Over last few years, I’ve put together a few utilities in R to visualise database performance data. One that was particularly useful for me is my own version of ASH viewer. I think it could be useful for many other DBAs and developers who deal with performance optimisation topics frequently enough, so I finally published it on github.
Month: June 2020
Slow insert values with array binding after JDBC upgrade to 19c
A very brief note to alert the community of a nasty JDBC bug affecting INSERT performance. It was noticed by our Java developers after upgrading their JDBC driver 184.108.40.206.0 to version 220.127.116.11.190416DBRU. They were inserting data in batches of 5,000 rows at a time, 250,000 total, and the time to process the entire batch went up from 16 to 102 minutes.
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Network performance in pictures
A few illustrations of patterns to look for when using Wireshark to understand poor network performance. I’ve already touched upon this topic in the past, but this time I just want to share a couple of screenshots with a few comments.
Offloading regex parsing using external table preprocessors
Loading data from flat files into an Oracle database is a very common task. Oracle’s implementation of external tables is fantastic, and in many cases it simplifies the job to such a degree that the developer is left with very little to do: just write a “create table” statement with a few additional details about the file’s structure and that’s pretty much it. Even if the information in the file is not in a simple format (like comma-separated or tab-delimited), this doesn’t make things much more complicated, as you can e.g. load the raw text and then use regex functions to process it.
So I’ve been using this feature in a broad variety of situations (some of them I covered in this blog, e.g. here), and one problem that I occasionally incur is that performance isn’t always great. For example, here is the DDL of what I use to parse listener log files:
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