eventstats command

Note

To see which AWS data source integrations support this PPL command, see Commands.

Use the eventstats command to enrich your event data with calculated summary statistics. It operates by analyzing specified fields within your events, computing various statistical measures, and then appending these results as new fields to each original event.

Key aspects of eventstats

It performs calculations across the entire result set or within defined groups.
The original events remain intact, with new fields added to contain the statistical results.
The command is particularly useful for comparative analysis, identifying outliers, or providing additional context to individual events.

Difference between stats and eventstats

The stats and eventstats commands are both used for calculating statistics, but they have some key differences in how they operate and what they produce.

Output format

stats: Produces a summary table with only the calculated statistics.
eventstats: Adds the calculated statistics as new fields to the existing events, preserving the original data.

Event retention

stats: Reduces the result set to only the statistical summary, discarding individual events.
eventstats: Retains all original events and adds new fields with the calculated statistics.

Use cases

stats: Best for creating summary reports or dashboards. Often used as a final command to summarize results.
eventstats: Useful when you need to enrich events with statistical context for further analysis or filtering. Can be used mid-search to add statistics that can be used in subsequent commands.

Syntax

Use the following syntax:


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

aggregation

Mandatory.
An aggregation function.
The argument of aggregation must be a field.

by-clause

Optional.
Syntax: by [span-expression,] [field,]...
The by clause can include fields and expressions such as scalar functions and aggregation functions. You can also use the span clause to split a specific field into buckets of equal intervals. The eventstats command then performs aggregation based on these span buckets.
Default: If you don't specify a by clause, the eventstats command aggregates over the entire result set.

span-expression

Optional, at most one.
Syntax: span(field_expr, interval_expr)
The unit of the interval expression is the natural unit by default. However, for date and time type fields, you need to specify the unit in the interval expression when using date/time units.

For example, to split the field age into buckets by 10 years, use span(age, 10). For time-based fields, you can split a timestamp field into hourly intervals using span(timestamp, 1h).

Available time units
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)

Aggregation functions

`COUNT`

COUNT returns a count of the number of expr in the rows retrieved by a SELECT statement.

For CloudWatch Logs use queries, COUNT is not supported.

Example:


os> source=accounts | eventstats count();
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+--------------------+------------+--------------------------+--------+-------+---------+
| account_number | balance  | firstname | lastname | age | gender | address            | employer   | email                    | city   | state | count() |
+----------------+----------+-----------+----------+-----+--------+--------------------+------------+--------------------------+--------+-------+---------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | *** Any Lane       | AnyCorp    | janedoe@anycorp.com      | Brogan | IL    | 4       |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street | AnyCompany | marymajor@anycompany.com | Dante  | TN    | 4       |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street     | AnyOrg     |                          | Nogal  | VA    | 4       |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court  |            | juanli@exampleorg.com    | Orick  | MD    | 4       |
+----------------+----------+-----------+----------+-----+--------+--------------------+------------+--------------------------+--------+-------+---------+

`SUM`

SUM(expr) returns the sum of expr.

Example:


os> source=accounts | eventstats sum(age) by gender;
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+------------+--------------------------+--------+-------+--------------------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer   | email                    | city   | state | sum(age) by gender |
+----------------+----------+-----------+----------+-----+--------+-----------------------+------------+--------------------------+--------+-------+--------------------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | 880 Any Lane          | AnyCorp    | janedoe@anycorp.com      | Brogan | IL    | 101                |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | AnyCompany | marymajor@anycompany.com | Dante  | TN    | 101                |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg     |                          | Nogal  | VA    | 28                 |
| 18             | 4180     | Juan      | Li       | 33  | M      | 467 Example Court     |            | juanli@exampleorg.com    | Orick  | MD    | 101                |
+----------------+----------+-----------+----------+-----+--------+-----------------------+------------+--------------------------+--------+-------+--------------------+

`AVG`

AVG(expr) returns the average value of expr.

Example:


os> source=accounts | eventstats avg(age) by gender;
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+------------+---------------------------+--------+-------+--------------------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | avg(age) by gender |
+----------------+----------+-----------+----------+-----+--------+-----------------------+------------+---------------------------+--------+-------+--------------------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | 880 Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 33.67              |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 33.67              |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg      |                          | Nogal  | VA    | 28.00              |
| 18             | 4180     | Juan      | Li       | 33  | M      | 467 Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 33.67              |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+--------------------+

MAX

MAX(expr) Returns the maximum value of expr.

Example


os> source=accounts | eventstats max(age);
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | max(age)  |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | 880 Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 36        |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 36        |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg      |                          | Nogal  | VA    | 36        |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 36        |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+

MIN

MIN(expr) Returns the minimum value of expr.

Example


os> source=accounts | eventstats min(age);
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | min(age)  |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | 880 Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 28        |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 28        |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | *** Any Street        | AnyOrg      |                          | Nogal  | VA    | 28        |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 28        |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+

STDDEV_SAMP

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

Example


os> source=accounts | eventstats stddev_samp(age);
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+------------------------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | stddev_samp(age)       |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+------------------------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | *** Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 3.304037933599835      |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 3.304037933599835      |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg      |                          | Nogal  | VA    | 3.304037933599835      |
| 18             | 4180     | Juan      | Li       | 33  | M      | 467 Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 3.304037933599835      |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+------------------------+

STDDEV_POP

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

Example


os> source=accounts | eventstats stddev_pop(age);
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+------------------------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | stddev_pop(age)        |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+------------------------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | 880 Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 2.****************     |
| 6              | 5686     | Mary      | Major    | 36  | M      | *** Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 2.****************     |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | *** Any Street        | AnyOrg      |                          | Nogal  | VA    | 2.****************     |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 2.****************     |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+------------------------+

PERCENTILE or PERCENTILE_APPROX

PERCENTILE(expr, percent) or PERCENTILE_APPROX(expr, percent) Return the approximate percentile value of expr at the specified percentage.

percent

The number must be a constant between 0 and 100.

Example


os> source=accounts | eventstats percentile(age, 90) by gender;
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+--------------------------------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | percentile(age, 90) by gender  |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+--------------------------------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | *** Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 36                             |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 36                             |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg      |                          | Nogal  | VA    | 28                             |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 36                             |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+--------------------------------+

Example 1: 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.


os> source=accounts | eventstats avg(age) as avg_age, sum(age) as sum_age, count() as count by gender;
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+-----------+-------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | avg_age   | sum_age   | count |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+-----------+-------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | *** Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 33.666667 | 101       | 3     |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 33.666667 | 101       | 3     |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg      |                          | Nogal  | VA    | 28.000000 | 28        | 1     |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 33.666667 | 101       | 3     |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+-----------+-----------+-------+

Example 2: Calculate the count by a span

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


os> source=accounts | eventstats count(age) by span(age, 10) as age_span
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+----------+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                    | city   | state | age_span |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+----------+
| 1              | 39225    | Jane      | Doe      | 32  | M      | *** Any Lane          | AnyCorp     | janedoe@anycorp.com      | Brogan | IL    | 3        |
| 6              | 5686     | Mary      | Major    | 36  | M      | 671 Example Street    | Any Company | marymajor@anycompany.com | Dante  | TN    | 3        |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | 789 Any Street        | AnyOrg      |                          | Nogal  | VA    | 1        |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com    | Orick  | MD    | 3        |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+--------------------------+--------+-------+----------+

Example 3: Calculate the count by a gender and span

The example gets the count of age by the interval of 5 years and group by gender.


os> source=accounts | eventstats count() as cnt by span(age, 5) as age_span, gender
fetched rows / total rows = 4/4
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+---------------------------+--------+-------+-----+
| account_number | balance  | firstname | lastname | age | gender | address               | employer    | email                     | city   | state | cnt |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+---------------------------+--------+-------+-----+
| 1              | 39225    | Jane      | Doe      | 32  | M      | *** Any Lane          | AnyCorp     | janedoe@anycorp.com       | Brogan | IL    | 2   |
| 6              | 5686     | Mary      | Majo     | 36  | M      | 671 Example Street    | Any Company | hattiebond@anycompany.com | Dante  | TN    | 1   |
| 13             | 32838    | Nikki     | Wolf     | 28  | F      | *** Any Street        | AnyOrg      |                           | Nogal  | VA    | 1   |
| 18             | 4180     | Juan      | Li       | 33  | M      | *** Example Court     |             | juanli@exampleorg.com     | Orick  | MD    | 2   |
+----------------+----------+-----------+----------+-----+--------+-----------------------+-------------+---------------------------+--------+-------+-----+

Usage

source = table | eventstats avg(a)
source = table | where a < 50 | eventstats avg(c)
source = table | eventstats max(c) by b
source = table | eventstats count(c) by b | head 5
source = table | eventstats distinct_count(c)
source = table | eventstats stddev_samp(c)
source = table | eventstats stddev_pop(c)
source = table | eventstats percentile(c, 90)
source = table | eventstats percentile_approx(c, 99)

Aggregations with span

source = table | eventstats count(a) by span(a, 10) as a_span
source = table | eventstats sum(age) by span(age, 5) as age_span | head 2
source = table | eventstats avg(age) by span(age, 20) as age_span, country | sort - age_span | head 2

Aggregations with time window span (tumble windowing function)

source = table | eventstats sum(productsAmount) by span(transactionDate, 1d) as age_date | sort age_date
source = table | eventstats sum(productsAmount) by span(transactionDate, 1w) as age_date, productId

Aggregations group by multiple levels

source = table | eventstats avg(age) as avg_state_age by country, state | eventstats avg(avg_state_age) as avg_country_age by country
source = table | eventstats avg(age) as avg_city_age by country, state, city | eval new_avg_city_age = avg_city_age - 1 | eventstats avg(new_avg_city_age) as avg_state_age by country, state | where avg_state_age > 18 | eventstats avg(avg_state_age) as avg_adult_country_age by country

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