The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
A software library and a framework for permitting the distributed processing of big data sets among computer clusters using with the help of noncomplex programming models is called Hadoop and is the project of Apache organization.
From scaling single computer systems up to thousands of systems for computing power and storage, Hadoop does the job with ease.
For creating the Hadoop framework there are a set of modules created by Hadoop.
The Primary Hadoop Framework Modules Are:
Hadoop Distributed File System (HDFS)
There are lots of other modules apart from the above modules and they are Hive, Ambari, Avro, Pig, Cassandra, Flume, Oozie and Sqoop which induces Hadoop’s power to reach big data applications and large data processing.
When dataset becomes very large or tough, Hadoop is used by most of the companies as their current solutions cannot process the information by taking lots of time.
The ideal text processing engine is none other than MapReduce and it is used to the best when compared to crawling and searching the web.
A rapid and a proper engine for big data processing used by most of the Apache Spark developers is called Spark. Hadoop’s big data framework is 800-lb gorilla and Spark is 130-lb big data cheetah.
The real-time data processing capability and MapReduce’s disk-bound engine are compared to and the real-time game is won by the former. Spark is also considered a module on Hadoop project page.
A cluster-computing framework called spark means it is contesting with lots of MapReduce than with the whole Hadoop.
The main difference between Spark and MapReduce is that persistent storage is used by MapReduce and Spark uses Resilient Distributed Datasets (RDDs) under the Fault Tolerance section.
The performance of processing in Spark is very fast because all the processing is done only in the memory and it can also use disk space for data that doesn’t fit in the memory. For gathering information on goingly this was installed and there was no need for this data in or near real-time.
Ease of Use
It is not good only in terms of performance but is also easy to use and is user-friendly for Scala, Python, Java, etc. Most of the users and developers use the interactive mode of Spark for its queries and other actions. There is no interactive mode in MapReduce but Pig and Hive make the operations quite easier.
Both Spark and MapReduce are the projects of Apache and they are opensource and there is no cost for these products. These products are made to run on commodity hardware and are called white box server systems. It is a well-known fact that Spark systems do costs more due to high requirements of RAM for running in the memory. Similarly, the number of systems needed is also significantly reduced.
Both Spark and MapReduce are working well with each other with respect to data sources, file formats, business intelligence tools like ODBC and JDBC.
MapReduce is a batch-processing engine. MapReduce operates in sequential steps by reading data from the cluster, performing its operation on the data, writing the results back to the cluster, reading updated data from the cluster, performing the next data operation, writing those results back to the cluster and so on.
A sequential step of operation is done in MapReduce which is a batch-processing engine and it does the operation on data and returns the result to the cluster and performs the next data operation and writing it back, so on and so forth.
A similar operation is done by spark but everything is done in one step and in memory. The data is read from the cluster and the operations are done on data and written back to the cluster.
Join DBA Course to learn more about Database and Analytics Tools.
Stay connected to CRB Tech for more technical optimization and other updates and information.