stats.rst

stats

Table of contents

• Description
• Syntax
• Aggregation Functions
  • COUNT
  • SUM
  • AVG
  • MAX
  • MIN
  • VAR_SAMP
  • VAR_POP
  • STDDEV_SAMP
  • STDDEV_POP
• By Clause
• Example 1: Calculate the count of events
• Example 2: Calculate the average of a field
• Example 3: Calculate the average of a field by group
• Example 4: Calculate the average, sum and count of a field by group
• Example 5: Calculate the maximum of a field
• Example 6: Calculate the maximum and minimum of a field by group
• Example 7: Calculate the distinct count of a field
• Example 8: Calculate the count by a span

Description

Using stats command to calculate the aggregation from search result.

The following table catalogs the aggregation functions and also indicates how the NULL/MISSING values is handled:

Function	NULL	MISSING
COUNT	Not counted	Not counted
SUM	Ignore	Ignore
AVG	Ignore	Ignore
MAX	Ignore	Ignore
MIN	Ignore	Ignore

Syntax

stats <aggregation>... [by-clause]...

• aggregation: mandatory. A aggregation function. The argument of aggregation must be field.
• by-clause: optional. The one or more fields to group the results by. Default: If no <by-clause> is specified, the stats command returns only one row, which is the aggregation over the entire result set.

Aggregation Functions

COUNT

Description

Usage: Returns a count of the number of expr in the rows retrieved by a SELECT statement.

Example:

os> source=accounts | stats count();
fetched rows / total rows = 1/1
+-----------+
| count()   |
|-----------|
| 4         |
+-----------+

SUM

Description

Usage: SUM(expr). Returns the sum of expr.

Example:

os> source=accounts | stats sum(age) by gender;
fetched rows / total rows = 2/2
+------------+----------+
| sum(age)   | gender   |
|------------+----------|
| 28         | F        |
| 101        | M        |
+------------+----------+

AVG

Description

Usage: AVG(expr). Returns the average value of expr.

Example:

os> source=accounts | stats avg(age) by gender;
fetched rows / total rows = 2/2
+--------------------+----------+
| avg(age)           | gender   |
|--------------------+----------|
| 28.0               | F        |
| 33.666666666666664 | M        |
+--------------------+----------+

MAX

Description

Usage: MAX(expr). Returns the maximum value of expr.

Example:

os> source=accounts | stats max(age);
fetched rows / total rows = 1/1
+------------+
| max(age)   |
|------------|
| 36         |
+------------+

MIN

Description

Usage: MIN(expr). Returns the minimum value of expr.

Example:

os> source=accounts | stats min(age);
fetched rows / total rows = 1/1
+------------+
| min(age)   |
|------------|
| 28         |
+------------+

VAR_SAMP

Description

Usage: VAR_SAMP(expr). Returns the sample variance of expr.

Example:

os> source=accounts | stats var_samp(age);
fetched rows / total rows = 1/1
+--------------------+
| var_samp(age)      |
|--------------------|
| 10.916666666666666 |
+--------------------+

VAR_POP

Description

Usage: VAR_POP(expr). Returns the population standard variance of expr.

Example:

os> source=accounts | stats var_pop(age);
fetched rows / total rows = 1/1
+----------------+
| var_pop(age)   |
|----------------|
| 8.1875         |
+----------------+

STDDEV_SAMP

Description

Usage: STDDEV_SAMP(expr). Return the sample standard deviation of expr.

Example:

os> source=accounts | stats stddev_samp(age);
fetched rows / total rows = 1/1
+--------------------+
| stddev_samp(age)   |
|--------------------|
| 3.304037933599835  |
+--------------------+

STDDEV_POP

Description

Usage: STDDEV_POP(expr). Return the population standard deviation of expr.

Example:

os> source=accounts | stats stddev_pop(age);
fetched rows / total rows = 1/1
+--------------------+
| stddev_pop(age)    |
|--------------------|
| 2.8613807855648994 |
+--------------------+

By Clause

The by clause could be the fields and expressions like scalar functions and aggregation functions. Besides, the span clause can also be used in the by clause to split specific field into buckets in the same interval, the stats then does the aggregation by these span buckets.

The span syntax is span(field_expr, interval_expr), the unit of the interval expression is the natural unit by default. If the field is a date and time type field, and the interval is in date/time units, you will need to specify the unit in the interval expression. For example, to split the field age into buckets by 10 years, it looks like span(age, 10). And here is another example of time span, the span to split a timestamp field into hourly intervals, it looks like span(timestamp, 1h).

Available time unit:

Span Interval Units
millisecond (ms)
second (s)
minute (m, case sensitive)
hour (h)
day (d)
week (w)
month (M, case sensitive)
quarter (q)
year (y)

Example 1: Calculate the count of events

The example show calculate the count of events in the accounts.

PPL query:

os> source=accounts | stats count();
fetched rows / total rows = 1/1
+-----------+
| count()   |
|-----------|
| 4         |
+-----------+

Example 2: Calculate the average of a field

The example show calculate the average age of all the accounts.

PPL query:

os> source=accounts | stats avg(age);
fetched rows / total rows = 1/1
+------------+
| avg(age)   |
|------------|
| 32.25      |
+------------+

Example 3: Calculate the average of a field by group

The example show calculate the average age of all the accounts group by gender.

PPL query:

os> source=accounts | stats avg(age) by gender;
fetched rows / total rows = 2/2
+--------------------+----------+
| avg(age)           | gender   |
|--------------------+----------|
| 28.0               | F        |
| 33.666666666666664 | M        |
+--------------------+----------+

Example 4: Calculate the average, sum and count of a field by group

The example show calculate the average age, sum age and count of events of all the accounts group by gender.

PPL query:

os> source=accounts | stats avg(age), sum(age), count() by gender;
fetched rows / total rows = 2/2
+--------------------+------------+-----------+----------+
| avg(age)           | sum(age)   | count()   | gender   |
|--------------------+------------+-----------+----------|
| 28.0               | 28         | 1         | F        |
| 33.666666666666664 | 101        | 3         | M        |
+--------------------+------------+-----------+----------+

Example 5: Calculate the maximum of a field

The example calculates the max age of all the accounts.

PPL query:

os> source=accounts | stats max(age);
fetched rows / total rows = 1/1
+------------+
| max(age)   |
|------------|
| 36         |
+------------+

Example 6: Calculate the maximum and minimum of a field by group

The example calculates the max and min age values of all the accounts group by gender.

PPL query:

os> source=accounts | stats max(age), min(age) by gender;
fetched rows / total rows = 2/2
+------------+------------+----------+
| max(age)   | min(age)   | gender   |
|------------+------------+----------|
| 28         | 28         | F        |
| 36         | 32         | M        |
+------------+------------+----------+

Example 7: Calculate the distinct count of a field

To get the count of distinct values of a field, you can use DISTINCT_COUNT (or DC) function instead of COUNT. The example calculates both the count and the distinct count of gender field of all the accounts.

PPL query:

os> source=accounts | stats count(gender), distinct_count(gender);
fetched rows / total rows = 1/1
+-----------------+--------------------------+
| count(gender)   | distinct_count(gender)   |
|-----------------+--------------------------|
| 4               | 2                        |
+-----------------+--------------------------+

Example 8: Calculate the count by a span

The example gets the count of age by the interval of 10 years.

PPL query:

os> source=accounts | stats count(age) by span(age, 10) as age_span
fetched rows / total rows = 2/2
+--------------+------------+
| count(age)   | age_span   |
|--------------+------------|
| 1            | 20         |
| 3            | 30         |
+--------------+------------+