Neo4j: The Definitive Guide. Hands-On Recipes for Production-Ready Graph Implementations Luanne Misquitta, Christophe Willemsen

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Spis treści książki

• Foreword
• Preface
  • Why We Wrote This Book
  • Is This Book for You?
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. How to Get Value from Graphs in Just Five Days
  • Dissonance at ElectricHarmony
  • Why Graph Databases?
  • Graph Use Cases
    • Ultimate Beneficial Ownership Networks
    • Real-Time Recommendations
    • Law Enforcement
    • Cybercrime Networks
  • Neo4j
    • Native Graph Databases
  • Cypher
  • The Song Recommendation System: A Proof of Concept
    • Day 1
      • Installing Neo4j
      • Ingesting your first datasets
      • Previewing the data
      • Designing your graph model
      • Walking the graph
      • Key takeaways
    • Day 2
      • Creating nodes and relationships
      • Querying across datasets
        • Merging (safely) to avoid duplicates
        • Exploration and refactoring
      • Key takeaways
    • Day 3
      • Indexes for boosting data ingestion speed
      • Minimum data quality
      • Finding similarities
      • Key takeaways
    • Day 4
      • Materializing similarities
      • Implicit relationships
      • Recommending a track when the playlist ends
      • Key takeaways
    • Day 5
  • Summary
• 2. Importing (Much) More Data
  • Database Transactions
    • The Beat Heap Box
      • The CALL IN TRANSACTIONS operation
    • Try It: Importing Data from Client Applications
      • Cypher query parameters
      • Processing batches of data with UNWIND
      • Batch sizing
    • Parallel Writes
      • Competing for memory
      • Locking mechanisms
      • Simultaneous updates to nodes and relationships
      • Creating nodes and relationships concurrently
      • Adding relationships concurrently
    • Offline Import
    • Exploring Other Ingestion Tools
    • Summary
• 3. Revisiting Modeling Decisions
  • It Depends
  • Principles of Modeling
  • Properties Versus Nodes
    • Properties That Decorate the Result
    • Key Takeaways
  • Traversing Across Commonalities
    • Key Takeaways
  • Modeling Concepts as Labels
    • Key Takeaways
  • Node Fanout
    • Key Takeaways
  • Supernodes
    • Key Takeaways
  • Relationship Granularity
    • Key Takeaways
  • Qualified Relationships
    • Bucketed Relationships
    • Key Takeaways
  • Bidirectional Relationships
    • Key Takeaways
  • Summary
• 4. Modeling and Refactoring Patterns
  • Hyperedges: N-way Relationships
    • Key Takeaways
  • Time-Based Versioning
    • Key Takeaways
  • Representing Sequences
    • Key Takeaways
  • Refactoring Patterns
    • Refactoring to Change the Type of a Relationship
      • Stage 1
      • Stage 2
      • Stage 3
    • Refactoring to Create a Node from a Property
    • Refactoring to Create a Node from a Relationship
      • Stage 1
      • Stage 2
      • Stage 3
      • Stage 4
    • Key Takeaways
  • Summary
• 5. Query Analysis and Tuning
  • Query Execution
    • Pattern Anchors
      • Global graph query
      • Querying based on labels
      • A more selective query
    • Query Profiling
      • Rows
      • Database hits
      • Page cache hits/misses
    • Row Cardinality
  • Matching Disconnected Patterns
  • Increasing Anchor Selectivity
    • Eliminating Redundant Filter Operations
    • Improving Anchor Selectivity in Queries with Predicates
    • Indexing Guidelines
  • Accessing Properties
  • Node Degrees
  • Dont Be Eager!
  • Sorting
  • I Want to Break Free (of the Planner)
  • Cypher Runtimes
  • Parameterizing Queries
  • Monitoring and Measuring Query Times
  • Summary
• 6. Securing Your Database
  • Spoofing
    • Authentication
      • Using the native auth provider
      • Using the LDAP provider
    • Securing Access via the Neo4j browser
    • Best Practices
  • Tampering
    • Securing Communication Channels
    • Securing Data at Rest
    • Using Consistency Checks
    • Defending Against Cypher Injection Attacks
    • Implementing Role-Based Access Control
    • Using the Load CSV Command
    • Audit Logs
    • Constraints
    • Backups
  • Repudiation
  • Information Disclosure
    • Query Logs
    • Fine-Grained Access Control
    • Property Encryption
  • Denial of Service
  • Elevation of Privilege
    • Immutable Privileges
    • Least Privileges
    • Extensions
    • User and Privilege Reviews
    • File Permissions
    • Patches
  • Summary
• 7. Search
  • What Is Search?
    • Text
    • Indexes
  • Searching for Data
    • Partial Searches
  • TEXT Indexes
  • Full-Text Indexes
    • Multitoken Searches
    • Phrase Searches
    • Wildcard Searches
    • Fuzzy Search
  • Additional Index and Query Considerations
    • Tokenization
    • Special Characters: Hashtags and Mentions
    • Identifiers, IP Addresses, and Other Nonword Terms
    • Stopwords: To Be or Not to Be
    • Performance with Graph Patterns
  • Summary
• 8. Advanced Graph Patterns
  • Subqueries
    • CALL Subqueries
    • Post-Union Processing
    • Concurrent Transactions with CALL
  • Fine-Grained Relationship Types
  • Modeling Resolved Entities
    • Entity Groups
    • Fused Entities
  • Quantified Path Patterns: An Entity-Resolution Use Case
  • Security Modeling: Labels Versus Properties
  • Summary
• 9. Backup and Restore
  • The Write Path
    • Checkpoints
    • Transaction-Log Retention
    • How Aggressive Is Aggressive?
    • A Guided Example
  • Backups
    • Types of Backups
      • Full backup
      • Incremental backup
  • Restoring Backups
  • Cloud Backups
  • Remote Backups and VM Separation
  • Designing a Backup Strategy
  • Summary
• 10. Clustering and Sharding
  • Clustering for High Availability
    • Raft Protocol
    • Fault Tolerance
    • Secondaries
  • Deploying a Cluster
    • Cluster Degradation
    • Multidatabase Clusters
    • How Network Latency Affects Clustering
  • Scaling Reads with Secondaries
  • Using Secondary Servers for Backups
  • Causal Consistency
  • The Mythical 1+1 Cluster
  • Sharding and Federation
  • Summary
• 11. Observability
  • Harnessing the Power of Logs
    • Types of Logs in Neo4j
    • Configuring Neo4j Logs
      • Appenders
      • Loggers
    • Inspecting Logs
      • Neo4j log
      • Security log
      • Debug log
      • Query log
      • GC log
    • Taming the Query Log
      • VERBOSE logging
      • Filtering out log events
      • Enriching the metadata
      • Identifying long-running queries
  • Unveiling the Power of Metrics
    • Enabling Metrics
    • Server Load Metrics
    • Neo4j Load Metrics
    • Neo4j Workload Metrics
      • Bolt metrics
      • Object count metrics
      • Throughput metrics
  • Bringing It All Together: Logs and Metrics with Grafana, Loki, and Prometheus
    • Setting Up the Observability Stack
    • Visualizing Metrics
    • Querying Logs
      • Step 1: Get all the logs
      • Step 2: Filter by application
      • Step 3: Filter by query time
      • Step 4: Combine them all
    • Other Tools
  • Summary
• 12. Practical Graph Data Science
  • Introduction to the Graph Data Science Library
    • Algorithms
    • The Graph Catalog
  • AI-Driven Playlist Communities
    • Building a Co-Occurrence Graph
      • Start small
        • Mark the experimental dataset
        • Skip low co-occurrences
      • Create the co-occurrence relationships
    • Using GDS
      • Installing the GDS plugin
      • Projecting the subgraph
      • Estimating memory usage
      • Project the graph
      • Executing the community detection algorithm
      • Memory estimation
        • Execution mode
        • Storing results
      • Analytical queries
      • Rinse and repeat
      • Creating a second experiment
  • Real-World Applications of Community Detection
    • Playlist recommendations
    • User segmentation
    • Influencer discovery
    • Behavioral clusters
    • Content licensing strategy
  • Summary
• 13. The Future of Graphs with Generative AI
  • Knowledge Graphs
    • Applications of Knowledge Graphs
    • Customer 360
    • Cybersecurity
    • Life Sciences
    • Retail
    • Criminal Investigations
  • GraphRAG
    • What About Vector Search?
  • Agentic AI Architectures
  • Knowledge Graph Creation
  • A Practical Example: Playlist Recommendations from Natural Language
    • Step 1: Communities from GDS
    • Step 2: Generate Summaries and Questions with an LLM
    • Step 3: Vectorize and Store in Neo4j
    • Step 4: The User Asks a Question
    • Step 5: Generate an answer
    • Step 6: Wrapping Up
  • Summary
• Index

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