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

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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 is to improve performance of lookup queries after data deletion in Big Data systems that use Eventual Consistency. We propose a scheme to improve lookup performance after data deletion by using Cuckoo Filter. Another scope for improvement is to avoid unnecessary network round-trip for querying in remote nodes in a distributed Big Data cluster when it is known that the nodes do not have requested partition of data. We propose a scheme using probabilistic filters that are looked up before querying remote nodes, so that queries resulting in no data can be skipped from passing through the network. We evaluate our schemes with Cassandra using real dataset and show that each scheme can improve performance of lookup queries for up to 100%.