Apache Spark

Basic Information

• Model: Apache Spark
• Version: Unified analytics engine for large-scale data processing.
• Release Date: Initial release was May 26, 2014.
• Minimum Requirements:
  • Java: Java 17/21 (for Spark 4.x), Java 8/11/17 (for Spark 3.5.x). Java 8 prior to 8u371 is deprecated as of Spark 3.5.0.
  • Scala: Scala 2.13 (for Spark 4.x), Scala 2.12/2.13 (for Spark 3.5.x). Applications must use the same Scala version Spark was compiled for.
  • Python: Python 3.9+ (for Spark 4.x), Python 3.8+ (for Spark 3.5.x).
  • R: R 3.5+ (deprecated in Spark 4.x).
• Supported Operating Systems: Windows, UNIX-like systems (e.g., Linux, macOS), and any platform that runs a supported version of Java. This includes JVMs on x86_64 and ARM64 architectures. Common Linux distributions like CentOS and Ubuntu are frequently used.
• Latest Stable Version: 4.0.1, released September 6, 2025.
• End of Support Date: Feature release branches are generally maintained with bug fix releases for a period of 18 months. The last minor release within a major release (LTS) is typically maintained for longer. For example, Spark 3.5.0 (LTS) is maintained until April 12, 2026.
• End of Life Date: Specific end-of-life dates apply to individual minor releases after their support period. For instance, Spark 3.3 reached EOL on December 9, 2023. Some vendor-specific add-ons may have their own EOL schedules, such as the Instaclustr Apache Spark add-on, which reached EOL on August 10, 2025.
• License Type: Apache License 2.0.
• Deployment Model:
  • Local Mode: Runs on a single machine for development and testing.
  • Standalone Mode: Spark's built-in cluster manager for simple resource management.
  • YARN Mode: Deploys Spark on top of Hadoop YARN.
  • Kubernetes Mode: Deploys Spark applications directly on Kubernetes.
  • Mesos Mode: Supported but deprecated in Spark 3.0.
  • Client Mode: The Spark driver runs on the client machine.
  • Cluster Mode: The Spark driver runs on one of the cluster nodes.

Technical Requirements

• RAM: Highly dependent on the dataset size and processing complexity. Spark leverages in-memory computation, so ample RAM is crucial for performance.
• Processor: Multi-core processors are essential to utilize Spark's distributed and parallel processing capabilities effectively.
• Storage: Distributed storage systems (e.g., HDFS, Amazon S3, Cassandra, Alluxio) are typically required for large-scale data. Local storage is used for temporary files and caching.
• Display: A standard display is sufficient for interacting with the Spark Web UI, which is browser-based.
• Ports: Various network ports are required for inter-component communication (RPC), Web UI access, and client-cluster interactions. Specific port ranges can be configured.
• Operating System: Windows, macOS, or Linux distributions (e.g., Ubuntu, CentOS) are supported, running on x86_64 or ARM64 architectures.

Analysis of Technical Requirements: Apache Spark's technical requirements are highly scalable and flexible, adapting to the scope of the data processing task. For local development and small datasets, a standard workstation with sufficient RAM and a multi-core processor suffices. For enterprise-level, large-scale data processing, Spark necessitates a distributed cluster environment with significant aggregate RAM, numerous CPU cores across worker nodes, and robust distributed storage solutions. The specific hardware configuration directly impacts performance and efficiency, making resource provisioning a critical consideration for optimal operation.

Support & Compatibility

• Latest Version: 4.0.1.
• OS Support: Compatible with Windows, macOS, and UNIX-like systems (Linux) on x86_64 and ARM64 architectures.
• End of Support Date: Minor releases typically receive bug and security fixes for 18 months. Long-Term Support (LTS) releases, which are the last minor release within a major version, are maintained for a longer duration (e.g., Spark 3.5.0 LTS until April 12, 2026).
• Localization: While Spark's UI is primarily in English, it supports programming in multiple languages including Java, Scala, Python, and R, allowing developers to work in their preferred language.
• Available Drivers: Spark provides high-level APIs for Java, Scala, Python, and R. Spark SQL also offers JDBC/ODBC server capabilities for connecting with external tools.

Analysis of Overall Support & Compatibility Status: Apache Spark boasts extensive support and compatibility across various operating systems and programming languages, making it a versatile tool for diverse development environments. Its commitment to maintaining feature and LTS releases ensures ongoing bug fixes and security updates for a significant period. The broad API support facilitates integration into existing technology stacks. However, users must actively monitor the versioning policy and EOL dates to ensure their deployments remain supported and secure, especially for non-LTS releases. The vibrant open-source community further enhances its support ecosystem.

Security Status

• Security Features:
  • Authentication: Supports Kerberos, shared secret mechanisms, OAuth 2.0, JSON Web Tokens (JWT), and Multi-Factor Authentication (MFA).
  • Authorization: Implements Access Control Lists (ACLs) and Role-Based Access Control (RBAC) for granular permissions. Integrates with external tools like Apache Ranger or Apache Sentry for fine-grained policy enforcement.
  • Encryption Support:
    • Data in Transit: SSL/TLS encryption for communication between Spark components (driver, executors, master, worker nodes) and Web UI. AES-based encryption is also available for RPC connections (legacy).
    • Data at Rest: Leverages underlying storage encryption like HDFS Transparent Data Encryption (TDE). Spark 3.2+ supports Parquet modular encryption for DataFrames, allowing column-specific or uniform encryption (Spark 3.4+).
  • Network Security: Recommendations include enabling firewalls and network isolation for Spark cluster nodes.
  • Auditing and Logging: Integrates with Hadoop audit logs for tracking user activities and resource access, aiding in compliance and monitoring.
• Known Vulnerabilities:
  • CVE-2023-32007: Shell command injection via Spark UI (update to 3.1.3+, 3.2.2+, or 3.3.1+).
  • CVE-2022-33891: Shell command injection via Spark UI when ACLs are enabled (affects 3.0.3 and earlier, 3.1.1-3.1.2, 3.2.0-3.2.1; fixed in later versions).
  • CVE-2023-22946: Proxy-user privilege escalation from malicious configuration class (versions prior to 3.4.0; fixed in 3.4.0+).
  • Cross-Site Scripting (XSS): Various XSS vulnerabilities, e.g., in Spark 3.2.1 and earlier, and 3.3.0, fixed in 3.2.2 or 3.3.1+.
• Blacklist Status: Not applicable in the traditional sense; however, known CVEs are publicly tracked and require mitigation.
• Certifications: While Apache Spark itself does not have a single official certification, several vendor-specific certifications exist, such as Databricks Certified Associate Developer for Apache Spark, Cloudera Certified Associate (CCA) Spark and Hadoop Developer, and MapR Certified Spark Developer.
• Authentication Methods: Kerberos, shared secret, OAuth 2.0, JWT, MFA.
• General Recommendations: Implement the principle of least privilege, regularly audit user roles, enable Multi-Factor Authentication, keep Spark and its underlying components updated, utilize comprehensive logging and monitoring, incorporate data masking for sensitive information, and integrate with dedicated Key Management Systems (KMS) for encryption keys. Avoid exposing Spark clusters directly to the public internet.

Analysis on the Overall Security Rating: Apache Spark provides a robust set of security features covering authentication, authorization, and encryption for both data in transit and at rest. However, its distributed nature and integration with various ecosystems mean that effective security relies heavily on proper configuration and adherence to best practices. The presence of known vulnerabilities (CVEs) underscores the critical need for timely updates and patches. Organizations must implement a multi-layered security approach, including strong authentication, fine-grained access control, comprehensive encryption, secure network configurations, and continuous monitoring, to maintain a secure Spark environment. The open-source nature allows for community scrutiny, but also places responsibility on users to stay informed and proactive about security.

Performance & Benchmarks

• Benchmark Scores: Specific benchmark scores are not provided in the search results, but Spark is widely recognized for its high performance in large-scale data processing.
• Real-World Performance Metrics: Spark is designed for speed and efficiency, often performing batch processing tasks 10 to 100 times faster than Hadoop MapReduce, primarily due to its in-memory computation capabilities and optimized execution engine. It excels in iterative algorithms and interactive queries.
• Power Consumption: Not directly addressed in the provided information. Power consumption is highly dependent on the underlying hardware, cluster size, and workload efficiency.
• Carbon Footprint: Not directly addressed in the provided information. This metric is influenced by the efficiency of the underlying infrastructure and data center operations.
• Comparison with Similar Assets: Apache Spark is frequently compared to Hadoop MapReduce, consistently demonstrating superior performance for many workloads due to its in-memory processing and DAG execution engine. It is also often compared with other stream processing engines like Apache Flink for real-time analytics.

Analysis of the Overall Performance Status: Apache Spark is engineered for high performance in big data analytics. Its core architecture, featuring Resilient Distributed Datasets (RDDs) and an optimized execution engine that supports general execution graphs, enables efficient in-memory processing and fault tolerance. This design allows Spark to significantly outperform disk-based processing frameworks for many data engineering, machine learning, and streaming workloads. While specific benchmark numbers vary by use case and configuration, its reputation for speed and scalability is well-established, making it a preferred choice for demanding data processing tasks.

User Reviews & Feedback

Summary of User Reviews and Feedback:

• Strengths:
  • Unified Analytics Engine: Praised for its ability to handle diverse workloads including batch processing, stream processing, machine learning, and graph processing within a single framework.
  • High-Level APIs: Developers appreciate the intuitive and expressive APIs available in Java, Scala, Python (PySpark), and R, which simplify complex data operations.
  • Performance: Consistently highlighted for its speed and efficiency, especially for in-memory computations, offering significant performance gains over older distributed processing frameworks.
  • Flexibility and Deployment Options: Users value the variety of deployment modes (Local, Standalone, YARN, Kubernetes) that cater to different environments from development to large-scale production.
  • Rich Ecosystem: The extensive set of higher-level tools like Spark SQL, MLlib, and Structured Streaming is a major advantage.
• Weaknesses:
  • Configuration Complexity: Setting up and optimizing Spark, especially in secure, multi-user environments, can be complex and requires deep technical knowledge.
  • Resource Management: While flexible, managing resources effectively across different deployment modes can be challenging, particularly for standalone mode's limited scheduling capabilities.
  • Security Implementation: Although Spark offers robust security features, their proper implementation and ongoing management (e.g., patching CVEs, key management) require diligent effort.
  • EOL Management: Keeping track of EOL dates for various minor releases and managing migrations can be a burden for users.
• Recommended Use Cases:
  • Large-scale data processing and ETL (Extract, Transform, Load) operations.
  • Real-time stream processing and analytics.
  • Machine learning model training and deployment (MLlib).
  • Interactive data analysis and SQL queries on big data.
  • Graph processing and analytics (GraphX).
  • Data engineering pipelines in cloud-native environments (Kubernetes).

Summary

Apache Spark stands as a powerful and versatile unified analytics engine for large-scale data processing. Its architecture, built around Resilient Distributed Datasets (RDDs) and an optimized execution engine, delivers high performance, often significantly faster than traditional batch processing frameworks. Spark offers a rich ecosystem of libraries, including Spark SQL for structured data, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for real-time analytics, all accessible via high-level APIs in Java, Scala, Python, and R. This broad language and platform compatibility, supporting Windows, macOS, and Linux on various architectures, makes it highly adaptable to diverse enterprise environments.

Key strengths include its exceptional speed for iterative and in-memory computations, its comprehensive suite of tools for various data workloads, and its flexible deployment options across local, standalone, YARN, and Kubernetes clusters. This flexibility allows it to scale from small development tasks to massive production deployments.

However, Spark's complexity in configuration and optimization, particularly in distributed and secure settings, can be a challenge. While it provides extensive security features—including strong authentication (Kerberos, OAuth 2.0), authorization (ACLs, RBAC), and encryption for data in transit (SSL/TLS) and at rest (HDFS TDE, Parquet encryption)—these require careful implementation and continuous management to mitigate known vulnerabilities. Users must remain vigilant about security updates and adhere to best practices to protect sensitive data.

In conclusion, Apache Spark is an indispensable tool for modern data-intensive applications, offering unparalleled capabilities for processing, analyzing, and transforming vast datasets. Its strengths in performance, versatility, and ecosystem integration make it a leading choice for data engineering, data science, and machine learning initiatives. Organizations should leverage its power while prioritizing robust security configurations, regular updates, and strategic resource management to maximize its benefits.

The information provided is based on publicly available data and may vary depending on specific device configurations. For up-to-date information, please consult official manufacturer resources.