neo4j interview questions 2

The Top Neo4j Interview Questions To Prepare For Your Next Tech Job

Getting hired as a Neo4j developer is no easy feat. You’ll need to demonstrate deep knowledge of this powerful graph database during the interview process. Luckily, we’ve got you covered!

In this comprehensive guide, I’ll be sharing the 25 most common Neo4j interview questions – including detailed explanations and sample answers. With over 10 years of experience using and teaching Neo4j, I’ve curated this list to include both basic and advanced queries you’re likely to encounter.

Whether you’re prepping for an upcoming interview or just want to level up your skills mastering these questions will get you one step closer to landing your dream role. So let’s dive in!

Neo4j Basics

Here are some common intro-level questions to ensure you have a solid grasp of Neo4j fundamentals:

1. What is Neo4j and how is it different from relational databases like MySQL?

Neo4j is an open-source NoSQL graph database that utilizes nodes, relationships, and properties to represent and store connected data. Unlike relational databases, Neo4j uses graph structures instead of tables to model data. This allows it to efficiently handle heavily linked data and run complex graph queries.

1. What are the main components of the Neo4j graph data model?

The building blocks of the Neo4j data model are nodes, relationships, and properties. Nodes represent entities like people or events. Relationships connect nodes and can have a direction. Properties are key-value pairs that contain metadata for nodes and relationships. This flexible data model allows capturing real-world connections and hierarchies.

1. What is index-free adjacency and how does it work in Neo4j?

Index-free adjacency refers to each node containing direct pointers to its adjacent nodes. This optimized storage eliminates the need for index lookups when traversing graphs. It enables constant-time query performance regardless of data size.

1. Does Neo4j support schema or is it schema-free?

Neo4j has a schema-optional design. It allows properties and labels to be added dynamically to nodes without enforcing a predefined schema. However, it also supports optional constraints and indexes for imposing schema requirements when needed.

1. What is the Cypher query language used in Neo4j?

Cypher is a declarative, SQL-inspired language for querying and updating graphs stored in Neo4j. It allows expressing what to retrieve from a graph without specifying how to retrieve it. Keywords like MATCH, WHERE, and RETURN are used to write Cypher queries.

Neo4j CRUD Operations

Now let’s move on to some frequently asked questions around performing CRUD (create, read, update, delete) operations in Neo4j using Cypher:

1. How do you create nodes and relationships in Cypher?

Nodes are created using the CREATE clause and specifying their labels. Relationships are formed by connecting nodes with -[:TYPE]->. For example:

CREATE (john:Person {name:”John Doe”})
CREATE (london:City {name:”London”})
CREATE (john)-[:LIVES_IN]->(london)

1. How are nodes queried in Cypher using MATCH?

The MATCH clause allows finding and retrieving nodes that satisfy given patterns. For example, this query finds all Person nodes:

MATCH (p:Person) RETURN p

We can also filter nodes using WHERE:

MATCH (p:Person) WHERE p.age > 30 RETURN p

1. How do you update a property of a node in Cypher?

Existing properties can be updated using the SET clause. For example:

MATCH (p:Person {name: “John Doe”})
SET p.age = 36
RETURN p

1. How are nodes deleted in Cypher?

Nodes can be deleted along with their relationships using the DETACH DELETE clause:

MATCH (p:Person {name:”John Doe”})
DETACH DELETE p

This will delete the matched node and all its relationships.

1. How do you delete relationships in Cypher without deleting nodes?

To delete relationships only, MATCH to find them and use DELETE:

MATCH (:Person)-[r:LIVES_IN]->(:City)
DELETE r

Advanced Cypher Queries

Let’s go deeper into some complex Cypher querying you may encounter:

1. How are WHERE clauses used to filter queries in Cypher?

WHERE clauses specify conditions that filter graph patterns to match. For example:

MATCH (p:Person)-[:KNOWS]->(friend)
WHERE p.name = “John”
RETURN friend

Here the result only includes John’s friends.

1. How do you handle ORDER BY and LIMIT in Cypher?

ORDER BY sorts the result set based on provided expressions. LIMIT constrains the number of results returned. For example:

MATCH (p:Person)
RETURN p.name, p.age
ORDER BY p.age DESC LIMIT 10

This returns the 10 oldest Person nodes sorted descending by age.

1. How are collections handled in Cypher?

Collections allow storing multiple values in properties. Square brackets [] are used to construct collections. Useful functions include size(), head(), tail() and extract().

For example, this stores an array of nicknames:

CREATE (p:Person {name:”John”, nicknames: [“JJ”, “JD”]})

1. Explain how the COUNT function works with aggregates in Cypher.

COUNT() returns the number of matching elements. It’s useful with aggregates for statistics about query results. For example:

MATCH (p:Person)-[:KNOWS]->(friend)
RETURN p.name, COUNT(friend)

This returns each person alongside their friend count.

1. How does OPTIONAL MATCH work in Cypher?

OPTIONAL MATCH allows matching patterns while still returning results even if no match is found. The missing data is returned as null. For example:

OPTIONAL MATCH (p)-[:WORKS_AT]->(company)
RETURN p.name, company.name

This will return all Person nodes regardless of whether a company is connected.

Schema, Indexes and Constraints

Here are some key questions around optimizing Neo4j model design:

1. How are indexes used in Neo4j?

Indexes speed up lookup of nodes/relationships with specific properties. They are created using:

CREATE INDEX ON :Label(property)

For example, this indexes Person nodes by name:

CREATE INDEX ON :Person(name)

1. What types of constraints exist in Neo4j?

UNIQUE constraints ensure the uniqueness of a property’s value for all nodes with a specific label.

Node KEY constraints make a property mandatory and unique for a label.

They are defined as:

CREATE CONSTRAINT ON (p:Person) ASSERT p.name IS UNIQUE

CREATE CONSTRAINT ON (p:Person) ASSERT p.id IS NODE KEY

1. How does MERGE work in Cypher?

MERGE creates nodes/patterns if they don’t exist or matches them if they do. This ensures nodes are unique when handling data imports. For example:

MERGE (john {name: “John Doe”})

will create the node only if another with that name doesn’t exist already.

1. What are some techniques to model trees and hierarchies in Neo4j?

Parent-child relationships can model trees effectively in Neo4j. The direction of relationships determines hierarchy.

For example, a parent node can be connected to children nodes using a :PARENT_OF relationship.

Nested data structures like arrays can also represent hierarchy within properties.

1. How are binary tree structures modeled in Neo4j?

A binary tree can be modeled using:

• A root node labeled :Root
• Left child relationships named :LEFT_CHILD
• Right child relationships named :RIGHT_CHILD

For example:

CREATE
(root:Root {value: 1})-[:LEFT_CHILD]->(left:Node {value: 2}),
(root)-[:RIGHT_CHILD]->(right:Node {value: 3})

Advanced Concepts

Let’s round out this list with some advanced Neo4j concepts you should know:

1. What are some advantages of graph databases like Neo4j over relational databases?

• Simplified modeling of complex connected data
• Superior performance for graph traversals and pattern matching
• Flexible schema allows easy evolution of data models
• No need for expensive join operations

1. How does Neo4j handle concurrency through ACID transactions?

Neo4j supports full ACID (Atomicity, Consistency, Isolation, Durability) transactions that group operations into an all-or-nothing unit. This ensures data integrity and consistency in concurrent access scenarios.

1. Explain Neo4j’s architecture and storage engine.

Neo4j stores data in a native graph format optimized for connections. Nodes and relationships are stored in linked fixed-size records grouped into stores accessed through direct pointers. The graph engine uses caching and indexing techniques for fast traversals.

1. What language drivers an