Analyzing Individual Run Details

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

Access these details by selecting a Run ID from the Run History table.

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

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Logs and Artifacts

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

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.

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.

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.

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.

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

💡 Learn how to configure source freshness in our documentation.

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

Use these artifacts to verify execution details, troubleshoot issues, and audit your dbt workflow performance.