---
title: as_count() in Monitor Evaluations
description: >-
  Understanding how as_count() and as_rate() modifiers affect monitor
  evaluations and arithmetic operations in different evaluation paths.
breadcrumbs: Docs > Monitors > Monitor Guides > as_count() in Monitor Evaluations
---

# as_count() in Monitor Evaluations

## Overview{% #overview %}

Queries using **`as_count()`** and **`as_rate()`** modifiers are calculated in ways that can yield different results in monitor evaluations. Monitors involving arithmetic and at least 1 **`as_count()`** modifier use a separate evaluation path that changes the order in which arithmetic and time aggregation are performed.

## Error rate example{% #error-rate-example %}

Suppose you want to monitor an error rate over 5 minutes using the metrics, `requests.error` and `requests.total`. Consider a single evaluation performed with these aligned timeseries points for the 5 min timeframe:

**Numerator**: `sum:requests.error{*}`

```text
| Timestamp           | Value |
|:--------------------|:------|
| 2018-03-13 11:00:30 | 1     |
| 2018-03-13 11:01:30 | 2     |
| 2018-03-13 11:02:40 | 3     |
| 2018-03-13 11:03:30 | 4     |
| 2018-03-13 11:04:40 | 5     |
```

**Denominator**: `sum:requests.total{*}`

```text
| Timestamp           | Value |
|:--------------------|:------|
| 2018-03-13 11:00:30 | 10    |
| 2018-03-13 11:01:30 | 10    |
| 2018-03-13 11:02:40 | 10    |
| 2018-03-13 11:03:30 | 10    |
| 2018-03-13 11:04:40 | 10    |
```

### 2 ways to calculate

Refer to this query as **`classic_eval_path`**:

```text
sum(last_5m): sum:requests.error{*}.as_rate() / sum:requests.total{*}.as_rate()
```

and this query as **`as_count_eval_path`**:

```text
sum(last_5m): sum:requests.error{*}.as_count() / sum:requests.total{*}.as_count()
```

Compare the result of the evaluation depending on the path:

| Path                     | Behavior                                       | Expanded expression                    | Result  |
| ------------------------ | ---------------------------------------------- | -------------------------------------- | ------- |
| **`classic_eval_path`**  | Aggregation function applied *after* division  | **(1/10 + 2/10 + 3/10 + 4/10 + 5/10)** | **1.5** |
| **`as_count_eval_path`** | Aggregation function applied *before* division | **(1+2+3+4+5) / (10+10+10+10+10)**     | **0.3** |

*Note that both evaluations above are mathematically correct. Choose a method that suits your intentions.*

It may be helpful visualize the **`classic_eval_path`** as:

```text
sum(last_5m):error/total
```

and the **`as_count_eval_path`** as:

```text
sum(last_5m):error
-----------------
sum(last_5m):total
```

In general, **`avg`** time aggregation with **`.as_rate()`** is reasonable, but **`sum`** aggregation with **`.as_count()`** is recommended for error rates. Aggregation methods other than **`sum`** do not make sense to use with (and cannot be used with) **`.as_count()`**.

[Reach out to the Datadog support team](https://docs.datadoghq.com/help/) if you have any questions.