SQL and JSON API for search, analytics, key/value, and events.

AmisaDB is a scalable search engine as a service that stores the inverted index entirely in memory and JSON objects and any blob on disk.
AmisaDB is architected for consistent high performance and low-latency, continuous availability, fault-tolerance, easy scalability and no administration.
Learn more about features and use cases

AmisaDB is for:
  • Low-latency ad-hoc queries using SQL
  • Full text search
  • Key value storage
  • DocumentDB wth the benefit of indexing
  • Real-time event monitoring
  • High performance analytics.
  • Datastructure server and cache servers
  • Datawarehousing and data archiving

C# .NET and JAVA Drivers + Http Access and Web Studio

Frequently Asked Questions
Q. How is AmisaDB different from other new search engine technologies? A. AmisaDB has been carefully architectured to unify the best of search engine, NoSQL and NewSQL database technologies to provide a comprehensive solution which can stand alone without having to integrate a database and search engine. We believe the best information retrieval is through massive parrallelism and in-memory indexes while the actual data is stored on flash or Disk. As such AmisaDB processes all SQL queries using a scalable in-memory inverted index without scanning any rows/documents to answer any query. If you seek the best performance for ad hoc queries or your application is under intensive writes, AmisaDB is the best tool for the job.

AmisaDB 1.0 is now available
CREATE schemaless no_schema {} CREATE Has_schema { student_id: "string keyvalueindex", enroll : "datetime index", courses: [{ grade: "float index frequency", course_name: "string" }] } SELECT * from has_schema WHERE arrayPredicateCount(courses.grade > 20) -- Count courses taken -- and enroll between dateadd('year', -4, now()) and now()-- Do range search -- and arrayPredicateAvg(courses.grade > 2.8) -- Calculate GPA --

"AmisaDB helped simplify our datalayer by eliminating our previous architecture which involved a cache layer, search engine layer and relational database. Today we are not writing integration code as AmisaDB provides us with in-memory indexing, full text search, analytics and storage in one platform." CTO and founder of Demanjo
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