Spark vs. Hadoop: Key Differences and Use Cases
Spark vs. Hadoop isn't the 1:1 comparison that many seem to think it is. While they are both involved in processing and analyzing big data, Spark and Hadoop are actually used for different purposes.
In this blog, our expert breaks down the differences between Spark and Hadoop, and explains how Hive, another Apache component, integrates with and complements Hadoop.
- Spark vs. Hadoop vs. Hive
- Apache Spark vs. Hadoop: Key Differences
- When to Use Spark
- When to Use Hadoop (and Hive)
- Final Thoughts
- Additional Resources
Spark vs. Hadoop vs. Hive
Spark is a real-time data analyzer, whereas Hadoop is a processing engine for very large data sets that do not fit in memory. Hive is a data warehouse system, like SQL, that is built on top of Hadoop.
Hadoop can handle batching of sizable data proficiently, whereas Spark processes data in real-time such as streaming feeds from Facebook and Twitter. Spark has an interactive mode allowing the user more control during job runs.
Spark is the faster option for ingesting real-time data, including unstructured data streams. Hadoop (with Hive) is optimal for running analytics using SQL.
What Is Apache Spark?
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. When Spark processes data, the least-recent data is evicted from RAM to keep the memory footprint manageable since disk access can be expensive.
What Is Apache Hadoop?
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.
What Is Hive?
Hive integrates with Hadoop by providing an SQL-like interface to query structured and unstructured data across a Hadoop cluster by abstracting away the complexity that would otherwise be required to write a Hadoop job to query the same dataset. Spark also has a similar interface, Spark SQL, which is part of the distribution and does not have to be added later.
Spark vs. Hadoop (and Hive): Key Differences
Hadoop has its own distributed file system, cluster manager, and data processing. In addition, it provides resource allocation and job scheduling as well as fault tolerance, flexibility, and ease of use.
Spark includes libraries for performing sophisticated analytics related to machine learning, AI, and a graphing engine. The scheduling implementation between Hadoop and Spark also differs. Spark provides a graphical view of where a job is currently running, has a more intuitive job scheduler, and includes a history server, which is a web interface to view job runs.
Hadoop is scalable by mixing nodes of varying specifications (e.g. CPU, RAM, and disk) to process a data set, which makes it cost-effective. Cheaper commodity hardware can be used with Hadoop. 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. Spark has been benchmarked to be up to 100 times faster than Hadoop for certain workloads.
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.
When to Use Spark
Spark is great for processing real-time, unstructured 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.
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 type and amount of data that can be stored in a Hadoop cluster. Additional data nodes can be added to address this requirement. It also integrates well with analytic tools like Apache Mahout, R, Python, MongoDB, HBase, and Pentaho.
Organizations today have more data at their disposal than ever before, and both Hadoop and Spark have a solid future in the realm of Big Data processing and analytics. 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.
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