Why does ctl_cyrusdb -r take so long with Berkeley DB? — Cyrus IMAP 3.0.9 (stable) documentation

- Home
- Download
- Contribute
- Support
- Cyrus SASL

# Why does ctl_cyrusdb -r take so long with Berkeley DB?

Berkeley DB maintains a log of all the transactions since the last checkpoint of the database. In order to ensure the database is in a consistent state, you must recover the log after any outage (thus the recommendation to run these processes when Cyrus starts). They can take a long time for a few reasons.

The most common one is that you need to checkpoint the cyrusdb more often. This can be done with a simple `ctl_cyrusdb -c`. If you do this very often, the amount of log that needs to be recovered will be significantly shorter. We recommend doing this at least once every half hour, and more often on busy sites.

The other reason is that your deliver.db may be very large. This is solvable by increasing the pruning interval (the -E parameter to ctl_deliver, which you should run on a regular basis), or (in a pinch) by just removing the database (since the effects of losing it do not prevent operation, they just cause vacation messages to be resent, and duplicate delivery suppression to possibly deliver duplicates).

- “by increasing the pruning interval”: My understanding is that the
    number after “-E” is the number of days after which entries are discarded. Is there a way to reduce it to a number of hours? Since most of our mail is internal mail should rarely be delayed by more then an hour or two.
- In case it’s useful to anyone we discovered that moving /var/lib/imap
    from an ext2 to an ext3 journaled filesystem made a vast difference for the worse. While recovering the database Berkeley DB does a vast quantity of small writes and that combined with the updates to the journal absolutely kills disk performance (with journalling it was taking about 40 minutes to start Cyrus on a mail server with about 500 users and 200G of mail). On the flip side moving /var/lib/imap to a hardware RAID system with a decent amount of onboard cache reduced this time to under 30 seconds. I think Berkeley DB could probably be optimized to deal with this better, but in the mean time avoid journaling filesystems, or at least be prepared to experiment to find something that works for you.