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count() function

The count() function counts the number of the specified values or rows.

  • (Native nGQL) You can use count() and GROUP BY together to group and count the number of specific values. Use YIELD to return.
  • (OpenCypher style) You can use count() and RETURN. GROUP BY is not necessary.

Syntax

count({expr | *})
  • count(*) returns the number of rows (including NULL).
  • count(expr) returns the number of non-NULL values that meet the expression.
  • count() and size() are different.

Examples

nebula> WITH [NULL, 1, 1, 2, 2] As a UNWIND a AS b \
        RETURN count(b), count(*), count(DISTINCT b);
+----------+----------+-------------------+
| count(b) | count(*) | count(distinct b) |
+----------+----------+-------------------+
| 4        | 5        | 2                 |
+----------+----------+-------------------+

# The statement in the following example searches for the people whom `player101` follows and people who follow `player101`, i.e. a bidirectional query.
nebula> GO FROM "player101" OVER follow BIDIRECT \
        YIELD properties($$).name AS Name \
        | GROUP BY $-.Name YIELD $-.Name, count(*);
+---------------------+----------+
| $-.Name             | count(*) |
+---------------------+----------+
| "LaMarcus Aldridge" | 2        |
| "Tim Duncan"        | 2        |
| "Marco Belinelli"   | 1        |
| "Manu Ginobili"     | 1        |
| "Boris Diaw"        | 1        |
| "Dejounte Murray"   | 1        |
+---------------------+----------+

The preceding example retrieves two columns:

  • $-.Name: the names of the people.
  • count(*): how many times the names show up.

Because there are no duplicate names in the basketballplayer dataset, the number 2 in the column count(*) shows that the person in that row and player101 have followed each other.

# a: The statement in the following example retrieves the age distribution of the players in the dataset.
nebula> LOOKUP ON player \
        YIELD player.age As playerage \
        | GROUP BY $-.playerage \
        YIELD $-.playerage as age, count(*) AS number \
        | ORDER BY $-.number DESC, $-.age DESC;
+-----+--------+
| age | number |
+-----+--------+
| 34  | 4      |
| 33  | 4      |
| 30  | 4      |
| 29  | 4      |
| 38  | 3      |
+-----+--------+
...

# b: The statement in the following example retrieves the age distribution of the players in the dataset.
nebula> MATCH (n:player) \
        RETURN n.player.age as age, count(*) as number \
        ORDER BY number DESC, age DESC;
+-----+--------+
| age | number |
+-----+--------+
| 34  | 4      |
| 33  | 4      |
| 30  | 4      |
| 29  | 4      |
| 38  | 3      |
+-----+--------+
...

# The statement in the following example counts the number of edges that Tim Duncan relates.
nebula> MATCH (v:player{name:"Tim Duncan"}) -- (v2) \
        RETURN count(DISTINCT v2);
+--------------------+
| count(distinct v2) |
+--------------------+
| 11                 |
+--------------------+

# The statement in the following example counts the number of edges that Tim Duncan relates and returns two columns (no DISTINCT and DISTINCT) in multi-hop queries.
nebula> MATCH (n:player {name : "Tim Duncan"})-[]->(friend:player)-[]->(fof:player) \
        RETURN count(fof), count(DISTINCT fof);
+------------+---------------------+
| count(fof) | count(distinct fof) |
+------------+---------------------+
| 4          | 3                   |
+------------+---------------------+
Last update: January 14, 2022
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