<commit_before>** Developer Documentation for SageMathCloud (SMC) **

These are extremely rough -- I literally haven't proof read them for a second!  Flow of consciousness.

 -- <NAME>, March 14, 2015



# How to setup your development environment

## Premade virtual machine

There is a VirtualBox image with an environment pre-configured for SMC development at ... (TODO: create and provide url).

## On Ubuntu 14.10

In this section we explain how to install and run your own copy of SMC on Ubuntu 14.10.

1. Install Ubuntu 14.10, create a user named "salvus", and login as this user.
1. Clone the SMC Git repository (todo: change source repo to https://gihub.com/sagemathinc/)
        git clone https://github.com/sagemath/cloud salvus
1. Build everything.  This will download various packages (e.g., Node.js, Cassandra, etc.) from the web, then build and install them into ~/salvus/salvus/data/local/
        cd ~/salvus/
        ./build.py --build_all
1. Create a symbolic link:
        cd /usr/local/bin/
        sudo ln -s /home/salvus/salvus/salvus/scripts/bup_storage.py .
1. Once the build completes successfully, start SMC running as follows:
        cd ~/salvus/salvus
        . environ
        bup_server start
        ipython
        In [1]: import admin; reload(admin); a = admin.Services('conf/deploy_local/')
        In [2]: a.start('all')
1. In order to open Sage worksheets, you must also have Sage installed and available system-wide.
1. Create the database schema.  Right now I do this by copying and pasting from `~/salvus/salvus/db_schema.cql`.
1. Copy the bup server's port, location, and secret key into the database (todo).



## Within a SageMathCloud project

Not currently supported.  (I made this work once via port proxying/forwarding and making all ports very configurable, and supporting a base URL.  It would probably not be difficult to get it to work again.)



# The Architecture of SMC


## Services

The components of SMC are:
- **DNS** -- Domain name servers pointed at the stunnel servers, with geographic load balancing.  DNS should be setup with health checks, so that if https://ip-address/alive fails on one machine DNS stops serving to that machine temporarily.
- **stunnel servers** -- Processes that users connect to via SSL, which support encryption. The DNS for SMC resolves to the ip addresses of the stunnel servers.
- **haproxy servers** -- Proxy servers that must run on the same nodes as the stunnel servers. These load balance and proxy decrypted traffic between stunnel and the hub (dynamic node.js) and nginx (static http) servers, depending on the url.
- **nginx servers** -- static http servers, which serve the contents of ~/salvus/salvus/static.  Traffic is sent to them via haproxy, which also does the load balancing.
- **hub servers** -- (file: `hub.coffee`) dynamic webservers, which coordinate account information, and all traffic flow between users and projects.  There are many of these processes running in each data center (currently each hub can easily handle over 100 simultaneous persistent connection using a single core and a few hundred MB's of RAM). They are the only service with access to the database.
- **Cassandra database cluster** -- distributed multi-datacenter aware NoSQL database, with no single point of failure.  Stores account and project information, state information about all projects (where located), collaboration, who logs in and what files are being edited, the blob key:value store (images in worksheets), stats about how many clients are connected, and much more.  Does **NOT** store user files.
- **project servers** (files: `bup_server.coffee`, `scripts/bup_storage.py`) -- tcp server that is used by the hub to carry out actions related to running user projects, including creating a project, getting current stats, creating/deleting accounts on demand, reading public files/directories from projects, periodically killing idle projects, creating bup snapshots of projects, replicating (via rsync) projects to other hosts, limiting resources via cgroups, etc.
- **local hub servers** --
- **console servers** -
- **SageMath servers** -
- **IPython servers** -
- **compute machines** -
- **internal monitoring** -
- **external monitoring** --

In March 2015, on a typical day, here's an estimate of the number of instances of each of the above servers that are running:

- dns: 6 ip addresses served via Amazon Route 53.
- stunnel: 6 (3 in each data center = dc)
- haproxy: 6 (3 in each dc)
- nginx: 16 nginx servers (8 in each dc)
- hub: 16 nginx servers (8 in each dc)
- cassandra database cluster: 16 nodes (8 in each dc)
- project servers / compute machines: 27, with 19 in Seattle.
- compute machines:
- local hub servers: around 500.
- console servers: around 500, one for each local hub server
- sage servers: around 500, one for each local hub server
- iPython servers: around 50, since these run on demand only when user decideds to open an IPython notebook.
- Internal monitoring: 2 sessions running in different physical locations
- External monitoring: Uses the Google compute monitoring health check system, which sends text messages, emails, etc. when things go wrong.

## Implementation Language

SMC has dependencies that are written in C/C++, Java, Javascript,
etc.  Both the client and server parts of SMC itself are written
almost entirely in CoffeeScript, with a small amount of Python 2
code.   (Aside: At many points I tried hard to write numerous parts of
the server backend using Python, but this always failed for one
reason or another, resulting in having to throw away months of work.
Python is a great language for some problems, e.g., it was an excellent
choice for implementing SageMath; however, it is definitely not as good
as Node.js for implementing an I/O-bound single-threaded asynchronous backend server.  Sorry, but if you want to work on SMC, you must learn
CoffeeScript.)

Here are how some of the services are implemented:

- dns: up to Amazon (or whatever)
- stunnel: third-party C program
- haproxy: third-party C program
- nginx: third-party C program
- hub: CoffeeScript node.js program
- cassandra database cluster: third-party Java program
- project servers / compute machines: CoffeeScript node.js server that handles connections and decides what to do; a Python script (`bup_storage.py`) that actually carries out actions.
- compute machines:
- local hub servers: around 500.
- console servers: around 500, one for each local hub server
- sage servers: around 500, one for each local hub server
- iPython servers: around 50, since these run on demand only when user decideds to open an IPython notebook.
- Internal monitoring: 2 sessions running in different physical locations
- External monitoring: Uses the Google compute monitoring health check system, which sends text messages, emails, etc. when things go wrong.


## The Fundamental Design constraints

SMC is a designed to be used as a large distributed
multi-data center web application, with the assumption that
things will fail.  It is nothing like current
IPython or Sagenb, which are built mainly to be used
by a single-user.  This informs the design at every step.
I have broken them at various points, but never in a way
that can't be fixed later fairly easily.

> **Fundamental design constraint:** no single points of failure

Properly setup _**everything**_ is redundant in multiple distinct
data centers.    One can of course set things up with
single points of failure, e.g., one of the development VM images
has no redundancy at all.

**This constraint is currently violated** in one place I'm aware of, which is that I (<NAME>) am the only person who knows how SMC works, and can fix things when human intervention is required.   _Your mission, if you choose to accept it, is to fix this!_

> **Fundamental design constraint:** everything must be event driven; no polling.

This is made much easier by websockets and the message passing code.

**This constraint is currently violated** in two places I'm aware of, which will be fixed:
 - the _notification system_ polls the database periodically to check for notifications for each connected user.  This will be replaced when we implement a mechanism for communicating between hub servers.
 - the _local hub watches for changes of files_ on the file system by calling stat every 5 seconds; instead it should use inotify.  Why not?  Because though node supports inotify, when I first implemented the local_hub to use them for this, it was very flaky. It might work fine now.


The following constraint is slightly less clear and well defined than the two above.

> **Fundamental design constraint:** linear horizontal scalability

Whenever possible, we choose designs where we can add more nodes in order to increase the number of users that we can handle, and in particular so we can scale up linearly to an arbitrary number of users.  Currently, we don't require that this scalability but instant or automatic -- it's fine if it takes a few hours to increase the number of servers, but not fine if it takes a month.  SMC grows very predictably, due to the academic calendar.  I do think that, given a day, I could make SMC handle a million simultaneous users via the current architecture; this would require increase quotas on Google Compute Engine, spinning up many new compute and web VM's, and... that's it.  It might be more expensive than it should be, due to the issue pointed above involving lack of pub/sub.

For a web application, the database is usually the main scalability constraint.  The Cassandra database scales linearly in the number of nodes, so by using it, we satisfy the above design constriant.  Many database-related problems are more difficult to solve using Cassandra than they
might be using PostgreSQL (a version of SMC in 2013 used PostgreSQL) or MySQL; however, the benefit is scalability.

**This constraint is currently violated** in backups of user projects, which are all made to a single disk on one machine at UW, which are then saved to other encrypted USB disks, some being disconnected from the internet.  This is causing real trouble with the backup machines being able to keep up with the right volume of the backups.  However, currently that is almost certainly a problem with ZFS being horrible for large volumes on HDD's, and this can be fixed by switching to ext4 for this purpose.  Also, in the long run, it may make sense to backup to Google Nearline Cloud Storage and/or Amazon Glacier, via some sort of service with completely different passwords, and no way to delete...  though I always want to have backups of user data that are not connected to the internet in any way.

The vaguest design constaint

> **Fundamental design constraint:** users are far more important than developers; if possible, cater to user needs rather than trying to change user behavior

For example, the persistent connection between SMC and client web browsers uses Websockets if possible, and otherwise falls back to several other approaches (e.g., long polling).   In contrast, IPython refuses to use anything except websockets, since they want to fix how people use the internet.  As another example, with SMC we regularly minify and combine a huge amount of HTML, etc., into a single download, so the user only has to download a few files, instead of a few hundred files.  This makes the developer workflow more painful, but the experience for users is better.  With IPython, they instead prefer to not seamlessly combine together many small files into a big one, since it makes working easier for developers.

This design constraint also impacts UI choices. Numerically, by far most users of SMC are students in courses, who are just encountering SageMath,
Python, LaTeX, etc. for the first time.  Thus toolbars that provide snippets of code, buttons to edit markdown, etc., should all be on by
default, with an advanced option to turn them off.   Also, it should be easy for users to ask for and get help, without having to jump through any hoops at all (e.g., joining a mailing list, signing up for github or stackoverflow.)  A good example of this is our `<EMAIL>` email address, which anybody can just email without having to joint a mailing list or anything else.

**This constraint is currently violated** in several ways.  For example, file move works badly, which makes SMC painful for new users. Also,
the gitter chat room is a very bad (and I think vastly under-used) because
it requires a GitHub login.  And of course there is even a complete lack of documentation about Sage worksheets.   The terminal should have a bar with all the command bash commands and wizards to use them.  Etc.


> **Fundamental design constraint:** No vendor lock in.

Right now SMC is mostly running on Google Compute Engine, but it does not rely on any special features of GCE.

This is the design constraint I'm least confident about.   The plus is that it means we can switch to Amazon EC2 or Azure or our own hardware fairly easily.  Also, making a supported enterprise private version of SMC that we sell is **possible**.  If it turns out that private installs don't matter and we don't need to switch from GCE in the first year or two, then this can be revisited.

## Messaging API

Local hubs, global hubs, storage servers, and (web browser) clients
communicate (mostly) via JSON text messages.  (The exception is communication between a browser and a console server, which uses a binary protocol that is multiplexed into the websocket connection.)

The messages are all defined in the file `salvus/message.coffee`.

### Messaging Framework
The actual code to implement sending and receiving of messages is just
low-level code written from scratch in CoffeeScript and Python, against
the TCP and websocket libaries.   SMC doesn't make any use of a
third-party messaging framework, for sending messages between web browsers,
and between Unix processes.  It was in fact a lot of work to write/debug the current messaging implementation, though it isn't really that much
code (files: `misc_node.coffee` and `sage_server.py`).
However, that work is done, and the messaging code works perfectly
as far as I know.

Note that there is currently no mechanism for hubs to communicate with each other.  The plan has always been to implement this via a direct tcp connection, which is made on demand when communication is required, and time out after a few minutes of activity.  Alternatively, we could use a messaging queue and pub/sub framework (like zmq, rabbitmq, what Google compute engine has, etc.)

# The Client Frontend




# Console Server

# Sage Server

# IPython Notebook Server

# Local Hub

# Global Hub

## Notifications

## Proxy Server

# Project Server

## Snapshots

# Cassandra Database

# Synchronization

- The **foundation** of sync in SMC is <NAME>ier's differential synchronization algorithm.  Everything else that involves sync is built on top of that.
- Some algorithms used during sync are blocking and can be very compute intensive, so it is **critical** to never use it in any server -- such as a hub -- that must respond to requests in less than a second.<commit_msg>documentation: something about how client works<commit_after>** Developer Documentation for SageMathCloud (SMC) **

These are extremely rough -- I literally haven't proof read them for a second!  Flow of consciousness.

 -- <NAME>, March 14, 2015



# How to setup your development environment

## Premade virtual machine

There is a VirtualBox image with an environment pre-configured for SMC development at ... (TODO: create and provide url).

## On Ubuntu 14.10

In this section we explain how to install and run your own copy of SMC on Ubuntu 14.10.

1. Install Ubuntu 14.10, create a user named "salvus", and login as this user.
1. Clone the SMC Git repository (todo: change source repo to https://gihub.com/sagemathinc/)
        git clone https://github.com/sagemath/cloud salvus
1. Build everything.  This will download various packages (e.g., Node.js, Cassandra, etc.) from the web, then build and install them into ~/salvus/salvus/data/local/
        cd ~/salvus/
        ./build.py --build_all
1. Create a symbolic link:
        cd /usr/local/bin/
        sudo ln -s /home/salvus/salvus/salvus/scripts/bup_storage.py .
1. Once the build completes successfully, start SMC running as follows:
        cd ~/salvus/salvus
        . environ
        bup_server start
        ipython
        In [1]: import admin; reload(admin); a = admin.Services('conf/deploy_local/')
        In [2]: a.start('all')
1. In order to open Sage worksheets, you must also have Sage installed and available system-wide.
1. Create the database schema.  Right now I do this by copying and pasting from `~/salvus/salvus/db_schema.cql`.
1. Copy the bup server's port, location, and secret key into the database (todo).



## Within a SageMathCloud project

Not currently supported.  (I made this work once via port proxying/forwarding and making all ports very configurable, and supporting a base URL.  It would probably not be difficult to get it to work again.)



# The Architecture of SMC


## Services

The components of SMC are:
- **DNS** -- Domain name servers pointed at the stunnel servers, with geographic load balancing.  DNS should be setup with health checks, so that if https://ip-address/alive fails on one machine DNS stops serving to that machine temporarily.
- **stunnel servers** -- Processes that users connect to via SSL, which support encryption. The DNS for SMC resolves to the ip addresses of the stunnel servers.
- **haproxy servers** -- Proxy servers that must run on the same nodes as the stunnel servers. These load balance and proxy decrypted traffic between stunnel and the hub (dynamic node.js) and nginx (static http) servers, depending on the url.
- **nginx servers** -- static http servers, which serve the contents of ~/salvus/salvus/static.  Traffic is sent to them via haproxy, which also does the load balancing.
- **hub servers** -- (file: `hub.coffee`) dynamic webservers, which coordinate account information, and all traffic flow between users and projects.  There are many of these processes running in each data center (currently each hub can easily handle over 100 simultaneous persistent connection using a single core and a few hundred MB's of RAM). They are the only service with access to the database.
- **Cassandra database cluster** -- distributed multi-datacenter aware NoSQL database, with no single point of failure.  Stores account and project information, state information about all projects (where located), collaboration, who logs in and what files are being edited, the blob key:value store (images in worksheets), stats about how many clients are connected, and much more.  Does **NOT** store user files.
- **project servers** (files: `bup_server.coffee`, `scripts/bup_storage.py`) -- tcp server that is used by the hub to carry out actions related to running user projects, including creating a project, getting current stats, creating/deleting accounts on demand, reading public files/directories from projects, periodically killing idle projects, creating bup snapshots of projects, replicating (via rsync) projects to other hosts, limiting resources via cgroups, etc.
- **local hub servers** --
- **console servers** -
- **SageMath servers** -
- **IPython servers** -
- **compute machines** -
- **internal monitoring** -
- **external monitoring** --

In March 2015, on a typical day, here's an estimate of the number of instances of each of the above servers that are running:

- dns: 6 ip addresses served via Amazon Route 53.
- stunnel: 6 (3 in each data center = dc)
- haproxy: 6 (3 in each dc)
- nginx: 16 nginx servers (8 in each dc)
- hub: 16 nginx servers (8 in each dc)
- cassandra database cluster: 16 nodes (8 in each dc)
- project servers / compute machines: 27, with 19 in Seattle.
- compute machines:
- local hub servers: around 500.
- console servers: around 500, one for each local hub server
- sage servers: around 500, one for each local hub server
- iPython servers: around 50, since these run on demand only when user decideds to open an IPython notebook.
- Internal monitoring: 2 sessions running in different physical locations
- External monitoring: Uses the Google compute monitoring health check system, which sends text messages, emails, etc. when things go wrong.

## Implementation Language

SMC has dependencies that are written in C/C++, Java, Javascript,
etc.  Both the client and server parts of SMC itself are written
almost entirely in CoffeeScript, with a small amount of Python 2
code.   (Aside: At many points I tried hard to write numerous parts of
the server backend using Python, but this always failed for one
reason or another, resulting in having to throw away months of work.
Python is a great language for some problems, e.g., it was an excellent
choice for implementing SageMath; however, it is definitely not as good
as Node.js for implementing an I/O-bound single-threaded asynchronous backend server.  Sorry, but if you want to work on SMC, you must learn
CoffeeScript.)

Here are how some of the services are implemented:

- dns: up to Amazon (or whatever)
- stunnel: third-party C program
- haproxy: third-party C program
- nginx: third-party C program
- hub: CoffeeScript node.js program
- cassandra database cluster: third-party Java program
- project servers / compute machines: CoffeeScript node.js server that handles connections and decides what to do; a Python script (`bup_storage.py`) that actually carries out actions.
- compute machines:
- local hub servers: around 500.
- console servers: around 500, one for each local hub server
- sage servers: around 500, one for each local hub server
- iPython servers: around 50, since these run on demand only when user decideds to open an IPython notebook.
- Internal monitoring: 2 sessions running in different physical locations
- External monitoring: Uses the Google compute monitoring health check system, which sends text messages, emails, etc. when things go wrong.


## The Fundamental Design constraints

SMC is a designed to be used as a large distributed
multi-data center web application, with the assumption that
things will fail.  It is nothing like current
IPython or Sagenb, which are built mainly to be used
by a single-user.  This informs the design at every step.
I have broken them at various points, but never in a way
that can't be fixed later fairly easily.

> **Fundamental design constraint:** no single points of failure

Properly setup _**everything**_ is redundant in multiple distinct
data centers.    One can of course set things up with
single points of failure, e.g., one of the development VM images
has no redundancy at all.

**This constraint is currently violated** in one place I'm aware of, which is that I (<NAME>) am the only person who knows how SMC works, and can fix things when human intervention is required.   _Your mission, if you choose to accept it, is to fix this!_

> **Fundamental design constraint:** everything must be event driven; no polling.

This is made much easier by websockets and the message passing code.

**This constraint is currently violated** in two places I'm aware of, which will be fixed:
 - the _notification system_ polls the database periodically to check for notifications for each connected user.  This will be replaced when we implement a mechanism for communicating between hub servers.
 - the _local hub watches for changes of files_ on the file system by calling stat every 5 seconds; instead it should use inotify.  Why not?  Because though node supports inotify, when I first implemented the local_hub to use them for this, it was very flaky. It might work fine now.


The following constraint is slightly less clear and well defined than the two above.

> **Fundamental design constraint:** linear horizontal scalability

Whenever possible, we choose designs where we can add more nodes in order to increase the number of users that we can handle, and in particular so we can scale up linearly to an arbitrary number of users.  Currently, we don't require that this scalability but instant or automatic -- it's fine if it takes a few hours to increase the number of servers, but not fine if it takes a month.  SMC grows very predictably, due to the academic calendar.  I do think that, given a day, I could make SMC handle a million simultaneous users via the current architecture; this would require increase quotas on Google Compute Engine, spinning up many new compute and web VM's, and... that's it.  It might be more expensive than it should be, due to the issue pointed above involving lack of pub/sub.

For a web application, the database is usually the main scalability constraint.  The Cassandra database scales linearly in the number of nodes, so by using it, we satisfy the above design constriant.  Many database-related problems are more difficult to solve using Cassandra than they
might be using PostgreSQL (a version of SMC in 2013 used PostgreSQL) or MySQL; however, the benefit is scalability.

**This constraint is currently violated** in backups of user projects, which are all made to a single disk on one machine at UW, which are then saved to other encrypted USB disks, some being disconnected from the internet.  This is causing real trouble with the backup machines being able to keep up with the right volume of the backups.  However, currently that is almost certainly a problem with ZFS being horrible for large volumes on HDD's, and this can be fixed by switching to ext4 for this purpose.  Also, in the long run, it may make sense to backup to Google Nearline Cloud Storage and/or Amazon Glacier, via some sort of service with completely different passwords, and no way to delete...  though I always want to have backups of user data that are not connected to the internet in any way.

The vaguest design constaint

> **Fundamental design constraint:** users are far more important than developers; if possible, cater to user needs rather than trying to change user behavior

For example, the persistent connection between SMC and client web browsers uses Websockets if possible, and otherwise falls back to several other approaches (e.g., long polling).   In contrast, IPython refuses to use anything except websockets, since they want to fix how people use the internet.  As another example, with SMC we regularly minify and combine a huge amount of HTML, etc., into a single download, so the user only has to download a few files, instead of a few hundred files.  This makes the developer workflow more painful, but the experience for users is better.  With IPython, they instead prefer to not seamlessly combine together many small files into a big one, since it makes working easier for developers.

This design constraint also impacts UI choices. Numerically, by far most users of SMC are students in courses, who are just encountering SageMath,
Python, LaTeX, etc. for the first time.  Thus toolbars that provide snippets of code, buttons to edit markdown, etc., should all be on by
default, with an advanced option to turn them off.   Also, it should be easy for users to ask for and get help, without having to jump through any hoops at all (e.g., joining a mailing list, signing up for github or stackoverflow.)  A good example of this is our `<EMAIL>` email address, which anybody can just email without having to joint a mailing list or anything else.

**This constraint is currently violated** in several ways.  For example, file move works badly, which makes SMC painful for new users. Also,
the gitter chat room is a very bad (and I think vastly under-used) because
it requires a GitHub login.  And of course there is even a complete lack of documentation about Sage worksheets.   The terminal should have a bar with all the command bash commands and wizards to use them.  Etc.


> **Fundamental design constraint:** No vendor lock in.

Right now SMC is mostly running on Google Compute Engine, but it does not rely on any special features of GCE.

This is the design constraint I'm least confident about.   The plus is that it means we can switch to Amazon EC2 or Azure or our own hardware fairly easily.  Also, making a supported enterprise private version of SMC that we sell is **possible**.  If it turns out that private installs don't matter and we don't need to switch from GCE in the first year or two, then this can be revisited.

## Messaging API

Local hubs, global hubs, storage servers, and (web browser) clients
communicate (mostly) via JSON text messages.  (The exception is communication between a browser and a console server, which uses a binary protocol that is multiplexed into the websocket connection.)

The messages are all defined in the file `salvus/message.coffee`.

### Messaging Framework
The actual code to implement sending and receiving of messages is just
low-level code written from scratch in CoffeeScript and Python, against
the TCP and websocket libaries.   SMC doesn't make any use of a
third-party messaging framework, for sending messages between web browsers,
and between Unix processes.  It was in fact a lot of work to write/debug the current messaging implementation, though it isn't really that much
code (files: `misc_node.coffee` and `sage_server.py`).
However, that work is done, and the messaging code works perfectly
as far as I know.

Note that there is currently no mechanism for hubs to communicate with each other.  The plan has always been to implement this via a direct tcp connection, which is made on demand when communication is required, and time out after a few minutes of activity.  Alternatively, we could use a messaging queue and pub/sub framework (like zmq, rabbitmq, what Google compute engine has, etc.)

# The Client Frontend


Most users use SMC via their web browsers, by visiting https://cloud.sagemath.com.  Their web browser downloads many of the files in `salvus/static`, then starts running a Javascript program (written mostly
in coffeescript).  That program connects using websockets (or a fallback mechanishm in case of old browsers or bad networks) via the Primus library to
exactly one backend hub.   The browser will always connect to the same hub,
unless it stops working or the user clears the SERVERID cookie
in their browser.

**NOTE:** this is just one way in which I envision people using the
SMC backend functionality.  The web-browser client frontend could
be replaced by a native application or a smartphone app or other
more lightweight applications that use the same messaging protocol but
have different functionality.  Henceforth we will assume that the
connection is via websockets below, but things work exactly the same
(except for speed) for fallbacks.

There is exactly **one** websocket connection (per browser tab)
between the client and the hub, even if
the user has multiple projects open in a
single tab.

Messages are sent in both ways between the client and the hub it connects
to encoded as a string created using JSON.stringify on a
Javascript object.

Most communication between the client and server uses messages over this
one websocket.  One exception is data involving a terminal, which is often
a single character.  Since sending an entire message for every character
would waste a lot of bandwidth, each terminal session gets a
"channel" associated to it.  When sending either binary data or a message
over the websocket, SMC first sends one character which indicates the channel,  with 0 being the JSON string channel, then the data.   To send
a single character over the terminal, SMC just sends the channel (one character), then the data, then null (?? not sure about null).

The client connects to the hub, which checks for secure signed
authentication cookies, which would have been saved if the user logged
in previously from this browser.  If the cookies are there, the
hub sends a message saying "you are signed in as XXX", and includes various
information about their account.   If there's no valid auth cookie,
the user sees either the sign-in screen or the create account screen,
depending on whether they have logged in before.

Account creation involves sending a message over the websocket to the hub saying "please create an account with these credentials".  The client then waits for a response for the server, either signing them in,
or telling them there is a problem with their account creation request.

Once the initial auth dance has concluded, the browser checks the URL and opens the corresponding
page.  For example, if the url is /projects/project_id, then it opens that project.  For example,
for public files on projects that the user is not collaborating on, it will then render them, even
if the user hasn't logged in yet.

Once the client is logged in, it requests a list of all their projects, which includes their associated metadata (e.g., a list
of collaborators on each project).

Most operations involve the following flow of data. Imagine the user
wants to do something, e.g., get a list of projects, find out how many
projects are active, create a new project, or change the title of a project.
There are hundreds of operations like this.  The file `message.coffee` defines
the structure of a every message between the client, hub and local hub
(and many more that should be deleted since they were never used).  For example,
the CoffeeScript

    # client --> hub
    message
        event          : 'create_account'
        id             : undefined
        first_name     : required
        last_name      : required
        email_address  : required
        password       : <PASSWORD>
        agreed_to_terms: required
        token          : undefined   # only required when token is set.

calls the function `message`, which is defined near the top of the file, with input the Javascript
object with keys `event`, `id`, ..., `token`.  This `message` function adds `create_account` to
the exported namespace of the `message` module, so now `f=require('message').create_account`
is defined.  In turn `f` must be called with an object that satisfies the constraints given
in the right above.  For example, `first_name` must be specified, but `token` need not be
defined.  The constraints are as defined in `misc.coffee`, with the only options being `required`
and `undefined`.  (At some point it might be a good idea to idea more interesting type checking,
e.g., instead of just `required` maybe `required_number` or `required_string`.)  The output of `f`
is a Javascript object that has the properties specified above.   Most code that calls
functions defined in `message.coffee` is located in `client.coffee`, `hub.coffee`, and `local_hub.coffee`.

To carry out an operation, the client constructs a message as above, then sends it
over the websocket connection to the hub it is connected to.  Some messages don't require
any response, e.g., there is a message that reports whenever the user sees a red error
alert message, which results in the hub writing the error message to a database for logging purposes.
Most messages assume that there will be a response from the server within a certain amount of time.
The file `client.coffee` implements a large number of functions, which are called all over in the
code in the `page/` directory, and which implements most cases of communication between
the client and the hub.  In particular, it calls tons of functions in `message.coffee`, and
handles timeouts, etc.  When sending a message requiring a response to the hub, the client
puts a random uuid in the id field above.

The code in `client.coffee`
also listens for incoming messages from the hub, and decides on what to
do with them.  For example, if the id field above is filled in, the client checks
for an associated callback (in a map with keys uuid's of messages that expect responses).
If it finds such a callback function, it calls it with the response message as input.
Also, many other messages arrive that are not in response to any previous message.
For example, notifications are pushed to the client without being requested.  Also,
document synchronization cycles can be initiated by the local_hub, which sends a
"please sync now" message to various hubs, which forwards them on to the appropriate
clients. 

**WARNING:** right now (March 19, 2015),
when a document is changed by a remote user, the client receives a message
saying "please sync this document now", and only then does the client
synchronize the document, by computing a local diff, then sending that upstreams,
waiting for another patch from upstream, and then applying that.  It would be way more
efficient to eliminate the first step and just directly send a sync diff
message from the remote (skipping the "you need to sync" message entirely).  I implemented
this in some version of notifications, but not in the general sync infrastructure.  It's
an optimization that needs to get done.










# Console Server

# Sage Server

# IPython Notebook Server

# Local Hub

# Global Hub

## Notifications

## Proxy Server

# Project Server

## Snapshots

# Cassandra Database

# Synchronization

- The **foundation** of sync in SMC is Neil Frasier's differential synchronization algorithm.  Everything else that involves sync is built on top of that.
- Some algorithms used during sync are blocking and can be very compute intensive, so it is **critical** to never use it in any server -- such as a hub -- that must respond to requests in less than a second.
{'fraction_non_alphanumeric': 0.05131617470921722, 'fraction_numerical': 0.004817545447286471, 'mean_word_length': 4.109056295893391, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 22, 'https://': 7, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 9, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4700564', 'n_tokens_mistral': 9505, 'n_tokens_neox': 8933, 'n_words': 5813}