I am looking at various options for developing a web app in the cloud and have been looking at GAE using Python. It has everything I need to develop the application. But I can not find a key-value store for it. In particular a key-value store that can scale. I have been looking at Redis but it is used for rails....
I have 2 questions regarding this.
1) Is a key-value store really needed for a high performance web app running on GAE?
2) Are there any well supported key-value stores for GAE?
All help will be greatly appreciated!
Google App Engine comes with a high performance, scalable Datastore implementation.
It also has support for a Memcached (key-value) implementation. It is up to your requirements for scale and functionality of the application which of the two options you chose.
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We have a Google App Engine application consist of several modules and we are storing our user's data in the Google Cloud DataStore.
Now we are going to implement some machine learning algorithms on this data and we are going to use DecisionTree algorithm.
We're looking to solve this by using one of the below methods:
Export the datas in the datastore to CSV file so we can use tools like Weka.
Process the data in the datastore and run google cloud's machine learning techniques. (But when I looked at the Google Cloud ML documents I couldnt find anything about running decision tree on datastore)
So does anyone know is it possible to accomplish the above methods in Google Cloud. If its can you show me a specific documentation or can you describe me the way to do it?
Based on your use case, I would say the best approach for your scenario is to use the new Beta release of Cloud ML Engine for scikit-learn. As you may already know, scikit-learn is a Machine Learning library for Python, and among its wide variety of possibilities, it includes Decision Trees. Note that this is a Beta release and therefore there may still be some rough edges, but I definitely think it should be a good option for you.
Cloud ML Engine has a tight integration with Google Cloud Storage, as it is the required storage option for input and output data, models, etc. That is why, regarding your mentioning of the storage of your data, I would say that the first option you mentioned "1. Export the data in the Datastore to CSV file so we can use tools like Weka" is the most suitable one. You will have to export your data to CSV files, upload them to Cloud Storage, and use ML Engine.
Finally, let me share with you some additional documentation pages that may be helpful to start working with ML Engine and scikit-learn:
Cloud ML Engine and scikit-learn quickstart
Using scikit-learn pipelines
scikit-learn documentation page
I am working on a project that we are going to put on Google Cloud.
There will be a member requirement so logins and profiles to store. Members will make projects that will be linked to their accounts. Other members can join these projects etc. Its not overly complex but I need it to be fast and scalable from the off.
I have a few (simple) questions about the best setup to go for.
Do I have a PHP front end if PHP is only in beta? Do I just use Python for the front end? Is there a better framework than others to use?
Do I create an App Engine API for the front end to call using Python or Java or something else?
Which database do I use? Do I go down the Compute Engine/MongoDB approach or just go straight for Google datastore? (MySQL is disregarded here)
Do I use a shared memcache or get a dedicated one?
These sort of things. It appears using Google Cloud is 'fairly' straight forward but would appreciate some pointers from those in the know who have already get their hands dirty, in a virtual sense of course!
Many thanks in advance
You appear to have four many-faceted Qs -- and apparently you aren't taking them to Google Groups so let me do my best here.
Do I have a PHP front end if PHP is only in beta? Do I just use Python
for the front end? Is there a better framework than others to use?
For guaranteed solidity use Python or Java - PHP and Go aren't quite as mature yet. Many Python frameworks are fine, from the very-lightweight webapp2 that comes with App Engine, through intermediate-weight ones such as "flask", all the way to rich "django". I'm personally a "frameworks shd stay out of my way!" guy so webapp2 is my own favorite.
Do I create an App Engine API for the front end to call using Python
or Java or something else?
Python and Java are both fully supported and stable. I personally of course prefer Python, but, hey!, that's just me! Endpoints, if that's what you mean by "an App Engine API", is also equally well supported each way, with Python perhaps a tad ahead in integration with the datastore thanks to https://github.com/GoogleCloudPlatform/endpoints-proto-datastore/tree/master/endpoints_proto_datastore .
Which database do I use? Do I go down the Compute Engine/MongoDB
approach or just go straight for Google datastore? (MySQL is
disregarded here)
I think the GAE datastore (with add-ons as needed, e.g to shunt images and videos off to Cloud Storage, or structured data for search including geo functionality to the Search API) is going to serve you fine.
Do I use a shared memcache or get a dedicated one?
Start with the shared (free) variety, then once you have it all working design and run stress load-tests and check how they perform with that vs a dedicated (paid) version. Do data-based decisions -- let the numbers guide you: how much better are you getting by paying $X/month for dedicated cache? Decide accordingly!-)
I am developing a project on my Final year at uni and this will be an Android application.
Basically, the "company" updates the database with jobs to be done around the country. Its field workers will use the app to display the jobs available in their location. Workers then select the jobs they are committing to do and send the selection back to database.
I would like to use Google App Engine for that and I am just studying it at the moment.
I came across two methods how to store the data on GAE: Datastore and Cloud SQL.
Personally, I would like to use NoSql Datastore in order to experiment and learn it.
What would you suggest me to use for my use case?
What are the pros and cons of using both mentioned methods?
If I go with Google Datastore, is this guide good for me to start with? https://developers.google.com/appengine/docs/java/datastore/
I would say both will work. If you want to discover the Google Datastore then go for it.
But I would suggest you have a look at Objectify, this library is excellent to make you the things easier with this technology.
go with Google App engine Database. its very efficient to use. yes tat document is enough to start.
Are there any cloud hosting solutions for geospatial data? I am currently writing a directory style app where businesses can sign up and then users can find nearby ones.
I am considering Google App Engine for this, but from what I can tell the GeoModel code is quite expensive (up to tens of thousands of dollars a year) to run since Google updated the pricing of App Engine. It doesn't seem like App Engine's database is really suited to this kind of query (though the SQL solution may be an answer).
I was hoping to find a service where I could send off a HTTP request to add data (a business' id, name and icon url) to a database, and then another one to find a list of businesses that are nearby to a given point. A service is preferable as this is work done for a client and we would like the solution to be managed with as little interaction from us or the client needed as possible.
EDIT:
I just found cartodb.com which uses PostgreSQL and is reasonably priced. Are the any other alternatives?
The App Engine Search API (currently in Experimental) supports GeoPoints and geosearch, and is great for exactly the kind of query that you describe.
See the Google Developers Academy (GDA) App Engine Search API classes for a bit more info and an example as well.
http://www.iriscouch.com/ is a cloud-based host for CouchDB and they support the geocouch extensions for CouchDB to store geoJSON data and perform spatial queries.
We have decided to go with cartodb.com because it looks like they have a good price to ease of use ratio.
You mentioned going with CartoDB, which is a good choice with a nice UI.
Just adding, if you were just looking for a scalable backend, you could use StormDB. It is a cloud hosted SQL database with geospatial extensions. You data is automatically distributed amongst multiple nodes for write, read, and parallel query scalability.
I want to use the Google App Engine to store my data and then query/display/ edit it using Google Spreadsheets as the user interface, with multiple concurrent users having their own view of the data. The problem I have now is that if I put everyone's data on the same Google Spreadsheet that everyone accesses, we can't each do sorting / filtering at the same time.
Is there a way to do this, and is it a good idea to build a simple system this way? I'll eventually need to query a series of Google Word Processor documents as well.
Can someone point me in the right direction on this or suggest other options?
I would ask what the advantage of doing something like this is as opposed to say hosting your application on Google App Engine and building a javascript front end with grids to help sort/filter and view data.
Anyway to answer your questions, you can build your interface over Google Spreadsheets using Google App Scripts. This will allow you to do things like authenticate your user, query, update and display data. If you want to merely display data it turns out that Google Spreadsheets has some built-in functions to do that.
Regarding consistency you should read up on GAE's Datastore as well as its features like transactions. The datastore is not an RDBMS, but is an object database which stores objects against keys. Again something to consider if you are planning to do a lot of data analysis and computation (summations, aggregations).
Overall I would recommend doing a rough design of your system without fixing on particular technologies (like GAE, and Google Spreadsheets). Once you identify what your key goals are for your application, then you can figure out which technologies and resources would make the most sense within your budget.