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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.

insideHPC

Original title and link: Big Data Search: Perfect Search (NoSQL database©myNoSQL)