Programming In Objective C Pdf 3rd Edition

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Run and Download Free Turbo C/C++ For Windows 8/7.The compatible version of Turbo C++ on different platform like Windows,Ubuntu,Android,Linux,Dosbox. This PDF is made available for personal use only during the relevant. C++, Objective-C is an extension to the C programming language, making it.

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• By John A. Davis, Steve Baca, Owen Thomas
• Published May 20, 2016 by VMware Press. Part of the VMware Press Certification series.

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• Your Price: $47.99
• List Price: $59.99

The Premium Edition eBook and Practice Test is a digital-only certification preparation product combining an eBook with enhanced Pearson IT Certification Practice Tests. Click on the 'Premium Edition' tab (on the left side of this page) to learn more about this product.

Your purchase will deliver:

• Link to download the enhanced Pearson IT Certification Practice Test exam engine
• Access code for question database
• eBook in the following formats, accessible from your Account page after purchase:

EPUBThe open industry format known for its reflowable content and usability on supported mobile devices.

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• The only official VMware study guide for VCP6-DCV, the newest version of the world's most popular VMware certification
• Engaging, comprehensive, and 100% authoritative: straight from VMware
• Covers every exam objective in 'blueprint' order, promoting efficient and systematic review
• Covers vCenter Server, VMware ESXi, vSphere networking and storage, VMs and vApps, service levels, troubleshooting, monitoring, alarm management, and more

Description

• Copyright 2016
• Dimensions: 7-3/8' x 9-1/8'
• Pages: 800
• Edition: 3rd

• Book
• ISBN-10: 0-7897-5648-X
• ISBN-13: 978-0-7897-5648-0

New! The authors have written a guide to using this book with the new 6.5 version of the exam; this can be found on the 'Updates' tab on the book's web page, http://www.pearsonitcertification.com/title/9780789756480.

VCP6-DCV Official Cert Guide presents you with an organized test-preparation routine through the use of proven series elements and techniques. “Do I Know This Already?” quizzes open each chapter and enable you to decide how much time you need to spend on each section. Exam topic lists make referencing easy. Chapter-ending Exam Preparation Tasks help you drill on key concepts you must know thoroughly.

• Master VMware VCP6-DCV exam topics
• Assess your knowledge with chapter-opening quizzes
• Review key concepts with exam preparation tasks
• Practice with realistic exam questions

VCP6-DCV Official Cert Guide focuses specifically on the objectives for the VMware Certified Professional 6 – Data Center Virtualization (VCP6-DCV) #2V0-621 exam. Leading VMware consultants, trainers, and data center experts John A. Davis, Steve Baca, and Owen Thomas share preparation hints and test-taking tips, helping you identify areas of weakness and improve both your conceptual knowledge and hands-on skills. Material is presented in a concise manner, focusing on increasing your understanding and retention of exam topics.

The companion website contains a powerful Pearson IT Certification Practice Test engine that enables you to focus on individual topic areas or take a complete, timed exam. The assessment engine tracks your performance and provides feedback on a module-by-module basis, laying out a complete assessment of your knowledge to help you focus your study where it is needed most.

Well regarded for its level of detail, assessment features, comprehensive design scenarios, and challenging review questions and exercises, this official study guide helps you master the concepts and techniques that will enable you to succeed on the exam the first time.

VCP6-DCV Official Cert Guide is part of a recommended learning path from VMware that includes simulation and hands-on training from authorized VMware instructors and self-study products from VMware Press. To find out more about instructor-led training, e-learning, and hands-on instruction offered worldwide, please visit www.vmware.com/training.

The official study guide helps you master all of the topics on the VCP6-DCV (#2V0-621) exam, including

• Securing vSphere environments
• Implementing advanced network virtualization policies, features, and Network I/O control (NIOC)
• Configuring and using VMware storage protocols, VSAN and VVOL software-defined storage, ESXi host interactions, and Storage I/O Control (SIOC)
• Upgrading vSphere deployments to 6.x, including vCenter Server and ESXi Hosts
• Planning and using resource pools
• Implementing backup/recovery with VMware Data Protection and vSphere Replication
• Troubleshooting performance, storage, networks, upgrades, clusters, and more
• Successfully configuring Auto Deploy environments with host profiles and virtualized workloads
• Configuring and administering vSphere high availability
• Using advanced VM settings, content libraries, and vCloud Air connectors

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The exciting new VCP6-DCV Official Cert Guide, Premium Edition eBook and Practice Test is a digital-only certification preparation product combining an eBook with enhanced Pearson IT Certification Practice Test.

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About the Premium Edition Practice Test

This Premium Edition contains an enhanced version of the Pearson IT Certification Practice Test (PCPT) software with four full practice exams. In addition, it contains all the chapter-opening assessment questions from the book. This integrated learning package

• Enables you to focus on individual topic areas or take complete, timed exams
• Includes direct links from each question to detailed tutorials to help you understand the concepts behind the questions
• Provides unique sets of exam-realistic practice questions
• Tracks your performance and provides feedback on a module-by-module basis, laying out a complete assessment of your knowledge to help you focus your study where it is needed most

Pearson IT Certification Practice Test minimum system requirements:

Windows XP (SP3), Windows Vista (SP2), or Windows 7; Microsoft .NET Framework 4.0 Client; Microsoft SQL Server Compact 4.0; Pentium class 1GHz processor (or equivalent); 512 MB RAM; 650 MB disc space plus 50 MB for each downloaded practice exam

About the Premium Edition eBook

VCP6-DCV Official Cert Guide focuses specifically on the objectives for the VMware Certified Professional 6 — Data Center Virtualization (VCP6-DCV #2VO-621) exam. Leading VMware consultants, trainers, and data center experts share preparation hints and test-taking tips, helping you identify areas of weakness and improve both your conceptual knowledge and hands-on skills. Material is presented in a concise manner, focusing on increasing your understanding and retention of exam topics.

VCP6-DCV Official Cert Guide presents you with an organized test-preparation routine through the use of proven series elements and techniques. “Do I Know This Already?” quizzes open each chapter and enable you to decide how much time you need to spend on each section. Exam topic lists make referencing easy. Chapter-ending Exam Preparation Tasks help you drill on key concepts you must know thoroughly.

Well regarded for its level of detail, assessment features, and challenging review questions and exercises, this official study guide helps you master the concepts and techniques that will enable you to succeed on the exam the first time.

This official study guide helps you master all the topics on the VCP6-DCV exam, including

• Securing vSphere environments
• Implementing advanced network virtualization policies, features, and Network I/O control (NIOC)
• Configuring and using VMware storage protocols, VSAN and VVOL software-defined storage, ESXi host interactions, and Storage I/O Control (SIOC)
• Upgrading vSphere deployments to 6.x, including vCenter Server and ESXi Hosts
• Planning and using resource pools
• Implementing backup/recovery with VMware Data Protection and vSphere Replication
• Troubleshooting performance, storage, networks, upgrades, clusters, and more
• Successfully configuring Auto Deploy environments with host profiles and virtualized workloads
• Configuring and administering vSphere high availability
• Using advanced VM settings, content libraries, and vCloud Air connectors

VCP6-DCV Official Cert Guide is part of a recommended learning path from VMware that includes simulation and hands-on training from authorized VMware instructors and self-study products from VMware Press. To find out more about instructor-led training, e-learning, and hands-on instruction offered worldwide, please visit www.vmware.com/training.

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Table of Contents

Introduction xxviii

Chapter 1 Security 3

“Do I Know This Already?” Quiz 3

Foundation Topics 6

Objective 1.1–Confi gure and Administer Role-based Access Control 6

Compare and Contrast Propagated and Explicit Permission Assignments 6

View/Sort/Export User and Group Lists 6

Add/Modify/Remove Permissions for Users and Groups on vCenter Server Inventory Objects 7

Determine How Permissions Are Applied and Inherited in vCenter Server 10

Create/Clone/Edit vCenter Server Roles 14

Confi gure VMware Directory Service 15

Apply a Role to a User/Group and to an Object or a Group of Objects 16

Change Permission Validation Settings 17

Determine the Appropriate Set of Privileges for Common Tasks in vCenter Server 18

Compare and Contrast Default System/Sample Roles 20

Determine the Correct Permissions Needed to Integrate vCenter Server with Other VMware Products 21

Objective 1.2–Secure ESXi, vCenter Server, and vSphere Virtual Machines 23

Harden Virtual Machine Access 23

Control VMware Tools Installation 24

Control VM Data Access 27

Configure Virtual Machine Security Policies 28

Harden a Virtual Machine Against Denial-of-Service Attacks 29

Control VM—VM Communications 29

Control VM Device Connections 29

Configure Network Security Policies 29

Harden ESXi Hosts 30

Enable/Configure/Disable Services in the ESXi Firewall 31

Change Default Account Access 34

Add an ESXi Host to a Directory Service 35

Apply Permissions to ESXi Hosts Using Host Profiles 36

Enable Lockdown Mode 37

Control Access to Hosts (DCUI/Shell/SSH/MOB) 38

Harden vCenter Server 39

Control Datastore Browser Access 39

Create/Manage vCenter Server Security Certificates 39

Control MOB Access 40

Change Default Account Access 40

Restrict Administrative Privileges 40

Understand the Implications of Securing a vSphere Environment 40

Objective 1.3–Enable SSO and Active Directory Integration 41

Describe SSO Architecture and Components 41

Differentiate Available Authentication Methods with VMware vCenter 42

Perform a Multi-site SSO Installation 42

Confi gure/Manage Active Directory Authentication 45

Confi gure/Manage Platform Services Controller (PSC) 48

Confi gure/Manage VMware Certifi cate Authority (VMCA) 50

Enable/Disable Single Sign-On (SSO) Users 51

Upgrade a Single/Multi-site SSO Installation 53

Confi gure SSO Policies 54

Add/Edit/Remove SSO Identity Sources 55

Add an ESXi Host to an AD Domain 55

Summary 56

Exam Preparation Tasks 56

Review All the Key Topics 56

Complete the Tables and Lists from Memory 56

Defi nitions of Key Terms 56

Answer Review Questions 57

Chapter 2 Networking, Part 1 61

“Do I Know This Already?” Quiz 61

Foundation Topics 65

Objective 2.1–Confi gure Advanced Policies/Features and Verify Network Virtualization Implementation 65

Compare and Contrast vSphere Distributed Switch (vDS) Capabilities 65

Create/Delete a vSphere Distributed Switch 68

Add/Remove ESXi Hosts from a vSphere Distributed Switch 72

Add/Confi gure/Remove dvPort Groups 82

Add/Remove Uplink Adapters to dvUplink Groups 86

Confi gure vSphere Distributed Switch General and dvPort Group Settings 90

Create/Confi gure/Remove Virtual Adapters 93

Migrate Virtual Machines to/from a vSphere Distributed Switch 98

Migrating Virtual Machines Individually 98

Migrating Multiple Virtual Machines 100

Confi gure LACP on dvUplink and dvPort Groups 101

Describe vDS Security Policies/Settings 109

Confi gure dvPort Group Blocking Policies 111

Confi gure Load Balancing and Failover Policies 113

Confi gure VLAN/PVLAN Settings for VMs Given Communication Requirements 114

Confi gure Traffi c Shaping Policies 119

Enable TCP Segmentation Offl oad Support for a Virtual Machine 122

Enable Jumbo Frames Support on Appropriate Components 123

Determine Appropriate VLAN Confi guration for a vSphere Implementation 127

Recognize Behavior of vDS Auto-Rollback 127

Confi gure vDS Across Multiple vCenter Servers to Support Long-Distance vMotion 129

Summary 131

Exam Preparation Tasks 131

Review All the Key Topics 131

Complete the Tables and Lists from Memory 131

Defi nitions of Key Terms 132

Answer Review Questions 132

Chapter 3 Networking, Part 2 135

“Do I Know This Already?” Quiz 135

Foundation Topics 138

Objective 2.2–Confi gure Network I/O Control (NIOC) 138

Defi ne Network I/O Control 138

Explain Network I/O Control Capabilities 138

Confi gure NIOC Shares/Limits Based on VM Requirements 142

Explain the Behavior of a Given Network I/O Control Setting 146

Determine Network I/O Control Requirements 148

Differentiate Network I/O Control Capabilities 148

Enable/Disable Network I/O Control 148

Monitor Network I/O Control 151

Summary 153

Exam Preparation Tasks 154

Review All the Key Topics 154

Complete the Tables and Lists from Memory 154

Defi nitions of Key Terms 154

Answer Review Questions 155

Chapter 4 Storage, Part 1 159

“Do I Know This Already?” Quiz 159

Foundation Topics 163

Objective 3.1–Manage vSphere Storage Virtualization 163

Storage Protocols 163

Identify Storage Adapters and Devices 163

Display Storage Adapters for a Host 164

Storage Devices for an Adapter 164

Fibre Channel Protocol 166

Fibre Channel over Ethernet Protocol 166

iSCSI Protocol 166

NFS Protocol 167

Authentication NFSv4.1 with Kerberos Authentication 167

Native Multipathing and Session Trunking 167

In-band, Mandatory, and Stateful Server-Side File Locking 167

Identify Storage Naming Conventions 168

Identify Hardware/Dependent Hardware/Software iSCSI Initiator Requirements 170

Discover New Storage LUNs 171

Confi gure FC/iSCSI/FCoE LUNs as ESXi Boot Devices 172

FC 172

iSCSI 172

FCoE 173

Create an NFS Share for Use with vSphere 173

Enable/Confi gure/Disable vCenter Server Storage Filters 173

iSCSI 175

Configure/Edit Hardware/Dependent Hardware Initiators 175

Enable/Disable Software iSCSI Initiator 176

Configure/Edit Software iSCSI Initiator Settings 177

Determine Use Case for Hardware/Dependent Hardware/Software iSCSI Initiator 177

Configure iSCSI Port Binding 178

Enable/Configure/Disable iSCSI CHAP 180

Determine Use Cases for Fibre Channel Zoning 183

Compare and Contrast Array and Virtual Disk Thin Provisioning 183

Array Thin Provisioning 183

Virtual Disk Thin Provisioning 184

Determine Use Case for and Configure Array Thin Provisioning 184

Summary 185

Exam Preparation Tasks 185

Review All the Key Topics 185

Complete the Tables and Lists from Memory 186

Defi nitions of Key Terms 186

Answer Review Questions 186

Chapter 5 Storage, Part 2 189

“Do I Know This Already?” Quiz 189

Foundation Topics 193

Objective 3.2–Confi gure Software-defi ned Storage 193

Explain VSAN and VVOL Architectural Components 193

VSAN 193

VVOL 194

Determine the Role of Storage Providers in VSAN 195

Determine the Role of Storage Providers in VVOLs 196

Explain VSAN Failure Domains Functionality 197

Confi gure/Manage VMware Virtual SAN 197

Create/Modify VMware Virtual Volumes (VVOLs) 201

Confi gure Storage Policies 202

Enable/Disable Virtual SAN Fault Domains 203

Create Virtual Volumes Given the Workload and Availability Requirements 204

Collect VSAN Observer Output 204

Create Storage Policies Appropriate for Given Workloads and Availability

Requirements 206

Confi gure VVOLs Protocol Endpoints 206

Objective 3.3–Confi gure vSphere Storage Multipathing and Failover 207

Explain Common Multipathing Components 207

Differentiate APD and PDL States 207

Compare and Contrast Active Optimized vs. Active non-Optimized Port Group States 208

Explain Features of Pluggable Storage Architecture (PSA) 209

MPP 209

NMP 210

SATP 210

PSP 210

Understand the Effects of a Given Claim Rule on Multipathing and Failover 210

Explain the Function of Claim Rule Elements 211

Change the Path Selection Policy Using the UI 213

Determine the Effect of Changing PSP on Multipathing and Failover 214

Determine the Effect of Changing SATP on Multipathing and Failover 215

Confi gure/Manage Storage Load Balancing 215

Differentiate Available Storage Load Balancing Options 216

Differentiate Available Storage Multipathing Policies 216

Confi gure Storage Policies 217

Locate Failover Events in the UI 218

Summary 218

Exam Preparation Tasks 219

Review All the Key Topics 219

Complete the Tables and Lists from Memory 220

Defi nitions of Key Terms 220

Answer Review Questions 221

Chapter 6 Storage, Part 3 225

“Do I Know This Already?” Quiz 225

Foundation Topics 229

Objective 3.4–Perform Advanced VMFS and NFS Confi gurations and Upgrades 229

Describe VAAI Primitives for Block Devices and NAS 229

Enable/Disable vStorage APIs for Array Integration (VAAI) 230

Differentiate VMware File System Technologies 231

Compare and Contrast VMFS and NFS Datastore Properties 232

Upgrade VMFS3 to VMFS5 232

Compare Functionality of New and Upgraded VMFS5 Datastores 233

Differentiate Physical Mode and Virtual Mode RDMs 233

Create a Virtual/Physical Mode RDM 234

Differentiate NFS 3.x and 4.1 Capabilities 235

Confi gure Bus Sharing 236

Confi gure Multi-writer Locking 237

Connect an NFS 4.1 Datastore Using Kerberos 238

Create/Rename/Delete/Unmount VMFS Datastores 239

Create a VMFS Datastore 239

Rename a VMFS Datastore 240

Delete a VMFS Datastore 240

Unmount a VMFS Datastore 240

Mount/Unmount an NFS Datastore 241

Mount an NFS Datastore 241

Unmount an NFS Datastore 242

Extend/Expand VMFS Datastores 242

Expandable 242

Extending 242

Place a VMFS Datastore in Maintenance Mode 243

Select the Preferred Path/Disable a Path to a VMFS Datastore 244

Given a Scenario, Determine a Proper Use Case for Multiple VMFS/NFS Datastores 245

Objective 3.5–Set Up and Confi gure Storage I/O Control 246

Describe the Benefi ts of SIOC 246

Enable and Confi gure SIOC 247

Confi gure/Manage SIOC 249

Monitor SIOC 250

Differentiate Between SIOC and Dynamic Queue Depth Throttling Features 252

Given a Scenario, Determine a Proper Use Case for SIOC 252

Compare and Contrast the Effects of I/O Contention in Environments With and Without

SIOC 253

Summary 253

Exam Preparation Tasks 254

Review All the Key Topics 254

Complete the Tables and Lists from Memory 255

Defi nitions of Key Terms 255

Answer Review Questions 255

Chapter 7 Upgrade a vSphere Deployment to 6.x 259

“Do I Know This Already?” Quiz 259

Foundation Topics 263

Objective 4.1–Perform ESXi Host and Virtual Machine Upgrades 263

Update Manager 263

Confi gure Download Source(s) 264

Set Up UMDS to Set Up Download Repository 265

Import ESXi Images 265

Create Baselines and/or Baseline Groups 267

Attach Baselines to vSphere Objects 267

Scan vSphere Objects 269

Stage Patches and Extensions 269

Remediate an Object 269

Upgrade a vSphere Distributed Switch 270

Upgrade VMware Tools 271

Upgrade Virtual Machine Hardware 272

Upgrade an ESXi Host Using vCenter Update Manager 273

Stage Multiple ESXi Host Upgrades 274

Objective 4.2–Perform vCenter Server Upgrades 276

Compare the Methods of Upgrading vCenter Server 276

Embedded Architecture Deployment 278

External Architecture Deployment 278

Back Up vCenter Server Database and Certifi cates 279

Embedded Windows vCenter Server 279

Embedded Linux vCenter Server Appliance Database 279

Certificates 280

Perform Update as Prescribed for Appliance or Installable 280

Pre-Upgrade Updates for Linux vCenter Appliance 280

Pre-Upgrade Updates for Windows Installer 281

Upgrade vCenter Server Appliance (vCSA) 281

Given a Scenario, Determine the Upgrade Compatibility of an Environment 283

Scenario Conclusion 284

Determine Correct Order of Steps to Upgrade a vSphere Implementation 284

Summary 285

Exam Preparation Tasks 285

Review All the Key Topics 285

Complete the Tables and Lists from Memory 286

Defi nitions of Key Terms 287

Answer Review Questions 287

Chapter 8 Resource Pools 289

“Do I Know This Already?” Quiz 289

Foundation Topics 293

Objective 5.1–Confi gure Advanced/Multilevel Resource Pools 293

Understand/Apply 293

Determine the Effect of the Expandable Reservation Parameter on

Resource Allocation 295

Create a Resource Pool Hierarchical Structure 296

Confi gure Custom Resource Pool Attributes 301

Determine How Resource Pools Apply to vApps 301

Describe vFlash Architecture 301

Create/Remove a Resource Pool 306

Add/Remove Virtual Machines from a Resource Pool 308

Create/Delete vFlash Resource Pool 309

Assign vFlash Resources to VMDKs 309

Given a Scenario, Determine Appropriate Shares, Reservations, and Limits for

Hierarchical Resource Pools 311

Summary 312

Exam Preparation Tasks 312

Review All the Key Topics 312

Complete the Tables and Lists from Memory 313

Defi nitions of Key Terms 313

Answer Review Questions 313

Chapter 9 Backup and Recovery 317

“Do I Know This Already?” Quiz 317

Foundation Topics 321

Objective 6.1–Confi gure and Administer a vSphere Backups/Restore/Replication Solution 321

Compare and Contrast vSphere Replication Compression Methods 321

Differentiate VMware Data Protection Capabilities 322

Confi gure Recovery Point Objective (RPO) for a Protected Virtual Machine 323

Explain VMware Data Protection Sizing Guidelines 324

Create/Delete/Consolidate Virtual Machine Snapshots 325

Install and Confi gure VMware Data Protection 327

Create a Backup Job with VMware Data Protection 334

Programming In Objective C Pdf Download

Backup/Restore a Virtual Machine with VMware Data Protection 335

Install/Confi gure/Upgrade vSphere Replication 340

Confi gure VMware Certifi cate Authority (VMCA) integration with vSphere Replication 341

Confi gure vSphere Replication for Single/Multiple VMs 342

Recover a VM Using vSphere Replication 345

Perform a Failback Operation Using vSphere Replication 346

Deploy a Pair of vSphere Replication Virtual Appliances 347

Summary 347

Exam Preparation Tasks 347

Review All the Key Topics 347

Complete the Tables and Lists from Memory 348

Defi nitions of Key Terms 348

Answer Review Questions 348

Chapter 10 Troubleshoot Common Issues 351

“Do I Know This Already?” Quiz 351

Foundation Topics 354

Objective 7.1–Troubleshoot vCenter Server, ESXi Hosts, and Virtual Machines 354

Monitor Status of the vCenter Server Service 354

Perform Basic Maintenance of a vCenter Server Database 357

Monitor Status of ESXi Management Agents 359

Determine ESXi Host Stability Issues and Gather Diagnostics Information 361

Monitor ESXi System Health 368

Locate and Analyze vCenter Server and ESXi Logs 369

Determine the Appropriate Command-Line Interface (CLI) Command for a Given Troubleshooting Task 372

Troubleshoot Common Issues 376

vCenter Server Service 377

Single Sign-On (SSO) 378

vCenter Server Connectivity 380

Virtual Machine Resource Contention, Configuration, and Operation 383

Platform Services Controller (PSC) 386

Problems with Installation 387

VMware Tools Installation 388

Fault Tolerant Network Latency 389

Summary 391

Exam Preparation Tasks 391

Review All the Key Topics 391

Complete the Tables and Lists from Memory 391

Defi nitions of Key Terms 392

Answer Review Questions 392

Chapter 11 Troubleshoot Storage, Networks, and Upgrades 395

“Do I Know This Already?” Quiz 395

Foundation Topics 399

Objective 7.2–Troubleshoot vSphere Storage and Network Issues 399

Identify and Isolate Network and Storage Resource Contention and Latency Issues 399

Monitor Networking and Storage Resources Using vROps Alerts and All Badges 399

Verify Network and Storage Confi guration 404

Verify a Given Virtual Machine Is Confi gured with the Correct Network Resources 410

Monitor/Troubleshoot Storage Distributed Resource Scheduler (SDRS) Issues 414

Recognize the Impact of Network and Storage I/O Control Confi gurations 418

Recognize a Connectivity Issue Caused by a VLAN/PVLAN 422

Troubleshoot Common Issues 423

Storage and Network 423

Virtual Switch and Port Group Configuration 426

Physical Network Adapter Configuration 427

VMFS Metadata Consistency 428

Objective 7.3–Troubleshoot vSphere Upgrades 430

Collect Upgrade Diagnostic Information 431

Recognize Common Upgrade Issues with vCenter Server and vCenter Server Appliance 431

Create/Locate/Analyze VMware Log Bundles 432

Determine Alternative Methods to Upgrade ESXi Hosts in the Event of Failure 434

VMware Update Manager 434

Interactively Using the ESXi Installer 434

Scripted Upgrades 435

vSphere Auto Deploy 437

ESXi Command Line 438

Confi gure vCenter Server Logging Options 439

Summary 442

Preparation Tasks 442

Review All the Key Topics 442

Complete the Tables and Lists from Memory 442

Defi nitions of Key Terms 442

Answer Review Questions 443

Chapter 12 Troubleshoot Performance 447

“Do I Know This Already?” Quiz 447

Foundation Topics 450

Objective 7.4–Troubleshoot and Monitor vSphere Performance 450

Monitor CPU and Memory Usage (Including vRealize Badges and Alerts) 450

Identify and Isolate CPU and Memory Contention Issues 451

Recognize Impact of Using CPU/Memory Limits, Reservations, and Shares 452

Describe and Differentiate Critical Performance Metrics 454

Describe and Differentiate Common Metrics–Memory 455

Describe and Differentiate Common Metrics–CPU 463

Describe and Differentiate Common Metrics–Network 465

Describe and Differentiate Common Metrics–Storage 466

Monitor Performance Through ESXTOP 467

Troubleshoot Enhanced vMotion Compatibility (EVC) Issues 473

Troubleshoot Virtual Machine Performance via vRealize Operations 477

Compare and Contrast Overview and Advanced Charts 483

Describe How Tasks and Events Are Viewed in vCenter Server 484

Identify Host Power Management Policy 489

Summary 491

Exam Preparation Tasks 491

Review All the Key Topics 491

Complete the Tables and Lists from Memory 491

Defi nitions of Key Terms 492

Answer Review Questions 492

Chapter 13 Troubleshoot Clusters 495

“Do I Know This Already?” Quiz 495

Foundation Topics 497

Objective 7.5–Troubleshoot HA and DRS Confi guration and Fault Tolerance 497

Troubleshoot Issues with DRS Workload Balancing 497

Troubleshoot Issues with HA Failover/Redundancy, Capacity, and Network Confi guration 500

Troubleshoot Issues with HA/DRS Cluster Confi guration 507

Troubleshoot Issues with vMotion/Storage vMotion Confi guration and/or Migration 511

Troubleshoot Issues with Fault Tolerance Confi guration and Failover Issues 514

Explain DRS Resource Distribution Graph and Target/Current Host Load Deviation 519

Explain vMotion Resource Maps 523

Summary 524

Exam Preparation Tasks 524

Review All the Key Topics 524

Complete the Tables and Lists from Memory 525

Defi nitions of Key Terms 525

Answer Review Questions 525

Chapter 14 Deploy and Consolidate 529

“Do I Know This Already?” Quiz 529

Foundation Topics 532

Objective 8.1–Deploy ESXi Hosts Using Auto Deploy 532

Describe the Components and Architecture of an Auto Deploy Environment 532

Use Auto Deploy Image Builder and PowerCLI Scripts 533

Implement Host Profi les with an Auto Deploy of an ESXi Host 537

Install and Confi gure Auto Deploy 539

Understand PowerCLI cmdlets for Auto Deploy 540

Deploy Multiple ESXi Hosts Using Auto Deploy 541

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Given a Scenario, Explain the Auto Deploy Deployment Model Needed to Meet a

Business Requirement 542

Objective 8.2–Customize Host Profi le Settings 542

Edit an Answer File to Customize ESXi Host Settings 542

Modify and Apply a Storage Path Selection Plug-in (PSP) to a Device Using Host Profi les 543

Modify and Apply Switch Confi gurations Across Multiple Hosts Using a Host Profi le 544

Create/Edit/Remove a Host Profi le from an ESXi Host 546

Import/Export a Host Profi le 548

Attach and Apply a Host Profi le to ESXi Hosts in a Cluster 549

Perform Compliance Scanning and Remediation of an ESXi Host and Clusters Using Host Profi les 552

Enable or Disable Host Profi le Components 555

Objective 8.3–Consolidate Physical Workloads Using VMware Converter 556

Install a vCenter Converter Standalone Instance 556

Convert Physical Workloads Using vCenter Converter 557

Modify Server Resources During Conversion 561

Interpret and Correct Errors During Conversion 562

Deploy a Physical Host as a Virtual Machine Using vCenter Converter 563

Collect Diagnostic Information During Conversion Operation 564

Resize Partitions During the Conversion Process 564

Given a Scenario, Determine Which Virtual Disk Format to Use 565

Summary 565

Exam Preparation Tasks 566

Review All the Key Topics 566

Complete the Tables and Lists from Memory 566

Defi nitions of Key Terms 566

Answer Review Questions 567

Chapter 15 Confi gure and Administer vSphere Availability Solutions 569

“Do I Know This Already?” Quiz 569

Foundation Topics 573

Objective 9.1–Confi gure Advanced vSphere HA Features 573

Modify vSphere HA Advanced Cluster Settings 573

Confi gure a Network for Use with HA Heartbeats 574

Apply an Admission Control Policy for HA 575

Enable/Disable Advanced vSphere HA Settings 576

Confi gure Different Heartbeat Datastores for an HA Cluster 578

Apply Virtual Machine Monitoring for a Cluster 579

Confi gure Virtual Machine Component Protection (VMCP) Settings 580

Implement vSphere HA on a Virtual SAN Cluster 581

Explain How vSphere HA Communicates with Distributed Resource Scheduler and

Distributed Power Management 581

Objective 9.2–Confi gure Advanced vSphere DRS Features 582

Confi gure VM-Host Affi nity/Anti-affi nity Rules 582

Confi gure VM-VM Affi nity/Anti-affi nity Rules 584

Add/Remove Host DRS Group 585

Add/Remove Virtual Machine DRS Group 585

Enable/Disable Distributed Resource Scheduler (DRS) Affi nity Rules 586

Confi gure the Proper Distributed Resource Scheduler (DRS) Automation Level Based on a

Set of Business Requirements 587

Explain How DRS Affi nity Rules Affect Virtual Machine Placement 588

Summary 589

Exam Preparation Tasks 589

Review All the Key Topics 589

Complete the Tables and Lists from Memory 589

Defi nitions of Key Terms 590

Answer Review Questions 590

Chapter 16 Virtual Machines 593

“Do I Know This Already?” Quiz 593

Foundation Topics 597

Objective 10.1–Confi gure Advanced vSphere Virtual Machine Settings 597

Determine How Using a Shared USB Device Impacts the Environment 597

Confi gure Virtual Machines for vGPUs, DirectPath I/O, and SR-IOV 598

Confi gure Virtual Machines for Multicore vCPUs 602

Differentiate Virtual Machine Confi guration Settings 603

Interpret Virtual Machine Confi guration File (.vmx) Settings 610

Enable/Disable Advanced Virtual Machine Settings 611

Objective 10.2–Create and Manage a Multi-site Content Library 612

Publish a Content Catalog 613

Subscribe to a Published Catalog 613

Determine Which Privileges Are Required to Globally Manage a Content Catalog 613

Compare the Functionality of Automatic Sync and On-Demand Sync 614

Confi gure Content Library to Work Across Sites 614

Confi gure Content Library Authentication 616

Set/Confi gure Content Library Roles 617

Add/Remove Content Libraries 618

Objective 10.3–Confi gure and Maintain a vCloud Air Connection 619

Create a VPN Connection Between vCloud Air and On-premise Site 619

Deploy a Virtual Machine Using vCloud Air 621

9780789756480_book.indb xxvi 4/15/16 1:51 PM

Contents xxvii

Migrate a Virtual Machine to vCloud Air 621

Verify VPN Connection Confi guration to vCloud Air 623

Confi gure vCenter Server Connection to vCloud Air 623

Confi gure Replicated Objects in vCloud Air Disaster Recovery Service 625

Given a Scenario, Determine the Required Settings for Virtual Machines Deployed in vCloud Air 627

Summary 628

Preparation Tasks 628

Review All the Key Topics 628

Complete the Tables and Lists from Memory 628

Defi nitions of Key Terms 628

Answer Review Questions 629

Chapter 17 Final Preparation 631

Getting Ready 631

Taking the Exam 634

Glossary 639

Appendix A Answers to the “Do I Know This Already?” Quizzes and Review

Questions 647

Appendix B Memory Tables 655

Appendix C Memory Tables Answer Key 689

TOC, 9780789756480, 4/19/2016

Updates

Updates & Corrections

Appendix E: Exam Updates (166 KB .pdf)

VCP6.5-DCV Exam Preparation
https://vloreblog.com/2017/05/05/vcp6-5-dcv-exam-preparation/

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A programming language is a formal language, which comprises a set of instructions that produce various kinds of output. Programming languages are used in computer programming to implement algorithms.

Most programming languages consist of instructions for computers. There are programmable machines that use a set of specific instructions, rather than general programming languages. Early ones preceded the invention of the digital computer, the first probably being the automatic flute player described in the 9th century by the brothers Musa in Baghdad, during the Islamic Golden Age.^[1] Since the early 1800s, programs have been used to direct the behavior of machines such as Jacquard looms, music boxes and player pianos.^[2] The programs for these machines (such as a player piano's scrolls) did not produce different behavior in response to different inputs or conditions.

Thousands of different programming languages have been created, and more are being created every year. Many programming languages are written in an imperative form (i.e., as a sequence of operations to perform) while other languages use the declarative form (i.e. the desired result is specified, not how to achieve it).

The description of a programming language is usually split into the two components of syntax (form) and semantics (meaning). Some languages are defined by a specification document (for example, the C programming language is specified by an ISO Standard) while other languages (such as Perl) have a dominant implementation that is treated as a reference. Some languages have both, with the basic language defined by a standard and extensions taken from the dominant implementation being common.

• 2History
• 3Elements
  • 3.2Semantics
  • 3.3Type system
• 4Design and implementation
• 6Use

Definitions[edit]

A programming language is a notation for writing programs, which are specifications of a computation or algorithm.^[3] Some authors restrict the term 'programming language' to those languages that can express all possible algorithms.^[3][4] Traits often considered important for what constitutes a programming language include:

Function and target

A computer programming language is a language used to write computer programs, which involves a computer performing some kind of computation^[5] or algorithm and possibly control external devices such as printers, disk drives, robots,^[6] and so on. For example, PostScript programs are frequently created by another program to control a computer printer or display. More generally, a programming language may describe computation on some, possibly abstract, machine. It is generally accepted that a complete specification for a programming language includes a description, possibly idealized, of a machine or processor for that language.^[7] In most practical contexts, a programming language involves a computer; consequently, programming languages are usually defined and studied this way.^[8] Programming languages differ from natural languages in that natural languages are only used for interaction between people, while programming languages also allow humans to communicate instructions to machines.

Abstractions

Programming languages usually contain abstractions for defining and manipulating data structures or controlling the flow of execution. The practical necessity that a programming language support adequate abstractions is expressed by the abstraction principle.^[9] This principle is sometimes formulated as a recommendation to the programmer to make proper use of such abstractions.^[10]

Expressive power

The theory of computation classifies languages by the computations they are capable of expressing. All Turing complete languages can implement the same set of algorithms. ANSI/ISO SQL-92 and Charity are examples of languages that are not Turing complete, yet often called programming languages.^[11][12]

Markup languages like XML, HTML, or troff, which define structured data, are not usually considered programming languages.^[13][14][15] Programming languages may, however, share the syntax with markup languages if a computational semantics is defined. XSLT, for example, is a Turing complete language entirely using XML syntax.^[16][17][18] Moreover, LaTeX, which is mostly used for structuring documents, also contains a Turing complete subset.^[19][20]

The term computer language is sometimes used interchangeably with programming language.^[21] However, the usage of both terms varies among authors, including the exact scope of each. One usage describes programming languages as a subset of computer languages.^[22] Similarly, languages used in computing that have a different goal than expressing computer programs are generically designated computer languages. For instance, markup languages are sometimes referred to as computer languages to emphasize that they are not meant to be used for programming.^[23]

Another usage regards programming languages as theoretical constructs for programming abstract machines, and computer languages as the subset thereof that runs on physical computers, which have finite hardware resources.^[24]John C. Reynolds emphasizes that formal specification languages are just as much programming languages as are the languages intended for execution. He also argues that textual and even graphical input formats that affect the behavior of a computer are programming languages, despite the fact they are commonly not Turing-complete, and remarks that ignorance of programming language concepts is the reason for many flaws in input formats.^[25]

History[edit]

Early developments[edit]

Very early computers, such as Colossus, were programmed without the help of a stored program, by modifying their circuitry or setting banks of physical controls.

Slightly later, programs could be written in machine language, where the programmer writes each instruction in a numeric form the hardware can execute directly. For example, the instruction to add the value in two memory location might consist of 3 numbers: an 'opcode' that selects the 'add' operation, and two memory locations. The programs, in decimal or binary form, were read in from punched cards, paper tape, magnetic tape or toggled in on switches on the front panel of the computer. Machine languages were later termed first-generation programming languages (1GL).

The next step was development of so-called second-generation programming languages (2GL) or assembly languages, which were still closely tied to the instruction set architecture of the specific computer. These served to make the program much more human-readable and relieved the programmer of tedious and error-prone address calculations.

The first high-level programming languages, or third-generation programming languages (3GL), were written in the 1950s. An early high-level programming language to be designed for a computer was Plankalkül, developed for the German Z3 by Konrad Zuse between 1943 and 1945. However, it was not implemented until 1998 and 2000.^[26]

John Mauchly's Short Code, proposed in 1949, was one of the first high-level languages ever developed for an electronic computer.^[27] Unlike machine code, Short Code statements represented mathematical expressions in understandable form. However, the program had to be translated into machine code every time it ran, making the process much slower than running the equivalent machine code.

At the University of Manchester, Alick Glennie developed Autocode in the early 1950s. As a programming language, it used a compiler to automatically convert the language into machine code. The first code and compiler was developed in 1952 for the Mark 1 computer at the University of Manchester and is considered to be the first compiled high-level programming language.^[28][29]

The second autocode was developed for the Mark 1 by R. A. Brooker in 1954 and was called the 'Mark 1 Autocode'. Brooker also developed an autocode for the Ferranti Mercury in the 1950s in conjunction with the University of Manchester. The version for the EDSAC 2 was devised by D. F. Hartley of University of Cambridge Mathematical Laboratory in 1961. Known as EDSAC 2 Autocode, it was a straight development from Mercury Autocode adapted for local circumstances and was noted for its object code optimisation and source-language diagnostics which were advanced for the time. A contemporary but separate thread of development, Atlas Autocode was developed for the University of Manchester Atlas 1 machine.

In 1954, FORTRAN was invented at IBM by John Backus. It was the first widely used high-level general purpose programming language to have a functional implementation, as opposed to just a design on paper.^[30][31] It is still a popular language for high-performance computing^[32] and is used for programs that benchmark and rank the world's fastest supercomputers.^[33]

Another early programming language was devised by Grace Hopper in the US, called FLOW-MATIC. It was developed for the UNIVAC I at Remington Rand during the period from 1955 until 1959. Hopper found that business data processing customers were uncomfortable with mathematical notation, and in early 1955, she and her team wrote a specification for an English programming language and implemented a prototype.^[34] The FLOW-MATIC compiler became publicly available in early 1958 and was substantially complete in 1959.^[35] FLOW-MATIC was a major influence in the design of COBOL, since only it and its direct descendant AIMACO were in actual use at the time.^[36]

Refinement[edit]

The increased use of high-level languages introduced a requirement for low-level programming languages or system programming languages. These languages, to varying degrees, provide facilities between assembly languages and high-level languages. They can be used to perform tasks which require direct access to hardware facilities but still provide higher-level control structures and error-checking.

The period from the 1960s to the late 1970s brought the development of the major language paradigms now in use:

• APL introduced array programming and influenced functional programming.^[37]
• ALGOL refined both structured procedural programming and the discipline of language specification; the 'Revised Report on the Algorithmic Language ALGOL 60' became a model for how later language specifications were written.
• Lisp, implemented in 1958, was the first dynamically typed functional programming language.
• In the 1960s, Simula was the first language designed to support object-oriented programming; in the mid-1970s, Smalltalk followed with the first 'purely' object-oriented language.
• C was developed between 1969 and 1973 as a system programming language for the Unix operating system and remains popular.^[38]
• Prolog, designed in 1972, was the first logic programming language.
• In 1978, ML built a polymorphic type system on top of Lisp, pioneering statically typedfunctional programming languages.

Each of these languages spawned descendants, and most modern programming languages count at least one of them in their ancestry.

The 1960s and 1970s also saw considerable debate over the merits of structured programming, and whether programming languages should be designed to support it.^[39]Edsger Dijkstra, in a famous 1968 letter published in the Communications of the ACM, argued that GOTO statements should be eliminated from all 'higher level' programming languages.^[40]

Consolidation and growth[edit]

The 1980s were years of relative consolidation. C++ combined object-oriented and systems programming. The United States government standardized Ada, a systems programming language derived from Pascal and intended for use by defense contractors. In Japan and elsewhere, vast sums were spent investigating so-called 'fifth-generation' languages that incorporated logic programming constructs.^[41] The functional languages community moved to standardize ML and Lisp. Rather than inventing new paradigms, all of these movements elaborated upon the ideas invented in the previous decades.

One important trend in language design for programming large-scale systems during the 1980s was an increased focus on the use of modules or large-scale organizational units of code. Modula-2, Ada, and ML all developed notable module systems in the 1980s, which were often wedded to generic programming constructs.^[42]

The rapid growth of the Internet in the mid-1990s created opportunities for new languages. Perl, originally a Unix scripting tool first released in 1987, became common in dynamic websites. Java came to be used for server-side programming, and bytecode virtual machines became popular again in commercial settings with their promise of 'Write once, run anywhere' (UCSD Pascal had been popular for a time in the early 1980s). These developments were not fundamentally novel, rather they were refinements of many existing languages and paradigms (although their syntax was often based on the C family of programming languages).

Programming language evolution continues, in both industry and research. Current directions include security and reliability verification, new kinds of modularity (mixins, delegates, aspects), and database integration such as Microsoft's LINQ.

Fourth-generation programming languages (4GL) are computer programming languages which aim to provide a higher level of abstraction of the internal computer hardware details than 3GLs. Fifth-generation programming languages (5GL) are programming languages based on solving problems using constraints given to the program, rather than using an algorithm written by a programmer.

Elements[edit]

All programming languages have some primitive building blocks for the description of data and the processes or transformations applied to them (like the addition of two numbers or the selection of an item from a collection). These primitives are defined by syntactic and semantic rules which describe their structure and meaning respectively.

Syntax[edit]

A programming language's surface form is known as its syntax. Most programming languages are purely textual; they use sequences of text including words, numbers, and punctuation, much like written natural languages. On the other hand, there are some programming languages which are more graphical in nature, using visual relationships between symbols to specify a program.

The syntax of a language describes the possible combinations of symbols that form a syntactically correct program. The meaning given to a combination of symbols is handled by semantics (either formal or hard-coded in a reference implementation). Since most languages are textual, this article discusses textual syntax.

Programming language syntax is usually defined using a combination of regular expressions (for lexical structure) and Backus–Naur form (for grammatical structure). Below is a simple grammar, based on Lisp:

This grammar specifies the following:

• an expression is either an atom or a list;
• an atom is either a number or a symbol;
• a number is an unbroken sequence of one or more decimal digits, optionally preceded by a plus or minus sign;
• a symbol is a letter followed by zero or more of any characters (excluding whitespace); and
• a list is a matched pair of parentheses, with zero or more expressions inside it.

The following are examples of well-formed token sequences in this grammar: 12345, () and (a b c232 (1)).

Not all syntactically correct programs are semantically correct. Many syntactically correct programs are nonetheless ill-formed, per the language's rules; and may (depending on the language specification and the soundness of the implementation) result in an error on translation or execution. In some cases, such programs may exhibit undefined behavior. Even when a program is well-defined within a language, it may still have a meaning that is not intended by the person who wrote it.

Using natural language as an example, it may not be possible to assign a meaning to a grammatically correct sentence or the sentence may be false:

• 'Colorless green ideas sleep furiously.' is grammatically well-formed but has no generally accepted meaning.
• 'John is a married bachelor.' is grammatically well-formed but expresses a meaning that cannot be true.

The following C language fragment is syntactically correct, but performs operations that are not semantically defined (the operation *p >> 4 has no meaning for a value having a complex type and p->im is not defined because the value of p is the null pointer):

If the type declaration on the first line were omitted, the program would trigger an error on undefined variable 'p' during compilation. However, the program would still be syntactically correct since type declarations provide only semantic information.

The grammar needed to specify a programming language can be classified by its position in the Chomsky hierarchy. The syntax of most programming languages can be specified using a Type-2 grammar, i.e., they are context-free grammars.^[43] Some languages, including Perl and Lisp, contain constructs that allow execution during the parsing phase. Languages that have constructs that allow the programmer to alter the behavior of the parser make syntax analysis an undecidable problem, and generally blur the distinction between parsing and execution.^[44] In contrast to Lisp's macro system and Perl's BEGIN blocks, which may contain general computations, C macros are merely string replacements and do not require code execution.^[45]

Semantics[edit]

The term semantics refers to the meaning of languages, as opposed to their form (syntax).

Static semantics[edit]

The static semantics defines restrictions on the structure of valid texts that are hard or impossible to express in standard syntactic formalisms.^[3] For compiled languages, static semantics essentially include those semantic rules that can be checked at compile time. Examples include checking that every identifier is declared before it is used (in languages that require such declarations) or that the labels on the arms of a case statement are distinct.^[46] Many important restrictions of this type, like checking that identifiers are used in the appropriate context (e.g. not adding an integer to a function name), or that subroutine calls have the appropriate number and type of arguments, can be enforced by defining them as rules in a logic called a type system. Other forms of static analyses like data flow analysis may also be part of static semantics. Newer programming languages like Java and C# have definite assignment analysis, a form of data flow analysis, as part of their static semantics.

Dynamic semantics[edit]

Once data has been specified, the machine must be instructed to perform operations on the data. For example, the semantics may define the strategy by which expressions are evaluated to values, or the manner in which control structures conditionally execute statements. The dynamic semantics (also known as execution semantics) of a language defines how and when the various constructs of a language should produce a program behavior. There are many ways of defining execution semantics. Natural language is often used to specify the execution semantics of languages commonly used in practice. A significant amount of academic research went into formal semantics of programming languages, which allow execution semantics to be specified in a formal manner. Results from this field of research have seen limited application to programming language design and implementation outside academia.

Type system[edit]

A type system defines how a programming language classifies values and expressions into types, how it can manipulate those types and how they interact. The goal of a type system is to verify and usually enforce a certain level of correctness in programs written in that language by detecting certain incorrect operations. Any decidable type system involves a trade-off: while it rejects many incorrect programs, it can also prohibit some correct, albeit unusual programs. In order to bypass this downside, a number of languages have type loopholes, usually unchecked casts that may be used by the programmer to explicitly allow a normally disallowed operation between different types. In most typed languages, the type system is used only to type check programs, but a number of languages, usually functional ones, infer types, relieving the programmer from the need to write type annotations. The formal design and study of type systems is known as type theory.

Typed versus untyped languages[edit]

A language is typed if the specification of every operation defines types of data to which the operation is applicable.^[47] For example, the data represented by 'this text between the quotes' is a string, and in many programming languages dividing a number by a string has no meaning and will not be executed. The invalid operation may be detected when the program is compiled ('static' type checking) and will be rejected by the compiler with a compilation error message, or it may be detected while the program is running ('dynamic' type checking), resulting in a run-time exception. Many languages allow a function called an exception handler to handle this exception and, for example, always return '-1' as the result.

A special case of typed languages are the single-typed languages. These are often scripting or markup languages, such as REXX or SGML, and have only one data type^[dubious]–—most commonly character strings which are used for both symbolic and numeric data.

In contrast, an untyped language, such as most assembly languages, allows any operation to be performed on any data, generally sequences of bits of various lengths.^[47] High-level untyped languages include BCPL, Tcl, and some varieties of Forth.

In practice, while few languages are considered typed from the type theory (verifying or rejecting all operations), most modern languages offer a degree of typing.^[47] Many production languages provide means to bypass or subvert the type system, trading type-safety for finer control over the program's execution (see casting).

Static versus dynamic typing[edit]

In static typing, all expressions have their types determined prior to when the program is executed, typically at compile-time. For example, 1 and (2+2) are integer expressions; they cannot be passed to a function that expects a string, or stored in a variable that is defined to hold dates.^[47]

Statically typed languages can be either manifestly typed or type-inferred. In the first case, the programmer must explicitly write types at certain textual positions (for example, at variable declarations). In the second case, the compiler infers the types of expressions and declarations based on context. Most mainstream statically typed languages, such as C++, C# and Java, are manifestly typed. Complete type inference has traditionally been associated with less mainstream languages, such as Haskell and ML. However, many manifestly typed languages support partial type inference; for example, C++, Java and C# all infer types in certain limited cases.^[48] Additionally, some programming languages allow for some types to be automatically converted to other types; for example, an int can be used where the program expects a float.

Dynamic typing, also called latent typing, determines the type-safety of operations at run time; in other words, types are associated with run-time values rather than textual expressions.^[47] As with type-inferred languages, dynamically typed languages do not require the programmer to write explicit type annotations on expressions. Among other things, this may permit a single variable to refer to values of different types at different points in the program execution. However, type errors cannot be automatically detected until a piece of code is actually executed, potentially making debugging more difficult. Lisp, Smalltalk, Perl, Python, JavaScript, and Ruby are all examples of dynamically typed languages.

Weak and strong typing[edit]

Weak typing allows a value of one type to be treated as another, for example treating a string as a number.^[47] This can occasionally be useful, but it can also allow some kinds of program faults to go undetected at compile time and even at run time.

Strong typing prevents these program faults. An attempt to perform an operation on the wrong type of value raises an error.^[47] Strongly typed languages are often termed type-safe or safe.

An alternative definition for 'weakly typed' refers to languages, such as Perl and JavaScript, which permit a large number of implicit type conversions. In JavaScript, for example, the expression 2 * x implicitly converts x to a number, and this conversion succeeds even if x is null, undefined, an Array, or a string of letters. Such implicit conversions are often useful, but they can mask programming errors.Strong and static are now generally considered orthogonal concepts, but usage in the literature differs. Some use the term strongly typed to mean strongly, statically typed, or, even more confusingly, to mean simply statically typed. Thus C has been called both strongly typed and weakly, statically typed.^[49][50]

It may seem odd to some professional programmers that C could be 'weakly, statically typed'. However, notice that the use of the generic pointer, the void* pointer, does allow for casting of pointers to other pointers without needing to do an explicit cast. This is extremely similar to somehow casting an array of bytes to any kind of datatype in C without using an explicit cast, such as (int) or (char).

Standard library and run-time system[edit]

Most programming languages have an associated core library (sometimes known as the 'standard library', especially if it is included as part of the published language standard), which is conventionally made available by all implementations of the language. Core libraries typically include definitions for commonly used algorithms, data structures, and mechanisms for input and output.

The line between a language and its core library differs from language to language. In some cases, the language designers may treat the library as a separate entity from the language. However, a language's core library is often treated as part of the language by its users, and some language specifications even require that this library be made available in all implementations. Indeed, some languages are designed so that the meanings of certain syntactic constructs cannot even be described without referring to the core library. For example, in Java, a string literal is defined as an instance of the java.lang.String class; similarly, in Smalltalk, an anonymous function expression (a 'block') constructs an instance of the library's BlockContext class. Conversely, Scheme contains multiple coherent subsets that suffice to construct the rest of the language as library macros, and so the language designers do not even bother to say which portions of the language must be implemented as language constructs, and which must be implemented as parts of a library.

Design and implementation[edit]

Programming languages share properties with natural languages related to their purpose as vehicles for communication, having a syntactic form separate from its semantics, and showing language families of related languages branching one from another.^[51][52] But as artificial constructs, they also differ in fundamental ways from languages that have evolved through usage. A significant difference is that a programming language can be fully described and studied in its entirety, since it has a precise and finite definition.^[53] By contrast, natural languages have changing meanings given by their users in different communities. While constructed languages are also artificial languages designed from the ground up with a specific purpose, they lack the precise and complete semantic definition that a programming language has.

Many programming languages have been designed from scratch, altered to meet new needs, and combined with other languages. Many have eventually fallen into disuse. Although there have been attempts to design one 'universal' programming language that serves all purposes, all of them have failed to be generally accepted as filling this role.^[54] The need for diverse programming languages arises from the diversity of contexts in which languages are used:

• Programs range from tiny scripts written by individual hobbyists to huge systems written by hundreds of programmers.
• Programmers range in expertise from novices who need simplicity above all else, to experts who may be comfortable with considerable complexity.
• Programs must balance speed, size, and simplicity on systems ranging from microcontrollers to supercomputers.
• Programs may be written once and not change for generations, or they may undergo continual modification.
• Programmers may simply differ in their tastes: they may be accustomed to discussing problems and expressing them in a particular language.

One common trend in the development of programming languages has been to add more ability to solve problems using a higher level of abstraction. The earliest programming languages were tied very closely to the underlying hardware of the computer. As new programming languages have developed, features have been added that let programmers express ideas that are more remote from simple translation into underlying hardware instructions. Because programmers are less tied to the complexity of the computer, their programs can do more computing with less effort from the programmer. This lets them write more functionality per time unit.^[55]

Natural language programming has been proposed as a way to eliminate the need for a specialized language for programming. However, this goal remains distant and its benefits are open to debate. Edsger W. Dijkstra took the position that the use of a formal language is essential to prevent the introduction of meaningless constructs, and dismissed natural language programming as 'foolish'.^[56]Alan Perlis was similarly dismissive of the idea.^[57] Hybrid approaches have been taken in Structured English and SQL.

A language's designers and users must construct a number of artifacts that govern and enable the practice of programming. The most important of these artifacts are the language specification and implementation.

Specification[edit]

The specification of a programming language is an artifact that the language users and the implementors can use to agree upon whether a piece of source code is a valid program in that language, and if so what its behavior shall be.

A programming language specification can take several forms, including the following:

• An explicit definition of the syntax, static semantics, and execution semantics of the language. While syntax is commonly specified using a formal grammar, semantic definitions may be written in natural language (e.g., as in the C language), or a formal semantics (e.g., as in Standard ML^[58] and Scheme^[59] specifications).
• A description of the behavior of a translator for the language (e.g., the C++ and Fortran specifications). The syntax and semantics of the language have to be inferred from this description, which may be written in natural or a formal language.
• A reference or model implementation, sometimes written in the language being specified (e.g., Prolog or ANSI REXX^[60]). The syntax and semantics of the language are explicit in the behavior of the reference implementation.

Implementation[edit]

An implementation of a programming language provides a way to write programs in that language and execute them on one or more configurations of hardware and software. There are, broadly, two approaches to programming language implementation: compilation and interpretation. It is generally possible to implement a language using either technique.

The output of a compiler may be executed by hardware or a program called an interpreter. In some implementations that make use of the interpreter approach there is no distinct boundary between compiling and interpreting. For instance, some implementations of BASIC compile and then execute the source a line at a time.

Programs that are executed directly on the hardware usually run much faster than those that are interpreted in software.^{[61][better source needed]}

One technique for improving the performance of interpreted programs is just-in-time compilation. Here the virtual machine, just before execution, translates the blocks of bytecode which are going to be used to machine code, for direct execution on the hardware.

Proprietary languages[edit]

Although most of the most commonly used programming languages have fully open specifications and implementations, many programming languages exist only as proprietary programming languages with the implementation available only from a single vendor, which may claim that such a proprietary language is their intellectual property. Proprietary programming languages are commonly domain specific languages or internal scripting languages for a single product; some proprietary languages are used only internally within a vendor, while others are available to external users.

Some programming languages exist on the border between proprietary and open; for example, Oracle Corporation asserts proprietary rights to some aspects of the Java programming language,^[62] and Microsoft's C# programming language, which has open implementations of most parts of the system, also has Common Language Runtime (CLR) as a closed environment.^[63]

Many proprietary languages are widely used, in spite of their proprietary nature; examples include MATLAB, VBScript, and Wolfram Language. Some languages may make the transition from closed to open; for example, Erlang was originally an Ericsson's internal programming language.^[64]

Use[edit]

Thousands of different programming languages have been created, mainly in the computing field.^[65]Software is commonly built with 5 programming languages or more.^[66]

Programming languages differ from most other forms of human expression in that they require a greater degree of precision and completeness. When using a natural language to communicate with other people, human authors and speakers can be ambiguous and make small errors, and still expect their intent to be understood. However, figuratively speaking, computers 'do exactly what they are told to do', and cannot 'understand' what code the programmer intended to write. The combination of the language definition, a program, and the program's inputs must fully specify the external behavior that occurs when the program is executed, within the domain of control of that program. On the other hand, ideas about an algorithm can be communicated to humans without the precision required for execution by using pseudocode, which interleaves natural language with code written in a programming language.

A programming language provides a structured mechanism for defining pieces of data, and the operations or transformations that may be carried out automatically on that data. A programmer uses the abstractions present in the language to represent the concepts involved in a computation. These concepts are represented as a collection of the simplest elements available (called primitives).^[67]Programming is the process by which programmers combine these primitives to compose new programs, or adapt existing ones to new uses or a changing environment.

Programs for a computer might be executed in a batch process without human interaction, or a user might type commands in an interactive session of an interpreter. In this case the 'commands' are simply programs, whose execution is chained together. When a language can run its commands through an interpreter (such as a Unix shell or other command-line interface), without compiling, it is called a scripting language.^[68]

Measuring language usage[edit]

Determining which is the most widely used programming language is difficult since the definition of usage varies by context. One language may occupy the greater number of programmer hours, a different one has more lines of code, and a third may consume the most CPU time. Some languages are very popular for particular kinds of applications. For example, COBOL is still strong in the corporate data center, often on large mainframes;^[69][70]Fortran in scientific and engineering applications; Ada in aerospace, transportation, military, real-time and embedded applications; and C in embedded applications and operating systems. Other languages are regularly used to write many different kinds of applications.

Various methods of measuring language popularity, each subject to a different bias over what is measured, have been proposed:

• counting the number of job advertisements that mention the language^[71]
• the number of books sold that teach or describe the language^[72]
• estimates of the number of existing lines of code written in the language – which may underestimate languages not often found in public searches^[73]
• counts of language references (i.e., to the name of the language) found using a web search engine.

Combining and averaging information from various internet sites, stackify.com reported the ten most popular programming languages as (in descending order by overall popularity): Java, C, C++, Python, C#, JavaScript, VB .NET, R, PHP, and MATLAB.^[74]

Dialects, flavors and implementations[edit]

A dialect of a programming language or a data exchange language is a (relatively small) variation or extension of the language that does not change its intrinsic nature. With languages such as Scheme and Forth, standards may be considered insufficient, inadequate or illegitimate by implementors, so often they will deviate from the standard, making a new dialect. In other cases, a dialect is created for use in a domain-specific language, often a subset. In the Lisp world, most languages that use basic S-expression syntax and Lisp-like semantics are considered Lisp dialects, although they vary wildly, as do, say, Racket and Clojure. As it is common for one language to have several dialects, it can become quite difficult for an inexperienced programmer to find the right documentation. The BASIC programming language has many dialects.

The explosion of Forth dialects led to the saying 'If you've seen one Forth.. you've seen one Forth.'

Taxonomies[edit]

There is no overarching classification scheme for programming languages. A given programming language does not usually have a single ancestor language. Languages commonly arise by combining the elements of several predecessor languages with new ideas in circulation at the time. Ideas that originate in one language will diffuse throughout a family of related languages, and then leap suddenly across familial gaps to appear in an entirely different family.

The task is further complicated by the fact that languages can be classified along multiple axes. For example, Java is both an object-oriented language (because it encourages object-oriented organization) and a concurrent language (because it contains built-in constructs for running multiple threads in parallel). Python is an object-oriented scripting language.

In broad strokes, programming languages divide into programming paradigms and a classification by intended domain of use, with general-purpose programming languages distinguished from domain-specific programming languages. Traditionally, programming languages have been regarded as describing computation in terms of imperative sentences, i.e. issuing commands. These are generally called imperative programming languages. A great deal of research in programming languages has been aimed at blurring the distinction between a program as a set of instructions and a program as an assertion about the desired answer, which is the main feature of declarative programming.^[75] More refined paradigms include procedural programming, object-oriented programming, functional programming, and logic programming; some languages are hybrids of paradigms or multi-paradigmatic. An assembly language is not so much a paradigm as a direct model of an underlying machine architecture. By purpose, programming languages might be considered general purpose, system programming languages, scripting languages, domain-specific languages, or concurrent/distributed languages (or a combination of these).^[76] Some general purpose languages were designed largely with educational goals.^[77]

A programming language may also be classified by factors unrelated to programming paradigm. For instance, most programming languages use English language keywords, while a minority do not. Other languages may be classified as being deliberately esoteric or not.

See also[edit]

• Computer science and Outline of computer science
• Metaprogramming
• Software engineering and List of software engineering topics

References[edit]

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Further reading[edit]

• Abelson, Harold; Sussman, Gerald Jay (1996). Structure and Interpretation of Computer Programs (2nd ed.). MIT Press. Archived from the original on 9 March 2018. Retrieved 22 October 2011.
• Raphael Finkel: Advanced Programming Language Design, Addison Wesley 1995.
• Daniel P. Friedman, Mitchell Wand, Christopher T. Haynes: Essentials of Programming Languages, The MIT Press 2001.
• Maurizio Gabbrielli and Simone Martini: 'Programming Languages: Principles and Paradigms', Springer, 2010.
• David Gelernter, Suresh Jagannathan: Programming Linguistics, The MIT Press 1990.
• Ellis Horowitz (ed.): Programming Languages, a Grand Tour (3rd ed.), 1987.
• Ellis Horowitz: Fundamentals of Programming Languages, 1989.
• Shriram Krishnamurthi: Programming Languages: Application and Interpretation, online publication.
• Bruce J. MacLennan: Principles of Programming Languages: Design, Evaluation, and Implementation, Oxford University Press 1999.
• John C. Mitchell: Concepts in Programming Languages, Cambridge University Press 2002.
• Benjamin C. Pierce: Types and Programming Languages, The MIT Press 2002.
• Terrence W. Pratt and Marvin V. Zelkowitz: Programming Languages: Design and Implementation (4th ed.), Prentice Hall 2000.
• Peter H. Salus. Handbook of Programming Languages (4 vols.). Macmillan 1998.
• Ravi Sethi: Programming Languages: Concepts and Constructs, 2nd ed., Addison-Wesley 1996.
• Michael L. Scott: Programming Language Pragmatics, Morgan Kaufmann Publishers 2005.
• Robert W. Sebesta: Concepts of Programming Languages, 9th ed., Addison Wesley 2009.
• Franklyn Turbak and David Gifford with Mark Sheldon: Design Concepts in Programming Languages, The MIT Press 2009.
• Peter Van Roy and Seif Haridi. Concepts, Techniques, and Models of Computer Programming, The MIT Press 2004.
• David A. Watt. Programming Language Concepts and Paradigms. Prentice Hall 1990.
• David A. Watt and Muffy Thomas. Programming Language Syntax and Semantics. Prentice Hall 1991.
• David A. Watt. Programming Language Processors. Prentice Hall 1993.
• David A. Watt. Programming Language Design Concepts. John Wiley & Sons 2004.

External links[edit]