a database web service offered by Google.com as part of their App Engine
development stack. The App Engine can be used to build and host web
applications. DataStore is a non-relational cloud database that can be used
along with the App Engine to store any data needed by the application. This
article will examine how the DataStore is accessed, populated, and queried.
What is Cloud Computing?
Wikipedia defines Cloud Computing as “a style of computing in which
resources are provided as a service over the internet”. For me personally,
Cloud Computing means developing or managing a machine or service I do not have
physical responsibility for and is located somewhere in the internet. I further
break down Cloud Computing into two roles of activity, either managing an
entire virtual asset (virtual machine or application), or just interacting with
a specific service. Google DataStore is the later because we interact with the
database through a service and are not responsible for any operating system
What is the DataStore?
The DataStore physically lives on Google’s servers. It is spread
across multiple servers to provide redundancy and performance. This is one of
the main benefits of developing on the App Engine; Google’s scalability is
leveraged. The DataStore was developed over Google’s Big Table, which hosts
many internal and external Google services.
is a non-relational database. This means unlike traditional databases, we don’t
build normalized tables then JOIN them for results. Instead, the DataStore is
optimized for Read speed. The JOIN command is not supported so we need to architect
the database as we would for a high volume reporting database, meaning more
columns and fewer tables.
datatypes are supported in the DataStore, including Sting, Int, Float, and DateTime.
In addition, there is Key (system assigned unique row id), Links (URL), phone
number, and postal address types. The complete list of Types is located at
this URL: http://code.google.com/appengine/docs/python/datastore/typesandpropertyclasses.html
on the DataStore is done though Application Programming Interfaces (API).
These can be accessed by either Python or JAVA. There is a Management Console,
but unlike the consoles in Oracle or MS SQL, the DataStore console isn’t really
designed to manipulate data. This is done though your own programming
instead. In addition, there isn’t any concept of a stored procedure or view. All
database code is maintained in your JAVA or Python application.
in the article will use Python 2.6.2 which can be downloaded free of charge at http://www.python.org/download/ .
In addition, you’ll also need the Google App Engine SDK (Software Development
Kit) which can be downloaded from http://code.google.com/appengine/downloads.html. To get started, create a login with the Google App Engine, located at http://code.google.com/appengine/. Low usage accounts are free of charge.
Once a login
is created with the App Engine, it will ask you to create an Account. Think of
an Account as a Database. All our Tables (called Models in the DataStore), and
data (Entities) will be saved inside an Application (Database).
your development environment is setup correctly, we’ll create a very small test
application. On your c:\ drive, create a helloworld directory. Next create a
file called app.yaml . The YAML is a runtime configuration file. Enter the
following text into the YAML file:
– url: /hello.*
first line “application” to your database name. The “handlers” section maps
URLs to Python pages. In this example, any URL starting with “hello” will map
to file we’re going to created called “hello.py”.
a file called “hello.py” and place it in the “helloworld” directory. Enter the
following code into this file:
print ‘Content-Type: text/plain’
print ‘Hello from Google’
mater what tool creates these files, notepad, the IDLE Python editor, or some
other text application.
step is to push our application up to the Google server. Open a DOS or Command
prompt and execute “appcfg.py update c:\helloworld”. You will be prompted for
your Google login. Below is the output from posting the application.
Now open a browser
and go to “your application name”.appspot.com/hello to run our test. If all is
correct you’ll receive the below confirmation.
In this next
example, we’ll create a Table (called a Model in the DataStore) and store some
test data (Entities). Open the YAML file created previously and add the
– url: /hello.*
– url: /write.*
map any URL starting with the word “write” to a Python page we’ll create called
“write.py”. Next, create the write.py page and locate it in the “helloworld”
directory. Add the following code into write.py:
from google.appengine.ext import db
make = db.StringProperty()
year = db.IntegerProperty()
myCar = Cars(make=”Ford”, year=2009)
line, “import db”, accesses the Google database API. Next, the class block
creates a Table (called a Model in the DataStore) of two columns; a string
called “make” and an Int called “year”. The Model will be called “Cars”. The
“myCar” line creates one row of data. Lastly the “put()” is executed. Put is
our method to “INSERT”.
the Google Administration Console to view the newly inserted data. The Console
can be accessed from: http://appengine.google.com/.
After logging in, the first screen will list our Applications (Databases).
Entering an Application will bring up the Dashboard pictured below.
gives us a snapshot of our processes and resources being utilized. On the left
of the chart is a link called “Data Viewer”. The Data Viewer is the web
interface to our data and shows the record just entered.
interface we can add, edit, and delete data as well as view. In addition,
there is a window to Query. Click the “Query” radio button towards the top of
the screen to open a query text area.
data in the DataStore is done with a SQL like language called GQL. For
example, this statement returns the newly created record:
SELECT * FROM Cars WHERE make = 'Ford'
The GQL is
not a robust language like PL/SQL or TSQL, but is adequate for presenting data
to forward facing web applications. There are WHERE, ORDER BY, and LIMIT
keywords. The OR command doesn’t exist for WHERE statements, but there is an
IN grouping clause that can be used. The GQL reference is located at this URL:
is a fast-distributed web service database located in the Google cloud. It’s
managed by APIs written in either Python or JAVA. There is a SQL like language
for working with data called GCL.