Today, most businesses need to work with large amounts of data and that is why finding a quality IT service provider is really important. Nowadays, traditional massively parallel systems like Hadoop are built on the batch computation model. which allow them to combine the features of discs. Processing with cluster scalability for handling problems by definition simple to parallelized. However, in this article, we are going to see the battle Spark vs MapReduce. This is going to be really interesting for businesses.
As you know, MapReduce is the model that is at the source of Hadoop. Despite its simplicity, it is not suitable for all issues, especially issues that involve interactive and iterative treatments. In fact, MapReduce was designed to run as a direct acyclic graph with 3 vertices:
Even if batch models like MapReduce make it possible to make maximum use of the “convenient” feature of clusters, their main drawback is that they are not optimized for some applications, especially those that perform many operations. Reuse data through algorithms such as most statistical learning algorithms, most iterative and interactive data analysis questions.
At the same time, Spark provides a satisfactory response to these limitations thanks to its core data abstraction called RDD (Resilient Distributed Dataset). RDD is a “collection” of elements partitioned and distributed through cluster nodes. Thanks to RDDs, Spark manages to excel in repetitive and interactive tasks while maintaining scalability and tolerance to cluster failures.
How to properly use Spark Resilient Distributed Dataset?
Spark exposes or makes RDDs available to users through APIs developed in Scala (the native language), Java, or Python. Data sets in RDDs are represented as objects (class instances) and transformations are applied using the methods of these objects. Furthermore, 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 different tasks in parallel. The Spark programming language provides 2 main abstractions for parallel programming:
- rdd transformation
- Parallel operations on these RDDs.
In fact, using RDDs is equivalent to making changes based on localized or non-localized data files on HDFS, and ends up using “actions”, which are functions that return a value to the application. There is 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 MapReduce
MapReduce is an algorithmic model that provides a “divide and conquer” functional programming style that automatically cuts data processing into tasks and separates them onto the nodes of a cluster. As we have already mentioned that this division is done in 3 steps (or 3 phases):
- a map stage,
- a shuffle step,
- one less step.
Let’s take a closer look at each of these steps. In the first step, the data file to be processed has already been partitioned in HDFS or the distributed file system of the cluster. Each partition of the data is assigned a map task. These map functions are actually functions that transform the partition to which each is assigned into key/value pairs.
The way the input data is converted to key/value is at the discretion of the user. Be aware, for people who work in database development, the term “key” can be confusing. The keys generated here are not “keys” in the sense of a “primary key” of a relational database, they are not unique, they are just numbers, arbitrary identifiers assigned to pair values.
However, the uniqueness is that all identical values in a partition are assigned the same key. To give you a better understanding of this, let’s take an example of word count in a stack of 3 documents.
Once all map tasks are completed (that is, when all nodes in the cluster have completed their assigned map tasks), the shuffle phase begins. This step consists of sorting by key, on the one hand, all the key/value pairs generated by the map step, and on the other hand grouping into a list for each key, all of its values scattered through the nodes for which the map works. has been assigned.
The shuffle phase ends with the creation of files that contain lists of keys/values that will serve as arguments for the reduce function. The purpose of this step is to collect the values of the keys obtained by shuffling and to vertically combine all the files to get the final result.
Your user defines in the reduce function the set he/she wants to use, for example, sum, count, etc., and what he/she wants to do with the results: either by using “print” statements to them Display or load them to a database or send them to another MapReduce job.
Modern features of working with data
While traditional Big Data systems rely on a direct cyclic batch model (such as MapReduce), which is not suitable for iterative calculations such as most data science or machine learning/deep learning algorithms (nets of neurons, clustering, k-means, logistic regression, etc.), Spark relies on a special abstraction called RDD. RDD is a collection where fault tolerance is achieved in traditional systems by replication of data across the cluster, in Spark is achieved by tracing all the operations required to retrieve the data contained in the RDD. This is why they are called self-resilient and they are the basis of performance of the Apache Spark framework.
As you can see in Big Data, mastery of Spark is mandatory in most cases. Professional IT Services provides you with specialized training that will allow you to become an expert in developing Spark applications. Now you know in more detail how these processes work and you will be able to choose the one that best suits your business.