Initially you have to format the configured HDFS file system, open namenode (HDFS server), and execute the following command.
$ hadoop namenode -format
After formatting the HDFS, start the distributed file system. The following command will start the namenode as well as the data nodes as cluster.
$ start-dfs.sh

Listing Files in HDFS

After loading the information in the server, we can find the list of files in a directory, status of a file, using ‘ls’. Given below is the syntax of ls that you can pass to a directory or a filename as an argument.
$ $HADOOP_HOME/bin/hadoop fs -ls <args>

Inserting Data into HDFS

Assume we have data in the file called file.txt in the local system which is ought to be saved in the hdfs file system. Follow the steps given below to insert the required file in the Hadoop file system.
Step 1
You have to create an input directory.
$ $HADOOP_HOME/bin/hadoop fs -mkdir /user/input
Step 2
Transfer and store a data file from local systems to the Hadoop file system using the put command.
$ $HADOOP_HOME/bin/hadoop fs -put /home/file.txt /user/input
Step 3
You can verify the file using ls command.
$ $HADOOP_HOME/bin/hadoop fs -ls /user/input

Retrieving Data from HDFS

Assume we have a file in HDFS called outfile. Given below is a simple demonstration for retrieving the required file from the Hadoop file system.
Step 1
Initially, view the data from HDFS using cat command.
$ $HADOOP_HOME/bin/hadoop fs -cat /user/output/outfile
Step 2
Get the file from HDFS to the local file system using get command.
$ $HADOOP_HOME/bin/hadoop fs -get /user/output/ /home/hadoop_tp/

Shutting Down the HDFS

You can shut down the HDFS by using the following command.
$ stop-dfs.sh

Hadoop – Command Reference

There are many more commands in “$HADOOP_HOME/bin/hadoop fs” than are demonstrated here, although these basic operations will get you started. Running ./bin/hadoop dfs with no additional arguments will list all the commands that can be run with the FsShell system. Furthermore, $HADOOP_HOME/bin/hadoop fs -help commandName will display a short usage summary for the operation in question, if you are stuck.
A table of all the operations is shown below. The following conventions are used for parameters −
“<path>” means any file or directory name.
“<path>…” means one or more file or directory names.
“<file>” means any filename.
“<src>” and “<dest>” are path names in a directed operation.
“<localSrc>” and “<localDest>” are paths as above, but on the local file system.
All other files and path names refer to the objects inside HDFS.

Sr.No	Command & Description
1	-ls <path> Lists the contents of the directory specified by path, showing the names, permissions, owner, size and modification date for each entry.
2	-lsr <path> Behaves like -ls, but recursively displays entries in all subdirectories of path.
3	-du <path> Shows disk usage, in bytes, for all the files which match path; filenames are reported with the full HDFS protocol prefix.
4	-dus <path> Like -du, but prints a summary of disk usage of all files/directories in the path.
5	-mv <src><dest> Moves the file or directory indicated by src to dest, within HDFS.
6	-cp <src> <dest> Copies the file or directory identified by src to dest, within HDFS.
7	-rm <path> Removes the file or empty directory identified by path.
8	-rmr <path> Removes the file or directory identified by path. Recursively deletes any child entries (i.e., files or subdirectories of path).
9	-put <localSrc> <dest> Copies the file or directory from the local file system identified by localSrc to dest within the DFS.
10	-copyFromLocal <localSrc> <dest> Identical to -put
11	-moveFromLocal <localSrc> <dest> Copies the file or directory from the local file system identified by localSrc to dest within HDFS, and then deletes the local copy on success.
12	-get [-crc] <src> <localDest> Copies the file or directory in HDFS identified by src to the local file system path identified by localDest.
13	-getmerge <src> <localDest> Retrieves all files that match the path src in HDFS, and copies them to a single, merged file in the local file system identified by localDest.
14	-cat <filen-ame> Displays the contents of filename on stdout.
15	-copyToLocal <src> <localDest> Identical to -get
16	-moveToLocal <src> <localDest> Works like -get, but deletes the HDFS copy on success.
17	-mkdir <path> Creates a directory named path in HDFS. Creates any parent directories in path that are missing (e.g., mkdir -p in Linux).
18	-setrep [-R] [-w] rep <path> Sets the target replication factor for files identified by path to rep. (The actual replication factor will move toward the target over time)
19	-touchz <path> Creates a file at path containing the current time as a timestamp. Fails if a file already exists at path, unless the file is already size 0.
20	-test -[ezd] <path> Returns 1 if path exists; has zero length; or is a directory or 0 otherwise.
21	-stat [format] <path> Prints information about path. Format is a string which accepts file size in blocks (%b), filename (%n), block size (%o), replication (%r), and modification date (%y, %Y).
22	-tail [-f] <file2name> Shows the last 1KB of file on stdout.
23	-chmod [-R] mode,mode,… <path>… Changes the file permissions associated with one or more objects identified by path…. Performs changes recursively with R. mode is a 3-digit octal mode, or {augo}+/-{rwxX}. Assumes if no scope is specified and does not apply an umask.
24	-chown [-R] [owner][:[group]] <path>… Sets the owning user and/or group for files or directories identified by path…. Sets owner recursively if -R is specified.
25	-chgrp [-R] group <path>… Sets the owning group for files or directories identified by path…. Sets group recursively if -R is specified.
26	-help <cmd-name> Returns usage information for one of the commands listed above. You must omit the leading ‘-‘ character in cmd.

So, this brings us to the end of blog. This Tecklearn ‘How to perform operations in Hadoop and commands used in Hadoop’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.
If you wish to learn Hive and build a career in Big Data or Hadoop domain, then check out our interactive, Big Data Hadoop-Architect (All in 1) Combo Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

BigData Hadoop-Architect (All in 1) | Combo Course

Big Data Hadoop-Architect (All in 1) Combo Training

About the Course

Tecklearn’s Big Data Hadoop-Architect (All in 1) combo includes the following Courses:
• BigData Hadoop Analyst
• BigData Hadoop Developer
• BigData Hadoop Administrator
• BigData Hadoop Tester
• Big Data Security with Kerberos

Why Should you take Big Data Hadoop Combo Training?

• Average salary for a Hadoop Administrator ranges from approximately $104,528 to $141,391 per annum – Indeed.com
• Average salary for a Spark and Hadoop Developer ranges from approximately $106,366 to $127,619 per annum – Indeed.com
• Average salary for a Big Data Hadoop Analyst is $115,819– ZipRecruiter.com

What you will Learn in this Course?

Introduction
• The Case for Apache Hadoop
• Why Hadoop?
• Core Hadoop Components
• Fundamental Concepts
HDFS
• HDFS Features
• Writing and Reading Files
• NameNode Memory Considerations
• Overview of HDFS Security
• Using the Namenode Web UI
• Using the Hadoop File Shell
Getting Data into HDFS
• Ingesting Data from External Sources with Flume
• Ingesting Data from Relational Databases with Sqoop
• REST Interfaces
• Best Practices for Importing Data
YARN and MapReduce
• What Is MapReduce?
• Basic MapReduce Concepts
• YARN Cluster Architecture
• Resource Allocation
• Failure Recovery
• Using the YARN Web UI
• MapReduce Version 1
Planning Your Hadoop Cluster
• General Planning Considerations
• Choosing the Right Hardware
• Network Considerations
• Configuring Nodes
• Planning for Cluster Management
Hadoop Installation and Initial Configuration
• Deployment Types
• Installing Hadoop
• Specifying the Hadoop Configuration
• Performing Initial HDFS Configuration
• Performing Initial YARN and MapReduce Configuration
• Hadoop Logging
Installing and Configuring Hive, Impala, and Pig
• Hive
• Impala
• Pig
Hadoop Clients
• What is a Hadoop Client?
• Installing and Configuring Hadoop Clients
• Installing and Configuring Hue
• Hue Authentication and Authorization
Cloudera Manager
• The Motivation for Cloudera Manager
• Cloudera Manager Features
• Express and Enterprise Versions
• Cloudera Manager Topology
• Installing Cloudera Manager
• Installing Hadoop Using Cloudera Manager
• Performing Basic Administration Tasks Using Cloudera Manager
Advanced Cluster Configuration
• Advanced Configuration Parameters
• Configuring Hadoop Ports
• Explicitly Including and Excluding Hosts
• Configuring HDFS for Rack Awareness
• Configuring HDFS High Availability
Hadoop Security
• Why Hadoop Security Is Important
• Hadoop’s Security System Concepts
• What Kerberos Is and How it Works
• Securing a Hadoop Cluster with Kerberos
Managing and Scheduling Jobs
• Managing Running Jobs
• Scheduling Hadoop Jobs
• Configuring the Fair Scheduler
• Impala Query Scheduling
Cluster Maintenance
• Checking HDFS Status
• Copying Data Between Clusters
• Adding and Removing Cluster Nodes
• Rebalancing the Cluster
• Cluster Upgrading
Cluster Monitoring and Troubleshooting
• General System Monitoring
• Monitoring Hadoop Clusters
• Common Troubleshooting Hadoop Clusters
• Common Misconfigurations
Introduction to Pig
• What Is Pig?
• Pig’s Features
• Pig Use Cases
• Interacting with Pig
Basic Data Analysis with Pig
• Pig Latin Syntax
• Loading Data
• Simple Data Types
• Field Definitions
• Data Output
• Viewing the Schema
• Filtering and Sorting Data
• Commonly-Used Functions
Processing Complex Data with Pig
• Storage Formats
• Complex/Nested Data Types
• Grouping
• Built-In Functions for Complex Data
• Iterating Grouped Data
Multi-Dataset Operations with Pig
• Techniques for Combining Data Sets
• Joining Data Sets in Pig
• Set Operations
• Splitting Data Sets
Pig Troubleshooting and Optimization
• Troubleshooting Pig
• Logging
• Using Hadoop’s Web UI
• Data Sampling and Debugging
• Performance Overview
• Understanding the Execution Plan
• Tips for Improving the Performance of Your Pig Jobs
Introduction to Hive and Impala
• What Is Hive?
• What Is Impala?
• Schema and Data Storage
• Comparing Hive to Traditional Databases
• Hive Use Cases
Querying with Hive and Impala
• Databases and Tables
• Basic Hive and Impala Query Language Syntax
• Data Types
• Differences Between Hive and Impala Query Syntax
• Using Hue to Execute Queries
• Using the Impala Shell
Data Management
• Data Storage
• Creating Databases and Tables
• Loading Data
• Altering Databases and Tables
• Simplifying Queries with Views
• Storing Query Results
Data Storage and Performance
• Partitioning Tables
• Choosing a File Format
• Managing Metadata
• Controlling Access to Data
Relational Data Analysis with Hive and Impala
• Joining Datasets
• Common Built-In Functions
• Aggregation and Windowing
Working with Impala
• How Impala Executes Queries
• Extending Impala with User-Defined Functions
• Improving Impala Performance
Analyzing Text and Complex Data with Hive
• Complex Values in Hive
• Using Regular Expressions in Hive
• Sentiment Analysis and N-Grams
• Conclusion
Hive Optimization
• Understanding Query Performance
• Controlling Job Execution Plan
• Bucketing
• Indexing Data
Extending Hive
• SerDes
• Data Transformation with Custom Scripts
• User-Defined Functions
• Parameterized Queries
Importing Relational Data with Apache Sqoop
• Sqoop Overview
• Basic Imports and Exports
• Limiting Results
• Improving Sqoop’s Performance
• Sqoop 2
Introduction to Impala and Hive
• Introduction to Impala and Hive
• Why Use Impala and Hive?
• Comparing Hive to Traditional Databases
• Hive Use Cases
Modelling and Managing Data with Impala and Hive
• Data Storage Overview
• Creating Databases and Tables
• Loading Data into Tables
• HCatalog
• Impala Metadata Caching
Data Formats
• Selecting a File Format
• Hadoop Tool Support for File Formats
• Avro Schemas
• Using Avro with Hive and Sqoop
• Avro Schema Evolution
• Compression
Data Partitioning
• Partitioning Overview
• Partitioning in Impala and Hive
Capturing Data with Apache Flume
• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
Spark Basics
• What is Apache Spark?
• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark
Working with RDDs in Spark
• A Closer Look at RDDs
• Key-Value Pair RDDs
• MapReduce
• Other Pair RDD Operations
Writing and Deploying Spark Applications
• Spark Applications vs. Spark Shell
• Creating the SparkContext
• Building a Spark Application (Scala and Java)
• Running a Spark Application
• The Spark Application Web UI
• Configuring Spark Properties
• Logging
Parallel Programming with Spark
• Review: Spark on a Cluster
• RDD Partitions
• Partitioning of File-based RDDs
• HDFS and Data Locality
• Executing Parallel Operations
• Stages and Tasks
Spark Caching and Persistence
• RDD Lineage
• Caching Overview
• Distributed Persistence
Common Patterns in Spark Data Processing
• Common Spark Use Cases
• Iterative Algorithms in Spark
• Graph Processing and Analysis
• Machine Learning
• Example: k-means
Preview: Spark SQL
• Spark SQL and the SQL Context
• Creating DataFrames
• Transforming and Querying DataFrames
• Saving DataFrames
• Comparing Spark SQL with Impala
Hadoop Testing
• Hadoop Application Testing
• Roles and Responsibilities of Hadoop Testing Professional
• Framework MRUnit for Testing of MapReduce Programs
• Unit Testing
• Test Execution
• Test Plan Strategy and Writing Test Cases for Testing Hadoop Application
Big Data Testing
• BigData Testing
• Unit Testing
• Integration Testing
• Functional Testing
• Non-Functional Testing
• Golden Data Set
System Testing
• Building and Set up
• Testing SetUp
• Solary Server
• Non-Functional Testing
• Longevity Testing
• Volumetric Testing
Security Testing
• Security Testing
• Non-Functional Testing
• Hadoop Cluster
• Security-Authorization RBA
• IBM Project
Automation Testing
• Query Surge Tool
Oozie
• Why Oozie
• Installation Engine
• Oozie Workflow Engine
• Oozie security
• Oozie Job Process
• Oozie terminology
• Oozie bundle
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