What is the key issues faced by Hadoop when reading and writing data from multiple disk in parallel?

Answer : D. Q 4 – What is the main problem faced while reading and writing data in parallel from multiple disks? A – Processing high volume of data faster.

Why is HDFS only suitable for large data sets and not the correct tool for many small files?

HDFS is more efficient for a large number of data sets, maintained in a single file as compared to the small chunks of data stored in multiple files. In simple words, more files will generate more metadata, that will, in turn, require more memory (RAM).

What is the command to remove a file under HDFS?

rm: Remove a file from HDFS, similar to Unix rm command. This command does not delete directories. For recursive delete, use command -rm -r .

How do I save in HDFS?

Follow the steps given below to insert the required file in the Hadoop file system.

1. You have to create an input directory. $ $HADOOP_HOME/bin/hadoop fs -mkdir /user/input.
2. Transfer and store a data file from local systems to the Hadoop file system using the put command.
3. You can verify the file using ls command.

What problems does Hadoop solve?

It can handle arbitrary text and binary data. So Hadoop can digest any unstructured data easily. We saw how having separate storage and processing clusters is not the best fit for big data. Hadoop clusters, however, provide storage and distributed computing all in one.

Why HDFS performs replication although it results in data redundancy?

Data remains Highly available and reliable and the important factor for this feature is Replication and Rack awareness. Once the data is written in HDFS it is immediately replicated along the cluster, so that different copies of data will be stored on different data nodes.

Why HDFS works well for big data not for regular file system explain the architecture of Hadoop to justify?

HDFS stores very large files running on a cluster of commodity hardware. It works on the principle of storage of less number of large files rather than the huge number of small files. HDFS stores data reliably even in the case of hardware failure. It provides high throughput by providing the data access in parallel.

What is Hdfs command?

ls: This command is used to list all the files. bin directory contains executables so, bin/hdfs means we want the executables of hdfs particularly dfs(Distributed File System) commands. mkdir: To create a directory. In Hadoop dfs there is no home directory by default.

Why is HDFS so hard to run?

This leads to an issue in HDFS — when you have a file system with 500 million to 700 million — the amount of RAM that needs to be reserved by the Namenode becomes large. Typically, in sizes of 256 GB or more. At this size, the JVM is hard at work too; since it has to do things like garbage collection.

How to move old data from one HDFS to another?

Alternatively, you can use Apache Nifi by watching a directory for old data and move it to new location. There’s nothing out of the box that will do that for you. You can also use apache falcon and build data retention policies for hdfs.

What is the default file size of HDFS?

The HDFS is a distributed file system. hadoop is mainly designed for batch processing of large volume of data. The default data block size of HDFS is 128 MB. When file size is significantly smaller than the block size the efficiency degrades.

What are unbounded files and why are they saved in HDFS?

Files could be the piece of a larger logical file. Since HDFS has only recently supported appends, these unbounded files are saved by writing them in chunks into HDFS. Another reason is some files cannot be combined together into one larger file and are essentially small. e.g.