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Spark and Hadoop MapReduce are both open-source frameworks from the Apache stable of Software. Since 2013 when Spark was released it has literally overtaken and acquired more than twice the number of Hadoop’s customers. And this lead is growing. However, big-data frameworks are directly linked to the customer’s need for a particular framework and its uses. Therefore a literal comparison is difficult and we need to discuss what Spark and MapReduce are used for and their differences to evaluate their performance.

The performance differences between Spark and MapReduce:

The main differences between the two are that is that MapReduce processing involves, reading from data and then writing it to the disk, whereas Spark process data within its memory. This feature makes Spark very fast at processing data. However, MapReduce has a far greater potential for processing data compared to Spark. Spark is faster by a 100-fold increase in speed and its ability to process data within the memory has scored with its customers preferring it over MapReduce.

Where MapReduce is useful:

As pointed out above the potential for data processing is high in MapReduce. It  is useful in applications using:

  • Large data sets linear-processing:

Hadoop-MapReduce enables very large data sets to be processed in a parallel fashion. It uses the simple technique of dividing the data into smaller sets processed on different nodes while gathering the results from these multi-nodes to produce a single set of results. When the resultant data set produced is bigger than the RAM capacity Spark will falter whereas MapReduce performance is better.

  • The solution is not for speedy processing: 

Where processing speed is not critically important Hadoop MapReduce is a viable and economical answer. Ex: If data can be processed at nights.

Where Spark is useful:

  • Rapid processing of data: 

Spark’s processing speeds are within the memory and about 10 fold better in terms of storage data and a 100 fold in terms of RAM data.

  • Repetitive data processing:

Spark’s RDDs allow it to map all operations with the memory. MapReduce will read and write the resultant set to the disk.

  • Instantaneous processing:

Spark enables such processing if instantaneous decision-making is required.

  • Processing of Graphs:

Spark scores in repetitive iterative tasks as in graphs because of its inbuilt API GraphX.

  • Machine learning:

Unlike MapReduce, Spark has an inbuilt ML library. MapReduce needs an ML library to be provided by an outside source to execute the same task. The library has many innovative algorithms that both Spark and MapReduce use while computing.

  • Combining datasets:

Spark is speedier and can combine data sets at high speeds. In comparison, MapReduce is better at combining very big data sets albeit slower than Spark.

Conclusion:

Spark outperforms Hadoop with real-time iterative data processing in memory in

  • Segmentation of customers demonstrating similar patterns of behaviour thus providing better customer experiences.
  • Management of risks in decision-making processes.
  • Detection of fraud in real-time is possible due to its ML library of algorithms being trained on data that is historical and inbuilt. 
  • Analysis of industrial big-data analysis in machinery breakdown is a plus feature of Spark.
  • It is compatible with Hive, RDDs and other Hadoop features.  
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