# Transaction group array OPC

**URL:** <https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609>\
**Category:** Ignition\
**Tags:** ignition80\
**Created:** [December 1, 2021, 8:50am UTC](https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609 "2021-12-01T08:50:18Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![IA\_old\_forum](https://avatars.discourse-cdn.com/v4/letter/i/7ab992/32.png) [@IA\_old\_forum](https://forum.inductiveautomation.com/u/IA_old_forum)\
**Post date:** [December 1, 2021, 8:50am UTC](https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609/1 "2021-12-01T08:50:18Z")

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I have an int array OPC that is sent from the PLC into Ignition. Inside the array different counted error signals are stored. I am trying to use the OPC array into a transaction group to write to my db but it gives errors

 ![image](https://us1.discourse-cdn.com/inductiveautomation/original/3X/c/3/c328632a8e3b96502a42431b034a1ac7256be2fd.png)  
Should I choose another data type or is there something I have to do ?

this is what I get when the transaction group runs:  
 ![image](https://us1.discourse-cdn.com/inductiveautomation/original/3X/c/e/ce5b16d63bbf9bb4447e5eec92d1ebd2a9c4f446.png)

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**Author:** ![pturmel](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/pturmel/32/3295_2.png) [@pturmel](https://forum.inductiveautomation.com/u/pturmel)\
**Post date:** [December 1, 2021, 1:24pm UTC](https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609/2 "2021-12-01T13:24:14Z")

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A plain transaction group only writes to _ **one** _ row in the database. JDBC doesn’t really have a generic way to pass arrays to a single entry in the DB. You should look at a block transaction group–you can generate multiple inserted rows with that, specifying the array subscript for each.

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<div class="post-metadata">

**Author:** ![IA\_old\_forum](https://avatars.discourse-cdn.com/v4/letter/i/7ab992/32.png) [@IA\_old\_forum](https://forum.inductiveautomation.com/u/IA_old_forum)\
**Post date:** [December 9, 2021, 10:33am UTC](https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609/3 "2021-12-09T10:33:04Z")

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I made a transaction group block and it seems to work the way I want it to. The numeric array is a counter for production errors of n-index of causes. When an "error" (array outputs) changes in value, the block group should insert a new row using insert changed row (in the table action parameter) and looks like this now (I use count(block\_ndx) to count the number of row and return te number of "errors" or instances for a particular time frame)

 ![image](https://us1.discourse-cdn.com/inductiveautomation/original/3X/f/2/f279d88528c7ae7dcee65ca29e079af40586a4e6.png)

But I am running into an issue where the values jump. Some sort of aliasing I suppose  
 ![](data:image/png;base64,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)

I have as trigger option, when values for all tags change. But the Machine cycle is 600 ms and should not jump, in this case, from 56 to 64 instances in less than 2 seconds (t\_stamp 10:28:29 to 10:30:19). In the PLC, the counter is working fine

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<div class="post-metadata">

**Author:** ![pturmel](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/pturmel/32/3295_2.png) [@pturmel](https://forum.inductiveautomation.com/u/pturmel)\
**Post date:** [December 9, 2021, 2:25pm UTC](https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609/4 "2021-12-09T14:25:56Z")

</div>

Did you set the group to only insert changed rows?

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<div class="post-metadata">

**Author:** ![IA\_old\_forum](https://avatars.discourse-cdn.com/v4/letter/i/7ab992/32.png) [@IA\_old\_forum](https://forum.inductiveautomation.com/u/IA_old_forum)\
**Post date:** [December 9, 2021, 2:30pm UTC](https://forum.inductiveautomation.com/t/transaction-group-array-opc/53609/5 "2021-12-09T14:30:13Z")

</div>

Yes, so that I only get a new row when one of the values changes (instead of the whole block) as I am trying to use the row number as a counter as a function of the timestamp