6/26/2023 0 Comments Acid transactions![]() Your data files are updated only when transfers are confirmed whole, ensuring easy and full recovery in case of an ungraceful server shutdown or another communication error. Our commitment to impeccable atomicity is maintained by means like safe collection of transmitted data into journal files. Multiple transactions are bundled into a single transfer, to reduce latency and zoom up performance by orders of magnitude. Transactional Merger is a great example for the simple and effective way in which Voron lifts RavenDB to a new level of rapidity and reliability. One of our latest improvements is the development of Voron, our custom-made storage engine, designed specifically to maximize RavenDB’s performance and eliminate integration issues.īy developing our own storage engine, we gained full control over RavenDB’s transactions and can now adjust both database and storage engine to gain diverse storing options, perfect reliability and immense speed. Improving performance while keeping ACID consistency throughout the system has been enough of a challenge to get us really creative. For demands higher than that, a single RavenDB server running on a machine of less than $1,000 can handle over 150,000 writes per second and over a million reads per second. ![]() This is more than enough for most small to medium applications. On a Raspberry PI, a $25 machine running on low powered ARM chips and a mere 1 GB of RAM, RavenDB can handle over 13,000 reads per second and over a 1,000 writes per second. In some cases, data is streamed from small edge points like sensors embedded in machinery, clothing and even the human body, and then relayed to servers for immediate processing. And the database must accomplish this while using up as little resources as possible, so teams with aging hardware will be able to run the database as if they had topnotch equipment. These requirements call for a database capable of handling massive amounts of data in real time, while guaranteeing its integrity for every transaction. Point Of Sale (POS) applications need to update inventory totals, regional sales and purchasing needs on the hour. ![]() Health care applications need to rapidly track test results and compare them to millions of similar cases, in order to assist doctors in choosing the best course of action. Trading algorithms need to update aggregate figures in real time, processing petabytes of trade data each moment. In today’s environment of Big Data, even the most reliable results are of little use if they come in late. Just having transactions isn’t enough if it’s at the cost of performance.
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