This practical guide provides application developers, sysadmins, and DevOps practitioners with a hands-on introduction to the most important aspects of Prometheus, including dashboarding and alerting, direct code instrumentation, and metric collection from third-party systems with exporters.This open source system has gained popularity over the past few years for good reason. With its simple yet powerful data model and query language, Prometheus does one thing, and it does it well. Author and Prometheus developer Brian Brazil guides you through Prometheus setup, the Node exporter, and the Alertmanager, then demonstrates how to use them for application and infrastructure monitoring.Know where and how much to apply instrumentation to your application codeIdentify metrics with labels using unique key-value pairsGet an introduction to Grafana, a popular tool for building dashboardsLearn how to use the Node Exporter to monitor your infrastructureUse service discovery to provide different views of your machines and servicesUse Prometheus with Kubernetes and examine exporters you can use with containersConvert data from other monitoring systems into the Prometheus format

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    Fully updated for the new Java SE 6 platform, this no-nonsense tutorial and reliable reference illuminates the most important language and library features with thoroughly tested real-world examples. The example programs have been carefully crafted to be easy to understand as well as useful in practice, so you can rely on them as an outstanding starting point for your own code. Volume I is designed to quickly bring you up to speed on what's new in Java SE 6 and to help you make the transition as efficiently as possible, whether you're upgrading from an earlier version of Java or migrating from another language. The authors concentrate on the fundamental concepts of the Java language, along with the basics of user-interface programming. You'll find detailed, insightful coverage ofJava fundamentalsObject-oriented programmingInterfaces and inner classesReflection and proxiesThe event listener modelGUI programming with SwingPackaging applicationsException handlingLogging and debuggingGeneric programmingThe collections frameworkConcurrencyFor detailed coverage of XML processing, networking, databases, internationalization, security, advanced AWT/Swing, and other advanced features, look for the forthcoming eighth edition of "Core Java(TM), Volume II--Advanced Features "(ISBN: 978-0-13-235479-0).

    Over the past five years, authors Dustin Boswell and Trevor Foucher have analyzed hundreds of examples of "bad code" (much of it their own) to determine why they’re bad and how they could be improved. Their conclusion? You need to write code that minimizes the time it would take someone else to understand it—even if that someone else is you.This book focuses on basic principles and practical techniques you can apply every time you write code. Using easy-to-digest code examples from different languages, each chapter dives into a different aspect of coding, and demonstrates how you can make your code easy to understand.Simplify naming, commenting, and formatting with tips that apply to every line of codeRefine your program’s loops, logic, and variables to reduce complexity and confusionAttack problems at the function level, such as reorganizing blocks of code to do one task at a timeWrite effective test code that is thorough and concise—as well as readable"Being aware of how the code you create affects those who look at it later is an important part of developing software. The authors did a great job in taking you through the different aspects of this challenge, explaining the details with instructive examples." —Michael Hunger, passionate Software Developer

    This authoritative book scrapes away these bad features to reveal a subset of JavaScript that's more reliable, readable, and maintainable than the language as a whole--a subset you can use to create truly extensible and efficient code.Considered the JavaScript expert by many people in the development community, author Douglas Crockford identifies the abundance of good ideas that make JavaScript an outstanding object-oriented programming language-ideas such as functions, loose typing, dynamic objects, and an expressive object literal notation. Unfortunately, these good ideas are mixed in with bad and downright awful ideas, like a programming model based on global variables.When Java applets failed, JavaScript became the language of the Web by default, making its popularity almost completely independent of its qualities as a programming language. In JavaScript: The Good Parts, Crockford finally digs through the steaming pile of good intentions and blunders to give you a detailed look at all the genuinely elegant parts of JavaScript, including:SyntaxObjectsFunctionsInheritanceArraysRegular expressionsMethodsStyleBeautiful featuresThe real beauty? As you move ahead with the subset of JavaScript that this book presents, you'll also sidestep the need to unlearn all the bad parts. Of course, if you want to find out more about the bad parts and how to use them badly, simply consult any other JavaScript book.With JavaScript: The Good Parts, you'll discover a beautiful, elegant, lightweight and highly expressive language that lets you create effective code, whether you're managing object libraries or just trying to get Ajax to run fast. If you develop sites or applications for the Web, this book is an absolute must.

    In addition to giving a strong foundation in the language itself, Lubanovic shows how to use it for a range of applications in business, science, and the arts, drawing on the rich collection of open source packages developed by Python fans.It's impressive how many commercial and production-critical programs are written now in Python. Developed to be easy to read and maintain, it has proven a boon to anyone who wants applications that are quick to write but robust and able to remain in production for the long haul.This book focuses on the current version of Python, 3.x, while including sidebars about important differences with 2.x for readers who may have to deal with programs in that version.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    The most common operation done with sed is substitution, replacing one block of text with another. awk is a complete programming language. Unlike many conventional languages, awk is "data driven" -- you specify what kind of data you are interested in and the operations to be performed when that data is found. awk does many things for you, including automatically opening and closing data files, reading records, breaking the records up into fields, and counting the records. While awk provides the features of most conventional programming languages, it also includes some unconventional features, such as extended regular expression matching and associative arrays. sed & awk describes both programs in detail and includes a chapter of example sed and awk scripts. This edition covers features of sed and awk that are mandated by the POSIX standard. This most notably affects awk, where POSIX standardized a new variable, CONVFMT, and new functions, toupper() and tolower(). The CONVFMT variable specifies the conversion format to use when converting numbers to strings (awk used to use OFMT for this purpose). The toupper() and tolower() functions each take a (presumably mixed case) string argument and return a new version of the string with all letters translated to the corresponding case. In addition, this edition covers GNU sed, newly available since the first edition. It also updates the first edition coverage of Bell Labs nawk and GNU awk (gawk), covers mawk, an additional freely available implementation of awk, and briefly discusses three commercial versions of awk, MKS awk, Thompson Automation awk (tawk), and Videosoft (VSAwk).

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    One slogan of Perl is that it makes easy things easy and hard things possible. "Intermediate Perl" is about making the leap from the easy things to the hard ones.Originally released in 2003 as "Learning Perl Objects, References, and Modules" and revised and updated for Perl 5.8, this book offers a gentle but thorough introduction to intermediate programming in Perl. Written by the authors of the best-selling "Learning Perl," it picks up where that book left off. Topics include: Packages and namespacesReferences and scopingManipulating complex data structuresObject-oriented programmingWriting and using modulesTesting Perl codeContributing to CPANFollowing the successful format of "Learning Perl," we designed each chapter in the book to be small enough to be read in just an hour or two, ending with a series of exercises to help you practice what you've learned. To use the book, you just need to be familiar with the material in "Learning Perl" and have ambition to go further.Perl is a different language to different people. It is a quick scripting tool for some, and a fully-featured object-oriented language for others. It is used for everything from performing quick global replacements on text files, to crunching huge, complex sets of scientific data that take weeks to process. Perl is what you make of it. But regardless of what you use Perl for, this book helps you do it more effectively, efficiently, and elegantly."Intermediate Perl" is about learning to use Perl as a programming language, and not just a scripting language. This is the book that turns the Perl dabbler into the Perl programmer.

    Try out more than 1,000 commands to find and get software, monitor system health and security, and access network resources. Then, apply the skills you learn from this book to use and administer desktops and servers running Ubuntu, Debian, and KNOPPIX or any other Linux distribution.

    And considering that the "one man" is Neal Stephenson, "the hacker Hemingway" (Newsweek) -- acclaimed novelist, pragmatist, seer, nerd-friendly philosopher, and nationally bestselling author of groundbreaking literary works (Snow Crash, Cryptonomicon, etc., etc.) -- the word is well worth hearing. Mostly well-reasoned examination and partial rant, Stephenson's In the Beginning... was the Command Line is a thoughtful, irreverent, hilarious treatise on the cyber-culture past and present; on operating system tyrannies and downloaded popular revolutions; on the Internet, Disney World, Big Bangs, not to mention the meaning of life itself.