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Apache Spark VS Grails

Compare Apache Spark VS Grails and see what are their differences

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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.

Grails logo Grails

An Open Source, full stack, web application framework for the JVM
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Grails Landing page
    Landing page //
    2021-10-17

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.

Grails features and specs

  • Rapid Development
    Grails promotes rapid development through its convention-over-configuration approach and powerful features, like scaffolding and GORM (Grails Object Relational Mapping), which speed up the coding process significantly.
  • Groovy Language Integration
    Being built on Groovy, a dynamic language for the Java platform, Grails provides the flexibility and expressiveness of Groovy while maintaining compatibility with Java libraries and tools.
  • Spring Boot Foundation
    Grails is built on top of Spring Boot, leveraging its robust dependency injection, security, and configuration management capabilities, which ensures the stability and scalability of applications.
  • Plugin Ecosystem
    Grails offers a rich ecosystem of plugins for extending the framework. This allows developers to easily integrate various functionalities without reinventing the wheel.
  • Convention-over-Configuration
    The framework emphasizes conventions for many aspects of the development process, reducing the need for extensive configuration and allowing developers to focus more on business logic.
  • Strong Community and Documentation
    Grails has a strong community and extensive documentation, which make it easier for developers to find solutions to problems, share knowledge, and get support.

Possible disadvantages of Grails

  • Learning Curve
    Despite its many conveniences, Grails has a steep learning curve, particularly for developers not familiar with Groovy or the underlying Spring framework.
  • Performance Overheads
    The abstraction layers and dynamic aspects of Groovy may introduce performance overheads, making Grails applications potentially slower than those built with more streamlined frameworks.
  • Limited Flexibility
    While Grails' conventions can be beneficial, they can also limit flexibility, forcing developers into certain patterns and practices even when they may not be ideal for all scenarios.
  • Less Popularity
    Compared to other frameworks like Spring Boot alone or Hibernate, Grails has a smaller market share, leading to fewer job opportunities and a smaller pool of resources.
  • Complex Debugging
    The dynamic nature of Groovy can sometimes make debugging more complex and challenging, especially for those accustomed to statically-typed languages like Java.
  • Dependency Management Issues
    Managing dependencies in Grails can occasionally be problematic, particularly when dealing with transitive dependencies or conflicts between plugins.

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

Grails videos

+ Add

BUYING MY SNEAKER GRAILS ON STOCKX!

More videos:

  • Review - TOP 5 SNEAKER GRAILS
  • Review - Top 5 Grails with Superpower Review | Berkfamily54comics

Category Popularity

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

Apache Spark

Grails

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

User comments

Share your experience with using Apache Spark and Grails. For example, how are they different and which one is better?
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Reviews

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

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

Grails Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
Although you have to write your code in Groovy, Grails works well with other Java-related technologies such as the Java Development Kit, Jakarta EE containers, Hibernate, and Spring. Under the hood, Grails is built on top of Spring Boot to make use of its productivity-friendly features like dependency injection. With Grails, you can achieve the same results with much less...
Source: raygun.com
10 Best Java Frameworks You Should Know
Grails is a web application framework developed using Apache Groovy Language. It is a Framework that follows the coding by convention method which provides a Standalone environment. Also, it supports instance development with no configuration required.
Source: www.simplilearn.com

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Grails. While we know about 70 links to Apache Spark, we've tracked only 6 mentions of Grails. 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 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

Grails mentions (6)

  • Mastering Node.js
    Trails is a modern web application framework. It builds on the pedigree of Rails and Grails to accelerate development by adhering to a straightforward, convention-based, API-driven design philosophy. - Source: dev.to / 10 months ago
  • RIFE2 web framework under development
    And frameworks like Grails build conventions and helpers on top of Spring. Source: over 2 years ago
  • Web app in Java with Template Engine
    I don't have any direct experience and am only suggesting it because you mentioned RoR...But Grails (https://grails.org/) is basically the JVM version of RoR (Groovy on Rails -> Grails). Source: over 2 years ago
  • Libraries other than Spring Boot for creating web APIs
    Grails - Spring under the hood. Much less boilerplate. Opinionated, which helps keep things consistent. Uses Spring-Security plugin for authentication. Source: almost 3 years ago
  • "get-it-done" MVC web framework like Django in Java?
    Also, Grails, which a Rails like framework build on Groovy, a JVM scripting language. Source: over 3 years ago
View more

What are some alternatives?

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

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

Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...

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

Django - The Web framework for perfectionists with deadlines

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

Meteor - Meteor is a set of new technologies for building top-quality web apps in a fraction of the time.

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