Achieve operational flexibility and reliability by using a Dockerized version of WURFL for your device detection operations. Users can simplify deployment and maintenance while achieving the accuracy and performance for which WURFL is known. Whether used for mobile optimization, advertising, or analytics use cases, WURFL Microservice for Docker device detection provides an enterprise’s devops team a reliable container that they can integrate into their larger architecture.
Users of WURFL Microservice for Docker device detection can select from over 500 WURFL capabilities. ScientiaMobile offers a wide selection of WURFL capabilities from which commercially-licensed users can choose. For example, “form_factor” will identify the device as “desktop”, “smartphone”, “tablet”, or several other classifications of devices. This saves developers effort to develop their own logic to address common business questions. Developers can use these capabilities to provide more fine-grained optimization, control, and analytics.
Likewise, WURFL’s premium capabilities can provide valuable insights into the economic profiles of your website visitors. For example, using our “MSRP,” you can instantly learn the price of a visitor’s smartphone. With this insight into the affluence and willingness to purchase of individual users, e-commerce and advertisers can tailor offers and ads appropriately.
ScientiaMobile is constantly updating and expanding its Device Description Repository (DDR) to ensure accurate coverage of the newest devices and the long-tail of older devices. WURFL Device Repository covers 55,000 device profiles, including smartphones, tablets, laptops, smart TVs, and game consoles. As the DDR grows, detection performance stays high through use of caching and efficient database architecture.
WURFL Microservice delivers the same standard of high accuracy that makes WURFL the industry leader. ScientiaMobile achieves over 99% accuracy by searching the internet and analyzing over 2 billion user agents per month. We quickly identify new devices, including long-tail Indian and Chinese devices, and deliver high-quality device intelligence updates to commercial customers every week. For even greater accuracy in identifying specific iPhone and iPad models, customers can add-on our WURFL.js Business Edition.
WURFL Microservice deploys with a fully functional Updater that will update its device database every week. WURFL Microservice automatically checks for a new device database snapshot, downloads, and reloads the microservice. It can do this with no interruptions to serving requests.
WURFL Microservice runs a fast, compiled version of WURFL InFuze under the hood. WURFL Microservice for Docker device detection is engineered to effectively multi-thread, allowing for effective scaling on multi-core processor servers. Multiple applications running the client library can interact with WURFL Microservice, establishing multiple threads with minimal contention for resources.
Many internet leaders use WURFL in their global, high-volume detection operations. Over 15 years, we have developed an efficient data structure and well-tuned API search algorithms. We deliver high-performance device detection that does not sacrifice quality and accuracy.
WURFL Microservice supports client API libraries for Java, PHP, .NET (C#), Node.js, and GoLang. The API supplies the HTTP request (and more specifically the user agent) to the the microservice and returns the results of the WURFL device capabilities. Developers can quickly integrate WURFL into their larger microservice application architecture. The client API leverages its local cache to provide high performance from the overall WURFL Microservice architecture.
Customers receive ticketed support. Support specialists with over 30 cumulative years experience in device detection provide fast responses to developers.
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