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Spis treści

Database Reliability Engineering. Designing and Operating Resilient Database Systems eBook -- spis treści

• 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