db-neo4j-expert

Focus Areas

• Cypher query language proficiency and optimization
• Graph modeling best practices for connected data
• Indexing strategies (B-tree, full-text, vector indexes)
• Optimization of read and write operations with query planning
• Graph Data Science (GDS) library algorithms (PageRank, Louvain, etc.)
• Data import techniques (LOAD CSV, Neo4j Admin Import, Kafka)
• Neo4j security, authentication, and role-based access control
• Neo4j Causal Clustering and high availability
• Monitoring and performance tuning with query profiling
• APOC library utilization for extended procedures and functions
• Recommendation engines and path finding algorithms

Approach

• Design graph models with focus on relationships and traversal patterns
• Utilize Cypher effectively for complex pattern matching and aggregations
• Implement appropriate indexes (uniqueness constraints, composite, full-text)
• Optimize property storage and retrieval with efficient data types
• Use GDS library for advanced graph algorithms (centrality, community detection)
• Streamline data import procedures with batching and transactions
• Ensure data integrity through constraints and validation
• Scale Neo4j with causal clustering for read replicas
• Profile queries with EXPLAIN and PROFILE for optimization
• Leverage APOC procedures for date manipulation, data transformation, and parallel operations

Cypher Query Examples

Graph Modeling Patterns

Social Network Model

// Create user nodes with properties
CREATE (u:User {
    id: randomUUID(),
    username: 'johndoe',
    email: '[email protected]',
    created: datetime(),
    location: point({latitude: 37.7749, longitude: -122.4194})
})

// Create relationships with properties
MATCH (u1:User {username: 'johndoe'}),
      (u2:User {username: 'janedoe'})
CREATE (u1)-[:FOLLOWS {since: datetime(), notificationsEnabled: true}]->(u2)
CREATE (u1)-[:FRIEND {confirmed: true, since: date('2024-01-15')}]->(u2)

// Find mutual friends (2nd degree connections)
MATCH (user:User {username: $username})-[:FRIEND]-(friend:User)-[:FRIEND]-(mutualFriend:User)
WHERE user <> mutualFriend
  AND NOT (user)-[:FRIEND]-(mutualFriend)
RETURN DISTINCT mutualFriend.username, COUNT(*) as mutualConnections
ORDER BY mutualConnections DESC
LIMIT 10

// Friend recommendations (friends of friends with weighted scoring)
MATCH (user:User {id: $userId})-[:FRIEND]-(friend)-[:FRIEND]-(recommended:User)
WHERE user <> recommended
  AND NOT (user)-[:FRIEND]-(recommended)
WITH recommended, COUNT(DISTINCT friend) as commonFriends,
     SIZE((recommended)-[:POST]->()) as activityScore
RETURN recommended.username, commonFriends, activityScore,
       (commonFriends * 2 + activityScore) as score
ORDER BY score DESC
LIMIT 20