Starting from an efficient multicore database system, we show that naive logging and checkpoints make normal-case execution slower, but that frequent disk synchronization allows us to keep up with many workloads with only a modest reduction in throughput. We design throughout for parallelism: during logging, during checkpointing, and during recovery. The result is fast.
We present ART, an adaptive radix tree (trie) for efficient indexing in main memory. Its lookup performance surpasses highly tuned, read-only search trees, while supporting very efficient insertions and deletions as well. At the same time, ART is very space efficient and solves the problem of excessive worst-case space consumption, which plagues most radix trees, by adaptively choosing compact and efficient data structures for internal nodes.
This paper presents SwapKV, which strives to retain both the advantages of DRAM and PMEM, aiming to achieve both high performance and large capacity simultaneously.
We present RStore to enable low and predictable latency (i.e. low tail latency) and efficient use of hardware resources such as CPU, memory and storage through the following design points:
Introduce two key techniques of distributed file system.
We present a novel scheme, called Log-assisted LSM-tree (L2SM), which adopts a small-size, multi-level log structure to isolate selected key-value items that have a disruptive effect on the tree structure, accumulates and absorbs the repeated updates in a highly efficient manner, and removes obsolete and deleted key-value items at an early stage.
How to analyse the source codes of snappy ? In this blog, the pseudocode of compression and uncompression using snappy is given, which is aimed to help you understand snappy algorithm.
Start from a new Ubuntu OS.
Have you ever seen “(void)val” in codes ?
