Table of contents
1.
Introduction
2.
A Brief Introduction to Apache Spark
3.
Advantages of Apache Spark
3.1.
Developer Friendly
3.2.
Speed
3.3.
Advanced Analytics
3.4.
Dynamic
3.5.
Multi-Language engine
3.6.
Powerful Engine
4.
Disadvantages of Apache Spark
4.1.
File Management System
4.2.
Lesser Algorithms
4.3.
Manual Optimization Process
4.4.
Small File Issues
4.5.
Window Criteria
4.6.
Handling Back Pressure
5.
Frequently Asked Questions
5.1.
What is Apache Spark?
5.2.
What is the primary difference between Apache Spark and Hadoop?
5.3.
What is the main use of Apache Spark?
5.4.
What is the main difference between Spark and Apache Spark?
5.5.
What are the disadvantages of Apache Spark?
6.
Conclusion
Last Updated: Mar 27, 2024
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Advantages and Disadvantages of Apache Spark

Author Ravi Khorwal
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Introduction

Hello ninjas! You must have heard terms like Apache Spark, big data analysismachine learning, fault tolerance, etc. Today, we will look at the advantages and disadvantages of Apache Spark. We will see various parameters for them.

Advantages and Disadvantages of Apache Spark

A Brief Introduction to Apache Spark

Apache Spark is an engine for the analysis of data.

  • It is used for large-scale datasets.
     
  • It is a multi-language and open-source system.
     
  • It is an interface for data science to program clusters, fault tolerance, etc.
     
  • It executes machine-learning algorithms on single-node machines.

 

It is a processing system for big data workloads. It uses in-memory caching and does query optimization for fast analysis of data. It is fast, developer-friendly, and can handle various workloads.

Let us look at the benefits and limitations of using Apache Spark.

S.No.

Benefits

Limitations

1

Developer Friendly

File Management System

2

Speed

Lesser Algorithms

3

Advanced Analytics

Manual Optimization Process

4

Dynamic

Small File Issues

5

Multi-Language Engine

Window Criteria

6

Powerful Engine

Handling Back Pressure

Let us study these in detail.

Advantages of Apache Spark

Now, let us discuss the advantages of Apache Spark.

Developer Friendly

Apache Spark has developer-friendly tools. It has APIs that are easy to work with over large datasets. It has above 80 high-level operators making complex computations fast and making parallel simulations.

Speed

Apache Spark is quick. And when dealing with Big Data, computation time matters greatly. It uses RAM(in-memory) computation. It can work with petabytes of data faster than Hadoop.

Advanced Analytics

Along with ‘MAP’ and ‘reduce’, it supports ML(Machine Learning) algorithms, graphs, SQL queries, streaming data, etc.

Dynamic

It is dynamic. It can make applications run in parallel. It is because Apache Spark offers more than 80 high-level operators.

Multi-Language engine

It supports multiple languages like JavaScalaPython, etc.

Powerful Engine

It has low latency in-memory data processing ability and thus can handle analytical and computational challenges. It has built libraries for ML and graph algorithms.

Disadvantages of Apache Spark

Let us discuss some drawbacks of Apache Spark.

File Management System

It does not have a file management system of its own. It depends on other cloud-based systems or Hadoop for file management.

Lesser Algorithms

The Machine Learning library MLib in Apache Spark has fewer algorithms. The number of available algorithms is less.

Manual Optimization Process

There is no automatic optimization process in Apache Spark. You have to optimize the code or dataset by yourself. A fixed number of partitions is required for parallelization. This is to be fixed and passed manually.

Small File Issues

If one uses Apache Spark with Hadoop, small file problems arise. This is because Hadoop Distributed File System supports only a limited number of large files and a large number of small files.

Window Criteria

All the data in Apache Spark is divided into time intervals. So, there will be time-based criteria rather than record-based criteria.

Handling Back Pressure

Back Pressure occurs when much data is stacked in the input/output switch when the buffer is already full to receive any data. Once the buffer is empty, only then further data is processed. Apache Spark cannot deal with this back pressure automatically, which is to be done manually.

Also see, Recursive Relationship in DBMS

Frequently Asked Questions

What is Apache Spark?

Apache Spark is an engine for data analysis for large-scale datasets. It is a multi-language and open-source system. It is an interface for data science to program clusters, data parallelism, and fault tolerance. It executes machine-learning algorithms on single-node machines.

What is the primary difference between Apache Spark and Hadoop?

The main difference is that Apache Spark has MLib, its machine-learning library. In contrast, Hadoop is interfaced with external sources for it. Also, Apache Spark is faster than Hadoop.

What is the main use of Apache Spark?

The primary use of Apache Spark is that it is an open-source engine that is a processing system for big data workloads. It uses in-memory caching and does query optimization for fast analysis of data.

What is the main difference between Spark and Apache Spark?

Spark is a Framework that comes under Microframeworks. In comparison, Apache Spark is an engine for the analysis of big data workloads. It is a Big Data Tool.

What are the disadvantages of Apache Spark?

It has no file management system of its own, no real-time processing support, has issues with small files, and has a lesser number of algorithms. These are the key disadvantages of Apache Spark.

Conclusion

Apache Spark is an engine for the analysis of data for large-scale datasets. It is a multi-language and open-source system. In this article, we studied the benefits and drawbacks of Apache Spark.

If you want to widen your horizon on this topic, do read the following:-

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