Introduction
To calculate aggregated data, Cypher offers aggregation, much like SQL’s GROUP BY.
Aggregate functions take multiple input values and calculate an aggregated value from them.
Examples are avg that calculates the average of multiple numeric values, or min that finds the smallest numeric value in a set of values.
Aggregation can be done over all the matching subgraphs, or it can be further divided by introducing key values. These are non-aggregate expressions, that are used to group the values going into the aggregate functions.
So, if the return statement looks something like this:
RETURN n, count(*)
We have two return expressions: n, and count(*).
The first, n, is no aggregate function, and so it will be the grouping key.
The latter, count(*) is an aggregate expression.
So the matching subgraphs will be divided into different buckets, depending on the grouping key.
The aggregate function will then run on these buckets, calculating the aggregate values.
If you want to use aggregations to sort your result set, the aggregation must be included in the RETURN to be used in your ORDER BY.
The last piece of the puzzle is the DISTINCT keyword.
It is used to make all values unique before running them through an aggregate function.
An example might be helpful. In this case, we are running the query against the following data:
Query
MATCH (me:Person)-->(friend:Person)-->(friend_of_friend:Person) WHERE me.name = 'A' RETURN count(DISTINCT friend_of_friend), count(friend_of_friend)
In this example we are trying to find all our friends of friends, and count them.
The first aggregate function, count(DISTINCT friend_of_friend), will only see a friend_of_friend once — DISTINCT removes the duplicates.
The latter aggregate function, count(friend_of_friend), might very well see the same friend_of_friend multiple times.
In this case, both B and C know D and thus D will get counted twice, when not using DISTINCT.
Result
| count(distinct friend_of_friend) | count(friend_of_friend) |
|---|---|
| 1 row | |
|
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; MATCH (me:Person)-->(friend:Person)-->(friend_of_friend:Person) WHERE me.name = 'A'RETURN count(distinct friend_of_friend), count(friend_of_friend)
The following examples are assuming the example graph structure below.
Figure 11.7. Graph
COUNT
COUNT is used to count the number of rows.
COUNT can be used in two forms — COUNT(*) which just counts the number of matching rows, and COUNT(<expression>), which counts the number of non-NULL values in <expression>.
Count nodes
To count the number of nodes, for example the number of nodes connected to one node, you can use count(*).
Query
MATCH (n { name: 'A' })-->(x)
RETURN n, count(*)
This returns the start node and the count of related nodes.
Result
| n | count(*) |
|---|---|
| 1 row | |
|
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n {name: 'A'})-->(x) return n, count(*)
Group Count Relationship Types
To count the groups of relationship types, return the types and count them with count(*).
Query
MATCH (n { name: 'A' })-[r]->()
RETURN type(r), count(*)
The relationship types and their group count is returned by the query.
Result
| type(r) | count(*) |
|---|---|
| 1 row | |
|
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n {name: 'A'})-[r]->() return type(r), count(*)
Count entities
Instead of counting the number of results with count(*), it might be more expressive to include the name of the variable you care about.
Query
MATCH (n { name: 'A' })-->(x)
RETURN count(x)
The example query returns the number of connected nodes from the start node.
Result
| count(x) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n {name: 'A'})-->(x) return count(x)
Count non-null values
You can count the non-NULL values by using count(<expression>).
Query
MATCH (n:Person) RETURN count(n.property)
The count of related nodes with the property property set is returned by the query.
Result
| count(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return count(n.property)
Statistics
sum
The sum aggregation function simply sums all the numeric values it encounters. NULLs are silently dropped.
Query
MATCH (n:Person) RETURN sum(n.property)
This returns the sum of all the values in the property property.
Result
| sum(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return sum(n.property)
avg
avg calculates the average of a numeric column.
Query
MATCH (n:Person) RETURN avg(n.property)
The average of all the values in the property property is returned by the example query.
Result
| avg(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return avg(n.property)
percentileDisc
percentileDisc calculates the percentile of a given value over a group, with a percentile from 0.0 to 1.0.
It uses a rounding method, returning the nearest value to the percentile.
For interpolated values, see percentileCont.
Query
MATCH (n:Person) RETURN percentileDisc(n.property, 0.5)
The 50th percentile of the values in the property property is returned by the example query. In this case, 0.5 is the median, or 50th percentile.
Result
| percentileDisc(n.property, 0.5) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return percentileDisc(n.property, 0.5)
percentileCont
percentileCont calculates the percentile of a 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.
Query
MATCH (n:Person) RETURN percentileCont(n.property, 0.4)
The 40th percentile of the values in the property property is returned by the example query, calculated with a weighted average.
Result
| percentileCont(n.property, 0.4) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return percentileCont(n.property, 0.4)
stdev
stdev calculates the standard deviation for a 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, stdevp should be used.
Query
MATCH (n) WHERE n.name IN ['A', 'B', 'C'] RETURN stdev(n.property)
The standard deviation of the values in the property property is returned by the example query.
Result
| stdev(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n) where n.name IN ['A','B','C'] return stdev(n.property)
stdevp
stdevp calculates the standard deviation for a 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.
Query
MATCH (n) WHERE n.name IN ['A', 'B', 'C'] RETURN stdevp(n.property)
The population standard deviation of the values in the property property is returned by the example query.
Result
| stdevp(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n) where n.name IN ['A','B','C'] return stdevp(n.property)
max
max find the largest value in a numeric column.
Query
MATCH (n:Person) RETURN max(n.property)
The largest of all the values in the property property is returned.
Result
| max(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return max(n.property)
min
min takes a numeric property as input, and returns the smallest value in that column.
Query
MATCH (n:Person) RETURN min(n.property)
This returns the smallest of all the values in the property property.
Result
| min(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return min(n.property)
collect
collect collects all the values into a list. It will ignore NULLs.
Query
MATCH (n:Person) RETURN collect(n.property)
Returns a single row, with all the values collected.
Result
| collect(n.property) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (n:Person) return collect(n.property)
DISTINCT
All aggregation functions also take the DISTINCT modifier, which removes duplicates from the values.
So, to count the number of unique eye colors from nodes related to a, this query can be used:
Query
MATCH (a:Person { name: 'A' })-->(b)
RETURN count(DISTINCT b.eyes)
Returns the number of eye colors.
Result
| count(distinct b.eyes) |
|---|
| 1 row |
|
Try this query live create (_0:`Person` {`name`:"A", `property`:13}) create (_1:`Person` {`eyes`:"blue", `name`:"B", `property`:33}) create (_2:`Person` {`eyes`:"blue", `name`:"C", `property`:44}) create (_3:`Person` {`eyes`:"brown", `name`:"D"}) create (_0)-[:`KNOWS`]->(_3) create (_0)-[:`KNOWS`]->(_2) create (_0)-[:`KNOWS`]->(_1) ; match (a:Person {name: 'A'})-->(b) return count(distinct b.eyes)