How Do I Load Data Into Neo4j?

The ability to load data into Neo4j is enabled through a variety of data loading APIs and tools. For processes where big data sets flow in or out of the Neo4j graph database, consideration needs to be taken to batch these read and write operations into batch sizes that are sympathetic to the master instances memory capacity as well the transactional overhead of data writes.

Neo4j provides a number of APIs to import big data sets including:

• the Cypher transactional endpoint, which uses the Cypher query language and is simple to utilize from any programing language because files containing CQL can be structured to bulk load data and write consistently.
• the Cypher data import capabilities exposed through LOAD CSV enable CSV files from a specified remote or local URL to be loaded and batching into desirable transaction sizes for importing massive data 

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Cypher is Awesome

Cypher is a declarative pattern matching language created by Neo4j for the purposes of describing graph data representations effectively. Cypher is considered to be one of the most powerful features fore effectively expressing graph database traversals for reading and writing graph database data into Neo4j.

Cypher makes it capable for queries to do something like: “bring back my friends’ friends right now” or “give me back all pages this page is linked to within the last day” in the form of several code lines. As such, graph database queries and operations across all languages and integrations with Neo4j are able to query in a consistent manner.

The reception to Cypher has been so great that Neo4j launched the OpenCypher initiative to make Cypher the SQL for graph databases. The organizations that are joining OpenCypher are very important to the graph database movement because supporting a common graph database query language means there will be more utilization and commonality across graph database implementations.

Cypher Query Syntax

Cypher looks a lot like ASCII art because it uses textual pattern representations to express nodes and relationships. The nodes are surrounded with parenthesis which appear like circles and the relationships consist of dashes with square brackets. Here’s an example: (graphs)-[:ARE]-(everywhere)

If we want to find all people and their preferences, the query will involve identifiers for the person and thing. A pattern like “(person)-[:LIKES]->(thing)” could be utilized so it can be referred to later say, for instance, to gain access to properties such as “person.name” and “thing.title.”

Writing and representing depth based queries is one place where cypher makes it really easy to do. To look at my friends of friends is as simple as “(me)-[:FRIEND*2]->(fof). It’s actually fun to create queries because its 

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Querying Your Neo4j Graph Database

There are different ways for querying, storing, managing and retrieving data in the Neo4j graph database. For a majority of users querying with Cypher is a great experience when it comes down to performing efficient and effective graph database traversals and interactions within the graph data model. For specific applications that entail further control on how graphs can be stored and queried in high performance, multi-threaded manner, a native Java API offers low-level access to graph database for granular control over the traversal and retrieval from the Neo4j graph database. When making use of the Java API, you’ll realize that you’re given great freedom and flexibility to communicate to the Neo4j graph database on how to best query your data for optimal results.

Querying with Cypher

The Cypher query language is an innovative SQL-like syntax designed for graphs that takes a more declarative approach. That means, you can tell Neo4j what you desire — not based on how to acquire it. When running a Cypher query, you’re expressing to the graph database what you need from it. In return, Neo4j has a compiler that translates the query into an executable plan describing data operation sets. The plan is conveniently arranged in a way that the obtained data from the graph is processed in a manner for each operation until a result is returned from the Cypher query.

The usual way for communicating with Neo4j consists of sending a Cypher query and parameters via an initiated POST request to the Neo4j database server. Frameworks or libraries managing wrappers around the REST API Neo4j methods from a programming language are called “drivers.” These drivers function by moving numerous queries and results over the network. Neo4j then operates by making further translation 

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Native Graph Database Benefits

Choosing a native graph database provides granular control over all operations from the transactional behavior to on-disk data organization to clustering and driver protocols. With complete control over every aspect of the native graph database, fine-tune graph traversal optimizations can be performed and choices sympathetic to graph principals for reliability and ACID transactional support can be made and implemented without restriction.

Durability and certainty of the graph database records are crucial to preserve. Choosing reliability and making sure failed graph database transactions roll back maintains a consistent data state in the native graph database.

Graph Database Reliability

For graph databases, reliability is far more essential than availability since the connectedness of the data make them more highly demanding than aggregate databases. The issue of placing a graph later over an existing datastore will boil down to how data is written and which record is factual since within a graph there are two perspectives: the node from each side.

If mutations are made through multiple requests simultaneously, it’ll lead to an uncertain relationship status. A non- native graph database will resolve this by means of complex algorithms, but in the end, they simply don’t work, leaving you with erroneous data. Such incorrectness can spread through the graph as well as 

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Native Graph Databases versus Non-Native Graph Databases

As with any graph database management system, native graph databases revolve around the idea of storage and use of query engines, which deals specifically with connected data persistence and traversal queries. The database query engine is in charge of operating queries, modifying, and extracting data. Native graph databases showcase the traversal of the graph data model paired with strategic index usage for locating the starting nodes for such operations. Storage involves how data can be physically housed and how it can be represented during extraction. Understanding graph database storage nuances is key to selecting the right graph database for your use case.

Relationships Matter: Non- and Native Graph Databases

Relationships are integral in any domain and requires frequent transversal. In a graph database, relationships are strictly explicit instead of being inferred. Creating explicit relationships can be gained either through the query engine on a non-native or native graph storage.

A graph database that depends on a non-native graph storage has relationships that will need to be inferred at runtime. For instance, if we intend to model an RDBMS graph, the processing engine will need to infer relationships through foreign keys while making the relationship concrete at runtime. This would be an expensive approach and won’t be feasible to traversing relationships due to the involvement of recursive joins.

Native Graph Databases and Index-Free Adjacency

As a native graph database, Neo4j turns relationships into first class entities in data records at store levels. It doesn’t place a layer of graph on an existing database storage engine. At the store level, Neo4j writes and reads data from the disk using techniques that are optimized for graph transversal.

Native graph databases utilizes a method known as the “index-free” adjacency.” It means that every data element is aimed directly to its incoming and outgoing relationships. This, in turn, point towards related 

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What is a Graph Database?

A graph database, or otherwise known as “graph-oriented database,” is a particular form of NoSQL database that makes use of a graph to query, house, and map out relationships. It consists of databases which specifically serve to store data structures that are graph-oriented.

A graph database is an example of a storage solution tha

t shows where linked elements are connected to each other in the absence of an index. Groups of a specific entity can be accessed by means of dereferencing a pointer.

There are various kinds of graphs that can be stored. They can range from a single undirected graph to property graphs to hyper-graphs.

A graph database usually meets the following requirements:

Storage is specifically arranged for the data to be represented as a graph, with provisions made for housing edges and vertices.
Storage is tweaked for graph transversal without making use of an index when following edges.
A graph database is arranged so queries leverage proximity data beginning from a single or multiple root 

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Using Neo4j Cypher MERGE Effectively

One of the areas in Neo4j and Cypher that brings the most questions and discussion when I’m giving Neo4j trainings or working with other engineers building with Neo4j, is around how to use Cypher

MERGE correctly and efficiently. The way I’ve explained Cypher MERGE to all our engineers and all the training attendees is this.

There are a few simple things to understand about how Neo4j handles Cypher MERGE operations to avoid undesired or unexpected behavior when using it.

1. The Cypher MERGE operation is a MATCH or CREATE of the entire pattern. This means that if any element of the pattern does NOT exist, Neo4j will attempt to create the entire pattern.
2. Always MERGE on each segment of the pattern that has the possibility to already exist.
3. After a MERGE operation you are guaranteed to have a useable reference to all identifiers established during the Cypher MERGE operation because they were either found or created.

Simple Cypher MERGE Examples

Let’s look at a couple examples of Cypher MERGE operations:
Assuming that a unique constraint exists on username for the User label and that (u:User {username: “neo”}) exists in the graph do you think these two statements are equivalent?
Statement 1:
MERGE (neo:User {username: “neo”})-[:KNOWS]->(trinity:User {username: “trinity”});

Statement 2:
MERGE (neo:User {username: “neo”})
MERGE (trinity:User {username: “trinity”})
MERGE (neo)-[:KNOWS]->(trinity);

The answer is no; they’re not equivalent. Here’s why they’re not:
In Statement 1 Neo4j will find that the entire pattern doesn’t MATCH because -[:KNOWS]->(trinity:User {username: “trinity”}) doesn’t exist in the graph.
This will cause Neo4j to attempt to create the entire pattern, which includes the already existing User node with a uniquely constrained

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