Use cases
The purpose of the jsonToTableFields transform is to take a JSON document stored in the "source_payload" field or one of the transform fields in an integration table, extract values from the JSON properties, and put these values into fields.
The primary use case for extracting values from JSON properties and putting them in table fields is to enable SQL queries to use the collected data for analytics, machine learning, troubleshooting, queries, reporting, sending data to Thingworx, and supporting ETL jobs to send data to external systems, and so forth.
Examples
The SQL examples in this section show how SQL can add value to collected data.
Return records where the temperature is greater than 60
SQL can filter records based on field values.
SELECT temperature_whole FROM air_quality_sensor WHERE temperature_whole > 60;
Combine the whole and fractional parts of a temperature to compute a temperature value
SQL can create calculated values from fields. This example returns records where the computed temperature is greater than 80.
SELECT (temperature_whole + temperature_fraction) AS temperature FROM air_quality_sensor WHERE (temperature_whole + temperature_fraction) > 80;
Create the integration table named air_quality_sensor.
If you want to run the previous SQL examples, you can use this JSON action.
{
"api": "hub",
"authToken": "replaceWithAuthTokenFromServer",
"action": "createIntegrationTable",
"params": {
"databaseName": "faircom",
"tableName": "air_quality_sensor",
"fields": [
{
"name": "temperature_whole",
"type": "integer"
},
{
"name": "temperature_fraction",
"type": "integer"
}
],
"metadata": {"description": "Custom integration table for air quality"},
"retentionPeriod": 30,
"retentionUnit": "day"
}
}