MongoDB Compass helps you visualize a collection’s shape and explore relationships between collections. Use its Schema tab for sampled field profiles and Data Modeling for relationship diagrams; for chart-based dashboards, use MongoDB Atlas Charts instead.
Choose the right MongoDB visualization tool
| What you need | Best fit | What it shows |
|---|---|---|
| Inspect field types, value distributions, ranges, nested fields, or arrays in one collection | Compass Schema tab | A sampled profile of the collection’s fields and values. |
| Explore how collections and fields relate | Compass Data Modeling | A diagram of selected collections and, optionally, inferred relationships. |
| Build chart visualizations or dashboards | MongoDB Atlas Charts | Charts and dashboards; each chart uses one data source, while a dashboard can combine charts from different collections. |
Compass is a free, source-available graphical interface for querying, aggregating, and analyzing MongoDB data, available for macOS, Windows, and Linux. Its Schema and Data Modeling features are for inspection and communication, not substitutes for a chart dashboard. See the MongoDB Compass overview and Atlas Charts overview.
How do I visualize a collection’s schema in Compass?
- Connect to your deployment. Open Compass and connect to an Atlas deployment or a locally hosted MongoDB deployment using an authorized connection.
- Open the collection. Select the database and collection whose structure you want to inspect.
- Open the Schema tab and analyze the schema. Compass profiles observed field types and shapes, value distributions and ranges, cardinality, nested documents and arrays, dates, and supported location values.
- Investigate a chart value. Click a value in a chart to create a query filter, then inspect the matching documents. You can combine filters to narrow the subset.
Fields with multiple observed types can be broken down by type. That view can help reveal inconsistent values—for example, a field containing both strings and numbers—but it is a signal to investigate, not proof that every document has been checked. MongoDB’s schema analysis documentation describes the available visualizations and filtering workflow.
Account for sampling and time limits
Schema analysis samples documents rather than guaranteeing a complete census. Rare fields or values may not appear in the sample, so use the profile to guide exploration rather than to certify that a field is absent or that all documents follow one shape. For a complete check, use an appropriate query or aggregation against the data.
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MongoDB notes that schema analysis can time out on very large collections. The query bar’s MAX TIME MS setting defaults to 60,000 milliseconds; if analysis needs more time, increase the setting as appropriate. A larger time limit may allow a query to run longer, but it does not change the fact that the profile is sample-based.
Can Compass show relationships between collections?
- Open Data Modeling. Select the connection and database to model.
- Choose the collections. Select the collections to include in the diagram.
- Set the sample size and relationship inference. Compass defaults to 100 sampled documents per collection. Enable relationship inference if you want Compass to identify possible links.
- Generate the diagram. Review the collection and field structure and any inferred relationships.
A larger sample may reveal fields or links missed by a smaller one, but it also increases analysis time and memory use. Choosing all documents is available; consider the dataset’s size and your device’s resources before doing so. Conversely, a small sample may miss infrequent fields or relationships. These behaviors and the workflow are described in MongoDB’s Data Modeling documentation.
Treat inferred links as candidates to verify against your data and application logic, not as a guarantee of an enforced relationship. A generated diagram is a snapshot: changes made to collection data afterward do not update it automatically. Regenerate the diagram when you need it to reflect newer data.
Can Compass turn a visualization into a reusable result?
For a shaped result, build an aggregation pipeline in Compass. A Compass view can expose the output of the pipeline’s final stage as a read-only result. A view is not a chart, and creating one does not save the pipeline itself. MongoDB explains the behavior in its views documentation.
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If you need to hand off a schema profile, Compass can export schema analysis in Standard, MongoDB, or Expanded format. Because the analysis is sampled, label or describe the export accordingly so that recipients do not mistake it for a complete inventory. See MongoDB’s schema export documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should I use Atlas Charts instead?
Choose Atlas Charts when the goal is to create charts or dashboards for communicating data, rather than to inspect a collection’s schema. A chart is tied to one data source; a dashboard can bring together charts based on different collections. For details, see MongoDB’s Atlas Charts documentation.
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When checking a chart for correctness, compare the displayed visualization with its underlying data. MongoDB notes that not every visualization option changes the data table, so a chart’s appearance alone may not show every relevant detail. The chart data documentation explains this distinction.
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