RocksDB: Evolution of Development Priorities in a Key-value Store Serving Large-scale Applications

This article is an eight-year retrospective on development priorities for RocksDB, a key-value store developed at Facebook that targets large-scale distributed systems and that is optimized for Solid State Drives (SSDs). We describe how the priorities evolved over time as a result of hardware trends and extensive experiences running RocksDB at scale in production at a number of organizations: from optimizing write amplification, to space amplification, to CPU utilization. We describe lessons from running large-scale applications, including that resource allocation needs to be managed across different RocksDB instances, that data formats need to remain backward- and forward-compatible to allow incremental software rollouts, and that appropriate support for database replication and backups are needed. Lessons from failure handling taught us that data corruption errors needed to be detected earlier and that data integrity protection mechanisms are needed at every layer of the system. We describe improvements to the key-value interface. We describe a number of efforts that in retrospect proved to be misguided. Finally, we describe a number of open problems that could benefit from future research.

Analysis and Design of Inventory Control Information Systems With Forecasting Methods: Moving Average and Exponential Smoothing

Inventory inventory on CV. Mitra Marga Sejahtera often experiences stockpiling of goods so that it wastes more costs and the manual process of recording goods using excel, so that they often experience data corruption and loss of sales data. Forecasting methods are usually used by the sales department in planning (sales planning) based on the results of sales forecasts, so that forecasting information can be useful for Production which uses Moving Average and Exponential Smoothing. the program that has been made using the forecasting method can help manage the stock of goods that will be needed in the coming months, so that store managers can save costs in stock items that are not excessive

THE TASK OF REDESIGNING LARGE DISTRIBUTED CORPORATE SYSTEM

The paper proposes a new problem to be solved in the process of redesigning large distributed corporate systems in which there is an exchange of many information flows through various channels. The variety of information transmission routes often leads to multiple duplication of information flows in physical channels. This increases the likelihood of data corruption on the network. At the same time, in large distributed systems, powerful and weak channels with low bandwidth or low reliability are used, which are often disconnected, and in this case it is necessary to solve the problem of information logistics - redistribution of information flows. The paper proposes the problem of optimizing the redistribution of information flows by the criterion of minimizing the total increment in the volume of transmitted information in the system, which is included in the integral indicator of the characteristics of logical and physical communication channels. The problem is solved at the application level when redesigning a corporate system by removing ineffective channels to obtain a minimum spanning tree structure.

Inconsistencies in countries COVID-19 data revealed by Benford’s law

Reporting of daily new cases and deaths on COVID-19 is one of the main tools to understand and menage the pandemic. However, governments and health authorities worldwide present divergent procedures while registering and reporting their data. Most of the bias in those procedures are influenced by economic and political pressures and may lead to intentional or unintentional data corruption, what can mask crucial information. Benford’s law is a statistical phenomenon, extensively used to detect data corruption in large data sets. Here, we used the Benford’s law to screen and detect inconsistencies in data on daily new cases of COVID-19 reported by 80 countries. Data from 26 countries display severe nonconformity to the Benford’s law (p< 0.01), what may suggest data corruption or manipulation.