Installing R shiny server on OCI compute instance (always free tier)


Oracle Compute Infrastructure (OCI) is the next generation cloud platform run by Oracle. Like some of its competitors, Oracle offers an always free tier where you can access a range of cloud products and services without having to pay anything ever. And while you will need to provide credit card information to prove that you are not a bot, it will not going to be charged unless you explicitly upgrade to a paid account.

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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 to version 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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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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Where did my RAM go?

In my previous posts (e.g. here and here) I showed how to use ps output (e.g. from ExaWatcher) visualization to spot performance problems in Linux. Here I’d like to show that this approach can be taken a little bit further, namely, to find the source of increase in memory usage.

The R code for this is quite straightforward. I also think it shouldn’t be much of a problem to do the same in Python, although I haven’t gotten around to try it myself. In the code below read_ps is the function that reads in a ps output file from ExaWatcher without unzipping it, sum_and_tidy does the aggregation, and visualize_rss does the plotting. There is also an auxillary function keep_top_n which is needed to keep the number of color bands to something reasonable.

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Memory fragmentation: the silent performance killer

In my last post, where I analyzed the problems caused by memory fragmentation on a Linux server, I said very little about memory fragmentation itself. I wanted to tell a story, so I had to dial down the technical stuff. But now that the story is told, I figured I should make a more technical post on the subject. I think it might be useful for many Linux DBAs or SAs, since there is not enough awareness of this problem within the Linux community. So if your system is experiencing cryptic performance and stability issues, keep reading, your problem may well be stemming from memory fragmentation.

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How to hang a server with a single ping, and other fun things we learned in a 18c upgrade

Every upgrade is a bit of a lottery. But for a long running, well established system with a lot of legacy code, it can be more of a Russian roulette. You are unlikely to gain much — your system has been running fine, thanks to various tweaks the apps team applied here and there over the years. All these nice and shiny features that arrive with the upgrade — well, sure, there can be a few that you might like and have big plans for, but most of them are not going to be very useful, at least not out of the box. While there’s a good chance that some of them would backfire in a really, really bad way.

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