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. To reproduce the family Feud demo, we will need to access the original text of the matched documents. In the end, one shard takes GB, so the overall size of the index would te Looking up a single keyword in our dictionary may end up taking close to a second. As a result, new segments produced are larger and less merging work is needed. Not bad… But where do we store this 17B index? It seems inactive today unfortunately.
Reproducing it at home On the off chance indexing Common-Crawl might interest businesses, academics or you, I made the code I used to download and index common-crawl available here. Around USD per month. Of course, 3 billions is far from exhaustive. Tantivy abstracts file accesses via a Directory trait. The payload is the last WET filename that got indexed. For instance, I might need rapidly a list of job titles. I can probably wait. The useful stuff First, we can use this to understand stereotypes. As a result, new segments produced are larger and less merging work is needed. The default dictionary in tantivy is based on a finite state transduce implementation: Looking up a single keyword in our dictionary may end up taking close to a second. So I randomly partitioned the 80, WET files into 80 shards of 10, files each. Fortunately tantivy has an undocumented alternative dictionary format that should help us here. 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. The problem is extremely easy to distribute over 80 instances, each of them in charge of WET files for instance. What about CPU time and download time? The 8msms random seek latency will be actually much more comfortable than the S3 solution. Exalead is a company with hundreds of servers to back this search engine. How would that go? Nothing to sneeze as really. Resuming I recently bought a house in Tokyo and the power installation was not too really suited with morning routine: I would assume it lacked financial support to cover server costs. Its speed will be dominated limited by your IO, so if you have more than one disc, you can speed up the results by spreading shards over different shards and query them in parallel. Finishing the job is only a matter of throwing time and money. What about indexing the whole thing on my desktop computer… Downloading the whole thing using my private internet connnection.
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