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Content was updated:Feb 3, 2025
This is an archived version of the documentation downloaded from LogScale Documentation Archives. Up to date documentation, and documentation for later versions, is available on LogScale Documentation

## `count()`

Counts the number of events in the repository, or streaming through the function. You can use this field name to pipe the results to other query functions or general use.

It's possible to specify a field and only events containing that field are counted. It's also possible to do a distinct count. When having many distinct values LogScale will not try to keep them all in memory. An estimate is then used, so the result will not be a precise match.

Parameter | Type | Required | Default Value | Description
--- | --- | --- | --- | ---
`as` | string | optional[a] | `_count` | The name of the output field.
`distinct` | boolean | optional[a] |  | When specified, counts only distinct values. When this parameter is set to `true`, LogScale always uses an estimate, which may give an inexact result as the value.
`field`[b] | string | optional[a] |  | The field for which only events are counted.
[a] Optional parameters use their default value unless explicitly set. [b] The parameter name `field` can be omitted.

Hide omitted argument names for this function

Show omitted argument names for this function

> Omitted Argument Names

> The argument name for `field` can be omitted; the following forms of this function are equivalent:

> logscale Syntax

>     count("value")

> and:

> logscale Syntax

>     count(field="value")

> These examples show basic structure only.

### Accuracy When Counting Distinct Values

When counting distinct values in a data stream, particularly when there are repeated elements in a limited memory environment, limitations exist in the accuracy of the count to avoid consuming too much memory in the process. For example, if counting 1,000,000 (million) events. If each event contains a different value, then memory is required to store the count for each of those million entries. Even if the field is only 10 bytes long, that is approximate 9MB of memory required to store the state. In LogScale, this affects the limits as outlined in State Sizes and Limits. As noted in that section, LogScale uses an estimation algorithm that produces an estimate of the number of distinct values while keeping the memory usage to a minimum.

While the algorithm in question doesn't give any guarantees on the relative error of the reported result, the typical accuracy (standard error) is less than 2%, with 2/3s of all results being within 1%, tests with up to 10^7 distinct values, the result at worst deviated by less than 0.02%. The worst results for each test can be seen in the table below:

Distinct Values | Result of distinct count | Deviation percentage
--- | --- | ---
10 | 10 | 0
100 | 100 | 0
1000 | 995 | -0.005025125628
10000 | 10039 | 0.003884849089
100000 | 100917 | 0.009086675189
1000000 | 984780 | -0.01545522858
10000000 | 10121302 | 0.01198482172

### Important

For less than 100 distinct values, the deviation percentage will be exacerbated. For example, if there are only 10 distinct values, a deviation of 1 is 10%, even though it is the smallest possible deviation from the actual number of distinct values.

More typically, values used for aggregations or counts for distinct values will have low cardinality (for example, a small number of distinct values against the overall set).

#### `count()` Syntax Examples

Below are several examples using the `count()` function. Some are simple and others are more complex, with functions embedded within others.

##### Count All Events

This a simple example using the `count()` function. The query just counts the number of events found in the repository for the period of time selected:

logscale

    count()

The result is just a single number, the total count.

_count
3886817

To format adding a thousands separator:

logscale

    count()
    | format("%,i", field=_count, as=_count)

Produces

_count |
3 | 886,817

##### Group & Count

In this example, the query uses the `count()` function within the `groupBy()` function. The first parameter given is the field upon which to group the data. In this case, it's the HTTP method (for example, `GET`, `PUT`, `POST`). The second parameter says to use the function `count()` to count the number occurrences for each method found.

logscale

    groupBy(field=method, function=count())

The result is a table with the column headings, method and _count, with the values for each:

method | _count
--- | ---
DELETE | 7375
GET | 153493
POST | 31654

##### Chart of Daily Counts

Figure 141. `count()` Chart of Daily Counts

You can use the `count()` function in conjunction with the `timeChart()` function to count the number occurrences of events or other factors. By default, the `timeChart()` function will aggregate the data by day. The results will look something like what you see in the screenshot shown in Figure 141, “`count()` Chart of Daily Counts”.

logscale

    timeChart(function=count())

##### Table of Daily Counts

When a user accesses a web site, the event is logged with a status. For instance, the status code `200` is returned when the request is successful, and `404` when the page is not found. To get a list of status codes returned and a count of each for a given period, you would enter the following query in the Search box:

logscale

    groupBy(field=status, function=count())

The sample output is shown below:

status | _count
--- | ---
101 | 9
200 | 55258
204 | 137834
307 | 2
400 | 2
401 | 4
403 | 57
404 | 265
504 | 62
stopping | 6
success | 6

### `count()` Examples

Click + next to an example below to get the full details.

#### Aggregate Status Codes by `count()` Per Minute

Time-based event counting with default buckets using the `bucket()` function

##### Query

logscale

    bucket(1min, field=status_code, function=count())

##### Introduction

Counts different HTTP status codes over time and buckets them into time intervals of 1 minute. Notice we group by two fields: status code and the implicit field _bucket.

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       bucket(1min, field=status_code, function=count())

   Sets the bucket interval to 1 minute, aggregating the count of the field status_code.

3. Event Result set.

##### Summary and Results

Bucketing allows for data to be collected according to a time range. Using the right aggregation function to quantify the value groups that information into the buckets suitable for graphing for example with a `Bar Chart`, with the size of the bar using the declared function result, `count()` in this example.

#### Aggregate Status Codes by `count()` Per Minute

Time series aggregate status codes by `count()` per minute into buckets

##### Query

logscale

    bucket(1min, field=status_code, function=count())

##### Introduction

In this example, the `bucket()` function is used with `count()` to count different HTTP status codes over time and bucket them into time intervals of 1 minute.

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       bucket(1min, field=status_code, function=count())

   Counts different HTTP status codes over time and buckets them into time intervals of 1 minute. Notice that we group by two fields: status_code field and the implicit field _bucket.

3. Event Result set.

##### Summary and Results

The query is used to optimizing data storage and query performance. Bucketing allows for data to be collected according to a time range. Using the right aggregation function to quantify the value groups that information into the buckets suitable for graphing for example with a `Bar Chart`, with the size of the bar using the declared function result, `count()` in this example.

#### Bucket Events Summarized by `count()`

Summarize events into buckets across selected timespan using the `count()` function

##### Query

logscale

    bucket(function=count())

##### Introduction

Divides the search time interval into buckets. As time span is not specified, the search interval is divided into 127 buckets. Events in each bucket are counted:

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       bucket(function=count())

   Summarizes events using `count()` into buckets across the selected timespan.

3. Event Result set.

##### Summary and Results

This query organizes data into buckets according to the count of events.

#### Calculate Events per Second by Host

Determine event rate for each host over a 5-minute period using an embedded expression within the`groupBy()` function

##### Query

logscale

    groupBy(host, function=[{count() | esp:=_count/300}])

##### Introduction

In this example, the `groupBy()` function is used with an embedded expression to calculate the total event count and events per second for each host over a 5-minute period.

Example incoming data might look like this:

@timestamp | host | service | status | response_time
--- | --- | --- | --- | ---
2025-08-06T10:00:00Z | server1 | web | ok | 120
2025-08-06T10:00:01Z | server2 | database | ok | 85
2025-08-06T10:00:02Z | server1 | web | ok | 95
2025-08-06T10:00:03Z | server3 | cache | ok | 45
2025-08-06T10:00:04Z | server2 | database | error | 250
2025-08-06T10:00:05Z | server1 | web | ok | 110
2025-08-06T10:00:06Z | server3 | cache | ok | 40
2025-08-06T10:00:07Z | server2 | database | ok | 90
2025-08-06T10:00:08Z | server1 | web | error | 300
2025-08-06T10:00:09Z | server3 | cache | ok | 42

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       groupBy(host, function=[{count() | esp:=_count/300}])

   Groups events by the host field and uses an embedded expression to count the number of events per host and calculate events per second in one operation.

   The `count()` function returns the count in a field named _count by default. The embedded expression then divides this value by `300` and stores the result in a new field named esp. This calculation provides the average events per second for each host over the time period.

   Using an embedded expression within the `groupBy()` function performs both the count and the calculation as part of the same aggregation. Both the original _count and the calculated esp field are included in the results.

3. Event Result set.

##### Summary and Results

The query is used to analyze event frequency patterns by calculating both the total event count and the average events per second for each host.

Note that the query aggregates against both the original count and the count/300 as one aggregate set. Using an embedded expression is more efficient for larger event sets.

This query is useful, for example, to monitor system load distribution across hosts, identify hosts with unusual event rates, or establish baseline activity patterns for capacity planning.

Sample output from the incoming example data:

host | _count | esp
--- | --- | ---
server1 | 4 | 0.013333
server2 | 3 | 0.010000
server3 | 3 | 0.010000

Note that the _count field shows the total number of events per host, and the esp field shows the calculated events per second (total events divided by 300 seconds)

This data is ideal for visualization using a Time Chart widget to show event rates over time. A Bar Chart widget could compare event rates across hosts, while a Gauge widget could show current event rates against predefined thresholds. Consider creating a dashboard that combines these visualizations with alerts for when event rates exceed normal ranges.

#### Count Total of Malware and Nonmalware Events

Count total of malware and nonmalvare events in percentage

##### Query

logscale

    [count(malware, as=_malware), count(nonmalware, as=_nonmalware)]
    | total := _malware + _nonmalware
    | nonmalware_pct_total := (_nonmalware/total)*100
    | malware_pct_total := (_malware/total)*100

##### Introduction

It is possible to use the `count()` function to show the count in percentage of two fields against total. In this example, the function `count()` function is used to count the field malware and the field nonmalware and have the results returned in percentage. A result set could, for example, be normalware 30% and nonmalware 70%.

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       [count(malware, as=_malware), count(nonmalware, as=_nonmalware)]

   Returns the counted results of the field malware in a field named _malware and the counted results of the field nonmalware in a field named _nonmalware.

3. logscale

       | total := _malware + _nonmalware

   Assigns the total of these events to a new field named total.

4. logscale

       | nonmalware_pct_total := (_nonmalware/total)*100
       | malware_pct_total := (_malware/total)*100

   Calculates the _malware and _nonmalware as a percentage of the total.

5. Event Result set.

##### Summary and Results

The query is used to get an overview of the total number of malware versus nonmalvare.

#### Get List of Status Codes

Get list of status codes returned and a count of each for a given period using the `groupBy()` function with `count()`

##### Query

logscale

    groupBy(field=status, function=count())

##### Introduction

In this example, the `groupBy()` function is used to get a list of status codes for logged events. For instance, the status code `200` is returned when the request is successful, and `404` when the page is not found.

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       groupBy(field=status, function=count())

   Groups events by the status field, and counts the number of events in each group.

   It is possible to enhance the query for more detailed analysis. The following query example groups by both the fields status and source, limits to 1000 results, and sorts by count descending. `groupBy([field=status, field=source], function=count(), limit=1000) | sort(_count, order=desc)`

3. Event Result set.

##### Summary and Results

The query is used to extract a list of status codes, each with a count of how many events have that status. The query is useful for summarizing and analyzing log data.

Sample output from the incoming example data:

status | _count
--- | ---
101 | 17
200 | 46183
204 | 3
307 | 1
400 | 2893
401 | 4
Failure | 1
Success | 8633

#### Group HTTP Methods and Count Status Codes

Analyze HTTP traffic patterns using nested `groupBy()` function

##### Query

logscale

    groupBy(method, function=[count(as=method_total),
            groupBy(statuscode, function=count(as=method_status_count))])

##### Introduction

In this example, the `groupBy()` function is used to analyze HTTP traffic patterns by grouping requests first by HTTP method and then by status code, providing counts at both levels.

Example incoming data might look like this:

@timestamp | method | statuscode | path | bytes
--- | --- | --- | --- | ---
2025-08-06T10:00:00Z | GET | 200 | /index.html | 1024
2025-08-06T10:00:01Z | POST | 201 | /api/users | 512
2025-08-06T10:00:02Z | GET | 404 | /missing.html | 256
2025-08-06T10:00:03Z | GET | 200 | /about.html | 768
2025-08-06T10:00:04Z | POST | 400 | /api/users | 128
2025-08-06T10:00:05Z | PUT | 200 | /api/users/1 | 896
2025-08-06T10:00:06Z | GET | 200 | /contact.html | 645
2025-08-06T10:00:07Z | POST | 201 | /api/orders | 789
2025-08-06T10:00:08Z | GET | 404 | /old-page.html | 234
2025-08-06T10:00:09Z | DELETE | 204 | /api/users/2 | 0

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       groupBy(method, function=[count(as=method_total),
               groupBy(statuscode, function=count(as=method_status_count))])

   Groups events first by the method field and performs two functions:

   - Counts the total number of events for each HTTP method using `count()` and returns the result in a new field named method_total.

   - Creates a nested grouping by statuscode within each method group, counting occurrences using `count()` and returns the result in a new field named method_status_count.

3. Event Result set.

##### Summary and Results

The query is used to analyze HTTP traffic patterns by providing a hierarchical view of request methods and their associated status codes.

This query is useful, for example, to identify patterns in API usage, detect potential issues with specific HTTP methods, or monitor the distribution of success and error responses across different request types.

Sample output from the incoming example data:

method | method_total | statuscode | method_status_count
--- | --- | --- | ---
GET | 5 | 200 | 3
GET | 5 | 404 | 2
POST | 3 | 201 | 2
POST | 3 | 400 | 1
PUT | 1 | 200 | 1
DELETE | 1 | 204 | 1

Note that the output shows both the total count per method (method_total) and the breakdown of status codes (method_status_count) within each method, providing a comprehensive view of the HTTP traffic distribution.

This data would be effectively visualized using a Sankey diagram widget to show the flow from HTTP methods to status codes, or a nested pie chart to display the distribution.

#### Group HTTP Methods and Status Codes Using Nested `groupBy()`

Analyze HTTP traffic patterns by method and status code using the `groupBy()` function

##### Query

logscale

    groupBy(method, function=[count(as=method_total), groupBy(statuscode, function=count(as=method_status_count))])

##### Introduction

In this example, the `groupBy()` function is used to analyze HTTP traffic patterns by grouping requests first by HTTP method and then by status code within each method.

Example incoming data might look like this:

@timestamp | method | statuscode | path | bytes
--- | --- | --- | --- | ---
2025-08-06T10:00:00Z | GET | 200 | /index.html | 1024
2025-08-06T10:00:01Z | POST | 201 | /api/users | 512
2025-08-06T10:00:02Z | GET | 404 | /missing.html | 256
2025-08-06T10:00:03Z | GET | 200 | /about.html | 768
2025-08-06T10:00:04Z | POST | 400 | /api/users | 128
2025-08-06T10:00:05Z | PUT | 200 | /api/users/1 | 384
2025-08-06T10:00:06Z | GET | 200 | /contact.html | 896
2025-08-06T10:00:07Z | DELETE | 204 | /api/users/2 | 0
2025-08-06T10:00:08Z | GET | 500 | /error.html | 1024
2025-08-06T10:00:09Z | POST | 201 | /api/orders | 756

##### Step-by-Step

1. Starting with the source repository events.

2. logscale

       groupBy(method, function=[count(as=method_total), groupBy(statuscode, function=count(as=method_status_count))])

   Groups events by the method field and performs two aggregations:

   - Counts total events for each HTTP method using `count()`, and returns the result in a new field named method_total.

   - Creates a nested grouping by statuscode within each method group, counting occurrences using `count()` and returns the result in a new field named method_status_count.

3. Event Result set.

##### Summary and Results

The query is used to analyze HTTP traffic patterns by providing a hierarchical view of request methods and their associated status codes.

This query is useful, for example, to identify patterns in API usage, detect potential issues with specific HTTP methods, or monitor the distribution of success and error responses across different request types.

Sample output from the incoming example data:

method | method_total | statuscode | method_status_count
--- | --- | --- | ---
GET | 5 | 200 | 3
GET | 5 | 404 | 1
GET | 5 | 500 | 1
POST | 3 | 201 | 2
POST | 3 | 400 | 1
PUT | 1 | 200 | 1
DELETE | 1 | 204 | 1

Note that the output shows the total count for each HTTP method in method_total and a breakdown of status codes and their counts within each method in method_status_count.

This data is well-suited for visualization using a Sankey diagram widget, which can effectively show the flow from HTTP methods to status codes.

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