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Apache Spark VS RegExr

Compare Apache Spark VS RegExr 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.

RegExr logo RegExr

RegExr.com is an online tool to learn, build, and test Regular Expressions.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • RegExr Landing page
    Landing page //
    2023-07-28

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.

RegExr features and specs

  • User-Friendly Interface
    RegExr offers an intuitive and visually appealing interface that makes it easy for users to write, test, and understand regular expressions.
  • Real-time Feedback
    Changes to the regular expression and input text are reflected immediately, allowing users to see the effects of their adjustments in real-time.
  • Built-in Cheatsheet
    RegExr includes a handy cheatsheet that provides quick access to common regex patterns and syntax, making it easier for users to learn and reference rules.
  • Community Examples
    Users can explore and share community-generated regex patterns, which can serve as valuable examples or starting points for creating their own regex.
  • Detailed Explanation
    Each part of the regex pattern can be hovered over to display detailed tooltips explaining its function, aiding in the understanding of complex expressions.
  • Cross-Platform Accessibility
    As a web-based tool, RegExr can be accessed from any modern browser without the need for installation, making it convenient to use on multiple devices.

Possible disadvantages of RegExr

  • Limited Offline Use
    Since RegExr is a web-based application, it requires an internet connection, limiting its utility for users who need to work offline.
  • Learning Curve
    While the tool is user-friendly, users still need to have a foundational understanding of regular expressions to use RegExr effectively.
  • Performance Issues
    For extremely large inputs or very complex regular expressions, the tool may experience performance lags or slowdowns.
  • Limited Advanced Features
    RegExr may lack some advanced features found in more specialized or professional regex tools, such as integration with development environments or extensive scripting capabilities.
  • Privacy Concerns
    Users inputting sensitive data need to be cautious, as the web-based nature of the tool could raise privacy or data security concerns.

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

RegExr videos

No RegExr videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Apache Spark and RegExr)
Databases
100 100%
0% 0
Programming Tools
0 0%
100% 100
Big Data
100 100%
0% 0
Regular Expressions
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 Apache Spark and RegExr

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...

RegExr Reviews

We have no reviews of RegExr yet.
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Social recommendations and mentions

Based on our record, RegExr should be more popular than Apache Spark. It has been mentiond 367 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.

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 / 23 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 / 25 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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RegExr mentions (367)

  • The importance of the environment in Regex pattern matching
    However - here it becomes weird - when testing the original regex rule (the first one, without the \u00A0 part) on the same string in an interactive visualiser (https://regexr.com/ for instance), there is a match:. - Source: dev.to / 7 months ago
  • Ask HN: How did you learn Regex?
    Learned regex in the 90's from the Perl documentation, or possibly one of the oreilly perl references. That was a time where printed language references were more convenient than searching the internet. Perl still includes a shell component for accessing it's documentation, that was invaluable in those ancient times. Perl's regex documentation is rather fantastic. `perldoc perlre` from your terminal. Or... - Source: Hacker News / 9 months ago
  • Ask HN: How did you learn Regex?
    I read a lot on https://www.regular-expressions.info and experimented on https://rubular.com since I was also learning Ruby at the time. https://regexr.com is another good tool that breaks down your regex and matches. One of the things I remember being difficult at the beginning was the subtle differences between implementations, like `^` meaning "beginning of line" in Ruby (and others) but meaning "beginning of... - Source: Hacker News / 9 months ago
  • Ask HN: How did you learn Regex?
    Mostly building things that needed complex RegEx, and debugging my regular expressions with https://regexr.com/. - Source: Hacker News / 9 months ago
  • Form Validation In TypeScipt Projects Using Zod and React Hook Form
    For username: You are using the min() function to make sure the characters are not below three and, then the max() function checks that the characters are not beyond twenty-five. You also make use of Regex to make sure the username must contain only letters, numbers, and underscore. - Source: dev.to / 10 months ago
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What are some alternatives?

When comparing Apache Spark and RegExr, 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.

regular expressions 101 - Extensive regex tester and debugger with highlighting for PHP, PCRE, Python and JavaScript.

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

rubular - A ruby based regular expression editor

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

Expresso - The award-winning Expresso editor is equally suitable as a teaching tool for the beginning user of regular expressions or as a full-featured development environment for the experienced programmer with an extensive knowledge of regular expressions.