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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