Problem in Active Real-Time Database Application

Problems in active real-time database applications Active and real-time databases are increasing recently. Real-time database systems are an important part of today's technology and now support real-time computing (RTC), automatic tracking, and object location applications. The Computer Integrated Manufacturing (CIM) process manages air traffic. Multimedia server for real-time streaming commerce and credit card transactions. In real-time database applications, data changes are very fast and dynamic.

An OLTP database is a database that runs a single computer application. Electronic Health Records (EHR) is the main example of such applications. The main advantage of the OLTP database is that it enables fast, real-time transaction processing. Designed with speed emphasis, it provides response time of less than 1 second. For example, when a patient appears in the foreground, you search her name in EHR and immediately see the result. Again, you enter her blood pressure into the EHR and the information is stored there immediately. This speed is considered obvious, but there is an OLTP database architecture that appreciates it.

The main focus of the main memory database is real-time application. These databases must be designed to provide concurrency control to efficiently schedule real-time transactions for applications that share data and directly access them. Research in this field includes Bell Labs researchers and DALI project, IBM and Starburst database system, and breakthrough system M proposed by Kenneth Salem and Hector Garcia-Molina. We briefly explain the transaction management method of these three systems.

Before proceeding, consider the time to DLT and consider and explain the traditional database. If you have a database and an application can write transactions to that database and read transactions from that database, this database is called the primary database. These have been circulating for decades. The agreement algorithm includes the rules (mathematics) that each node follows to reach agreement. These algorithms describe the steps that need to be taken. For example, the profit verification algorithm can define rules such that the creator of the next block is selected by random selection, pile or various combinations of age.

The problem solved by INFURA is "reading" activity, which is the other side of the equation. Most database activities are performed through a generic web interface application. I like to use examples: if you opened Facebook on your phone. All the data you see will be read from the database. Sometimes, you may click the "Like" button. Like traditional databases, the read activity far exceeds the write activity of Ethereum block chains. The Ethereum block chain currently handles up to 500,000 writes per day, but as part of working with INFURA, a read request of 7 billion per day for read activity occurred. This is a dApp that looks for data similar to a block browser and checks the content of Etherdelta via a meta mask.