation: 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 should expect ms 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 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. Around USD per month.
By the way, my estimates were not too far from reality. I did not take in account the WET file headers, that ends up being thrown away.
Also, some of the document which passed our English language detector are multilingual. The tokenizer is configured to discard all tokens that do not contain exclusively characters in [a-zA-Z].
What about indexing the whole thing on my desktop computer… Downloading the whole thing using my private internet connnection.
Think about it, a 4TB hard drive nowadays on amazon Japan cost around 85 dollars. I could buy three or four of these and store the index there.
The 8msms random seek latency will be actually much more comfortable than the S3 solution. What about CPU time and download time? I can probably wait.
Once again, indexing at this speed is really not a problem. In fact, my bandwidth is only fast enough to keep two indexing threads busy, leaving me plenty of CPU to watch netflix and code.
On my laptop, 1 thread would probably be ok. Explicitely limiting the number of threads has the positive side effect of allocating more RAM to each segment being indexed.
As a result, new segments produced are larger and less merging work is needed. So I randomly partitioned the 80, WET files into 80 shards of 10, files each.
I then started indexing these shards sequentially. For each shard, after having indexed all documents, I force-merge all of the segments into a single very large segment.
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