gae

Keys in the GAE datastore are also unique, just as they would be in a traditional database.

Note that this same class, with changes for package name and class name, can be used in each of your GAE projects.

Click OK in the form of a query and you've indicated that you are prepared to pay for a database operation.

You need to register your application on GAE before you deploy, as you need its name in app. yaml (as shown in Part 1).

Developers are reminded that the GAE Go environment is still experimental, so it is wise not to start writing commercial applications yet.

If your JVM is swapped out of memory, GAE must spend additional time to start your entire application the next time a request comes in.

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As it turns out, GAE is an excellent example of a message-oriented middleware system.

The user's access token is no longer saved in a plain text file but in GAE's datastore, which is different from a traditional database.

Despite XML's ubiquity across the internet, GAE does not provide a bulk upload service for data stored in XML documents.

This was inspired by the popular open source distributed-cache memcached, but is a specialized implementation for the GAE.

Once you start to leverage GAE and store and retrieve real data from it, indexing becomes a real-world (as in real dollar) concern.

About NoSQL systems: Datastore is hardly a good example of a good one. It's good enough for App Engine, but you now know its caveats.

A task in a GAE queue is just a servlet.

When you sign up for a GAE account, you automatically get 200 indexes for free; beyond that, there is a price.

Queueing longer running processes in order to manage the duration of main processes isn't just a GAE thing, however.

I have found that GAE is one of the best and easiest places for Java developers to get started learning NoSQL.

Likewise, large enterprise applications that are already developed are likely to use APIs that GAE prohibits.

Conclusions Acanthamoeba is an opportunistic pathogenic free living amoeba which can produce fatal GAE.

Specifically, GAE queues make it possible to unload long-running processes from web requests.

Running on GAE does not change this in any way, but it can take advantage of the scalability features of our application.

Recognizing the limitations of BigTable for most developers, GAE provides access to hosted MySQL services via its paid business offerings.

Using GAE, you dump URLs that point to servlets or JSPs onto message queues, which are then picked up and processed by GAE services.

Launched in April 2008, Google App Engine (GAE) (see Resources for a link) is a free web application hosting service from Google.

GAE requires that all the data columns involved in a query be indexed, and the index can't contain BLOB or text columns.

This makes use of the GAE's user's API to look up the User object based on the e-mail, then querying Bigtable for the account.

Add both the created output jar and wsc-gae. jar file to the lib folder of your project.

Objectify also permits relationships and exposes a query interface that supports the GAE notions of filtering and sorting.

To create each employee, you will use a SOAP service running on GAE, one at a time.

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Both GAE and Beanstalk are cloud platforms especially tailored to Java application development.

GAE offers an option for developers to pay and keep the inactive JVM in memory for more-consistent performance.

For demonstration purposes, the code in Listing 11 simply displays an exception message when a GAE exception is thrown.

Beanstalk's advantages over GAE are rooted in fundamentally different cloud-based service models.

If you are developing a new application and can live with GAE's constraints, GAE is an excellent and free choice.

GAE's quota on memcache calls is higher than on datastore calls, so it makes sense to use memcache whenever possible.

Now we have seen how the data gets served from our application running on GAE.

Google Storage is also the most complex solution to work with, however, because its libraries currently don't support GAE.

I didn't really have to do much else, because GAE manages things like database management and caching.

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Nothing wrong with the code, just something wrong with the communication between GAE and the del. icio. us Web site.

Namely, I need to have a User instance created (and saved into the GAE datastore) before I can logically add related Retweets.

Add these files to a standard GAE Java project and launch the test server.

GAE has supported the Java platform as a development and deployment environment since 2009.

In some cases, GAE is simply ruled out for this very reason.

To make the GAE scalable, it cannot be tied up by long-running requests.

A key innovation of GAE is the use of a truly scalable data store: Google BigTable.

Luckily, life has options, and GAE offers some particularly powerful ones for storage.

SES is especially interesting and offers a good comparison point with the GMail API in GAE.

Gaelyk is a Groovy framework whose terse syntax and coding shortcuts I'll leverage to quickly build my GAE web application.

The GAE does not allow writing to the local file system, which is a prerequisite of the Spring AxiomSoapMessageFactory class.

It used the Expando property feature of GAE to store the URLs of the services.

Deciding which index to create is a significant burden for GAE developers.

Luckily, the GAE provides an additional API for faster access to data: Memcache.

Making matters worse, GAE provides no easy way to delete indexes that are no longer in use.

That makes GAE an unreliable platform for mashup-type applications.

Developers can write and test Go applications locally on Linux and Mac OS X and run them on GAE.

In my own anecdotal experience, GAE often takes 1 to 3 seconds to respond to database-related requests.

Google App Engine SDK 1. 5. 2 comes with a new addition, tools to build applications written with Go and to run them on GAE.

Compared to EC2, GAE has up till now provided a faster, easier path for cloud-based application development.

We can now focus on seeing how each of the GAE storage options interacts with the application workflow, starting with Bigtable.

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When you build a GAE web app, you don't get to say where your data will live.

If a query uses a combination of columns that are not indexed, GAE will only throw an exception at run time when the query is executed.

In Part 1, we built a small application for aggregating content feeds and serving them through the GAE.

The GAE SDK includes Django, so we are using Django's django. utils. simplejson function to serialize our Python objects to JSON.

Now, the logging could be viewed using the Logs console in GAE, as shown below.

In this article, we'll build an example application that implements each GAE storage option in turn.

The GAE datastore leverages an index for any query issued.

This is a major limitation of GAE for the increasing number of applications that make use of third-party web services APIs.

In GAE, you can't write files to disk because there's no useable file system.

However, GAE's Java support is limited and not standards-compliant.

This class uses very simple queries using GAE's GQL syntax.

On the whole, the Objectify API makes domain modeling for GAE easier and more intuitive, which is a boost to developer productivity.

We do not want that, so we will make greater use of the GAE's data-modeling and Bigtable features to improve performance.

We'll use the workflow to explore each GAE storage option.

One tip: Set up a cron job inside GAE to load your own website every 2 to 3 minutes to keep the JVM active.

We'll explore those trade-offs further next month, when we start preparing the sample application for deployment on GAE.

For its part, GAE limits the returned dataset of each query to 1, 000 rows.

If you want to build your apps on GAE, there are a few rules you must obey.

The final article will guide you through how to deploy the Web application developed in Part 2 to the Google App Engine (GAE).

That's fine, except that GAE allows only 100 indexes per table.

Figure 4 shows the application running on GAE.

That is a problem in general, but it is especially a problem for anything running on the GAE.

Let's see how to deal with this in the GAE.

If a servlet takes longer than that time to send out a response, GAE generates an error in the form of an HTTP 500 code.

The biggest prerequisite for it is knowledge of Python, as this is the programming language used on it (currently, Python V2. 5. 2).

It requires Python 2. 5, which is really old.

You can also use any Java library you like, which is another area where GAE can be fussy.

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Asynchronous processing in GAE

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