Hi,
I’m running an API request to download a file, but the response is base64 encoded. Is there any way I can decode in Knime?
I would try to use a R package that does the decoding. Could you provide us with a sample of the data or a file?
As an example, I’m getting the following response.
The decoded contentBytes is an xlsx file with a table. I need the table 
“contentBytes”: “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Did you try using the Binary Objects to Files node?
https://hub.knime.com/knime/nodes/Binary_Objects_to_Files*nrqUIwETRxhSxpBV
1 Like
contentBytes is part of a Java response, so after I extract it, it’s a string and then String to Binary (ASCII) -> Binary to File just returns the same code.
OK I toyed around with this example and with R I got to a certain point where the outcome looks something like a binary XML/XLSX file but my Mac cannot read it. It could be it is not a valid Excel file or there is something wrong with UTF-8.
I upload my current status so someone could toy around with it some more. I also was not able to create a simple conversion with just the raw data via any online tool.
What could help would be a real or complete file or an example where you were sure it worked. Then we could verify solutions.
kn_example_base64.knwf (127.7 KB)
2 Likes
Thank you for your effort!
This is a real file in the example code, just with mock up data.
It does work here: https://www.base64decode.org
Could you provide us with a file in a ZIP so we have an example that is like it is on your machine. I was not able to convert your example. Maybe if you could put your base64 file and your excel into a ZIP we would have an example.
OK now it does work. The R package can convert the original code.txt into an Excel file.
output=the name and path of the Excel file
file=the path and the original name of the file
Since you mentioned that the base64 string can come via some stream I have loaded the content of the TXT file into knime and saved it again and converted it again to see if there is a way to do it without having an original TXT file.
There should be a way to directly convert a block via R, but it is possible that there is a bug or something. It does not yet.
kn_example_base64_02.knwf (123.7 KB)
this is the basic R code
library(base64enc)
base64decode(output = c(knime.flow.in[[“base64_result_file1”]]), file=c(knime.flow.in[[“base64_file”]]))
2 Likes
Still can’t get it sorted.
I’m getting Execute failed: Could not get value of knime.out from workspace.