A fast and compact hybrid memory resident datastore for text analytics with autonomic memory allocation

This paper describes a high-performance and space-efficient memory-resident datastore for text analytics systems based on a hash table for fast access, a dynamic trie for staging and a list of Level-Order Unary Degree Sequence (LOUDS) tries for compactness. We achieve efficient memory allocation and data placement by placing freqently access keys in the hash table, and infrequently accessed keys in the LOUDS tries without using conventional cache algorithms. Our algorithm also dynamically changes memory allocation sizes for these data structures according to the remaining available memory size. This technique yields 38.6% to 52.9% better throughput than a double array trie - a conventional fast and compact datastore.