I haven't looked into the details of your configuration, but in
general, Jupyter and the python kernels have no problem using whatever
RAM the system makes available to it. I have created single NumPY
arrays with 1TB of RAM with no problem. If you are running out of RAM,
it is a system level thing related to the configuration of the system,
physical RAM of the system and your deployment of Jupyter.

On Mon, Nov 20, 2017 at 11:58 AM, Karthik Ram <[email protected]> wrote:
> Argg. Thank you Thomas. It did run longer(17 min as opposed to 10 min) this
> time after I un-commented those lines, but still saw the same issue. Is
> there any limitation in Jupyter that it cannot handle more than certain GB
> data or query more than certain million or billion rows from post gre SQL DB
> ?
>
> Its strange because when I do run the SQL script from SQL work bench locally
> on my machine(which is less powerful than the server on which jupyter is
> running), I do get the resulting rows.
>
> On Mon, Nov 20, 2017 at 3:35 AM, Thomas Kluyver <[email protected]> wrote:
>>
>> On 19 November 2017 at 22:28, Karthik Ram <[email protected]> wrote:
>>>
>>> I also changed the following in jupyterhub_config.py file. But still
>>> seeing the issue.
>>>
>>> ...
>>>
>>> #c.Spawner.mem_guarantee = 8G
>>>
>>
>> You'll need to uncomment these lines for them to affect anything, i.e.
>> remove the # from the beginning. It also probably needs to be a string - put
>> "8G" inside quotation marks.
>>
>> Thomas
>>
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>
>
>
>
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> "In the depth of winter, I finally learned that within me there lay an
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-- 
Brian E. Granger
Associate Professor of Physics and Data Science
Cal Poly State University, San Luis Obispo
@ellisonbg on Twitter and GitHub
[email protected] and [email protected]

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