Performance issues are difficult to track, so I give you my collection of discussions and tips of what to do about it.
Could you be a little bit more specific about what kind and size of data you are dealing with and especially what kind of model you are trying to use (from my experience the Weka nodes are especially ‘hungry’).
In general if your machine is not powerful enough there is litte choice but to upgrade the machine or reduce the data set. The last part can be done in a meaningful way by dimensionality reduction, just to give a few hints:
remove highly correlated variables (that basically contain the same information)
remove variables with little variance
reduce dimensions thru PCA (principal component analysis) or smth.
KNIME performance
Process 900+ CSV files
A few remarks since it is difficult to judge from the screenshots:
if you have to process that many files and lines it could make sense to do it in chunks and have some method in place to log the current status and be able to restart at a certain point. Eg. write results to disk after each 50 out of 900 processed files or something. Because if you do not have a very powerful infrastructure, compartmentalization might be a thing for you
also this saving of steps in-between might function as som…
Will the large dataset be imported and the problems will occur later or do you experience problems also when loading data? If you have problems with import it might help to use the R library Readr instead of KNIME file reader.
I know of no formal restriction for data in KNIME but it depends on how much power your system might bring. These points might be able to help you
check out the links below with hints from KNIME and other information about performance
with large datasets from CSV it mig…
you could try and increase the assigned memory in the knime.ini configuration file but with only 4 GB there are limits to that (https://www.knime.com/blog/optimizing-knime-workflows-for-performance ).
On other option is to set the “Memory Policy” of the (sorter) node to “Write tables to disc”. Obviously this increases the load on the hard drive but it might save on RAM.
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Also you could try and close other workflows, restart KNIME an try to use the garbage collector.
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You might want to check your virus scanner. On certain systems with quite aggressive virus scanners I experienced KNIME to be slow. Since it can contain several 1,000 single files an ‘agressive’ virus scanner might scan all of them every time they are accessed/opened. When we changed the settings KNIME started faster. Of course there is a trade off between security and speed. But modern virus scanners might be able to have a flexible configuration.
But of course the number of AddOns and the ove…
That is certainly odd. To the best of my knowledge, there haven’t been any recent major changes to KNIME that are known to have a degrading effect on performance. From my experience, the most dominating performance factor in KNIME is the materialization of intermediate data tables on disk.
Have you noticed the performance of your hard disk degrading independently of KNIME?
While we have some performance improvements for KNIME in the pipeline, here are some small adjustments you can try out:
Y…
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