Spark vs. Hadoop vs. Hive: Key Differences and Use Cases
Organizations today have more data at their disposal than ever before. And, in order to make use of that data, many of these organizations are adopting big data processing and analysis technologies like Spark and Hadoop. But, as we explore in this article, comparing Spark vs. Hadoop isn't the 1:1 comparison that many seem to think it is.
- Is Spark vs. Hadoop the Right Comparison?
- Apache Spark vs. Hive
- When to Use Spark
- When to Use Hadoop (and Hive)
- Final Thoughts
- Additional Resources
Is Spark vs. Hadoop the Right Comparison?
Hadoop is a data processing engine, whereas Spark is a real-time data analyzer.
Hadoop can handle very large data in batches proficiently, whereas Spark processes data in real-time such as feeds from Facebook and Twitter. Spark has an interactive mode allowing the user more control during job runs.
Spark and Hadoop have similar features, but they are used for different purposes. If batch processing is required, then Hadoop will be the best option. Spark is best used for ingesting real-time data, including unstructured data streams.
Apache Spark Overview
Spark was initially started in 2009 then open sourced in 2010. It is now covered under the Apache License 2.0. Its foundational concept is a read-only set of data distributed over a cluster of machines, which is called a resilient distributed dataset (RDD). RDDs were developed due to limitations in MapReduce computing, which read data from disk by reducing the results into a map. RDDs work faster on a working set of data which is stored in memory which is ideal for real-time processing and analytics.
Apache Hadoop Overview
Compared to Spark, Hadoop is a slightly older technology. It uses a network of computers to solve large data computation using the MapReduce programming model. Hadoop is also fault tolerant. It knows hardware failures can and will happen and adjusts accordingly. Hadoop splits the data across the cluster and each node in the cluster processes the data in parallel very similar to divide and conquer problem solving.
Apache Hive Overview
Hive integrates with Hadoop by providing an SQL-like interface to query distributed data across a Hadoop cluster which also uses the MapReduce programming model. Hive abstracts the complexity that would otherwise be required to write a Hadoop job to query the same dataset.
Apache Spark vs. Hive
Spark is used for running big data analytics and is a faster option than MapReduce, whereas Hive is optimal for running analytics using SQL.
Apache Hadoop is open-source and scalable by providing distributed processing via MapReduce. In addition, it provides resource allocation and job scheduling as well as fault tolerance, flexibility, and ease of use.
Apache Spark can process large disparate real-time data at high speeds by storing data in memory (and sparingly using disk). This is a big win especially when performing sophisticated analytics, including machine learning and graphing.
Hadoop has a solid MapReduce algorithm, but Spark uses MapReduce more efficiently, which lends itself to faster processing. Hadoop accesses the disk frequently when processing data with MapReduce, which can yield a slower job run.
Another performance differentiator for Spark is that it does not access to disk as much, thus relying on data being stored in memory. Consequently, this makes Spark more expensive due to memory requirements. Cheaper commodity hardware can be used with Hadoop.
With Hadoop, a developer must write the code that is going to process the data in a batch, which is different than Spark where Resilient Distribute Datasets (RDDs) are used. Hadoop requires additional tools for Machine Learning and Streaming which is already included in Spark. Hadoop can be very complex to use with its low-level APIs, while Spark abstracts away these details using high-level operators. Hadoop requires an external job scheduler, whereas Spark does not require a scheduler because all data is computed in-memory.
When to Use Spark
Spark is great for processing real-time data from various sources such as IoT, sensors, or financial systems and using that for analytics. The analytics can be used to target groups for campaigns or machine learning. Spark has support for multiple languages like Java, Python, Scala, and R which is helpful if a team already has experience in these languages. Spark has been benchmarked to be 100 times faster than Hadoop Hive without refactoring code.
When to Use Hadoop (and Hive)
Hadoop is great for parallel processing of diverse sets of large amounts of data. There is no limit to the amount of data that can be stored in a Hadoop cluster. Additional datanodes can be added to address this requirement. It also integrates well with analytic tools like Apache Mahout, R, Python, MongoDB, HBase, and Pentaho.
Both Apache Hadoop and Spark have a solid future in the realm of Big Data processing and analytics. Apache Spark has a vibrant and active community including 2,000 developers from thousands of companies which include 80% of the Fortune 500.
For those thinking that Spark will replace Hadoop, it won't. In fact, Hadoop adoption is increasing -- especially in banking, entertainment, communication, healthcare, education, and government. It's clear that there's enough room for both to thrive, and plenty of use cases to go around for both of these open source technologies.
Technical Support for Hadoop, Spark, and Beyond
Whether you need help planning your data layer, implementing advanced data processing and analytic software like Hadoop or Spark, or need an expert in your corner to help troubleshoot technical issues, OpenLogic is here to help. Learn more about how we can support your team by talking with an expert today.