One of my current CKAN setups is a pretty basic Source install running on Ubuntu 16.04 LTS. For a few different reasons we set up the server to be as small as possible which for the most part is great but it does have it’s limitations.
The VM is on Azure and is a tiny A1 Basic General purpose machine with i VCPUs and 1.75 GB of RAM. Good news, its cheap. Also, depending what you’re doing with CKAN it seems to handle it relatively well.
Where it starts to stumble is when doing some imports of datasets.
For importing in this case I wrote a python script that loops through a CSV export from another system. I then wrangle the data into my schema. During this process I have to clean many of the values to be more appropriate and clean. I also have to use Selenium to scrape another site to get some additional information for some of the records.
It turns out it was the web scraping that really killed my VM. It would start nice, run for awhile then start to slow down and the CPU would continually increase to around 90%. Eventually I would kill the process and remove some records from the CSV that were already imported and start again but this sucked.
I ended up resizing the VM to an A2 Basic General Purpose VM with 2 VCPUs and 3.5 GB or RAM. I also added a couple tweaks to my import scipt in the Selenium department.
For Selenium I added some arguments to ensure it was a bit more streamlined.
I went from an implicit wait to an explicit wait (should have had this to begin with.)
And finally, I added a very small
time.sleep(0.2) just before I call a
function that handles creating and dealing with the driver.
This combination seems to help keep my VM relatively small but handle a larger amount of work without killing my VMs CPU.
Yes, there are other ways to do imports such as the ckanapi and the harvester extension but I’ve found that when I need to wrangle the data to align with my schema it’s easier to write a script that handles it.