Well, today Spark and MapReduce are often debated. About Apache Spark talk and write. Professionals use it more and more often. Analysts call it the next big thing in the big data field. So, mapreduce vs. spark? Today, we will try to find the answer.
As for Spark, its creators call it “a high-performance system for parallel processing of large-scale data.” It works on clusters of the Hadoop environment, the use of which made it possible to significantly minimize funds for storing and processing data.
Savings are achieved by the fact that the framework provides good horizontal scalability: computing power can be increased by adding inexpensive mass-level nodes instead, to spend unimaginable funds to buy and maintain powerful high-performance servers and storage systems.
Previously, the main technology for parallelizing tasks running on Hadoop was MapReduce, developed by Google. However, not so long ago, she lost her monopoly, and Spark began to appear among her main competitors. Experts say that the latter surpasses the classic technology that has become classic. Especially in the speed of work. In addition, Spark can process streaming data in real-time. And they are much easier to manage. As well, for the best results, you can ask for the help of professionals, like Visual Flow.
MapReduce vs. Spark: the speed and other features
The work of MapReduce corresponds to the title. There is a cluster consisting of a certain number of nodes on which the procedures are executed. First procedure – Map, pre-processing of input data. The main node receives data that is distributed to other nodes for pre-processing. During the second procedure (Reduce), the received data is rolled up, after which the result is passed to the main node, which forms the final solution to the problem.
MapReduce has proven to be very effective for complex package problems. However, to ensure fault tolerance, MapReducev primarily processes information stored on hard drives. It also uses a single-pass calculation model. All this makes the platform not too suited for low latency applications and iterative computing, such as graph algorithm implementations or machine learning, – says Justin Kestelyn (Cloudera). He also argues that the Apache Spark framework breaks down these limitations by generalizing the MapReduce computational model.
Spark can perform batch task processing many times faster by reducing the number of read/write operations on the hard disk.
The framework can track the data of each operator individually, and process and securely store them in RAM. The developers claim that this approach greatly increases the speed of solving iterative tasks compared to MapReduce.
Stream data processing
Unlike MapReduce, Spark can handle not only stored data packets but also real-time streams.
Moreover, the processing of packets and data streams, as well as machine learning, can take place on the same data cluster. This feature allows you to greatly simplify the processes of deployment, maintenance, and development of applications.
More about MapReduce
Apache Hadoop MapReduce is a software platform for creating jobs that handle large amounts of data. Input data is broken down into independent blocks, which are then processed in parallel on the cluster nodes. MapReduce has two functions.
Mapper (Mapper Module) – receives input data, analyzes it (usually with filtering and sorting), and transmits tuples (pairs “key-value”).
Reducer(Reducer) – Accepts tuples formed in the mapping module and performs a summary operation that generates a smaller result, combining data from the mapping module.
The output of this job represents the frequency of use of each word in the text:
The mapping procedure takes each line from the input text as input data and breaks it down into words. It generates a pair of “key-value” each time a word is encountered, followed by 1. Before the reducer is sent, the output is sorted. The reducer then summarizes these separate counters for each word and produces one “key-value” pair containing the word, followed by the frequency of its use.
MapReduce can be implemented in different languages. Java is the most common implementation used in this document as an example.
More about Spark
Apache Spark is an integrated computer system with a set of libraries for parallel data processing on clusters of computers.
Reasons for using Spark:
- Spark is an all-in-one for working with big data. Spark is designed to help solve a wide range of data analysis tasks, from simple data loading and SQL queries to machine learning and streaming computing, using the same computing tool with an unchanged set of APIs.
- Speed. Apache Spark is a computing tool that runs at the speed of light. By reducing the amount of read-write to disk and storing intermediate data in memory, Spark runs applications 100 times faster in memory and 10 times faster on disk than MapReduce.
- Easy to use. Many Spark libraries make it easy to perform many basic high-level operations with RDD.
- Iterative processing. If the task condition is to process data over and over again, Spark will defeat Hadoop MapReduce. Spark RDD activates many memory operations, while Hadoop MapReduce must write intermediate results to disk.
- lmost real-time processing. If a business needs immediate insights, then you should use Spark and its processing directly in memory.
- raph processing. The computational model Spark is good for iterative computing, which is often needed in graph processing. And Apache Spark has GraphX, the API for graph computation.
Wrapping it up
All the mentioned features of Spark look promising and, given the support and attention that this project receives, its future looks bright.
Apache Spark is already used by many large companies. And some time ago, the Apache Software Foundation (ASF), which supports various open-source projects, announced the transition of Apache Spark to the top-level project (TLP).
Therefore, experts are sure that Spark will become a strong player in the field of big data. Although due to the unchanging nature of the basic Spark abstraction (RDD, Resilient Distributed Datasets), it is not a universal solution. Even the authors themselves admit that Spark is not best suited for those operations that require changing a small number of data records at the same time. On the other hand, it may be difficult to use Spark if you are not tech-savvy. Luckily, there is an attractive solution for everyone who looks for it. It is to rely on a well-known team of IT experts, like Visual Flow. Contact them to get the answers to all the questions you may have.
