What is the difference in idea, design and code, between Apache Spark and Apache Hadoop?

Apache Spark and Apache Hadoop are two prominent big data processing frameworks, each with distinct characteristics in terms of ideas, design, and code. Here's a comparative overview: ### 1. **Idea/Concept** - **Apache Hadoop:** - **Batch Processing:** Primarily designed for batch processing of large datasets. - **MapReduce Paradigm:** Utilizes the MapReduce programming model to process and generate large data sets with a parallel, distributed algorithm on a cluster. - **Disk-Based Storage:** Data is read from and written to disk, making it suitable for processing large-scale, unstructured data where latency is not a primary concern. - **Apache Spark:** - **In-Memory Processing:** Optimized for in-memory computing, which speeds up processing times significantly for iterative algorithms and interactive queries. - **Unified Analytics Engine:** Provides capabilities for batch processing, streaming, machine learning, and graph processing. - **RDDs (Resilient Distributed Datasets):** Core abstraction for distributed data processing, allowing for fault tolerance and parallel processing across a cluster. ### 2. **Design** - **Apache Hadoop:** - **Components:** Consists of Hadoop Distributed File System (HDFS) for storage and YARN (Yet Another Resource Negotiator) for resource management. - **MapReduce Framework:** Built around the MapReduce programming model, which splits tasks into map and reduce phases. - **Disk I/O:** Heavy reliance on disk I/O between steps, which can slow down performance for iterative processes. - **Apache Spark:** - **Components:** Includes Spark Core (basic functionalities), Spark SQL, Spark Streaming, MLlib (machine learning), and GraphX (graph processing). - **Directed Acyclic Graph (DAG):** Uses a DAG for task scheduling, which allows for better optimization and performance improvements over the MapReduce model. - **In-Memory Computation:** Minimizes disk I/O by keeping intermediate data in memory, significantly boosting performance for iterative processing. ### 3. **Code and API** - **Apache Hadoop:** - **MapReduce API:** Requires developers to write Mapper and Reducer classes, often leading to verbose and complex code. - **Java-Based:** Primarily uses Java for coding, though there are APIs for other languages (e.g., Python with Hadoop Streaming). - **Data Flow:** Explicitly managed by developers through the MapReduce phases, with significant boilerplate code required for each job. - **Apache Spark:** - **High-Level APIs:** Offers high-level APIs in Java, Scala, Python, and R, making it more accessible to a broader range of developers. - **Simpler Syntax:** Provides concise and expressive code for data transformations and actions through its RDD, DataFrame, and Dataset APIs. - **Lazy Evaluation:** Operations are lazily evaluated, meaning transformations are not executed until an action is called, allowing for optimization of the execution plan. ### Summary - **Apache Hadoop** is more suitable for large-scale batch processing and long-term storage of massive datasets, while its MapReduce paradigm is highly robust but can be slower due to disk I/O. - **Apache Spark** excels in performance for both batch and real-time data processing due to its in-memory computation model and more flexible, high-level APIs, which facilitate easier and faster development. These differences highlight how Apache Spark is generally preferred for applications requiring fast, iterative processing, while Apache Hadoop remains a strong choice for large-scale, long-term batch processing needs. read less