An Efficient Crash Recovery Technique for Real-Time Main Memory Database

This paper presents an efficient recovery scheme suitable for real-time mainmemory database. In the recovery scheme, log records are stored in non-volatile RAM which is dividedinto four different partitions based on transaction types. Similarly, a main memory database isdivided into four partitions based data types. When the using ratio of log store area exceeds thethreshold value, checkpoint procedure is triggered. During executing checkpoint procedure, someuseless log records are deleted. During restart recovery after a crash, partition reloading policyis adopted to assure that critical data are reloaded and restored in advance, so that the databasesystem can be brought up before the entire database is reloaded into main memory. Therefore downtime is obvionsly reduced. Simulation experiments show our recovery scheme obviously improves thesystem performance, and does a favor to meet the dtadlints of real-time transactions.

Cache-Conscious Index Mechanism for Main-Memory Databases

Recent studies have addressed that the cache be havior is important in the design of main memory index structures. Cache-conscious indices such as the CSB^＋-tree are shown to outperform conventional main memory indices such as the AVL-tree and the T-tree. This paper proposes a cacheconscious version of the T-tree, CST-tree, defined according to the cache-conscious definition. To separate the keys within a node into two parts, the CST-tree can gain higher cache hit ratio.

Prefetching J^+-Tree:A Cache-Optimized Main Memory Database Index Structure

As the speed gap between main memory and modern processors continues to widen, the cache behavior becomes more important for main memory database systems （MMDBs）. Indexing technique is a key component of MMDBs. Unfortunately, the predominant indexes -B^＋-trees and T-trees -- have been shown to utilize cache poorly, which triggers the development of many cache-conscious indexes, such as CSB^＋-trees and pB^＋-trees. Most of these cache-conscious indexes are variants of conventional B^＋-trees, and have better cache performance than B^＋-trees. In this paper, we develop a novel J^＋-tree index, inspired by the Judy structure which is an associative array data structure, and propose a more cacheoptimized index -- Prefetching J^＋-tree （pJ^＋-tree）, which applies prefetching to J^＋-tree to accelerate range scan operations. The J^＋-tree stores all the keys in its leaf nodes and keeps the reference values of leaf nodes in a Judy structure, which makes J^＋-tree not only hold the advantages of Judy （such as fast single value search） but also outperform it in other aspects. For example, J^＋-trees can achieve better performance on range queries than Judy. The pJ^＋-tree index exploits prefetching techniques to further improve the cache behavior of J^＋-trees and yields a speedup of 2.0 on range scans. Compared with B^＋-trees, CSB^＋-trees, pB^＋-trees and T-trees, our extensive experimental Study shows that pJ^＋-trees can provide better performance on both time （search, scan, update） and space aspects.

Real-Time Memory Data Optimization Mechanism of Edge IoT Agent

With the full development of disk-resident databases(DRDB)in recent years,it is widely used in business and transactional applications.In long-term use,some problems of disk databases are gradually exposed.For applications with high real-time requirements,the performance of using disk database is not satisfactory.In the context of the booming development of the Internet of things,domestic real-time databases have also gradually developed.Still,most of them only support the storage,processing,and analysis of data values with fewer data types,which can not fully meet the current industrial process control system data types,complex sources,fast update speed,and other needs.Facing the business needs of efficient data collection and storage of the Internet of things,this paper optimizes the transaction processing efficiency and data storage performance of the memory database,constructs a lightweight real-time memory database transaction processing and data storage model,realizes a lightweight real-time memory database transaction processing and data storage model,and improves the reliability and efficiency of the database.Through simulation,we proved that the cache hit rate of the cache replacement algorithm proposed in this paper is higher than the traditional LRU(Least Recently Used)algorithm.Using the cache replacement algorithm proposed in this paper can improve the performance of the system cache.

A Partition Checkpoint Strategy Based on Data Segment Priority

A partition checkpoint strategy based on data segment priority is presented to meet the timing constraints of the data and the transaction in embedded real-time main memory database systems（ERTMMDBS） as well as to reduce the number of the transactions missing their deadlines and the recovery time.The partition checkpoint strategy takes into account the characteristics of the data and the transactions associated with it;moreover,it partitions the database according to the data segment priority and sets the corresponding checkpoint frequency to each partition for independent checkpoint operation.The simulation results show that the partition checkpoint strategy decreases the ratio of trans-actions missing their deadlines.