Overview

An aggregate function performs a calculation on a set of values and returns a single scalar value.

Vertical aggregation is supported which takes a set of values from different rows and aggregates into a single value. Horizontal aggregation which takes a set of values from a group list value and aggregates into a single value is not yet supported.

DISTINCT

All aggregate functions support the use of the set quantifier DISTINCT to deduplicate values before aggregation.

Null Values

Rows containing null values are ignored by all aggregate functions, except count(*).

Example Graph

The following examples run against this graph:

avg()

Computes the average of a set of numeric values.

MATCH (n)
RETURN avg(n.score)

Result:

collect_list()

Collects a set of values into a list.

MATCH (n)
RETURN collect_list(n.title)

Result:

count()

Returns the number of rows in the input.

MATCH (n)
RETURN count(n)

Result:

count(*)

count(*) returns the number of rows in the current working table.

Comparing the following two queries, the null values are only considered when using count(*):

FOR item IN [1, "a", "2", "b3", null]
RETURN count(item)

Result:

FOR item IN [1, "a", "2", "b3", null]
RETURN count(*)

Result:

count(DISTINCT)

You can include the set quantifier DISTINCT in count() to return the number of distinct rows in the input.

FOR item IN [1, 1, "a", "2", "b3"]
RETURN count(DISTINCT item)

Result:

max()

Returns the maximum value in a set of values.

MATCH (n)
RETURN max(n.score)

Result:

FOR item IN [1, "a", "2.1", "b3"]
RETURN max(item)

Result:

min()

Returns the minimum value in a set of values.

MATCH (n)
RETURN min(n.score)

Result:

FOR item IN [3, "a", "0.2", "b2"]
RETURN min(item)

Result:

percentile_cont()

Computes the continuous percentile value over a set of numeric values.

percentile_cont() is computed using the following steps:

• Sort the values in ascending order.
• Compute the percentile position as p = percentile × (n − 1) + 1, where n is the number of non-null values.
• Determine the percentile value using linear interpolation:
  • If p is an integer, the corresponding value at that position is the percentile value.
  • If p is a decimal between two integers p1 and p2 (p1 < p < p2), interpolate between the value v1 at position p1 and the value v2 at position p2 to compute the percentile value as v1 + (p - p1) × (v2 - v1).

FOR item IN [3, 9, 4, 7, 6]
RETURN percentile_cont(item, 0.4)

Result:

FOR item IN [3, 9, 4, 7, 6]
RETURN percentile_cont(item, 0.5)

Result:

percentile_disc()

Computes the discrete percentile value over a set of numeric values.

percentile_disc() is computed using the following steps:

• Sort the values in ascending order.
• Compute the percentile position as p = percentile × (n − 1) + 1, where n is the number of non-null values.
• Determine the percentile value:
  • If p is an integer, the value at that exact position is selected as the percentile value.
  • If p is not an integer, it is rounded to p', the value at the position p' is selected as the percentile value.

FOR item IN [3, 9, 4, 7, 6]
RETURN percentile_disc(item, 0.4)

Result:

FOR item IN [3, 9, 4, 7, 6]
RETURN percentile_disc(item, 0.5)

Result:

stddev_pop()

Computes the population standard deviation of a set of numeric values.

MATCH (n)
RETURN stddev_pop(n.score)

Result:

stddev_samp()

Computes the sample standard deviation of a set of numeric values.

MATCH (n)
RETURN stddev_samp(n.score)

Result:

sum()

Computes the sum of a set of numeric values.

MATCH (n)
RETURN sum(n.score)

Result: