Hadoop vs Spark: 7 Key Differences and Full Explanation in Plain English

Apache Hadoop and Apache Spark are two of the most widely used big data analytics platforms, each with its own strengths and shortcomings. This comprehensive yet accessible guide compares Hadoop and Spark across 7 key dimensions and helps you determine which is the right choice for your needs.

A Brief History

Hadoop originated out of Google research papers in the early 2000s describing Google‘s approach to large scale, distributed data processing across clusters of commodity servers. Doug Cutting and others took these ideas and created an open-source implementation of MapReduce on top of a distributed file system, which became Apache Hadoop.

Spark emerged in 2009 out of UC Berkeley‘s AMPlab, focused on speeding up iterative and interactive data workloads by caching data in memory rather than reading from disk between iterations. The Spark research focused on overcoming limitations of Hadoop, especially for use cases requiring lower latency like machine learning.

Both projects became top-level Apache projects driving significant innovation in big data. Today, Hadoop and Spark both represent multi-component ecosystems solving a range of large-scale data processing needs.

Core Functionality and Components

Hadoop provides distributed data storage and batch data processing across clusters based on its core components:

  • HDFS (Hadoop Distributed File System) – Storage layer that divides data and replicates it across nodes
  • YARN (Yet Another Resource Manager) – Cluster resource management
  • MapReduce – Batch data processing programming model

Complementary projects like Hive, Pig, HBase and more are often bundled into Hadoop distributions.

Spark also handles distributed data processing but focuses more on speed through an in-memory processing engine based on:

  • RDDs (Resilient Distributed Datasets) – Fault tolerant abstraction for working with data in clusters
  • Spark SQL – Module for structured data and queries
  • Spark Streaming – Processing and analyzing real-time streams
  • MLlib – Machine learning toolbox
  • GraphX – Graph and network algorithms

Data Processing Models

Hadoop‘s core batch processing model, MapReduce, uses disk-based processing across two primary phases:

  • Map – Processes data as key-value pairs in parallel
  • Reduce aggregates outputs from map phase

This works well for long running, deep analytical queries across large datasets but less optimal for iterative workloads.

Spark‘s in-memory engine processes data more rapidly using a directed acyclic graph (DAG) model allowing caching of results in memory rather than reading from disk. This speeds up interactive queries and algorithms requiring multiple passes over the same dataset.

Data Storage and Management

Hadoop‘s HDFS distributes data in blocks across nodes in a cluster and handles replication. This aims to exploit data locality during processing. HDFS optimized for streaming access of large files rather than latency of small reads/writes.

Spark uses RDDs which represent distributed datasets partitioned across nodes. Spark can run standalone, on HDFS, or integrate with many other storage layers (S3, Cassandra etc). Spark RDDs handle both streaming and batch workloads.

Ease of Use

Hadoop MapReduce requires writing custom mapper and reducer functions in Java which can represent a steep learning curve. Languages like Pig and Hive reduce this complexity via SQL-like abstraction.

Spark‘s native APIs for Java, Python, Scala, R provide more flexibility. Frameworks like Spark SQL and DataFrames simplify coding through a declarative style. Overall Spark represents an easier on-ramp for analysts and developers.

Performance and Optimization

Hadoop optimizes batch workloads through parallelism, data locality, and deliberate scheduling across stages. However, disk I/O tends to constrain overall performance especially for iterative algorithms.

Spark minimizes disk I/O via in memory caching. Other optimizations include data partitioning, DAG computation planning, and optimized joins/shuffles. In a cluster setting, Spark processes data much faster than native Hadoop – often 100x for iterative algorithms when data fits in aggregate memory.

Real Time Stream Processing

Native Hadoop focuses mainly on batch processing rather than real time data. Separate stream processing engines built on top of Hadoop like Storm, Samza, or Flink can handle streaming.

Spark Streaming has native support for stream processing workloads with minimal code changes needed. Micro batching allow streams to be smoothly handled with windowing, stateful processing, and event time management.

Fault Tolerance

Both Hadoop and Spark provide fault tolerance for node failures inherent in distributed environments.

Hadoop achieves this through data replication across nodes via HDFS. If a node fails, another can process replicated block. Failed map/reduce tasks also rescheduled.

Spark uses lineage tracking to recreate lost data and RDD partitioning helps parallelize data reconstruction after failures. Although Spark also works with HDFS, it does not depend on replication for recovery.

Machine Learning Capabilities

Hadoop itself does not directly provide machine learning capabilities, however complementary engines like Mahout or Hama can integrate with Hadoop for distributed ML tasks. These require pulling data from HDFS into other tools and environments.

Spark‘s MLlib provides distributed machine learning algorithms directly within the Spark ecosystem letting you write applications that mix SQL, structured data processing, and machine learning using languages like Python, R, Scala, and Java. Tight integration through shared data structures like DataFrames and RDDs simplifies development.

So in summary, key advantages of Hadoop include:

  • Mature ecosystem supporting wide range of workloads
  • Industry-standard for open source distributed processing
  • High degree of stability and reliability
  • Cost-effective data storage and processing at scale

Spark advantages include:

  • Speed and performance, especially for iterative/interactive processing
  • Ease of use through intuitive APIs and languages
  • Real-time stream processing capabilities
  • Native machine learning libraries

Understanding these contrasts allows you to evaluate both platforms against your specific data infrastructure and analytics objectives. Often companies adopt a hybrid data architecture leveraging strengths from both the Hadoop and Spark ecosystems.

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