Deciding between Apache Hadoop and Spark for your big data analytics needs? As experienced data engineers, we often get this question on which framework to use. While both are useful in their own ways, Hadoop and Spark have key architectural differences that impact their use cases.
In this comprehensive guide, we’ll clarify the contrasts between the two platforms to help you determine the right choice based on your specific needs.
In a nutshell: Hadoop provides scalable, fault-tolerant storage and batch processing of huge datasets using MapReduce jobs on commodity hardware. Spark, on the other hand, focuses on speed through in-memory processing, making it great for streaming, machine learning and interactive workloads.
Now let’s dive deeper and compare Hadoop vs Spark across various criteria:
A Brief History
First, some background. Hadoop was created in 2006 by Doug Cutting and Mike Cafarella, inspired by Google’s MapReduce paper on distributed data processing published in 2004.
As an open source project under Apache, Hadoop offered a scalable platform for storing and analyzing massive datasets by dividing work across cheap commodity servers. Its distributed file system HDFS and batch-oriented MapReduce engine fueled many big data analytics use cases.
A few years later in 2009, Spark originated at UC Berkeley’s AMPLab to improve upon MapReduce limitations. Instead of disk-based storage, it leveraged in-memory computing for faster performance. Spark entered the Apache ecosystem in 2013 and has gained immense popularity since.
Architectural Building Blocks
The core Hadoop framework consists of:
- HDFS (Hadoop Distributed File System): Stores and processes data across distributed commodity hardware. Highly scalable and fault-tolerant.
- YARN (Yet Another Resource Negotiator): Cluster resource management capabilities like job scheduling and monitoring.
- MapReduce: Programming paradigm using ‘Map’ and ‘Reduce’ to process data in parallel.
Additional components like Hive, Pig and HBase provide higher level abstractions for analytical querying, data warehousing and NoSQL storage on top of Hadoop.
In Spark, fundamental concepts include:
- RDDs (Resilient Distributed Datasets): In-memory distributed data collections that let users persist intermediate results and operate on them repeatedly.
- DAG Engine: Based on directed acyclic graphs, advanced execution engine to run Spark jobs more efficiently.
- MLlib: Standard library of machine learning algorithms for common tasks like classification, clustering, recommendation systems etc.
Spark also provides libraries for SQL & structured data (Spark SQL), streaming (Spark Streaming) and graph processing (GraphX).
Hadoop vs Spark – Key Differences
| Criteria | Hadoop | Spark |
|---|---|---|
| Data Processing Approach | Batch processing | Real-time, iterative processing |
| Processing Model | MapReduce | DAG execution |
| Primary Storage Medium | Disk (HDFS) | In-memory (RDDs) |
| Execution Speed | Slower due to disk I/O | Faster due to in-memory computing |
| Fault Tolerance Mechanism | Data replication | RDD lineage tracking |
| Machine Learning Capabilities | Limited to Mahout lib | Rich via MLlib algorithms |
| Ease of Use | Complex Java APIs | Concise DSL-like APIs – Python, Java, Scala, SQL/DataFrames |
| Streaming Analytics Support | Bolt-on (Storm, Flink) | Native via Spark Streaming |
| Common Use Cases | Data lakes, warehouses, ETL | Real-time dashboards, data science apps |
Source: Databricks Runtime 5.1 vs Hadoop 2.9 Benchmark
As you can see from the table above, Hadoop and Spark have fundamentally different architectures. Let’s explore some key contrasts in more depth:
Data Processing Approach
Hadoop uses HDFS and MapReduce intended for high volume, batch oriented tasks that involve reading and writing from disk. This makes it suitable for sequentially accessing large datasets.
Spark‘s in-memory RDDs and DAG execution engine optimize job pipelines for low latency queries, iterating rapidly over cached working sets in memory. Hence it shines for applications needing real-time response.
Storage and Execution
Hadoop relies on replication across commodity hardware disks for failover. Spark prevents data loss using RDD lineage tracking to reconstruct objects.
MapReduce schedules batch jobs, while Spark DAG engine handles data flow optimization. Avoiding disk spills to memory makes Spark faster for most workloads.
Ease of Use
Hadoop APIs are Java-centric and interlinked across components like HDFS and YARN. This can translate to more verbose, low level coding.
Spark offers Python and Scala APIs alongside Java, plus higher level abstractions like SQL, DataFrames, MLlib. So faster development cycles.
Built-in Libraries
Want to operationalize machine learning models on streaming data? Spark comes baked in with MLlib for model building and Spark Streaming for ingestion. Hadoop would require stitching separate specialty engines.
To summarize, Spark optimizes for reduced engineering complexity and accelerated time-to-insight – fit for data science apps. Hadoop suits massive scale, long term batch analytics typical in enterprises.
Key Use Cases and Examples
Based on their strengths, some common use cases include:
Hadoop is a great fit for:
- Building enterprise data lakes and cloud data warehouses on AWS, Azure
- Powering analytics pipelines and ETL workflows
- Log aggregation and processing – think web server, application logs
- Extracting, transforming and loading large datasets from transactional systems
- High performance computing operations using vast amounts of data
Spark shines in these scenarios:
- Building machine learning models over fast moving data streams
- Complex event stream processing and monitoring
- Real-time data visualization and dashboards for business metrics
- Iterative algorithms like page rank or k-means clustering
- Large scale graph computation like social network or fraud analysis
- Development of data science applications such as recommendation systems
Of course, the Hadoop and Spark ecosystems integrate well. For example, running Spark workloads on HDFS storage using YARN for resource allocation is common.
You could also envision pipelines that write batch results from Hadoop to Spark for interactive analysis. Blending both frameworks helps balance data engineering complexity with speed.
Key Takeaways
To wrap up, remember:
- Hadoop focuses on highly scalable and fault tolerant storage and batch processing
- Spark enables faster computation through in-memory processing
- Hadoop stores data on disk while Spark uses memory
- Hadoop uses MapReduce programming, Spark utilizes more optimized DAG execution
- Hadoop suits long term, batch oriented workloads at scale
- Spark better fits speed needs of data science, real-time apps
We hope this guide has helped demystify Hadoop vs Spark for your big data environment. Assess your requirements around existing infrastructure, volume, speed needs and risk tolerance while deciding. Architecting the right data platform requires thoughtful iteration – we are happy to help advise!
Let us know if you have any other questions.
