Over half a billion videos are watched on JW Player video player every day resulting in about 7 billion events a day which generates approximately 1.5 to 2 terabytes of compressed data every day. We, in the data team here at JW Player, have built various batch and real time pipelines to crunch this data in order to provide analytics to our customers. For more details about our infrastructure, you can look at JW at Scale and Fast String Matching. In this post, I am going to discuss how we got Hive with Tez running in our batch processing pipelines.
We’ve been hard at work on JW 7.2 for the past few months. We’ve crushed quite a few bugs and gotten a lot of your feedback via our beta testing program. (Check our release notes for full details!) Today, we’re happy to announce that 7.2 is publicly available for all. This blog post explores its new features and improvements.
The major challenge with automated integration testing for a web player via browser automation tools is that these tools better serve the purposes of web applications such as e-commerce sites, single page applications, and social networks. How do you take a product like JW Player which is embedded on over 2 million websites where publishers are always coming up with unique ways to use the player, and build an automated testing framework that will ensure the quality of that player? I would like to walk through how we’ve taken on that challenge.
Paul Mandal, Rik Heijdens & Henry Lee, JW Mobile Mavens
This week, Apple started taking pre-orders for the new Apple TV. Previously, the devices had only been available to developers. Shortly after the Apple TV announcement in September, I received my developer device from Apple. Like most people, I have been very impressed with the device and development environment, but have found some crucial functionality missing from the new tvOS. But more on that in a minute.
Let’s get down to business: We know what it takes to excel with Google publishing solutions
As one of Google’s Certified Publishing Partners, JW Player can help your business thrive. Google selected us for this program based on our proven expertise in DoubleClick AdExchange and DoubleClick for Publishers Small Business. We'll help you monetize your sites -- and earn as much as possible from every ad impression -- as we work with you to provide a great video ad experience.
With the release of JW7, we had an exciting opportunity to leverage our new skinning model for a new skin design as well as to reassess the user experience of the player overall. Here I’ll recap the creative process that led to the new “seven” skin and what’s next for the design of the player in our upcoming releases.
There has been much ado in the news lately about Chrome and its active pausing of smaller Flash-based content. A recent post regarding this behavior was published when our VPAID 2.0 support was launched. We’re now happy to report that our latest release, JW Player 7.1.4, goes a step further. We're now able to provide a better experience for viewers by intelligently reacting to Chrome’s Flash throttling.
With nearly 15 billion plays each month globally, JW Player understands that our customers’ wide-ranging, diverse audiences are key to any successes in the ever-expanding world of online video. In some industries, especially for our customers in the public sector, video captions are even the law. Whether for accessibility or for multi-language support, video accessibility has become table stakes for anyone serious about online video.
At JW Player our analytics pipeline currently receives 4M pings per minute at peak times, providing the basis for insights to the publishers on our dashboards. Recently we have moved some of our offline classification processes to the beginning of our real-time and batch pipelines, which required us to optimize our string matching implementations. We employed finite state automata, rolling hash functions, and bloom filters to achieve 6x and 3x speedup in NSFW classification and ad classification respectively. In this article, we will discuss these classification problems, then the algorithms, implementations, as well as evaluation of our solutions.