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Jupyter JupyterQ

Jupyter kernel for kdb+.

  • Syntax highlighting for q
  • Code completion for q keywords, .z, .h, .Q, .j namespace functions, and user-defined variables
  • Code help for q keywords and basic help (display and type information) for user-defined objects
  • Script-like execution of code (multiline input)
  • Inline display of charts created using embedPy and matplotlib
  • Console stdout/stderr capture and display in notebooks
  • Inline loading and saving of scripts into and from notebook cells


  • kdb+ ≥ v3.5 64-bit
  • Anaconda Python 3.x
  • embedPy

Build and install

Download the code from KxSystems/JupyterQ and follow the instructions there.

Using notebooks

See the notebook kdb+Notebooks.ipynb for full interactive examples and explanation, also viewable on GitHub.

Running code

The simplest case is running some code and getting a result. Note here:

  • Each line of code which would produce output at a console produces output in the notebook.
  • stderr/stdout are printed separately to the output in the usual way for notebooks. Note that if your print statement, such as -1"hello world!", has an output (here -1) then the output will be displayed. You can suppress this with a semicolon at the end of a statement as usual.
  • Execution is script-like, i.e. you can use the normal rules of indentation for functions, if/while blocks, and select/update/delete statements.


System commands

System commands can be used with the \ escape character at the start of a line in a code cell.

\d does not currently work.

Code completion and getting help

The notebook supports code completion of q keywords and anything in the .h, .Q, .z and .j directories of q.

Completion also works on user-defined variables, provided they exist on the server. If you’ve defined variables in the same cell they won’t exist yet in the server process before the cell is first executed, but notebooks will complete these for you locally.

Code completion in notebooks is accessed via the Tab key.



Help is available on q keywords and built in commands, embedPy and Python foreign objects, and user defined variables. For user defined variables the console representation along with datatype information is displayed.

In notebooks help is accessed with Shift+Tab. This should pop up a window in the notebook. To see an HTML version of the help, with links to the online documentation for the function, press Shift+Tab four times in succession (or use the buttons in the Help window).



Errors are displayed in red and fall into two classes.

Parse errors

Parsing of the cell content is checked by the kernel before sending to the server for evaluation. The usual q parsing rules apply. Note that for foreign languages (lines preceded by p) for example) parsing is not checked.

Evaluation errors

Runtime errors are reported from the server. One important thing to note is that as with q scripts, lines up to where the error occurred will have been executed.


will result in a having a value of 1.



Loading and saving code


In addition to loading code with \l , code from a script on the server can be loaded directly into a cell using a ‘magic’ command

/%loadscript filename

This will not attempt to execute the code and any code in the rest of the cell will not execute.



/%savescript filename [overwrite]

Will save contents of the cell as a script, optionally overwriting the script if it already exists


Python and inline display


Along with k and q code, python code can be run in a q process using embedPy, lines preceded by p) will be executed as Python code. Charts created using matplotlib will be displayed inline in the notebook.


Cells with /%python anywhere in the cell at the start of a line will be evaluated entirely as Python code. This is intended only to make it easier to copy and paste Python snippets into kdb+ notebooks. The language for syntax highlighting, code completion and help is still q.


Server command-line arguments

When opening a notebook or running a console, Jupyter starts the kdb+ kernel. (You do not connect to an existing kdb+ process.) The kdb+ kernel is split into two processes:

  • one handles all communication with the Jupyter front end (jupyterq_kernel.q)
  • another is where code is actually executed (jupyterq_server.q)

To set command-line arguments for these q processes, edit the Jupyter configuration file kernel.json. Here you can set arguments for either the kernel process or the server process. In practice you will likely only want to modify options for the server process as it is where all your code and data reside. The kernel process only manages communication with the Jupyter front end.

Restrict connections using passwords

The command-line argument -u is not supported but -U is. See next section for how to restrict the access to the notebooks to authorised users.

Suppose you wanted to set the default timer interval to 1 second, and a workspace limit of 500 MB. You would change the kernel.json file from the default:

 "argv": [
 "display_name": "Q 3.5",
 "language": "q",

To this:

 "argv": [
 "display_name": "Q 3.5",
 "language": "q",
 "env": {"JUPYTERQ_SERVERARGS":"-t 1000 -w 500"}

To locate the config file after install run:

jupyter kernelspec list

The kernel.json file is located in the directory listed for the qpk kernel.

Restrict access to a notebook using passwords

To open a port for the server or kernel process with user authentication enabled, specify -U (not -u) in command-line options for the server and kernel. You need to provide a valid username and password to allow connections to the kernel and server, in an environment variable JUPYTERQ_LOGIN e.g. kernel.json might look like this:

 "argv": [
 "display_name": "Q (kdb+)",
 "language": "q",
 "env": {"JUPYTERQ_SERVERARGS":"-U servuser.txt","MPLBACKEND":"Agg"}

You can then use separate password files for the kernel and server, but JUPYTERQ_LOGIN must contain a username password combination valid for both processes.

$ cat servuser.txt # password stored hashed
$ cat kernuser.txt # passwords can also be stored in plaintext
$ export JUPYTERQ_LOGIN=user1:password
#should work
$ jupyter-console --kernel=qpk
Jupyter console 5.1.0

KDB+ v3.6 2018.06.01 kdb+ kernel for jupyter, jupyterQ vdevelopment
In [1]: 

#shouldn't work as login not valid for both kernel and server 
$ JUPYTERQ_LOGIN=user2:password jupyter-console --kernel=qpk processes
** Wrong user:password in JUPYTERQ_LOGIN **
Press Ctrl+C

Opening a connection

Set the listening port before opening a connection with another process (as it is set to 0 for both kernel and server processes when JupyterQ is initialized), e.g. \p 1234

$ jupyter-console --kernel=qpk
Jupyter console 5.1.0

KDB+ v3.6 2018.06.01 kdb+ kernel for jupyter, jupyterQ vdevelopment
In [1]: \p 

In [2]: \p 1234


Frequently-asked questions

Can I run the kernel remotely?

Yes, see the Jupyter documentation. To set up a notebook server for multiple users Jupyter recommends JupyterHub

Additional setup for JupyterQ under JupyterHub

If you see this error when running the Jupyter console on the server you’re installing on:

You may need to set LD_LIBRARY_PATH/DYLD_LIBRARY_PATH to your 
python distribution's library directory: $HOME/anaconda3/lib

You will need to export the LD_LIBRARY_PATH and add this to your configuration file for JupyterHub


Why is setting LD_LIBRARY_PATH/DYLD_LIBRARY_PATH required with Anaconda python?

Anaconda packages libraries which may conflict with the system versions of these libraries loaded by q at startup e.g. libssl or libz. There is a conda packaged version of q which doesn't require setting LD_LIBRARY_PATH, if you are already using Anaconda then you can install it with

conda install -c kx kdb

How can I save the contents of a notebook to a q script?

To dump the entire contents of the code cells in a notebook to a q script use File > Download as > Q (.q).

save q script

To save the contents of individual cells as q scripts use /%savescript in a cell.

Loading and saving scripts

Can I mix Python and q code in the same notebook?

Yes, either with p) or /%python.


Is there a Docker image available?

Yes, if you have Docker installed, you can run:

docker run -it --rm -p 8888:8888 kxsys/jupyterq

Further instructions for running headless and building the image are available

Always Linux

Even if you are running Docker on macOS or Windows the version of kdb+ is 64-bit Linux, and the Docker image is Linux.

Can I restrict the access to my notebook using passwords?

Yes, you can initialise a notebook which requires other processes to provide a valid username:password pair when trying to open a connection with the notebook.

Restrict access