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Javalin VS Apache Spark

Compare Javalin VS Apache Spark and see what are their differences

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Javalin logo Javalin

Simple REST APIs for Java and Kotlin

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.
  • Javalin Landing page
    Landing page //
    2022-10-26
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Javalin features and specs

  • Lightweight
    Javalin is a lightweight framework with minimal dependencies, making it easy to integrate into existing projects and reducing overhead.
  • Simplicity
    The framework is simple to use and has a straightforward API, which makes it easier for developers to understand and work with.
  • Kotlin & Java Support
    Javalin natively supports both Kotlin and Java, making it flexible for projects written in either language.
  • WebSocket Support
    It includes built-in support for WebSockets, enabling real-time communication between the client and server without additional dependencies.
  • Extensive Documentation
    Javalin comes with comprehensive documentation and guides, which help developers get up to speed quickly.

Possible disadvantages of Javalin

  • Limited Features
    As a minimalist framework, Javalin may lack some advanced features present in more comprehensive frameworks, requiring additional implementations from developers.
  • Community Support
    While growing, the community around Javalin is not as large as other established frameworks, which may result in fewer resources or third-party libraries.
  • Performance Overhead
    Though lightweight, Javalin may not offer the same level of performance optimization as frameworks specifically designed for high-performance use cases.
  • Limited Middleware
    Compared to other frameworks, the middleware support in Javalin is more limited, potentially requiring additional code for customization.

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.

Javalin videos

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Apache Spark videos

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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 Javalin and Apache Spark)

Javalin

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 Javalin and Apache Spark

Javalin Reviews

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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 Javalin. 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.

Javalin mentions (36)

  • Year After Switching from Java to Go: Our Experiences
    But Javas has so many of these web frameworks?! * Spring (https://spring.io/) * Spring Boot (https://spring.io/projects/spring-boot) * Helidon (https://helidon.io/) * Micronaut (https://micronaut.io/) * Quarkus (https://quarkus.io/) * JHipster (https://www.jhipster.tech/) * Vaadin (https://vaadin.com/) That's just to mention the bigger ones, there's lots of mini frameworks like Javalin (https://javalin.io/) and... - Source: Hacker News / 3 months ago
  • Latudio – a language acquisition app with a listening-oriented approach
    - like Sentences exercise, but you can select your own set of sentences. You can also set goals and view statistics about your progress. None of this would be possible without the great help from hundreds of our contributors [3], who translated, mapped and recorded content. All the content you find in the app was reviewed multiple times by several people and recordings are made by native speakers. No story in the... - Source: Hacker News / 5 months ago
  • Show HN: Donobu – Mac App for Web Automation and Testing
    - Javalin 6 for the web framework (https://javalin.io/). - Source: Hacker News / 7 months ago
  • Spark – A web micro framework for Java and Kotlin
    I'd recommend Javalin (https://javalin.io/) instead. Same idea, only executed better and it is actively maintained. - Source: Hacker News / about 1 year ago
  • Spark – A web micro framework for Java and Kotlin
    SparkJava has an actively developed fork/successor called Javalin[1]. It's straightforward to convert from SparkJava to Javalin. The latter is written in Kotlin, but works fine with ordinary Java. While the rest of the Java world was devolving into annotation hell, AOP and other nightmares, these Java microframeworks showcased what happens when you forego legacy Java and leverage modern Java language features... - Source: Hacker News / about 1 year 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 / 18 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 / 20 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 Javalin and Apache Spark, you can also consider the following products

Spark Framework - Spark Framework is a simple and lightweight Java web framework built for rapid development.

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

vert.x - From Wikipedia, the free encyclopedia

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

Micronaut Framework - Build modular easily testable microservice & serverless apps

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

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