LearningNeo4j

Content

Graph Database Fundamentals
Neo4J
- Query Language Cypher
- Fraud Detection
Index
References

Graph Database Fundamentals

A graph database can store any kind of data using a few simple concepts:

Nodes - graph data records
Labels - specifies the type of the node
Relationships - connect nodes
Properties - key-value pair properties

The simplest graph has just a single node with some named values called Properties:

Nodes are the name for data records in a graph and the data is stored as Properties that can be simple key-value pairs.

Nodes can be grouped together by applying a Label to each member. In the example above we can set to that node the label Person. Is important to know that a label is not a object and can't have any properties, is used only to categorize the nodes in a graph. A node can have zero or more labels based on the definition of that node.

To add more records we can simply add more nodes.

Similar nodes can have different properties with different type: string, number or even boolean. The dimension of a graph like this can be infinite because there is no limit to the number of nodes that can be added.

One of the properties of a database is to connect data, in a graph database the link is made by Relationships. To associate two nodes we can add Relationship between them wich describe how the records are related.

A relationship are data records that need to have two properties: direction and type, and can also contains properties like nodes.

A Graph database is an online database management system with Create, Read, Update and Delete (CRUD) operations working on a graph data model. Graph database are generally build for use with OLTP systems, they are normally optimized for transactional performance, and engineered with transactional integrity and operational availability in mind.

Unlike the other databases, relationships take first priority in graph databases so the foreign keys or out-of-band processing is no more necessary to link a data to another.

By assembling the simple abstractions of nodes and relationships into connected structures, graph databases enable us to build sophisticated models that map closely to out problem domain.

Many applications' data is modeled as relational data, indeed there are some similarities between a relational model nad a graph model:

Relational	Graph
Rows	Nodes
Joins	Relationships
Table names	Labels
columns	Properties

There are even difference between this two databases:

Relational	Graph
Each column must have field value	Nodes with the same label arent' required to have the same set of properties
Joins are calculated at query time	Relationships are stored on disk when they are created
A row can belong to one table	A node can have many labels
Try to get the schema defined and then make minimal changes to it after that	It's common for the schema to evolve with the application
More abstract focus when modeling	Common to use actual data items when modeling

Here is the relational model:

And here is the correspondig graph model:

The graph model can be more versatile and can be upgrade without efforts, for example we want to add the confederation and country:

Neo4J

video youtube

Connected information is everywhere in the world around us. Neo4j was build to efficiently store, handle, and query higly-connected data in your data model.

Neo4J is a high performance graph store with all the feature expected of a mature and robust database. The network structure is made by nodes and relationships rather than static tables.

Some definitions:

Index free adjacency

With index free adjaceny, when a node or relationship is written to the database, it is stored in the database as connected and any subsequent access to the data is done using pointer navigation wich is very fast. Since Neo4j is a native graph database, it supports very large graphs where connected data can be traversed in constant time without the need for an index.

To know more read -> Index-free adjacency
ACID

Transactionality is very important for robust applications that require an atomicity, consistency, isolation, and durability guarantees for their data. If a relationship between nodes is created, not only is the relationship created, but the nodes are updated as connected. All of these updates to the database must all succeed or fail.

To know more read -> ACID
Clusters

Neo4j supports clusters that provide high availablity, scalability for read access to the data and failover which is important to many enterprises.

To know more read -> Cluster
Graph engine

The Neo4j graph engine is used to interpret Cypher statements and also executes kernel-level code to store and retrive data, whether it is on disk, or cached in memory.
Bolt

Neo4j supports Java, JavaScript, Python, C#, and Go drivers that use Neo4j's bolt protocol for binary access to the database layer. Bolt is an efficiant binary protocol that compresses data sent over the wire as well encrypting the data. It's possible to create a java application that uses the bolt driver to access the Neo4j database and the application may use other packages that allow data integration between Neo4j and other data stores or uses as common framework such as spring.
Tools

Neo4j browser is an application that uses the JavaScript Bolt driver to access the graph engine of the Neo4j database server.

Bloom enables you to visualize a graph without knowing much about Cypher (youtube video).

ETL used to importing and exporting data between flat files and a neo4j Database.

To use Neo4j there are two options:

desktop application

" The Neo4j Desktop includes the Neo4j Database server which includes the graph engine and kernel so that Cypher statements can be executed to access a database on your system. It includes an application called Neo4j Browser. Neo4j Browser enables you to access a Neo4j database using Cypher. You can also call built-in procedures that communicate with the database server. There are a number of additional libraries and drivers for accessing the Neo4j database from Cypher or from another programming language that you can install in your development environment. If you are looking to use your system for application development and you want to be able to create multiple Neo4j databases on your machine, you should consider downloading the Neo4j Desktop (free download). The Neo4j Desktop runs on OS X, Linux, and Windows. "

How to use on:
1. OSX: youtube video
2. Windows: youtube video
3. Linux: youtube video
browser sandbox

" The Neo4j sandbox is another way that you can begin development with Neo4j. It is a temporary, cloud-based instance of a Neo4j Server with its associated graph that you can access from any Web browser. The database in a Sandbox may be blank or it may be pre-populated. It is started automatically for you when you create the Sandbox.

By default, the Neo4j sandbox is available for three days, but you can extend it for up to 10 days. If you do not want to install Neo4j Desktop on your system, consider creating a Neo4j sandbox. You must make sure that you extend your lease of the sandbox, otherwise you will lose your graph and any saved Cypher scripts you have created in the sandbox. However, you can use Neo4j Browser Sync to save Cypher scripts from your sandbox. We recommend you use the Desktop for a real development project. The Sandbox is intended as a temporary environment or for learning about the features of Neo4j as well as specific graph use-cases. "

youtube video - Creating a Neo4j Sandbox

Both of them use Neo4j Browser application to perform querying in the database -> GettingStartedBrowser

Cypher

This notes below can be read on neo4J browser sandbox by type the command: :play cypher

All of the query are run in the Neo4J browser sandbox or on Neo4J desktop

Neo4J's Cypher language is purpose built for working with graph data, is a declarative query language that allows for expressive and efficient querying and updating of graph data. It uses patterns to describe graph data and is familiar to sql-like clauses. This query language allows users to store and retrive data from the Neo4J graph database with a visual and logical syntax to match patterns of nodes and relationships in the graphs. It allow to state what we want to select, insert, update, or delete from our graph data without a description of exactly how to do it:

" Describing what to find and not how to find it "

This means that complex database queries can easily be expressed through Cypher, allowing you to focus on your domain instead of getting lost in the syntax of database access. Also give an expressive and efficient queries to handle needed create, read, update, and delete functionality (also know as CRUD operations).

The unwritten rule wants to rappresents the nouns as the nodes of the graph, the verbs as the relationships, the adjectives and adverbs are the properties:

Graph patterns are expressed in Cypher using ASCII-art like syntax to make queries more self-explanatory:

NODES uses a pair of parentheses like () or (node) to rapresent a node, similar to a circle on witheboard. An anonymous node () represents one or more nodes during a query processing where there are no restrictions of the type of the node, a name inside the parentheses (node) tells the query processor that for this query is used the variable called node to rapresents all the nodes of the graph.
LABELS are used to group nodes and filter queries against the graph and is defined with a colon (:Label). A node can have zero or more labels for example (node), (node:Label), (node:Label1:Label2), (:Label), (:Label1:Label2).
RELATIONSHIPS are defined within square brackets [] and optionally we can specify type and direction like ()<-[:RELATIONSHIP]-().
ALIASES are used to referred elements to later in the query defined by a name before a name like (node1:Label1)<-[relationship:RELATIONSHIP]-(node2:Label2) where node1, node2 and relationship are aliases.
Predicates are filters that can be applied to limit the matching paths: boolean logi operators, regular expressions and string comparison operators.

The properties of a node are accessed using {variable}.{property_key}, for example emil.name or movie.title.

The Cypher language are case insensitive and sensitive:

Sensitive	Insensitive
Node labels	Cypher keywords
Relationship type	-----
Property keys	----

Later on the cypher keywords are upper-case, this is a coding convention and is described in the Cypher Style Guyide.

Part one

Comments

You can place comments anywhere in the query and to specify that the rest of the line is interpreted as a comment you need to put a double slash // comment.

Null

Null represents missing or undefined values. You do not store a null value in a property. It just doesen't exist on that particular node. Warning: null=null is not true but the result will be null because we don't know the value of a null property

Match

on neof4j browser run the command `:help MATCH`

youtube video - how to execute a MATCH statement

The most widely used Cypher clause is MATCH, this performs a pattern match against the data in the graph. During the query processing, the graph engine traverses the graph to find all nodes that match the graph pattern.

A query with match need to be present with the RETURN clause. This clause must be the last of a query to the graph. Here some examples:

// returns all nodes in the graph
MATCH (variable)
RETURN variable

// returns all Label nodes in the graph
MATCH (variable:Label)
RETURN variable // returns

When you specify a pattern for a MATCH clause, you should always specify a node label if possible. In doing so, the graph engine uses an index to retrive the nodes which will perform better than not using a label for the MATCH.

Type of query output

The output of a query can be different:

by graph:

by table:

{
    "name": "Emil",
    "from": "Sweden",
    "klout": 99
}

by text:

╒══════════════════════════════════════════╕
│"ee"                                      │
╞══════════════════════════════════════════╡
│{"name":"Emil","from":"Sweden","klout":99}│
└──────────────────────────────────────────┘

Exercises part one

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 1 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 1.1 Retrive all nodes from the database

MATCH (n)
RETURN n

Exercise 1.2 Examine the schema of your database

CALL db.schema()

Exercise 1.3 Retrive all Person nodes

MATCH (p:Person)
RETURN p

Exercise 1.4 Retrive all Movie nodes

MATCH (m:Movie)
RETURN m

Part two

Properties

In Neo4j a node can have properties that are used for further define a node. A property is identified with a key and defined for a node and not for a type of node. All nodes of the same type need not have the same properties.

For example in the Movie graph all Movie nodes have both title and released properties, however it is not requirement that every Movie node has a property tagline:

Properties can be used to filter queries so that a subset of the graph is retrived. In addition, with the RETURN clause, you can return property values from the retrived nodes, rather than the nodes.

The propertiy keys of a graph can be view by execute CALL db.propertyKeys which call the Neo4j library method that returns the property keys for the graph. For example run this command in the movie graph returns the result stream contains all property keys in the graph:

Nodes properties filtering

youtube video - using match to return propery values

It's possible to filter the nodes of the graph to a specify a value for a property, any node that matches the value will be retrived. Here some examples:

MATCH (variable {propertyKey: propertyValue})
RETURN variable

MATCH (variable:Label {propertyKey: propertyValue})
RETURN variable

MATCH (variable:Label {propertyKey1: propertyValue1, propertyKey2: propertyValue2})
RETURN variable

It's possible to retrive a property values of nodes in a query and return on output:

MATCH (variable {property1: value})
RETURN variable.property2

MATCH (variable:Label {property1: value})
RETURN variable.property2

MATCH (variable:Label {property1: value, property2: value})
RETURN variable.property2, variable.property3

In the graph database we can filter the person born on 1970:

MATCH (p:Person {born: 1970})
RETURN p.name, p.born

Aliases

To customize the headings for a table containing property value it can be use aliases:

MATCH (variable:Label {property1: value, property2: value})
RETURN variable.property2 AS alias1, variable.property3 AS alias2

In the graph database we can specify aliases for the returned property values:

MATCH (p:Person {born: 1970})
RETURN p.name AS name, p.born AS `birth year`

Exercises part two

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 2 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 2.1: Retrieve all Movie nodes that have a released property value of 2003.

MATCH (m:Movie {released: 2003})
RETURN m

Exercise 2.2: View the retrieved results as a table.

MATCH (m:Movie {released: 2003})
RETURN m

Exercise 2.3: Query the database for all property keys.

CALL db.propertyKeys

Exercise 2.4: Retrieve all Movies released in a specific year, returning their titles.

MATCH (m:Movie {released: 2006})
RETURN m.title

Exercise 2.5: Display title, released, and tagline values for every Movie node in the graph.

MATCH (m:Movie)
RETURN m.title, m.released, m.tagline

Exercise 2.6: Display more user-friendly headers in the table.

MATCH (m:Movie)
RETURN m.title AS `Movie title`, m.released AS `Released date`, m.tagline AS `Tag line`

Part three

Relationships

A relationship is a directed connection between two nodes that has a relationship type (name). In addiction, a relationship can have properties, just like nodes. In a match clause it's possible to specify nodes and their relationships to traverse the graph and quickly find the data of interest:

() // a node
()--() // 2 nodes have some type of relationship
()-->() // the first node has a relationship to the second node
()<--() // the second node has a relationship to the first node The relationship can be specified with or without direction.

Here some examples for retrieving a set of nodes that satisfy one or more directed and type relationships:

MATCH (node1)-[:REL_TYPE]->(node2)
RETURN node1, node2

MATCH (node1)-[:REL_TYPEA | :REL_TYPEB]->(node2)
RETURN node1, node2

Where:


node1, node2	is a specification of a node where you may include node labels and property values for filtering
:REL_TYPE	is the type for the relationship from node1 to node2
:REL_TYPEA, :REL_TYPEB	are the relationships from node1 to node2, the nodes are returned if at least one of the relationships exists

In the movie graph to retrive the nodes Person that have acted in the Movie "The Matrix" we need to use relationships:

MATCH (node1:Person)-[relation:ACTED_IN]->(node2:Movie {title: "The Matrix"})
RETURN node1, relation, node2

There is a build-in function type() that returns the relationship type of a relationship. Here is an example where we use the rel variable to hold the relationships retrived:

MATCH (p:Person)-[rel]->(:Movie {title: "The Matrix"})
RETURN p.name, type(rel)

The output list will be:

p.name	type(rel)
"Hugo Weaving"	"ACTED_IN"
"Lilly Wachowski"	"DIRECTED"
"Laurence Fishburne"	"ACTED_IN"
"Lana Wachowski"	"DIRECTED"
"Keanu Reeves"	"ACTED_IN"
"Carrie-Anne Moss"	"ACTED_IN"
"Joel Silver"	"PRODUCED"
"Emil Eifrem"	"ACTED_IN"

Even relationships can have properties, this enables the graph model to provide more data about the relationships between the nodes. Just as can be specify property values for filtering nodes for a query, you can specify property values for a relationships. Here is an example with the movie graph:

// Returns the name of the person who gave the movie "the da vinci code" a rating of 65
MATCH (p:Person)-[:REVIEWED {rating: 65}]->(:Movie {title: "The Da Vinci Code"})
RETURN p.name

Since relationships are directionals queries can have multiple type of matching:

right direction
```
MATCH (n)-[r]->(m)
RETURN n, r, m
```
left direction
```
MATCH (n)<-[r]-(m)
RETURN n, r, m
```
both direction
```
MATCH (n)-[r]-(m)
RETURN n, r, m
```
traversing relationships
```
MATCH (n)-[r]->(m)-[p]->(o)
RETURN n, m, o
```
with query like this it can be usefull to assign a variable to the path and return the path:
```
MATCH path = (n)-[r]->(m)-[p]->(o)
RETURN path
```

Centralising relationships

MATCH (n)-[r]->(m)<-[p]-(o)
RETURN n, m, o

Style recommendations

Node labels are CamelCase and begin with an upper-case letter, like Person or NetworkAddress.
Property keys, variables, parameters, aliases, and functions are camelCase and begin with a lower-case letter, like title or businessAddress.
Relationship type are in upper-case and can use the underscore, like ACTED_IN or FOLLOWS.
Cypher keywords are upper-case, like MATCH or RETURN.
String constats are in single quotes, unless the string contains a quote or apostrophe, like 'The Matrix' or "Something's Gotta Give".
Specify variables only when needed for use later in the cypher statement.
Place named nodes and relationships before anonymous nodes and relationships in the MATCH clauses when possible.
Specify anonymous relationships with -->, --, or <--.

Exercises part three

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 3 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 3.1: Display the schema of the database.

CALL db.schema

Exercise 3.2: Retrieve all people who wrote the movie Speed Racer.

MATCH (p:Person)-[:WROTE]->(:Movie {title: "Speed Racer"})
RETURN p

Exercise 3.3: Retrieve all movies that are connected to the person, Tom Hanks.

MATCH (m:Movie)--(:Person {name: 'Tom Hanks'})
RETURN m

Exercise 3.4: Retrieve information about the relationships Tom Hanks had with the set of movies retrieved earlier.

MATCH (m:Movie)-[relation]-(:Person {name: 'Tom Hanks'})
RETURN m.title, type(relation)

Exercise 3.5: Retrieve information about the roles that Tom Hanks acted in

MATCH (m:Movie)-[acted:ACTED_IN]-(:Person {name: 'Tom Hanks'})
RETURN m.title, acted.roles

Part four

Where

on neof4j browser run the command :help WHERE

The most common clause to filter queries is WHERE that follows a MATCH clause. This clause is the answer for "how we filter the result for a particular match", so this filter all of the nodes and relationships. In the WHERE clause it is possible to place conditions that are evaluated at runtime to filter the query. The potential of this clause is that is possible to specify complex conditions for the query.

Some examples:

Example 1

MATCH (m:Movie)
WHERE m.title = "The Matrix"
RETURN m

Example 2

MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE m.released = 2008
RETURN p, m

Example 3

MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE m.released = 2008 OR m.released = 2009
RETURN p, m

This clause accept conditions that return a boolean value of true or false. It can be use several comparison operators: =, <>, <, >, <=, >=, IS NULL, IS NOT NULL, =~. There are 4 boolean operators that it can use: AND, OR, XOR, NOT.

An example:

MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)
WHERE
    m.released > 2000 OR
    (1994 < m.released <= 1997 AND m.title='As Good as It Gets')
RETURN p.name, m.title, m.released

It is opssible to filter node labels in the WHERE clause, for example this two queries:

MATCH (p:Person)
RETURN p.name

MATCH (p:Person)-[:ACTED_IN]->(:Movie {title: 'The Matrix'})
RETURN p.name

can be rewritten usign WHERE clauses:

MATCH (p)
WHERE p:Person
RETURN p.name

MATCH (p)-[:ACTED_IN]->(m)
WHERE p:Person AND m:Movie AND m.title = 'The Matrix'
RETURN p.name

Since we are talking about graph database not all the nodes with the same label have the same properties, with the WHERE clause and the build-in function exists(property) we can filter the nodes that doesn't have value for the property requested. For example:

MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name='Jack Nicholson' AND exists(m.tagline)
RETURN m.title, m.tagline

There are also a set of string-related keywords to test string property values: STARTS WITH, ENDS WITH, and CONTAINS.

MATCH (p:Person)-[:ACTED_IN]->()
WHERE p.name STARTS WITH 'Michael'
RETURN p.name

Regular expressions can be used to test property values, to specify the request it's necessary to use the =~ in the condition:

MATCH (p:Person)
WHERE p.name =~'Tom.*'
RETURN p.name

The query above retrive all Person nodes with a name property that begins with Tom, the result can be something like this:

p.name
"Tom Cruise"
"Tom Hanks"
"Tom Skerritt"
"Tom Tykwer"

Some more filtering for relationships can be used during a query, for example can be possible to use the NOT in a WHERE clause:

MATCH (p:Person)-[:WROTE]->(m:Movie)
WHERE NOT exists( (p)-[:DIRECTED]->() )
RETURN p.name, m.title

This query exclude Person who directed and not wrote the movie.

One more filtering is IN that can be used to compare each property with values on the list:

MATCH (p:Person)
WHERE p.born IN [1965, 1970]
RETURN p.name as name, p.born as yearBorn

and the return is:

name	yearBorn
"Lana Wachowsky"	1965
"Jay Mohr"	1970
"River Phoenix"	1970
"Brooke Langton"	1970

Exercises part four

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 4 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 4.1: Retrieve all movies that Tom Cruise acted in.

MATCH (tom:Person {name: 'Tom Cruise'})-[:ACTED_IN]-(movie:Movie)
RETURN movie.title

or

MATCH (tom:Person)-[:ACTED_IN]-(movie:Movie)
WHERE tom.name = 'Tom Cruise'
RETURN movie.title

Exercise 4.2: Retrieve all actors that were born in the 70’s, return name and year born.

MATCH (p:Person)-[:ACTED_IN]->(:Movie)
WHERE 1970 <= p.born <= 1979
RETURN p.name AS Name, p.born AS `Year Born`

Exercise 4.3: Retrieve the actors who acted in the movie The Matrix who were born after 1960, return name and year born.

MATCH (act:Person)-[:ACTED_IN]->(m:Movie)
WHERE m.title = 'The Matrix' AND act.born > 1960
RETURN act.name as Name, act.born as `Year Born`

Exercise 4.4: Retrieve all movies released in 2000 by testing the node label and released property, return the title of the movie.

MATCH (mov:Movie)
WHERE mov.released = 2000
RETURN mov.title

Exercise 4.5: Retrieve all people that wrote movies by testing the relationship between two nodes, return the name of the people and the title of the movie.

MATCH (p)-[rel]->(mov)
WHERE p:Person AND type(rel) ='WROTE' AND mov:Movie
RETURN p.name AS Name, mov.title AS `Movie title`

Exercise 4.6: Retrieve all people in the graph that do not have a the born property and return there name.

MATCH (p)
WHERE p:Person AND NOT exists(p.born)
RETURN p.name AS Name

Exercise 4.7: Retrieve all people related to movies where the relationship has the rating property, than return their name, movie, title and the rating.

MATCH (p:Person)-[rel]-(m:Movie)
WHERE exists(rel.rating)
RETURN
    p.name AS Name,
    m.title AS `Movie title`,
    rel.rating AS Rating

Exercise 4.8: Retrieve all actors whose name begins with James.

MATCH (p:Person)
WHERE p.name =~'James.*'
RETURN p.name

or

MATCH (p:Person)
WHERE p.name STARTS WITH 'James'
RETURN p.name

Exercise 4.9: Retrieve all all REVIEW relationships from the graph with filtered results.

MATCH ()-[rel:REVIEWED]->(movie:Movie)
WHERE toLower(rel.summary) CONTAINS 'fun'
RETURN
    movie.title as `Movie Title`,
    rel.rating as `Rating`,
    rel.summary as `Summary`

Exercise 4.10: Retrieve all people who have produced a movie, but have not directed a movie.

MATCH (a:Person)-[:PRODUCED]->(m:Movie)
WHERE NOT ((a)-[:DIRECTED]->(:Movie))
RETURN a.name, m.title

Exercise 4.11: Retrieve the movies and their actors where one of the actors also directed the movie.

MATCH (actor:Person)-[:ACTED_IN]->(mov:Movie)<-[:DIRECTED]-(dir:Person)
WHERE (dir)-[:ACTED_IN]->(mov)
RETURN actor.name, dir.name, mov.title

Exercise 4.12: Retrieve all movies that were released in a set of years.

MATCH (m)
WHERE
    m:Movie AND
    m.released IN [2004, 2008, 2000]
RETURN m.title, m.released

Exercise 4.13: Retrieve the movies that have an actor’s role that is the name of the movie.

MATCH (act:Person)-[rel:ACTED_IN]->(mov:Movie)
WHERE mov.title in rel.roles
RETURN mov.title, rel.roles

Part five

Multiple Match patterns

The MATCH clause includes a pattern specified by two paths separated by a comma:

MATCH (a:Person)-[:ACTED_IN]->(m:Movie),
    (m:Movie)<-[:DIRECTED]-(d:Person)
WHERE m.released = 2000
RETURN a.name, m.title, d.name

Setting path variables

It's possible to assign to a variable a path that can be reuse later in the same query or if it's needed to return that path:

MATCH megPath = (meg:Person)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d:Person),
    (other:Person)-[:ACTED_IN]->(m)
WHERE meg.name = 'Meg Ryan'
RETURN megPath

Varying lenght paths

Any graph that represents social networking, trees, or hierarchies will most likely have multiple paths of varying lengths.

To get this far you need to use this format (nodeA)-[:REALTYPE*<number_of_hops>]->(nodeB) or (nodeA)-[:REALTYPE*n..m]->(nodeB) where n and m are the extremes of an interval.

Finding the shortest path

A built-in function that you may find useful in a graph that has many ways of traversing the graph to get to the same node is the shortestPath() function. Using the shortest path between two nodes improves the performance of the query.

Here an example:

MATCH p = shortestPath((m1:Movie)-[*]-(m2:Movie))
WHERE m1.title = 'A Few Good Men' AND
    m2.title = 'The Matrix'
RETURN p

When you use the shortestPath() function, the query editor will show a warning that this type of query could potentially run for a long time. You should heed the warning, especially for large graphs. Read the Graph Algorithms documentation about the shortest path algorithm.

When you use ShortestPath(), you can specify a upper limits for the shortest path. In addition, you should aim to provide the patterns for the from an to nodes that execute efficiently. For example, use labels and indexes.

Optional pattern matching

This clause OPTIONAL MATCH is just like the MATCH but if no matches are found, this clause will use null for missing parts of the pattern. Here is an examples:

MATCH (p:Person)
WHERE p.name STARTS WITH 'James'
OPTIONAL MATCH (p)-[r:REVIEWED]->(m:Movie)
RETURN p.name, type(r), m.title

The return will be a table like this:

p.name	type(r)	m.title
"James Marshall"	null	null
"James L. Brooks"	null	null
"James Cromwell"	null	null
"James Thompson"	"REVIEWED"	"The Replacements"
"James Thompson"	"REVIEWED"	"The Da Vinci Code"

Aggregation in Cypher

In Cypher is not need to specify a grouping key, all non-aggregated result columns become grouping keys, The grouping is implicity done beased upon the filds in the RETURN clause.

For example, in Cypher statement, all rows returned with the same values for a.name and d.name are counted and only return once:

MATCH (a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d)
RETURN a.name, d.name, count(*)

a.name	d.name	count(*)
"Emil Eifrem"	"Lana Wachowski"	1
"Hugo Weaving"	"Lana Wachowski"	4
"Laurence Fishburne"	"Lana Wachowski"	3
"Carrie-Anne Moss"	"Lana Wachowski"	3
"Keanu Reeves"	"Lana Wachowski"	3
"Emil Eifrem"	"Lilly Wachowski"	1
"Hugo Weaving"	"Lilly Wachowski"	4
"Laurence Fishburne"	"Lilly Wachowski"	3
"Carrie-Anne Moss"	"Lilly Wachowski"	3
"Keanu Reeves"	"Lilly Wachowski"	3
"Al Pacino"	"Taylor Hackford"	1

This function is very useful when you want to count the number of occurrences of a particular query result. It's possible to specify the occurrences of an alias count(n) and the graph engine calculates the number of occurrences of n. If we want to calculates the number of rows retrived, including those with null values the count argument need to be a *. Last one is the count() without argument and this will implicit group by based upon the aggregation.

There are more aggregating functions such as min() or max() that can also use in queries.

Collecting results

Cypher has a built-in function collect() that enables you to aggregate value into a list:

MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name = 'Tom Cruise'
RETURN collect(m.title) AS `movies for Tom Cruise`

And the result will be a list called movies for Tom Cruise with the values ["Jerry Maguire", "Top Gun", "A Few Good Men"].

Additional processing using `WITH`

During the execution of a MATCH clause, is possible to specify some intermediate calculations or values that will be used for further processing of the query, or for limiting the number of results before further processing is done. With the WITH clause it's possible to perform intermediate processing ro data flow operations.

Here is an example:

MATCH (a:Person)-[:ACTED_IN]->(m:Movie)
WITH a, count(a) AS numMovies, collect(m.title) AS movies
WHERE numMovies > 1 AND numMovies < 4
RETURN a.name, numMovies, movies

This example return the actors name only if they acted on 2 or 3 movies, with a reference of the numbers of the films and a list of that.

Be carefull with this clause because in the WITH body are specify some variables from the previous part of the query that need to be part of the next section of the query, all the aliases for the next part are the only one defined in the body.

Remember to name all expressions with an alias in a WITH that are not simple variables.

One more example:

MATCH (p:Person)
WITH p, size((p)-[:ACTED_IN]->(:Movie)) as movies
WHERE movies>=5
OPTIONAL MATCH (p)-[:DIRECTED]->(m:Movie)
RETURN p.name, m.title

This is a simple query to retrive all the actor that are acted in at least 5 movies and if they also directed a movie than return the name of that movie.

p.name	m.title
"Keanu Reeves"	null
"Hugo Weaving"	null
"Jack Nicholson"	null
"Meg Ryan"	null
"Tom Hanks"	"That Thing You Do"

Exercises part five

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 5 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 5.1: Write a Cypher query that retrieves all movies that Gene Hackman has acted it, along with the directors of the movies. In addition, retrieve the actors that acted in the same movies as Gene Hackman. Return the name of the movie, the name of the director, and the names of actors that worked with Gene Hackman.

MATCH (gene:Person)-[:ACTED_IN]->(movie:Movie)
WHERE gene.name = 'Gene Hackman'
OPTIONAL MATCH
    (other:Person)-[:ACTED_IN]->(movie),
    (dir:Person)-[:DIRECTED]->(movie)
WITH
    movie,
    collect(other.name) AS Actors,
    collect(dir.name) AS Directors
RETURN
    movie.title AS `Title of movie`,
    Actors AS `Co-Actors`,
    Directors

Exercise 5.2: Retrieve all nodes that the person named James Thompson directly has the FOLLOWS relationship in either direction.

MATCH (james:Person)-[:FOLLOWS]-(other:Person)
WHERE james.name = 'James Thompson'
RETURN james, other

Exercise 5.3: Modify the query to retrieve nodes that are exactly three hops away.

MATCH (james:Person)-[:FOLLOWS*3]-(other:Person)
WHERE james.name = 'James Thompson'
RETURN james, other

Exercise 5.4: Modify the query to retrieve nodes that are one and two hops away.

MATCH (james:Person)-[:FOLLOWS*1..2]-(other:Person)
WHERE james.name = 'James Thompson'
RETURN james, other

Exercise 5.5: Modify the query to retrieve particular nodes that are connected no matter how many hops are required.

MATCH (james:Person)-[:FOLLOWS*]-(other:Person)
WHERE james.name = 'James Thompson'
RETURN james, other

Exercise 5.6: Write a Cypher query to retrieve all people in the graph whose name begins with Tom and optionally retrieve all people named Tom who directed a movie.

MATCH (tom:Person)
WHERE toLower(tom.name) STARTS WITH 'tom'
OPTIONAL MATCH (tom)-[:DIRECTED]->(movie:Movie)
WITH tom, collect(movie.title) as DirMov
RETURN tom.name AS `Tom* name`, DirMov AS `Directed movies`

Exercise 5.7: Retrieve actors and the movies they have acted in, returning each actor’s name and the list of movies they acted in.

MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)
WITH actor.name AS Actor, collect(movie.title) as Movies
RETURN Actor, Movies

Exercise 5.8: Retrieve all movies that Tom Cruise has acted in and the co-actors that acted in the same movie, returning the movie title and the list of co-actors that Tom Cruise worked with.

MATCH (tom:Person {name: "Tom Cruise"})-[:ACTED_IN]->(movie:Movie)<-[:ACTED_IN]-(other:Person)
RETURN movie.title AS `Movie`, collect(other.name) AS `Co-Actors`

Exercise 5.9: Retrieve all people who reviewed a movie, returning the list of reviewers and how many reviewers reviewed the movie.

MATCH (reviewer:Person)-[:REVIEWED]->(movie:Movie)
RETURN movie.title AS `Movie`, collect(reviewer.name) AS `reviewers`, count(reviewer) as `Number of reviewers`

Exercise 5.10: Retrieve all directors, their movies, and people who acted in the movies, returning the name of the director, the number of actors the director has worked with, and the list of actors.

MATCH (dir:Person)-[:DIRECTED]->(movie:Movie)<-[:ACTED_IN]-(actor:Person)
WITH dir.name as Director, count(actor) as ActorsNumber, collect(actor.name) AS ActorsList
RETURN Director, ActorsList AS `Actors` , ActorsNumber AS `Number of actors`

Exercise 5.11: Retrieve the actors who have acted in exactly five movies.

MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)
WITH actor, count(movie) as movies, collect(movie.title) as moviesList
WHERE movies = 5
RETURN actor.name AS `Name of actor`, moviesList as `Movies`

Exercise 5.12: Retrieve the movies that have at least 2 directors, and optionally the names of people who reviewed the movies.

MATCH (dir:Person)-[:DIRECTED]->(movie:Movie)
WITH movie, count(dir) AS numDir
WHERE numDir >= 2
OPTIONAL MATCH (movie)<-[:REVIEWED]-(reviewer:Person)
WITH movie.title AS title, collect(reviewer.name) as listRev
RETURN title, listRev AS `reviewer`

Part six

Eliminating duplication

To eliminating duplicated results it can be used the DISTINCT keyword

This is the exercise 5.1, we can use the DISTINCT clause to avoid repeting the directors in the list:

MATCH (gene:Person)-[:ACTED_IN]->(movie:Movie)
WHERE gene.name = 'Gene Hackman'
OPTIONAL MATCH
    (other:Person)-[:ACTED_IN]->(movie),
    (dir:Person)-[:DIRECTED]->(movie)
WITH
    movie,
    collect(other.name) AS Actors,
    collect(DISTINCT dir.name) AS Directors
RETURN
    movie.title AS `Title of movie`,
    Actors AS `Co-Actors`,
    Directors

This clause can be use in several uses, like this:

MATCH (p:Person)-[:DIRECTED | :ACTED_IN]->(m:Movie)
WHERE p.name = 'Tom Hanks'
WITH DISTINCT m
RETURN m.released, m.title

Ordering result

If you want the results to be sorted, you specify the expression to use for the sort usign the ORDER BY keyword and whether you want the order to be descending using the DESC keyword. Ascending order is the default. It can be use multiple sort expressions and the result will be sorted in that order.

This is an example where there is a specification for the release date of the movies for Tom Hanks:

MATCH (p:Person)-[:DIRECTED | :ACTED_IN]->(m:Movie)
WHERE p.name = 'Tom Hanks' AND m.released >= 2000
RETURN m.released, collect(DISTINCT m.title) AS movies ORDER BY m.released DESC

m.released	movies
2012	["Cloud Atlas"]
2007	["Charlie Wilson's War"]
2006	["The Da Vinci Code"]
2004	["The Polar Express"]
2000	["Cast Away"]

Limiting the number of results

Although you can filter queries to reduce the number of results returned, you may also want to limit the number of results. This is useful if you have very large result sets and you only need to see the beginning or end of a set of ordered results. LIMIT is the right choice to do something like this.

Example:

MATCH (m:Movie)
RETURN m.title as title, m.released as year
ORDER BY m.released DESC
LIMIT 10

Exercises part six

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 6 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 6.1: You want to know what actors acted in movies in the decade starting with the year 1990. First write a query to retrieve all actors that acted in movies during the 1990s, where you return the released date, the movie title, and the collected actor names for the movie. For now do not worry about duplication.

MATCH (per:Person)-[:ACTED_IN]->(mov:Movie)
WHERE 1990 <= mov.released < 2000
RETURN mov.released, mov.title, collect(per.name)

Exercise 6.2: Modify the query to eliminate duplication.

MATCH (per:Person)-[:ACTED_IN]->(mov:Movie)
WHERE 1990 <= mov.released < 2000
RETURN mov.released, collect(mov.title), collect(per.name)

Exercise 6.3: Modify the query to eliminate more duplication.

MATCH (per:Person)-[:ACTED_IN]->(mov:Movie)
WHERE 1990 <= mov.released < 2000
RETURN mov.released, collect(DISTINCT mov.title), collect(DISTINCT per.name)

Exercise 6.4: Sort results returned.

MATCH (per:Person)-[:ACTED_IN]->(mov:Movie)
WHERE 1990 <= mov.released < 2000
RETURN mov.released, collect(DISTINCT mov.title), collect(DISTINCT per.name)
ORDERED BY mov.released DESC

Exercise 6.5: Retrieve the top 5 ratings and their associated movies.

MATCH (:Person)-[rel:REVIEWED]->(mov:Movie)
RETURN mov.title, rel.rating as rat
ORDER BY rat DESC
LIMIT 5

Exercise 6.6: Retrieve all actors that have not appeared in more than 3 movies.

MATCH (act:Person)-[:ACTED_IN]->(mov:Movie)
WITH act, count(mov) AS numberMovies, collect(mov.title) AS movies
where numberMovies <= 3
RETURN act.name, movies

Part Seven

List

There are many build-in Cypher functions that you can use to build or access elements in lists. A Cypher map is list of key/value pairs where each element of the list is of the format key: value.

It's possible to collect values for a list during a query and with this it's possible to sort by the size of the list using the size() function as follows:

MATCH (a:Person)-[:ACTED_IN]->(m:Movie)
WITH m, count(m) AS numCast, collect(a.name) AS cast
RETURN m.title, cast, numCast
ORDER BY size(cast)

Unwinding lists

There my be some situations where you want to perform the opposite of collecting results, but rather separate the lists into separate rows. This functionality is done using the unwind clause.

Here is an example where we create a list with three elements, unwind the list and then return the values. Since there are three elements, three rows are returned with the values:

WITH [1, 2, 3] AS list
UNWIND list AS row
RETURN row, list

and the result will be:

row	list
1	[1,2,3]
2	[1,2,3]
3	[1,2,3]

The unwind clause is frequently used when importing data into a graph.

Dates

Cypher has a built-in date() function, as well as other temporal values and functions that you can use to calculate temporal values. You use a combination of numeric, temporal, spatial, list and string functions to calculate values that are useful to your application.

For example we want to calculate all the age value of the actors given the born year:

MATCH (actor:Person)-[:ACTED_IN]->(:Movie)
WHERE exists(actor.born)
WITH DISTINCT actor, date().year - actor.born AS age
RETURN actor.name, age AS `age today`
ORDER BY actor.born DESC

Exercises part seven

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 7 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 7.1: Write a Cypher query that retrieves all actors that acted in movies, and also retrieves the producers for those movies. During the query, collect the names of the actors and the names of the producers. Return the movie titles, along with the list of actors for each movie, and the list of producers for each movie making sure there is no duplication of data. Order the results returned based upon the size of the list of actors.

MATCH (act:Person)-[:ACTED_IN]->(mov:Movie)<-[:PRODUCED]-(prod:Person)
WITH mov, collect(act.name) AS actList, collect(DISTINCT prod.name) AS prodList
RETURN mov.title AS `Title`, actList AS `Actors`, prodList AS `Producers`
ORDER BY size(actList) DESC

Exercise 7.2: Write a Cypher query that retrieves all actors that acted in movies, and collects the list of movies for any actor that acted in more than five movies. Return the name of the actor and the list.

MATCH (act:Person)-[:ACTED_IN]->(mov:Movie)
WITH act, collect(mov.title) as movs
WHERE size(movs) > 5
RETURN act.name AS `Name`, movs AS `Movies`

Exercise 7.3: Modify the query you just wrote so that before the query processing ends, you unwind the list of movies and then return the name of the actor and the title of the associated movie

MATCH (act:Person)-[:ACTED_IN]->(mov:Movie)
WITH act, collect(mov.title) as movs
WHERE size(movs) > 5
UNWIND movs as movie
RETURN act.name, movie

Exercise 7.4: Write a query that retrieves all movies that Tom Hanks acted in, returning the title of the movie, the year the movie was released, the number of years ago that the movie was released, and the age of Tom when the movie was released.

MATCH (tom:Person)-[:ACTED_IN]->(mov:Movie)
WHERE tom.name = "Tom Hanks"
WITH mov, date().year - mov.released AS yearsAgo, mov.released - tom.born AS yearTom
RETURN mov.title, mov.released, yearsAgo, yearTom
ORDER BY yearsAgo

Part Eight

Create

on neof4j browser run the command `:help CREATE`

Recall that a node is an element of a graph representing a domain entity that has zero or more labels, properties, and relationships to or from other nodes in the graph.

When you create a node, you can add it to the graph without connecting it to another node.

Here is the simplified syntax for creating a node:

CREATE (optionalVariable optionalLabels {optionalProperties})

Let's create a small social graph using this query language.

If you plan on referencing the newly created node, you must provide a variable. Whether you provide labels or properties at node creation time is optional. In most cases, you will want to provide some label and property values for a node when created. This will enable you to later retrive the node. Provided you have a reference to the node.

Here some examples:

1 -> Create node of type Movie with the title Batman Begins:
```
CREATE  (:Movie {title: 'Batman Begins'})
```
2 -> Create a node with two labels to the graph of types Movie and Action with the title Batman Begins:
```
CREATE (:Movie:Action {title: 'Batman Begins'})
```

3 -> Create the same node of example 2 and return the title:

CREATE (m:Movie:Action {title: 'Batman Begins'})
RETURN m.title

4 -> Create the same node but with more properties:

CREATE (:Movie:Action {title: 'Batman Begins', released: 2005})

When the graph engine creates a node, it automatically assigns a read-only, unique ID to the node but this value is not accessible like the other properties but is an internal value.

If all of this 4 queries are execute, this will generate 4 differents nodes this is because the graph engine will create a node with the same properties of a node that already exists. To prevent this "issue" you can use the MERGE clause rather than CREATE or you can add constraints to the graph.

More create at once

Create clauses can create many nodes at once:

CREATE
(:Person {name: 'Michael Caine', born: 1933}),
(:Person {name: 'Liam Neeson', born: 1952})

CREATE
(:Person {name: 'Katie Holmes', born: 1978}),
(:Person {name: 'Benjamin Melniker', born: 1913})

There are two ways to create multiple nodes at once: by separating the nodes specified with commas, or by specifying multiple CREATE statements.

Add and remove the labels

on neof4j browser run the command :help SET

Obviously it can be added more lable or lables for a node before it's creations using the SET clause.

Adding 1 label to the x node:

SET x:Label

Adding 2 labels to the x node:

SET x:Label1:Label2

The SET processing is ignored if that label already exists in that node.

Example:

MATCH (m:Movie)
WHERE m.title = 'Batman Begins`
SET m:Action
RETURN labels(m)

This query used the build-in function label() that return the set of labels for the node

It's possible to remove labels from a node by using the REMOVE clause with the same syntax of the SET clause.

Example: Remove the Action label from the previous created node.

MATCH (m:Movie:Action)
WHERE m.title = 'Batman Begins'
REMOVE m:Action
RETURN labels(m)

Adding properties to a node

The SET clause can be use even to add properties to a node.

Some examples:

SET x.propertyName = value

SET x.propertyName1 = value1, x.propertyName2 = value2

SET x = {propertyName1: value1, propertyName2: value2}

SET x += {propertyName1: value1, propertyName2: value2}

If the property does not exist, it is added to the node. If the property exists, its value is updated. If the value specified is null, the property is removed.

The type of data for a property is not enforced, so you can assign a string value to a property that was once a numeric value and visa versa.

When the assignment used is the = than the properties and their values must exists and their values are overwritten; however += is used to update if exists already, alternatively the properties and values are created.

Example: Add the properties released and lengthInMinutes to the movie Batman Begins.

MATCH (m:Movie)
WHERE m.title = 'Batman Begins'
SET m.released = 2005, m.lengthInMinutes = 140
RETURN m

MATCH (m:Movie)
WHERE m.title = 'Batman Begins'
SET  m = {title: 'Batman Begins',
          released: 2005,
          lengthInMinutes: 140,
          videoFormat: 'DVD',
          grossMillions: 206.5}
RETURN m

MATCH (m:Movie)
WHERE m.title = 'Batman Begins'
SET m += {
        grossMillions: 300,
        awards: 66
        }
RETURN m

Removing properties from a node

There are two ways that you can remove a property from a node:

Using the REMOVE keyword:
```
REMOVE x.propertyName
```
Set the property's value to null
```
SET x.propertyName = null
```

For example if we want to remove the video format and the gross millions from the batman begins film we can execute this query:

MATCH (m:Movie)
WHERE m.title = 'Batman Begins'
SET m.grossMillions = null
REMOVE m.videoFormat
RETURN m

Exercises part eight

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 8 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 8.1: Create a Movie node for the movie with the title, Forrest Gump.

CREATE (:Movie {title: 'Forrest Gump'})

Exercise 8.2: Retrieve the newly-created node.

MATCH (forrest:Movie {title: 'Forrest Gump'})
RETURN forrest

Exercise 8.3: Create a Person node for the person with the name, Robin Wright.

CREATE (rob:Person {name: 'Robin Wright'})

Exercise 8.4: Retrieve the newly-created node.

MATCH (rob:Person {name: 'Robin Wright'})
RETURN rob

Exercise 8.5: Add the label OlderMovie to any Movie node that was released before 2010.

MATCH (oldMov:Movie)
WHERE oldMov.released < 2010
SET oldMov:OlderMovie
RETURN DISTINCT labels(oldMov)

Exercise 8.6: Retrieve all older movie nodes to test that the label was indeed added to these nodes.

MATCH (old:OlderMovie)
RETURN old.title, old.released
ORDER BY old.released DESC

Exercise 8.7: Add the label Female to all Person nodes that has a person whose name starts with Robin.

MATCH (female:Person)
WHERE female.name STARTS WITH 'Robin'
SET female:Female
RETURN DISTINCT labels(female)

Exercise 8.8: Retrieve all Female nodes.

MATCH (female:Female)
RETURN female

Exercise 8.9: We’ve decided to not use the Female label. Remove the Female label from the nodes that have this label.

MATCH (female:Female)
REMOVE female:Female
RETURN female

Exercise 8.10: View the current schema of the graph.

CALL db.schema

Exercise 8.11: Add the following properties to the movie, Forrest Gump: released: 1994, tagline: Life is like a box of chocolates…you never know what you’re gonna get., lengthInMinutes: 142,

MATCH (mov:Movie {title: 'Forrest Gump'})
SET mov += {
    released: 1994,
    tagline: 'Life is like a box of chocolates…you never know what you’re gonna get.',
    leghtInMinutes: 142
}
SET mov:OlderMovie

Exercise 8.12: Retrieve this OlderMovie node to confirm that the properties and label have been properly set.

MATCH (p:OlderMovie)
WHERE p.title = 'Forrest Gump'
RETURN p

Exercise 8.13: Add the following properties to the person, Robin Wright: born: 1966 and birthPlace: Dallas

MATCH (rob:Person)
WHERE rob.name = 'Robin Wright'
SET rob += {
    born: 1996,
    birthPlace: "Dallas"
}
RETURN rob.born, rob.birthPlace

Exercise 8.14: Retrieve this Person node to confirm that the properties have been properly set.

MATCH (rob:Person {name: 'Robin Wright'})
RETURN rob

Exercise 8.15: Remove the lengthInMinutes property from the movie, Forrest Gump.

MATCH (mov:Movie {title: 'Forrest Gump'})
SET mov.legthInMinutes = null
return mov

Exercise 8.16: Retrieve the Forrest Gump node to confirm that the property has been removed.

MATCH (mov:Movie {title: 'Forrest Gump})
return mov

Exercise 8.17: Remove the birthPlace property from the person, Robin Wright.

MATCH (rob:Person)
WHERE rob.name = 'Robin Wright'
REMOVE rob.birthPlace
RETURN rob

Exercise 8.18: Retrieve the Robin Wright node to confirm that the property has been removed.

MATCH (rob:Person)
WHERE rob.name = 'Robin Wright'
RETURN rob

Part Nine

Creating relationships

The relationships are created by defined the left node x and the right node y:

CREATE (x)<-[:NAME_OF_RELATIONSHIP {name_of_property: value_of_property}]-(y)

CREATE (x)-[:NAME_OF_RELATIONSHIP {name_of_property: value_of_property}]->(y)

When a relationship is created it must have a direction unlike the non creational queries. The connections capture the semantic relationships and context of the nodes in the graph.

In most cases, unless you are connecting nodes at creation time, you will retrive the two nodes, each with their own variables, for example, by specifying a WHERE clause to fine them, and then use the variables to connect them.

Here is an example: Creation of a relationship between Michael Caine with the movie Batman Begins

MATCH (a:Person), (m:Movie)
WHERE a.name = 'Michael Caine' AND m.title = 'Batman Begins'
CREATE (a)-[:ACTED_IN]->(m)
RETURN a, m

Another example is the creation of multiple relationships at once bu simply providing the pattern for the creation that includes the relationship types, their directions and the nodes that you want ot connect.

MATCH (a:Person), (m:Movie), (p:Person)
WHERE a.name = 'Liam Neeson' AND
      m.title = 'Batman Begins' AND
      p.name = 'Benjamin Melniker'
CREATE (a)-[:ACTED_IN]->(m)<-[:PRODUCED]-(p)
RETURN a, m, p

Adding properties to relationships

Just like the adding properties to a node, the same is for the relationships.

SET relationship.propertyName = value

SET relationship.propertyName1 = value1 , relationship.propertyName2 = value2

SET r = {propertyName1: value1, propertyName2: value2}

SET r += {propertyName1: value1, propertyName2: value2}

The same rules of adding properties node is apply to the properties of relationships.

Here is an example: Add the roles property to the ACTED_IN relationship from Christian Bale to Batman Begins right after creation of the relationship.

MATCH (act:Person), (mov:Movie)
WHERE act.name = 'Christian Bale' AND mov.title = 'Batman Begins'
CREATE (act)-[rel:ACTED_IN]->(mov)
SET rel.roles = ['Bruce Wayne', 'Batman']
RETURN act, rel, mov

To prevent the useless executions of create and set it can be possible to add a condition:

MATCH (act:Person), (mov:Movie)
WHERE act.name = 'Christian Bale' AND
      mov.title = 'Batman Begins' AND
      NOT exists((a)-[:ACTED_IN]->(MOV))
CREATE (act)-[rel:ACTED_IN]->(mov)
SET rel.roles = ['Bruce Wayne', 'Batman']
RETURN act, rel, mov

Removing properties to relationships

Same as removing properties from nodes it can be possible to remove properties from relationships.

Example:

MATCH (a:Person)-[rel:ACTED_IN]->(m:Movie)
WHERE a.name = 'Christian Bale' AND
      m.title = 'Batman Begins'
REMOVE rel.roles
RETURN a, rel, m

Exercises part nine

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 9 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 9.1: Create the ACTED_IN relationship between the actors, Robin Wright, Tom Hanks, and Gary Sinise and the movie, Forrest Gump.

MATCH (mov:Movie)
WHERE mov.title = 'Forrest Gump'
MATCH (act:Person)
WHERE act.name = 'Robin Wright' OR
      act.name = 'Tom Hanks' OR
      act.name = 'Gary Sinise'
CREATE (act)-[:ACTED_IN]->(mov)
RETURN act, mov

Exercise 9.2: Create the DIRECTED relationship between Robert Zemeckis and the movie, Forrest Gump.

MATCH (act:Person), (mov:Movie)
WHERE mov.title = 'Forrest Gump' AND
      act.name = 'Robert Zemeckis'
CREATE (act)-[rel:DIRECTED]->(mov)
RETURN act, rel, mov

Exercise 9.3: Create a new relationship, HELPED from Tom Hanks to Gary Sinise.

MATCH (tom:Person), (gar:Person)
WHERE tom.name = 'Tom Hanks' AND gar.name = 'Gary Sinise'
CREATE (tom)-[rel:HELPED]->(gar)
RETURN tom, rel, gar

Exercise 9.4: Write a Cypher query to return all nodes connected to the movie, Forrest Gump, along with their relationships.

MATCH (pers:Person)-[rel]-(mov:Movie)
WHERE mov.title = 'Forrest Gump' AND exists(rel)
RETURN pers,rel, mov

Exercise 9.5: Add the roles property to the three ACTED_IN relationships that you just created to the movie, Forrest Gump using this information: Tom Hanks played the role, Forrest Gump. Robin Wright played the role, Jenny Curran. Gary Sinise played the role, Lieutenant Dan Taylor.

MATCH (actor:Person)-[rel:ACTED_IN]->(mov:Movie)
WHERE mov.title = 'Forrest Gump'
SET rel.roles = CASE actor.name
                    WHEN 'Tom Hanks'
                        THEN ['Forrest Gump']
                    WHEN 'Robin Wright'
                        THEN ['Jenny Curran']
                    WHEN 'Gary Sinise'
                        THEN ['Lieutenant Dan Taylor']
                END
RETURN actor, rel, mov

Exercise 9.6: Add a new property, research to the HELPED relationship between Tom Hanks and Gary Sinise and set this property’s value to war history.

MATCH (tom:Person)-[rel:HELPED]->(gar:Person)
WHERE tom.name = 'Tom Hanks' AND gar = 'Gary Sinise'
SET rel.research = 'war history'

Exercise 9.7: View the current list of property keys in the graph.

CALL db.propertyKeys

Exercise 9.8: View the current schema of the graph.

CALL db.schema

Exercise 9.9: Query the graph to return the names and roles of actors in the movie, Forrest Gump.

MATCH (act:Person)-[rel:ACTED_IN]->(mov:Movie)
WHERE mov.title = 'Forrest Gump'
RETURN act.name, rel.roles

Exercise 9.10: Query the graph to retrieve information about any HELPED relationships.

MATCH (pers:Person)-[help:HELPED]->(someone:Person)
RETURN pers.name, help, someone.name

Exercise 9.11: Modify the role that Gary Sinise played in the movie, Forrest Gump from Lieutenant Dan Taylor to Lt. Dan Taylor.

MATCH (:Person {name: 'Gary Sinise'})-[rel:ACTED_IN]->(:Movie {title: 'Forrest Gump'})
SET rel.roles = 'Lt. Dan Taylor'
RETURN rel.roles

Exercise 9.12: Remove the research property from the HELPED relationship from Tom Hanks to Gary Sinise.

MATCH (:Person {name: 'Tom Hanks'})-[rel:HELPED]->(:Person {name: 'Gary Sinise'})
REMOVE rel.research
RETURN rel

Exercise 9.13: Query the graph to confirm that your modifications were made to the graph.

MATCH (p:Person)-[]->(m:Movie {title: 'Forrest Gump'})
RETURN p, m

Part Ten

Deleting nodes and relationships

If a node has no relationships to any other nodes, it's possible to simply delete it from the graph using the DELETE.

The graph engine return errors if attempt to delete a node in the graph that has relationships in or out of the node, this is because deleting such a node will leave orphaned relationships in the graph.

To delete a relationship between nodes it's necessary to find it first in the graph and then deleting it.

Example:

Delete the ACTED_IN relationship between Christian Bale and the movie Batman Begins:

MATCH (a:Person)-[rel:ACTED_IN]->(m:Movie)
WHERE a.name = 'Christian Bale' AND 'm.title = 'Batman Begins'
DELETE rel
RETURN a, m

Delete the node Benjamin Melniker and his relationships to movie nodes. To make this works it's necessary to remove the relationships to prevent errors:
```
MATCH (p:Person)-[rel:PRODUCED]->(:Movie)
WHERE p.name = 'Benjamin Melniker'
DELETE rel, p
```

The most efficient way to delete a node and its corresponding relationships is to specify DETACH DELETE. With this specification, the relationships to and from the node are deleted then the node is deleted.

Examples:

This gives errors:

MATCH (p:Person)
WHERE p.name = 'Liam Neeson'
DELETE p

This not gives errors:

MATCH (p:Person)
WHERE p.name = 'Liam Neeson'
DETACH DELETE p

Exercises part ten

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 10 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 10.1: Delete the HELPED relationship from the graph.

MATCH ()-[rel:HELPED]-()
DELETE rel
return rel

Exercise 10.2: Query the graph to confirm that the relationship no longer exists.

CALL db.relationshipTypes

Exercise 10.3: Query the graph to display Forrest Gump and all of its relationships.

MATCH (movie:Movie)-[rel]-(nod)
WHERE movie.title = 'Forrest Gump'
RETURN movie, rel, nod

Exercise 10.4: Try deleting the Forrest Gump node without detaching its relationships.

MATCH (movie:Movie)
WHERE movie.title = 'Forrest Gump'
DELETE movie

Exercise 10.5: Delete Forrest Gump, along with its relationships in the graph.

MATCH (movie:Movie)
WHERE movie.title = 'Forrest Gump'
DETACH DELETE movie

Exercise 10.6: Query the graph to confirm that the Forrest Gump node has been deleted.

MATCH (movie:Movie)-[rel]-(pers)
WHERE movie.title = 'Forrest Gump'
RETURN movie, rel, pers

Part Eleven

Merge

The MERGE clause is used either to create new nodes and relationships or to make structural changes to existing nodes and relationships.

Recap of the CREATE clause:

	The result with `CREATE`
Node	If a node with the same property value exists, a duplicate node is creted
Label	If the label already exists for the node, the node is not updated
Property	If the node or relationship property already exists, it is updated with the new value
Relationship	If a relationship exists, a duplicate relationship is created

It's difficult to have a graph where we want some duplicates, and the solution for this is the MERGE clause. This clause is used to find elements in the graph and if the element is not found then it will be created:

Create a unique node based on label and key information for a property and if it exists, optionally update it
Create a unique relationship
Create a node and relationship to it uniquely in the context of another node

MERGE (variable:Label{nodeProperties})
RETURN variable

This is an example:

MERGE (a:Person {name: 'Michael Caine'}) SET a.born = 1933 RETURN a

We can repeat this query with the MERGE clause but no more Actor will be created.

This clause can be used also to create relationships:

MERGE (variable:Label {nodeProperties})-[:REL_TYPE]->(otherNode)
RETURN variable

It's possible to leave out the direction of the relationship, if the relationship not exists than will be created assuming the left-to-right arrow.

Using this clause to create relationships is expensive and the suggestion is to do only when it's needed to ensure that a relationship is unique and you are not sure it already exists.

To prevent this the MATCH clause:

MATCH (p:Person), (m:Movie)
WHERE m.title = 'Batman Begins' AND p.name ENDS WITH 'Caine'
MERGE (p)-[:ACTED_IN]->(m)
RETURN p,m

You must be aware of the behavior of the MERGE clause and how it will automatically create nodes and relationships. This clause tries to find a full pattern and if it doesn't find it, it creates that full pattern. That's why in most cases it's suggested to first MERGE the nodes and then the relationships afterwards.

Another property of the MERGE clause is ON CREATE used to assign specific values to a node being created as a result of an attempt to merge.

Example:

MERGE (a:Person {name: 'Sir Michael Caine'})
ON CREATE SET a.birthPlace = 'London',
              a.born = 1934
RETURN a

The SET clause will be used only if the node is not found, the clause will be skipped otherwise.

Like ON CREATE it exists another clause for the matching case ON MATCH, used when the node/relationship already exists.

Example:

MERGE (a:Person {name: 'Sir Michael Caine'})
ON CREATE SET a.born = 1934
              a.birthPlace = 'UK'
ON MATCH SET a.birthPlace = 'UK'
RETURN a

Exercises part eleven

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 11 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 11.1: Use MERGE to create (ON CREATE) a node of type Movie with the title property, Forrest Gump. If created, set the released property to 1994.

MERGE (mov:Movie {title: 'Forest Gump'})
ON CREATE
    SET mov.released = 1994
RETURN mov

Exercise 11.2: Use MERGE to update (ON MATCH) a node of type Movie with the title property, Forrest Gump. If found, set the tagline property to "Life is like a box of chocolates…you never know what you’re gonna get.".

MERGE (mov:Movie {title: 'Forrest Gump'})
ON CREATE
    SET mov.released = 1994
ON MATCH
    SET mov.tagline = 'Life is like a box of chocolates…you never know what you’re gonna get.'
RETURN mov

Exercise 11.3: Use MERGE to create (ON CREATE) a node of type Production with the title property, Forrest Gump. If created, set the property year to the value 1994.

MERGE (prod:Production {title: 'Forrest Gump'})
ON CREATE
    SET prod.year = 1994
RETURN prod

Exercise 11.4: Query the graph to find labels for nodes with the title property, Forrest Gump.

MATCH (forrest)
WHERE forrest.title = 'Forrest Gump'
RETURN labels(forrest)

Exercise 11.5: Use MERGE to update (ON MATCH) the existing Production node for Forrest Gump to add the company property with a value of Paramount Pictures.

MERGE (forrest:Production {title: 'Forrest Gump'})
ON MATCH
    SET forrest.property = 'Paramount Pictures'
RETURN forrest

Exercise 11.6: Use MERGE to add the OlderMovie label to the movie, Forrest Gump.

MERGE (mov:Movie {title: 'Forrest Gump'})
ON MATCH
    SET mov:OlderMovie
RETURN labels(mov)

Exercise 11.7: Execute the following Cypher statement that uses MERGE to create two nodes and a single relationship

MERGE (p:Person {name: 'Robert Zemeckis'})-[:DIRECTED]->(m {title: 'Forrest Gump'})

This statement first finds all Person nodes that have only the name property value of Robert Zemeckis. It then finds all nodes with only the title property set to Forrest Gump. There are no Person or other nodes that have only these properties so the graph engine creates them. Then the graph engine creates the relationship between these two nodes. That is, this MERGE operation creates two nodes and a single relationship. If we had provided all of the property values for the nodes, we would not have created the extra nodes.

In fact, you should never create nodes and relationships together like this! This example is here to show you how powerful Cypher can be. A best practice is to create nodes first, then relationships.

Exercise 11.8: Repeat the execution of the previous statement.

It should do nothing. A best practice is to always use MERGE to create relationships to ensure that there will be no duplication in the graph.

MERGE (p:Person {name: 'Robert Zemeckis'})-[:DIRECTED]->(m {title: 'Forrest Gump'})

Exercise 11.9: Find the correct Person node to delete

You query the nodes before you delete them to ensure you have the correct MATCH clauses.

Execute this query:

MATCH (p:Person {name: 'Robert Zemeckis'})-[rel]-(x)
WHERE NOT EXISTS (p.born)
RETURN p, rel, x

Exercise 11.10: Delete this Person node, along with its relationships.

MATCH (p:Person {name: 'Robert Zemeckis'})-[rel]-(x)
WHERE NOT EXISTS (p.born)
DETACH DELETE p
RETURN p, rel, x

Exercise 11.11: Find the correct Forrest Gump node to delete by executing this statement:

MATCH (m)
WHERE m.title = 'Forrest Gump' AND labels(m) = []
RETURN m, labels(m)

Exercise 11.12: Delete the Forrest Gump node.

MATCH (m)
WHERE m.title = 'Forrest Gump' AND labels(m) = []
DETACH DELETE m

Exercise 11.13: Use MERGE to create the DIRECTED relationship between Robert Zemeckis and the Movie, Forrest Gump.

MERGE (rob:Person {name: 'Robert Zemeckis})-[:DIRECTED]->(mov:Movie {title: 'Forrest Gump})
RETURN rob, mov

Exercise 11.14: Use MERGE to create the ACTED_IN relationship between the actors, Tom Hanks, Gary Sinise, and Robin Wright and the Movie, Forrest Gump.

MATCH (pers:Person), (m:Movie)
WHERE
    pers.name IN ['Tom Haks', 'Gary Sinise', 'Robin Wright'] AND
    m.title = 'Forrest Gump'
MERGE (pers)-[:ACTED_IN]->(m)
RETURN pers, m

Exercise 11.15: Modify the relationship property, role for their roles in Forrest Gump: Tom Hanks is Forrest Gump, Gary Sinise is Lt. Dan Taylor, Robin Wright is Jenny Curran

MATCH (pers:Person)-[rel:ACTED_IN]->(m:Movie)
WHERE m.title = 'Forrest Gump'
SET rel.roles =
    CASE pers.name
        WHEN 'Tom Hanks'
            THEN ['Forrest Gump']
        WHEN 'Gary Sinise'
            THEN ['Lt. Dan Taylor']
        WHEN 'Robin Wright'
            THEN ['Jenny Curran']
    END
RETURN pers, m

Part Twelve

In a deployed application, it's not necessary to hard code the Cypher statements. In this case we are going to use a variety of values to test, but we don't want to chenge every time the values of test. In addition, typically we include Cypher statements in an application where parameters are passed in the Cypher statement before it executes. For these scenarios, we need to parameterize values in the Cypher statements.

Parameters

In the Cypher statements, a parameter name begins with the $ symbol.

Example:

MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name = $actorName
RETURN m.released, m.title
ORDER BY m.released DESC

At runtime, if the parameter $actorName has a value, it will be used in the Cypher statement when it runs in the graph engine.

In Neo4j Browser to set values of parameters we can use the command :param:

:param actorName => 'Tom Hanks'

This set the parameter actorName with the value Tom Hanks.

The result of this command is:

{
  "actorName": "Tom Hanks"
}
See :help param for usage of the :param command.

This is a client-side browser command, so it not work outside of that.

Once this parameter is setted, it can be used to do queries.

Example:

MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name = $actorName
RETURN m.released, m.title
ORDER BY m.released DESC

Eventually the parameter can be reset with another value and the query can be reused without touch the statements.

To set value for more than one parameters it can be use the JSON-style format:

:params {actorName: 'Tom Cruise`, movieName: `Top Gun`}

To remove a parameter from the list simply run the command without the parameter unwanted.

To show the list of the parameters in use for the session execute the :param command without any definition of parameters.

Exercises part twelve

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 12 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 12.1: Write and execute a Cypher query that returns the names of people who reviewed movies and the actors in these movies by returning the name of the reviewer, the movie title reviewed, the release date of the movie, the rating given to the movie by the reviewer, and the list of actors for that particular movie.

MATCH (rev:Person)-[review:REVIEWED]->(mov:Movie),
      (act:Person)-[:ACTED_IN]->(mov)
WITH rev, review, mov, collect(act.name) AS actors
RETURN DISTINCT rev.name AS `Reviewer`,
       mov.title AS `Movie`,
       mov.released AS `Released`,
       review.rating AS `Rated`,
       actors AS `Actors`

Exercise 12.2: Add a parameter named year to your session with a value of 2000.

:param year => 2000

Exercise 12.3: Modify the Cypher query you just wrote to filter by the year parameter.

MATCH (rev:Person)-[review:REVIEWED]->(mov:Movie),
      (act:Person)-[:ACTED_IN]->(mov)
WITH rev, review, mov, collect(act.name) AS actors
WHERE mov.released = $year
RETURN DISTINCT rev.name AS `Reviewer`,
       mov.title AS `Movie`,
       review.rating AS `Rated`,
       actors AS `Actors`

Exercise 12.4: Modify the year parameter to be a different value, 2006, and retest your query.

:param year => 2006

Exercise 12.5: Add a parameter named ratingValue to your session with a value of 65.

:param ratingValue => 65

Exercise 12.6: Modify the query you wrote previously to also filter the result returned by the rating for the movie.

MATCH (rev:Person)-[review:REVIEWED]->(mov:Movie),
      (act:Person)-[:ACTED_IN]->(mov)
WITH rev, review, mov, collect(act.name) AS actors
WHERE mov.released = $year AND
      review.rating = $ratingValue
RETURN DISTINCT rev.name AS `Reviewer`,
       mov.title AS `Movie`,
       review.rating AS `Rated`,
       actors AS `Actors`

Exercise 12.7: Modify the ratingValue parameter to be a different value, 60, and retest your query.

:param ratingValue => 60

Part Thirteen

Explain Profile

on neof4j browser run the command :help EXPLAIN

The graphs studies during this notes is a very small graphs and the problems for the rapresentation become much more harder when the dataset grows larger and larger.

With larger graphs it will be important to not only add appropriate indexes but also write Cypher statements that execute as efficently as possible.

There are two Cypher keywords to use as prefix with a Cypher statement to analyze a query:

EXPLAIN provides estimates of the graph engine processing that will occur, but does not execute the statement.
PROFILE provides real profiling information for what has occurred in the graph engine during the query and executes the statement.

The EXPLAIN option provides the query plan. With this it can be possible to compare different statements to understand the stages of processing that will occur when the Cypher executes.

Example:

EXPLAIN
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name = $actorName AND
      m.released < $year
RETURN p.name, m.title, m.released

This query return the phases of the Cypher execution, it can be possible to examine what code is expected to run. Each phase of the query presents an estimante of the number of rows expected to be returned.

For a better metric for analyzing how the Cypher statement will run is needed to run the PROFILE prefix keyword which runs the statement and gives the run-time performance metrics.

Example:

PROFILE
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
WHERE p.name = $actorName AND
      m.released < $year
RETURN p.name, m.title, m.released

This query above show the cache hits and most importantly the number of times that the engine accessed the database (db hints). This is the metric that will affect the performance of the Cypher statement at run-time.

There are two reasons why a query may take long time:

The query returns a lot of data -> the query completes execution in the graph engine, but it takes log time to create the result stream:
```
MATCH (a)--(b)--(c)--(d)--(e)--(f)
RETURN a
```
The query takes a long time to execute in the graph engine:
```
MATCH (a),(b),(c),(d),(e)
RETURN count(id(a))
```

If the query executes and then returns a lots of data, there is no way to monitor it or kill the query, the only think to do is to close the session of the browser and start a new one. If the server has many of these rogue querise running, it will slow down considerably so you should aim to limit these types of queries. If the query is a long-running query, it's possible to monitor it buy using the :queries command. This command calls the dbms.listQueries under the hood. It can be possible to kill a query by using the icon to the right, alternatively execute the statement CALL dbms.killQuery('query-id').

Exercises part thirteen

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 13 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 13.1: View the query plan for a Cypher statement.

For this Part of the exercise, you will use the query that you wrote previously using Cypher parameters. It assumes that you have set the year and ratingValue Cypher parameters:

MATCH (r:Person)-[rel:REVIEWED]->(m:Movie)<-[:ACTED_IN]-(a:Person)
WHERE m.released = $year AND
      rel.rating > $ratingValue
RETURN  DISTINCT r.name, m.title, m.released, rel.rating, collect(a.name)

View the query plan for this Cypher statement.

EXPLAIN
MATCH (r:Person)-[rel:REVIEWED]->(m:Movie)<-[:ACTED_IN]-(a:Person)
WHERE m.released = $year AND
      rel.rating > $ratingValue
RETURN  DISTINCT r.name, m.title, m.released, rel.rating, collect(a.name)

Exercise 13.2: View the metrics for the query when the previous statement executes.

PROFILE
MATCH (r:Person)-[rel:REVIEWED]->(m:Movie)<-[:ACTED_IN]-(a:Person)
WHERE m.released = $year AND
      rel.rating > $ratingValue
RETURN  DISTINCT r.name, m.title, m.released, rel.rating, collect(a.name)

Exercise 13.3: Remove the labels from the nodes and relationships in the query and again view the metrics. Compare the db hits from the previous version of the statement.

PROFILE
MATCH (r)-[rel]->(m)<-[]-(a)
WHERE m.released = $year AND
      rel.rating > $ratingValue
RETURN  DISTINCT r.name, m.title, m.released, rel.rating, collect(a.name)

Exercise 13.4: Recall that a query may run for a long time because there are a lot of results to return

The query takes a long time to execute in the graph engine.

You will perform these steps to gain some experience with monitoring and killing queries by having your original Neo4j Browser window open and then opening another Neo4j Browser window.

Open a second Neo4j Browser session. This session will be used for monitoring queries as they run.

:queries

Exercise 13.5: Execute this long-running query in your original Neo4j Browser session that returns a lot of results. In the second Neo4j Browser window, monitor the running queries.

Here is a very bad Cypher statement to use that returns a lot of results:

PROFILE MATCH (a)--(b)--(c)--(d)--(e)--(f)--(g)
RETURN a

What do you see in the second Neo4j Browser window where you are monitoring queries?

While the query in the original Neo4j Browser window is executing, in the second Neo4j Browser window i can see the state of the PROFILE query.

Exercise 13.6: Execute this long-running query in your original Neo4j Browser session and monitor the query in the second Neo4j Browser session.

Here is a very bad Cypher statement that takes a lot of time to execute in the graph engine:

PROFILE MATCH (a), (b), (c), (d), (e) , (f), (g)
RETURN count(id(a))

What do you see in the second Neo4j Browser window where you are monitoring queries?

Same as exercise 5 but this query never finished yet.

Exercise 13.7: In the second Neo4j Browser window, kill the long-running query.

Double click in the minus button.

Part Fourteen

Managing constraints and node keys

We are already said that we need to prevent the duplications of the nodes and data, it's not possible to prevent duplication by checking the existence of the exact node (with properties) as this type of test is not cluster or multi-thread safe as mp òpcls are ised. This is why the MERGE clause is preferred over the CREATE because MERGE does use locks.

To define some rules above this problems we can:

Add a uniqueness constraint that ensures that a value for a property is unique for all nodes of that type.
Add an existence constraint that ensure that when a node or relationship is created or modified, ti must have certain properties set.
Add a node key that ensures that a set of values for properties of a node of a given type is unique.

Constraint properties

We add a uniqueness constraint to the graph by creating a constraint that asserts that a particular node property is unique in the graph for a particular type of node.

Example:

CREATE CONSTRAINT ON (m:Movie) ASSERT m.title IS UNIQUE

This statement will fail if the graph already has multiple Movie nodes with the same value for the title property.

If we attempt to create a Movie with the title The Matrix the Cypher statement will fail baceuse the graph already has a movie with that title. Also when we attempt to modify the value of a property where the uniqueness assertion fails the Cypher statement will fail.

If we attempt to add a new constraint to a property of some label and this property is not set for some nodes with that label, than the query will fail because we cannot set a new constraint without first make this new rule applicable.

We can also create a constraint on properties of relationships, it's not necessary to define the direction of the relationship:

CREATE CONSTRAINT ON ()-[rel:REVIEWED]-() ASSERT exists(rel.rating)

To retrieving constraints defined for the graph we can run the command :schema to view existing indexes and contraints defined for the graph.

The CALL db.constraints() will return the statements of all the constraints.

To delete a constraints it can be run the DROP clause followed by the statements of the constraints to delete:

DROP CONSTRAINT ON ()-[rel:REVIEWED]-() ASSERT exists(rel.rating)

Constraint node keys

A node key is used to define the uniqueness constraint for multiple properties of a node of a certain type. A node key is also used as a composite index in the graph.

Example:

CREATE CONSTRAINT ON (p:Person) ASSERT (p.name, p.born) IS NODE KEY

This prevent the duplication of nodes.

Exercises part fourteen

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 14 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 14.1: Add a uniqueness constraint to the Person nodes in the graph.

Suppose we want to ensure that every Person in the graph must have a unique name.

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

Exercise 14.2: Add Tom Hanks to the graph.

CREATE (tom:Person {name: 'Tom Hanks'})
RETURN tom.name

Exercise 14.3: Attempt to add an existence constraint to the Person nodes in the graph.

Suppose we want to ensure that every Person node in the graph has a born property.

CREATE CONSTRAINT ON (p:Person) ASSERT exists(p.born)

Exercise 14.4: Update the existing Person nodes so that you set the born property to 0 for any nodes that do not exist.

MATCH (per:Person)
WHERE NOT exists(per.born)
SET per.born = 0

Exercise 14.5: Add the existence constraint to the graph for the born property.

CREATE CONSTRAINT ON (per:Person) ASSERT exists(per.born)

Exercise 14.6: Add Sean Penn to the graph where you do not specify a value for born.

CREATE (:Person {name: 'Sean Penn'})
RETURN p

Exercise 14.7: Add an existence constraint to the ACTED_IN relationship in the graph.

Suppose we want to ensure that every ACTED_IN relationship must have a value for the roles property.

CREATE CONSTRAINT ON ()-[act:ACTED_IN]-() ASSERT exists(act-roles)

Exercise 14.8: Add an ACTED_IN relationship from the person, Emil Eifrem to the movie, Forrest Gump where the roles property is not set.

MATCH (em:Person {name: 'Emil Eifrem'}), (movie:Movie {title: 'Forrest Gump'})
MERGE (em)-[:ACTED_IN]->(movie)
RETURN em

Exercise 14.9: Suppose we want to ensure that the graph will never contain a movie with the same title and the same year. For example, the movie studio decides to release a movie with the title, Back to the Future in 2018. There already is a movie in the graph with this title that was released in 1985. We want to allow this. To implement this type of constraint on the graph, you must add a constraint as a node key since it uses two properties of the node.

Note: If you have created a uniqueness constraint to the graph for the title property (as shown in the module content), you must first drop the constraint as it no longer makes sense if we are to allow duplicate titles in the graph. You must write and execute the statement to drop the constraint if it exists:

DROP CONSTRAINT ON (m:Movie) ASSERT m.title IS UNIQUE

Add a node key to the graph that will ensure that the combined values of title and released are unique for all Movie nodes.

CREATE CONSTRAINT ON (m:Movie) ASSERT (m.title, m.released) IS NODE KEY

Exercise 14.10: Add the movie, Back to the Future with a released value of 1985 and a tagline value of Our future..

CREATE (m:Movie {title: 'Back to the Future', released: 1985, tagline: 'hello future'})

Exercise 14.11: Add the movie, Back to the Future with a released value of 2018 and a tagline value of The future is ours..

CREATE (m:Movie {title: 'Back to the Future', released: 2018, tagline: 'this future'})

Exercise 14.12: Try adding the 2018 movie again.

CREATE (m:Movie {title: 'Back to the Future', released: 2018, tagline: 'this future'})

Exercise 14.13: Display the list of constraints defined in the graph.

CALL db.constraints

Exercise 14.14: Drop the constraint that requires the ACTED_IN relationship to have a property, roles.

DROP CONSTRAINT ON ()-[ acted_in:ACTED_IN ]-() ASSERT exists(acted_in.roles)

Part Fiveteen

Managing indexes

The uniqueness and node key constraints are essentially single-property and composite indexes respectively. Indexes are used to improve initial node lookup performance, but they require additional storage in the graph to maintain and also add to the cost of creating or modifying property values that are indexed. Indexes store redundant data that points to nodes with the specific property value or values. Unlike SQL, there is no such thing as a primary key in Neo4j, but nodes can have multiple properties that must be unique.

This are single-property indexes used:

Equality checks: =
Range comparisons: >,>=,<, <=
List membership: IN
String comparisons: STARTS WITH, ENDS WITH, CONTAINS
Existence checks: exists()
Spatial distance searches: distance()
Spatial bounding searches: point()

Composite indexes are used only for equality checks and list membership.

For more about indexes -> neo4j operations manual

Because index maintenance incurs additional overhead when nodes are created so it's not raccomend to create indexes in a small graph. The indexes of a graph can be view by use the build-in command schema.

The index for a property of a node can greatly reduce the number of nodes that the engine needs to visit in oredr to satisy a query.

Example:

MATCH (m:Movie)
WHERE 1990 < m.released < 2000
SET m.videoFormat = 'DVD'

The graph engine will find the pointers to all nodes that satisfy the query without having to visit all of the nodes:

Indexes improve graph engine performance. A unique constraint on a property is an index so it's not needed to create an index for any properties created with uniqueness constraints for, but an index on its own does not guarantee uniqueness.

To create a single-property index it's necessary to use the CREATE INDEX ON:

CREATE INDEX ON :Movie(released)

If a set of properties for a node must be unique for every node, then it's recommend to create a constraint as a node key, rather than an index. If there can be duplication for a set of property values, but the faster access it's necessary than the composite index is necessary.

Example:

MATCH (m:Movie)
WHERE m.released >= 2000
SET m.videoFormat = 'DVD';
MATCH (m:Movie)
WHERE m.released < 2000
SET m.videoFormat = 'VHS';
CREATE INDEX ON :Movie(released,videoFormat)

This queries set two new properties and with the last query create composite index on these properties.

To retrive indexes there is a build-in method call :schema, similar to CALL db.constraints(), that return a list of the indexes, and the unique constraints and node key shown as indexes in the graph.

To drop an existing index created it's possible to use the clause DROP instead of CREATE:

DROP INDEX ON :Movie(released, videoFormat)

Exercises part fiveteen

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 15 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 15.1: Create a single-property index on the born property of a Person node.

CREATE INDEX ON :Person(born)

Exercise 15.2: View the indexes defined for the graph.

:schema

Exercise 15.3: Drop the single-property index you just created for the born property of the Person nodes.

DROP INDEX ON :Person(born)

Part Sixteen

From relational to graph

youtube video - (RDBMS+SQL) to (Graphs+Cypher)

In many applications the datas comes from a .csv files or files of other types and with these we want to populate our graph. There are many nuances and best practices for loading data into graph from files.

To convert a relational database into a graph database with one step you can use the ETL tool (Extract Transform Load) -> Neo4J ETL

In Cypher it's possible to:

Load data from a URL (http(s) or file)
Process data as a stream of records
Create or update the graph with the data being loaded
Use transactions during the data load
Transform and convert values from the load stream
Load up to 10M nodes and relationships

Commonly to import data into a graph is used the csv files, to do that it's necessary to develop a model that describes how data need to be represents in the graph.

Importing normalized data

The LOAD CSV clause parses a local in the import directory of the neo4j installation or a remote file into a stream of rows which represent maps (with eaders) or lists. Once done this it's possible to use Cypher operations to create nodes or relationships or merge existing graph.

Syntax of LOAD CSV:

LOAD CSV WITH HEADERS FROM url-value
AS row

The row is a variable that is used to extract data from file.
The first line of the file must contain a comma-separated list of column names.
The url-value can be a resource or a file on the system.

Each line of this file must contains data that is interpreted as values for each column name. When each line is read from the file, it's possible to perform the necessary processing to create or merge data into the graph.

As CSV file usually represent either node or relationship lists and to create nodes and relationships separately it's necessary to parse several time that file.

Example of csv file:

id,title,country,year,summary
1,Wall Street,USA,1987, Every dream has a price.
2,The American President,USA,1995, Why can't the most powerful man in the world have the one thing he wants most?
3,The Shawshank Redemption,USA,1994, Fear can hold you prisoner. Hope can set you free.

Before loading data from CSV files into graph, we need to confirm that the data retrived looks ok. To do this we can first print the lines of the file and get some information about the data to be loaded.

Example:

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies_to_load.csv'
as LINE
RETURN count(*)

Or even better we probably want to see how the datas are stored:

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies_to_load.csv'
AS line
RETURN * LIMIT 1

Since all the lines are with the same format we can limit the visualization to 1.

The output of the last query should be:

line
{
  "summary": " Every dream has a
price.",
  "country": "USA",
  "year": "1987",
  "id": "1",
  "title": "Wall Street"
}

Notice that the summary column's data has an extra space before the data in the file, to make sure not to load data like this we need to beutify the output:

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies_to_load.csv'
AS line
RETURN line.id, line.title, toInteger(line.year), trim(line.summary)

The output will be formatted to this:

line.id	line.title	toInteger(line.year)	trim(line.summary)
"1"	"Wall Street"	1987	"Every dream has a price."
"2"	"The American President"	1995	"Why can't the most powerful man in the world have the one thing he wants most?"
"3"	"The Shawshank Redemption"	1994	"Fear can hold you prisoner. Hope can set you free."

Now we can create nodes and relationships from this file:

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies_to_load.csv'
AS line
CREATE (movie:Movie {movieId: line.id, title: line.title, released: toInteger(line.year), tagline: trim(line.summary)})

Another example can be the file that holds the data of Person:

Id,name,birthyear
1,Charlie Sheen, 1965
2,Oliver Stone, 1946
3,Michael Douglas, 1944
4,Martin Sheen, 1940
5,Morgan Freeman, 1937

In this case we need to pay more attention at the people in the database that have already this name to prevent duplications. Instead of creating them, we need to use the MERGE to ensure unique entries after the import.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/person_to_load.csv'
AS line
MERGE (actor:Person {personId: line.Id})
ON CREATE
    SET actor.name = line.name, actor.born = toInteger(trim(line.birthyear))

Now let's create relationships between the movies and the actors using the csv file:

personId,movieId,role
1,1,Bud Fox
4,1,Carl Fox
3,1,Gordon Gekko
4,2,A.J. MacInerney
3,2,President Andrew Shepherd
5,3,Ellis Boyd 'Red' Redding

The relative query will be:

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/roles_to_load.csv'
AS line
MATCH (movie:Movie { movieId: line.movieId })
MATCH (person:Person { personId: line.personId })
CREATE (person)-[:ACTED_IN { roles: [line.role]}]->(movie)

Importing denormalized data

When file that we need to import are denormalized a supplementary step is needed.

There are a specifications for the field terminator of the file, this is an example:

title;released;summary;actor;birthyear;characters
Back to the Future;1985;17 year old Marty McFly got home early last night. 30 years early.;Michael J. Fox;1961;Marty McFly
Back to the Future;1985;17 year old Marty McFly got home early last night. 30 years early.;Christopher Lloyd;1938;Dr. Emmet Brown

From this we want to create person and movies:

LOAD CSV WITH HEADERS
FROM 'https://data.neo4j.com/intro-neo4j/movie_actor_roles_to_load.csv'
AS line FIELDTERMINATOR ';'
MERGE (movie:Movie { title: line.title })
ON CREATE SET movie.released = toInteger(line.released),
              movie.tagline = line.summary
MERGE (actor:Person { name: line.actor })
ON CREATE SET actor.born = toInteger(line.birthyear)
MERGE (actor)-[r:ACTED_IN]->(movie)
ON CREATE SET r.roles = split(line.characters,',')

Two main things to notice:

the definition of the semi-colon as a field terminator rather than comma;
the build-in method split() to create the list for the roles property.

It's suggested to create nodes and relationships separately in multiple passes, dependes on the complexity of the operations and the experienced performance.

To import a larger amount of data (more than 10k rows), it is recommended to prefix the load csv clause with a PERIODIC COMMIT hint. This allow the database to regularly commit the import transactions to avoid memory churn for large transaction-states.

Exercises part sixteen

on neof4j browser run the command :play intro-neo4j-exercises and follow exercise 16 instructions

First of all use the script found at Cypher/exercises/part_one/createGraph.cql to create the basic graph:

Added 171 labels, created 171 nodes, set 564 properties, created 253 relationships, completed after 24 ms.

Exercise 16.1: You are given the name of a file, http://data.neo4j.com/intro-neo4j/actors.csv that you must load into your graph.

Write the Cypher statement to read the actor data from a file.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/actors.csv'
AS row
RETURN count(*)

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/actors.csv'
AS row
RETURN row
LIMIT 1

Exercise 16.2: Read the data and return it, ensuring that the data returned is properly formatted.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/actors.csv'
AS row
RETURN row.id, row.name, toInteger(trim(row.birthYear))

Exercise 16.3: Load the data into your graph.

Hint: Use MERGE because the graph already contains some of these actors.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/actors.csv'
AS row
MERGE (actor:Person {name: row.name, born: toInteger(trim(row.birthYear))})
    ON CREATE SET actor.born = toInteger(trim(row.birthYear)), actor.id = row.id
    ON MATCH SET actor.id = row.id

Exercise 16.4: You are given the name of a file, http://data.neo4j.com/intro-neo4j/movies.csv that you must load into your graph.

Write the Cypher statement to read the movie data from a file.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies.csv'
AS row
RETURN count(*)

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies.csv'
AS row
RETURN row
LIMIT 1

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies.csv'
AS row
RETURN row.id, row.title, row.year, row.tagLine
LIMIT 10

Exercise 16.5: Read the data and return it, ensuring that the data returned is properly formatted.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies.csv'
AS row
RETURN row.id, row.title, toInteger(trim(row.year)), trim(row.tagLine)
LIMIT 10

Exercise 16.6: Load the data into your graph.

Hint: Use MERGE because the graph already contains some of these movies.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/movies.csv'
AS row
MERGE (movie:Movie {title: row.title})
    ON CREATE SET movie.tagLine = trim(row.tagLine), movie.id = row.id, movie.released = toInteger(trim(row.year))
    ON MATCH SET movie.id = row.id

Exercise 16.7: You are given the name of a file, http://data.neo4j.com/intro-neo4j/roles.csv that you must load into your graph.

Write the Cypher statement to read the relationship data from a file.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/roles.csv'
AS row FIELDTERMINATOR ';'
RETURN count(*)

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/roles.csv'
AS row FIELDTERMINATOR ';'
RETURN row
LIMIT 1

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/roles.csv'
AS row FIELDTERMINATOR ';'
RETURN row.Role, row.personId, row.movieId
LIMIT 10

Exercise 16.8: Read the data and return it, ensuring that the data returned is properly formatted.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/roles.csv'
AS row FIELDTERMINATOR ';'
RETURN split(row.Role, ','), row.personId, row.movieId
LIMIT 10

Exercise 16.9: Load the data into your graph.

LOAD CSV WITH HEADERS
FROM 'http://data.neo4j.com/intro-neo4j/roles.csv'
AS row FIELDTERMINATOR ';'
MATCH (pers:Person {id: row.personId}), (mov:Movie {id: row.movieId})
MERGE (pers)-[rol:ACTED_IN {role: split(row.Role, ',')}]->(mov)
RETURN pers, rol, mov

Libraries

Neo4j has a published, open source Cypher library, Awesome Procedures on Cypher (APOC) that contain many useful procedures you can call from Cypher.

Another Cypher library is the Graph Algorithms library to help users to analyze data in graphs.
github -> github.com/neo4j-contrib/neo4j-apoc-procedures
neo4j docs -> neo4j-contrib.github.io/neo4j-apoc-procedures/

How to learn more

There are many resources available to you for learning more about Neo4j and Cypher.

Resources for developers
Neo4j Community Site where you can ask or answer questions about Neo4j and discuss with other users -> Community
Neo4j documentation
Neo4j Sandboxes for experimenting with graphs
Videos on YouTube
GitHub repository
Neo4j events all over the world
Neo4j online and classroom training
Graph Gists for learning more use cases for Neo4j
Become a Neo4j certified developer
Attend a Neo4j meetup
View questions/answers raised about Neo4j

Example - Simple Graph

This example below is found on lesson 5 neo4j

Let's create this graph:

To see the graph result you can use this query:

MATCH (p)
RETURN p

Creation of the nodes:

CREATE (dan:Person {name:"Dan", born:"Dec 5, 1975"})
CREATE (ann:Person {name:"Ann", born: "May 29, 1970", twitter: "@ann"})
CREATE (car:Car {brand:"Volvo", model:"V70"})

result:

Query find on: creationNodes

Creation of realtionships:

MATCH
    (dan:Person {name: "Dan"}),
    (ann:Person {name: "Ann"}),
    (car:Car {model:"V70"})
CREATE
    (dan)-[:LOVES]->(ann),
    (ann)-[:LOVES]->(dan),
    (ann)-[:LIVES_WITH]->(dan),
    (dan)-[:OWNS]->(car),
    (dan)-[:DRIVES]->(car),
    (ann)-[:DRIVES]->(car)

result:

Query find on: creationRelationships

Basic query

"Find who loves Ann":

MATCH
    (ann:Person {name: "Ann"})<-[:LOVES]-(op)
RETURN
    op

The result is simple "Dan".

Query find on: lovesAnn

"Find the Ann's Car":

MATCH
    (ann:Person {name: "Ann"})-[:DRIVES]-(car:Car)
RETURN
    car

The result is simple "Volvo".

Query find on: annCar

"Find the Dan's Volvo car and update value of that car with the number of the wheels":

MATCH
    (ann:Person {name: "Dan"})-[:OWNS]-(car:Car)
WHERE
    car.brand="Volvo"
SET
    car.wheels=4
RETURN
    car.wheels

The result is simple 4.

Query find on: updateDanCar

Ensuring uniqueness: We don't want a bunch of nodes rapresenting the same object so to prevent this we can use the constraint on and unique properties

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

If we create a node Person with the same value of name of others than neo4j throws error. An example:

CREATE (a:Person {name:"Ann"})
CREATE (a)-[:HAS_PET]->(:Dog {name:"Sam"})

Output:

Neo.ClientError.Schema.ConstraintValidationFailed: Node(1) already exists with label `Person` and property `name` = 'Ann'

To prevent this error we can use the MERGE clause:

MERGE (a:Person {name:"Ann"})
CREATE (a)-[:HAS_PET]->(:Dog {name:"Sam"})

The node with name Ann will be created if is not exists and if exists then it not create that. The new graph will be:

In the same way if the dog already exists the above query will give you an error, to prevent is necessary use MERGE instead of CREATE for the Dog creation. An example:

MERGE (a:Person {name:"Ann"})
ON CREATE SET
    a.facebook = "@annie"
MERGE (a)-[:HAS_PET]->(:Dog {name:"Sam"})

On create set will be use only if the merge create the node, if you run this query before all the queries above the facebook parameter will not be created.

Query find on: annDogCreation

Application - Movie Graph

on neof4j browser run the command :play movie graph

Let's create a more complex example with a mini application containing actors and directors that are related through the movies they've collaborated on.

We can create more than one nodes and relationships with a single block query statement composed of multiple CREATE clauses:

CREATE (TheMatrix:Movie {title:'The Matrix', released:1999, tagline:'Welcome to the Real World'})
CREATE (Keanu:Person {name:'Keanu Reeves', born:1964})
CREATE (Carrie:Person {name:'Carrie-Anne Moss', born:1967})
CREATE (Laurence:Person {name:'Laurence Fishburne', born:1961})
CREATE (Hugo:Person {name:'Hugo Weaving', born:1960})
CREATE (LillyW:Person {name:'Lilly Wachowski', born:1967})
CREATE (LanaW:Person {name:'Lana Wachowski', born:1965})
CREATE (JoelS:Person {name:'Joel Silver', born:1952})
CREATE
  (Keanu)-[:ACTED_IN {roles:['Neo']}]->(TheMatrix),
  (Carrie)-[:ACTED_IN {roles:['Trinity']}]->(TheMatrix),
  (Laurence)-[:ACTED_IN {roles:['Morpheus']}]->(TheMatrix),
  (Hugo)-[:ACTED_IN {roles:['Agent Smith']}]->(TheMatrix),
  (LillyW)-[:DIRECTED]->(TheMatrix),
  (LanaW)-[:DIRECTED]->(TheMatrix),
  (JoelS)-[:PRODUCED]->(TheMatrix)

You can find all the query block here: Cypher/MovieGraph/createGraph.cql

Source query of movie graph -> here

The script add lot of nodes with relative relationships and properties, with this we can test some query:

Find the actor named "TOM Hanks"

MATCH (tom {name: "Tom Hanks"})
RETURN tom

or

MATCH (tom:Person)
WHERE tom.name="Tom Hanks"
RETURN tom

Find the movie with title "Cloud Atlas"

MATCH (cloud:Movie)
WHERE cloud.title="Cloud Atlas"
RETURN cloud

or

MATCH (cloud:Movie {title: "Cloud Atlas"})
RETURN cloud

Find 10 people
```
MATCH (people:Person)
RETURN people.name
LIMIT 10
```
The LIMIT clause is use to limitate the number of nodes in output.

Find movies released in the 1990s

MATCH (movie:Movie)
WHERE movie.released >= 1990 AND movie.released < 2000
RETURN movie.title

Find the films where "Tom Hanks" acted and show the director of the film

MATCH (tom:Person)-[:ACTED_IN]->(movie)<-[:DIRECTED]-(dir)
WHERE tom.name = "Tom Hanks"
RETURN tom,movie,dir

graph result:

List of Tom Hanks movies

MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie)
RETURN tom,movie

graph result:

Who directed "Cloud Atlas"

MATCH (dir:Person)-[:DIRECTED]->(movie)
WHERE movie.title = "Cloud Atlas"
RETURN dir

Tom Hanks co-actor (actor in the same film)

MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->(movie)
WHERE film.title = "Cloud Atlas"
RETURN dir

How people are related to "Cloud Atlas"

MATCH (people:Person)-[relatedTo]-(:Movie {title: "Cloud Atlas"})
RETURN people.name,Type(relatedTo),relatedTo

The Type() is use to get the type of nodes or relationships.

Movies and actors up to 4 "hops" away from Kevin Bacon
```
MATCH (bacon:Person {name: "Kevin Bacon"})-[*1..4]-(person)
RETURN DISTINCT person
```
The result of this query block count 135 nodes and 180 relationships.
Kevin Bacon the shortest path of any relationships to Meg Ryan
```
MATCH p=shortestPath(
    (bacon:Person {name: "Kevin Bacon"})-[*]-(meg:Person {name: "Meg Ryan"})
)
RETURN p
```
Result graph:

The shortestPath({}-[]-{}) is a function that take a relation of 2 nodes and return the shortest path from them.

To know more about the shortest path problem you can visit the wikipedia page: Shortest_path_problem

Extend Tom Hanks co-actors, to find co-co-actors who haven't worked with Tom Hanks

MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->()<-[:ACTED_IN]-(coactor:Person),
    (coactor)-[:ACTED_IN]->()<-[:ACTED_IN]-(cocoactor:Person)
WHERE NOT (tom)-[:ACTED_IN]->()<-[:ACTED_IN]-(cocoactor) AND tom <> cocoactor
RETURN cocoactor.name AS Racommended, count(*) AS Strength ORDER BY Strength DESC

Find someone to introduce Tom Hanks to Tom Cruise

MATCH (tomh:Person {name: "Tom Hanks"})-[]->(movie)<-[]-(someone:Person),
    (someone)-[]->(movie2)<-[]-(tomc:Person {name: "Tom Cruise"})
RETURN tomh,movie, someone,movie2, tomc

Result graph:

Let's now clean the graph by delete all the nodes and relationships:

MATCH (n)
DETACH
DELETE n

With this the engine take all the nodes, for every one it detach from any relationships and then delete it. To be sure and prove that the graph is gone the query is:

MATCH (n)
RETURN n

All the query above is on the source directory of Cypher here

Application - Northwind Graph

on neof4j browser run the command :play northwind graph

This application demostrates how to migrate from a relational database to Neo4j, to do this we need to transform all the data on th relational tables to the nodes and relationships of a graph. Pratically how to get this:

From this:

This example is a sellers of food products for a few categories provided by suppliers. The database actually in use is a relational table of product catalog. The first step to transform this is with the LOAD CSV clause that retrive a CSV file from a valid URL and create a named map:

Products nodes:

LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/products.csv" AS row
CREATE (n:Product)
SET n = row,
    n.unitPrice = toFloat(row.unitPrice),
    n.unitinStock = toInteger(row.unitsInStock), n.unitOnOrder = toInteger(row.unitsOnOrder),
    n.reorderLevel = toInteger(row.reorderLevel), n.discontinued = (row.discontinued <> "0")

Categories nodes:

LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/categories.csv" AS row
CREATE (n:Category)
SET n = row

Suppliers nodes:

LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/suppliers.csv" AS row
CREATE (n:Supplier)
SET n = row

It's important to define an index for a node with the purpose of making searches of related data more efficent:

Product index:
```
CREATE INDEX ON :Product(productID)
```
Category index:
```
CREATE INDEX ON :Category(productID)
```
Supplier index:
```
CREATE INDEX ON :Supplier(productID)
```

This 3 type of nodes are related one to another with foreign keys references we can use this properties to create the relationships from node to node. A product is a part of a category and a supplier supplies product, let's create this two relationship:

Product-part_of->Category:

MATCH (p:Product), (c:Category)
WHERE p.categoryID = c.categoryID
CREATE (p)-[:PART_OF]->(c)

Supplier-supplies->Product:

MATCH (p:Product), (s:Supplier)
WHERE p.supplierID = s.supplierID
CREATE (s)-[:SUPPLIES]->(p)

We can now test what we crated:

List the product categories proviced by each supplier:

MATCH (s:Supplier)-[*]->(c:Category)
RETURN
    s.companyName as Company,
    collect(distinct c.categoryName) as Categories
ORDER BY Company DESC

Find the produce suppliers:

MATCH (s:Supplier)-[*]->(c:Category {categoryName: "Produce"})
RETURN DISTINCT s.companyName as ProduceSuppliers

We can expand this graph with more datas and infos about Orders and Costumers:

We can do the same thing we had before to load and index the records:

Customers nodes

LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/customers.csv" AS row
CREATE (n:Customer)
SET n = row

Orders nodes

LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/orders.csv" AS row
CREATE (n:Order)
SET n = row

Index customer
```
CREATE INDEX ON :Customer(customerID)
```
Index order
```
CREATE INDEX ON :Order(orderID)
```

Create relationship customer-purchased-order

MATCH
    (c:Customer),
    (o:Order)
WHERE c.customerID = o.customerID
CREATE (c)-[:PURCHASE]->(o)

Notice that the Order Details are always part of an Order and are the description of the relation between an order and a product, this is a sign of a data relationship indicating shared information between two other records:

LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/order-details.csv" AS row
MATCH (p:Product), (o:Order)
WHERE
    p.productID = row.productID AND
    row.orderID = o.orderID
CREATE (o)-[details:ORDERS]->(p)
set details = row,
    details.quantity = toInteger(row.quantity)

Example of query:

List of costumers with the number of products purchased for the Produce category:

MATCH
    (c:Customer)-[:PURCHASED]->(:Order)-[o:ORDERS]->(p:Product),
    (p)-[:PART_OF]->(cat:Category {categoryName: "Produce"})
RETURN DISTINCT
    c.contactName AS CustomerName,
    SUM(o.quantity) AS TotalProductsPurchased

ACID consistency model

The acronym stands for:

Atomic -> All operations in a transaction succeed or every operation is rolled back
Consistent -> On the completition of a transaction, the database is structurally sound
Isolated -> Transactions do not contend with one another. Contentious access to data is moderated by database so that transactions appear to run sequentially.
Durable -> The results of applying a transaction are permanent, even in the presence of failures

This four properties mean that once a transaction is complete, its data is consistent and stable on disk.

Most of graph databases (Neo4j incuded) use an ACID consistency model to ensure data is safe and consistently stored.

Cluster

A cluster is when data is assembled around one particular value, on graph usually happens when there are several nodes thet seem to gather in a certain area. In other word a cluster is a group that are placed closely next to each other in a certain area, with a few nodes scattered in other places on the graph.

For example this is a social netowork graph:

There are several aggregations of similar nodes that are colored.

index-free adjacency

A graph database is any storage system that provides index-free adjacency and this means that every element contains a direct pointer to its adjacent element and no index lookups are necessary. This is one of the most element of distinction from relational databases that are based on a key-value store. The idea of an index-free adjacency is analogous to that of a pointer where the time to follow the relationship is O(1) with respect to the size of the graph, and this is not the cost of found a relationship in a relational. To be more precise a relational database use indexes but the aren't used for adjacency or link purposes. With Neo4j tipically a query will find the node to start from with an index and then the resto fo the query simply follows relationships to compute the answer. When create a new set of nodes it's typical to create an index of that type, see northwind graph, this reduce the cost of every query run. Example:

In a relational database if we want to switch from data A to data B the step are:

The db prohibited you to access direct items via pointers, so you need to ask for the permission.
The request permission line can be full of other request, wait for your turn.
The central index of db search for the data you want in a list of other data , the cost of search is O(n) with n number of the data stored.
Once you get the memory pointer of that data you can go to B by follow the memory allocations by hopping on memory.
The cost of this hopping is O(n), but at the end you can switch to B and do your operations.

In a graph database like Neo4j all the nodes are connected to each other by relationships, so in order to go from A to B:

Get the relationships pointer
Point to node B wherever the type of the relationships are
Do operations on B

The cost in this case is O(1), a big difference in performance between a classical relational db.

OLTP

This is an acronym that stands for Online transaction processing. The term transaction in the realm of database transactions it denotes an atomic change of state. The goals of OLTP applications are availability, speed, cuncurrency and recoverability.

An OLTP system is a common data processing system like order entry, retail sales and financial transaction systems.

The main characteristics of an OLTP environment are:

Short response time
Small transactions
Data maintenance operations
Large user populations
High concurrency
Large data volumes
High availability
Lifecycle-related data usage

The benefits of this environments are:

Support for bigger databases
Partition maintenance operations for data maintenance
Potential higher concurrency through elimination of hot spots

Regular expression

The phrase regular expressions, and consequently, regexes, is often used to mean the specific, standard textual syntax for representing patterns for matching text. Each character in a regular expression is either a metacharacter, having a special meaning, or a regular character that has a literal meaning. For example, in the regex a., a is a literal character which matches just 'a', while '.' is a meta character that matches every character except a newline. Therefore, this regex matches, for example, 'a ', or 'ax', or 'a0'. Together, metacharacters and literal characters can be used to identify text of a given pattern, or process a number of instances of it. Pattern matches may vary from a precise equality to a very general similarity, as controlled by the metacharacters. For example, . is a very general pattern, [a-z] (match all lower case letters from 'a' to 'z') is less general and a is a precise pattern (matches just 'a'). The metacharacter syntax is designed specifically to represent prescribed targets in a concise and flexible way to direct the automation of text processing of a variety of input data, in a form easy to type using a standard ASCII keyboard.

A very simple case of a regular expression in this syntax is to locate a word spelled two different ways in a text editor, the regular expression seriali[sz]e matches both "serialise" and "serialize". Wildcards also achieve this, but are more limited in what they can pattern, as they have fewer metacharacters and a simple language-base.

The usual context of wildcard characters is in globbing similar names in a list of files, whereas regexes are usually employed in applications that pattern-match text strings in general. For example, the regex ^[ \t]+|[ \t]+$ matches excess whitespace at the beginning or end of a line. An advanced regular expression that matches any numeral is [+-]?(\d+(.\d+)?|.\d+)([eE][+-]?\d+)?.

For more read wikipedia -> regex

References

Graph database Fundamentals:

neo4J browser sandbox by type the command :play concepts
graphacademy

Others:

Name		Name	Last commit message	Last commit date
Latest commit History 71 Commits
Cypher		Cypher
adoc		adoc
out		out
resources		resources
LICENSE		LICENSE
README.md		README.md

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LearningNeo4j

Content

Graph Database Fundamentals

Neo4J

Cypher

" Describing what to find and not how to find it "

Part one

Comments

Null

Match

on neof4j browser run the command :help MATCH

Type of query output

Exercises part one

Part two

Properties

Nodes properties filtering

Aliases

Exercises part two

Part three

Relationships

Style recommendations

Exercises part three

Part four

Where

Exercises part four

Part five

Multiple Match patterns

Setting path variables

Varying lenght paths

Finding the shortest path

Optional pattern matching

Aggregation in Cypher

Collecting results

Additional processing using WITH

Exercises part five

Part six

Eliminating duplication

Ordering result

Limiting the number of results

Exercises part six

Part Seven

List

Unwinding lists

Dates

Exercises part seven

Part Eight

Create

on neof4j browser run the command :help CREATE

More create at once

Add and remove the labels

Adding properties to a node

Removing properties from a node

Exercises part eight

Part Nine

Creating relationships

Adding properties to relationships

Removing properties to relationships

Exercises part nine

Part Ten

Deleting nodes and relationships

Exercises part ten

Part Eleven

Merge

Exercises part eleven

Part Twelve

Parameters

Exercises part twelve

Part Thirteen

Explain Profile

Exercises part thirteen

Part Fourteen

Managing constraints and node keys

Constraint properties

Constraint node keys

on neof4j browser run the command `:help MATCH`

Additional processing using `WITH`

on neof4j browser run the command `:help CREATE`