We may see more open interfaces and metadata framework, but they also have drawbacks.
Depth study, no server functionality and streaming have in common? They become a major trend in computing in addition to outside support these changes to open source projects are a new and perhaps unique way to build. In each area, we have emerged only dedicated open source API interfaces that must be used with one of the many supported independent backend. This model might benefit the industry, because this model for developers to bring a little rewriting, and easy to adopt, performance improvements and commitments for the company profitable. In this article I will explain this model, and provide first-hand information about it, discuss it open source meaning, then we should explore how to nurture this model.
Typically, a new open source project providing new technology implementation, but also provides the user programming to use the API. API defined to a certain extent, can be regarded as a creative activity, which draws on a similar concept (such as the Storm of spouts and bolts, or Kubernetes the pods) from the history to help users quickly learn how to use this new thing. API specific to the project execution engine: together they form a single whole. Read the user to install the software project documentation, interactive interface, and benefit from the execution engine.
Several important new projects in different structures. They do not execution engine; rather, they provide a common interface framework element (metaframeworks) for several different execution engines. Keras is the second most popular deep learning framework, is an example of this trend. As the creator of Fran? Ois Chollet as recent attempts to explain, "Keras is an interface, not an end to end framework." The same, Apache Beam, a large, data processing framework, "programming model is also a self-described . " ? What does it mean you can use your own programming model what you do with it The answer is:? What really do not, because these two projects require an external back-end. Beam Take this example, the pipeline can execute user-written in eight different "runner (runner)", including six open-source system (in which five from Apache) and three proprietary vendor systems. The same, Keras also supports TensorFlow, Microsoft's cognitive toolkit (Microsoft's Cognitive Toolkit, CNTK), Theano, Apache MxNet and so on. Chollet on GitHub recent exchange a brief description of this approach: "In fact, we will expand in the future even more than the back-end aspects of ...... Keras Keras is the front end of the depth of learning, rather than the front end TensorFlow.."
The similarities stop there. Beam and Keras originally by Google employees at the same time (2015) and related fields (data processing and machine learning) created. However, it seems that these two groups have independently got this model. This is exactly how it happened? This is the model What does it mean?
Beam story
2015, I served as a product manager at Google, focusing on Cloud Dataflow (cloud data stream). Dataflow engineering team of legendary status dates back to the famous treatise MapReduce Jeff Dean and Sanjay Ghemawat published in 2004. As with most projects, MapReduce define an execution method and a programming model to take advantage of it. Although the model is still execute batch of the latest technology, but using the programming model of experience is not so pleasant, so Google quickly developed a simpler programming model abstraction Flume (the first step in Fig. 1) Meanwhile, process needs low latency, resulting in a new project, the project using a common execution and programming model, becomes (i.e., the second step in FIG. 1) MillWheel. Interestingly, these teams around such an idea came together, Flume is an abstract programming model for batch processing, with some extensions, you can also stream programming model (third step). The key insight is the core programming model Beam, then called Dataflow Model (data flow model).
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