12.2 Distributed computing with Hadoop and Spark

Distributed computing revolutionized big data processing. Hadoop and Spark, two powerful frameworks, tackle massive datasets by dividing tasks across computer clusters. They offer scalability, fault tolerance, and data locality, making them essential tools in modern data science.

Hadoop excels in batch processing huge datasets, while Spark shines in real-time analytics and iterative algorithms. Both use commodity hardware and open-source software, providing cost-effective solutions for organizations dealing with ever-growing data volumes and complex computations.

Hadoop Ecosystem Architecture

Core Components of Hadoop

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• Hadoop Distributed File System (HDFS) stores large data sets reliably and streams them at high bandwidth to user applications
• YARN (Yet Another Resource Negotiator) manages system resources and schedules tasks across the cluster
• MapReduce programming model processes vast amounts of data in parallel on large clusters
• Hadoop Common provides utilities and libraries supporting other Hadoop modules

Extended Hadoop Ecosystem

• ZooKeeper maintains configuration information, naming, distributed synchronization, and group services
• Hive data warehousing tool facilitates querying and managing large datasets stored in distributed storage
• Pig high-level data flow language simplifies the creation of MapReduce programs
• HBase non-relational distributed database provides real-time read/write access to large datasets

Distributed Computing with Hadoop and Spark

Fundamental Principles

• Distributed computing divides problems into tasks solved by multiple computers over a network
• Data locality moves computation to the data, minimizing network transfer of large datasets
• Fault tolerance ensures job completion despite individual node failures in the cluster
• Scalability allows addition of commodity hardware to increase processing power and storage

Comparative Strengths

• Hadoop excels in batch processing of large datasets (terabytes to petabytes)
• Spark specializes in real-time processing and iterative algorithms using in-memory computing
• Both frameworks provide cost-effective solutions utilizing commodity hardware and open-source software
• Spark offers a more flexible programming model supporting multiple languages (Java, Scala, Python, R)

Data Processing with Hadoop and Spark

Hadoop MapReduce Implementation

• MapReduce jobs typically use Java, defining Map and Reduce functions for key-value pair processing
• Mapper processes input key-value pairs to generate intermediate key-value pairs
• Reducer merges all intermediate values associated with the same intermediate key
• Supports various input/output formats (text files, sequence files, database connections)

Spark Data Processing

• Primary programming abstraction uses Resilient Distributed Datasets (RDDs)
• DataFrame and Dataset APIs offer user-friendly interfaces for structured/semi-structured data
• Spark SQL integrates SQL queries with Spark programs for seamless data manipulation
• MLlib library simplifies implementation of machine learning algorithms
• Supports multiple input/output formats similar to Hadoop

Hadoop vs Spark: Performance and Use Cases

Performance Comparison

• Spark outperforms Hadoop in processing speed, especially for iterative algorithms and interactive analysis
• Hadoop better handles very large datasets that don't fit in memory
• Spark's in-memory computing accelerates data processing tasks
• HDFS provides robust, scalable storage for extremely large datasets

Suitability for Different Use Cases

• Hadoop suits batch processing of massive datasets (log processing, data warehousing)
• Spark excels in real-time processing, machine learning, and interactive data exploration
• Hadoop preferred for organizations with legacy systems or strict data governance requirements
• Spark favored for agile and diverse data processing needs (stream processing, graph computations)

Factors Influencing Choice

• Existing infrastructure and team expertise impact framework selection
• Data size and processing requirements guide decision-making
• Budget constraints affect choice between Hadoop and Spark implementations
• Spark's user-friendly API and multi-language support ease adoption for developers