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vert.x VS Apache Spark

Compare vert.x VS Apache Spark and see what are their differences

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vert.x logo vert.x

From Wikipedia, the free encyclopedia

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.
  • vert.x Landing page
    Landing page //
    2022-06-12
  • Apache Spark Landing page
    Landing page //
    2021-12-31

vert.x features and specs

  • Performance
    Vert.x is designed to be highly performant, leveraging a non-blocking, event-driven architecture which makes it suitable for handling many concurrent requests efficiently.
  • Polyglot
    Vert.x supports multiple programming languages, including Java, Kotlin, JavaScript, Groovy, Ruby, and more. This allows developers to use the language they are most comfortable with.
  • Modular
    Vert.x is modular and lightweight, enabling developers to use only the parts they need and easily integrate with other libraries and tools.
  • Reactive Ecosystem
    Vert.x provides a robust ecosystem for building reactive applications, including asynchronous APIs, event bus, and reactive streams.
  • Scalability
    The architecture of Vert.x allows for easy scaling both vertically and horizontally, as it can efficiently manage resources and load balancing.

Possible disadvantages of vert.x

  • Learning Curve
    The event-driven and asynchronous nature of Vert.x can be challenging for developers who are accustomed to traditional synchronous programming paradigms.
  • Community and Resources
    While growing, the Vert.x community is smaller compared to more established frameworks, which may result in fewer resources, tutorials, and third-party integrations.
  • Complexity
    As applications grow in size, managing asynchronous code and callback structures can become complex, requiring careful planning and architecture decisions.
  • Tooling
    Tooling support, while improving, may not be as comprehensive as other established frameworks, which might impact development speed and debugging.

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.

vert.x videos

+ Add

From Zero to Back End in 45 Minutes with Eclipse Vert.x

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 vert.x and Apache Spark)

vert.x

Apache Spark

Web Frameworks
100 100%
Web Frameworks
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
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 vert.x and Apache Spark

vert.x Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
As Vert.x is an event-driven and non-blocking framework, it can handle a lot of concurrencies using only a minimal number of threads. Vert.x is also quite lightweight, with the core framework weighing only about 650 KB. It has a modular architecture that allows you to use only the modules you need so that your app can stay as slick as possible. Vert.x is an ideal choice if...
Source: raygun.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 – 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 should be more popular than vert.x. It has been mentiond 70 times since March 2021. 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.

vert.x mentions (29)

  • Java News: WildFly 36, Spring Milestones, and Open Liberty Updates
    The sixth release candidate of Eclipse Vert.x 5.0.0 provides support for the Java Platform Module System and a new VerticleBase class. Further details are available in the release notes. - Source: dev.to / 26 days ago
  • Rust, C++, and Python trends in jobs on Hacker News (February 2025)
    I see your point, but I still don't think you can just say "If you want to get get a job as a Go developer, you must know gRPC." Even more so for Kafka, I've only heard about it being popular in the Java world. You can't even say "If you want to get a job as a Java developer, you must know Spring." Nowadays, sane Java projects use https://vertx.io, it's just too good. I would argue that Spring is for legacy... - Source: Hacker News / 3 months ago
  • Error handlers and failure handlers in Vert.x
    Vert.x is a toolkit for developing reactive applications on the JVM. I wrote a short introductory post about it earlier, when I used it for a commercial project. I had to revisit a Vert.x-based hobby project a few weeks ago, and I learned that there were some gaps in my knowledge about how Vert.x handles failures and errors. To fill those gaps, I did some experiments, wrote a few tests, and then wrote this blog post. - Source: dev.to / 6 months ago
  • Spark – A web micro framework for Java and Kotlin
    Https://vertx.io/ It's actively maintained with full time developers, performant, supports Kotlin out of the box, and has more features? - Source: Hacker News / about 1 year ago
  • Reactive database access on the JVM
    Hibernate Reactive integrates with Vert.x, but an extension allows to bridge to Project Reactor if wanted. - Source: dev.to / almost 2 years ago
View more

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 19 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 21 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing vert.x and Apache Spark, you can also consider the following products

Micronaut Framework - Build modular easily testable microservice & serverless apps

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

Javalin - Simple REST APIs for Java and Kotlin

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

helidon - Helidon Project, Java libraries crafted for Microservices

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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