While 8k is the default block size, Oracle supports other block sizes, too. Smaller block sizes (more commonly, 4k) are encouraged for OLTP workloads to reduce concurrency, and even smaller block size, 2k, is recommended for databases running on “Advanced format” (or “4K”) storage. Oracle documentation warns us of possible implications when storing larger rows in 2k/4k blocks, such as potentially larger space overhead due to metadata, or even possibility of row chaining. What it doesn’t warn us about, is that there is also a big difference in the way DML operations work, which has very serious implications for their performance.
Nowadays, data in databases is wrapped in may layers of cache: result cache, buffer cache, OS page cache, storage hardware cache… They greatly improve performance, but they also make it less stable and harder to predict. And when I/O performance takes a turn for worse, one has to go through multiple layers of cache trying to understand what went wrong and why. I had such a case not too long ago.
The incident took place on an Oracle 18.104.22.168 database running on a Solaris 10 server. The first symptom was decreased throughput of one of batch processes in the database by about 30 percent. Since the process was spending more than half of its time doing I/O (as ASH indicated), it was easy to establish that the slowdown was linked to increased time of db file sequential reads by plotting average wait times from DBA_HIST_SYSTEM_EVENT (the SQL code for this and other queries in this blog post can be found below, in the Appendix):