Table of Contents
Is HDFS and MapReduce same?
In brief, HDFS and MapReduce are two modules in Hadoop architecture. The main difference between HDFS and MapReduce is that HDFS is a distributed file system that provides high throughput access to application data while MapReduce is a software framework that processes big data on large clusters reliably.
Why is GFS good for MapReduce?
The fact that GFS stores multiple copies of each chunk on different machines increases the likelihood of MapReduce being able to schedule computation close to the data. Whenever using the optimal machine is not feasible, MapReduce tries to use a machine close to the data.
What is the difference between HDFS and Hadoop?
The main difference between Hadoop and HDFS is that the Hadoop is an open source framework that helps to store, process and analyze a large volume of data while the HDFS is the distributed file system of Hadoop that provides high throughput access to application data. In brief, HDFS is a module in Hadoop.
What are the differences between GFS and HDFS explain with example?
File serving: In GFS, files are divided into units called chunks of fixed size. Chunk size is 64 MB and can be stored on different nodes in cluster for load balancing and performance needs. In Hadoop, HDFS file system divides the files into units called blocks of 128 MB in size5. The HDFS has “DistributedCache”.
What is Mapreducer explain with example?
MapReduce is a programming framework that allows us to perform distributed and parallel processing on large data sets in a distributed environment. MapReduce consists of two distinct tasks – Map and Reduce. As the name MapReduce suggests, the reducer phase takes place after the mapper phase has been completed.
What is the difference between HDFS and MapReduce?
In brief, HDFS and MapReduce are two modules in Hadoop architecture. The main difference between HDFS and MapReduce is that HDFS is a distributed file system that provides high throughput access to application data while MapReduce is a software framework that processes big data on large clusters reliably.
What is the difference between Hadoop and Google GFS?
Hadoop Distributed File System (HDFS) is an Apache project. It’s a file system which is used to store the initial and ‘reduced’ data once the data is processed using MapReduce. Google File System (GFS) was the database created by Google initially to store the website indexing data for the search engine.
Are GFS and HDFS distributed file systems used for big data?
Using comarision techniques for architecture and development of GFS and HDFS, allows us use to deduce that both GFS and HDFS are considered two of the most used distributed file systems for dealing with huge clusters where big data lives.
What are the alternatives to Hadoop MapReduce and Bigtable?
One example is that there have been so many alternatives to Hadoop MapReduce and BigTable-like NoSQL data stores coming up. For MapReduce, you have Hadoop Pig, Hadoop Hive, Spark, Kafka + Samza, Storm, and other batch/streaming processing frameworks.