"Data Limit Exceeded" Even if Under 50,000 Records

[SantiagoVega]

We are currently working on a dashboard to display some time series temperature and humidity data. Using a serial chart, we set a time series and set the category to be "temperature," then split the field by name to get multiple series.

However, we are getting the "Data limit exceeded" warning, which would naturally occur if maxPaginationRecords was exceeded. Yet, our data set is nowhere near 50,000 records, but instead closer to 5,500.

I would like to see if anyone knows how to deal with this issue! This is causing a lot of slowdowns and bugs with our data display.

For reference, our table structure is "timestamp," "temp," "rh," "name," "heat_index," "FID"

Thank you!

[VenkataKondepati · Accepted solution]

Hey there,

We’ve run into the same issue before — the "Data limit exceeded" warning in ArcGIS Dashboards can be misleading, especially when your dataset is well below the 50,000 record limit.

In your case (around 5,500 records), the problem is likely tied to how the data is grouped and split in the serial chart. When you split by the "name" field, Dashboards internally generates a separate series for each unique name, and each one triggers its own query behind the scenes.

So if you have, say, 100 unique names, you're potentially generating 100 queries — and that can hit performance bottlenecks or internal data limits (especially if your service has tight maxRecordCount or if the feature layer has pagination settings configured on the backend).

Suggestions:

1. Reduce the number of splits – Try limiting how many unique "name" values are shown at once, maybe by using a filter or category selector.

2. Enable server-side pagination – Check the service settings (if hosted by you) and make sure the layer supports pagination with supportsPagination = true.

3. Use an external data aggregation step – If performance continues to lag, consider summarizing your data using a Python script or data pipeline, then feeding the dashboard a pre-aggregated view.

4. Optimize the chart settings – Try using "Grouped Values" instead of "Split By" if it fits your use case better.

Hope that helps! Would love to hear if anyone has found clever workarounds in production dashboards.

— Venkat

[SantiagoVega]

Thank you! Some of our series had badly misaligned timestamp data, which we believe to have caused issues with querying. Truncating the misaligned data and creating better filters resolved our issue (at least for now).