Improving lookup and query execution performance in distributed Big Data systems using Cuckoo Filter

Abstract Performance is a critical concern when reading and writing data from billions of records stored in Big Data warehouse. We introduce two scopes for query performance improvement. One to improve the lookup queries after deletion systems that use Eventual Consistency. propose scheme by using Cuckoo Filter. Another scope improvement avoid unnecessary network round-trips querying remote nodes distributed cluster it known do not have requested partition data. probabilistic filters are looked up before so resulting no can be skipped passing through network. evaluate our schemes with Cassandra real dataset show each 2 x .