Catching Up on Processing in Data Thread when Data Queues are corrupt or unusable

Data queues, while very efficient, are prone to occasional damage. Sometimes this damage can render the data queue unusable. Checking the status of the data queue will show that it is damaged or corrupt.

If it is damaged or corrupt, the solution is to delete that data queue and stop and restart the Data Thread jobs. Data Thread will automatically re-create that data queue upon startup.

Listing the data queues:

To list the data queues and their status, you have the following options:

  • Option 12 from the DataThread Main Menu
    • Page down and you will see the data queues and the status of them. If they are white, they are working and have a status of ‘ok’.
    • If they are red, they are corrupt or damaged and you can put a ‘4’ next to that data queue to delete it. Data Thread will recreate the data queues upon restart.
    • To restart:

Take Option 17 (Data Thread Manager) from the Main Menu.

Enter the command ENDALL to end all the jobs in the Data Thread SBS.

Enter the command STRMGR to restart those jobs and the data queue will be automatically rebuilt.

Once the Data queue has been re-created, the monitor job (IDT450) will automatically catch-up cleaning out the Data queues and copy the data to the data vault within DataThread.

Alternatively,

  • Enter the following command from the command line to list the data queues:

WRKOBJ OBJ(DATATHREAD/IDTLOGQ*) OBJTYPE(*DTAQ)

This will list the IDTLOGQ data queues A-G. Often an 8 will show text 'Object is Damaged '.  If so, select a 4 next to IDTLOGQ and press ENTER to delete it. DataThread will recreate the queues upon restart.

To restart:

Take Option 17 (Data Thread Manager) from the Main Menu.

Enter the command ENDALL to end all the jobs in the Data Thread SBS.

Enter the command STRMGR to restart those jobs and the data queue will be automatically rebuilt.

Once the Data queue has been re-created, the monitor job (IDT450) will automatically catch-up cleaning out the Data queues and copy the data to the data vault within DataThread.


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Last Modified On: April 30, 2018