{"id":16865,"date":"2016-10-11T14:00:10","date_gmt":"2016-10-11T21:00:10","guid":{"rendered":"https:\/\/blogs.technet.microsoft.com\/dataplatforminsider\/?p=16865"},"modified":"2024-01-22T22:50:35","modified_gmt":"2024-01-23T06:50:35","slug":"1000000-predictions-per-second","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/sql-server\/blog\/2016\/10\/11\/1000000-predictions-per-second\/","title":{"rendered":"1,000,000 predictions per second"},"content":{"rendered":"

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.<\/em><\/p>\n

Transactional Workloads + Intelligence<\/h2>\n

Online transaction processing (OLTP) database applications have powered many enterprise use-cases in recent decades, with numerous implementations in banking, e-commerce, manufacturing and many other domains. Today, I\u2019d like to highlight a new breed of applications that marry the latest OLTP advancements with advanced insights and machine learning. In particular, I\u2019d like to describe how companies can predict a million events per second<\/strong> with the very latest algorithms<\/strong>, using readily available software. We have shown this demo at the Microsoft Machine Learning and Data Science Summit<\/a> and my General Session at Ignite<\/a> in Atlanta, Georgia. You can watch both online. The predictive model was based on a boosted decision tree algorithm with 50 trees and 33 features.<\/p>\n

\"Machine<\/a><\/p>\n

Take credit card transactions, for instance. These can trigger a set of decisions that are best handled with predictive models. Financial services companies need to determine whether a particular transaction is fraudulent or legitimate.<\/p>\n

As the number of transactions per second (TPS) increase, so does the number of predictions per second (PPS) that organizations need to make. The Visa network, for instance, was capable of handling 56,000 TPS last year and managed over 100 billion yearly transactions. With each transaction triggering a set of predictions and decisions, modern organizations have a need for a powerful platform that combines OLTP with a high-speed prediction engine. We expect that an increasing number of companies will need to hit 1 million predictions per second (PPS) or more<\/strong> in coming years.<\/p>\n

What kind of architecture would enable such use cases? At Microsoft, we believe that computing needs to take place where data lives. This minimizes data movement, eliminates the costs and security risks associated with data movement and the prediction engine sits close to the database (i.e., in-database analytics). Moreover, the predictive models can be shared by multiple applications. That\u2019s precisely how SQL Server 2016 was designed.<\/p>\n

Take the credit card fraud detection example I mentioned above \u2013 one can handle it in the following manner:<\/p>\n