Which constraint does not allow someone to create an order for a nonexistent customer?

Understanding non-existent properties and working with nulls

In Neo4j, since there is no table schema or equivalent to restrict possible properties, non-existence and null are equivalent for node and relationship properties. That is, there really is no such thing as a property with a null value; null indicates that the property doesn’t exist at all.

This is in contrast with a relational db with a table schema, where a field exists for a row of a table even if the value for that column is null.

As an example, if we had a graph of :Person nodes, the following two queries are considered the same:

MATCH (p:Person)
WHERE p.id = $personId
RETURN NOT EXISTS(p.email)

MATCH (p:Person)
WHERE p.id = $personId
RETURN p.email IS NULL

It does not matter if the property in question is one that is used on other nodes (just missing on this one), or if it is a nonsensical or never-used property in the graph (such as p.pinkElephant). If the property doesn’t exist, a NULL check as above will return true.

Likewise, the following two queries result in the removal of the property:

MATCH (p:Person)
WHERE p.id = $personId
SET p.email = null

MATCH (p:Person)
WHERE p.id = $personId
REMOVE p.email

This can be useful if you need a way to clear several properties dynamically from nodes, using a map to update node properties:

WITH {email:null, dob:null} as clearProps
MATCH (p:Person)
WHERE p.id = $personId
SET p += clearProps

NULL map values

This also demonstrates that although non-existence and null node and relationship properties are equivalent, this does not hold true for maps or collections. A map with an entry with a null value, as above, is different than a map that is missing the entry.

This differentiation can be especially useful when using map projection. Map projection is a map view of a node’s properties, and can be used to force the display of a property for the returned data, even if the property doesn’t exist on that node.

For example, in this query, the email property in the returned node data may or may not be returned, depending on if the property exists on each returned node:

MATCH (p:Person)
RETURN p

But if we wanted the email property to always display, showing NULL if the property doesn’t exist, we would use map projection like so:

MATCH (p:Person)
RETURN p {.*, .email}

NULL list values

NULLs are also allowed in lists:

However, aggregations over node or relationship properties skip or disregard null values:

MATCH (p:Person)
RETURN count(p.email) as emailCount

The count does not include null values.

MATCH (p:Person)
RETURN collect(p.email) as emails

This skips null values; the returned collection does not contain any nulls.

Working with NULL in logical expressions and other contexts

The Neo4j developers manual has an extensive section on working with NULL in logical expressions and details when expressions will return NULL.

Use COALESCE() to use a default for a null value

In some cases we may want to display a fallback default value when a property doesn’t exist on a node or relationship. Or we may want to perform an equality or inequality comparison against a possible null value, and use a default if the value happens to be null.

COALESCE() allows handling of these cases. It accepts any number of parameters, and returns the first non-null value encountered.

MATCH (p:Person)
RETURN collect(COALESCE(p.email, 'NOT SET')) as emails

Rather than ignoring the null values as before, we see 'NOT SET' included in the list.

MATCH (p:Person)
WHERE COALESCE(p.optedIn, false) <> true
RETURN p

With COALESCE() we can treat a null as a different value for the purpose of comparison. Note that equality or inequality comparisons against null, such as null <> true, results in null, not a boolean, so COALESCE() is especially helpful here.

MATCH (p:Person)
RETURN COALESCE(p.email, p.backupEmail, p.backupBackupEmail, 'NOT SET') as email

Multiple fallbacks are allowed, the first non-null value will be used.

Matching and Merging with NULL

Null node variables

While you can perform a MATCH where a node variable in the match is null (such as from a failed OPTIONAL MATCH), you cannot CREATE or MERGE a pattern with a null node variable.

When there are no :PinkElephant nodes, this works, but won’t return records:

OPTIONAL MATCH (node1:PinkElephant)
WITH node1
MATCH (node1)-[:ACTED_IN]->(m:Movie)
RETURN node1

However both of these will throw errors:

OPTIONAL MATCH (node1:PinkElephant)
WITH node1
MERGE (node1)-[:ACTED_IN]->(m:Movie)
RETURN node1

and

OPTIONAL MATCH (node1:PinkElephant)
WITH node1
CREATE (node1)-[:ACTED_IN]->(m:Movie)
RETURN node1

Null property values

When we instead use null property values, we can MATCH or CREATE using a null property value, but we cannot use MERGE:

These work:

OPTIONAL MATCH (ele:PinkElephant)
WITH ele
MATCH (node1:Person {name:ele.name})-[:ACTED_IN]->(m:Movie)
RETURN node1

and

OPTIONAL MATCH (ele:PinkElephant)
WITH ele
CREATE (node1:Person {name:ele.name})-[:ACTED_IN]->(m:Movie)
RETURN node1

but this throws an error:

OPTIONAL MATCH (ele:PinkElephant)
WITH ele
MERGE (node1:Person {name:ele.name})-[:ACTED_IN]->(m:Movie)
RETURN node1

Which of the following systems allow users to create read update and delete data in a relational database?

Database Management Systems (DBMS) are software systems used to store, retrieve, and run queries on data. A DBMS serves as an interface between an end-user and a database, allowing users to create, read, update, and delete data in the database.

Which of the following systems allows users to create read update and delete?

A database management system (DBMS) is system software for creating and managing databases. A DBMS makes it possible for end users to create, protect, read, update and delete data in a database.

What is the term for the time it takes for data to be stored or retrieved?

Data latency is the time it takes for data packets to be stored or retrieved. In business intelligence (BI), data latency is how long it takes for a business user to retrieve source data from a data warehouse or BI dashboard.

What is data that is not defined does not follow a specified format and is typically freeform text such as emails twitter tweets and text messages?

Unstructured data is not defined and does not follow a speci- fied format and is typically free-form text such as emails, Twitter tweets, and text messages. Unstructured data accounts for about 80 percent of the data that surrounds us.