A design prototype for DocNow to learn with
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An early experiment in automating a series of actions with Twitter data for docnow. If you want to install dnflow and don't want to manually set things up yourself give our Ansible playbook a try.

Uses Luigi for workflow automation.

running it for yourself

First create your dnflow configuration file, and add your Twitter application keys to it:

cp dnflow.cfg.template dnflow.cfg

If you are running on a non-standard HTTP port, such as the flask default, localhost:5000, be sure to include the port number in the value of HOSTNAME, e.g.:

HOSTNAME = 'localhost:5000'

The current summarize.py is set up to collect a handful of tweets based on a search, then execute a series of counts against it. This will result in one data file (the source tweets) and several count files (with the same name under data/ but with extensions like -urls, -hashtags added on.

Assuming you either have an activated virtualenv or similar sandbox, install the requirements first:

% pip install -r requirements

Start the luigid central scheduler, best done in another terminal:

% luigid

To test the workflow, run the following to kick it off (substituting a search term of interest):

% python -m luigi --module summarize RunFlow --term lahoreblast

It may take a moment to execute the search, which will require repeated calls to the Twitter API. As soon as it completes, you should have all the mentioned files in your data/ directory. The naming scheme isn't well thought out. This is only a test.

While you're at it, take a look at the web ui for luigi's scheduler at:


(Assuming you didn't change the port when you started luigid.)

adding the flask UI

ui.py contains a simple web app that allows a search to be specified through the web, queueing workflows to execute in the background, and showing workflow process status and links to completed summaries as well. Running the web UI takes a few more steps.

Redis can be run without configuration changes, best done in another terminal:

% redis-server

RQ requires a running instance of Redis and one or more workers, also best done in another terminal.

% rq worker
  • Create the flask UI backend

A simple SQLite3 database tracks the searches you will create and their workflow status. Within your dnflow virtual environment:

% sqlite3 db.sqlite3 < schema.sql

The flask UI shows a list of existing searches, lets you add new ones, and links to completed search summaries. Again, within your dnflow virtual environment, and probably in yet another terminal window:

% python ui.py

The flow, for now

The luigi workflow is not automated; it needs to be invoked explicitly. The web UI is the wrong place to invoke the workflow because the workflow can run for a long time, yet the UI needs to remain responsive. For these reasons, the process is separated out with the queue.

When a search is added, dnflow adds a job to the queue by defining a Python subprocess to call the luigi workflow from the commandline. RQ enqueues this task for later processing. If one or more RQ workers are available, the job is assigned and begins. Because dnflow's enqueueing of the job is very fast, it can return an updated view promptly.

The luigi workflow takes as long as it needs, generating static files in a distinct directory for each requested search.

Integration between the web UI and workflows occurs in the UI's SQLite database, where search terms are stored with a job id. When the workflow is assigned to an RQ worker, that search record is updated through an HTTP PUT to the web app at the URL /job, with a reference to the job id and its output directory. Each individual task within the workflow further updates this same URL with additional PUTs upon task start, success, or failure. This is handled using Luigi's event model and HTTP callbacks/hooks to the UI keep the integration between the two pieces of the environment simple. During a workflow, the most recent task status will be recorded in the database, and is available for display in the UI.

With these pieces in place, several requests for new searches can be added rapidly within the UI. Each search will be run by the next available RQ worker process, so if only one process is available, they will execute in succession, but with more than one worker running, multiple workflows can run in parallel. The main limitation here is the rate limit on Twitter's API.