AmisaDB - The Fastest Search and Analytics Service
Low latency and high throughput performance even at large scale for indexing and searching structured, semi-structured and unstructured data in any language.
SQL and JSON API for search, analytics, key/value, and events.

AmisaDB is a hybrid in-memory/on disk distributed multi-model ACID compliant database that supports SQL for queries and stores highly compressed JSON objects.

AmisaDB is architected for consistent high performance and low-latency, continuous availability, fault-tolerance, easy scalability and easy administration.

Frequently Asked Questions
Q. How is AmisaDB different from other new database technologies? A. AmisaDB has been carefully architected 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 several database technologies. We believe the best information retrieval is through massive parallelism 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 (Posting list is encoded as a sparse bit map for memory efficiency and speed) without scanning any rows/documents to answer any query. If you seek the best performance for ad hoc queries, aggregations or your application is under intensive writes, AmisaDB is the best tool for the job.

The only requirement to use AmisaDB is knowledge of JSON and SQL. We backup your data daily, replicate to multiple regions for high availability and automatically spread your data across nodes and shards transparently behind the scenes.

Read more on features of AmisaDB.



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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