Today, most businesses need to work with huge amounts of data and that’s why it’s really important to find a quality IT service provider, Nowadays, conventional massively parallel systems such as Hadoop are built on batch calculation models that allow them to combine the features of disk processing with cluster scalability to deal with problems by definition that are simple to parallelized. However, in this article, we are going to see the battle Spark vs. Mapreduce. It’s going to be really interesting for businesses.
As you may know, MapReduce is the model that is at the source of Hadoop. Despite its simplicity, it is not suitable for all issues, especially those involving interactive and iterative treatment. Indeed, the MapReduce was designed to run as a direct acyclic graph with 3 vertices:
Even if batch models such as MapReduce make it possible to make the most of the “convenient” feature of clusters, their main flaw is that they are not adapted for certain applications, especially those that reuse data through multiple operations such as most statistical learning algorithms, most iterative, and interactive data analysis queries.
At the same time, Spark provides a satisfactory response to these limits thanks to its main data abstraction called RDD (Resilient distributed dataset). The RDD is a “collection” of elements partitioned and distributed through the cluster nodes. Thanks to the RDD, Spark manages to excel in iterative and interactive tasks while maintaining scalability and tolerance to cluster failures.
How to Correctly Use Spark Resilient Distributed Dataset?
Spark exposes or makes available to users RDDs through an API developed in Scala (basic language), Java, or Python. Data sets in RDDs are represented as objects (class instances) and transformations are invoked using the methods of these objects. Moreover, the functional aspect of Scala lends itself very well to this style of operation.
To use Spark, you write a pilot program (a driver) that implements the high-level control flow of your application and launches various operations in parallel. Spark provides 2 main abstractions for parallel programming in a programming language:
- RDD transformations
- parallel operations on these RDDs.
In fact, using RDDs is equivalent to performing transformations based on either localized or non-localized data files on the HDFS, and ends up using “actions”, which are functions that return a value to the application. There’s no doubt that only professional programmers, such as the Visual Flow team, can build this system for your business.
All You Need to Know About Different Phases of the MapReduce
Mapreduce is an algorithmic model that provides a ” divide & conquers” functional programming style that automatically cuts data processing into tasks and isolates them on the nodes of a cluster. As we have already mentioned this division is carried out in 3 phases (or 3 steps):
- a map phase,
- a shuffle phase,
- a reduce phase.
Let’s take a very close look at each of these phases. In the first phase, the data file to be processed has already been partitioned in the HDFS or the distributed file system of the cluster. Each partition of the data is assigned a map task. These Map tasks are actually functions that transform the partition to which each is assigned into key/value pairs.
The way in which input data is transformed into a key/Value is at the user’s discretion. Be careful, for those who work in the development of databases, the term “key” can generate confusion. The keys generated here are not the “keys” in the sense of the “primary key” of relational databases, they are not unique, they are just numbers, arbitrary identifiers that are assigned to the values of the pair.
The specificity, however, is that all identical values in the partition are assigned the same key. To make you better understand this, let’s take the illustration of word counting in a stack of 3 documents.
Once all the Map tasks are completed (i.e. when all the nodes in the cluster have finished executing the Map function assigned to them), the Shuffle phase starts. This phase consists on the one hand of sorting by key, all the key/value pairs generated by the Map phase, and on the other hand of grouping in a list for each key, all its values scattered through the nodes to which the Map function has been assigned.
The Shuffle phase ends with the construction of the files containing the lists of keys/values that will serve as arguments for the Reduce function. The purpose of this phase is to aggregate the values of the keys received by the Shuffle and to vertically join all the files to obtain the final result.
Your users define in the Reduce function the aggregate he/she wants to use, for example, the sum, counting, etc., and what he wants to do with the results: either display them using a “print” statement or load them into a database or send them to another MapReduce job.
Modern Features of Working with Data
Whereas conventional Big Data systems rely on a direct acyclic batch model (such as MapReduce), which is not suitable for iterative calculations such as the majority of data science or machine learning/deep learning algorithms (net of neurons, clustering, k-means, logistic regression, etc.), Spark relies on a particular abstraction called the RDD. The RDD is a collection Where fault tolerance in conventional systems is obtained by replication of data in the cluster, in Spark it is obtained by tracing all the operations necessary to obtain the data present in the RDD. This is why these are called self-resilient and are the foundation of the performance of the Apache Spark framework.
As you can see in Big Data, mastery of Spark is mandatory in most cases. Professional IT services provide you with specialized training that will allow you to become a specialist in the development of Spark applications. Now you know in more detail how these processes work and will be able to choose the one that suits your business the most.