Hey all,
So I'm currently using InfluxDB (a time-series database) to store all of my measurement data. I have developed a tool, using LabVIEW, to provide users with access to this data through a nice interface. This tool uses the InfluxDB's Restful API to query data, which responds in JSON format. I have used other toolkits, such as JKI JSON, to tackle the issue I'm having, but these toolkits are MUCH slower that the native LabVIEW "Unflatten JSON String", in my experience. And since I am often posting requests for hundreds of thousands of points, this is no good. So, here is an example of a JSON string response for such a query:
{
"results": [{
"statement_id": 0,
"series": [{
"name": "ac_phase3",
"columns": ["time", "value"],
"values": [["2018-09-19T23:00:12.63546368Z", 0.07106], ["2018-09-19T23:00:12.637463552Z", 0.235248], ["2018-09-19T23:00:12.639463936Z", -0.011033], ["2018-09-19T23:00:12.641463808Z", -0.093127]],
"partial": true
}
],
"partial": true
}
]
}
Take notice of the 2D array "values". Each row contains a timestamp(string) AND a value (DBL). This structure is not possible in LabVIEW, without the use of clusters (which reflects a different JSON structure). Technically, a 2D array of variants would be the most accurate representation, but the VI breaks when I do this (JKI toolkit does not, but again it's really slow). I would like to be able to use this native VI, because it is fast! However, I don't see a way of getting this to work, efficiently... Is there something I can possibly be missing? Would love to hear some other ideas. I know that I can write something up in Python to handle this structure, and pass it into LabVIEW, but I'd like to avoid that, if possible (but will certainly do so, if it comes down to it). Thanks!
-Alex W.
