What are the key differences between Hadoop and Spark?

Hadoop and Spark are both distributed computing frameworks, but they differ significantly in their processing models, architecture, and use cases. Hadoop relies on a disk-based batch processing model using MapReduce, while Spark emphasizes in-memory computation for faster performance. Additionally, Hadoop includes its own distributed file system (HDFS) and resource manager (YARN), whereas Spark focuses on processing and integrates with external storage systems.

The core difference lies in their processing approaches. Hadoop’s MapReduce breaks tasks into stages that read from and write to disk between each phase, making it suitable for large-scale batch jobs but slower for iterative workloads. For example, training a machine learning model with multiple iterations would require repeated disk I/O in Hadoop, increasing latency. Spark, by contrast, retains intermediate data in memory using Resilient Distributed Datasets (RDDs), enabling iterative algorithms (e.g., gradient descent) or interactive queries to run much faster. Spark can also spill to disk if memory is limited, but its default in-memory approach reduces latency significantly compared to Hadoop’s disk-heavy model.

Architecturally, Hadoop is a complete ecosystem. HDFS provides fault-tolerant storage by replicating data across nodes, while YARN manages cluster resources and schedules tasks. Spark, however, does not include its own storage layer and instead integrates with HDFS, cloud storage (e.g., S3), or databases. It can run on cluster managers like YARN, Mesos, or its standalone mode. For example, a common setup uses HDFS for storage and Spark for processing, combining Hadoop’s reliable storage with Spark’s speed. Spark’s unified engine also supports diverse workloads (streaming, SQL, machine learning) through libraries like Spark Streaming and MLlib, whereas Hadoop requires additional tools like Apache Hive or Mahout for similar tasks.

Performance and use cases further distinguish the two. Hadoop is cost-effective for large-scale, one-time batch processing (e.g., log analysis), where latency isn’t critical. Spark excels at iterative workloads (machine learning), near-real-time processing (streaming), and interactive data exploration. For instance, Spark can process streaming data in micro-batches with sub-second latency, while Hadoop’s MapReduce would struggle with such low-latency demands. However, Hadoop’s disk-based approach can handle datasets larger than cluster memory, which might be a limitation for Spark if not managed carefully. Developers often choose Spark for its speed and richer APIs (Python, Scala, Java), while Hadoop remains relevant for legacy systems or scenarios prioritizing storage reliability over processing speed.