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Kakoune VS Apache Spark

Compare Kakoune VS Apache Spark and see what are their differences

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Kakoune logo Kakoune

Vim inspired — Faster as in less keystrokes — Multiple selections — Orthogonal design

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.
  • Kakoune Landing page
    Landing page //
    2023-10-13
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Kakoune features and specs

  • Modal Editing
    Kakoune uses a modal editing style similar to Vim, which can be more efficient for experienced users who prefer to keep their hands on the keyboard.
  • Interactive and Asynchronous
    Kakoune is designed to be both interactive and asynchronous, providing immediate feedback for commands which can enhance the editing experience.
  • Selections
    Kakoune treats text editing as a multiple selections-oriented operation, enabling powerful, simultaneous edits across multiple text locations, which can speed up complex text manipulations.
  • Simplicity in Configuration
    Kakoune’s configuration files are written in a simple, declarative syntax, making it easier for users to customize their setup without extensive scripting.
  • Performance
    Kakoune is designed with performance in mind, resulting in a lightweight and fast editor even with large files.
  • Extensible Plugin System
    Kakoune supports various plugins and has a community-driven set of extensions, allowing users to extend functionality as needed.
  • Language Support
    Built-in support for syntax highlighting and other language-specific features for many programming languages.

Possible disadvantages of Kakoune

  • Learning Curve
    Kakoune has a steep learning curve, especially for users not familiar with modal editing or coming from different types of text editors.
  • Plugin Ecosystem
    While Kakoune has an extensible plugin system, its plugin ecosystem is not as mature or extensive as more established editors like Vim or Emacs.
  • Limited GUI Support
    Kakoune primarily operates in the terminal, with limited graphical user interface support, which might be a drawback for users who prefer more visual editing environments.
  • Smaller User Base
    Kakoune has a smaller user base compared to more mainstream editors, potentially resulting in fewer community resources, tutorials, and third-party tool integrations.
  • Reliance on Command Line
    Heavy reliance on command-line operations can be intimidating or cumbersome for users who are not comfortable with the terminal.
  • Limited IDE Features
    Kakoune lacks some of the advanced integrated development environment (IDE) features out-of-the-box, such as integrated debugging or project management tools.

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.

Kakoune videos

Kakoune Is A More Efficient Text Editor

Apache Spark videos

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 Kakoune and Apache Spark)
Text Editors
100 100%
0% 0
Databases
0 0%
100% 100
IDE
100 100%
0% 0
Big Data
0 0%
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 Kakoune and Apache Spark

Kakoune 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...
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...
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...

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Kakoune. 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.

Kakoune mentions (10)

  • Show HN: Ki Editor
    Fascinating idea! To summarize for those who know [Kakoune](https://github.com/mawww/kakoune), the idea is that every command has the form ["selection mode" -> "movement" -> "action"](https://ki-editor.github.io/ki-editor/comparisons/modal-editors.html) instead of Kakoune's movement->action. So, instead of having separate commands for "next character", "next word", "next structural element", there is one command... - Source: Hacker News / 8 months ago
  • Helix: Release 24.03 Highlights
    Helix's modal editing is based on Kakoune's modal editing which is like an evolution to Vim's modal editing. You can think of it as being always in selection (visual) mode. https://github.com/mawww/kakoune?tab=readme-ov-file#selectio.... - Source: Hacker News / about 1 year ago
  • I don't need your query language
    You might like kakoune (https://github.com/mawww/kakoune), which does exactly that: first you select the range (which can even be disjoint, e.g. All words matching a regex), then you operate on it. By default, the selected range is the character under cursor, and multiple cursors work out of the box. It also generally follows the Unix philosophy, e.g. By using shell... - Source: Hacker News / almost 2 years ago
  • I use nano BTW.
    It might be worth checking out kakoune if you are experimenting with editors. It’s supposed to be equally powerful to vim but much easier to learn. Source: over 2 years ago
  • Mle is a small, flexible, terminal-based text editor written in C
    For that, try Kakoune[1], which is modal with a mostly-postfix language instead of vi's usually-prefix one and uses this to also be a multiple-selections editor with immediate visual feedback. It falls too much into the uncanny valley of almost-but-not-quite-vi for some people, though. [1] https://kakoune.org/, https://github.com/mawww/kakoune. - Source: Hacker News / over 2 years ago
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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 / 21 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 / 23 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
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What are some alternatives?

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

Vim - Highly configurable text editor built to enable efficient text editing

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

Light Table - Light Table is a new interactive IDE that lets you modify running programs and embed anything from...

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

VS Code - Build and debug modern web and cloud applications, by Microsoft

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