Bulk Importing and Editing Data Points

Overview

When deploying Mango by Radix IoT at scale, manually creating data points one at a time is not practical. A building automation system might need hundreds of Modbus registers, a BACnet installation could have thousands of objects, and an enterprise deployment may span dozens of data sources. Mango provides CSV and JSON import/export tools that let you create, modify, and manage large numbers of data points efficiently using spreadsheet software or scripts.

Prerequisites

• Mango 4.x or 5.x
• Superadmin — Bulk data point edit and Configuration import/export are both superadmin-only pages. The REST data point endpoints instead check permission on the points involved
• A spreadsheet application (Excel, LibreOffice Calc, Google Sheets) for CSV editing
• Familiarity with your data source type and its point configuration fields

Step 1: Open the Bulk Data Point Edit Page

Go to Data integration > Bulk data point edit.

The page opens with a point browser on the left and an empty table on the right. Nothing loads until you select something in that browser — a watch list, a data source, or a device. The CSV controls change depending on what you pick, so make this selection first.

Both CSV controls are icon buttons in the table toolbar, and each one opens a menu rather than acting immediately:

Step 2: Export Existing Points as CSV

Start by downloading the current configuration as a template:

1. Select the data source or watch list whose points you want in the point browser
2. Optionally filter by tags or point name to narrow the export
3. Click the Download CSV icon and choose Data points
4. Save the file (e.g., data-points-export.csv)

Step 3: Understand the CSV Format

The CSV is a flattened view of Mango’s data point REST model. Nested objects become slash-separated column headers — loggingProperties/loggingType, not loggingType. Two columns sit outside the point model itself:

Everything else is a field of the point:

loggingProperties/loggingType accepts six values, not four:

Both periodType columns accept MILLISECONDS, SECONDS, MINUTES, HOURS, DAYS, WEEKS, MONTHS, YEARS.

Special cell values

Mango’s CSV reader treats a few cell values as literals rather than text:

The Action Column

The action column controls what happens to each row. It takes four values:

Step 4: Modify or Create Points in the Spreadsheet

Adding New Points

1. Copy an existing row as a template
2. Change the xid to a new unique value (e.g., DP_TEMP_002)
3. Clear the originalXid cell — there is no existing point to look up
4. Set the action column to CREATE. This is required, not optional; a blank action is treated as UPDATE and the row will fail
5. Update the name, deviceName, and other fields as needed
6. Repeat for each new point

Bulk Editing Existing Points

To change a property across many points at once:

1. Export the points you want to modify
2. Use spreadsheet features (find-and-replace, formulas, fill-down) to make changes
3. For example, to change all logging types from ALL to ON_CHANGE, filter the loggingProperties/loggingType column and replace
4. Leave action blank or set it to UPDATE — both mean update
5. Save the CSV

Bulk Editing Tags

Tags are key-value pairs used for organizing and querying data points. In the CSV:

• Each tag key is its own column, written as tags/<key> — for example tags/site, tags/floor, tags/equipment
• Add a new tag by adding a header like tags/building and filling in values
• To clear a tag, write NULL in the cell. Leaving it blank changes nothing.

Step 5: Import the CSV

1. With your selection still made in the point browser, click the Upload CSV icon
2. Choose Data points
3. Select your modified CSV file

Mango uploads the file and runs the whole batch immediately, then reports the outcome per row:

• CREATE / UPDATE: the action Mango took for that row
• Errors: rows that failed, with the reason (XID not found, validation failure, missing required fields)

Review the report and fix any errors, then re-upload just the corrected rows.

Step 6: Data Points and Related Data (the Extended Export)

The plain Data points export covers the point model. The Data points and related data export additionally carries:

• the data-source-specific point locator fields, as pointLocator/... columns
• event detectors attached to each point
• published points, where configured

Use it when you need to bulk-edit protocol addressing:

1. Select a data source or device in the point browser — not a watch list, or the matching Upload option will be hidden
2. Click Download CSV and choose Data points and related data
3. Edit the pointLocator/... columns in your spreadsheet
4. Upload with Upload CSV > Data points and related data

Locator column names come from the data source type. For example:

• Modbus: pointLocator/range, pointLocator/offset, pointLocator/modbusDataType, pointLocator/slaveId, pointLocator/registerCount
• BACnet: the BACnet locator’s object type, instance number, and property identifier fields
• SQL: the configured table and field names

Step 7: JSON Configuration Import/Export

For whole-configuration work, Mango has a single Configuration import/export page. Its menu item is under Data integration, and the same page has a button on the Utilities card of Administration > Admin home. Unlike the CSV tools, it covers every configuration element — data sources, data points, event handlers, users, mailing lists — not just points.

Export as JSON

1. Go to Data integration > Configuration import/export
2. Tick the object types to export (data sources, data points — which carry their event detectors — event handlers, and so on), or Select all
3. Click Export. The JSON document appears in the editor
4. Click Download JSON file to save it

Import from JSON

1. Go to the same Configuration import/export page
2. Paste or upload the JSON configuration
3. Click Import
4. Review the results summary

The JSON format uses flat property names, which are not the same as the CSV column paths. In JSON a point’s logging type is loggingType; in CSV it is loggingProperties/loggingType.

Example JSON Data Point

{
  "dataPoints": [
    {
      "xid": "DP_ZONE_TEMP_101",
      "name": "Zone 101 Temperature",
      "enabled": true,
      "loggingType": "ON_CHANGE",
      "tolerance": 0.5,
      "purgeType": "MONTHS",
      "purgePeriod": 12,
      "unit": "°F",
      "dataSourceXid": "DS_MODBUS_01",
      "pointLocator": {
        "offset": 100,
        "modbusDataType": "TWO_BYTE_INT_SIGNED",
        "range": "HOLDING_REGISTER"
      },
      "tags": {
        "site": "Building A",
        "floor": "1",
        "zone": "101"
      }
    }
  ]
}

Step 8: REST API Bulk Operations

For automated or CI/CD workflows, use the Mango REST API to perform bulk operations programmatically.

Export and re-import a configuration

Use the json-emport endpoint for both halves of the round trip. The data point query endpoint returns a paged query result, not a configuration document, so it cannot be fed back into an import.

GET /json-emport requires an exportElements parameter naming what to include — a bare GET is rejected. GET /json-emport/list returns the available element names.

# Export the data sources and data points
curl -X GET "https://mango-server:8443/rest/latest/json-emport?exportElements=dataSources&exportElements=dataPoints" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Accept: application/json" > config.json

# Import the modified configuration
curl -X POST "https://mango-server:8443/rest/latest/json-emport" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d @config.json

The element names double as the JSON document’s top-level keys, which is why an exported document opens with dataSources and dataPoints arrays. Other values include users, eventHandlers, eventDetectors, mailingLists, publishers, publishedPoints, jsonData, systemSettings, roles, and permissions.

Bulk Create/Update via CSV

The same CSV the UI uploads can be posted directly. Send it as text/csv:

curl -X POST "https://mango-server:8443/rest/latest/data-points/bulk" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: text/csv" \
  --data-binary @data-points-export.csv

Bulk Tag Update

The bulk endpoints take an action, a shared body, and a requests array — one entry per point:

curl -X POST "https://mango-server:8443/rest/latest/data-point-tags/bulk-sync" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "action": "MERGE",
    "body": {"region": "Northeast"},
    "requests": [
      {"xid": "DP_TEMP_001"},
      {"xid": "DP_TEMP_002"},
      {"xid": "DP_TEMP_003"}
    ]
  }'

action accepts GET, SET, MERGE, or UPDATE (a synonym for MERGE). MERGE adds and overwrites the listed tags and leaves other tags alone; SET replaces the point’s whole tag set.

Verification

Confirm your bulk import was successful:

• Import report shows the expected number of created/updated points
• No error rows in the import results
• New data points appear in the data source and on watch lists
• Point properties (names, tags, units, logging) match your spreadsheet
• Data sources are polling and new points are receiving values
• Tags are searchable and correct in the tag manager

Troubleshooting

New rows fail with “not found” or nothing is created
The action cell is blank, which Mango reads as UPDATE. Set action to CREATE on every new row.

“XID already exists” errors on import
The row is marked CREATE but the XID is already in use. Either change the xid, or switch the row to UPDATE and put the existing XID in originalXid.

A column I edited had no effect
The cell was blank, and blank means “no change”. Write NULL to clear a field, or EMPTY for an empty string.

Tags will not clear
Same cause. Blanking a tags/<key> cell leaves the tag as it was; write NULL.

CSV encoding issues (garbled characters)
Save the CSV as UTF-8 in your spreadsheet application. Special characters in point names or units — degree signs especially — corrupt if the wrong encoding is used.

Missing columns after editing in Excel
Excel sometimes drops empty columns on save. Verify all required column headers are present before uploading. Re-export a fresh template if needed.

Import succeeds but points have wrong data types
The data type comes from the point locator, not a CSV column of its own. Correct it in the pointLocator/... columns of an extended export, or delete and recreate the point.

REST API returns 403 Forbidden
Check which endpoint you called. The data point and tag endpoints need read/edit permission on the points involved; json-emport needs the superadmin role. Also confirm the bearer token is valid and not expired.

A spreadsheet added an unnamed first column
Delete it before uploading. Mango’s own export never writes an unnamed column, but spreadsheets sometimes add a row-number column on save. A column with a missing header is ignored harmlessly, but a column with an empty header and a value in it makes Mango read the whole row as that single value and discard the rest of the row, so the row fails.