Hitting the memory wall: implications of the obvious
ACM SIGARCH Computer Architecture News
Efficient locking for concurrent operations on B-trees
ACM Transactions on Database Systems (TODS)
Making B+- trees cache conscious in main memory
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Burst tries: a fast, efficient data structure for string keys
ACM Transactions on Information Systems (TOIS)
A Study of Index Structures for Main Memory Database Management Systems
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Organization and maintenance of large ordered indexes
Software pioneers
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Query processing on prefix trees live
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Growing main memory capacities and an increasing number of hardware threads in modern server systems led to fundamental changes in database architectures. Most importantly, query processing is nowadays performed on data that is often completely stored in main memory. Despite of a high main memory scan performance, index structures are still important components, but they have to be designed from scratch to cope with the specific characteristics of main memory and to exploit the high degree of parallelism. Current research mainly focused on adapting block-optimized B+-Trees, but these data structures were designed for secondary memory and involve comprehensive structural maintenance for updates. In this paper, we present the KISS-Tree, a latch-free in-memory index that is optimized for a minimum number of memory accesses and a high number of concurrent updates. More specifically, we aim for the same performance as modern hash-based algorithms but keeping the order-preserving nature of trees. We achieve this by using a prefix tree that incorporates virtual memory management functionality and compression schemes. In our experiments, we evaluate the KISS-Tree on different workloads and hardware platforms and compare the results to existing in-memory indexes. The KISS-Tree offers the highest reported read performance on current architectures, a balanced read/write performance, and has a low memory footprint.