Hi,
I have a Block transaction group with 750 item in the block .As you already know The value ,timestamp ,the row/block id is sent to the database as a column. Is it possible to add a column with the Item name of every item in the block?
Thanks
I’m a bit stumped… each item has it’s own target column, so you already have a 1:1 map ?
If it’s for documentation purposes, consider exporting your transaction group as XML and parsing that separately.
You can also add an expression item that returns a constant string of your choice, and storing that in a column.
Might be a dummie question i’m new to this database thing but look at the image.
I want to add every item name related to the source value in a new column .
Im not sure how to do this that was my question if it’s more clear.
Item name is arbitrary–not used for anything outside the transaction group. When dragging and dropping, it is simply a copy of the OPC item address. The target name is the column name actually created in the database. Double-click on your items to see the full configuration dialog for that item.
There isn’t a setting on the group to add the name/tag path.
However, you could add another block item, then add static values to the new block item (change the Item Type to Static Value), and simply enter the names of the tags there.
If we’re talking best practices, it would probably be better to have a separate table in the DB that contained the tag names, and then do a look up based on the row_id. The first thing I suggested is just easier to get started with.
Consider just using the tag historian. If you are just trying to store one tag value per row in the DB. That’s how the historian works. Transaction groups are best suited to situations where multiple values need to be stored in their own columns with a shared timestamp.
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FWIW, we’re simply using a simple tag historian and pulling the data from a (currently) Postgres database into a Snowflake data warehouse. join this with sqlth_te on id = tagid and then we have another table with user friendly descriptions of the data, in this case you join on tagpath, which is pretty constant.
We then create pivoted VIEW of use in BI tools, which give us much more flexibility than the historical group.
This let’s us keep massive amounts of raw data with very few tables, and create multiple specialize views for analysis.