Neo4j: The Definitive Guide. Hands-On Recipes for Production-Ready Graph Implementations - Helion

ebook

Autor: Luanne Misquitta, Christophe Willemsen
ISBN: 9781098165611
stron: 410, Format: ebook
Data wydania: 2025-07-23
Księgarnia: Helion

Cena książki: 220,15 zł (poprzednio: 255,99 zł)
Oszczędzasz: 14% (-35,84 zł)

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Looking to improve the performance of Cypher queries or learn how to model graphs to support business use cases? A graph database like Neo4j can help. In fact, many enterprises are leveraging Neo4j to power their business-critical applications. This book offers practical and concise recipes on how and when to successfully leverage Neo4j into architectures.

Authors Christophe Willemsen and Luanne Misquitta walk you through typical Neo4j implementation strategies from proof of concept to iterative improvements and, finally, to production readiness and beyond. By the end of this book, you should understand how to:

Make practical decisions in the proof of concept stage to maximize value
Revisit and revise your decisions when transitioning to production
Configure and implement observability features for in-production data graphs
Integrate graph databases into existing enterprise architectures

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Spis treści

Neo4j: The Definitive Guide. Hands-On Recipes for Production-Ready Graph Implementations eBook -- spis treści

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