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The Practice of Cloud System Administration: DevOps and SRE Practices for Web Services, Volume 2 by Thomas A. Limoncelli, Strata R. Chalup, Christina J. Hogan
active measures, Amazon Web Services, anti-pattern, barriers to entry, business process, cloud computing, commoditize, continuous integration, correlation coefficient, database schema, Debian, defense in depth, delayed gratification, DevOps, domain-specific language, en.wikipedia.org, fault tolerance, finite state, Firefox, functional programming, Google Glasses, information asymmetry, Infrastructure as a Service, intermodal, Internet of things, job automation, job satisfaction, Kickstarter, load shedding, longitudinal study, loose coupling, Malcom McLean invented shipping containers, Marc Andreessen, place-making, platform as a service, premature optimization, recommendation engine, revision control, risk tolerance, side project, Silicon Valley, software as a service, sorting algorithm, standardized shipping container, statistical model, Steven Levy, supply-chain management, The future is already here, Toyota Production System, web application, Yogi Berra
First printing, September 2014 Contents at a Glance Contents Preface About the Authors Introduction Part I Design: Building It Chapter 1 Designing in a Distributed World Chapter 2 Designing for Operations Chapter 3 Selecting a Service Platform Chapter 4 Application Architectures Chapter 5 Design Patterns for Scaling Chapter 6 Design Patterns for Resiliency Part II Operations: Running It Chapter 7 Operations in a Distributed World Chapter 8 DevOps Culture Chapter 9 Service Delivery: The Build Phase Chapter 10 Service Delivery: The Deployment Phase Chapter 11 Upgrading Live Services Chapter 12 Automation Chapter 13 Design Documents Chapter 14 Oncall Chapter 15 Disaster Preparedness Chapter 16 Monitoring Fundamentals Chapter 17 Monitoring Architecture and Practice Chapter 18 Capacity Planning Chapter 19 Creating KPIs Chapter 20 Operational Excellence Epilogue Part III Appendices Appendix A Assessments Appendix B The Origins and Future of Distributed Computing and Clouds Appendix C Scaling Terminology and Concepts Appendix D Templates and Examples Appendix E Recommended Reading Bibliography Index Contents Preface About the Authors Introduction Part I Design: Building It 1 Designing in a Distributed World 1.1 Visibility at Scale 1.2 The Importance of Simplicity 1.3 Composition 1.3.1 Load Balancer with Multiple Backend Replicas 1.3.2 Server with Multiple Backends 1.3.3 Server Tree 1.4 Distributed State 1.5 The CAP Principle 1.5.1 Consistency 1.5.2 Availability 1.5.3 Partition Tolerance 1.6 Loosely Coupled Systems 1.7 Speed 1.8 Summary Exercises 2 Designing for Operations 2.1 Operational Requirements 2.1.1 Configuration 2.1.2 Startup and Shutdown 2.1.3 Queue Draining 2.1.4 Software Upgrades 2.1.5 Backups and Restores 2.1.6 Redundancy 2.1.7 Replicated Databases 2.1.8 Hot Swaps 2.1.9 Toggles for Individual Features 2.1.10 Graceful Degradation 2.1.11 Access Controls and Rate Limits 2.1.12 Data Import Controls 2.1.13 Monitoring 2.1.14 Auditing 2.1.15 Debug Instrumentation 2.1.16 Exception Collection 2.1.17 Documentation for Operations 2.2 Implementing Design for Operations 2.2.1 Build Features in from the Beginning 2.2.2 Request Features as They Are Identified 2.2.3 Write the Features Yourself 2.2.4 Work with a Third-Party Vendor 2.3 Improving the Model 2.4 Summary Exercises 3 Selecting a Service Platform 3.1 Level of Service Abstraction 3.1.1 Infrastructure as a Service 3.1.2 Platform as a Service 3.1.3 Software as a Service 3.2 Type of Machine 3.2.1 Physical Machines 3.2.2 Virtual Machines 3.2.3 Containers 3.3 Level of Resource Sharing 3.3.1 Compliance 3.3.2 Privacy 3.3.3 Cost 3.3.4 Control 3.4 Colocation 3.5 Selection Strategies 3.6 Summary Exercises 4 Application Architectures 4.1 Single-Machine Web Server 4.2 Three-Tier Web Service 4.2.1 Load Balancer Types 4.2.2 Load Balancing Methods 4.2.3 Load Balancing with Shared State 4.2.4 User Identity 4.2.5 Scaling 4.3 Four-Tier Web Service 4.3.1 Frontends 4.3.2 Application Servers 4.3.3 Configuration Options 4.4 Reverse Proxy Service 4.5 Cloud-Scale Service 4.5.1 Global Load Balancer 4.5.2 Global Load Balancing Methods 4.5.3 Global Load Balancing with User-Specific Data 4.5.4 Internal Backbone 4.6 Message Bus Architectures 4.6.1 Message Bus Designs 4.6.2 Message Bus Reliability 4.6.3 Example 1: Link-Shortening Site 4.6.4 Example 2: Employee Human Resources Data Updates 4.7 Service-Oriented Architecture 4.7.1 Flexibility 4.7.2 Support 4.7.3 Best Practices 4.8 Summary Exercises 5 Design Patterns for Scaling 5.1 General Strategy 5.1.1 Identify Bottlenecks 5.1.2 Reengineer Components 5.1.3 Measure Results 5.1.4 Be Proactive 5.2 Scaling Up 5.3 The AKF Scaling Cube 5.3.1 x: Horizontal Duplication 5.3.2 y: Functional or Service Splits 5.3.3 z: Lookup-Oriented Split 5.3.4 Combinations 5.4 Caching 5.4.1 Cache Effectiveness 5.4.2 Cache Placement 5.4.3 Cache Persistence 5.4.4 Cache Replacement Algorithms 5.4.5 Cache Entry Invalidation 5.4.6 Cache Size 5.5 Data Sharding 5.6 Threading 5.7 Queueing 5.7.1 Benefits 5.7.2 Variations 5.8 Content Delivery Networks 5.9 Summary Exercises 6 Design Patterns for Resiliency 6.1 Software Resiliency Beats Hardware Reliability 6.2 Everything Malfunctions Eventually 6.2.1 MTBF in Distributed Systems 6.2.2 The Traditional Approach 6.2.3 The Distributed Computing Approach 6.3 Resiliency through Spare Capacity 6.3.1 How Much Spare Capacity 6.3.2 Load Sharing versus Hot Spares 6.4 Failure Domains 6.5 Software Failures 6.5.1 Software Crashes 6.5.2 Software Hangs 6.5.3 Query of Death 6.6 Physical Failures 6.6.1 Parts and Components 6.6.2 Machines 6.6.3 Load Balancers 6.6.4 Racks 6.6.5 Datacenters 6.7 Overload Failures 6.7.1 Traffic Surges 6.7.2 DoS and DDoS Attacks 6.7.3 Scraping Attacks 6.8 Human Error 6.9 Summary Exercises Part II Operations: Running It 7 Operations in a Distributed World 7.1 Distributed Systems Operations 7.1.1 SRE versus Traditional Enterprise IT 7.1.2 Change versus Stability 7.1.3 Defining SRE 7.1.4 Operations at Scale 7.2 Service Life Cycle 7.2.1 Service Launches 7.2.2 Service Decommissioning 7.3 Organizing Strategy for Operational Teams 7.3.1 Team Member Day Types 7.3.2 Other Strategies 7.4 Virtual Office 7.4.1 Communication Mechanisms 7.4.2 Communication Policies 7.5 Summary Exercises 8 DevOps Culture 8.1 What Is DevOps? 8.1.1 The Traditional Approach 8.1.2 The DevOps Approach 8.2 The Three Ways of DevOps 8.2.1 The First Way: Workflow 8.2.2 The Second Way: Improve Feedback 8.2.3 The Third Way: Continual Experimentation and Learning 8.2.4 Small Batches Are Better 8.2.5 Adopting the Strategies 8.3 History of DevOps 8.3.1 Evolution 8.3.2 Site Reliability Engineering 8.4 DevOps Values and Principles 8.4.1 Relationships 8.4.2 Integration 8.4.3 Automation 8.4.4 Continuous Improvement 8.4.5 Common Nontechnical DevOps Practices 8.4.6 Common Technical DevOps Practices 8.4.7 Release Engineering DevOps Practices 8.5 Converting to DevOps 8.5.1 Getting Started 8.5.2 DevOps at the Business Level 8.6 Agile and Continuous Delivery 8.6.1 What Is Agile?
While traditionally change has been seen as a potential destabilizer, DevOps shows that infrastructure change can be done rapidly and frequently in a way that increases overall stability. DevOps is not a job title; you cannot hire a “DevOp.” It is not a product; you cannot purchase “DevOps software.” There are teams and organizations that exhibit DevOps culture and practices. Many of the practices are aided by one software package or another. But there is no box you can purchase, press the DevOps button, and magically “have” DevOps. Adam Jacob’s seminal “Choose Your Own Adventure” talk at Velocity 2010 (Jacob 2010) makes the case that DevOps is not a job description, but rather an inclusive movement that codifies a culture.
As a result availability becomes the problem for the entire organization, not just for the system administrators. DevOps is not just about developers and system administrators. In his blog post “DevOps is not a technology problem. DevOps is a business problem,” Damon Edwards (2010) emphasizes that DevOps is about collaboration and optimization across the whole organization. DevOps expands to help the process from idea to customer. It isn’t just about leveraging cool new tools. In fact, it’s not just about software. The organizational changes involved in creating a DevOps environment are best understood in contrast to the traditional software development approach. The DevOps approach evolved because of the drawbacks of such methods when developing custom web applications or cloud service offerings, and the need to meet the higher availability requirements of these environments. 8.1.1 The Traditional Approach For software packages sold in shrink-wrapped packages at computer stores or downloaded over the Internet, the developer is finished when the software is complete and ships.
Seeking SRE: Conversations About Running Production Systems at Scale by David N. Blank-Edelman
Affordable Care Act / Obamacare, algorithmic trading, Amazon Web Services, backpropagation, bounce rate, business continuity plan, business process, cloud computing, cognitive bias, cognitive dissonance, commoditize, continuous integration, crowdsourcing, dark matter, database schema, Debian, defense in depth, DevOps, domain-specific language, en.wikipedia.org, fault tolerance, fear of failure, friendly fire, game design, Grace Hopper, information retrieval, Infrastructure as a Service, Internet of things, invisible hand, iterative process, Kubernetes, loose coupling, Lyft, Marc Andreessen, microaggression, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, pull request, RAND corporation, remote working, Richard Feynman, risk tolerance, Ruby on Rails, search engine result page, self-driving car, sentiment analysis, Silicon Valley, single page application, Snapchat, software as a service, software is eating the world, source of truth, the scientific method, Toyota Production System, web application, WebSocket, zero day
When introducing toil limits, you will need to educate your people to socialize the concept of toil and to get them to understand why toil is destructive to both the individual and the organization. Leverage Existing Enthusiasm for DevOps Although DevOps was once the exclusive domain of web-scale startups, it has become an accepted ideal in most enterprises. Born in 2009, DevOps is a broad cultural and professional movement focused on “world-class quality, reliability, stability, and security at ever lower cost and effort; and accelerated flow and reliability throughout the technology value stream.”12 There is quite a bit of overlap between the goals of DevOps and SRE. There is also quite a bit of overlap between the theoretical underpinnings of DevOps and SRE. Benjamin Treynor Sloss, the Google leader who first coined the term SRE and presided over the codification of Google’s SRE practices, sees a clear overlap between DevOps and SRE: One could view DevOps as a generalization of several core SRE principles to a wider range of organizations, management structures, and personnel.
Benjamin Treynor Sloss, the Google leader who first coined the term SRE and presided over the codification of Google’s SRE practices, sees a clear overlap between DevOps and SRE: One could view DevOps as a generalization of several core SRE principles to a wider range of organizations, management structures, and personnel. One could equivalently view SRE as a specific implementation of DevOps with some idiosyncratic extensions.13 Within the enterprise, DevOps has been applied most often to the limited scope that starts with software development and moves through the service delivery pipeline (from source code check-in to automated deployment). In these enterprises, the penetration of the DevOps transformation is minimal beyond deployment and the bulk of operations practices have remained unchanged.
There is overlap, where deployment/delivery to an operational site is a shared domain with SRE. There is also opposing ideals where DevOps is integrated across the pipeline, SRE is only on the operational infrastructure, and would be considered a silo under strict DevOps philosophy. — Joaquin Menchaca, Senior DevOps engineer, NinjaPants Consulting ◆ ◆ ◆ While many consider DevOps to be a single framework, it is in fact an umbrella for a pipeline of practices that span the organization’s value stream from concept to value creation. Most DevOps practices focus on the stages from development through deployment as Continuous Integration, Continuous Delivery and Continuous Deployment.
Ansible Playbook Essentials by Gourav Shah
Our problem statement includes the following: Create a devops user on all hosts. This user should be part of the devops group. Install the "htop" utility. Htop is an improved version of top—an interactive system process monitor. Add the Nginx repository to the web servers and start it as a service. Now, we will create our first playbook and save it as simple_playbook.yml containing the following code: --- - hosts: all remote_user: vagrant sudo: yes tasks: - group: name: devops state: present - name: create devops user with admin privileges user: name: devops comment: "Devops User" uid: 2001 group: devops - name: install htop package action: apt name=htop state=present update_cache=yes - hosts: www user: vagrant sudo: yes tasks: - name: add official nginx repository apt_repository: repo: 'deb http://nginx.org/packages/ubuntu/ lucid nginx' - name: install nginx web server and ensure its at the latest version apt: name: nginx state: latest - name: start nginx service service: name: nginx state: started Our playbook contains two plays.
Since we are only going to specify tasks, we just need one subdirectory inside the base: $ mkdir -p roles/base/tasks Create the main.yml file inside roles/base/tasks to specify tasks for the base role. Edit the main.yml file and add the following code:--- # essential tasks. should run on all nodes - name: creating devops group group: name=devops state=present - name: create devops user user: name=devops comment="Devops User" uid=2001 group=devops - name: install htop package action: apt name=htop state=present update_cache=yes Creating an Nginx role We will now create a separate role for Nginx and move the previous code that we wrote in the simple_playbook.yml file to it, as follows: Create the directory layout for the Nginx role: $ mkdir roles/nginx $ cd roles/nginx $ mkdir tasks meta files $ cd tasks Create the install.yml file inside roles/base.
Tasks are a sequence of actions performed against a group of hosts that match the pattern specified in a play. Each play typically contains multiple tasks that are run serially on each machine that matches the pattern. For example, take a look at the following code snippet: - group: name:devops state: present - name: create devops user with admin privileges user: name: devops comment: "Devops User" uid: 2001 group: devops In the preceding example, we have two tasks. The first one is to create a group, and second is to create a user and add it to the group created earlier. If you notice, there is an additional line in the second task, which starts with name:.
Terraform: Up and Running: Writing Infrastructure as Code by Yevgeniy Brikman
It may still make sense to have a sepa‐ rate Dev team responsible for the application code and an Ops team responsible for the operational code, but it’s clear that Dev and Ops need to work more closely together. This is where the DevOps movement comes from. DevOps isn’t the name of a team or a job title or a particular technology. Instead, it’s a set of processes, ideas, and techniques. Everyone has a slightly different definition of DevOps, but for this book, I’m going to go with the following: The goal of DevOps is to make software delivery vastly more efficient. Instead of multi-day merge nightmares, you integrate code continuously and always keep it in a deployable state.
And instead 18 | Chapter 1: Why Terraform of constant outages and downtime, you build resilient, self-healing systems, and use monitoring and alerting to catch problems that can’t be resolved automatically. The results from companies that have undergone DevOps transformations are astounding. For example, Nordstrom found that after applying DevOps practices to their organization, they were able to double the number of features they delivered per month, reduce defects by 50%, reduce lead times (the time from coming up with an idea to running code in production) by 60%, and reduce the number of production incidents by 60% to 90%. After HP’s LaserJet Firmware division began using DevOps practices, the amount of time their developers spent on developing new features went from 5% to 40% and overall development costs were reduced by 40%.
After HP’s LaserJet Firmware division began using DevOps practices, the amount of time their developers spent on developing new features went from 5% to 40% and overall development costs were reduced by 40%. Etsy used DevOps practices to go from stressful, infrequent deployments that caused numerous outages to deploying 25-50 times per day.1 There are four core values in the DevOps movement: Culture, Automation, Measure‐ ment, and Sharing (sometimes abbreviated as the acronym CAMS).2 This book is not meant as a comprehensive overview of DevOps (check out ??? for recommended reading), so I will just focus on one of these values: automation. The goal is to automate as much of the software delivery process as possible. That means that you manage your infrastructure not by clicking around a webpage or manually executing shell commands, but through code.
Ansible for DevOps: Server and Configuration Management for Humans by Jeff Geerling
AGPL, Amazon Web Services, cloud computing, continuous integration, database schema, Debian, defense in depth, DevOps, fault tolerance, Firefox, full text search, Google Chrome, inventory management, loose coupling, microservices, Minecraft, MITM: man-in-the-middle, Ruby on Rails, web application
Since we’re only going to run a simple example, we will create a playbook in Tower’s default projects directory located in /var/lib/awx/projects: Log into the Tower VM: vagrant ssh Switch to the awx user: sudo su - awx Go to Tower’s default projects directory: cd /var/lib/awx/projects Create a new project directory: mkdir ansible-for-devops && cd ansible-for-devops Create a new playbook file, main.yml, within the new directory, with the following contents: 1 --- 2 - hosts: all 3 gather_facts: no 4 connection: local 5 6 tasks: 7 - name: Check the date on the server. 8 command: date Switch back to your web browser and get everything set up to run the test playbook inside Ansible Tower’s web UI: Create a new Organization, called ‘Ansible for DevOps’. Add a new User to the Organization, named John Doe, with the username johndoe and password johndoe1234. Create a new Team, called ‘DevOps Engineers’, in the ‘Ansible for DevOps’ Organization.
Create a new Team, called ‘DevOps Engineers’, in the ‘Ansible for DevOps’ Organization. Under the Team’s Credentials section, add in SSH credentials by selecting ‘Machine’ for the Credential type, and setting ‘Name’ to Vagrant, ‘Type’ to Machine, ‘SSH Username’ to vagrant, and ‘SSH Password’ to vagrant. Under the Team’s Projects section, add a new Project. Set the ‘Name’ to Tower Test, ‘Organization’ to Ansible for DevOps, ‘SCM Type’ to Manual, and ‘Playbook Directory’ to ansible-for-devops (Tower automatically detects all folders placed inside /var/lib/awx/projects, but you could also use an alternate Project Base Path if you want to store projects elsewhere).
Removed ‘Variables’ chapter (variables will be covered in-depth elsewhere). Added Appendix B - Ansible Best Practices and Conventions. Started tagging code in Ansible for DevOps GitHub repository to match manuscript version (starting with this version, 0.50). Fixed various layout issues. Version 0.49 (2014-04-24) Completed history of SSH in chapter 10. Clarified definition of the word ‘DevOps’ in chapter 1. Added section “Testing Ansible Playbooks” in chapter 14. Added links to Ansible for DevOps GitHub repository in the introduction and chapter 4. Version 0.47 (2014-04-13) Added Apache Solr example in chapter 4. Updated VM diagrams in chapter 4.
Making Work Visible: Exposing Time Theft to Optimize Workflow by Dominica Degrandis, Tonianne Demaria
,” interview by Jeremy Hobson, Here and Now, March 31, 2014, www.wbur.org/hereandnow/2014/03/31/saying-no-psychology. 2. Todd Watts, “Addressing the Detrimental Effects of Context Switching with DevOps,” DevOps Blog, Software Engineering Institute at Carnegie Mellon University, March 5, 2015, https://insights.sei.cmu.edu/devops/2015/03/addressing-the-detrimental-effects-of-context-switching-with-devops.html. 3. “Context Switching,” OSDev.org, last modified December 29, 2015, http://wiki.osdev.org/Context_Switching. 4. Harry F. Harlow, as quoted in Daniel H. Pink, Drive: The Surprising Truth about What Motivates Us, (New York: Riverhead Books, 2011), 3. 5.
CONCLUSION: CALIBRATION Never let formal education get in the way of your learning. —Mark Twain Mountain View, California, September 2011 Following the first ever Kanban for DevOps class in Mountain View, California, a man sporting a kilt and long locks asked, “How do you integrate kanban with ticket systems without slowing down high-throughput Ops teams?” The man, whose name was Ben, wrote the question on a large, orange sticky note while standing at the back of the DevOps meetup room. Now, looking at the orange note, which I saved, I remember that I didn’t know how to answer the question at the time. It’s as valid a question today as it wasin 2011.
Troy Magennis, “Entangled: Solving the Hairy Problem of Team Dependencies,” Agile Alliance conference video, 1:15:15, August 5, 2015, https://www.agilealliance.org/resources/videos/entangled-solving-the-hairy-problem-of-team-dependencies/. 2. Maura Thomas, “Your Team’s Time Management Problem Might Be a Focus Problem,” Harvard Business Review, February 28, 2017, https://hbr.org/2017/02/your-teams-time-management-problem-might-be-a-focus-problem. 1.3 1. 2016 State of DevOps Report, (Portland, OR: Puppet Labs, 2016) 26, https://puppet.com/resources/whitepaper/2016-state-of-devops-report. 1.4 1. Ross Garber, as quoted in Gary Keller, The ONE Thing: The Surprisingly Simple Truth Behind Extraordinary Results (London: John Murray, 2013), 19. 1.5 1. Michael Feathers, Working Effectively with Legacy Code, (Upper Saddle River, NJ: Prentice Hall, 2004), xvi. 2.
Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith by Sam Newman
Airbnb, business process, continuous integration, database schema, DevOps, fault tolerance, ghettoisation, inventory management, Jeff Bezos, Kubernetes, loose coupling, microservices, MVC pattern, price anchoring, pull request, single page application, software as a service, source of truth, sunk-cost fallacy, telepresence
You can provide help and training, add new people to the team (perhaps by embedding people from the current operations team in delivery teams). No matter what change you want to bring about, just as with our software, you can make this happen in an incremental fashion. DevOps Doesn’t Mean NoOps! There is widespread confusion around DevOps, with some people assuming that it means that developers do all the operations, and that operations people are not needed. This is far from the case. Fundamentally, DevOps is a cultural movement based on the concept of breaking down barriers between development and operations. You may still want specialists in these roles, or you might not, but whatever you want to do, you want to promote common alignment and understanding across the people involved in delivering your software, no matter what their specific responsibilities are.
You may still want specialists in these roles, or you might not, but whatever you want to do, you want to promote common alignment and understanding across the people involved in delivering your software, no matter what their specific responsibilities are. For more on this, I recommend Team Topologies,9 which explores DevOps organizational structures. Another excellent resource on this topic, albeit broader in scope, is The Devops Handbook.10 Making a Change So if you shouldn’t just copy someone else’s structure, where should you start? When working with organizations that are changing the role of delivery teams, I like to begin with explicitly listing all the activities and responsibilities that are involved in delivering software within that company.
Introducing EventStorming. Leanpub, 2019. http://bit.ly/2n0zCLU. Brooks, Frederick P. The Mythical Man-Month, 20th Anniversary Edition. Addison Wesley, 1995. Bryant, Daniel. “Building Resilience in Netflix Production Data Migrations: Sangeeta Handa at QCon SF.” http://bit.ly/2m1EwHT. Devops Research & Assessment. Accelerate: State Of Devops Report 2018. http://bit.ly/2nPDNLe. Evans, Eric. Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley, 2003. Feathers, Michael. Working Effectively with Legacy Code. Prentice-Hall, 2004. Fowler, Martin. “Strangler Fig Application.” http://bit.ly/2p5xMKo.
Agile Project Management with Kanban (Developer Best Practices) by Eric Brechner
Amazon Web Services, cloud computing, continuous integration, crowdsourcing, DevOps, don't repeat yourself, en.wikipedia.org, index card, loose coupling, minimum viable product, pull request, software as a service
See also Williams, Laurie, and Robert Kessler. Pair Programming Illuminated. Reading, MA: Addison-Wesley, 2002. DevOps DevOps is the practice of developers collaborating closely with service operators to create and maintain software services together. When a service is being designed, service operators directly contribute. When there is a serious production issue, the developer (or developers) who wrote the service that’s affected are directly engaged. DevOps is often tied to testing in production (TIP) and continuous deployment. DevOps can be incorporated into Kanban as described earlier in the “Continuous deployment” section in Chapter 6.
For large projects (100+ people), the two roles are broken up, with a specialized PM, called a release manager, taking on the project-management responsibilities, and feature-team PMs acting mostly as analysts, but who are also responsible for reporting their team’s status to the release manager. PM is a tricky role at Microsoft—good PMs are highly valued. My current teams don’t have testers. Developers, automation, or partners validate improvements before they are used by larger customer audiences, basically running a DevOps model (see Chapter 9, “Further resources and beyond,” for details on DevOps). We still have a Validate step because that is real work that must be tracked for each item, even though many of the people performing that step might not be on our team. The more you use Kanban, the more you focus on the smooth flow of work than on getting caught up in the people assigned.
In addition, the Validate step may be performed by developers instead of by testers. For a DevOps team, the example of the Validate done rule from the Kanban quick-start guide (Chapter 2) is particularly pertinent: “The work is deployed to production and tried by a significant subset of real customers. All issues found are resolved.” See also Humble, Jez, and David Farley. Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation. Upper Saddle River, NJ: Addison-Wesley, 2010. All of my current and past Xbox teams use DevOps. Many of my teams use TDD and refactoring, and some have used pair programming.
WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly
4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, disinformation, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, strong AI, TaskRabbit, telepresence, the built environment, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar
We organized a summit to host the leaders of the emerging field of web operations, and soon thereafter launched our Velocity Conference to host the growing number of professionals who worked behind the scenes to make Internet sites run faster and more effectively. The Velocity Conference brought together a community working on a new discipline that came to be called DevOps, a portmanteau word combining software development and operations. (The term was coined a few months after the first Velocity Conference by Patrick Debois and Andrew “Clay” Shafer, who ran a series of what they called “DevOps Days” in Belgium.) The primary insight of DevOps is that there were traditionally two separate groups responsible for the technical infrastructure of modern web applications: the developers who build the software, and the IT operations staff who manage the servers and network infrastructure on which it runs.
And those two groups typically didn’t talk to each other, leading to unforeseen problems once the software was actually deployed at scale. DevOps is a way of seeing the entire software life cycle as analogous to the lean manufacturing processes that Toyota had identified for manufacturing. DevOps takes the software life cycle and workflow of an Internet application and turns it into the workflow of the organization, building in measurement, identifying key choke points, and clarifying the network of essential communication. In an appendix to The Phoenix Project, a novelized tutorial on DevOps created by Gene Kim, Kevin Behr, and George Spafford as homage to The Goal, the famous novel about the principles of lean manufacturing, Gene Kim notes that speed is one of the key competitive advantages that DevOps brings to an organization.
“Just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he writes, “continuous experimentation . . . improve[s] the way we optimize business processes in our organizations.” But DevOps also brings higher reliability and better responsiveness to customers. Gene Kim characterizes what happens in a high-performance DevOps organization: “Instead of upstream Development groups causing chaos for those in the downstream work centers (e.g., QA, IT operations, and Infosec), Development is spending twenty percent of its time helping ensure that work flows smoothly through the entire value stream, speeding up automated tests, improving deployment infrastructure, and ensuring that all applications create useful production telemetry.”
Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale by Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George
Amazon Web Services, barriers to entry, bitcoin, business intelligence, business process, cloud computing, commoditize, computer vision, continuous integration, create, read, update, delete, database schema, Debian, DevOps, domain-specific language, fault tolerance, Firefox, functional programming, Google Chrome, Induced demand, Infrastructure as a Service, Internet of things, job automation, Kickstarter, Kubernetes, loose coupling, microservices, natural language processing, Network effects, platform as a service, source of truth, statistical model, web application
Note When you run in the public cloud, as you will see in Chapter 16, you can also choose PaaS/SaaS solutions, which reduce the footprint of roles (as well as your flexibility and control over infrastructure components). Do I Need DevOps? In a sense, the rise of the term DevOps mirrors the rise of corporate distributed computing. In some definitions DevOps signals a merging of the developer and operator roles into one, often skipping any compartmentalization as described earlier. Others simply define it as operators who automate as much as possible in code, which is certainly beneficial in the world of distributed systems.
skill profile, Big data engineer split responsibilities with other team roles, Split Responsibilities bigdata-interop project (Google), Hadoop integration binary releases of Hadoop, Installation Choices bind (LDAP), LDAP Authentication BitLocker, Encryption in Microsoft Azure bits per second (bps), translating to bytes per second (B/s), Measuring throughput blob storage (Azure), Azure storage options, Azure storage options, Blob storageencryption in, Encryption in Microsoft Azure in Hadoop, Azure storage options integration with Hadoop, Azure Blob storage block blobs (Azure), Azure storage options block encryption key (BEK), Encryption in Microsoft Azure block I/O prioritization, Linux kernel cgroups and, Requirements for Multitenancy block locality, Erasure Coding Versus Replicationlocality opitmization, Locality optimization block reports, HDFS blocks, HDFSblock size, The Linux Page Cache block-level filesystems, Filesystems-Filesystems different meanings of, Sequential I/O performance mlocked by the DataNode, Short-Circuit and Zero-Copy Reads placement of, using replication, Erasure Coding Versus Replication replication of, Replication bring your own key (BYOK), Options for Encryption in the Cloud, BYOK, Recommendations and Summary for Cloud Encryption brokers (Kafka), Kafka bucket policies (Amazon S3), Amazon Simple Storage Service bucketsin Amazon S3, AWS storage options, Caveats and service limits in Google Cloud Storage (GCS), Storage options business continuity team, Policies and Objectives business intelligence project case study, Case Study: A Typical Business Intelligence Project-Do I Need a Center of Excellence/Competence?center for excellence/competence in solution with Hadoop, Do I Need a Center of Excellence/Competence? DevOps and solution with Hadoop, Do I Need DevOps? new team setup for solution with Hadoop, New Team Setup solution overview with Hadoop, Solution Overview with Hadoop split responsibilities in solution with Hadoop, Split Responsibilities traditional solution approach, The Traditional Approach-The Traditional Approach typical team setup, Typical Team Setup-Systems engineer C C++, Apache Impala, Everything Is Java, Web UIsImpala and Kudu implementations, The role of the x86 architecture server certificate verification, Application Integration X.509 certificate format, Converting Certificates cablingcross-cabling racks, Network in stacked networks, Stacked network cabling considerations using high-speed cables in network Layer 1, Layer 1 Recommendations caches, Commodity Serverscache coherence, Commodity Servers disk cache, Disk cacheenabled and disabled, throughput testing, Disk cache HDFS, implementation of, Important System Calls Kerberos tickets stored in, Kerberos Clients L3 cache size and core count, CPU Specifications Linux page cache, The Linux Page Cache Linux, access for filesystem I/O, User Space simulating a cache miss, Sequential I/O performance storage controller cache, Controller cacheguidelines for, Guidelines read-ahead caching, Read-ahead caching throughput testing, Disk cache write-back caching, Write-back caching caching, Hadoop and the Linux Storage Stackenabling name service caching, OS Configuration for Hadoop HDFS, Short-Circuit and Zero-Copy Readscache administration commands, Short-Circuit and Zero-Copy Reads instructing Linux to minimize, The Linux Page Cache of Sentry roles and permissions by services, Impala Canonical Name (CNAME) records (DNS), DNS round robin CAP theorem, Quorum spanning with two datacenters catalog, Impala daemons catalog server, Catalog server categories (cable), Layer 1 Recommendations center of excellence or competence, Do I Need a Center of Excellence/Competence?
in the public cloud, security of, Assessing the Risk ingest and intercluster connectivity, Ingest and Intercluster Connectivity-Hardwarehardware, Hardware software, Software replacements and repair, Replacements and Repairoperational procedures, Operational Procedures space and racking constraints, Space and Racking Constraints typical pitfalls, Typical Pitfalls DataNode, HDFS, The Linux Page Cachechecksumming, opting out of, Short-Circuit and Zero-Copy Reads failure during consistency operations, Disk cache Datasets, Apache Spark dd tool, Disk cache, Single writes and readsusing to measure sequential I/O performance for disks, Sequential I/O performance-Sequential I/O performance Debian, OS Choicesobtaining sysbench, Validation approaches package management, Installation Process dedicated subnets, Layer 3 Recommendations dedicated switches, Layer 1 Recommendations deep learning, Data scientist default group, Hue delegation tokens, Delegation Tokens, Impersonation, Security, Deployment considerations, Temporary security credentialspersistent store for, in Hive high availability, Deployment considerations dependencieshigh availability for Kafka dependencies, Deployment considerations in Hadoop stack, A Tour of the Landscape deployment, Hadoop Deployment-Summarycloud deployment for Hadoop, Network Architecture deploying HBase for high availability, Deployment considerations deploying HDFS for high availability, Deployment recommendations deploying Hue for high availability, Deployment options deploying Impala for high availability, Deployment considerations deploying Kafka for high availability, Deployment considerations deploying KMS for high availability, Deployment considerations deploying Oozie for high efficiency, Deployment considerations deploying Solr for high availability, Deployment considerations deploying YARN for high availability, Deployment recommendations deploying ZooKeeper for high availability, Deployment considerations deployment risks in the cloud, Deployment Risksmitigation, Mitigation Hadoop distribution architecture, Distribution Architecture-Distribution Architecture Hadoop distributions, Hadoop Distributions-Hadoop Distributions installation choices for Hadoop distributions, Installation Choices-Installation Choices installation process for Hadoop platform, Installation Process-Summary of long-lived clusters in the cloud, Configuration and Templating-Post-install tasksone-click deployment, One-Click Deployments developer keys (GCP Cloud Storage), GCP Cloud Storage developers, Software developer developmentdata replication for software development, Replication for Software Development multiple clusters for software development, Multiple Clusters for Software Developmentvariations in cluster sizing, Variation in cluster sizing DevOps, Do I Need DevOps? digest scheme (ZooKeeper), ZooKeeper direct bind (LDAP), LDAP Authentication disaster recovery, Multiple Clusters for Resiliency, Data Replication, Alternative solutions(see also backups and disaster recovery) operational procedures for, in datacenters, Operational Procedures disaster tolerance, cluster spanning used for, Cluster Spanning disksdata encryption on, reasons for, At-Rest Encryption dedicated disks in ZooKeeper deployment, Deployment considerations disk and network tests with TeraGen, Disk and network tests disk layer storage, Disk Layer-Disk cachecharacteristics of hard disk drive types, Disk Layer disk cache, Disk cache disk sizes, Disk sizes SAS, SATA, and Nearline SAS drives, SAS, Nearline SAS, or SATA (or SSDs)?
Puppet 3 Beginner's Guide by John Arundel
Developers, who now often build applications, services, and businesses by themselves, couldn't do what they do without knowing how to set up and fix servers. The term "devops" has begun to be used to describe the growing overlap between these skill sets. It can mean sysadmins who happily turn their hand to writing code when needed, or developers who don't fear the command line, or it can simply mean the people for whom the distinction is no longer useful. Devops write code, herd servers, build apps, scale systems, analyze outages, and fix bugs. With the advent of CM systems, devs and ops are now all just people who work with code.
Introduction to Puppet The problem Configuration management A day in the life of a sysadmin Keeping the configuration synchronized Repeating changes across many servers Self-updating documentation Coping with different platforms Version control and history Solving the problem Reinventing the wheel A waste of effort Transferable skills Configuration management tools Infrastructure as code Dawn of the devop Job satisfaction The Puppet advantage Welcome aboard The Puppet way Growing your network Cloud scaling What is Puppet? The Puppet language Resources and attributes Summary Configuration management What Puppet does The Puppet advantage Scaling The Puppet language 2. First steps with Puppet What you'll need Time for action – preparing for Puppet Time for action – installing Puppet Your first manifest How it works Applying the manifest What just happened?
We can adopt the tools and techniques that regular programmers—who write code in Ruby or Java, for example—have used for years: Powerful editing and refactoring tools Version control Tests Pair programming Code reviews This can make us more agile and flexible as system administrators, able to deal with fast-changing requirements and deliver things quickly to the business. We can also produce higher-quality, more reliable work. Dawn of the devop Some of the benefits are more subtle, organizational, and psychological. There is often a divide between "devs", who wrangle code, and "ops", who wrangle configuration. Traditionally, the skill sets of the two groups haven't overlapped much. It was common until recently for system administrators not to write complex programs, and for developers to have little or no experience of building and managing servers.
Chaos Engineering: System Resiliency in Practice by Casey Rosenthal, Nora Jones
Amazon Web Services, Asilomar, autonomous vehicles, barriers to entry, blockchain, business continuity plan, business intelligence, business process, cloud computing, complexity theory, continuous integration, cyber-physical system, database schema, DevOps, fault tolerance, hindsight bias, Kubernetes, linear programming, loose coupling, microservices, MITM: man-in-the-middle, node package manager, pull request, ransomware, risk tolerance, Silicon Valley, six sigma, Skype, software as a service, statistical model, the scientific method, WebSocket
About the Author John Allspaw has worked in software systems engineering and operations for over twenty years in many different environments. John’s publications include The Art of Capacity Planning and Web Operations (both O’Reilly) as well as the foreword to The DevOps Handbook (IT Revolution Press). His 2009 Velocity talk with Paul Hammond, “10+ Deploys Per Day: Dev and Ops Cooperation” helped start the DevOps movement. John served as CTO at Etsy, and holds an MSc in Human Factors and Systems Safety from Lund University. 1 David D. Woods, STELLA: Report from the SNAFUcatchers Workshop on Coping with Complexity (Columbus, OH: The Ohio State University, 2017). 2 Principles of Chaos Engineering, retrieved June 10, 2019, from https://principlesofchaos.org. 3 Deploying a change in software with its execution path gated “on” only for some portion of users is known as a “dark” deploy.
Adoption can be split into four considerations: Who bought into the idea How much of the organization participates The prerequisites The obstacles Who Bought into Chaos Engineering Early in the adoption cycle, it is likely that the individual contributors (ICs) who are closest to the repercussions of an outage or security incident are most likely to adopt Chaos Engineering or seek out the discipline for obvious reasons. This is often followed by internal championing, with advocacy often found in DevOps, SRE, and Incident Management teams. In more traditional organizations, this often falls to Operations or IT. These are the teams that understand the pressure of being paged for an availability incident. Of course, the urgency around getting the system back online sets up obstacles for learning.
Since Chaos Engineering can be applied to infrastructure, inter-application, security, and other levels of the sociotechnical system, there are many opportunities for the practice to spread to different parts of the organization. Ultimate adoption occurs when practicing Chaos Engineering is accepted as a responsibility for all individual contributors throughout the organization, at every level of the hierarchy, even if a centralized team provides tooling to make it easier. This is similar to how in a DevOps-activated organization, every team is responsible for the operational properties of their software, even though centralized teams may specifically contribute to improving some of those properties. Prerequisites There are fewer prerequisites for Chaos Engineering than most people think. The first question to ask an organization considering adopting the practice is whether or not they know when they are in a degraded state.
Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century by Jeff Lawson
Airbnb, AltaVista, Amazon Web Services, barriers to entry, big data - Walmart - Pop Tarts, big-box store, bitcoin, business process, call centre, Chuck Templeton: OpenTable:, cloud computing, coronavirus, Covid-19, COVID-19, create, read, update, delete, cryptocurrency, David Heinemeier Hansson, DevOps, Elon Musk, financial independence, global pandemic, global supply chain, Internet of things, Jeff Bezos, Lean Startup, loose coupling, Lyft, Marc Andreessen, Mark Zuckerberg, microservices, minimum viable product, Mitch Kapor, move fast and break things, move fast and break things, Paul Graham, peer-to-peer, ride hailing / ride sharing, risk tolerance, Ruby on Rails, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, software is eating the world, sorting algorithm, Startup school, Steve Ballmer, Steve Jobs, Telecommunications Act of 1996, Toyota Production System, transaction costs, transfer pricing, Uber and Lyft, uber lyft, ubercab, web application, Y Combinator
But beneath his easygoing manner there’s an intensity and discipline that he learned way back in Marine boot camp. This combination was crucial as we started to embrace a methodology called DevOps in building our developer platform. Even if you don’t work directly in technology you might have heard the term DevOps without really understanding what it is. A cynic might say DevOps has become kind of the “flavor of the month” for software development, the way Agile and Lean Startup did before. Amazon lists more than a thousand books on the topic. You could spend years learning everything about DevOps, but for our purposes I’m going to provide an extremely simplified explanation, which goes like this: Once upon a time, software development organizations broke the process of producing a piece of code into multiple roles.
At each step there could be delays as a developer waited for a test engineer or release engineer to finish other projects and then get to theirs. Multiply all those potential delays by the number of steps, and you can see how things could get bogged down. DevOps, first conceived about a decade ago, represents an attempt to speed things up by having one developer handle all of the steps. The concept is reflected in the name itself: instead of having “developers” who write code and “operators” who do everything else, you combine all of the duties in one person. In a DevOps environment, the same developer writes the code, tests the code, packages it, monitors it, and remains responsible for it after it goes into production.
Yet if every team has to become domain experts, and build their own automation for each of these categories, it would take forever. That’s where Jason’s team comes in. Jason defines his job, and that of the platform team—a group of about one hundred engineers across thirteen small teams—as “to provide software that will enable a traditional software developer to be successful in a DevOps culture without having a deep background in all of these specialized disciplines.” They don’t develop software that ships to customers. They make software that developers use to write, test, deploy, and monitor software. If anything about our process resembles an assembly line, this is probably the closest thing.
The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake
A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Swan, blockchain, borderless world, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, DevOps, disinformation, don't be evil, Donald Trump, Edward Snowden, Exxon Valdez, global village, immigration reform, Infrastructure as a Service, Internet of things, Jeff Bezos, Julian Assange, Kubernetes, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, ransomware, Richard Thaler, Sand Hill Road, Schrödinger's Cat, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, software as a service, Steven Levy, Stuxnet, technoutopianism, The future is already here, Tim Cook: Apple, undersea cable, WikiLeaks, Y2K, zero day
a concept borrowed from the military: For a thorough discussion of the OODA loop, see Daniel Ford, Vision So Noble: John Boyd, the OODA Loop, and America’s War on Terror (n.p.: CreateSpace Independent Publishing Platform, 2010). DevOps, short for “development and operations”: For a kind and gentle explanation of DevOps (in novel form) see Gene Kim, Kevin Behr, and George Spafford, The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win (Glenside, Penn.: IT Revolution Press, 2013). According to data from Spamhaus: The “Spamhaus Botnet Threat Report 2017” put Amazon at number two on its list, behind the French hosting provider OVH.
In short, these technologies are being designed with security built in. While Yu notes that since the 1980s, security and IT have been diverging and CISOs and CIOs are increasingly reporting to different leaders (and at one another’s throats), he sees trends such as DevOps, bring your own device, and the ever-present specter of shadow IT bringing them back together. DevOps, short for “development and operations,” shortens the software development life cycle by bringing the development team and the operations team in closer alignment so they can rapidly push out new versions of software. The fact that employees tend to prefer to carry around one device and not two has forced most companies to allow work to be done on personal devices.
But some companies are starting to embrace these trends despite the apprehension of their security teams. If carried out with security in mind, they are finding, doing so has security benefits. Yu argues that by embracing trends in technology rather than fighting against them, security can harness the speed of modern businesses as a weapon to be wielded against malicious cyber actors. With DevOps, companies may be releasing updated versions of their software dozens of times a day. That means that when bugs are discovered, they can be fixed immediately. It also means that bugs may be eliminated in rewrites before an attacker can identify and exploit them. The concept of chaos engineering pioneered at Netflix has corporations running a constant stream of experiments to test the resilience of their systems.
Kill It With Fire: Manage Aging Computer Systems by Marianne Bellotti
anti-pattern, barriers to entry, cloud computing, cognitive bias, computer age, continuous integration, create, read, update, delete, Daniel Kahneman / Amos Tversky, database schema, DevOps, fault tolerance, fear of failure, Google Chrome, iterative process, loose coupling, microservices, minimum viable product, platform as a service, pull request, QWERTY keyboard, Richard Stallman, risk tolerance, Schrödinger's Cat, side project, software as a service, Steven Levy, web application, Y Combinator, Y2K
The problem with seeing Conway’s law as prescriptive is that technology is filled with little shifts in perception like this. The technology in our example has not fundamentally changed, but our groupings of what belongs with what have changed. We could tell the same story in reverse: what if we want to transition away from a traditional operations team to a DevOps model? Do our operations people now get moved to the product engineering teams? Do backend engineers learn the DevOps tools with operations acting as an oversight authority? Do we keep operations where it is and just ask them to automate? Reorgs Are Traumatic The reorg is the matching misused tool of the full rewrite. As the software engineer gravitates toward throwing everything out and starting over to project confidence and certainty, so too does the software engineers’ manager gravitate toward the reorg to fix all manner of institutional ills.
One of the reasons the DevOps and SRE movements have had such a beneficial effect on software development is that they seek to re-establish accountability. If product engineering teams play a role in running and maintaining their own infrastructure, they are the ones who feel the impact of their own decisions. When they build something that doesn’t scale, they are the ones who are awakened at 3 am with a page. Making software engineers responsible for the health of their infrastructure instead of a separate operations team unmutes the feedback loop. But anyone who has ever tried to run an SRE or DevOps team will tell you that maintaining the expectation that product engineering teams should be responsible for their infrastructure is easier said than done.
See failure drills chief information officer (CIO), 15 CloudFlare, 204 Code Yellow, 116–122, 156, 193 Collins Aerospace, 204 column width, 18 commercial cloud, 3, 15, 69, 86 Committee on Data Systems Languages (CODASYL), 29 compiler design, 71 complexity, 41, 46–50, 61, 103, 108, 137, 146, 173, 207 compliance, 90 configuration management, 65 containerization, 65 continuous integration, 72, 183 contract testing, 110 control flow graphs, 72 conventions, 106 Conway, Melvin, 140, 144 Conway’s law, 98, 140–141, 149–152, 156, 159 costs, 9 coupling, 46–50, 56, 64, 66, 85, 101, 103, 173 cross-compatibility, 64, 69 D databases, 36 data contracts, 102–110, 171 data flow graphs, 72 Deep Impact probe, the, 198 Dekker, Sidney, 145, 167 delays, 211 Department of Justice, 24 Department of Treasury, 15 Department of Veterans Affairs, 68 dependencies, 68, 111, 115 graphs, 71 management, 64 deprecations, 179 development environments, 72 development view, 173 DevOps, 150, 218 diagnosis-policies-actions, 184–187 drift, 145–146 E ECMA Office Open XML specification, 61 encoding, 20 enterprise architects, 77 enterprise service buses (ESB), 7–8 Etsy, 166 Excel, 61 F FAA (Federal Aviation Administration), 204 Facebook, 114 failovers, 55 failure drills, 114, 153, 172, 178 Falcone, Rino, 168 Feathers, Michael, 55 feature parity, 79 Federal Aviation Administration (FAA), 204 feedback loops, 210–211, 218–219 filetypes PDF, 67 fixed-point, 70 Flickr, 102 floating-point, 70 flows, 210 Fog Creek Software, 33 Ford, Neal, 105 formal methods, 109 Alloy, 110 Petri nets, 110 TLA+, 110 formal specification, 109–110 Fowler, Chad, 33 frameworks Angular.js, 150 Node.js, 36, 68 React.js, 36, 150 Vue.js, 150 G garbage collection, 44, 206 Gawker Media, 204 Glidden, Carlos, 19 GNU, 25 Google, 113, 117–118, 169, 205, 207 Chrome 119 GPS, 202–204 Groupon, 102 H Hadoop, 15 hard cutoff, 57 hardware lifecycles, 196 Harvard Business Review, 140 Harvard’s Kennedy School for Government, 162 Hölzle, Urs, 119 hooks, 65 HTTPS (HyperText Transfer Protocol Secure), 114 human factors, 145 I IBM, 19, 140, 198 Simon, 5 incentives, 34, 122, 140–144, 148–156, 163–165 incident commander, 121 incident response, 109, 188 InsightMaker, 212 Instagram, 204 International Telegraph Alphabet No. 1.
The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, George Spafford
air freight, anti-work, business intelligence, business process, centre right, cloud computing, continuous integration, dark matter, database schema, DevOps, friendly fire, index card, inventory management, Lean Startup, shareholder value, Toyota Production System
He says, “I want you to write a book, describing the Three Ways and how other people can replicate the transformation you’ve made here at Parts Unlimited. Call it The DevOps Cookbook and show how IT can regain the trust of the business and end decades of intertribal warfare. Can you do that for me?” Write a book? He can’t be serious. I reply, “I’m not a writer. I’ve never written a book before. In fact, I haven’t written anything longer than an e-mail in a decade.” Unamused, he says sternly, “Learn.” Shaking my head for a moment, I finally say, “Of course. It would be an honor and a privilege to write The DevOps Cookbook for you while I embark on what will probably be the most challenging three years of my entire career.”
With my drink in hand, I ponder how far we’ve come. During the Phoenix launch, I doubt anyone in this group could have imagined being part of a super-tribe that was bigger than just Dev or Ops or Security. There’s a term that we’re hearing more lately: something called “DevOps.” Maybe everyone attending this party is a form of DevOps, but I suspect it’s something much more than that. It’s Product Management, Development, IT Operations, and even Information Security all working together and supporting one another. Even Steve is a part of this super-tribe. In that moment, I let myself feel how incredibly proud I am of everyone in this room.
Old instincts kicking in, I urgently look around the room for Patty who is making a beeline toward me, her phone already in her hand. “First off, congratulations, boss,” she says, with a half smile on her face. “You want the bad news or the good news first?” Turning to her, I say with a sense of calm and inner peace, “What have we got, Patty?” To access more free resources on IT, DevOps, and helping your business win, visit: http://itrevolution.com/next Join us in spreading the word by leaving a review on Amazon or GoodReads, writing a blog post or telling a friend! Acknowledgements First and foremost, I want to acknowledge all the support from my loving wife, who put up with far more than I promised, Margueritte, and my sons, Reid, Parker, and Grant.
Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy
Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business process, Checklist Manifesto, cloud computing, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, meta-analysis, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, web application, zero day
For example, the decision to stop releases for the remainder of the quarter once an error budget is depleted might not be embraced by a product development team unless mandated by their management. DevOps or SRE? The term “DevOps” emerged in industry in late 2008 and as of this writing (early 2016) is still in a state of flux. Its core principles—involvement of the IT function in each phase of a system’s design and development, heavy reliance on automation versus human effort, the application of engineering practices and tools to operations tasks—are consistent with many of SRE’s principles and practices. One could view DevOps as a generalization of several core SRE principles to a wider range of organizations, management structures, and personnel.
If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-491-92912-4 [LSI] Foreword Google’s story is a story of scaling up. It is one of the great success stories of the computing industry, marking a shift towards IT-centric business. Google was one of the first companies to define what business-IT alignment meant in practice, and went on to inform the concept of DevOps for a wider IT community. This book has been written by a broad cross-section of the very people who made that transition a reality. Google grew at a time when the traditional role of the system administrator was being transformed. It questioned system administration, as if to say: we can’t afford to hold tradition as an authority, we have to think anew, and we don’t have time to wait for everyone else to catch up.
., [Gla02] for more details. 2 For our purposes, reliability is “The probability that [a system] will perform a required function without failure under stated conditions for a stated period of time,” following the definition in [Oco12]. 3 The software systems we’re concerned with are largely websites and similar services; we do not discuss the reliability concerns that face software intended for nuclear power plants, aircraft, medical equipment, or other safety-critical systems. We do, however, compare our approaches with those used in other industries in Chapter 33. 4 In this, we are distinct from the industry term DevOps, because although we definitely regard infrastructure as code, we have reliability as our main focus. Additionally, we are strongly oriented toward removing the necessity for operations—see Chapter 7 for more details. 5 In addition to this great story, she also has a substantial claim to popularizing the term “software engineering.”
Mastering Ansible by Jesse Keating
He has worked on both start-ups and big established companies. His interests include SDN, NFV, Network Automation, DevOps, and Cloud technologies. He also likes to try out and follow open source projects in these areas. You can find him on his blog at https://sreeninet.wordpress.com/. Tim Rupp has been working in various fields of computing for the last 10 years. He has held positions in computer security, software engineering, and most recently, in the fields of Cloud computing and DevOps. He was first introduced to Ansible while at Rackspace. As part of the Cloud engineering team, he made extensive use of the tool to deploy new capacity for the Rackspace Public Cloud.
Sawant Shah is a passionate and experienced full-stack application developer with a formal degree in computer science. Being a software engineer, he has focused on developing web and mobile applications for the last 9 years. From building frontend interfaces and programming application backend as a developer to managing and automating service delivery as a DevOps engineer, he has worked at all stages of an application and project's lifecycle. He is currently spearheading the web and mobile projects division at the Express Media Group—one of the country's largest media houses. His previous experience includes leading teams and developing solutions at a software house, a BPO, a non-profit organization, and an Internet startup.
The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture by Scott Belsky
23andMe, 3D printing, Airbnb, Albert Einstein, Anne Wojcicki, augmented reality, autonomous vehicles, Ben Horowitz, bitcoin, blockchain, Chuck Templeton: OpenTable:, commoditize, correlation does not imply causation, cryptocurrency, delayed gratification, DevOps, Donald Trump, Elon Musk, endowment effect, hiring and firing, Inbox Zero, iterative process, Jeff Bezos, knowledge worker, Lean Startup, Lyft, Mark Zuckerberg, Marshall McLuhan, minimum viable product, move fast and break things, move fast and break things, NetJets, Network effects, new economy, old-boy network, pattern recognition, Paul Graham, ride hailing / ride sharing, Silicon Valley, slashdot, Snapchat, Steve Jobs, subscription business, TaskRabbit, the medium is the message, Travis Kalanick, Uber for X, uber lyft, WeWork, Y Combinator, young professional
He had only a small amount of engineering experience before he joined us, but the team fell for him after his very first interview. Malcolm joined us as the third member of our dev-ops team, which is a group of engineers dedicated to the infrastructure, stability, and security of Behance’s platform. The dev-ops team is at the front line of every nightmare situation: spam problems, security breaches, latency in the speed of millions of portfolios loading for millions of visitors every day, and, when the site goes down, the dev-ops team diagnoses the problem and fixes it. Putting out fires all day—and trying to make the company flame retardant—is a stressful job, compounded by the constant battery of questions and concerns coming from all corners.
Putting out fires all day—and trying to make the company flame retardant—is a stressful job, compounded by the constant battery of questions and concerns coming from all corners. On paper, Malcolm wasn’t the perfect fit for the job based on his past experience. But his level of enthusiasm and willingness to take on any responsibility and master it helped him not only succeed but also elevate the dev-ops culture more broadly. Malcolm transformed the team and became a leader we all admired. Skills may be shared, but sheer initiative (and the energy and enthusiasm that comes along with it) helps the culture and spreads like wildfire—the good kind of fire. HOW DO YOU HIRE FOR INITIATIVE? Past initiative is the best indicator of future initiative.
This doesn’t make for a good politician, at least not in difficult moments. But over time, people come to respect candidness and directness. When you feel like a problem is being obfuscated by disclaimers, delicateness, or a lack of intellectual honesty, try to simplify it and compartmentalize the issues. One of my most frequent questions to our dev-ops team at Behance—the folks responsible for keeping our services up and running for millions of people to use every day—was “What’s keeping you up at night right now?” I was always trying to get beneath the surface of the progress we were making to unearth the real vulnerabilities. When you’re proposing a solution to a problem and meet resistance, take a step back to make sure everyone understands the problem first.
Python Network Programming Cookbook by M. Omar Faruque Sarker
So, this book covers less theory, but it's packed with practical materials. This book is written with a "devops" mindset where a developer is also more or less in charge of operation, that is, deploying the application and managing various aspects of it, such as remote server administration, monitoring, scaling-up, and optimizing for better performance. This book introduces you to a bunch of open-source, third-party Python libraries, which are awesome to use in various usecases. I use many of these libraries on a daily basis to enjoy automating my devops tasks. For example, I use Fabric for automating software deployment tasks and other libraries for other purposes, such as, searching things on the Internet, screen-scraping, or sending an e-mail from a Python script.
Faruque Sarker Reviewers Ahmed Soliman Farghal Vishrut Mehta Tom Stephens Deepak Thukral Acquisition Editors Aarthi Kumarswamy Owen Roberts Content Development Editor Arun Nadar Technical Editors Manan Badani Shashank Desai Copy Editors Janbal Dharmaraj Deepa Nambiar Karuna Narayanan Project Coordinator Sanchita Mandal Proofreaders Faye Coulman Paul Hindle Joanna McMahon Indexer Mehreen Deshmukh Production Coordinator Nilesh R. Mohite Cover Work Nilesh R. Mohite About the Author Dr. M. O. Faruque Sarker is a software architect, and DevOps engineer who's currently working at University College London (UCL), United Kingdom. In recent years, he has been leading a number of Python software development projects, including the implementation of an interactive web-based scientific computing framework using the IPython Notebook service at UCL.
Learn Algorithmic Trading by Sebastien Donadio
active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, buy and hold, buy low sell high, cryptocurrency, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, p-value, paper trading, performance metric, prediction markets, quantitative trading / quantitative ﬁnance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game
Despite all of these precautions, software implementation bugs do slip into live trading markets, so we should always be aware and cautious because software is never perfect and the cost of mistakes/bugs is very high in the algorithmic trading business, and even higher in the HFT business. DevOps risk DevOps risk is the term that is used to describe the risk potential when algorithmic trading strategies are deployed to live markets. This involves building and deploying correct trading strategies and configuring the configuration, the signal parameters, the trading parameters, and starting, stopping, and monitoring them.
Choice of IDE – Pycharm or Notebook Our first algorithmic trading (buy when the price is low, and sell when the price is high) Setting up your workspace PyCharm 101 Getting the data Preparing the data – signal Signal visualization Backtesting Summary Section 2: Trading Signal Generation and Strategies Deciphering the Markets with Technical Analysis Designing a trading strategy based on trend- and momentum-based indicators Support and resistance indicators Creating trading signals based on fundamental technical analysis Simple moving average Implementation of the simple moving average Exponential moving average Implementation of the exponential moving average Absolute price oscillator Implementation of the absolute price oscillator Moving average convergence divergence Implementation of the moving average convergence divergence Bollinger bands Implementation of Bollinger bands Relative strength indicator Implementation of the relative strength indicator Standard deviation Implementing standard derivatives Momentum Implementation of momentum Implementing advanced concepts, such as seasonality, in trading instruments Summary Predicting the Markets with Basic Machine Learning Understanding the terminology and notations Exploring our financial dataset Creating predictive models using linear regression methods Ordinary Least Squares Regularization and shrinkage – LASSO and Ridge regression Decision tree regression Creating predictive models using linear classification methods K-nearest neighbors Support vector machine Logistic regression Summary Section 3: Algorithmic Trading Strategies Classical Trading Strategies Driven by Human Intuition Creating a trading strategy based on momentum and trend following Examples of momentum strategies Python implementation Dual moving average Naive trading strategy Turtle strategy Creating a trading strategy that works for markets with reversion behavior Examples of reversion strategies Creating trading strategies that operate on linearly correlated groups of trading instruments Summary Sophisticated Algorithmic Strategies Creating a trading strategy that adjusts for trading instrument volatility Adjusting for trading instrument volatility in technical indicators Adjusting for trading instrument volatility in trading strategies Volatility adjusted mean reversion trading strategies Mean reversion strategy using the absolute price oscillator trading signal Mean reversion strategy that dynamically adjusts for changing volatility Trend-following strategy using absolute price oscillator trading signal Trend-following strategy that dynamically adjusts for changing volatility Creating a trading strategy for economic events Economic releases Economic release format Electronic economic release services Economic releases in trading Understanding and implementing basic statistical arbitrage trading strategies Basics of StatArb Lead-lag in StatArb Adjusting portfolio composition and relationships Infrastructure expenses in StatArb StatArb trading strategy in Python StatArb data set Defining StatArb signal parameters Defining StatArb trading parameters Quantifying and computing StatArb trading signals StatArb execution logic StatArb signal and strategy performance analysis Summary Managing the Risk of Algorithmic Strategies Differentiating between the types of risk and risk factors Risk of trading losses Regulation violation risks Spoofing Quote stuffing Banging the close Sources of risk Software implementation risk DevOps risk Market risk Quantifying the risk The severity of risk violations Differentiating the measures of risk Stop-loss Max drawdown Position limits Position holding time Variance of PnLs Sharpe ratio Maximum executions per period Maximum trade size Volume limits Making a risk management algorithm Realistically adjusting risk Summary  Section 4: Building a Trading System Building a Trading System in Python Understanding the trading system Gateways Order book management Strategy Order management system  Critical components Non-critical components Command and control Services Building a trading system in Python LiquidityProvider class Strategy class OrderManager class MarketSimulator class TestTradingSimulation class Designing a limit order book Summary Connecting to Trading Exchanges Making a trading system trade with exchanges Reviewing the Communication API Network basics Trading protocols FIX communication protocols Price updates Orders Receiving price updates Initiator code example Price updates Sending orders and receiving a market response Acceptor code example Market Data request handling Order Other trading APIs Summary Creating a Backtester in Python Learning how to build a backtester  In-sample versus out-of-sample data Paper trading (forward testing) Naive data storage HDF5 file Databases Relational databases Non-relational databases Learning how to choose the correct assumptions For-loop backtest systems Advantages Disadvantages Event-driven backtest systems Advantages Disadvantages Evaluating what the value of time is Backtesting the dual-moving average trading strategy For-loop backtester Event-based backtester Summary Section 5: Challenges in Algorithmic Trading Adapting to Market Participants and Conditions Strategy performance in backtester versus live markets Impact of backtester dislocations Signal validation Strategy validation Risk estimates Risk management system Choice of strategies for deployment Expected performance Causes of simulation dislocations Slippage Fees Operational issues Market data issues Latency variance Place-in-line estimates Market impact Tweaking backtesting and strategies in response to live trading Historical market data accuracy Measuring and modeling latencies Improving backtesting sophistication Adjusting expected performance for backtester bias Analytics on live trading strategies Continued profitability in algorithmic trading Profit decay in algorithmic trading strategies Signal decay due to lack of optimization Signal decay due to absence of leading participants Signal discovery by other participants Profit decay due to exit of losing participants Profit decay due to discovery by other participants Profit decay due to changes in underlying assumptions/relationships Seasonal profit decay Adapting to market conditions and changing participants Building a trading signals dictionary/database Optimizing trading signals Optimizing prediction models Optimizing trading strategy parameters Researching new trading signals Expanding to new trading strategies Portfolio optimization Uniform risk allocation PnL-based risk allocation PnL-sharpe-based risk allocation Markowitz allocation Regime Predictive allocation Incorporating technological advances Summary Final words Other Books You May Enjoy Leave a review - let other readers know what you think Preface In modern times, it is increasingly difficult to gain a significant competitive edge just by being faster than others, which means relying on sophisticated trading signals, predictive models, and strategies.
Most modern trading firms trade markets electronically almost 23 hours a day, and they have a large number of staff whose only job is to keep an eye on the automated algorithmic trading strategies that are deployed to live markets to ensure they are behaving as expected and no erroneous behavior goes uninvestigated. They are known as the Trading Desk, or TradeOps or DevOps. These people have a decent understanding of software development, trading rules, and exchange for provided risk monitoring interfaces. Often, when software implementation bugs end up going to live markets, they are the final line of defense, and it is their job to monitor the systems, detect issues, safely pause or stop the algorithms, and contact and resolve the issues that have emerged.
Puppet Essentials by Felix Frank
Thomas Dao has spent over two decades playing around with various Unix flavors as a Unix administrator, build and release engineer, and configuration manager. He is passionate about open source software and tools, so Puppet was something he naturally gravitated toward. Currently employed in the telecommunications industry as a configuration analyst, he also divides some of his time as a technical editor at devops.ninja. I would like to thank my lovely wife, whose patience with me while I'm glued to my monitor gives me the inspiration to pursue my passions, and my dog, Bento, who is always by my side, giving me company. Brian Moore is a senior product engineer, a father of two, and a quintessential hacker.
Table of Contents Preface Chapter 1: Writing Your First Manifests Getting started Introducing resources and properties Interpreting the output of the puppet apply command Dry-testing your manifest Adding control structures in manifests Using variables Variable types Controlling the order of evaluation Declaring dependencies Error propagation Avoiding circular dependencies Implementing resource interaction Examining the most notable resource types The user and group types The exec resource type The cron resource type The mount resource type Summary Chapter 2: The Master and Its Agents The Puppet master Setting up the master machine Creating the master manifest Inspecting the configuration settings Setting up the Puppet agent The agent's life cycle 1 7 8 10 11 12 13 14 14 16 17 20 21 22 25 26 27 29 29 30 31 31 32 33 35 35 38 Table of Contents Renewing an agent's certificate Running the agent from cron Performance considerations Switching to Phusion Passenger Using Passenger with Nginx Basic tuning Troubleshooting SSL issues Summary Chapter 3: A Peek Under the Hood – Facts, Types, and Providers Summarizing systems with Facter Accessing and using fact values Extending Facter with custom facts Simplifying things using external facts Goals of Facter Understanding the type system The resource type's life cycle on the agent side Substantiating the model with providers Providerless resource types Summarizing types and providers Putting it all together Summary Chapter 4: Modularizing Manifests with Classes and Defined Types Introducing classes and defined types Defining and declaring classes Creating and using defined types Understanding and leveraging the differences Structured design patterns Writing comprehensive classes Writing component classes Using defined types as resource wrappers Using defined types as resource multiplexers Using defined types as macros Exploiting array values using defined types Including classes from defined types Nesting definitions in classes Establishing relationships among containers Passing events between classes and defined types [ ii ] 40 41 42 43 45 46 47 48 49 50 52 53 55 57 57 58 59 61 61 62 64 65 66 66 67 69 71 71 73 74 76 77 78 81 82 83 83 Table of Contents Ordering containers Limitations Performance implications of container relationships Mitigating the limitations 86 86 89 90 Making classes more flexible through parameters Caveats of parameterized classes Preferring the include keyword Summary 92 92 93 94 The anchor pattern The contain function Chapter 5: Extending Your Puppet Infrastructure with Modules 90 91 95 An overview of Puppet's modules Parts of a module How the content of each module is structured Documentation in modules Maintaining environments Configuring environment locations Obtaining and installing modules Modules' best practices Putting everything in modules Avoiding generalization Testing your modules 96 96 97 98 99 100 101 102 102 103 104 Building a specific module Naming your module Making your module available to Puppet Implementing the basic module functionality Creating utilities for derived manifests 105 106 106 106 110 Safe testing with environments Adding configuration items Allowing customization Removing unwanted configuration items Dealing with complexity Enhancing the agent through plugins Replacing a defined type with a native type Enhancing Puppet's system knowledge through facts Refining the interface of your module through custom functions Making your module portable across platforms Finding helpful Forge modules Identifying modules' characteristics Summary [ iii ] 104 111 113 114 115 116 118 125 126 128 130 130 131 Table of Contents Chapter 6: Leveraging the Full Toolset of the Language 133 Chapter 7: Separating Data from Code Using Hiera 157 Templating dynamic configuration files Learning the template syntax Using templates in practice Avoiding performance bottlenecks from templates Creating virtual resources Realizing resources more flexibly using collectors Exporting resources to other agents Exporting and importing resources Configuring the master to store exported resources Exporting SSH host keys Managing hosts files locally Automating custom configuration items Simplifying the Nagios configuration Maintaining your central firewall Overriding resource parameters Making classes more flexible through inheritance Understanding class inheritance in Puppet Naming an inheriting class Making parameters safer through inheritance Saving redundancy using resource defaults Avoiding antipatterns Summary Understanding the need for separate data storage Consequences of defining data in the manifest Structuring configuration data in a hierarchy Configuring Hiera Storing Hiera data Choosing your backends Retrieving and using Hiera values in manifests Working with simple values Binding class parameter values automatically Handling hashes and arrays Converting resources to data Choosing between manifest and Hiera designs Using Hiera in different contexts A practical example Debugging Hiera lookups Summary [ iv ] 134 134 135 136 137 140 141 142 142 143 144 144 145 146 147 148 149 151 151 152 154 155 158 159 161 163 164 165 165 166 167 170 172 175 175 177 179 180 Table of Contents Chapter 8: Configuring Your Cloud Application with Puppet 181 Index 207 Typical scopes of Puppet Common data center use – roles and profiles Taking Puppet to the cloud Initializing agents in the cloud Using Puppet's cloud-provisioner module Building manifests for the cloud Mapping functionalities to nodes Choosing certificate names Creating a distributed catalog Composing arbitrary configuration files Handling instance deletions Preparing for autoscaling Managing certificates Limiting round trip times Ensuring successful provisioning Adding necessary relationships Testing the manifests Summary [v] 182 183 184 185 186 187 187 190 191 194 197 198 198 200 202 203 204 205 Preface The software industry is changing and so are its related fields. Old paradigms are slowly giving way to new roles and shifting views on what the different professions should bring to the table. The DevOps trend pervades evermore workflows. Developers set up and maintain their own environments, and operations raise automation to new levels and translate whole infrastructures to code. A steady stream of new technologies allows for more efficient organizational principles. One of these newcomers is Puppet.
What you have learned will most likely satisfy your immediate requirements. For information beyond these lessons, don't hesitate to look up the excellent online documentation at https://docs. puppetlabs.com/ or join the community and ask your questions on chat or in the mailing list. Thanks for reading, and have lots of fun with Puppet and its family of DevOps tools. [ 206 ] Index A agents initializing, in cloud 185 resources, exporting to 141 anchor pattern about 90 URL 91 antipatterns avoiding 154, 155 apt-get command 8 arrays 15 autorequire feature 125 autoscaling feature about 198 certificates, managing 198-200 round trip times, limiting 200-202 autosigning URL 200 autosigning script 198 B backends selecting 165 URL, for online documentation 165 beaker about 105 URL 105 before metaparameter 19, 21, 24 C classes about 66 component classes, writing 73, 74 comprehensive classes, writing 71, 72 creating, with parameters 92 declaring 66, 67 defining 66, 67 definitions, nesting 82 differentiating, with defined types 69, 70 include keyword, preferring 93 parameterized classes, consequences 92, 93 class inheritance 149 cloud agents, initializing in 185 manifests, building for 187 cloud-provisioner module using 186 collectors used, for realizing resources 140, 141 component classes writing 73, 74 composite design 71 comprehensive classes writing 71, 72 configuration data structuring, in hierarchy 161, 162 containers events, passing between classes and defined types 83-85 limitations 86-89 limitations, mitigating 90 ordering 86 relationships, establishing among 83 containers, limitations anchor pattern 90 contain function 91 control structures adding, in manifest 13, 14 creates parameter 28 cron resource type 29 custom attribute 191 custom facts about 53 Facter, extending with 53-55 custom functions about 96 used, for refining custom module interface 126-128 custom module building 105 enhancing, through facts 125 implementing 106-109 interface, refining through custom functions 126-128 making, portable across platforms 128, 129 naming 106 using 106 utilities, creating for derived manifests 110 custom types 117 D data resources, converting to 172-174 data, defining in manifest consequences 159, 160 defined types about 66 creating 67-69 differentiating, with classes 69, 70 used, for exploiting array values 78-81 using 67-69 using, as macros 77, 78 using, as resource multiplexers 76 using, as resource wrappers 74, 75 dependency 20 documentation, modules 98, 99 domain-specific language (DSL) 8 dynamic configuration files templating 134 dynamic scoping 154 E enabled property 10 ensure property 10 environment.conf file 100 environment locations configuring 100, 101 environments maintaining 99, 100 modules, installing 101, 102 modules, obtaining 101, 102 used, for testing modules 104, 105 evaluation order circular dependencies, avoiding 21, 22 controlling 16 dependencies, declaring 17-20 error propagation 20 events about 23 passing, between classes and defined types 83-85 exec resource type 27 external facts using 55, 56 External Node Classifiers (ENCs) 174 F Faces 186 Facter example 62 extending, with custom facts 53-55 goals 57 systems, summarizing with 50, 51 facts URL, for documentation 125 used, for enhancing custom module 125 fact values accessing 52, 53 using 52, 53 flexibility, providing to classes about 148 class inheritance 149 inheriting class, naming 151 parameters, making safer through inheritance 151 [ 208 ] Forge modules' characteristics, identifying 130 URL 130 used, for searching modules 130 fqdn_rand function 41 fully qualified domain name (FQDN) 52 G group resource type 26 H hashes 14 Hiera arrays, handling 170-172 class parameter values, binding 167-169 configuring 163 data, storing 164 hashes, handling 170-172 lookups, defining 179 practical example 177, 178 using, in different contexts 175, 176 values, retrieving 165 values, using in manifest 165 working with simple values 166, 167 hiera_array function 170 hiera_hash function 171 hierarchy configuration data, structuring in 161, 162 I immutability, variables 14 include keyword preferring 93 Infrastructure as a Service (IaaS) 184 Infrastructure as Code paradigm 105 inheriting class naming 151 installation, modules 101, 102 instances method 123 M manifest about 182 control structures, adding in 13, 14 dry-testing 12 structure 9 manifest, and Hiera designs selecting between 175 manifest, building for cloud about 187 arbitrary configuration files, composing 194-196 certificate names, selecting 190, 191 distributed catalog, creating 191-194 functionality, mapping to nodes 187-189 instance deletions, handling 197, 198 metaparameters 18 model substantiating, with providers 59, 60 modules about 96 agent, enhancing through plugins 116, 117 best practices 102 content structure 97, 98 documentation 98, 99 generalization, avoiding 103 identifying, in Forge 130 important parts 96 installing 101, 102 manifest files, gathering 102, 103 obtaining 101, 102 searching, in Forge 130 testing 104 testing, with environments 104, 105 URL, for publishing 98 monolithic implementation 71 mount resource type 29, 30 N Nginx about 45 Phusion Passenger, using with 45, 46 nodes file 100 Notice keyword 20 [ 209 ] O operatingsystemrelease fact 53 output interpreting, of puppet apply command 11, 12 P Proudly sourced and uploaded by [StormRG] Kickass Torrents | TPB | ExtraTorrent | h33t parameterized classes consequences 92, 93 parameters versus properties 10 parser functions 96 performance bottlenecks avoiding, from templates 136 performance considerations about 42 basic tuning 46 Passenger, using with Nginx 45 switching, to Phusion Passenger 43, 44 Phusion Passenger switching to 43, 44 URL, for installation instructions 45 using, with Nginx 45, 46 Platform as a Service (PaaS) 184 plugins about 116 custom types, creating 118 custom types, naming 118 management commands, declaring 121 provider, adding 121 provider, allowing to prefetch existing resources 123, 124 provider functionality, implementing 122, 123 resource names, using 120 resource type interface, creating 119 sensible parameter hooks, designing 120 types, making robust 125 used, for enhancing modules agent 116, 117 plugins, types custom facts 116 parser functions 116 providers 116 types 116 processorcount fact 52 properties about 10 versus parameters 10 providerless resource types 61 provider parameter 10 providers model, substantiating with 59, 60 summarizing 61 Puppet about 182 installing 8 modules 96 typical scopes 182 URL 182 Puppet agent certificate, renewing 40 life cycle 38, 39 running, from cron 41 setting up 35-37 puppet apply command about 9, 31 output, interpreting of 11, 12 PuppetBoard 186 Puppet Dashboard 186 Puppet Explorer 186 Puppet Labs URL 8 URL, for advanced approaches 43 URL, for core resource types 61 URL, for style guide 52 URL, for system installation information 32 URL, for Troubleshooting section 47 puppetlabs-strings module URL 99 Puppet master about 31 configuration settings, inspecting 35 master machine, setting up 32 master manifest, creating 33, 34 tasks 32 puppetmaster system service 33 puppet module install command 101 Puppet support, for SSL CSR attributes URL 199 [ 210 ] Puppet, taking to cloud about 184 agents, initializing 185 cloud-provisioner module, using 186 Puppet toolchain 46 rspec-puppet module about 105 URL 105 R separate data storage need for 158 singletons 135 site manifest 33 SSL troubleshooting 47, 48 stdlib module 101 strings 15 subscribe metaparameter 23 successful provisioning, ensuring about 202 manifests, testing 204, 205 necessary relationships, adding 203 systems summarizing, with Facter 50, 51 S realize function 138, 139 redundancy saving, resource defaults used 152, 153 relationships, containers performance implications 89 require metaparameter 19 resource chaining 17 resource defaults used, for saving redundancy 152, 153 resource interaction implementing 22-24 resource parameters overriding 147, 148 resources about 10 converting, to data 172-174 exporting 142 exporting, to agents 141 importing 142 realizing, collectors used 140, 141 resources, exporting about 141 central firewall, maintaining 146 custom configuration, automating 144 hosts files, managing 144 master configuration, for storing exported resources 142 Nagios configuration, simplifying 145, 146 SSH host keys, exporting 143 resource type life cycle, agent side 58, 59 resource types cron 29 examining 25, 26 exec 27, 28 group 26 mount 29, 30 user 26 revocation 39 Roles and Profiles pattern 183 T templates performance bottlenecks, avoiding from 136 using 135, 136 template syntax learning 134, 135 transaction 57 Trusted Facts 189 types about 117 summarizing 61 type system 57 typical scopes, Puppet about 182 profiles 183, 184 roles 183, 184 U user resource type 26 utilities, custom module complexity, dealing 115, 116 configuration items, adding 111, 112 creating, for derived manifests 110 [ 211 ] customization, allowing 113 unwanted configuration items, removing 114, 115 W Warning keyword 20 V Y Vagrant 182 variables using 14 variable types about 14 arrays 15 hashes 14 strings 15 virtual resources creating 137, 138 yum command 8 [ 212 ] Thank you for buying Puppet Essentials About Packt Publishing Packt, pronounced 'packed', published its first book "Mastering phpMyAdmin for Effective MySQL Management" in April 2004 and subsequently continued to specialize in publishing highly focused books on specific technologies and solutions.
Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski, Adam Stubblefield
anti-pattern, barriers to entry, bash_history, business continuity plan, business process, Cass Sunstein, cloud computing, continuous integration, correlation does not imply causation, create, read, update, delete, cryptocurrency, cyber-physical system, database schema, Debian, defense in depth, DevOps, Edward Snowden, fault tolerance, fear of failure, general-purpose programming language, Google Chrome, Internet of things, Kubernetes, load shedding, margin call, microservices, MITM: man-in-the-middle, performance metric, pull request, ransomware, revision control, Richard Thaler, risk tolerance, self-driving car, Skype, slashdot, software as a service, source of truth, Stuxnet, Turing test, undersea cable, uranium enrichment, Valgrind, web application, Y2K, zero day
For many years, my colleagues and I have argued that security should be a first-class and embedded quality of software. I believe that embracing an SRE-inspired approach is a logical step in that direction. Since arriving at Google, I’ve learned more about how the SRE model was established here, how SRE implements DevOps philosophies, and how SRE and DevOps have evolved. Meanwhile, I’ve been translating my IT security experience in the financial services industry to the technical and programmatic security capabilities at Google. These two sectors are not unrelated, but each has its own history worth understanding. At the same time, enterprises are at a critical point where cloud computing, various forms of machine learning, and a complicated cybersecurity landscape are together determining where an increasingly digital world is going, how quickly it will get there, and what risks are involved.
Ever since I began working in the tech industry, across organizations of varying sizes, I’ve seen people struggling with the question of how security should be organized: Should it be centralized or federated? Independent or embedded? Operational or consultative? Technical or governing? The list goes on…. When the SRE model, and SRE-like versions of DevOps, became popular, I noticed that the problem space SRE tackles exhibits similar dynamics to security problems. Some organizations have combined these two disciplines into an approach called “DevSecOps.” Both SRE and security have strong dependencies on classic software engineering teams. Yet both differ from classic software engineering teams in fundamental ways: Site Reliability Engineers (SREs) and security engineers tend to break and fix, as well as build.
In my previous roles, I looked for a more formal exploration of these questions; I hope that a variety of teams inside and outside of security organizations find this discussion useful as approaches and tools evolve. This project has reinforced my belief that the topics it covers are worth discussing and promoting in the industry—particularly as more organizations adopt DevOps, DevSecOps, SRE, and hybrid cloud architectures along with their associated operating models. At a minimum, this book is another step in the evolution and enhancement of system and data security in an increasingly digital world. Royal Hansen, Vice President, Security Engineering Foreword by Michael Wildpaner At their core, both Site Reliability Engineering and Security Engineering are concerned with keeping a system usable.
Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst
algorithmic trading, bioinformatics, business intelligence, business process, call centre, cloud computing, create, read, update, delete, data acquisition, DevOps, fault tolerance, linked data, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, Watson beat the top human players on Jeopardy!, web application
Training is a prerequisite for understanding the paradigm shift that Big Data offers. Without that insider knowledge, it becomes difficult to explain and communicate the value of data, especially when the data are public in nature. Next on the list is the integration of development and operations teams (known as DevOps), the people most likely to deal with the burdens of storing and transforming the data into something usable. Much of the process of moving forward will lie with the business executives and decision makers, who will also need to be brought up to speed on the value of Big Data. The advantages must be explained in a fashion that makes sense to the business operations, which in turn means that IT pros are going to have to do some legwork.
See Business intelligence (BI) Big Data and Big Data analytics analysis categories application platforms best practices business case development challenges classifications components defined evolution of examples of 4Vs of goal setting introduction investment in path to phases of potential of privacy issues processing role of security (See Security) sources of storage team development technologies (See Technologies) value of visualizations Big Science BigSheets Bigtable Bioinformatics Biomedical industry Blekko Business analytics (BA) Business case best practices data collection and storage options elements of introduction Business intelligence (BI) as Big Data analytics foundation Big Data analytics team incorporation Big Data impact defined extract, transform, and load (ETL) information technology and in-memory processing limitations of marketing campaigns risk analysis storage capacity issues unstructured data visualizations Business leads Business logic Business objectives Business rules C Capacity of storage systems Cassandra Census data CERN Citi Classification of data Cleaning Click-stream data Cloud computing Cloudera Combs, Nick Commodity hardware Common Crawl Corpus Communication Competition Compliance Computer security officers (CSOs) Consulting firms Core capabilities, data analytics team Costs Counterintelligence mind-set CRUD (create, retrieve, update, delete) applications Cryptographic keys Culture, corporate Customer needs Cutting, Doug D Data defined growth in volume of value of See also Big Data and Big Data analytics Data analysis categories challenges complexity of as critical skill for team members data accuracy evolution of importance of process technologies Database design Data classification Data discovery Data extraction Data integration technologies value creation Data interpretation Data manipulation Data migration Data mining components as critical skill for team members defined examples methods technologies Data modeling Data protection. See Security Data retention Data scientists Data sources growth of identification of importation of data into platform public information Data visualization Data warehouses DevOPs Discovery of data Disk cloning Disruptive technologies Distributed file systems. See also Hadoop Dynamo E e-commerce Economist e-discovery Education 80Legs Electronic medical records compliance data errors data extraction privacy issues trends Electronic transactions EMC Corporation Employees data analytics team membership monitoring of training Encryption Entertainment industry Entity extraction Entity relation extraction Errors Event-driven data distribution Evidence-based medicine Evolution of Big Data algorithms current issues future developments modern era origins of Expectations Expediency-accuracy tradeoff External data Extract, transform, and load (ETL) Extractiv F Facebook Filters Financial controllers Financial sector Financial transactions Flexibility of storage systems 4Vs of Big Data G Gartner General Electric (GE) Gephi Goal setting Google Google Books Ngrams Google Refine Governance Government agencies Grep H Hadoop advantages and disadvantages of design and function of event-processing framework future origins of vendor support Yahoo’s use HANA HBase HDFS Health care Big Data analytics opportunities Big Data trends compliance evolution of Big Data See also Electronic medical records Hibernate High-value opportunities History.
Learning Puppet 4: A Guide to Configuration Management and Automation by Jo Rhett
Most important of all, this book will cover how to scale your Puppet installation to handle thousands of nodes. You’ll learn multiple strategies for handling diverse and heterogenous environments, and reasons why each of these approaches may be appropriate or not for your needs. Who this book is for This book is primarily aimed at System Administrators and Operations or DevOps Engineers. If you are responsible for development or production nodes, this book will provide you with immediately useful tools to make your job easier than ever before. If you run a high-uptime production environment, you’re going to learn how Puppet ix www.it-ebooks.info can enforce your existing standards throughout the implementation.
I owe a drink and many thanks to the many people who provided input and feedback on the book during the writing process, including but definitely not limited to the technical reviewers: • Chris Barbour, Taos Mountain And finally, I’d like to thank my O’Reilly editor, Brian Anderson, who gave me excel‐ lent guidance on the book and was a pleasure to work with. Jo Rhett, DevOps Architect, Net Consonance xii | Preface www.it-ebooks.info Introduction What is Puppet? Puppet brings computer systems into compliance with a policy you design. Puppet manages configuration data on these systems, including users, packages, processes, services; any component of the system you can define.
Puppet 3 Cookbook by John Arundel
Herman Carlos Nilton Araújo Corrêa Daniele Sluijters Dao Thomas Acquisition Editor Kartikey Pandey Lead Technical Editor Madhuja Chaudhari Technical Editors Anita Nayak Larissa Pinto Indexers Hemangini Bari Monica Ajmera Mehta Graphics Ronak Dhruv Production Coordinator Kyle Albuquerque Cover Work Kyle Albuquerque About the Author John Arundel is a devops consultant, which means he solves difficult problems for a living. (He doesn't get called in for easy problems.) He has worked in the tech industry for 20 years, and during that time has done wrong (or seen done wrong) almost everything that you can do wrong with computers. That comprehensive knowledge of what not to do, he feels, is one of his greatest assets as a consultant.
You'll find links and references to more information on every topic, so that you can explore further for yourself. Whatever your level of Puppet experience, there's something for you, from simple workflow tips to advanced, high-performance Puppet architectures. I've tried hard to write the kind of book that would be useful to me in my day-to-day work as a devops consultant. I hope it will inspire you to learn, to experiment, and to come up with your own new ideas in this exciting and fast-moving field. Preface What this book covers You'll find the following chapters in this book: Chapter 1, Puppet Infrastructure, shows how to set up Puppet for the first time, including instructions on installing Puppet, creating your first manifests, using version control with Puppet, building a distributed Puppet architecture based on Git, writing a script to apply Puppet manifests, running Puppet automatically, using Rake to bootstrap machines and deploy changes, and using Git hooks to automatically syntax-check your manifests.
The New Kingmakers by Stephen O'Grady
AltaVista, Amazon Web Services, barriers to entry, cloud computing, correlation does not imply causation, crowdsourcing, David Heinemeier Hansson, DevOps, Jeff Bezos, Khan Academy, Kickstarter, Marc Andreessen, Mark Zuckerberg, Netflix Prize, Paul Graham, Ruby on Rails, Silicon Valley, Skype, software as a service, software is eating the world, Steve Ballmer, Steve Jobs, The future is already here, Tim Cook: Apple, Y Combinator
Internally, Netflix oriented its business around its developers. As cloud architect Adrian Cockcroft put it: The typical environment you have for developers is this image that they can write code that works on a perfect machine that will always work, and operations will figure out how to create this perfect machine for them. That’s the traditional dev-ops, developer versus operations contract. But then of course machines aren’t perfect and code isn’t perfect, so everything breaks and everyone complains to each other. So we got rid of the operations piece of that and just have the developers, so you can’t depend on everybody and you have to assume that all the other developers are writing broken code that isn’t properly deployed.
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn
A Pattern Language, Airbnb, algorithmic trading, automated trading system, business intelligence, business process, combinatorial explosion, computer vision, continuous integration, Covid-19, COVID-19, DevOps, discrete time, en.wikipedia.org, iterative process, Kubernetes, microservices, mobile money, natural language processing, Netflix Prize, optical character recognition, pattern recognition, performance metric, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, speech recognition, statistical model, the payments system, web application
The full code can be found in our GitHub repository. We strongly encourage you to peruse the code as you read the pattern description. Machine Learning Terminology Because machine learning practitioners today may have different areas of primary expertise—software engineering, data analysis, DevOps, or statistics—there can be subtle differences in the way that different practitioners use certain terms. In this section, we define terminology that we use throughout the book. Models and Frameworks At its core, machine learning is a process of building models that learn from data. This is in contrast to traditional programming where we write explicit rules that tell programs how to behave.
Using a machine with more powerful GPUs, for example, typically helps to improve the performance of deep learning models. Choosing a machine with multiple accelerators and/or threads helps improve the number of requests per second. Using an autoscaling cluster of machines can help lower cost on spiky workloads. These kinds of tweaks are often done by the ML/DevOps team; some are ML-specific, some are not. Language-neutral Every modern programming language can speak REST, and a discovery service is provided to autogenerate the necessary HTTP stubs. Thus, Python clients can invoke the REST API as follows. Note that there is nothing framework specific in the code below.
Powerful ecosystem Because web application frameworks are so widely used, there is a lot of tooling available to measure, monitor, and manage web applications. If we deploy the ML model to a web application framework, the model can be monitored and throttled using tools that software reliability engineers (SREs), IT administrators, and DevOps personnel are familiar with. They do not have to know anything about machine learning. Similarly, your business development colleagues know how to meter and monetize web applications using API gateways. They can carry over that knowledge and apply it to metering and monetizing machine learning models.
Ansible: Up and Running: Automating Configuration Management and Deployment the Easy Way by Lorin Hochstein
Amazon Web Services, cloud computing, continuous integration, Debian, DevOps, domain-specific language, don't repeat yourself, general-purpose programming language, Infrastructure as a Service, job automation, MITM: man-in-the-middle, pull request, side project, smart transportation, web application
I didn’t know what else to recommend, so I decided to write something to fill the gap — and here it is. Alas, this book comes too late for him, but I hope you’ll find it useful. Who Should Read This Book This book is for anyone who needs to deal with Linux or Unix-like servers. If you’ve ever used the terms systems administration, operations, deployment, configuration management, or (sigh) DevOps, then you should find some value here. Although I have managed my share of Linux servers, my background is in software engineering. This means that the examples in this book tend toward the deployment end of the spectrum, although I’m in agreement with Andrew Clay Shafer ([webops]) that the distinction between deployment and configuration is unresolved.
Declarative A type of programming language where the programmer describes the desired output, not the procedure for how to compute the output. Ansible’s playbooks are declarative. SQL is another example of a declarative language. Contrast with procedural languages, such as Java and Python. Deployment The process of bringing software up onto a live system. DevOps IT buzzword that gained popularity in the mid-2010s. Dry run See Check mode. DSL Domain-specific language. In systems that use DSLs, the user interacts with the systems by writing text files in the domain-specific language and then runs those files through the system. DSLs are not as powerful as general-purpose programming language, but (if designed well) they are easier to read and write than general-purpose programming language.
Brave New Work: Are You Ready to Reinvent Your Organization? by Aaron Dignan
"side hustle", activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, Black Swan, blockchain, Buckminster Fuller, Burning Man, butterfly effect, cashless society, Clayton Christensen, clean water, cognitive bias, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, David Heinemeier Hansson, deliberate practice, DevOps, disruptive innovation, don't be evil, Elon Musk, endowment effect, Ethereum, ethereum blockchain, Frederick Winslow Taylor, future of work, gender pay gap, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, hiring and firing, hive mind, impact investing, income inequality, information asymmetry, Internet of things, Jeff Bezos, job satisfaction, Kevin Kelly, Kickstarter, Lean Startup, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, race to the bottom, remote working, Richard Thaler, shareholder value, Silicon Valley, six sigma, smart contracts, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, source of truth, Stanford marshmallow experiment, Steve Jobs, TaskRabbit, The future is already here, the High Line, too big to fail, Toyota Production System, Tragedy of the Commons, uber lyft, universal basic income, WeWork, Y Combinator, zero-sum game
We need to ensure that we’ve thought about, and made space for, evolution at all levels. Thought Starters Innovation Everywhere. Because of our obsession with efficiency, most organizations like to keep innovation and operations separate. They build things and they run things. But within the last decade, DevOps has emerged as a software engineering culture and practice that has completely upended that notion. As teams release software faster and more frequently, the interaction between development, quality assurance, and operations was stressed. Developers want change. Testers want risk reduction. And operators want stability.
(Catmull), 191 criticality, 193, 216–18 Crunchbase, 253 cultural differences, 258 culture, company, 180–81, 190 hiring and, 142–43 Dalio, Ray, 152, 153–54 David, Joshua, 188 de Blok, Jos, 34–36, 105, 144 debt, 27 organizational, 27–29, 91 decentralized autonomous organizations (DAOs), 250–51 Deci, Edward, 41–42 decision making, 67, 121, 132, 152 advice process in, 70, 72–73 consent in, 70–73, 195 decision stack in, 72–73 discipline about, 69 emotion in, 174–75 information and, 136 Integrative Decision Making, 71–73 waterline principle and, 69–70, 72 defaults vs. standards, 106–7 degradation, graceful, 29–34 Deming, W. Edwards, 53, 87, 165 DeSteno, David, 236 DevOps, 104 Doerr, John, 87 Donovan, William J., 6–7 Dunbar’s number, 197 Dweck, Carol, 154 dynamic networks, 77–78 dynamic teaming, 81 economy, economics, 27, 246–47, 248 Edmondson, Amy, 221 education, 257–58 Einstein, Albert, 247 email, 131, 133–35 Emergent Inc., 183–85, 195–96, 199, 206, 212, 222, 234–35, 237–38, 239 Emerson, Harrington, 20, 25 emotions, 174–75 empowerment, 136 enabling constraints, 46 Endenburg, Gerard, 70–71 Enspiral, 99–100, 133 Ernst & Young, 35 essential intent, 62 Essentialism (McKeown), 62 Etsy, 157 eudaemonic purpose, 59 even over statements, 88–90 Everlane, 130–31, 259 Everyone Culture, An (Kegan and Lahey), 153 Evolutionary Organizations, 13, 14, 16, 21, 34–37, 48, 53, 244, 248 Complexity Conscious mindset in, see Complexity Conscious mindset list of, 267–70 operating system for, see Operating System Canvas People Positive mindset in, see People Positive mindset exaptation, 103, 104 experiments, conducting, 213–16 Facebook, 62–63, 84, 88, 235, 252, 268 facilitators, 122–23 failure, 68, 74 FAVI, 16, 37–38, 42–43 Fayol, Henri, 24 fear, 141, 222 Federal Reserve System, 252 Fifth Discipline, The (Senge), 153, 202 Firms of Endearment (Sisodia, Sheth, and Wolfe), 60 Fitzgerald, F.
Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success by Sean Ellis, Morgan Brown
Airbnb, Amazon Web Services, barriers to entry, Ben Horowitz, bounce rate, business intelligence, business process, correlation does not imply causation, crowdsourcing, DevOps, disruptive innovation, Elon Musk, game design, Google Glasses, Internet of things, inventory management, iterative process, Jeff Bezos, Khan Academy, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, market design, minimum viable product, Network effects, Paul Graham, Peter Thiel, Ponzi scheme, recommendation engine, ride hailing / ride sharing, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, software as a service, Steve Jobs, subscription business, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, working poor, Y Combinator, young professional
If you’re just starting to form a growth team, then bringing over one or two individuals from different departments to get the team started may be a good way to get the ball rolling, and the size of the team can grow over time. In some cases, as the process is learned, additional teams can be formed. At IBM, for example, a growth team was formed to work specifically on growing the adoption of its Bluemix DevOps product, a software development package for engineers, by assigning five engineers and five other staff, from business operations and marketing, to make up the team. At Inman, Morgan comprised his growth team of a data scientist, three marketers, and their Web developer to start the growth hacking process.
If you are the head of a small team and want to give the process a try, it’s best to set your team up for success by getting buy-in first, even if it is just with a few peers and a supervisor. You will make mistakes, experiments will fail, webpages will break—it’s an inevitable part of the experimentation process. Having the support of higher-ups can alleviate the blowback from such eventualities. Lauren Schaefer, who was the growth hacking lead on the Bluemix DevOps team at IBM, launched a test early in the process of experimenting with growth hacking that crippled the product’s home page. But her boss was a supporter of the effort, and she and her growth team got past that stumble.14 It’s just as important that the growth team machine not be put into drive too early.
Roads and Bridges by Nadia Eghbal
AGPL, Airbnb, Amazon Web Services, barriers to entry, Benevolent Dictator For Life (BDFL), corporate social responsibility, crowdsourcing, cryptocurrency, David Heinemeier Hansson, Debian, DevOps, en.wikipedia.org, Firefox, GnuPG, Guido van Rossum, Khan Academy, Kickstarter, Marc Andreessen, market design, Network effects, platform as a service, pull request, Richard Stallman, Ruby on Rails, side project, Silicon Valley, Skype, software is eating the world, Tragedy of the Commons, Y Combinator
The Art of Monitoring by James Turnbull
Amazon Web Services, anti-pattern, cloud computing, continuous integration, correlation does not imply causation, Debian, DevOps, domain-specific language, failed state, functional programming, Kickstarter, Kubernetes, microservices, performance metric, pull request, Ruby on Rails, software as a service, source of truth, web application, WebSocket
Instrument schemas Time and the observer effect Metrics Application metrics Business metrics Monitoring patterns, or where to put your metrics The utility pattern The external pattern Building metrics into a sample application Logging Adding our own structured log entries Adding structured logging to our sample application Working with your existing logs Health checks, endpoints, and external monitoring Checking an internal endpoint Deployments Adding deployment notifications to our sample application Working with our deployment events Tracing Summary Notifications Our current notifications Updating expired event configuration Upgrading our email notifications Formatting the email subject Formatting the email body Adding graphs to notifications Defining our data source Defining our query parameters Defining our graph panels and rows Rendering the dashboard Adding our dashboard to the Riemann notification Some sample scripted dashboards Other context Adding Slack as a destination Adding PagerDuty as a destination Maintenance and downtime Learning from your notifications Other alerting tools Summary Monitoring Tornado: a capstone The Tornado application Application architecture Monitoring strategy Tagging our Tornado events Monitoring Tornado — Web tier Monitoring HAProxy Monitoring Nginx Addressing the Web tier monitoring concerns Setting up the Tornado checks in Riemann The webtier function Adding Tornado checks to Riemann Summary Monitoring Tornado: Application Tier Monitoring the Application tier JVM Configuring collectd for JMX Collecting our Application tier JVM logs Monitoring the Tornado API application Addressing the Tornado Application tier monitoring concerns Summary Monitoring Tornado: Data tier Monitoring the Data tier MySQL server Using MySQL data for metrics Query timing Monitoring the Data tier's Redis server Addressing the Tornado Data tier monitoring concerns The Tornado dashboard Expanding monitoring beyond Tornado Summary An Introduction to Clojure and Functional Programming A brief introduction to Clojure Installing Leiningen Clojure syntax and types Clojure functions Lists Vectors Sets Maps Strings Creating our own functions Creating variables Creating named functions Learning more Clojure Cover Table of contents The Art of Monitoring Who is this book for? This book is for engineers, developers, sysadmins, operations staff, and those with an interest in monitoring and DevOps. It provides a simple, hands-on introduction to the art of modern application and infrastructure monitoring. There is an expectation that the reader has basic Unix/Linux skills and is familiar with the command line, editing files, installing packages, managing services, and basic networking. Credits and Acknowledgments Ruth Brown, who continues to be the most amazing person in my life.
Monitoring provides data that measures quality or service and provides data that helps IT justify budgets, costs, or new projects. Much of this data is provided directly to business units, application teams, and other relevant parties via dashboards and reports. This is typical in web-centric organizations and many mature startups. This type of approach is also commonly espoused by organizations that have adopted a DevOps culture/methodology. Monitoring will still largely be managed by an operations team, but responsibility for ensuring new applications and services are monitored may be delegated to application developers. Products will not be considered feature complete or ready for deployment without monitoring.
Test-Driven Development With Python by Harry J. W. Percival
Try and leave yourself in a position where you can freely make changes to the design and layout, without having to go back and adjust tests all the time. 130 | Chapter 7: Prettification: Layout and Styling, and What to Test About It www.it-ebooks.info CHAPTER 8 Testing Deployment Using a Staging Site Is all fun and game until you are need of put it in production. — Devops Borat It’s time to deploy the first version of our site and make it public. They say that if you wait until you feel ready to ship, then you’ve waited too long. Is our site usable? Is it better than nothing? Can we make lists on it? Yes, yes, yes. No, you can’t log in yet. No, you can’t mark tasks as completed.
This is one of the chapters I’m most pleased with, and it’s one that people often write to me saying they were really glad they stuck through it. If you’ve never done a server deployment before, it will demystify a whole world for you, and there’s nothing like the feeling of seeing your site live on the actual Internet. Give it a buzzword name like “DevOps” if that’s what it takes to convince you it’s worth it. 131 www.it-ebooks.info Why not ping me a note once your site is live on the web, and send me the URL? It always gives me a warm and fuzzy feeling … obeythe email@example.com. TDD and the Danger Areas of Deployment Deploying a site to a live web server can be a tricky topic.
Vagrant: Up and Running by Mitchell Hashimoto
about, Preface, An Introduction to Vagrant alternatives to, Alternatives to Vagrant setting up, Setting Up Vagrant–Conflicting RubyGems Installation common mistakes, Common Mistakes installation, Installing Vagrant–Linux VirtualBox, Installing VirtualBox Vagrantfile about, The Vagrantfile defaults, Setting Vagrantfile Defaults VAGRANT_CWD, VAGRANT_CWD VAGRANT_HOME, VAGRANT_HOME VAGRANT_LOG, VAGRANT_LOG VAGRANT_NO_PLUGINS, VAGRANT_NO_PLUGINS VAGRANT_VAGRANTFILE, VAGRANT_VAGRANTFILE validation, plug-in configuration, Validation version 1 plug-ins, Plug-In Definition version control, Up versions, Installing Vagrant virtual machine, plug-in custom commands, Working with the Virtual Machine–Parsing Command-Line Options VirtualBox export, Box Format installation, Installing VirtualBox installing guest additions, Installing VirtualBox Guest Additions machine, Creating the VirtualBox Machine using Vagrant without, Using Vagrant Without VirtualBox virtualization, Preface, Plain Desktop Virtualization W Windows environmental variable, Troubleshooting and Debugging installing Vagrant, Windows working directory, Hiera Data About the Author Mitchell Hashimoto is a passionate engineer, professional speaker, and entrepreneur. Mitchell has been creating and contributing to open source software for almost a decade. He has spoken at dozens of conferences about his work, such as VelocityConf, OSCON, FOSDEM, and more. Mitchell is the founder of HashiCorp, a company whose goal is to make the best DevOps tools in the world, including Vagrant. Prior to HashiCorp, Mitchell spent five years as a web developer and another four as an operations engineer. Colophon The animal on the cover of Vagrant: Up and Running is a blue rock pigeon (Columba livia). The cover image is from Wood’s Animate Creations.
Architecting For Scale by Lee Atchison
Amazon Web Services, business process, cloud computing, continuous integration, DevOps, Internet of things, microservices, platform as a service, risk tolerance, software as a service, web application
Once you’ve mastered these skills, your applications will be able to reliably handle huge quantities of traffic as well as huge variability in traffic without affecting the quality your customers expect. Who Should Read This Book This book is intended for software engineers, architects, engineering managers, and directors who build and operate large-scale applications and systems. If you manage software developers, system reliability engineers, or DevOps engineers, or you run an organization that contains large-scale applications and systems, the suggestions and guidance provided in this book will help you make your applications run smoother and more reliably. If your application started small and has seen incredible growth (and is now suffering from some of the growing pains associated with that growth), you might be suffering from reduced reliability and reduced availability.
Army of None: Autonomous Weapons and the Future of War by Paul Scharre
active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day
id=100706&ver=0. 201 speeds measured in microseconds: Michael Lewis, Flash Boys: A Wall Street Revolt (New York: W. W. Norton, 2015), 63, 69, 74, 81. 201 shortest route for their cables: Ibid, 62–63. 201 optimizing every part of their hardware for speed: Ibid, 63–64. 201 test them against actual stock market data: D7, “Knightmare: A DevOps Cautionary Tale,” Doug Seven, April 17, 2014, https://dougseven.com/2014/04/17/knightmare-a-devops-cautionary-tale/. 202 “The Science of Trading, the Standard of Trust”: Jeff Cox, “ ‘Knight-Mare’: Trading Glitches May Just Get Worse,” August 2, 2012, http://www.cnbc.com/id/48464725. 202 Knight’s trading system began flooding the market: “Knight Shows How to Lose $440 Million in 30 Minutes,” Bloomberg.com, August 2, 2012, https://www.bloomberg.com/news/articles/2012-08-02/knight-shows-how-to-lose-440-million-in-30-minutes. 202 neglected to install a “kill switch”: D7, “Knightmare.” 202 executed 4 million trades: “How the Robots Lost: High-Frequency Trading’s Rise and Fall,” Bloomberg.com, June 7, 2013, https://www.bloomberg.com/news/articles/2013-06-06/how-the-robots-lost-high-frequency-tradings-rise-and-fall. 202 Knight was bankrupt: D7, “Knightmare.” 202 “Knightmare on Wall Street”: For a theory on what happened, see Nanex Research, “03-Aug-2012—The Knightmare Explained,” http://www.nanex.net/aqck2/3525.html. 203 Waddell & Reed: Waddell & Reed was not named in the official SEC and CFTC report, which referred only to a “large fundamental trader (a mutual fund complex).”
Learning Ansible 2 - Second Edition by Fabio Alessandro Locati
Amazon Web Services, anti-pattern, cloud computing, continuous integration, Debian, DevOps, don't repeat yourself, Infrastructure as a Service, inventory management, Kickstarter, revision control, source of truth, web application
Since Ansible is an open source project, I thank all companies that decided to invest into it as well as all people that decided to volunteer their time to the project. About the Reviewer Tim Rupp has been working in various fields of computing for the last 10 years. He has held positions in computer security, software engineering, and, most recently, in the fields of cloud computing and DevOps. He was first introduced to Ansible while at Rackspace. As part of the cloud engineering team, he made extensive use of the tool to deploy new capacity for the Rackspace public cloud. Since then, he has contributed patches, provided support for, and presented on Ansible topics at local meetups. Tim is currently a senior software engineer at F5 Networks, where he works on data plane programmability.
Learning Flask Framework by Matt Copperwaite, Charles Leifer
ISBN 978-1-78398-336-0 www.packtpub.com www.allitebooks.com Credits Authors Project Coordinator Matt Copperwaite Shipra Chawhan Charles Leifer Proofreaders Stephen Copestake Reviewers Abhishek Gahlot Safis Editing Burhan Khalid Indexer Commissioning Editor Mariammal Chettiyar Ashwin Nair Production Coordinator Acquisition Editor Conidon Miranda Subho Gupta Cover Work Content Development Editor Conidon Miranda Mamata Walkar Technical Editors Siddhesh Ghadi Siddhesh Patil Copy Editor Sonia Mathur www.allitebooks.com About the Authors Matt Copperwaite graduated from the University of Plymouth in 2008 with a bachelor of science (Hons) degree in computer systems and networks. Since then, he has worked in various private and public sectors in the UK. Matt is currently working as a Python software developer and DevOps engineer for the UK Government, focusing mainly on Django. However, his first love is Flask, with which he has built several products under the General Public License (GPL). Matt is also a trustee of South London Makerspace, a hackerspace community in South London; a cohost of The Dick Turpin Road Show, a podcast for free and open source software; and LUG Master of Greater London Linux User Group.
Clean Agile: Back to Basics by Robert C. Martin
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, c2.com, continuous integration, DevOps, disinformation, double entry bookkeeping, en.wikipedia.org, failed state, Frederick Winslow Taylor, index card, iterative process, Kubernetes, loose coupling, microservices, remote working, revision control, Turing machine
I was careful to isolate each of these concepts into their own modules and to use those names exclusively throughout the application. Those names were my Ubiquitous Language. The Ubiquitous Language is used in all parts of the project. The business uses it. The developers use it. QA uses it. Ops/Devops use it. Even the customers use those parts of it that are appropriate. It supports the business case, the requirements, the design, the architecture, and the acceptance tests. It is a thread of consistency that interconnects the entire project during every phase of its lifecycle.4 4. “It’s an energy field created by all living things.
The AI-First Company by Ash Fontana
23andMe, Amazon Mechanical Turk, Amazon Web Services, autonomous vehicles, barriers to entry, blockchain, business intelligence, business process, business process outsourcing, call centre, chief data officer, Clayton Christensen, cloud computing, combinatorial explosion, computer vision, crowdsourcing, data acquisition, DevOps, en.wikipedia.org, independent contractor, industrial robot, inventory management, John Conway, knowledge economy, Kubernetes, Lean Startup, minimum viable product, natural language processing, Network effects, optical character recognition, Pareto efficiency, performance metric, price discrimination, recommendation engine, Ronald Coase, software as a service, source of truth, speech recognition, the scientific method, transaction costs, yield management
You don’t need to get this whole loop running in order to get started; you can be successful with just part of it in motion. This chapter aims to increase your awareness of what could go wrong, and when, so that you can stay one step ahead of potential problems. Here we’ll relate model management to concepts familiar to those in the technology industry such as agile development, DevOps, and statistical process control (SPC), while also introducing novel ideas specific to managing intelligent systems. We have two diagrams to guide you through the two parts to this chapter: Implementation and Management. STEPS TO ACCEPTANCE IMPLEMENTATION Taking a model from the lab to live typically involves lots of people, processes, and pieces of software.
Forge Your Future with Open Source by VM (Vicky) Brasseur
AGPL, anti-pattern, Benevolent Dictator For Life (BDFL), call centre, continuous integration, Debian, DevOps, don't repeat yourself, en.wikipedia.org, Firefox, Guido van Rossum, Internet Archive, Larry Wall, microservices, Perl 6, premature optimization, pull request, Richard Stallman, risk tolerance, Turing machine
Second Edition A single dramatic software failure can cost a company millions of dollars—but can be avoided with simple changes to design and architecture. This new edition of the best-selling industry standard shows you how to create systems that run longer, with fewer failures, and recover better when bad things happen. New coverage includes DevOps, microservices, and cloud-native architecture. Stability antipatterns have grown to include systemic problems in large-scale systems. This is a must-have pragmatic guide to engineering for production systems. Michael Nygard (376 pages) ISBN: 9781680502398 $47.95 Your Code as a Crime Scene Jack the Ripper and legacy codebases have more in common than you’d think.
Upscale: What It Takes to Scale a Startup. By the People Who've Done It. by James Silver
Airbnb, augmented reality, Ben Horowitz, blockchain, business process, call centre, credit crunch, crowdsourcing, DevOps, family office, future of work, Google Hangouts, high net worth, hiring and firing, Jeff Bezos, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, minimum viable product, Network effects, pattern recognition, ride hailing / ride sharing, Silicon Valley, Skype, Snapchat, software as a service, Uber and Lyft, uber lyft, WeWork, women in the workforce, Y Combinator
‘If you don’t have good people taking control of those areas, who are able to step up and be accountable for key issues like hiring, product and growth, then you won’t really know where to turn as a founder.’ ‘Communication and ongoing dialogue are critical.’ As a founder, you need to make sure that your teams - right down to specialists, if you’re a software company, such as your back-end and front-end developers, data scientists and DevOps person - are talking to one another, and that people are pointed in the same direction. Obviously the more people you have in the organisation, the more challenging that becomes to manage and orchestrate. ‘That’s where things like strategy, culture, goals and objectives become really important because the larger, the more complex the organisation becomes, the more you need things that really bind people together.’
Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist
3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, DevOps, digital twin, fault tolerance, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, The future is already here, trade route, undersea cable, web application, WebRTC, Y2K
However, they are correct to stress the importance of operational efficiency as it is paramount to all business and Industry 4.0 lends itself to increased productivity, efficiency, and customer engagement. Merge OT with IT The biggest problem with merging OT (operational technology) with IT (Information technology) is that they have completely different goals and aspirations. It is actually similar to merging operations and development into devops. In reality, OT is about manufacturing and OT workers and technicians have evolved via a different mindset. OT workers have come through the industrial workforce, where employees are labor-oriented and expect that the job they do is vital to the manufacturing of the product. OT staff work hard in difficult conditions and they work to meet production targets and work closely with the factory workforce as part of a team.
Bad Data Handbook by Q. Ethan McCallum
Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, functional programming, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative ﬁnance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application
Using a Production Environment for Ad-Hoc Analysis The use cases of performing exploratory analysis or any other data R&D effort are very different than the use cases for running production analytics processes. Generally, the design of production systems specify that they have to meet certain service level agreements (SLAs), such as for uptime (availability) and speed. These systems are maintained by an operations or devops teams, and are usually locked down, have very tight user space quotas, and may be located in self-contained environments for protection. The production processes that run on these systems are clearly defined, consistent, repeatable, and reliable. In contrast, the process of performing ad-hoc analytical tasks is nonlinear, error-prone, and usually requires tools that are in varying states of development, especially when using open source software.
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann
active measures, Amazon Web Services, bitcoin, blockchain, business intelligence, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, database schema, DevOps, distributed ledger, Donald Knuth, Edward Snowden, Ethereum, ethereum blockchain, fault tolerance, finite state, Flash crash, full text search, functional programming, general-purpose programming language, informal economy, information retrieval, Internet of things, iterative process, John von Neumann, Kubernetes, loose coupling, Marc Andreessen, microservices, natural language processing, Network effects, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, statistical model, surveillance capitalism, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons
Use tools in preference to unskilled help to lighten a programming task, even if you have to detour to build the tools and expect to throw some of them out after you’ve finished using them. This approach—automation, rapid prototyping, incremental iteration, being friendly to experimentation, and breaking down large projects into manageable chunks— sounds remarkably like the Agile and DevOps movements of today. Surprisingly little has changed in four decades. 394 | Chapter 10: Batch Processing The sort tool is a great example of a program that does one thing well. It is arguably a better sorting implementation than most programming languages have in their standard libraries (which do not spill to disk and do not use multiple threads, even when that would be beneficial).
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann
active measures, Amazon Web Services, bitcoin, blockchain, business intelligence, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, database schema, DevOps, distributed ledger, Donald Knuth, Edward Snowden, Ethereum, ethereum blockchain, fault tolerance, finite state, Flash crash, full text search, functional programming, general-purpose programming language, informal economy, information retrieval, Infrastructure as a Service, Internet of things, iterative process, John von Neumann, Kubernetes, loose coupling, Marc Andreessen, microservices, natural language processing, Network effects, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, statistical model, surveillance capitalism, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons
Use tools in preference to unskilled help to lighten a programming task, even if you have to detour to build the tools and expect to throw some of them out after you’ve finished using them. This approach—automation, rapid prototyping, incremental iteration, being friendly to experimentation, and breaking down large projects into manageable chunks—sounds remarkably like the Agile and DevOps movements of today. Surprisingly little has changed in four decades. The sort tool is a great example of a program that does one thing well. It is arguably a better sorting implementation than most programming languages have in their standard libraries (which do not spill to disk and do not use multiple threads, even when that would be beneficial).
in derived data systems, Derived Data materialized views, Aggregation: Data Cubes and Materialized Views updating derived data, Single-Object and Multi-Object Operations, The need for multi-object transactions, Combining Specialized Tools by Deriving Data versus normalization, Deriving several views from the same event log derived data, Derived Data, Stream Processing, Glossaryfrom change data capture, Implementing change data capture in event sourcing, Deriving current state from the event log-Deriving current state from the event log maintaining derived state through logs, Databases and Streams-API support for change streams, State, Streams, and Immutability-Concurrency control observing, by subscribing to streams, End-to-end event streams outputs of batch and stream processing, Batch and Stream Processing through application code, Application code as a derivation function versus distributed transactions, Derived data versus distributed transactions deterministic operations, Pros and cons of stored procedures, Faults and Partial Failures, Glossaryaccidental nondeterminism, Fault tolerance and fault tolerance, Fault tolerance, Fault tolerance and idempotence, Idempotence, Reasoning about dataflows computing derived data, Maintaining derived state, Correctness of dataflow systems, Designing for auditability in state machine replication, Using total order broadcast, Databases and Streams, Deriving current state from the event log joins, Time-dependence of joins DevOps, The Unix Philosophy differential dataflow, What’s missing? dimension tables, Stars and Snowflakes: Schemas for Analytics dimensional modeling (see star schemas) directed acyclic graphs (DAGs), Graphs and Iterative Processing dirty reads (transaction isolation), No dirty reads dirty writes (transaction isolation), No dirty writes discrimination, Bias and discrimination disks (see hard disks) distributed actor frameworks, Distributed actor frameworks distributed filesystems, MapReduce and Distributed Filesystems-MapReduce and Distributed Filesystemsdecoupling from query engines, Diversity of processing models indiscriminately dumping data into, Diversity of storage use by MapReduce, MapReduce workflows distributed systems, The Trouble with Distributed Systems-Summary, GlossaryByzantine faults, Byzantine Faults-Weak forms of lying cloud versus supercomputing, Cloud Computing and Supercomputing detecting network faults, Detecting Faults faults and partial failures, Faults and Partial Failures-Cloud Computing and Supercomputing formalization of consensus, Fault-Tolerant Consensus impossibility results, The CAP theorem, Distributed Transactions and Consensus issues with failover, Leader failure: Failover limitations of distributed transactions, Limitations of distributed transactions multi-datacenter, Multi-datacenter operation, The Cost of Linearizability network problems, Unreliable Networks-Can we not simply make network delays predictable?
The Startup Way: Making Entrepreneurship a Fundamental Discipline of Every Enterprise by Eric Ries
activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, autonomous vehicles, barriers to entry, basic income, Ben Horowitz, Black-Scholes formula, call centre, centralized clearinghouse, Clayton Christensen, cognitive dissonance, connected car, corporate governance, DevOps, Elon Musk, en.wikipedia.org, fault tolerance, Frederick Winslow Taylor, global supply chain, hockey-stick growth, index card, Jeff Bezos, Kickstarter, Lean Startup, loss aversion, Marc Andreessen, Mark Zuckerberg, means of production, minimum viable product, moral hazard, move fast and break things, move fast and break things, obamacare, peer-to-peer, place-making, rent-seeking, Richard Florida, Sam Altman, Sand Hill Road, secular stagnation, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Steve Jobs, the scientific method, time value of money, Toyota Production System, Uber for X, universal basic income, web of trust, Y Combinator
In government alone, projects like RFP-EZ11 (Request for Proposal-EZ), one of the first Presidential Innovation Fellows projects, which created an online marketplace where small businesses could bid for government work; and the Agile Blanket Purchase Agreement (Agile BPA),12 which gives the entire government access to contractors and vendors who provide agile delivery services like DevOps, user-centered design, and agile software development have both cut the requirements and time needed for purchasing, leading to faster resolution of critical problems. But that’s not all. Even the Nuclear Codes Need Procurement Reform It may seem highly improbable that procurement reform, which many consider inherently uninteresting, could be connected to something as critical and sensitive as generating nuclear codes.
Python Data Analytics: With Pandas, NumPy, and Matplotlib by Fabio Nelli
Amazon Web Services, backpropagation, centre right, computer vision, Debian, DevOps, functional programming, Google Earth, Guido van Rossum, Internet of things, optical character recognition, pattern recognition, sentiment analysis, speech recognition, statistical model, web application
About the Technical Reviewer Raul Samayoa is a senior software developer and machine learning specialist with many years of experience in the financial industry. An MSc graduate from the Georgia Institute of Technology, he’s never met a neural network or dataset he did not like. He’s fond of evangelizing the use of DevOps tools for data science and software development. Raul enjoys the energy of his hometown of Toronto, Canada, where he runs marathons, volunteers as a technology instructor with the University of Toronto coders, and likes to work with data in Python and R. © Fabio Nelli 2018 Fabio NelliPython Data Analyticshttps://doi.org/10.1007/978-1-4842-3913-1_1 1.
Overwhelmed: Work, Love, and Play When No One Has the Time by Brigid Schulte
8-hour work day, affirmative action, Bertrand Russell: In Praise of Idleness, blue-collar work, Burning Man, business cycle, call centre, cognitive dissonance, David Brooks, deliberate practice, desegregation, DevOps, East Village, Edward Glaeser, epigenetics, fear of failure, feminist movement, financial independence, game design, gender pay gap, glass ceiling, helicopter parent, hiring and firing, income inequality, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge economy, knowledge worker, labor-force participation, meta-analysis, new economy, profit maximization, Results Only Work Environment, Richard Feynman, Ronald Reagan, Saturday Night Live, sensible shoes, sexual politics, Silicon Valley, Skype, Steve Jobs, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, women in the workforce, working poor, Zipcar, éminence grise
Robinson based some of her conclusions on a white paper written by her computer game designer husband, Evan Robinson: “Why Crunch Modes Doesn’t Work: Six Lessons,” International Game Developers Association, www.igda.org/why-crunch-modes-doesnt-work-six-lessons. 16. Christopher P. Landrigan et al., “Effect of Reducing Interns’ Work Hours on Serious Medical Errors in Intensive Care Units,” New England Journal of Medicine 351 (2004): 1838–48, doi: 10.1056/NEJMoa041406. 17. Klint Finley, “What Research Says About Working Long Hours,” Devops Angle, April 18, 2012, http://devopsangle.com/2012/04/18/what-research-says-about-working-long-hours/. 18. www.businessinsider.com/best-buy-ending-work-from-home-2013-3. 19. U.S. Department of Commerce, Economics and Statistics Administration, “Women-Owned Businesses in the 21st Century,” White House Council on Women and Girls, October 2010, www.esa.doc.gov/sites/default/files/reports/documents/women-owned-businesses.pdf.
The Rust Programming Language by Steve Klabnik, Carol Nichols
Through efforts such as this book, the Rust teams want to make systems concepts more accessible to more people, especially those new to programming. Companies Hundreds of companies, large and small, use Rust in production for a variety of tasks. Those tasks include command line tools, web services, DevOps tooling, embedded devices, audio and video analysis and transcoding, cryptocurrencies, bioinformatics, search engines, Internet of Things applications, machine learning, and even major parts of the Firefox web browser. Open Source Developers Rust is for people who want to build the Rust programming language, community, developer tools, and libraries.
The Art of Community: Building the New Age of Participation by Jono Bacon
barriers to entry, Benevolent Dictator For Life (BDFL), collaborative editing, crowdsourcing, Debian, DevOps, do-ocracy, en.wikipedia.org, Firefox, game design, Guido van Rossum, Johann Wolfgang von Goethe, Jono Bacon, Kickstarter, Larry Wall, Mark Shuttleworth, Mark Zuckerberg, openstreetmap, Richard Stallman, side project, Silicon Valley, Skype, slashdot, social graph, software as a service, telemarketer, union organizing, VA Linux, web application
As such, we needed to pick which events we wanted him to attend, and pick wisely. With this in mind I asked Jorge to put together a spreadsheet that listed all the events that could be interesting for us to attend. The focus of this list was clear: these need to be cloud events and oriented around technology (as opposed to business events) and DevOps (the audience we were focusing on). I asked Jorge to gather this list of events and to determine the following characteristics for each one: Location and venue Date(s) of the event Typical attendance size Number of sessions and average talk audience size Team priority Each of these pieces of information helped to provide an overview of each event and its respective details.