This paper studies how RAID (redundant array of independent disks) could take full advantage of modern SSDs (solid-state drives) with built-in transparent compression. In current practice, RAID users are forced to choose a specific RAID level (e.g., RAID 10 or RAID 5) with a fixed storage cost vs. speed performance trade-off. The commercial market is witnessing the emergence of a new family of SSDs that can internally perform hardware-based lossless compression on each 4KB LBA (logical block address) block, transparent to host OS and user applications. Beyond straightforwardly reducing the RAID storage cost, such modern SSDs make it possible to relieve RAID users from being locked into a fixed storage cost vs. speed performance trade-off. In particular, RAID systems could opportunistically leverage higher-than-expected runtime user data compressibility to enable dynamic RAID level conversion to improve the speed performance without compromising the effective storage capacity. This paper presents techniques to enable and optimize the practical implementation of such elastic RAID systems. We implemented a Linux software-based elastic RAID prototype that supports dynamic conversion between RAID 5 and RAID 10. Compared with a baseline software-based RAID 5, under sufficient runtime data compressibility that enables the conversion from RAID 5 to RAID 10 over 60% of user data, the elastic RAID could improve the 4KB random write IOPS (I/O per second) by 42% and 4KB random read IOPS in degraded mode by 46%, while maintaining the same effective storage capacity.

This paper studies the design of a key-value (KV) store that can take full advantage of modern storage hardware with built-in transparent compression capability. Many modern storage appliances/drives implement hardware-based data compression, transparent to OS and applications. Moreover, the growing deployment of hardware-based compression in Cloud infrastructure leads to the imminent arrival of Cloud-based storage hardware with built-in transparent compression. By decoupling the logical storage space utilization efficiency from the true physical storage usage, transparent compression allows data management software to purposely waste logical storage space in return for simpler data structures and algorithms, leading to lower implementation complexity and higher performance. This work proposes a table-less hash-based KV store, where the basic idea is to hash the key space directly onto the logical storage space without using a hash table at all. With a substantially simplified data structure, this approach is subject to significant logical storage space under-utilization, which can be seamlessly mitigated by storage hardware with transparent compression. This paper presents the basic KV store architecture, and develops mathematical formulations to assist its configuration and analysis. We implemented such a KV store KallaxDB and carried out experiments on a commercial SSD with built-in transparent compression. The results show that, while consuming very little memory resource, it compares favorably with the other modern KV stores in terms of throughput, latency, and CPU usage.

This paper presents an approach to enable relational database take full advantage of modern storage hardware with built-in transparent compression. Advanced storage appliances (e.g., all-flash array) and some latest SSDs (solid-state drives) can perform hardware-based data compression, transparently from OS and applications. Moreover, the growing deployment of hardware-based compression capability in Cloud storage infrastructure leads to the imminent arrival of cloud-based storage hardware with built-in transparent compression. To make relational database better leverage modern storage hardware, we propose to deploy a dual in-memory vs. on-storage page format: While pages in database cache memory retain the conventional row-based format, each page on storage devices has a column-based format so that it can be better compressed by storage hardware. We present design techniques that can further improve the on-storage page data compressibility through additional light-weight column data transformation. We the impact of compression algorithms on the selection of column data transformation techniques. We integrated the design techniques into MySQL/InnoDB by adding only about 600 lines of code, and ran Sysbench OLTP workloads on a commercial SSD with built-in transparent compression. The results show that the proposed solution can bring up to 45% additional reduction on the storage cost at only a few percentage of performance degradation.