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Apache Struts VS Apache Spark

Compare Apache Struts VS Apache Spark and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Apache Struts logo Apache Struts

Apache Struts is an open-source web application framework for developing Java EE web applications.

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Apache Struts Landing page
    Landing page //
    2022-04-27
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Apache Struts features and specs

  • Robust Framework
    Apache Struts is a mature and well-established framework for Java web applications, providing stable and reliable tools for enterprise-level applications.
  • MVC Architecture
    Struts adheres to the Model-View-Controller (MVC) design pattern, which separates business logic, presentation, and navigation, making code maintenance and development easier.
  • Extensive Documentation
    Struts has comprehensive documentation and a wealth of online resources, including tutorials, community forums, and user guides, which can support developers throughout their projects.
  • Rich Tag Library
    It comes with a rich set of custom tags that enhance the JSP (JavaServer Pages) to create dynamic web content easily.
  • Plugin Support
    Apache Struts supports various plugins that can extend its functionality, allowing developers to integrate additional features without much effort.

Possible disadvantages of Apache Struts

  • Steep Learning Curve
    New developers might find Struts challenging to learn due to its complexity and the need for a good understanding of the MVC architecture and Java web application development.
  • Configuration Overhead
    The framework requires extensive XML configuration, which can be cumbersome and time-consuming compared to convention-over-configuration frameworks.
  • Performance
    Struts can be slower than some newer, lighter frameworks due to its broader feature set and the overhead associated with its extensive configuration.
  • Security Vulnerabilities
    Struts has had notable security vulnerabilities in the past. Although patches and updates are available, it necessitates proactive monitoring and maintenance.
  • Outdated Compared to Modern Frameworks
    With the advent of modern frameworks like Spring MVC and JavaServer Faces, some developers consider Struts to be less up-to-date with the latest web development standards and practices.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of Apache Struts

Overall verdict

  • Apache Struts is a robust framework, suitable for building Java-based web applications, but it's crucial to stay vigilant regarding security updates.

Why this product is good

  • Apache Struts is known for its MVC framework, which is useful for creating well-structured and maintainable Java applications. It provides a range of comprehensive features like a flexible tag library, integration with other Java frameworks, and a strong support community. However, it has faced some high-profile security vulnerabilities in the past, underscoring the importance of keeping the framework timely updated.

Recommended for

  • Organizations developing enterprise-level Java applications
  • Developers familiar with Java and looking for a robust MVC framework
  • Teams interested in integrating their web applications with other Java technologies

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Apache Struts videos

+ Add

Finding and Fixing Apache Struts CVE-2017-5638 with Black Duck Hub

More videos:

  • Review - Apache Struts 2 - remote command execution
  • Review - Dark ambient drone music | Vulnerable Apache Struts installation under attack (Java, Jakarta)

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Apache Struts and Apache Spark)

Apache Struts

Apache Spark

Developer Tools
100 100%
Developer Tools
0% 0
Databases
0 0%
Databases
100% 100
Web Frameworks
100 100%
Web Frameworks
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Struts and Apache Spark

Apache Struts Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
You can integrate Struts with other Java frameworks to perform tasks that arenโ€™t built into the platform. For instance, you can use the Spring plugin for dependency injection or the Hibernate plugin for object-relational mapping. Struts also allows you to use different client-side technologies such as Jakarta Server Pages to build the frontend of your application.
Source: raygun.com
10 Best Java Frameworks You Should Know
Followed by Struts Framework, the next leading framework currently being used in the IT industry is the Wicket.
Source: www.simplilearn.com

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing โ€“ batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Apache Struts. While we know about 80 links to Apache Spark, we've tracked only 2 mentions of Apache Struts. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Struts mentions (2)

Apache Spark mentions (80)

  • MLOps Lifecycle: Stages, Workflow, and Best Practices
    Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / 2 months ago
  • 7 Free Tools for Data Pipeline Reconciliation and Cross-Source Validation
    Apache Spark provides distributed in-memory data processing and is the appropriate tool when the data set to be reconciled does not fit in a single machine's memory, or when parallelizing the comparison across a cluster would reduce runtime from hours to minutes. - Source: dev.to / 3 months ago
  • Why Apache IoTDB Is Written in Java: A Decade of Engineering Trade-offs
    When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVMโ€”such as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 5 months ago
  • I Scraped 47M+ Hacker News Items Into Parquet Files โ€“ Here's What I Discovered About HN's Hidden Data Patterns
    For handling even larger datasets or building production applications, Apache Spark provides excellent Parquet support with distributed processing capabilities. - Source: dev.to / 5 months ago
  • Show HN: Spark โ€“ Zero-config IoT deployment tool written in Rust
    You may want to consider renaming this project. The name "Spark" already refers to: A popular data analytics framework of the Apache Foundation: https://spark.apache.org/ A subset of the Ada programming language used for formal verification: https://learn.adacore.com/courses/intro-to-spark/chapters/01_Overview.html An Nvidia AI development system: https://www.nvidia.com/en-us/products/workstations/dgx-spark/. - Source: Hacker News / 7 months ago
View more

What are some alternatives?

When comparing Apache Struts and Apache Spark, you can also consider the following products

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Grails - An Open Source, full stack, web application framework for the JVM

Hadoop - Open-source software for reliable, scalable, distributed computing

Spark Mail - Spark helps you take your inbox under control. Instantly see whatโ€™s important and quickly clean up the rest. Spark for Teams allows you to create, discuss, and share email with your colleagues

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

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