Hadoop and Apache Spark are both the present booming open-source Big data platforms. Even though these two tools don’t do the same thing in Big data analytics, they are interrelated. The need for Hadoop is everywhere for Big data processing. However, Hadoop has a noteworthy disadvantage despite its numerous essential highlights and advantages for big data handling. MapReduce, which is the native data processing engine of Hadoop, isn’t as fast as Spark.
What’s more, that is the place Spark takes an edge over Hadoop is the vast majority of the present workloads request massive data handling with faster response. MapReduce isn’t equipped to deal with it and can process just cluster information. Besides, when the time has come to low latency, handling a huge set of data Map Reduce fails to perform well.
Hence, comes the need to run Spark on top of Hadoop. Spark has its own hybrid framework and resilient distributed dataset (RDD) using which data can be stored transparently in-memory while you run Spark. In any case, does that mean there is dependably a need for Hadoop to run Spark? We should investigate the technical detail to validate it.
Role of Hadoop to run Apache Spark
Hadoop and Spark are not fundamentally unrelated and can work together. Ongoing and quicker data processing isn’t possible without Spark. Then again, Spark doesn’t have any file system of its own.
Most of the Big data analytics projects handle petabytes of data that needs to be stored in a file system. Hadoop Distributed File System (HDFS) is utilized alongside YARN(Yet Another Resource Negotiator) for this purpose. To run Spark in the distributed mode, it is installed on top of YARN. Hence, HDFS is the primary need to run Spark in distributed mode.
Read more – The Purpose of Cloud for Big data analytics
Various approaches to run Spark in Hadoop
There are three approaches to send and run Spark:
1.Standalone
2.Over YARN
3.In MapReduce (SIMR)
Standalone deployment:
This is the easiest method of deployment. In the independent mode, resources are statically distributed on all or subsets of nodes in the Hadoop cluster. In any case, you can run Spark parallel with MapReduce. This is the preferred deployment decision for Hadoop 1.x.
Over YARN deployment:
It is a simple method for integration between Hadoop and Spark as it does not ask for any admin access. For a big Hadoop cluster, it is the best possible way.
Start In MapReduce (SIMR):
In this method of deployment, there is no requirement for YARN. Or maybe Spark jobs can be launched inside MapReduce.
You can run Spark in Standalone mode
While Spark and Hadoop are better together, Hadoop isn’t a must to run Spark. In standalone mode, you can run Spark independently. Resources like YARN or Mesos are enough to run Spark in this situation. Besides, you can run Spark without Hadoop with Mesos, and you don’t need to for any library from the Hadoop ecosystem.
Why do organizations like to run Spark with Hadoop?
Spark has its own ecosystem which comprises of –
1.Spark core –Basis for data processing
2.Spark SQL – Based on Shark and helps to extract, load and transform data.
3.Spark streaming – Light API which helps in batch processing and streaming of data
4.Machine learning library – Helps to implement machine learning algorithm
5.Graph Analytics(GraphX) – Helps to represent Resilient Distributed Graph
6.Spark Cassandra Connector
7.Spark R integration
Here is the layout of the components in the ecosystem –
Hadoop is commercially accredited. And maybe this is the most compelling reason enterprises prefer to run Spark on top of Hadoop despite many issues with their ecosystem.
How might you run Spark without HDFS?
HDFS is only one of the file systems that Spark supports and not the last answer. If you don’t have Hadoop set up in the environment, what might you do?
Spark is not a file system and a data processing framework. Hence, any local or external file storage system like a no SQL database like Apache Cassandra or HBase or Amazon’s S3 can work with it. Also, you don’t have to run HDFS unless you are utilizing any record from HDFS.
Conclusion:
To conclude, Spark is made to be an effective solution for distributed computing in multi-node mode. Hence, to achieve the maximum benefit of big data processing running Spark with HDFS is the optimum solution. Moreover, like Hadoop and Spark, both are Apache products, and open-source integrating them is easier than with a third-party tool.
So, the answer to our question – ‘Do you really need Hadoop to run Spark?’ is – we can go either way. However, as Spark was designed for Hadoop, the best solution is to follow compatibility.
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