Made changes to data parallelism flow

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@@ -42,12 +42,14 @@ by: "Jingjing and Abhilash"
 
 **MapReduce** {% cite dean2008mapreduce  --file big-data %} is a programming model proposed by Google to initially satisfy their demand of large-scale indexing for web search service. It provides a simple user program interface: *map* and *reduce* functions and automatically handles the parallelization and distribution.
 
-The MapReduce model is simple and powerful, and quickly became very popular among developers. However, when developers start writing real-world applications, they often end up chaining together MapReduce stages. The pipeline of MapReduce forces programmers to write additional coordinating codes, i.e. the development style goes backward from simple logic computation abstraction to lower-level coordination management. Besides, Developers mostly need to understand the execution model to do manual optimizations. **FlumeJava** {%cite chambers2010flumejava --file big-data%} library intends to provide support for developing data-parallel pipelines. It defers the evaluation, constructs an execution plan from parallel collections, optimizes the plan, and then executes underlying MR primitives. The optimized execution is comparable with hand-optimized pipelines, so there's no need to write raw MR programs directly.
+The MapReduce model is simple and powerful, and quickly became very popular among developers. However, when developers start writing real-world applications, they often end up chaining together MapReduce stages. The pipeline of MapReduce forces programmers to write additional coordinating codes, i.e. the development style goes backward from simple logic computation abstraction to lower-level coordination management. In map reduce, programmers need to reason about data representation on disk or in storage services such as a database. Besides, developers need to clearly understand the map reduce execution model  to do manual optimizations[ref]. **FlumeJava** {%cite chambers2010flumejava --file big-data%} library intends to provide support for developing data-parallel pipelines by abstracting away the complexity involved in data representation and implicitly handling the optimizations. It defers the evaluation, constructs an execution plan from parallel collections, optimizes the plan, and then executes underlying MR primitives. The optimized execution is comparable with hand-optimized pipelines, so there's no need to write raw MR programs directly.
 
-Microsfot **Dryad** {% cite isard2007dryad --file big-data %} designed differently from MapReduce and can support more general computations. It abstracts individual computation tasks as vertices, and constructs a communication graph between those vertices. What programmers need to do is to describe this DAG graph and let Dryad execution engine to construct the execution plan and manage scheduling and optimization. One of the advantages of Dryad over MapReduce is that Dryad vertices can process an arbitrary number of inputs and outputs, while MR only supports to a single input and a single output for each vertex.   Besides the flexibility of computations, Dryad also supports different types of communication channel: file, TCP pipe and shared-memory FIFO.
+An alternative approach to data prallelism is to construct complex, multi-step directed acyclic graphs (DAGs) of work from the user instructions execute those DAGs all at once. This eliminates the costly synchronization required by MapReduce and makes applications much easier to build and reason about. Dryad, a Microsoft Research project used internally at Microsoft was one such project which leveraged this model of computation.
 
+Microsfot **Dryad** {% cite isard2007dryad --file big-data %} abstracts individual computational tasks as vertices, and constructs a communication graph between those vertices. What programmers need to do is to describe this DAG graph and let Dryad execution engine construct the execution plan and manage scheduling and optimization. One of the advantages of Dryad over MapReduce is that Dryad vertices can process an arbitrary number of inputs and outputs, while MR only supports a single input and a single output for each vertex. Besides the flexibility of computations, Dryad also supports different types of communication channel: file, TCP pipe and shared-memory FIFO.
 
-Dryad expresses computation as acyclic data flows, which might be too expensive for some complex applications, e.g. iterative machine learning algorithms. **Spark** {% cite zaharia2010spark --file big-data%} is a framework that uses functional programming and pipelining to provide such support. It is largely inspired by MapReduce, however, instead of writing data to disk for each job as MapReduce does, user program in Spark can explicitly cache an RDD in memory and reuse the same dataset across multiple parallel operations. This feature makes Spark suitable for iterative jobs and interactive analytics and also has better performance.
+
+Dryad expresses computation as acyclic data flows, which might be too expensive for some complex applications, e.g. iterative machine learning algorithms. **Spark** {% cite zaharia2010spark --file big-data%} is a framework that uses functional programming and pipelining to provide such support. It is largely inspired by MapReduce's model and builds upon the ideas behind DAG, lazy evaluation of DryadLinq. Instead of writing data to disk for each job as MapReduce does Spark can cache the results across jobs. Spark explicitly caches computational data in memory thorugh specialized immutable datasets named Resilient Distributed Sets(RDD) and reuse the same dataset across multiple parallel operations. The Spark builds upon RDD to achieve fault tolerance by reusing the lineage information of the lost RDD. This results in lesser overhead than what is seen in fault tolerance achieved by checkpoint in Distribtued Shared Memory systems. Moreover, Spark powers a stack of other libraries, e.g..SQL&DataFrames, GraphX, and can easily combine those libraries in one single application. These feature makes Spark the best fit for iterative jobs and interactive analytics and also helps it in providing better performance. Above all, any system can be easily expressed by Spark enabling other models to leverage the specific advantages of Spark systems and still retain the process of computation without any changes to Spark system[ref].
 
 
 Following four sections discuss about the programming models of MapReduce, FlumeJava, Dryad and Spark.