Python frameworks and features for working with Hadoop

The software development framework Hadoop has been developed in Apache and used to cluster storage as well as process data-sets on large scale in multiple hardwares. The main reason behind the creation of Hadoop was to enhance the search capability over several servers. This framework uses the factor of distributed computing to enable High Performance Computing. Java is the language in which the framework has been developed but other languages can be used as well like C++ or Python. Looking at the power as well as popularity of Python most people are prompted to use it. However, during that time the framework used must be Python-specific. These make writing in Python really easy as well as convenient. Some of the frameworks along with their associated features are discussed here.

HadoopStreaming- Hadoop Streaming is the number 1 choice for many developers since it is considered the most transparent as well as fastest option. It also encourages great text processing. This canonical method helps supply executables to Hadoop as reducer or mapper. Agreed-upon semantics are used for direct streaming. By default a tab character is used for separation of value and key. This helps in precise as well as clean functioning without the need of configuring a separate framework.

Pydoop- Pydoop script is another popular option which helps in writing of simple MapReduce programs. The reducers as well as mapper functions can be enabled with very few lines of code. When you need more functions than that provided by the Pydoop Script, Pydoop API can be switched over which is far more complete. With this Python RecordWriter, RecordReader as well as Partitioner can be implemented. It has certain unique features; it wraps Hadoop Pipes and claims to provide an interface which is quite rich.

mrjob- This open source framework has been developed actively by Yelp; it wraps Streaming. The operation between Yelp and Amazon Web Services makes integration between mrjob and EMR really easy as well as smooth. A pythonic API is provided by mrjob which enables users to use mappers as well as keys to work with all kinds of objects. The jobs can be run either on Hadoop cluster, locally for testing or on EMR. Multi-step jobs can be written with minimal setup needed.

Hadoopy- This Python wrapper has been written in Cython. This fast as well as simple framework is tested on more than 700 node clusters. Despite its tiny size, it is extremely well documented, transparent, fast as well as efficient. It can efficiently handle even complicated programs and is compatible with dumbo which allows switching back and forth between the two. The debugging feature is quite nice where messages can be written directly to stdout/stderr, that too without any disruptions to the Streaming process.

dumbo- The project for wrapping of Streaming is quite broadly used. This project allows easy writing along with running of Hadoop programs. In fact, it is often considered as a convenient Python API to write MapReduce programs. Its main identifying features are that it is efficient, easy, mature and flexible. Its simplicity does not stand in the way- it can perform low level things along with tricky actions. It relies on typed bytes to communicate with the framework. On top of that, writing resource-intensive elements natively in a job becomes very easy in Java. It is not only easy to write but easy to read too. It even provides several additional features along with boilerplate functionality.

Apart from the ones mentioned here, there are some other frameworks like octopi, Disco, happy, Mortar, Luigi, etc which can be used. These have several features and benefits of using. So considering everything, users have plenty of options when looking for the perfect Python framework to work with Hadoop. You can get in touch with a custom python development company who can help you develop web application within allocated budgets and time schedules.

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