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Apache Spark VS Drools

Compare Apache Spark VS Drools 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.

Drools logo Drools

Drools introduces the Business Logic integration Platform which provides a unified and integrated platform for Rules, Workflow and Event Processing.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Drools Landing page
    Landing page //
    2023-09-16

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.

Drools features and specs

  • Declarative Programming Model
    Drools uses a rule-based approach, allowing you to specify what the desired outcome is rather than detailing the control flow to achieve it. This can simplify complex decision-making logic and make the codebase easier to understand and maintain.
  • Separation of Business Logic
    It separates business rules from application code, which makes the rules easier to modify, understand, and manage without needing to redeploy the entire application.
  • Flexibility and Adaptability
    Drools allows for real-time decision-making changes. Business users can adjust the rules dynamically based on changing requirements, increasing the system's responsiveness to business needs.
  • Integration with Java
    As a Java-based rules engine, Drools integrates seamlessly with Java applications, making it a versatile choice for Java developers.
  • Comprehensive Toolkit
    Drools provides a rich set of features including a web-based interface for authoring and managing rules, decision tables, a rules repository, and more, providing a comprehensive toolkit for rule management.

Possible disadvantages of Drools

  • Complexity
    The learning curve for Drools can be steep for beginners or those unfamiliar with rule-based systems. It requires a good understanding of both the Drools framework and rule-based logic.
  • Performance Overhead
    In some scenarios, especially with a large number of complex rules, performance issues may arise. Fine-tuning and optimization may be necessary to ensure acceptable performance levels.
  • Debugging and Testing
    Debugging and testing rules can be challenging because the flow of control is not explicitly defined in the code. Specialized testing strategies and tools may be required to ensure rule correctness.
  • Dependency Management
    Drools, being a Java library with many dependencies, can add complexity to project setup and dependency management, especially in large projects.
  • Limited Support for Non-Java Environments
    Drools is primarily designed for Java environments, which can limit its applicability in projects using other languages. Integration with non-Java environments can be cumbersome.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Analysis of Drools

Overall verdict

  • Drools is a good choice for projects that require advanced rule processing capabilities, especially when dealing with complex interdependencies among rules. It offers a high level of configurability and is well-suited for enterprise applications that demand precision in decision automation.

Why this product is good

  • Drools is a powerful business rule management system (BRMS) and rule engine, which is part of the JBoss Community under Red Hat. It is known for its flexibility and ability to handle complex rule processing scenarios. Drools supports both forward and backward chaining, allowing for sophisticated rule execution. With a strong community, extensive documentation, and active development, Drools is considered a robust choice for implementing business rules that require dynamic, rule-based decision-making.

Recommended for

  • Enterprise applications requiring complex rule processing
  • Systems that need dynamic business rule management
  • Applications that benefit from leveraging both forward and backward chaining
  • Projects that can benefit from integration with other JBoss tools
  • Development teams familiar with Java as Drools is Java-based

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

Drools videos

+ Add

Dog Feed :- Drools Dog Food Review (In Hindi) By Dog N Dogs

More videos:

  • Review - drools focus dog food review with PROOF
  • Review - Pet Care - Know About All Drools Product - Special guest - Bhola Shola

Category Popularity

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

Apache Spark

Drools

Databases
100 100%
Databases
0% 0
BPM
0 0%
BPM
100% 100
Big Data
100 100%
Big Data
0% 0
Automation
0 0%
Automation
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 Drools

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

Drools Reviews

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

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Drools. While we know about 70 links to Apache Spark, we've tracked only 6 mentions of Drools. 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 / about 2 months 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 / about 2 months 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 / 3 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 / 3 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 / 4 months ago
View more

Drools mentions (6)

  • Ask HN: What is the current state of "logical" AI?
    See https://cacm.acm.org/magazines/2023/6/273222-the-silent-revolution-of-sat/fulltext and also modern production rules engines like https://drools.org/ Oddly, back when “expert system shells” were cool people thought 10,000 rules were difficult to handle, now 1,000,000 might not be a problem at all. Back then the RETE algorithm was still under development and people were using linear search and not hash tables... - Source: Hacker News / over 1 year ago
  • How Expert Systems AI is Transforming Industries
    Drools – an open-source business rule management system that allows developers to create and manage complex decision logic. Source: about 2 years ago
  • Your views and opinions on Python's rule-engine package
    - Drools - Available in JVM environments (Java, Scala and similar) - uses FEEL for expression language. Source: about 2 years ago
  • 🚀 Introducing GoRules: Open-Source Business Rules Engine
    GoRules is a modern, open-source rules engine designed for high performance and scalability. Our mission is to democratise rules engines and drive early adoption. Rules engines are very useful as they allow business users to easily understand and modify core business logic with little help from developers. You can think of us as a modern, less memory-hungry version of Drools that will be available in many... Source: about 2 years ago
  • A General Workflow Engine
    Is this something like Drools? It's quite uncommon but it is used in situations where certain sets of business rules change a lot and you want business analysts to be able to quickly change them in a simple graphical UI. Source: over 3 years ago
View more

What are some alternatives?

When comparing Apache Spark and Drools, 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.

DecisionRules.io - Business rule engine that lets you create and deploy business rules, while all your rules run in a secure and scalable cloud. Unlike other rule engines, you can create your first rule in 5 minutes and make 100k decisions in a minute via API.

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

Camunda - The Universal Process Orchestrator

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

GoRules.io - Open-source business rules engine for automating decisions

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