How long will my database upgrade take? Revisited …

I received this question twice within a few days. And I remember that a tiny little while ago I wrote something on the blog about it. But as this “while” is 11 years ago, I came to conclusion to refresh this topic – and write something about How long will my database upgrade take? Revisited …

How long will my database upgrade take? Revisited ...

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Is there a query?

At first, before I go into details, an important fact upfront: There is neither any query nor any tool giving you a rough estimate or precise figure of how long your upgrade will take. There is exactly one way to gather this information: You need to test the upgrade on comparable hardware. With a copy of the database you will upgrade later in production.

Testing in an OVM environment with just two CPU cores assigned to it, is not a comparable system when your production database runs on an Exadata with 96 CPU cores.

Progress bars are as inaccurate as possible – that’s why neither AutoUpgrade nor “dbpgrade” give you more than the precise information about the current phase. AutoUpgrade tells you the percentage it has completed already – but not in relation to time. And dbupgrade tells you in which of the 100-something phases the upgrade is. That’s the best we can do.

Why do CDB/PDBs always take longer?

Especially in the case where you have a CDB with only one PDB (single tenant), the upgrade takes typically more than twice as long as a regular non-CDB upgrade. At first, as a matter of fact, a PDB has a dictionary. Hence, a PDB needs to be upgraded, too.

When you attempt to upgrade a single tenant, the following things happen:

  • Upgrade of the CDB$ROOT with a maximum of 8 parallel workers
    • This is exactly the same process as upgrading a non-CDB
  • Upgrade of the PDB$SEED and the PDB in parallel
    • Each of them will have two workers

Now in addition, the PDB$SEED has a forced hard-wired recompilation phase per design by the Multitenant team. This leads to the following effect, regardless if you have only one or many PDBs. While your PDBs get upgraded by two workers for each of them by default, the PDB$SEED hits the recompilation part. I guess it will recompile with 2x the number of CPU_CORES – and therefore has to take away CPU capabilities form the other workers upgrading your regular user-created PDBs at the same time. We saw in many real world projects that PDB$SEED took the longest from all PDBs due to the recompilation but also slowed down PDB upgrades happening in parallel if you are short on CPU capabilities.

Instead, you can unplug and plug, and then upgrade. Then your upgrade duration is similar to a non-CDB upgrade but you’ll have to add the unplug/plug operation.

Do tools matter?

Regardless of which tools you choose, AutoUpgrade, dbugrade or the DBUA: The database upgrade process underneath the covers is always exactly the same. will utilize and drive the upgrade in parallel wherever possible. The DBUA had an issue in older releases when it didn’t allow you to choose the parallel degree but chose the wrong one by itself.Β  But I guess that this has been fixed in the newest releases (I didn’t check).

Does size matter?

Size clearly doesn’t matter for the duration of an upgrade. At least not the size of your database. Whether it is 40TB or just 40GB, the upgrade usually doesn’t touch data.

There are very rare exceptions, for instance in the past we have seen a situation where SPATIAL data needed a refresh. And of course, then it mattered if you really used Spatial Data Option, or not. And the time zone adjustment is a special candidate. Even this is only optional, you should be aware that the time zone change can add plenty of downtime post upgrade if YOU have a lot of timestamp with timezone data in your database. Due to locks, your application data may not be accessible during the apply script run. So basically this means downtime.

But apart from such edge cases, the database upgrade process is independent of the physical size of your database.

Less components, faster upgrade

On the blog you find a longer series about how to remove components from a database. I’m not a fan of installing everything into a database two simple reasons:

  • More components mean: More scripts need to be run. And this means: longer upgrade duration
  • Most component upgrade scripts are serial, and can’t even parallelized with other component upgrades

This is also the reason why you should not use the seed databases but create your own databases, ideally with DBCA templates.

The biggest factor deciding how long an upgrade takes is the number of components. Query DBA_/CDB_REGISTRY to see whether you have just a minimal set of components – or if you have all 17 or 18 or 19 components.

And just on the side, we removed the APEX upgrade from the standard upgrade process as well with Oracle Database for two reasons:

  • It is an application layer, and hence needs to be verified separately from the database upgrade
  • Upgrade APEX upfront saves downtime

Dictionary complexity may matter

If you have millions of objects in the dictionary, then the upgrade may take longer for obvious reasons. Typical examples are EBS databases where you have really a huge dictionary. In this case, a lot of operations can take significantly longer. But there is a good way to deal with this. See below …

Statistics matter!

We have seen really brutal cases where an upgrade took 10 hours. And the key to speed it up was gathering stats on some cluster indexes or on an XDB table. So as a matter of fact, you should refresh your dictionary statistics before the upgrade. As a standard task, the AutoUpgrade does it for you. And also the preupgrade_fixups.sql scripts gather stats for fixed objects and for the dictionary. You may also gather stats directly for the SYS and maybe the SYSTEM schema, too.

But let me show you an example we’ve dealt with a 2 years ago.

This is the upgrade output from the database upgrade:

More than 10 hours alone for the OJVM sounds a bit … odd. But once we simply refreshed the dictionary and the fixed objects stats, the upgrade completed in 52 minutes and 42 seconds. That’s 93% faster πŸ˜‰ And I swear, this case is not made it – it was a real case Roy had to deal with.

How about CPU, IO?

Well, CPU may influence the upgrade a lot when you upgrade many PDBs at the same time. Actually, in this case the CPU power of your machine matters a lot. But for a single PDB, or a non-CDB or the CDB$ROOT, the CPU power does not influence the upgrade duration significantly. Of course, when you have only two virtual cores available, then CPU matters. But on a correctly sized system, the CPU power of clock won’t have a bigger influence on the upgrade.

For instance, Roy’s Mac has 8 cores – and mine is an outdated old dual core CPU which can pretend to have 4 cores via hyperthreading. The lab upgrades run twice as fast on his machine than on mine. But in reality we don’t compare laptops but database upgrades on real servers.

IO may matter only when we have a super-huge dictionary or one of the edge cases above. But a database upgrade is not IO intense.

But we do a lot of COMMIT operations during an upgrade. But still, even if your redo logs are on super-fast devices and perfectly well sized, you won’t see a big impact (unless you put copy your database to my laptop).

Can you speed it up?

Yes, you can! Less components mean faster upgrades. This is the biggest differentiator. By far actually. Dictionary statistics matter, too.

And there are some edge cases such as a huge volume of synopsis for incremental statistics – and between different releases the underlying partitioning structure in the dictionary got altered. More synopsis meant longer runtime. You could truncate the synopsis and recreate them afterwards. Or just wait a bit …

But again, this is an edge case.

Less components mean faster upgrades. And the amount of user data does not matter for the duration of a database upgrade.

That’s how simple as it is.

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