Category: Computer

First, why does this occur? When you run a query, Oracle retains that data in a “snapshot”. The underlying tables in that query might continue to get changed, but you will see the data as it was when you executed the query. You can keep moving back and forth (rows) within the snapshot using the cursor. However, as you might expect: Oracle cannot hold that snapshot for ever. For how long it retains the snapshot is defined via the UNDO_RETENTION parameter.

So one way to solve this problem might be to increase the limit defined by this parameter. However, that is not always the best solution.

This problem normally occurs when a process opens a cursor (by running the query), and processes each row one by one. For example, let’s assume the process runs a query that returns 10000 rows. Processing each row takes, on average, 10 seconds. It goes on to the next row after processing the previous. Hence the total processing of all these rows will take around 28 hours. If your UNDO_RETENTION is defined as 10 hours, this process will fail on the snapshot too old error.

One of the best ways to solve this problem is to execute performance tuning on this process. This should be carried out specifically on the part of the processes that runs within the query in question, and should be targeted at reducing the time it takes to process one row. For example, if we can get our processing time down to 3 seconds, we will be done within about 8.5 hours, which is below our current setting for UNDO_RETENTION. In most cases, this can actually be done. (Read more here and here.)

A second way to solve the problem is to use a temporary table. For example, suppose you want to analyse all open purchase orders. From the table containing POs, pull the ones that are open, and put them into the temporary table. Since the temporary table is being used only by your process, Oracle will not have to hold the “snapshot” for you. Again the main driver query is the candidate for putting into temporary table. This will also make your process faster overall if it’s based on a not-so-small subset of a large table.

However, a third solution is also possible. For our problem we had a process that had to run for days and days, rather than doing something and finishing. So obviously, we got this error.

To solve the problem, we exited the loop after every n rows, and then reentered it. For example, if the pseudocode looked as below prior to the fix:

select something from somewhere;
while (rows) {
  do process
} 

We changed it as below:

hasAtleastOneRow = True;
while (hasAtleastOneRow) {
  hasAtleastOneRow = False;
  select something from somewhere where rownum<n;
  while (rows) {
    do process
    hasAtleastOneRow = True;
  }
} 

Note that the SELECT statement must have a mechanism to prevent picking up rows that have already been processed earlier. This could be a flag-condition or ‘check’ another table. For example:

select po_id from po_table where po_status='O' and rownum<100
and not exists(select 1 from po_temp where po_table.po_id = po_temp.po_id)

As part of the 'do process' then, we should insert into po_temp.

How do we select the value of 'n'? You will have to do some timing and hit-and-try here. Try to keep the highest value that is guaranteed to take lower processing time compared to the undo retention window.