We are currently using the trendminer‑interface SDK to create and populate custom tags (e.g. predictions / calculated KPIs). During implementation and testing, we have identified a major limitation that becomes critical at scale.
When using:
client.io.tag.save(tag_data_dict, index=False)to write data to an existing tag:
- Saving a new dataset overwrites existing historical data for that tag.
- It is not possible to append new time‑series data to an existing tag.
- Writing data for a new time range clears previously uploaded history.
Although the documentation refers to this operation as an “update”, the actual behavior is a full overwrite of the time series, not an incremental append.
Why this is a problem?
Because appending is not supported, every update would require:
- Re‑uploading the entire historical dataset for each tag
- Maintaining full history externally
This leads to:
- Significant performance and bandwidth overhead
- Long execution times
- High operational risk (partial uploads or failures can wipe history)
- A design that does not scale for near‑real‑time or historian‑like use cases
Questions to the comunity/Trendminer team
- Is there any supported way to append data to an existing tag time series (via SDK or API)?
- Is this overwrite behavior by design, or is append functionality planned?
- If append is intentionally not supported, what is the recommended scalable approach ?