On one of the databases I’m looking after (18.104.22.168, Solaris, non-RAC), several different INSERT statements (all into tablespaces with manually managed segments) suffer from occasional hiccups. The symptoms are always the same: in one of the sessions, the INSERT gets stuck doing lots of single-block I/O against one of the indexes on the inserted table, and if other sessions are running similar INSERTs, they hang on enq: TX – index contention. The situation can last just a few seconds, but sometimes it’s much longer than this (several minutes), in which case the impact on the application is quite serious.
Nested loop join appears like the simplest thing there could be — you go through one table, and as you go, per each row found you probe the second table to see if you find any matching rows. But thanks to a number of optimizations introduced in recent Oracle releases, it has become much more complex than that. Randolf Geist has written a great series of posts about this join mechanism (part 1, part 2 and part 3) where he explores in a great detail how numerous nested loop optimization interact with various logical I/O optimizations for unique and non-unique indexes. Unfortunately, it doesn’t cover the physical I/O aspects, and that seems to me like the most interesting part — after all, that was the primary motivation behind introducing all those additional nested loop join mechanism on the top of the basic classical nested loop. So I conducted a study on my own, and I’m presenting my results in the mini-series that I’m opening with this post.
Not every commit results in a redo write. This is because there are multiple optimizations (some controlled by the user e.g. with COMMIT_LOGGING parameter, some automatic) that aim at reducing the number of redo writes caused by commits by grouping redo records together. Such group or “piggyback” commits are important for understanding log file sync waits and various statistics around it. In particular, “piggyback” commits play a key role when many sessions commit concurrently at a high rate, as described in my previous post. I made myself a little demo to actually see this mechanism in work with my own eyes. I think it could be of interest for others, so I’m sharing it here. Since the demo involves stopping and resuming background process, I wouldn’t recommend running it on anything other than a designated private sandbox environment.