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Apache Spark VS Sourcegraph

Compare Apache Spark VS Sourcegraph 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.

Sourcegraph logo Sourcegraph

Sourcegraph is a free, self-hosted code search and intelligence server that helps developers find, review, understand, and debug code. Use it with any Git code host for teams from 1 to 10,000+.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Sourcegraph Landing page
    Landing page //
    2023-08-06

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.

Sourcegraph features and specs

  • Code Search
    Sourcegraph offers powerful, fast, and precise code search across large codebases, which helps developers quickly find references, definitions, or implementations.
  • Cross-Repository Search
    Allows searching across multiple repositories within the same interface, enhancing discoverability and productivity.
  • Integrations
    Sourcegraph integrates with popular code hosting platforms like GitHub, GitLab, Bitbucket, and more, providing a seamless experience.
  • Code Intelligence
    Supports advanced code intelligence features like hover tooltips, go-to-definition, and find-references, making code navigation easier.
  • Extensibility
    Developers can extend Sourcegraph's functionality with custom extensions, adapting it to their specific needs.
  • Data Privacy
    Sourcegraph can be self-hosted, giving organizations control over their code and data privacy.
  • Multi-Language Support
    Supports a wide range of programming languages and continuously adds more, catering to diverse development environments.

Possible disadvantages of Sourcegraph

  • Complex Setup
    Setting up Sourcegraph, especially self-hosted versions, can be complicated and time-consuming, requiring a good understanding of DevOps practices.
  • Resource Intensive
    Sourcegraph can be resource-heavy, necessitating significant computational power and memory, especially for large codebases.
  • Cost
    While there is a free tier, advanced features and self-hosted options can be expensive for small teams or individual developers.
  • Learning Curve
    The myriad of features and customizations can result in a steep learning curve for new users, potentially slowing down initial adoption.
  • Limited Offline Support
    While Sourcegraph provides robust online features, its functionality is limited when offline, which can impact productivity in environments with restricted internet access.
  • Dependency on Code Hosts
    Sourcegraph's heavy reliance on integrations with external code hosting platforms can introduce friction if there are changes or issues with those services.

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

Sourcegraph videos

+ Add

Code review with IDE powers: Sourcegraph Chrome extension

More videos:

  • Review - Better code reviews on GitHub with the Sourcegraph browser extension
  • Review - Sourcegraph's new GitLab native integration

Category Popularity

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

Apache Spark

Sourcegraph

Databases
100 100%
Databases
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Big Data
100 100%
Big Data
0% 0
Git
0 0%
Git
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 Sourcegraph

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

Sourcegraph Reviews

We have no reviews of Sourcegraph yet.
Be the first one to post

Social recommendations and mentions

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

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 / 26 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 / 28 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

Sourcegraph mentions (34)

  • Ask HN: Cursor or Windsurf?
    This is a product by Sourcegraph https://sourcegraph.com who already have a solution in this space. Is this something wildly different to Cody, your existing solution, or just a "subtle" attempt to gain more customers? - Source: Hacker News / 7 days ago
  • Ask HN: Who is hiring? (April 2025)
    Sourcegraph | San Francisco / Remote | Full-Time | SWE, Database Platform Eng, Forward Deployed Eng, Solutions Eng, Dev Advocate (all roles write code) | https://sourcegraph.com Sourcegraph is how enterprises industrialize software development with AI. We accelerate and automate how software is built in the world's most important companies, including 7/10 top software companies by market cap and 4/6 top US banks.... - Source: Hacker News / about 2 months ago
  • Quickly build UI components with AI
    Cody by Sourcegraph can transform how you build UI components, from basic buttons to complex, dynamic systems. It handles the heavy lifting so you can focus on crafting good UI/UX designs. Whether you’re customising components or managing complex UI systems, Cody provides the tools to make the process faster and more efficient. - Source: dev.to / 2 months ago
  • 22 Unique Developer Resources You Should Explore
    URL: https://sourcegraph.com What it does: A universal code search tool for navigating large codebases. Why it's great: Quickly locate what you need in vast repositories — ideal for collaboration! - Source: dev.to / 4 months ago
  • Copilot vs. Cody: All you need to know
    What is Sourcegraph Cody? Cody, introduced by Sourcegraph, is an AI-powered coding assistant designed to use advanced search and codebase context to help you understand, write, and fix code faster. Launched in 2023, Cody aims to provide deeper context and more accurate code suggestions, particularly for complex and large-scale projects. - Source: dev.to / 6 months ago
View more

What are some alternatives?

When comparing Apache Spark and Sourcegraph, 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.

OpenGrok - OpenGrok is a fast and usable source code search and cross reference engine.

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

Atlassian Fisheye - With FishEye you can search code, visualize and report on activity and find for commits, files, revisions, or teammates across SVN, Git, Mercurial, CVS and Perforce.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

codespell.ai - AI-Powered Code Completion for Faster SDLC

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