Big Data Search: Perfect Search
Tim Stay (CEO) talks about Perfect Search a solution for searching Big Data that:
- offers a unique architectural approach that significantly reduces the total computations required to query
- creates terms and pattern indexes (basically combinations of terms at indexing time)
- uses jump tables and bloom filters
- heavily optimizes disk I/O
- doesn’t require indexes in memory
- “can often do same query with less than 1% computations”
- “when compared to Oracle/MS SQL, Perfect Search can be from 10x to over 1000x faster”
- according to the chart, the significant speed improvements are for cached results, while for first time queries I see numbers from 2 to 59
- if Perfect Search is a search engine why comparing with relational databases?
- “Google takes over 100 servers to search 1 billion documents. Perfect Search can do it with 1 server”
- Google is using 100 servers for reliability and guaranteeing the speed of results
- “Lucene: 0.1 billion documents per server; CPU maxing at 100%. Perfect Search 1.6 billion documents per server; CPU idling at 15%”
With this preamble, you can watch the video after the break:
Here is the thing. When I hear about a product claiming 10x better results, 100x better speed, 1000x whatever followed by no mention of limitations or trade-offs, I raise a red flag. Actually… I raise an army of red flags.
Original title and link: Big Data Search: Perfect Search (©myNoSQL)
