I’ve been thinking about the transition of App Engine to Python 3 and have come to the conclusion that it will never happen — App Engine will eventually be deprecated in favour of Managed VMs. Let’s break this apart to see why this is. First, consider the effort required by Google to develop App Engine. The Python runtime environment was modified to enforce the sandbox of the App Engine environment. To provide a Python 3 environment for App Engine as we know it, the Python 3 runtime would need to be modified with the same restrictions. Even imagining that this would happen for Python 3.4, the effort to upgrade to Python 3.5 would require additional effort by Google to modify the runtime. ...

A common problem with Python development for large-scale teams is sharing internal libraries. At Vendasta we’ve been solving this problem using a private PyPI installation running on Google App Engine with Python eggs and wheels being served by Google Cloud Storage. Today, we are announcing the open source version of this tool — CloudPyPI. CloudPyPI is a modification of pypiserver for running on Google App Engine. We’ve also introduced a simple user management system to allow authenticated access to your Python packages. Together, we’ve found this to be a robust tool for distributing private Python libraries internally. If this is a problem you’ve been trying to solve, give CloudPyPI a try — contributions and feature requests are always welcome. ...

View all articles in the Pipeline API Series. This article will serve as a reminder of the Pipeline UI as much for the writer as for the reader. The Pipeline UI requires the MapMeduce library to be installed. If you are not familiar with MapReduce please refer to the MapReduce API Series of articles. Once MapReduce is installed you will need to add a few indices to index.yaml to properly query for pipeline records for display in the UI. ...

View all articles in the Pipeline API Series. This article will cover fully asynchronous pipelines. The term ‘asynchronous’ is misleading here — all piplines are asynchronous in the sense that yielding a pipeline is a non-blocking operation. An asynchronous refers to a pipeline that remains in a RUN state until outside action is taken, for example, a button is clicked or a task is executed. Marking a pipeline as an asynchronous pipeline is as simple as setting the async class property to True. ...

View all articles in the Pipeline API Series. We’ve learned how to execute and chain together pipelines, now let’s take a look at how pipelines execute under the hood. If necessary, you can refer to the source code of the pipelines project to clarify any details. The Pipeline Data Model Let’s start with the pipeline data model. Note that each Kind defined by the pipelines API is prefixed by _AE_Pipeline, making it easy to view individual pipeline details by viewing the datastore entity. ...

View all articles in the Pipeline API Series. Last time, we studied how to connect two pipelines together. In this post, we expand on this topic, exploring how to fan-out to do multiple tasks in parallel, fan-in to combine multiple tasks into one, and how to do sequential work. ...

View all articles in the Pipeline API Series. Last time, we discussed basic pipeline instantiation and execution. This time, we will cover sequential pipelines, answering the question “How do I connect the output of one pipeline with the input of another pipeline”? ...

View all articles in the Pipeline API Series. The Pipelines API is a general purpose workflow engine for App Engine applications. With the Pipelines API we can connect together complex workflows into a coherent run time backed by the Datastore. This article provides a basic overview of the Pipelines API and how it can be used for abritrary computational workflows. In the most basic sense a Pipeline is an object that takes input, performs some logic or computation on that input, and produces output. Pipelines can take two general forms – synchronous or asynchronous. Synchronous pipelines act as basic functions that must complete during a single request. Asynchronous pipelines spawn child pipelines and connect them together into a workflow by passing input and output parameters around. ...

tldr; DatastoreDict. What’s a durabledict? Good question. Durabledict is a Python implementation of a persistent dictionary. The dictionary values are cached locally and sync with the datastore whenever a value in the datastore changes. Disqus provides concrete implementations for Redis, Django, ZooKeeper and in memory. This blog post details an implementation using the App Engine datastore and memcache. Creating your own durabledict By following the guide the durabledict README we can create our own implementation. We need to subclass durabledict.base.DurableDict and implement the following interface methods. Strictly speaking, _pop and _setdefault do not have to be implemented but doing so makes your durabledict behave like a base dict in all cases. ...

What if you could deliver more value, with more speed and with more stability? What if you could triage bugs faster? What if you could fix bugs easier and with less user facing impact? You can, with continuous delivery. Terminology First, some terminology. What distinguishes continuous integration, continuous deployment and continuous delivery? Continuous integration revolves around the continuous automated testing of software whenever change to the software is made. Continuous deployment is the practice of automatically deploying any change to the code. Continuous delivery implies that you can deploy any change to production but for any number of reasons you may choose not to. The focus of this article is on continuous delivery. ...