## 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 X-Pack machine learning features include the following sum functions:

#### 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)

Example 1: Analyzing total expenses with the sum function.

```{
"function" : "sum",
"field_name" : "expenses",
"by_field_name" : "costcenter",
"over_field_name" : "employee"
}```

If you use this `sum` function in a detector in your 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.

```{
"function" : "high_sum",
"field_name" : "cs_bytes",
"over_field_name" : "cs_host"
}```

If you use this `high_sum` function in a detector in your 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.

#### 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)

Population analysis (that is to say, use of the `over_field_name` property) is not applicable for this function.
```{
If you use this `high_non_null_sum` function in a detector in your 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.