Hadoop Spark Integration: Quick Guide
There is one question always arise in mind, that how does Apache Spark fit in the Hadoop ecosystem. Also, sometimes question strikes how one can run spark in an existing Hadoop cluster.
In this blog, we will answer all those questions regarding Hadoop Spark Integration. We will also learn that in how many ways Spark can work with Hadoop.
Hadoop Spark Integration
Generally, people say Spark is replacing Hadoop. Although, Apache Spark is enhancing Hadoop, not replace. As we know Spark does not have its own file storage system. Hence, it was designed either to read and write data from/to HDFS or other storage systems, for example, HBase and Amazon’s S3.
Furthermore, Hadoop users can also enrich their processing capabilities by integration process. Such as Integration of Spark with Hadoop MapReduce, HBase, and other big data frameworks.
In addition, for every Hadoop user, it is as easy as possible to take advantage of Spark’s capabilities. Even if we run Hadoop 1.x or Hadoop 2.0 (YARN). Although there is a way for us to run Spark, it doesn’t matter whether we have administrative privileges to configure the Hadoop cluster or not.
Basically, we can deploy Spark in a Hadoop cluster in three ways, such as standalone, YARN, and SIMR. Let’s understand each in detail.
1. Standalone deployment
There is one major advantage of standalone deployment. We can statically allocate resources on all or a subset of machines in a Hadoop cluster, also can run Spark side by side with Hadoop MR.
Afterwards, the user can then run arbitrary Spark jobs on their HDFS data. Hence, due to this simplicity, for many Hadoop 1.x users, it is a choice of deployment.
2. Spark Yarn deployment:
We can simply run Spark on YARN without any pre-installation or administrative access required. It turned out as a good decision for those who have already deployed or are planning to deploy it.
It’s the best part is, it allows users to easily integrate Spark in their Hadoop stack. Also, leverages advantage of the full power of Spark, with other components running on top of Spark.
3. Spark In MapReduce (SIMR):
It is one of the beautiful options for those who are not running YARN yet. In addition to the standalone deployment, one can use SIMR to launch Spark jobs inside MapReduce. Users can start experimenting with Spark With SIMR.
Also, after downloading it, within a couple of minutes we can use its shell. Hence it lowers the barrier of deployment. Ultimately, it lets virtually everyone play with Spark.
Two ways of Hadoop and Spark Integration
Basically, for Spark Hadoop Integration project, there are two main approaches available. Such as:
a. Independence
Both Apache Spark and Hadoop can run separate jobs. Even with Spark pulling data from the HDFS on the basis of their business priorities. Hence, it is a very common setup because of its simplicity.
b. Speed
While there is already Hadoop YARN running, we can use Spark despite MapReduce. It provides faster read/write from HDFS. In addition, it is true for several types of apps, such as apps with machine learning requirements as well as similar AI projects.
Conclusion
As a result, we have seen that Apache Spark is enhancing Hadoop MapReduce. Although, some are also saying, Apache Spark is the future of Hadoop. Hence, currently, it is difficult to say that Spark is replacing Hadoop.
Ultimately, we have seen how Spark Hadoop Integration takes place. Also, we have learned how they are working together. Thus, we have answered all the questions regarding Spark Hadoop Integration.
Yet, if you still feel any queries, please let us know through Comment Section.