I'm mostly working on backend staff, except now in a project I need to use python to do computing and visualize the results on google maps. Think about it as, for example, compute the geographical clusters of people tweeting in new york city.
In the python program, it runs about 10 seconds, and then output one iteration of data, which is a json object for coordinates. I'm wondering how should I connect this data to google maps?
What I thought was let python write data into a file and JS would listen to that file every few milliseconds. However that sounds too hacky. Just wondering is there a better way to do it?
I'm really a newbie to js. please forgive my ignorance.
Thanks
The normal way a HTML page gets data from a backend service (like your coordinate generator every 10 seconds) is to poll a web service (usually, a JSON feed) for updates.
All of the dynamic Google Maps stuff happens within a browser, and that page polls a JSON endpoint, or uses something fancier like websockets to stream data into the browser window.
For the frontend, consider using jQuery, which makes polling JSON dead simple. Here's some examples.
Your "python program" should dump results into a simple database. While relational and traditional databases like MySQL or PostgreSQL should suffice, i'd encourage you to use a NoSQL database, which handles capped collections. This prevents you from having to clean old data out from a cron schedule. It additionally allows storing data in ranged buckets for some cool playback style histories.
You should then have a simple web server which can handle the JSON requests from the HTML frontend page, and simply pulls data from the MongoDB. This can be done quickly in any one of the python web frameworks like Flask, Bottle or Pyramid. You could also play with something a little sexier like node.js. The only requirement here is that a database driver exists for it.
Hope that gives a 10,000 foot view of what you need to do now.
Related
Good afternoon people !
Programming language : Python.
Web Framework : Django.
Graphic framework : Bokeh.
Currently trying to set up a Dashboard-like application for my company where each factory would be able to upload their own csv-like data and have dynamic results presentation after data cleaning and processing, and some predictions from machine-learning if enough data is provided.
The dynamic part is mostly using widgets to let them choose how to visualize, what to visualize, different parts of the data analysis process etc... with either JS Callbacks of the Bokeh server functionality.
Until now I decided not to use DataBase Storage but server-side storage in different big CSV, it seems like I have faster access to those on server-side than filtering each time my tables for a given factory upload. Also thought about creating temporary tables/models but didn't feel it right.
Before getting into any hard time programming that, I'd like to know if there is any easy answer to my questions, didn't find suitable solution online regarding this, considering the fact that they might upload a bunch of CSV at once.
Is there anyway to save all the data (after cleaned on the server) in client-side cache so I have to send it only one time and then just sending the adapted graph structures, which would take the data from client-side cache to display them ?
Is there any smarter way to do so between JS callbacks and Bokeh Server ? The documentation didn't help me much to decide between them.
I am taking any tips, and thanks for your time.
Have a nice week-end !
I have a basic personal project website that I am looking to learn some web dev fundamentals with and database (SQL) fundamentals as well (If SQL is even the right technology to use??).
I have the basic skeleton up and running but as I am new to this, I want to make sure I am doing it in the most efficient and "correct" way possible.
Currently the site has a main index (landing) page and from there the user can select one of a few subpages. For the sake of understanding, each of these sub pages represents a different surf break and they each display relevant info about that particular break i.e. wave height, wind, tide.
As I have already been able to successfully scrape this data, my main questions revolve around how would I go about inserting this data into a database for future use (historical graphs, trends)? How would I ensure data is added to this database in a continuous manner (once/day)? How would I use data that was scraped from an earlier time, say at noon, to be displayed/used at 12:05 PM rather than scraping it again?
Any other tips, guidance, or resources you can point me to are much appreciated.
This kind of data is called time series. There are specialized database engines for time series, but with a not-extreme volume of observations - (timestamp, wave heigh, wind, tide, which break it is) tuples - a SQL database will be perfectly fine.
Try to model your data as a table in Postgres or MySQL. Start by making a table and manually inserting some fake data in a GUI client for your database. When it looks right, you have your schema. The corresponding CREATE TABLE statement is your DDL. You should be able to write SELECT queries against your table that yield the data you want to show on your webapp. If these queries are awkward, it's a sign that your schema needs revision. Save your DDL. It's (sort of) part of your source code. I imagine two tables: a listing of surf breaks, and a listing of observations. Each row in the listing of observations would reference the listing of surf breaks. If you're on a Mac, Sequel Pro is a decent tool for playing around with a MySQL database, and playing around is probably the best way to learn to use one.
Next, try to insert data to the table from a Python script. Starting with fake data is fine, but mold your Python script to read from your upstream source (the result of scraping) and insert into the table. What does your scraping code output? Is it a function you can call? A CSV you can read? That'll dictate how this script works.
It'll help if this import script is idempotent: you can run it multiple times and it won't make a mess by inserting duplicate rows. It'll also help if this is incremental: once your dataset grows large, it will be very expensive to recompute the whole thing. Try to deal with importing a specific interval at a time. A command-line tool is fine. You can specify the interval as a command-line argument, or figure out out from the current time.
The general problem here, loading data from one system into another on a regular schedule, is called ETL. You have a very simple case of it, and can use very simple tools, but if you want to read about it, that's what it's called. If instead you could get a continuous stream of observations - say, straight from the sensors - you would have a streaming ingestion problem.
You can use the Linux subsystem cron to make this script run on a schedule. You'll want to know whether it ran successfully - this opens a whole other can of worms about monitoring and alerting. There are various open-source systems that will let you emit metrics from your programs, basically a "hey, this happened" tick, see these metrics plotted on graphs, and ask to be emailed/texted/paged if something is happening too frequently or too infrequently. (These systems are, incidentally, one of the main applications of time-series databases). Don't get bogged down with this upfront, but keep it in mind. Statsd, Grafana, and Prometheus are some names to get you started Googling in this direction. You could also simply have your script send an email on success or failure, but people tend to start ignoring such emails.
You'll have written some functions to interact with your database engine. Extract these in a Python module. This forms the basis of your Data Access Layer. Reuse it in your Flask application. This will be easiest if you keep all this stuff in the same Git repository. You can use your chosen database engine's Python client directly, or you can use an abstraction layer like SQLAlchemy. This decision is controversial and people will have opinions, but just pick one. Whatever database API you pick, please learn what a SQL injection attack is and how to use user-supplied data in queries without opening yourself up to SQL injection. Your database API's documentation should cover the latter.
The / page of your Flask application will be based on a SQL query like SELECT * FROM surf_breaks. Render a link to the break-specific page for each one.
You'll have another page like /breaks/n where n identifies a surf break (an integer that increments as you insert surf break rows is customary). This page will be based on a query like SELECT * FROM observations WHERE surf_break_id = n. In each case, you'll call functions in your Data Access Layer for a list of rows, and then in a template, iterate through those rows and render some HTML. There are various Javascript and Python graphing libraries you can feed this list of rows into and get graphs out of (client side or server side). If you're interested in something like a week-over-week change, you should be able to express that in one SQL query and get that dataset directly from the database engine.
For performance, try not to get in a situation where more than one SQL query happens during a page load. By default, you'll be doing some unnecessary work by going back to the database and recomputing the page every time someone requests it. If this becomes a problem, you can add a reverse proxy cache in front of your Flask app. In your case this is easy, since nothing users do to the app cause its content to change. Simply invalidate the cache when you import new data.
I have a DB table which refers to itself. For example let's say I have a list of employees, some of them are other employees' bosses. Of course I have SQLAlchemy objects of this table.
I want to visualize the connections with a graph - each employee is a node, and a line is connecting each employee to his boss.
Is there a way to do it using flask? Or any other python or web framework?
You could render this client-side using a JavaScript graphing library like D3 or Google Charts. Retrieve and process your records using SQLAlchemy and Python, structure the data as per your graphing library's specifications, set up your element with a little JS, send your data over, and then render it in HTML. You'll get a pretty, interactive, and mutable graph of your choosing.
Still, this is a Python question. If you're allergic to JavaScript, you could use a Python library, such as the ones recommended here. A lot of those are more geared towards generating static plots, though. That's fine if you want to embed an image into your page or prepare it for a presentation, but it isn't very lively.
If you want to go interactive in the browser, but really don't want to touch any JS, you could conceivably use a Python wrapper for one of the aforementioned graphing libraries and let the wrapper write the JS for you. You're going to run into some JS at one point or another, whether you're the one generating it or not. Why not have the satisfaction and flexibility of doing it yourself? :)
At my work, we use Oracle for our database. Which works great. I am not the main db admin, but I do work with it. One thing I like is that the DB has a built in logic layer using PL/SQL which ca handle logic related to saving the data and retrieve it. I really like this because it allows our MVC application (PHP/Zend Framework) to be lighter, and makes it easier to tie in another platform into the data, such as desktop or mobile.
Although, I have a personal project where I want to use couchdb or mongodb, and I want to try and accomplish a similar goal. outside of the mvc/framework, I want to have an API layer that the main applications talk to. they dont actually talk directly to the database. They specify the design document (couchdb) or something similar for mongo, to get the results. And that API layer will validate the incoming data and make sure that data itself is saved and updated properly. Such as saving a new user, in the framework I only need to send a json obejct with the keys/values that need to be saved and the api layer saves the data in the proper places where needed.
This API would probably have a UI, but only for administrative purposes and to make my life easier. In general it will always reply with json strings, or pre-rendered/cached html in some cases. Since each api layer would be specific to the application anyways.
I was wondering if anyone has done anything like this, or had any tips on nethods I could accomplish this. I am currently looking to write my application in python, and the front end will likely be something like Angularjs. Although I am also looking at node.js for a back end.
We do this exact thing at my current job. We have MongoDB on the back end, a RESTful API on top of it and then PHP/Zend on the front end.
Most of our data is read only, so we import that data into MongoDB and then the RESTful API (in Java) just serves it up.
Some things to think about with this approach:
Write generic sorting/paging logic in your API. You'll need this for lists of data. The user can pass in things like http://yourapi.com/entity/1?pageSize=10&page=3.
Make sure to create appropriate indexes in Mongo to match what people will query on. Imagine you are storing users. Make an index in Mongo on the user id field, or just use the _id field that is already indexed in all your calls.
Make sure to include all relevant data in a given document. Mongo doesn't do joins like you're used to in Oracle. Just keep in mind modeling data is very different with a document database.
You seem to want to write a layer (the middle tier API) that is database agnostic. That's a good goal. Just be careful not to let Mongo specific terminology creep into your exposed API. Mongo has specific operators/concepts that you'll need to mask with more generic terms. For example, they have a $set operator. Don't expose that directly.
Finally after having a decent amount of experience with CouchDB and Mongo, I'd definitely go with Mongo.
I would like to know what would be the best way to plot real-time tweets by their location onto a map application such as leaflet.
At the moment I am getting the tweets by the streaming api(filtered) and storing into a mongodb. I would like to know how I can best achieve this. I'm using python and the flask framework.
So a tweet's information is stored into a DB and plotted in real-time onto the map concurrently.
Thanks
This repo shows how to use Flask and Mongo with Leaflet
https://github.com/openshift-quickstart/openshift-mongo-flask-example
If you can either write a timer loop in your JS page to do an Ajax request at a regular interval or you will need something like WebSockets on the server and the client