# Spark NQL capabilities

Use Nexthink Query Language (NQL) in the [Investigations](/platform/user-guide/investigations/creating-investigations/nql-editor.md) module to access Spark conversations data.

The system stores each conversation in the `agent.conversations` NQL table. Refer to [NQL data model > Namespace agent](/platform/understanding-key-data-platform-concepts/nql-data-model.md#agent) for more details on available fields.

The following are examples of common NQL queries:

<details>

<summary>Key Spark KPIs</summary>

Retrieve key Spark KPIs from the last 30 days, such as the number of active users, total number of conversations and the number of conversations that are currently active.

{% code overflow="wrap" lineNumbers="true" %}

```
agent.conversations during past 30d
| summarize active_users = user.count(), total_conversations = conversation_id.count(), active_conversations = conversation_id.countif((state == in_progress or state == null))
```

{% endcode %}

</details>

<details>

<summary>Conversation number and duration per device</summary>

Retrieve the number of conversations and avarage conversation duration per device.

```
devices during past 30d
| include agent.conversations during past 30d
| where agent.conversations.outcome in [resolved, escalated]
| compute number_of_conversations_ = conversations.number_of_conversations.sum(), average_resolution_time = agent.conversations.conversation_duration.avg()
| where number_of_conversations_ != 0
| where average_resolution_time != 0
| list device.name, device.entity, device.hardware.model, device.hardware.type, device.operating_system.name, number_of_conversations_, average_resolution_time
| sort number_of_conversations_ desc
```

</details>

<details>

<summary>Resolved conversations by resolution reason</summary>

Retrieve the number of resolved conversations in the last 7 days broken down by resolution reason.

{% code overflow="wrap" lineNumbers="true" %}

```
agent.conversations during past 7d
| where outcome == resolved
| summarize number_of_conversations_by_reason = count() by reason
```

{% endcode %}

</details>

RELATED TOPICS

* [Monitoring Spark](/platform/user-guide/spark/monitoring-spark.md)
* [NQL data model](/platform/understanding-key-data-platform-concepts/nql-data-model.md#agent)


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# Agent Instructions: Querying This Documentation

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```
GET https://docs.nexthink.com/platform/user-guide/spark/spark-nql-capabilities.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.