[role="xpack"]
[[ml-sum-functions]]
= Sum functions

The sum functions detect anomalies when the sum of a field in a bucket is
anomalous.

If you want to monitor unusually high totals, use high-sided functions.

If want to look at drops in totals, use low-sided functions.

If your data is sparse, use `non_null_sum` functions. Buckets without values are
ignored; buckets with a zero value are analyzed.

The {ml-features} include the following sum functions:

* xref:ml-sum[`sum`, `high_sum`, `low_sum`]
* xref:ml-nonnull-sum[`non_null_sum`, `high_non_null_sum`, `low_non_null_sum`]

[discrete]
[[ml-sum]]
== Sum, high_sum, low_sum

The `sum` function detects anomalies where the sum of a field in a bucket is
anomalous.

If you want to monitor unusually high sum values, use the `high_sum` function.

If you want to monitor unusually low sum values, use the `low_sum` function.

These functions support the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `over_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties, see the
{ref}/ml-put-job.html#ml-put-job-request-body[create {anomaly-jobs} API].

.Example 1: Analyzing total expenses with the sum function
[source,js]
--------------------------------------------------
{
  "function" : "sum",
  "field_name" : "expenses",
  "by_field_name" : "costcenter",
  "over_field_name" : "employee"
}
--------------------------------------------------
// NOTCONSOLE

If you use this `sum` function in a detector in your {anomaly-job}, it
models total expenses per employees for each cost center. For each time bucket,
it detects when an employee’s expenses are unusual for a cost center compared
to other employees.

.Example 2: Analyzing total bytes with the high_sum function
[source,js]
--------------------------------------------------
{
  "function" : "high_sum",
  "field_name" : "cs_bytes",
  "over_field_name" : "cs_host"
}
--------------------------------------------------
// NOTCONSOLE

If you use this `high_sum` function in a detector in your {anomaly-job}, it
models total `cs_bytes`. It detects `cs_hosts` that transfer unusually high
volumes compared to other `cs_hosts`. This example looks for volumes of data
transferred from a client to a server on the internet that are unusual compared
to other clients. This scenario could be useful to detect data exfiltration or
to find users that are abusing internet privileges.

[discrete]
[[ml-nonnull-sum]]
== Non_null_sum, high_non_null_sum, low_non_null_sum

The `non_null_sum` function is useful if your data is sparse. Buckets without
values are ignored and buckets with a zero value are analyzed.

If you want to monitor unusually high totals, use the `high_non_null_sum`
function.

If you want to look at drops in totals, use the `low_non_null_sum` function.

These functions support the following properties:

* `field_name` (required)
* `by_field_name` (optional)
* `partition_field_name` (optional)

For more information about those properties, see the
{ref}/ml-put-job.html#ml-put-job-request-body[create {anomaly-jobs} API].

NOTE: Population analysis (that is to say, use of the `over_field_name` property)
is not applicable for this function.

.Example 3: Analyzing employee approvals with the high_non_null_sum function
[source,js]
--------------------------------------------------
{
  "function" : "high_non_null_sum",
  "fieldName" : "amount_approved",
  "byFieldName" : "employee"
}
--------------------------------------------------
// NOTCONSOLE

If you use this `high_non_null_sum` function in a detector in your {anomaly-job},
it models the total `amount_approved` for each employee. It ignores any buckets
where the amount is null. It detects employees who approve unusually high
amounts compared to their past behavior.