Dynamically serving multiple bokeh applications

Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,

Amogh

I’m in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json “apps”, so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks “live” if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I’m open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

···

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:

Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,

Amogh

Well, looks like this is not supported in bokeh after all (seeing the response on this thread and the documentation).

I think there are other alternatives to bokeh if one has such an architecture in mind (adding the applications on the go). Dash by plotly for example. It looks much cleaner too.

···

On Sun, Mar 4, 2018 at 9:25 PM, [email protected] wrote:

I’m in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json “apps”, so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks “live” if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I’m open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:

Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,

Amogh

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Amogh Kulkarni
EMAIL: [email protected]
PHONE: +1 (615)810-7561

Alternatively, you could help stem the open source sustainability crisis by becoming a contributor. I’m certainly always happy to help any one get started. In particular, since this kind of feature would largely be on the python side only, it should be much more accessible to be contributors.

Thanks,

Bryan

···

On Sun, Mar 4, 2018 at 9:25 PM, [email protected] wrote:

I’m in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json “apps”, so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks “live” if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I’m open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:

Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,

Amogh

You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.

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For more options, visit https://groups.google.com/a/continuum.io/d/optout.

Amogh Kulkarni
EMAIL: [email protected]
PHONE: +1 (615)810-7561

I would be happy to contribute. Although I have a very limited experience in developing production software, I can certainly give it a shot and at least get it working so that it satisfies preliminary requirements.

Can you provide any pointers as to where I should start from?
Sent with Shift

···

On Tue, Mar 6, 2018 at 1:46 PM, Bryan Van de ven [email protected] wrote:

Alternatively, you could help stem the open source sustainability crisis by becoming a contributor. I’m certainly always happy to help any one get started. In particular, since this kind of feature would largely be on the python side only, it should be much more accessible to be contributors.

Thanks,

Bryan

On Mar 6, 2018, at 11:34, Amogh Kulkarni [email protected] wrote:

Well, looks like this is not supported in bokeh after all (seeing the response on this thread and the documentation).

I think there are other alternatives to bokeh if one has such an architecture in mind (adding the applications on the go). Dash by plotly for example. It looks much cleaner too.

You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.

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For more options, visit https://groups.google.com/a/continuum.io/d/optout.

On Sun, Mar 4, 2018 at 9:25 PM, [email protected] wrote:

I’m in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json “apps”, so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks “live” if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I’m open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:

Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,

Amogh

You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.

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For more options, visit https://groups.google.com/a/continuum.io/d/optout.


Amogh Kulkarni
EMAIL: [email protected]
PHONE: +1 (615)810-7561

Amogh Kulkarni
EMAIL: [email protected]
PHONE: +1 (615)810-7561

I’m happy to contribute too! And a thousand thanks for all your hard work on bokeh— it really is a magnificently flexible piece of software.

I think the main piece I’m missing is just understanding exactly how the pieces would fit together. My sense from the documentation (and code) is that Bokeh Server as it stands today is intentionally lean— it deliberately doesn’t handle a lot of the stuff around resource contention, multi-user, etc. Given that, it seems like “Persistent Bokeh Server” is something which should potentially be an outside project that extends the baseline bokeh server:

  • Simple Flask app which maintains an internal dictionary of “active” plots (eg, some activity has occurred in the last X hours/seconds) and corresponding Bokeh Server instances.

  • These are all in-process instances part of a single monolithic Python process.

  • When you access one that’s already active, your request is passed through to the corresponding Bokeh Server.

  • When you access one that’s not active, it instantiates it and passes your request through.

  • When you access a URL which doesn’t have a corresponding py file in the target folder, you get a 404.

  • Some internal process periodically garbage collects unused Server instances.

It looks like the “json” representation of a plot is not suitable for consumption by Bokeh Server, so I need to introduce my own format here to avoid re-running the expensive analysis which prepares the plots from my data source. Essentially I would pickle my DataFrames and then have a scheme for rehydrating that data and preparing the plots apart from doing the full data preparation from source.

Does all this seem reasonable?

···

On Tuesday, March 6, 2018 at 2:46:59 PM UTC-5, Bryan Van de ven wrote:

Alternatively, you could help stem the open source sustainability crisis by becoming a contributor. I’m certainly always happy to help any one get started. In particular, since this kind of feature would largely be on the python side only, it should be much more accessible to be contributors.

Thanks,

Bryan

On Mar 6, 2018, at 11:34, Amogh Kulkarni [email protected] wrote:

Well, looks like this is not supported in bokeh after all (seeing the response on this thread and the documentation).

I think there are other alternatives to bokeh if one has such an architecture in mind (adding the applications on the go). Dash by plotly for example. It looks much cleaner too.

On Sun, Mar 4, 2018 at 9:25 PM, [email protected] wrote:

I’m in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json “apps”, so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks “live” if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I’m open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:

Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,

Amogh

You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.

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For more options, visit https://groups.google.com/a/continuum.io/d/optout.


Amogh Kulkarni
EMAIL: amogh.s…@vanderbilt.edu
PHONE: +1 (615)810-7561

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For more options, visit https://groups.google.com/a/continuum.io/d/optout.

Hi,

I think it's accurate to describe the Bokeh server as lean, tho I do think that a provision to add apps to an already running Bokeh server could be in scope. So, I'm not sure I'd start down the road of a separate higher level orchestration project just yet. That said the use case you describe is not identical with "late app adding", so maybe you'd still want to. In any case I will try to delineate the different use cases in a reply to the other message. That said, some of the points below I don't necessarily agree with, specifically:

- Simple Flask app which maintains an internal dictionary of "active" plots (eg, some activity has occurred in the last X hours/seconds) and corresponding Bokeh Server instances.

The Bokeh server itself keeps track of all sessions, active, inactive, time since last use, cleanup after timeout, etc. I think having something else try to also keep track of all this would be both duplicative and fragile.

- Some internal process periodically garbage collects unused Server instances.

Not sure if this is worth the bother. A server with no active sessions takes up minimal RAM and no CPU.

It looks like the "json" representation of a plot is not suitable for consumption by Bokeh Server, so I need to introduce my own format here to avoid re-running the expensive analysis which prepares the plots from my data source. Essentially I would pickle my DataFrames and then have a scheme for rehydrating that data and preparing the plots apart from doing the full data preparation from source.

This statement I don't quite understand. The Bokeh JSON format is the same everywhere, modulo some optimizations that for binary array transport that only happen when there are websockets present (i.e. server apps or inside a notebook). Since Bokeh apps are just Python code, they can "rehydrate" in any number of ways, from files, from databases, etc. If I were trying to design a general orchestration facility on top of Bokeh I don't think I'd force a specific mechanism on users (but you might be talking about a specific use case you have, I'm not sure).

Thanks,

Bryan

···

On Mar 7, 2018, at 06:37, Mike Purvis <[email protected]> wrote:

I'm happy to contribute too! And a thousand thanks for all your hard work on bokeh— it really is a magnificently flexible piece of software.

I think the main piece I'm missing is just understanding exactly how the pieces would fit together. My sense from the documentation (and code) is that Bokeh Server as it stands today is intentionally lean— it deliberately doesn't handle a lot of the stuff around resource contention, multi-user, etc. Given that, it seems like "Persistent Bokeh Server" is something which should potentially be an outside project that extends the baseline bokeh server:

- Simple Flask app which maintains an internal dictionary of "active" plots (eg, some activity has occurred in the last X hours/seconds) and corresponding Bokeh Server instances.
- These are all in-process instances part of a single monolithic Python process.
- When you access one that's already active, your request is passed through to the corresponding Bokeh Server.
- When you access one that's not active, it instantiates it and passes your request through.
- When you access a URL which doesn't have a corresponding py file in the target folder, you get a 404.
- Some internal process periodically garbage collects unused Server instances.

It looks like the "json" representation of a plot is not suitable for consumption by Bokeh Server, so I need to introduce my own format here to avoid re-running the expensive analysis which prepares the plots from my data source. Essentially I would pickle my DataFrames and then have a scheme for rehydrating that data and preparing the plots apart from doing the full data preparation from source.

Does all this seem reasonable?

On Tuesday, March 6, 2018 at 2:46:59 PM UTC-5, Bryan Van de ven wrote:
Alternatively, you could help stem the open source sustainability crisis by becoming a contributor. I'm certainly always happy to help any one get started. In particular, since this kind of feature would largely be on the python side only, it should be much more accessible to be contributors.

Thanks,

Bryan

On Mar 6, 2018, at 11:34, Amogh Kulkarni <[email protected]> wrote:

Well, looks like this is not supported in bokeh after all (seeing the response on this thread and the documentation).

I think there are other alternatives to bokeh if one has such an architecture in mind (adding the applications on the go). Dash by plotly for example. It looks much cleaner too.

On Sun, Mar 4, 2018 at 9:25 PM, <[email protected]> wrote:
I'm in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json "apps", so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks "live" if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I'm open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:
Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,
Amogh

--
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--
Amogh Kulkarni
EMAIL: amogh.s....@vanderbilt.edu
PHONE: +1 (615)810-7561

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Hi,

That's great to hear. Since a few people have expressed interest in a couple of different variants of these ideas, I thought I would lay out the different cases (as I see them) so that work can be planned with full information.

The general problem statement "add new apps dynamically". There are two approaches to this:

* Allow a running bokeh server to add new apps

* Have an orchestration layer that can spin Bokeh servers up and down

The latter is more along what Mime suggested in the other reply. I think this is an interesting and useful thing, but also out of scope for the core project. So I will try to outline some different possibilities for "Allow a running bokeh server to add new apps" which I do think is in scope, and which might be useful for many simpler use cases. I think it's worth discussing what the actual best approach might be first, then I can point to specifics in the repo and docs for making changes.

Approaches

···

==========

"Config File" approach
----------------------

Currently apps are specified on the command line at startup:

    bokeh server app1 app2 ...

One possibility that has been mentioned is to allow Bokeh server to take its app configuration from some sort of YaML/JSON/whatever config file, instead of just the command line. I think this is a fairly simple and straightforward task for someone with moderate Python experience, and could be done as a small initial PR. Once this is done, the next step would be to add a way for the Bokeh server to re-read the config file, and update the installed apps based on the new contents. There are a couple of possibilities:

* Make Bokeh server re-read the config file when it gets a HUP signal, i.e. "kill -15 <server pid>" would cause the server to reload the config. This is definitely a well known pattern on Unix-like systems. I'm not sure if something similar exists on Windows. If not, this approach is probably not a good one.

* Make Bokeh server monitor the file for changes. This could be accomplished either with explicit polling in some kind of periodic callback, or by using something like the "watchdog" project in a separate thread, and sending some async signal to the bokeh server to "reload" when a change happens. The latter is probably more efficient and performant, but also probably a bit more work.

There are issues to consider with this overall approach. I.e., if running multiple bokeh servers behind a load balancer, for instance, they all need to update. That probably points to the second approach as more reasonable.

Also neither of these touch on the "reload a page for a changed app" case that some user's have asked about. (i.e. the "Flask debug mode" analogue). That would require some BokehJS work and I think is out of scope for this discussion (and would not be an appropriate mode for "production" use in any case).

"On Demand" approach
--------------------

Another idea, which similar to one part of Mike's orchestration idea, is to have Bokeh simply try to look for the appropriate file or directory whenever and unknown URL comes in, i.e. an end user navigates to the URL

    https://foo.com/some_app

And there is no "some_app" loaded, Bokeh could look for an appropriate "some_app.py" or "some_app" directory to load. For this the Bokeh server would need a new configuration option to explicitly state the locations that are OK to search in, and then it would need a new "catchall" endpoint in the Bokeh Tornado app to do the searching and app loading and installing.

"Automatic Detection" approach
------------------------------

This is basically similar to the last idea, a specified location for Bokeh server to look, but instead of loading apps from there on demand, the Bokeh server monitors it for new scripts and directories via polling or watchdog (similar to the first approach) This approach is a bit of a hybrid of the two ideas above.

Considerations

As mentioned above, these notions do not cover a "debug" mode where changing existing app source files cause the browser sessions to reload.

I think perhaps my initial inclination would be to lean toward the "On demand" approach. I think it avoids the complications of polling or watchdog. It would scale automatically when there are multiple bokeh servers behind a load balancer, though each "first hit" on a given server would incur the "on demand" startup cost. So it's maybe not ideal for the use case where the app loading itself is expensive. However, if app loading itself is expensive that has the possibility of blocking existing sessions in *any* case, which could be problematic. It's one reason starting all apps up front and using higher level orchestration would be recommended. It's possible in any of these cases that app loading could happen in a thread but that will start to get complicated.

I guess it would be good to get your thoughts re: do these proposals actually cover the use cases that people have in mind, and secondarily do any of them sounds more or less approachable or better for some reason.

Thanks,

Bryan

On Mar 6, 2018, at 23:26, Amogh Kulkarni <[email protected]> wrote:

I would be happy to contribute. Although I have a very limited experience in developing production software, I can certainly give it a shot and at least get it working so that it satisfies preliminary requirements.

Can you provide any pointers as to where I should start from?

Sent with Shift

On Tue, Mar 6, 2018 at 1:46 PM, Bryan Van de ven <[email protected]> wrote:
Alternatively, you could help stem the open source sustainability crisis by becoming a contributor. I'm certainly always happy to help any one get started. In particular, since this kind of feature would largely be on the python side only, it should be much more accessible to be contributors.

Thanks,

Bryan

On Mar 6, 2018, at 11:34, Amogh Kulkarni <[email protected]> wrote:

Well, looks like this is not supported in bokeh after all (seeing the response on this thread and the documentation).

I think there are other alternatives to bokeh if one has such an architecture in mind (adding the applications on the go). Dash by plotly for example. It looks much cleaner too.

On Sun, Mar 4, 2018 at 9:25 PM, <[email protected]> wrote:
I'm in a similar position. My ideal scenario would be to have a single bokeh server instance simply watching a directory into which I (or a CI process, whatever) can drop serialized json "apps", so that when users visit bokeh.mycompany.com/my-fancy-thing, it checks "live" if my-fancy-thing.json exists, and if so, establishes a session to decimate and stream the data from it to that user as they pan and zoom the plots in their browser.

Is there anything like this supported or on the horizon? I'm open to wrapping Bokeh Server in a small flask app that can do what I want by just spinning up new instances of the server on a per-app basis, but it would be helpful to have any pointers on this from others who have tried it.

M.

On Wednesday, February 28, 2018 at 8:50:43 PM UTC-5, Amogh Kulkarni wrote:
Hi,

I have used Bokeh previously in Jupyter notebooks but I am new to using Bokeh to build standalone applications. Can I run an instance of Bokeh server and keep on adding the applications on the go?

For example - I can run an instance of the bokeh server with the command

bokeh serve --show myapp.py

and it will serve the application written in myapp.py. Can I add another such file myanotherapp.py and make the same instance of the server serve it? I am avoiding having to spawn another instance of the bokeh server again.

Thanks,
Amogh

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Amogh Kulkarni
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Amogh Kulkarni
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