# Analyzing Individual Run Details

Investigate specific schedule executions to diagnose performance, troubleshoot issues, and verify data processing results. 

{% hint style="info" %}
Access these details by selecting a Run ID from the [Run History table](https://docs.paradime.io/app-help/~/changes/rDjfrwa4QH3nWWrOItTq/documentation/bolt/viewing-run-log-history#run-history).
{% endhint %}

### Run Details Overview

Analyze your run execution through different visualization tools:

#### DAGs View

Visualize the execution flow of scheduled commands through a directed acyclic graph (DAG), showing:

* **Command dependencies -** Understand which models must complete before others can start
* **Execution order -** Track the sequence of operations to optimize pipeline flow
* **Process relationships -** Identify critical paths and potential parallelization opportunities

#### **Model Timeline**

Track the temporal sequence of model execution within your run:

* **Individual model execution times -** Spot which models are taking longer than expected
* **Parallel processing visualization -** See which models run simultaneously to maximize efficiency
* **Performance bottleneck identification -** Find slow-running models that delay your entire pipeline

{% @arcade/embed flowId="fSI4ZiydwsvJGa9CYKMx" url="<https://app.arcade.software/share/fSI4ZiydwsvJGa9CYKMx>" %}

***

### **Logs and Artifacts**

Explore additional metadata for each run ID, including a breakdown of run logs, source freshness, and artifacts.

{% @arcade/embed flowId="TgVn754OtMX4OBm6Z9rV" url="<https://app.arcade.software/share/TgVn754OtMX4OBm6Z9rV>" %}

#### Run Logs

The **Run Logs** section provides a detailed breakdown of each scheduled run, offering insights into execution status, performance, and any encountered issues. This section is divided into three tabs: **Summary**, **Console Logs**, and **Debug Logs**.

{% tabs %}
{% tab title="Summary" %}
**Overview**\
Displays high-level execution details for commands, showing the overall completion status, duration, and success metrics.

**Warnings and Errors**\
Lists any warnings or errors encountered during the run, such as deprecated configurations or unused paths.

**Suggested Actions**\
Recommendations to address identified warnings and errors, including updates for alignment with best practices.

**Use Case:**\
Quickly assess if the run was successful, spot any configuration issues, and take corrective actions.

<figure><img src="https://2337193041-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FHET0AD04uHMgdeLAjptq%2Fuploads%2FCHfmfLFxOankLn3K1eyO%2Fimage.png?alt=media&#x26;token=fe107a9d-fa51-45c0-90a2-582009510150" alt=""><figcaption></figcaption></figure>
{% endtab %}

{% tab title="Console Logs" %}
**Overview**\
Shows a line-by-line output of the command execution, starting with initialization and progressing through each execution step, including model or task processing times and completion summaries.

**Use Case**\
Ideal for reviewing the detailed flow of the run, identifying stages where issues may have occurred, and monitoring performance.

<figure><img src="https://2337193041-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FHET0AD04uHMgdeLAjptq%2Fuploads%2F3QQBVI4Zug7QPG1QsXdx%2Fimage.png?alt=media&#x26;token=35ee131f-4b60-4417-8fca-63683946d25f" alt=""><figcaption></figcaption></figure>
{% endtab %}

{% tab title="Debug Logs" %}
**Overview**\
Provides in-depth technical details, including system interactions, resource allocation, thread usage, and connection status to support troubleshooting.

**Use Case:**\
Best for advanced diagnostics and analyzing system performance, especially when investigating anomalies or unexpected behaviors.

<figure><img src="https://2337193041-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FHET0AD04uHMgdeLAjptq%2Fuploads%2FdWw8ARyKX38pl7Mzoo1U%2Fimage.png?alt=media&#x26;token=a4873804-65a6-4231-a5e4-8d1bee1f20ad" alt=""><figcaption></figcaption></figure>
{% endtab %}
{% endtabs %}

Each tab gives a targeted view of run details, offering a complete understanding of pipeline performance.

#### Source Freshness

When your schedule includes the `dbt source freshness` command, you can:

* Monitor when each source table was last updated
* Track if data freshness meets your defined SLAs
* Identify stale or outdated data sources

<figure><img src="https://2337193041-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FHET0AD04uHMgdeLAjptq%2Fuploads%2FDOt6oQCvLG8tTho4JAlx%2Fimage.png?alt=media&#x26;token=b7a65dcc-f180-402a-be6c-6c37b5006e4a" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
💡 Learn how to configure source freshness in our [documentation](https://docs.paradime.io/app-help/~/changes/rDjfrwa4QH3nWWrOItTq/documentation/bolt/managing-schedules/configuring-source-freshness#how-to-configure-source-freshness).&#x20;
{% endhint %}

#### Artifacts

The **Artifacts** section provides access to files that dbt generates after each run. These files help you analyze and troubleshoot your workflows:

#### SQL Files

* **Run SQL** - View the actual SQL statements executed during the run
* **Compiled SQL** - Examine the optimized SQL used in your data warehouse

#### Execution Metadata

* `manifest.json` - Shows project structure (models, sources, and tests)
* `catalog.json` - Contains schema information and column details
* `run_results.json` - Provides execution outcomes of dbt commands
* `sources.json` - Tracks source table metadata and freshness history

{% hint style="info" %}
Use these artifacts to verify execution details, troubleshoot issues, and audit your dbt workflow performance.
{% endhint %}

<figure><img src="https://2337193041-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FHET0AD04uHMgdeLAjptq%2Fuploads%2FYRixww3ogi6HLLCKuih3%2Fimage.png?alt=media&#x26;token=1279378f-feb7-48e3-b0dd-46a418eed132" alt=""><figcaption></figcaption></figure>