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Yes, Hadoop is different from its parallel computing system. It will let you store and handle a great amount of data on machine clouds and handle data redundancy. The first benefit of Hadoop is that it stores data in several nodes. This method of storing is better than the distributed manner. Each of these nodes processes the data stored on it instead of moving it over to other networks.
The relational database computing system, you can easily query data in real-time, but this may not be efficient to store data in tables, records, and also, the columns only when the data is in greater size.
The best part, Hadoop will allow you to build a column database with HadoopHBase, for runtime queries on rows.
There are 3 modes on which Hadoop runs, and they are the standalone mode, pseudo-distributed mode, and fully distributed mode.
3.Name the two benefits of distributed cache?
The two benefits of distributed cache are:-
It will distribute simple, read-only text/data files and also, complex types like jars, archives, and others. These archives are then un-archived at slave node. And the second benefit is that the distributed cache will track the modification timestamp of cache files. It will notify the files that shouldn’t be modified until a particular job is executed.
The common input format in Hadoop is the text input format that is the default input format in the Hadoop, a key value input format which is used for plan test files. Here, the files are broken into lines. The last is the sequence file input format where it is used for reading the files in sequence.
5.What does the job tracker do in Hadoop?
The job tracker manages resources. It also tracks the resources which are available and also manages the life cycle tasks. It separates the nodes, but not on the Data Node. It communicates with Name Node in order to identify the data location. It also finds the best tracker nodes that execute the tasks given on the nodes. The job tracker also monitors the individual task trackers and submits this to the overall job back to the client. Lastly, it tracks the execution of Map Reduce workloads local to the slave nodes.
The storage system for Hadoop is the HDFS while there is no storage type or system for Spark. Hadoop has an average speed of processing, while the spark has an excellent processing speed. In Hadoop, the libraries are separated by tools, and in Spark, the libraries are spark core, SQL, streaming, MLlibm, and graph.
7.Mention the three core methods of a reducer?
The three core methods of the reducer are setup() used for configuring various parameters like input data size and distributed cache, reduce() is the heart of reducer also, called once per key with associated reduced task public void reduce, and cleanup() is the method of cleaning the temporary files.
The record reader in Hadoop will slit the data into a single record.
9.What is the outcome when you run Hadoop job with an output directory?
If you run the Hadoop job with an output directory, it will throw an exception saying that the output file directory already existed. And to run the MapReduce Job, you need to ensure that the output directory will not exist before in the HDFS. And to delete the directory before running the job, you need to utilize the shell : Hadoopfs-rmr/path/to/your/output or use the JAVA API: FileSystem.getlocal(conf).delete(outputDir,true);
10.Name few companies using Hadoop
IBM, Intel, Microsoft, Teradata, Amazon Web Services.
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