A SAS kernel for Jupyter Notebooks. Jupyter Notebooks are capable of running programs in a variety of programming languages and it is the kernel that enables this ability. The SAS kernel enables Jupyter Notebook to provide the following programming experience:
After installing the SAS kernel, you can use a notebook and a SAS installation to write, document, and submit SAS programming statements.
Dependencies¶Previous release of the SAS kernel supported connecting to SAS on Linux only. For this release, you can connect to SAS on any platform that is supported for the specified SAS releases.
Jupyter has a number of dependencies. See the subsections for steps on installing Jupyter on your system.
Jupyter magics¶The %%prompt4var magic is written specifically for the SAS kernel. The purpose of the magic is to prompt for sensitive information such as a password and store the value in a SAS macro variable.
Seasoned SAS programmers might notice that Python magics begin with a percent sign (%) and that SAS macro variables also begin with a percent sign. To ensure that magics are interpreted by Python, they must be specified in the first line of a notebook cell. Otherwise, the magic (%xxxxx) is submitted to SAS.
If you need to run a SAS macro as the first statement, then insert a blank line as the first line in the notebook cell and the macro on the second line. The blank line prevents Python from intrepreting the macro variable as a magic.
NBExtensions¶There are a few NBExtensions to make working with notebooks more productive and pleasant. These are largely the result of pain points and gotchas. These include:
You can install the extensions after you install the SAS kernel. Installation information is provided in this documentation. The source code for the extensions can be found at https://github.com/sassoftware/sas_kernel/tree/master/sas_kernel/nbextensions.
Integration with other notebook software¶ FAQ¶Do I need to buy SAS to use this kernel?
The SAS kernel is simply a program that enables Jupyter to communicate with SAS. As such, if SAS is not installed, then this kernel is not helpful. For information about purchasing SAS, see http://www.sas.com/en_us/software/how-to-buy.html.
How does Jupyter communicate with SAS?
Behind a Jupyter Notebook is a Python session. The Python session submits code to SAS and receives responses through socket I/O communication. The submit and receive strategy leverages the stdin, stdout, and stderr capabilities that have been supported in SAS for a long time.
If stdin, stdout, and stderr have been supported for so long why do I need to have SAS 9.4 or newer?
First, SAS 9.4 was released in July 2013, so it is not a bleeding edge requirement. The reason for the prerequisite is that SAS 9.4 introduced support for creating of HTML5 documents. The SAS kernel relies on the HTML5 output so that it can render attractive tables and graphs automagically.
For information about SAS Viya, see http://www.sas.com/viya.
How can I see my SAS log, I only see the listing output?
SAS is different from many other programming languages in that it has two useful information streams, the log (which details the technical details of what happened and how long it took) and the lst (which includes the tables and graphics from the analysis). The SAS kernel attempts to show you what we think you want. Here are the rules:
LOG LST DISPLAYED NOTES Yes No LOG This happens when you run DATA step or a PROC with thenoprint option. Yes Yes LST — Yes (with ERROR message(s)) Yes ERROR messages with context from the log, then the listing output. — Yes (with ERROR message(s)) No LOG —
If you want to see the log but it was not displayed, you can use the SAS log NBExtension. The extension shows the log for the last executed cell or the entire log since the last (re)start of the notebook.
Will this leave a bunch of SAS sessions hanging around?
A SAS session is started for each notebook that you have open. For example, if you open 5 notebooks, you start 5 SAS sessions. Those sessions remain active as long as the notebook is running. If you shut down your notebook, the associated SAS session terminates. In JupyterHub, there are configuration options to shut down inactive sessions and the SAS kernel complies with those directives.
I restarted my SAS kernel and now my Work library is now empty. What happened?
When you restart the kernel in a notebook, you terminate the SAS session and start a new one. All of the temporary artifacts, such as data sets in the Work library, assigned librefs, filerefs, Work macros, and so on, are destroyed.
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