An FTL-Agnostic Layer to Improve Random Write on Flash Memory

International audienceFlash memories are considered a competitive alternative to rotating disks as non-volatile data storage for database management systems. However, even if the Flash Translation Layer - or FTL - allows both technologies to share the same block interface, they have different preferred access patterns. Database management systems could potentially benefit from flash memories as they provide fast random access for read operations although random writes are generally not as efficient as sequential writes.In this paper, we propose a simple data placement algorithm designed for flash memories, to reorganize inefficient random writes in a quasi-sequential access pattern. This access pattern is first established encouraging for a subset of flash devices by identifying a strong correlation between spatial locality and write performances, with a distance being defined to quantify this effect. This design is then validated by a formalization with a mathematical model, along with experimental results. With this optimization, random write potentially become as efficient as sequential write, improving random write speed by up to two orders of magnitude

Time Series Management Systems:A Survey

The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD