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Database Reliability Engineering. Designing and Operating Resilient Database Systems - Helion

Database Reliability Engineering. Designing and Operating Resilient Database Systems
ebook
Autor: Laine Campbell, Charity Majors
ISBN: 978-14-919-2621-5
stron: 294, Format: ebook
Data wydania: 2017-10-26
Ksi─Ögarnia: Helion

Cena ksi─ů┼╝ki: 152,15 z┼é (poprzednio: 176,92 z┼é)
Oszczędzasz: 14% (-24,77 zł)

Dodaj do koszyka Database Reliability Engineering. Designing and Operating Resilient Database Systems

The infrastructure-as-code revolution in IT is also affecting database administration. With this practical book, developers, system administrators, and junior to mid-level DBAs will learn how the modern practice of site reliability engineering applies to the craft of database architecture and operations. Authors Laine Campbell and Charity Majors provide a framework for professionals looking to join the ranks of today’s database reliability engineers (DBRE).

You’ll begin by exploring core operational concepts that DBREs need to master. Then you’ll examine a wide range of database persistence options, including how to implement key technologies to provide resilient, scalable, and performant data storage and retrieval. With a firm foundation in database reliability engineering, you’ll be ready to dive into the architecture and operations of any modern database.

This book covers:

  • Service-level requirements and risk management
  • Building and evolving an architecture for operational visibility
  • Infrastructure engineering and infrastructure management
  • How to facilitate the release management process
  • Data storage, indexing, and replication
  • Identifying datastore characteristics and best use cases
  • Datastore architectural components and data-driven architectures

Dodaj do koszyka Database Reliability Engineering. Designing and Operating Resilient Database Systems

 

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Dodaj do koszyka Database Reliability Engineering. Designing and Operating Resilient Database Systems

Spis tre┼Ťci

Database Reliability Engineering. Designing and Operating Resilient Database Systems eBook -- spis tre┼Ťci

  • Foreword
  • Preface
    • Why We Wrote This Book
    • Who This Book Is For
    • How This Book Is Organized
    • Conventions Used in This Book
    • OReilly Safari
    • How to Contact Us
  • 1. Introducing Database Reliability Engineering
    • Guiding Principles of the DBRE
      • Protect the Data
      • Self-Service for Scale
      • Elimination of Toil
      • Databases Are Not Special Snowflakes
      • Eliminate the Barriers Between Software and Operations
    • Operations Core Overview
    • Hierarchy of Needs
      • Survival and Safety
      • Love and Belonging
      • Esteem
      • Self-actualization
    • Wrapping Up
  • 2. Service-Level Management
    • Why Do I Need Service-Level Objectives?
    • Service-Level Indicators
      • Latency
      • Availability
      • Throughput
      • Durability
      • Cost or Efficiency
    • Defining Service Objectives
      • Latency Indicators
      • Availability Indicators
        • Resiliency versus robustness in availability
        • Designing for downtime allowed
      • Throughput Indicators
        • Cost/efficiency indicators
        • Considerations
    • Monitoring and Reporting on SLOs
      • Monitoring Availability
      • Monitoring Latency
      • Monitoring Throughput
      • Monitoring Cost and Efficiency
    • Wrapping Up
  • 3. Risk Management
    • Risk Considerations
      • Unknown Factors and Complexity
      • Availability of Resources
      • Human Factors
      • Group Factors
    • What Do We Do?
    • What Not to Do
    • A Working Process: Bootstrapping
      • Service Risk Evaluation
      • Architectural Inventory
      • Prioritization
        • Severe impact (immediate SLO violation)
        • Major (imminent SLO violation)
        • Moderate (could contribute to SLO violation with other incidents in the same period)
          • Minor
        • Control and Decision Making
        • Identification
        • Evaluation
        • Mitigation and controls
        • Implementation
    • Ongoing Iterations
    • Wrapping Up
  • 4. Operational Visibility
    • The New Rules of Operational Visibility
      • Treat OpViz Systems Like BI Systems
      • Distributed Ephemeral Environments Trending to the Norm
      • Store at High Resolutions for Key Metrics
      • Keep Your Architecture Simple
    • An OpViz Framework
    • Data In
      • Telemetry/Metrics
      • Events
      • Logs
    • Data Out
    • Bootstrapping Your Monitoring
      • Is the Data Safe?
      • Is the Service Up?
      • Are the Consumers in Pain?
    • Instrumenting the Application
      • Distributed Tracing
      • Events and Logs
    • Instrumenting the Server or Instance
      • Events and Logs
    • Instrumenting the Datastore
    • Datastore Connection Layer
      • Utilization
      • Saturation
      • Errors
    • Internal Database Visibility
      • Throughput and Latency Metrics
      • Commits, Redo, and Journaling
      • Replication State
      • Memory Structures
      • Locking and Concurrency
    • Database Objects
    • Database Queries
    • Database Asserts and Events
    • Wrapping Up
  • 5. Infrastructure Engineering
    • Hosts
      • Physical Servers
      • Operating a System and Kernel
        • User resource limits
        • I/O scheduler
        • Memory allocation and fragmentation
        • Swapping
        • Non-Uniform memory access
        • Network
        • Storage
        • Storage capacity
        • Storage throughput
        • Storage latency
        • Storage availability
        • Durability
      • Storage Area Networks
      • Benefits of Physical Servers
      • Cons of Physical Servers
    • Virtualization
      • Hypervisor
      • Concurrency
      • Storage
      • Use Cases
    • Containers
    • Database as a Service
      • Challenges of DBaaS
      • The DBRE and the DBaaS
    • Wrapping Up
  • 6. Infrastructure Management
    • Version Control
    • Configuration Definition
    • Building from Configuration
    • Maintaining Configuration
      • Enforcement of Configuration Definitions
        • Configuration synchronization
        • Component redeploys
    • Infrastructure Definition and Orchestration
      • Monolithic Infrastructure Definitions
      • Separating Vertically
      • Separated Tiers (Horizontal Definitions)
    • Acceptance Testing and Compliance
    • Service Catalog
    • Bringing It All Together
    • Development Environments
    • Wrapping Up
  • 7. Backup and Recovery
    • Core Concepts
      • Physical versus Logical
      • Online versus Offline
      • Full, Incremental, and Differential
    • Considerations for Recovery
    • Recovery Scenarios
      • Planned Recovery Scenarios
        • New production nodes and clusters
        • Building different environments
        • ETL and pipeline processes for downstream datastores
        • Operational tests
      • Unplanned Scenarios
        • User error
        • Application errors
        • Infrastructure services
        • OS and hardware errors
        • Hardware failures
        • Datacenter failures
      • Scenario scope
      • Scenario Impact
    • Anatomy of a Recovery Strategy
      • Building Block 1: Detection
        • User error
        • Application errors
        • Infrastructure services
        • OS and hardware errors
        • Hardware and datacenter failures
      • Building Block 2: Tiered Storage
        • Online, high performance storage
        • Online, low-performance storage
        • Offline storage
        • Object storage
      • Building Block 3: A Varied Toolbox
        • Full physical backups
        • Incremental physical backups
        • Full and incremental logical backups
        • Object stores
      • Building Block 4: Testing
    • A Recovery Strategy Defined
      • Online, Fast Storage with Full and Incremental Backups
        • Use Cases
        • Detection
        • Tiered storage
        • Toolbox
        • Testing
      • Online, Slow Storage with Full and Incremental Backups
        • Use cases
        • Detection
        • Tiered storage
        • Toolbox
        • Testing
      • Offline Storage
        • Use cases
        • Detection
        • Tiered storage
        • Toolbox
        • Testing
      • Object Storage
        • Use cases
        • Detection
        • Testing
    • Wrapping Up
  • 8. Release Management
    • Education and Collaboration
      • Become a Funnel
      • Foster Conversations
      • Domain-Specific Knowledge
        • Architecture
        • Data model
        • Best Practices and Standards
        • Tools
      • Collaboration
    • Integration
      • Prerequisites
        • Version control system
        • Database build automation
        • Test data
        • Database migrations and packaging
        • CI server and test framework
    • Testing
      • Test-Friendly Development Practices
        • Abstraction and encapsulation
        • Being efficient
      • Post-Commit Testing
        • Pre-build
        • Build
        • Post-build
      • Full Dataset Testing
      • Downstream Tests
      • Operational Tests
    • Deployment
      • Migrations and Versioning
      • Impact Analysis
        • Locking of objects
        • Saturation of resources
        • Data integrity issues
        • Replication stalls
      • Migration Patterns
        • Pattern: locking operations
        • Pattern: high resource utilization operations
        • Pattern: rolling migrations
        • Migration testing
        • Rollback testing
      • Manual or Automated
    • Wrapping Up
  • 9. Security
    • The Purpose of Security
      • Protecting Data from Theft
      • Protecting from Purposeful Damage
      • Protecting from Accidental Damage
      • Protecting Data from Exposure
      • Compliance and Auditing Standards
    • Database Security as a Function
      • Education and Collaboration
      • Self-Service
      • Integration and Testing
      • Operational Visibility
        • Application layer instrumentation
        • Database layer instrumentation
        • OS instrumentation
    • Vulnerabilities and Exploits
      • STRIDE
      • DREAD
      • Basic Precautions
      • Denial of Service
        • Mitigation
        • Resource management and load shedding
        • Continual improvement of database access and workloads
        • Logging and monitoring
      • SQL Injection
        • Mitigation
        • Prepared statements
        • Input validation
        • Harm reduction
        • Monitoring
      • Network and Authentication Protocols
    • Encryption of Data
      • Financial Data
      • Personal Health Data
      • Private Individual Data
      • Military or Government Data
      • Confidential/Sensitive Business Data
      • Data in Transit
        • Anatomy of a cipher suite
        • Communication within the network
        • Communications outside of the network
        • Establishing secure data connections
          • Basic connection encryption
          • Securely stored secrets
          • Dynamically built database users
      • Data in the Database
        • Application-level security
        • Database plug-in encryption
        • Transparent database encryption
        • Query performance considerations
      • Data in the Filesystem
        • Data encryption above the filesystem
        • Filesystem encryption
        • Device-level encryption
    • Wrapping Up
  • 10. Data Storage, Indexing, and Replication
    • Data Structure Storage
      • Database Row Storage
        • B-tree structures
          • Binary tree writes
      • Sorted-String Tables and Log-Structured Merge Trees
        • Bloom filters
        • Implementations
      • Indexing
        • Hash indexes
        • Bitmap indexes
        • Permutations of B-trees
      • Logs and Databases
    • Data Replication
      • Single-Leader
        • Replication models
        • Replication log formats
          • Statement-based logs
          • Write-ahead logs
          • Row-based replication
          • Block-level replication
          • Other methods
        • Single-leader replication uses
          • Availability
          • Scalability
          • Locality
          • Portability
        • Single leader replication challenges
          • Building replicas
          • Keeping replicas synchronized
          • Single leader failovers
        • Single leader replication monitoring
          • Replication lag and latency
          • Replication availability and capacity
          • Replication consistency
          • Operational processes
      • Multi-Leader Replication
        • Multileader use cases
          • Availability
          • Locality
          • Disaster recovery
        • Conflict resolution in traditional multidirectional replication
          • Eliminate conflicts
          • Last write wins
          • Custom resolution options
          • Conflict-free replicated datatypes
        • Write-anywhere replication
          • Eventual consistency
          • Read and write quorums
          • Sloppy quorums
          • Anti-entropy
    • Wrapping Up
  • 11. Datastore Field Guide
    • Conceptual Attributes of a Datastore
      • The Data Model
        • The relational model
        • The keyvalue model
        • The document model
        • The navigational model
      • Transactions
        • ACID
        • Atomicity
        • Consistency
        • Isolation
        • Durability
      • BASE
    • Internal Attributes of a Datastore
      • Storage
      • The Ubiquitous CAP Theorem Section
        • Consistency
        • Availability
        • Partition tolerance
      • Consistency Latency Trade-offs
      • Availability
    • Wrapping Up
  • 12. A Data Architecture Sampler
    • Architectural Components
      • Frontend Datastores
      • Data Access Layer
      • Database Proxies
        • Availability
        • Data Integrity
        • Scalability
        • Latency
      • Event and Message Systems
        • Availability
        • Data integrity
        • Scalability
        • Latency
      • Caches and Memory Stores
        • Availability
        • Data integrity
        • Scalability
        • Latency
    • Data Architectures
      • Lambda and Kappa
        • Lambda architecture
        • Kappa architecture
      • Event Sourcing
      • CQRS
    • Wrapping Up
  • 13. Making the Case For DBRE
    • A Culture of Database Reliability
      • Breaking-Down Barriers
        • The architectural process
        • Database development
        • Production migrations
        • Infrastructure design and deployment
      • Data-Driven Decision Making
      • Data Integrity and Recoverability
    • Wrapping Up
  • Index

Dodaj do koszyka Database Reliability Engineering. Designing and Operating Resilient Database Systems

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