Optimizations in Syntax Highlighting Visual Studio Code version 1.9 includes a cool performance improvement that we've been working on and I wanted to tell its story. TL;DR TextMate themes will look more like their authors intended in VS Code 1.9, while being rendered faster and with less memory consumption. Syntax Highlighting Syntax Highlighting usually consists of two phases. Tokens are assigned to source code, and then they are targeted by a theme, assigned colors, and voilà, your source code is rendered with colors. It is the one feature that turns a text editor into a code editor. Tokenization in VS Code (and in the Monaco Editor ) runs line-by-line, from top to bottom, in a single pass. A tokenizer can store some state at the end of a tokenized line, which will be passed back when tokenizing the next line. This is a technique used by many tokenization engines, including TextMate grammars, that allows an editor to retokenize only a small subset of the line...
A large fraction of big data projects fail to deliver return of investment, or take years before they do so. The reasons are typically a combination of project management, leadership, organisation, available competence, and technical failures. In this presentation, I will focus on the technical aspects, and present the most common or costly data engineering mistakes that I have experienced when building scalable data processing technology over the last five years, as well as advice for how to avoid them. The presentation includes war stories from large scale production environments, some that lead to reprocessing of petabytes of data, or DDoSing critical services with a Hadoop cluster, and what we learnt from the incidents. EVENT: #bbuzz 2018 SPEAKER: Lars Albertsson PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). CREDITS: Original video source: https://www.youtube.com/watch?v=mv7PL...
Microsoft R Server 9.1.0, Microsoft's R distribution with added big-data, in-database, and integration capabilities, is released in April 2017 and is available for download. The release has several exciting features, including new machine-learning capabilities to support text and image processing and improved operationalization. The update includes new functionality to MicrosoftML. This package provides state-of-the-art, fast and scalable machine learning algorithms for common data science tasks including featurization, classification and regression. Some of the new functions include: Added support for most MRS platforms including Spark, , and Linux Out of the box image featurization with several deep neural pre-trained models Easy to use sentiment analysis functionality Support for Ensembling and parallel learning Improved operationalization on web and SQL
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