To better explain this, let us first try to understand what Hadoop and Spark are in layman terms.
What is Apache Hadoop?
Hadoop is a cluster computing framework developed to solve the problem of Big Data. It is a combination of two components: HDFS and MapReduce. HDFS helps store data while MapReduce helps process it. Hadoop is a Java-based robust framework that is capable of handling any type of data that may be structured or semi-structured or even unstructured.
The power of handling all types of data comes from HDFS, which is a distributed file system in the Hadoop ecosystem. HDFS stores data in a distributed fashion. MapReduce processes the collected data in HDFS in key-value pairs.
There are mainly two versions in Hadoop, popularly known as Hadoop1.x and Hadoop 2.x.
- Hadoop1.x: This was the very first version of Hadoop that was built to handle Big Data. HDFS and MapReduce are the two steps involved in processing data in the Hadoop1.x architecture. It processes data in batches, this process can be named as batch processing.
- Hadoop2.x: To enhance the features of Hadoop1.x and to overcome problems in the previous version, Hadoop2.x was introduced. A new concept called YARN (Yet Another Resource Negotiator) was introduced in Hadoop2.x. HDFS+YARN is now used to process the data.
With the introduction of YARN, Hadoop is now able to include some more tools in it to process the data, like the MapReduce, Hive, Pig, Spark, etc.
This was about Hadoop in brief. Now let us see what Spark is.
What is Apache spark?
Apache Spark is a cluster computing framework which runs on Hadoop and handles different types of data. It is a one-stop solution to many problems. Spark has rich resources for handling data and most importantly, it is 10–20x faster than Hadoop’s MapReduce.
It attains these speeds of computation by its in-memory primitives. The data is cached and is present in the memory (RAM) and performs all the computations in-memory.
Spark’s rich resources has almost all the components of Hadoop. For example, we can perform batch processing in Spark and real-time data processing too. It has its own streaming engine called the Spark Streaming Engine that can process the streaming data.
We can perform various functions with Spark.
SQL Operations: It has its own SQL engine called Spark SQL. It covers features of both SQL and Hive.
Machine Learning: Spark has a Machine Learning Library, MLib. It can perform Machine Learning without the help of MAHOUT.
Graph Processing: Graph performs Graph processing by using GraphX component.
All the above features are in-built for Spark.
These can be run on different types of cluster managers such as Hadoop, YARN framework, and the Apache Mesos framework. It has its own standalone scheduler to get started, if other frameworks are not available.
Apache Spark provides the access and ease of storing data, plus, it can be run on many file systems. For example, HDFS, Hbase, MongoDB, and Cassandra can store data in its local files system.
This was about Spark in brief.
So, can Spark replace Hadoop?
Spark can never be a replacement for Hadoop! Spark is a processing engine that functions on top of the Hadoop ecosystem. Both Hadoop and Spark have their own advantages. Spark is built to increase the processing speed of the Hadoop ecosystem and to overcome the limitations of MapReduce.
Still, there are many companies that are still using Hadoop. Depending on the requirement, we can switch between either of them. Hadoop has two phases HDFS+MapReduce; HDFS is used for storing and MapReduce for processing data. Spark comes on top of the Hadoop ecosystem to process data.
As shown in the above architecture, Spark comes in place of MapReduce in the Hadoop ecosystem. There are other components in the Hadoop architecture to process data like Pig and Hive. Spark can be used in either ways by integrating with Hadoop or without Hadoop. Finally, it is our choice to use the elements that are provided by the framework!
Spark offers many more features than Hadoop but still there are a few things that can be limiting in case of Spark. Its “in-memory” capability can become a bottleneck when it comes to cost-efficient processing of big data.
Besides, Spark does not have its own file management system, so you need to integrate with Hadoop, or other cloud based data platforms. For many of the input and output formats, Spark still uses Hadoop’s IO classes.
So, Spark needs to come a long way in order to replace Hadoop. It may be safe to say that Spark could be integrated with Hadoop, rather than have it as a replacement.
We hope this post helped you in clearing your confusion about the possibility of Spark acting as a replacement for Hadoop. Keep visiting our site www.acadgild.com for more updates on Big Data and other technologies.