Updated June 15, 2023
Differences Between Mahout vs Spark
Machine learning is the new boom in the software industry which helps in training the computer to think, organize and process data by itself. The main intent of machine learning is that the machine learns to observe data, extract important information from it and grasp on its own to predict, recommend or alter any action without any human mediation. This requires various algorithms over varied systems. For the ease of these algorithms, Apache has come up with frameworks Mahout vs Spark, which in its different ways helps in implementing machine learning in a better way.
Head To Head Comparison Between Mahout vs Spark (Infographics)
Mahout vs Spark both has its advantages and disadvantages. Let us have a look at the major differences between them.
Key Differences Between Mahout vs Spark
Some key differences are explained below between Mahout vs Spark
The variations in both these machine learning frameworks are as follows:
- Mahout mainly focuses on extensively gathering data, separating it, and having a library of machine learning algorithms that is built on Hadoop. It helps in improving performance when there is a large amount of data. Spark on the other hand mainly focuses on accelerated retrieval of data when compared to MapReduce. It also has an additional plus that it can be used with Scala, Java, and Python.
- Mahout provides precise recommenders that help in recommending various trends like customers buying a book, web visitors watching a particular video, visibility of a map, and ratings of a particular product that is on sale on an e-commerce website. A mahout has classifiers that help in high-quality implementation. It uses sequential processing instead of parallel processing which results in slow retrieval of data. It provides various algorithms in a systematic way. It also has an information retrieval library named Lucene. Spark, on the other hand, uses MLlib which helps in really fast retrieval of data. It is primarily used for sophisticated analytics. It also supports predictions about data which leads to business growth exponentially. It can run along with other Hadoop tools like Pig and Hive. It is an iterative algorithm that helps in the fast running and retrieval of data on the Hadoop cluster. As a result, its algorithms are much faster when compared to Mahout’s equivalent algorithms.
- A mahout has its own advantages with having different components like math library, clustering, decomposition, and recommendations. The math library provides operations like basic linear algebra, statistical sampling, good clustering, and extensibility, especially for sparse data. Though mahout does not provide us with the expected speed, a complete set of algorithms, and proper optimization of data, it helps in giving as legitimate results. Spark MLlib consists of two packages namely, MLlib and ML. MLlib comprises of original APIs built on top of RDDs whereas, ML provides higher-level API built on data frames for ML pipelines which is faster than MLlib. As mentioned above, Spark is the transformation operation of RDD which will obtain a predicate as a part of an argument. Spark will go ahead and pass this argument and produce on source RDD. The predicate which does not satisfy will be filtered out and a new RDD will be created of those elements which satisfy the predicate arguments passed. The filter transformation used is lazily evaluated.
- A mahout has various clustering algorithms like Canopy, Mean Shift, etc. Clustering algorithms need input and they can organize and group the cluster on its own. K means when N is a number of elements and K is a number of clusters, it searches for k centroid points and groups them together. Spark comes up with clustering algorithms, K-means being the most commonly used algorithm. The MLlib consists of lateral variants of K-means. With all these advantages and speedy performance, Spark has a few disadvantages where it does not support real-time processing of data. Also when a small file is passed, then efficiency is hampered as RDDs need to be re-partitioned. In addition to this, it consumes memory and the issue is not resolved in a user-friendly manner.
- Filtering in Mahout is not supervised and the clusters help in determining the group. This grouping is done based on size and it is not labeled. The recommendation algorithm finds a similarity between users and items and accordingly filtering is done. In Spark, recommendations use MLlib to build models of billions of records. It implements Alternating Least Squares which helps in reducing errors in observed ratings
Mahout vs Spark Comparison Table
Below is the Comparison table between Mahout vs Spark
|Basis for comparison||Mahout||Spark|
|Basic difference||Mahout is a framework that helps in collective refining, gathering, and segregating data to carry out extensible machine learning algorithms.||Spark is an open-source processing engine built to speed up the process of analytics. It speeds up the process of analyzing large amounts of data when compared with MapReduce.|
|Use||Data that is stored in Hadoop needs to have a meaningful outcome. Mahout provides this to data science tools by automatically finding patterns in big data sets.||Spark helps in handling large amounts of data with speed.|
|Framework||It is a Hadoop MapReduce framework||Spark provides MLlib which helps in speedy retrieval of data.|
|Clustering||Mahout organizes data on the basis of similarity and groups all data together. Each item is grouped in naturally occurring associations.||1) Spark clustering depends on the MLlib package which contains the following models:
2) K- means
3) Gaussian Mixture
4) Power Iteration Cluster
5) Latent Dirichlet allocation
6) Bisecting K means
7) Streaming K means
|Collective Filtering||Filtering in Mahout is based on the Taste framework. It helps in processing efficiency and integration with various web applications.||Spark filter uses the transformation operation of Resilient Distributed Datasets (RDD). It uses some parameters with Boolean values of true and false which is stored in predicate function.
|Classification of data||Mahout creates a new model based on existing learning algorithms. Post creation this model is tested and its class is determined.||In Spark, we need to initialize context and make the necessary transformation. Dataset is to be created and loaded into a data frame. The process includes reading documents, creating a data frame and slicing the data into the frame.|
Mahout vs Spark both provides machine learning a cutting edge and assist prediction and recommendation in an easier way. Spark provides MLlib which helps in rapid data retrieval. Mahout, on the other hand, handles large amounts of data and helps in easier content clustering. Data frames in Spark use APIs and hence can be used across languages. Mahout focuses on algorithms that will help in exponential growth in business. Both frameworks hence have their own pros and cons, with Spark having extra features like batch processing, which uses data to predict the future of a business and grow exponentially.
This has been a guide to Differences Between Mahout vs Spark, their Meaning, Head to Head Comparison, Key Differences, Comparison Table, and Conclusion. You may also look at the following articles to learn more –