TeksDB

Abstract

In this paper, we examine the design tradeoffs of existing in-memory data structures of a state-of-the-art key-value store. We observe that no data structures provide both fast point-accesses and consistent ranged- retrievals, and naive amalgamations of existing structures fail to get the best of both worlds. Furthermore, our experiments reveal a performance anomaly when increasing the memory size: as more key-value pairs are maintained in memory, the shortcomings of the data structures exacerbate. To address the above problems, we present TeksDB, a fast and consistent key-value store with a novel in-memory data structure, which effciently handles both point- and ranged- accesses at a modest increase in memory footprint. Our evaluation demonstrates that TeksDB outperforms RocksDB by 3.6×, 9×, and 4.5× for get, scan, and range_query, respectively. The effectiveness of TeksDB extends to real-world workloads, achieving up to 3.3× speedup for YCSB.

JellyFish

“…FloDB [7] and TeksDB [28] both augment the in-memory skip list by integrating it with a hash data structure. More specifically, FloDB places a small hash table in front of the sorted skip list, and TeksDB uses both the hash table and the skip list at the same level that both point to the same value.…”

“…In doing so, they achieve fast response time for point accesses, while enjoying the benefit of sorted data retrievals. However, FloDB [7] unfortunately does not support MVCC, while TeksDB [28] incurs overhead in synchronizing the two complementary data structures.…”