Aggregating functions

Introduction

Aggregating functions take a set of values and calculate an aggregated value over them. Aggregation can be computed over all the matching paths, or it can be further divided by introducing grouping keys. Grouping keys are non-aggregate expressions that are used to group the values going into the aggregate functions.

For example, given the following query containing two return expressions, n and count(*):

RETURN n, count(*)

The first, n is not an aggregate function, so it will be the grouping key. The latter, count(*) is an aggregate expression. The matching paths will be divided into different buckets, depending on the grouping key. The aggregate function will then be run on these buckets, calculating an aggregate value per bucket.

The input expression of an aggregation function can contain any expression, including expressions that are not grouping keys. However, not all expressions can be composed with aggregation functions. The example below will throw an error since n.x, which is not a grouping key, is combined with the aggregation expression count(*). For more information, see Grouping keys.

RETURN n.x + count(*)

To use aggregations to sort the result set, the aggregation must be included in the RETURN to be used in the ORDER BY.

The DISTINCT operator works in conjunction with aggregation. It is used to make all values unique before running them through an aggregate function. More information about DISTINCT can be found in Syntax → Aggregation operators.

Example graph

The following graph is used for the examples below:

graph aggregating functions

To recreate the graph, run the following query against an empty Neo4j database:

CREATE
  (keanu:Person {name: 'Keanu Reeves', age: 58}),
  (liam:Person {name: 'Liam Neeson', age: 70}),
  (carrie:Person {name: 'Carrie Anne Moss', age: 55}),
  (guy:Person {name: 'Guy Pearce', age: 55}),
  (kathryn:Person {name: 'Kathryn Bigelow', age: 71}),
  (speed:Movie {title: 'Speed'}),
  (keanu)-[:ACTED_IN]->(speed),
  (keanu)-[:KNOWS]->(carrie),
  (keanu)-[:KNOWS]->(liam),
  (keanu)-[:KNOWS]->(kathryn),
  (carrie)-[:KNOWS]->(guy),
  (liam)-[:KNOWS]->(guy)

avg() - Numeric values

The function avg() returns the average of a set of numeric values.

avg(expression)

Returns:

Either an Integer or a Float, depending on the values returned by expression and whether or not the calculation overflows.

Arguments:

Name Description

expression

An expression returning a set of numeric values.

Considerations:

Any null values are excluded from the calculation.

avg(null) returns null.
Example 1. avg()
Query
MATCH (p:Person)
RETURN avg(p.age)

The average of all the values in the property age is returned:

Table 1. Result
avg(p.age)

61.8

Rows: 1

avg() - Durations

The function avg() returns the average of a set of Durations.

avg(expression)

Returns:

A Duration.

Arguments:

Name Description

expression

An expression returning a set of Durations.

Considerations:

Any null values are excluded from the calculation.

avg(null) returns null.
Example 2. avg()
Query
UNWIND [duration('P2DT3H'), duration('PT1H45S')] AS dur
RETURN avg(dur)

The average of the two supplied Durations is returned:

Table 2. Result
avg(dur)

P1DT2H22.5S

Rows: 1

collect()

The function collect() returns a single aggregated list containing the values returned by an expression.

collect(expression)

Returns:

A list containing heterogeneous elements; the types of the elements are determined by the values returned by expression.

Arguments:

Name Description

expression

An expression returning a set of values.

Considerations:

Any null values are ignored and will not be added to the list.

collect(null) returns an empty list.
Example 3. collect()
Query
MATCH (p:Person)
RETURN collect(p.age)

All the values are collected and returned in a single list:

Table 3. Result
collect(p.age)

[58, 70, 55, 55, 71]

Rows: 1

count()

The function count() returns the number of values or rows, and appears in two variants:

count(*)

returns the number of matching rows.

count(expr)

returns the number of non-null values returned by an expression.

count(expression)

Returns:

An Integer.

Arguments:

Name Description

expression

An expression.

Considerations:

count(*) includes rows returning null.

count(expr) ignores null values.

count(null) returns 0.

Using count(*) to return the number of nodes

The function count(*) can be used to return the number of nodes; for example, the number of nodes connected to a node n.

Example 4. count()
Query
MATCH (p:Person {name: 'Keanu Reeves'})-->(x)
RETURN labels(p), p.age, count(*)

The labels and age property of the start node Keanu Reeves and the number of nodes related to it are returned:

Table 4. Result
labels(p) p.age count(*)

["Person"]

58

4

Rows: 1

Using count(*) to group and count relationship types

The function count(*) can be used to group the type of matched relationships and return the number of types.

Example 5. count()
Query
MATCH (p:Person {name: 'Keanu Reeves'})-[r]->()
RETURN type(r), count(*)

The type of matched relationships are grouped and the group count of relationship types is returned:

Table 5. Result
type(r) count(*)

"ACTED_IN"

1

"KNOWS"

3

Rows: 2

Counting non-null values

Instead of simply returning the number of rows with count(*), the function count(expression) can be used to return the number of non-null values returned by the expression.

Example 6. count()
Query
MATCH (p:Person)
RETURN count(p.age)

The number of nodes with the label Person and a property age is returned: (To calculate the sum, use sum(n.age))

Table 6. Result
count(p.age)

5

Rows: 1

Counting with and without duplicates

This example tries to find all friends of friends of Keanu Reeves and count them.

count(DISTINCT friendOfFriend)

Will only count a friendOfFriend once, as DISTINCT removes the duplicates.

count(friendOfFriend)

Will consider the same friendOfFriend multiple times.

Example 7. count()
Query
MATCH (p:Person)-->(friend:Person)-->(friendOfFriend:Person)
WHERE p.name = 'Keanu Reeves'
RETURN friendOfFriend.name, count(DISTINCT friendOfFriend), count(friendOfFriend)

The nodes Carrie Anne Moss and Liam Neeson both have an outgoing KNOWS relationship to Guy Pearce. The Guy Pearce node will, therefore, get counted twice when not using DISTINCT.

Table 7. Result
friendOfFriend.name count(DISTINCT friendOfFriend) count(friendOfFriend)

"Guy Pearce"

1

2

max()

The function max() returns the maximum value in a set of values.

max(expression)

Returns:

A property type, or a list, depending on the values returned by expression.

Arguments:

Name Description

expression

An expression returning a set containing any combination of property types and lists thereof.

Considerations:

Any null values are excluded from the calculation.

In a mixed set, any numeric value is always considered to be higher than any string value, and any string value is always considered to be higher than any list.

Lists are compared in dictionary order, i.e. list elements are compared pairwise in ascending order from the start of the list to the end.

max(null) returns null.
Example 8. max()
Query
UNWIND [1, 'a', null, 0.2, 'b', '1', '99'] AS val
RETURN max(val)

The highest of all the values in the mixed set — in this case, the numeric value 1 — is returned:

The value '99' (a string), is considered to be a lower value than 1 (an integer), because '99' is a string.
Table 8. Result
max(val)

1

Rows: 1
Example 9. max()
Query
UNWIND [[1, 'a', 89], [1, 2]] AS val
RETURN max(val)

The highest of all the lists in the set — in this case, the list [1, 2] — is returned, as the number 2 is considered to be a higher value than the string 'a', even though the list [1, 'a', 89] contains more elements.

Table 9. Result
max(val)

[1,2]

Rows: 1
Example 10. max()
Query
MATCH (p:Person)
RETURN max(p.age)

The highest of all the values in the property age is returned:

Table 10. Result
max(p.age)

71

Rows: 1

min()

The function min() returns the minimum value in a set of values.

min(expression)

Returns:

A property type, or a list, depending on the values returned by expression.

Arguments:

Name Description

expression

An expression returning a set containing any combination of property types and lists thereof.

Considerations:

Any null values are excluded from the calculation.

In a mixed set, any string value is always considered to be lower than any numeric value, and any list is always considered to be lower than any string.

Lists are compared in dictionary order, i.e. list elements are compared pairwise in ascending order from the start of the list to the end.

min(null) returns null.
Example 11. min()
Query
UNWIND [1, 'a', null, 0.2, 'b', '1', '99'] AS val
RETURN min(val)

The lowest of all the values in the mixed set — in this case, the string value "1" — is returned. Note that the (numeric) value 0.2, which may appear at first glance to be the lowest value in the list, is considered to be a higher value than "1" as the latter is a string.

Table 11. Result
min(val)

"1"

Rows: 1
Example 12. min()
Query
UNWIND ['d', [1, 2], ['a', 'c', 23]] AS val
RETURN min(val)

The lowest of all the values in the set — in this case, the list ['a', 'c', 23] — is returned, as (i) the two lists are considered to be lower values than the string "d", and (ii) the string "a" is considered to be a lower value than the numerical value 1.

Table 12. Result
min(val)

["a","c",23]

Rows: 1
Example 13. min()
Query
MATCH (p:Person)
RETURN min(p.age)

The lowest of all the values in the property age is returned:

Table 13. Result
min(p.age)

55

Rows: 1

percentileCont()

The function percentileCont() returns the percentile of the given value over a group, with a percentile from 0.0 to 1.0. It uses a linear interpolation method, calculating a weighted average between two values if the desired percentile lies between them. For nearest values using a rounding method, see percentileDisc.

percentileCont(expression, percentile)

Returns:

A Float.

Arguments:

Name Description

expression

A numeric expression.

percentile

A numeric value between 0.0 and 1.0.

Considerations:

Any null values are excluded from the calculation.

percentileCont(null, percentile) returns null.
Example 14. percentileCont()
Query
MATCH (p:Person)
RETURN percentileCont(p.age, 0.4)

The 40th percentile of the values in the property age is returned, calculated with a weighted average:

Table 14. Result
percentileCont(p.age, 0.4)

56.8

Rows: 1

percentileDisc()

The function percentileDisc() returns the percentile of the given value over a group, with a percentile from 0.0 to 1.0. It uses a rounding method and calculates the nearest value to the percentile. For interpolated values, see percentileCont.

percentileDisc(expression, percentile)

Returns:

Either an Integer or a Float, depending on the values returned by expression and whether or not the calculation overflows.

Arguments:

Name Description

expression

A numeric expression.

percentile

A numeric value between 0.0 and 1.0.

Considerations:

Any null values are excluded from the calculation.

percentileDisc(null, percentile) returns null.
Example 15. percentileDisc()
Query
MATCH (p:Person)
RETURN percentileDisc(p.age, 0.5)

The 50th percentile of the values in the property age is returned:

Table 15. Result
percentileDisc(p.age, 0.5)

58

Rows: 1

stDev()

The function stDev() returns the standard deviation for the given value over a group. It uses a standard two-pass method, with N - 1 as the denominator, and should be used when taking a sample of the population for an unbiased estimate. When the standard variation of the entire population is being calculated, stdDevP should be used.

stDev(expression)

Returns:

A Float.

Arguments:

Name Description

expression

A numeric expression.

Considerations:

Any null values are excluded from the calculation.

stDev(null) returns 0.
Example 16. stDev()
Query
MATCH (p:Person)
WHERE p.name IN ['Keanu Reeves', 'Liam Neeson', 'Carrie Anne Moss']
RETURN stDev(p.age)

The standard deviation of the values in the property age is returned:

Table 16. Result
stDev(p.age)

7.937253933193772

Rows: 1

stDevP()

The function stDevP() returns the standard deviation for the given value over a group. It uses a standard two-pass method, with N as the denominator, and should be used when calculating the standard deviation for an entire population. When the standard variation of only a sample of the population is being calculated, stDev should be used.

stDevP(expression)

Returns:

A Float.

Arguments:

Name Description

expression

A numeric expression.

Considerations:

Any null values are excluded from the calculation.

stDevP(null) returns 0.
Example 17. stDevP()
Query
MATCH (p:Person)
WHERE p.name IN ['Keanu Reeves', 'Liam Neeson', 'Carrie Anne Moss']
RETURN stDevP(p.age)

The population standard deviation of the values in the property age is returned:

Table 17. Result
stDevP(p.age)

6.48074069840786

Rows: 1

sum() - Numeric values

The function sum() returns the sum of a set of numeric values.

sum(expression)

Returns:

Either an Integer or a Float, depending on the values returned by expression.

Arguments:

Name Description

expression

An expression returning a set of numeric values.

Considerations:

Any null values are excluded from the calculation.

sum(null) returns 0.
Example 18. sum()
Query
MATCH (p:Person)
RETURN sum(p.age)

The sum of all the values in the property age is returned:

Table 18. Result
sum(p.age)

309

Rows: 1

sum() - Durations

The function sum() returns the sum of a set of durations.

sum(expression)

Returns:

A Duration.

Arguments:

Name Description

expression

An expression returning a set of Durations.

Considerations:

Any null values are excluded from the calculation.
Example 19. sum()
Query
UNWIND [duration('P2DT3H'), duration('PT1H45S')] AS dur
RETURN sum(dur)

The sum of the two supplied Durations is returned:

Table 19. Result
sum(dur)

P2DT4H45S

Rows: 1

Grouping keys

Aggregation expressions are expressions which contain one or more aggregation functions. A simple aggregation expression consists of a single aggregation function. For instance, SUM(x.a) is an aggregation expression that only consists of the aggregation function SUM( ) with x.a as its argument. Aggregation expressions are also allowed to be more complex, where the result of one or more aggregation functions are input arguments to other expressions. For instance, 0.1 * (SUM(x.a) / COUNT(x.b)) is an aggregation expression that contains two aggregation functions, SUM( ) with x.a as its argument and COUNT( ) with x.b as its argument. Both are input arguments to the division expression.

For aggregation expressions to be correctly computable for the buckets formed by the grouping key(s), they have to fulfill some requirements. Specifically, each sub expression in an aggregation expression has to be either:

  • an aggregation function, e.g. SUM(x.a),

  • a constant, e.g. 0.1,

  • a parameter, e.g. $param,

  • a grouping key, e.g. the a in RETURN a, count(*)

  • a local variable, e.g. the x in count(*) + size([ x IN range(1, 10) | x ]), or

  • a subexpression, all whose operands are operands allowed in an aggregation expression.

Examples of aggregation expressions.

Example 20. Simple aggregation without any grouping keys:
Query
MATCH (p:Person)
RETURN max(p.age)
Table 20. Result
max(p.age)

71

Rows: 1
Example 21. Addition of an aggregation and a constant, without any grouping keys:
Query
MATCH (p:Person)
RETURN max(p.age) + 1
Table 21. Result
max(p.age) + 1

72

Rows: 1
Example 22. Subtraction of a property access and an aggregation.

Note that p is a grouping key:

Query
MATCH (p:Person{name:'Keanu Reeves'})-[:KNOWS]-(f:Person)
RETURN p, p.age - max(f.age)
Table 22. Result
p p.age - max(f.age)

{{"name":"Keanu Reeves","age":58}}

-13

Rows: 1
Example 23. Subtraction of a property access and an aggregation.

Note that p.age is a grouping key:

Query
MATCH (p:Person {name:'Keanu Reeves'})-[:KNOWS]-(f:Person)
RETURN p.age, p.age - max(f.age)
Table 23. Result
p.age p.age - max(f.age)

58

-13

Rows: 1

Grouping keys themselves can be complex expressions. For better query readability, Cypher® only recognizes a sub-expression in aggregation expressions as a grouping key if the grouping key is either:

  • A variable - e.g. the p in RETURN p, p.age - max(f.age)

  • A property access - e.g. the p.age in RETURN p.age, p.age - max(f.age)

  • A map access - e.g. the p.age in WITH {name:'Keanu Reeves', age:58} AS p RETURN p.age, p.age - max(p.age)

If more complex grouping keys are needed as operands in aggregation expression, it is always possible to project them in advance with WITH.

Using the property p.age will throw an exception, since p.age is not a grouping key. Therefore, it cannot be used in the expressions which contain the aggregating function. The below two queries would consequently return the same error message:

Query
MATCH (p:Person {name:'Keanu Reeves'})-[:KNOWS]-(f:Person)
RETURN p.age - max(f.age)
Query
MATCH (p:Person {name:'Keanu Reeves'})-[:KNOWS]-(f:Person)
RETURN p.age + p.age, p.age + p.age - max(f.age)
Error message
Aggregation column contains implicit grouping expressions. For example, in 'RETURN n.a, n.a + n.b + count(*)' the aggregation expression 'n.a + n.b + count(*)' includes the implicit grouping key 'n.b'. It may be possible to rewrite the query by extracting these grouping/aggregation expressions into a preceding WITH clause. Illegal expression(s): n.age

However, the latter query would work if rewritten to:

Query
MATCH (p:Person {name:'Keanu Reeves'})-[:KNOWS]-(f:Person)
WITH p.age + p.age AS groupingKey, f
RETURN groupingKey, groupingKey - max(f.age)
Table 24. Result
groupingKey groupingKey - max(f.age)

116

45

Rows: 1