Real time Hadoop and Big data interview questions
Real time Hadoop and Big data interview questions |
What is BIG DATA?
Big Data is
nothing but an assortment of such a huge and complex data that it becomes very
tedious to capture, store, process, retrieve and analyze it with the help of
on-hand database management tools or traditional data processing techniques.
Can you give some examples of Big Data?
There are many real
life examples of Big Data! Facebook is generating 500+ terabytes of data per
day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade
data per day, a jet airline collects 10 terabytes of censor data for every 30
minutes of flying time. All these are day to day examples of Big Data!
Can you give a detailed overview about the Big Data being
generated by Facebook?
As of December 31,
2012, there are 1.06 billion monthly active users on facebook and 680 million
mobile users. On an average, 3.2 billion likes and comments are posted every
day on Facebook. 72% of web audience is on Facebook. And why not! There are so
many activities going on facebook from wall posts, sharing images, videos,
writing comments and liking posts, etc. In fact, Facebook started using
Hadoop in mid-2009 and was one of the initial users of Hadoop.
According to IBM, what are the three characteristics of Big Data?
According to IBM, the
three characteristics of Big Data are:
Volume: Facebook generating 500+ terabytes of
data per day.
Velocity: Analyzing 2 million records each day to identify the reason for losses.
Variety: images, audio, video, sensor data, log files, etc.
Velocity: Analyzing 2 million records each day to identify the reason for losses.
Variety: images, audio, video, sensor data, log files, etc.
How Big is ‘Big Data’?
With time, data volume
is growing exponentially. Earlier we used to talk about Megabytes or Gigabytes.
But time has arrived when we talk about data volume in terms of terabytes,
petabytes and also zettabytes! Global data volume was around 1.8ZB in 2011 and
is expected to be 7.9ZB in 2015. It is also known that the global information
doubles in every two years!
How analysis of Big Data is useful for organizations?
Effective analysis of
Big Data provides a lot of business advantage as organizations will learn which
areas to focus on and which areas are less important. Big data analysis
provides some early key indicators that can prevent the company from a huge
loss or help in grasping a great opportunity with open hands! A precise
analysis of Big Data helps in decision making! For instance, nowadays people
rely so much on Facebook and Twitter before buying any product or service. All
thanks to the Big Data explosion.
Who are ‘Data Scientists’?
Data scientists are
soon replacing business analysts or data analysts. Data scientists are experts
who find solutions to analyze data. Just as web analysis, we have data
scientists who have good business insight as to how to handle a business
challenge. Sharp data scientists are not only involved in dealing business
problems, but also choosing the relevant issues that can bring value-addition
to the organization.
What is Hadoop?
Hadoop is a framework
that allows for distributed processing of large data sets across clusters of
commodity computers using a simple programming model.
Why the name ‘Hadoop’?
Hadoop doesn’t have
any expanding version like ‘oops’. The charming yellow elephant you see is
basically named after Doug’s son’s toy elephant!
Why do we need Hadoop?
Everyday a large
amount of unstructured data is getting dumped into our machines. The major
challenge is not to store large data sets in our systems but to retrieve and
analyze the big data in the organizations, that too data present in
different machines at different locations. In this situation a necessity for
Hadoop arises. Hadoop has the ability to analyze the data present in different
machines at different locations very quickly and in a very cost effective way.
It uses the concept of MapReduce which enables it to divide the query into
small parts and process them in parallel. This is also known as parallel
computing.
What are some of the characteristics of Hadoop framework?
Hadoop framework is
written in Java. It is designed to solve problems that involve analyzing large
data (e.g. petabytes). The programming model is based on Google’s MapReduce.
The infrastructure is based on Google’s Big Data and Distributed File System.
Hadoop handles large files/data throughput and supports data intensive
distributed applications. Hadoop is scalable as more nodes can be easily added
to it.
Give a brief overview of Hadoop history.
In 2002, Doug Cutting
created an open source, web crawler project.
In 2004, Google published MapReduce, GFS papers.
In 2006, Doug Cutting developed the open source, Mapreduce and HDFS project.
In 2008, Yahoo ran 4,000 node Hadoop cluster and Hadoop won terabyte sort benchmark.
In 2009, Facebook launched SQL support for Hadoop.
In 2004, Google published MapReduce, GFS papers.
In 2006, Doug Cutting developed the open source, Mapreduce and HDFS project.
In 2008, Yahoo ran 4,000 node Hadoop cluster and Hadoop won terabyte sort benchmark.
In 2009, Facebook launched SQL support for Hadoop.
Give examples of some companies that are using Hadoop structure?
A lot of companies are
using the Hadoop structure such as Cloudera, EMC, MapR, Hortonworks, Amazon,
Facebook, eBay, Twitter, Google and so on.
What is the basic difference between traditional RDBMS and Hadoop?
Traditional RDBMS is
used for transactional systems to report and archive the data, whereas Hadoop is an approach to store huge amount of data in
the distributed file system and process it. RDBMS will be useful when you want
to seek one record from Big data, whereas, Hadoop will be useful when you want
Big data in one shot and perform analysis on that later.
What is structured and unstructured data?
Structured data is the data that is easily identifiable as
it is organized in a structure. The most common form of structured data is a
database where specific information is stored in tables, that
is, rows and columns. Unstructured data refers to any data that
cannot be identified easily. It could be in the form of images, videos,
documents, email, logs and random text. It is not in the form of rows and
columns.
What are the core components of Hadoop?
Core components of
Hadoop are HDFS and MapReduce. HDFS is basically used to store large data sets
and MapReduce is used to process such large data sets.
What is HDFS?
HDFS is a file system
designed for storing very large files with streaming data access patterns,
running clusters on commodity hardware.
What are the key features of HDFS?
HDFS is highly
fault-tolerant, with high throughput, suitable for applications with large data
sets, streaming access to file system data and can be built out of commodity
hardware.
What is Fault Tolerance?
Suppose you have a
file stored in a system, and due to some technical problem that file gets
destroyed. Then there is no chance of getting the data back present in that
file. To avoid such situations, Hadoop has introduced the feature of fault
tolerance in HDFS. In Hadoop, when we store a file, it automatically gets
replicated at two other locations also. So even if one or two of the systems
collapse, the file is still available on the third system.
Replication causes data redundancy then why is is pursued in HDFS?
HDFS works with
commodity hardware (systems with average configurations) that has high chances
of getting crashed any time. Thus, to make the entire system highly
fault-tolerant, HDFS replicates and stores data in different places. Any data
on HDFS gets stored at atleast 3 different locations. So, even if one of them
is corrupted and the other is unavailable for some time for any reason, then
data can be accessed from the third one. Hence, there is no chance of losing
the data. This replication factor helps us to attain the feature of Hadoop
called Fault Tolerant.
Since the data is replicated thrice in HDFS, does it mean that any
calculation done on one node will also be replicated on the other two?
Since there are 3
nodes, when we send the MapReduce programs, calculations will be done only on
the original data. The master node will know which node exactly has
that particular data. In case, if one of the nodes is not responding,
it is assumed to be failed. Only then, the required calculation will be done on
the second replica.
What is throughput? How does HDFS get a good throughput?
Throughput is the amount of
work done in a unit time. It describes how fast the data is getting accessed
from the system and it is usually used to measure performance of the system. In
HDFS, when we want to perform a task or an action, then the work is divided and
shared among different systems. So all the systems will be executing the
tasks assigned to them independently and in parallel. So the work will be
completed in a very short period of time. In this way, the HDFS gives good
throughput. By reading data in parallel, we decrease the actual time to read
data tremendously.
What is streaming access?
As HDFS works on the principle of ‘Write Once, Read Many‘,
the feature of streaming access is extremely important in HDFS. HDFS
focuses not so much on storing the data but how to retrieve it at the
fastest possible speed, especially while analyzing logs. In HDFS, reading the
complete data is more important than the time taken to fetch a single record
from the data.
What is a commodity hardware? Does commodity hardware include
RAM?
Commodity hardware is
a non-expensive system which is not of high quality or high-availability.
Hadoop can be installed in any average commodity hardware. We don’t need super
computers or high-end hardware to work on Hadoop. Yes, Commodity hardware
includes RAM because there will be some services which will be running on RAM.
What is a Namenode?
Namenode is the master
node on which job tracker runs and consists of the metadata. It maintains and
manages the blocks which are present on the datanodes. It is a
high-availability machine and single point of failure in HDFS.
Is Namenode also a commodity?
No. Namenode can never
be a commodity hardware because the entire HDFS rely on it. It
is the single point of failure in HDFS. Namenode has to be a high-availability
machine.
What is a metadata?
Metadata is the
information about the data stored in datanodes such as location of the file,
size of the file and so on.
What is a Datanode?
Datanodes are the
slaves which are deployed on each machine and provide the actual storage. These
are responsible for serving read and write requests for the clients.
Why do we use HDFS for applications having large data sets and not
when there are lot of small files?
HDFS is more suitable
for large amount of data sets in a single file as compared to small amount of
data spread across multiple files. This is because Namenode is a very expensive
high performance system, so it is not prudent to occupy the space in the
Namenode by unnecessary amount of metadata that is generated for multiple small
files. So, when there is a large amount of data in a single file, name node
will occupy less space. Hence for getting optimized performance, HDFS supports
large data sets instead of multiple small files.
What is a daemon?
Daemon is a process or
service that runs in background. In general, we use this word in UNIX
environment. The equivalent of Daemon in Windows is “services” and in Dos is ”
TSR”.
What is a job tracker?
Job tracker is a
daemon that runs on a namenode for submitting and tracking MapReduce jobs in
Hadoop. It assigns the tasks to the different task tracker. In a Hadoop
cluster, there will be only one job tracker but many task trackers. It is the
single point of failure for Hadoop and MapReduce Service. If the job tracker
goes down all the running jobs are halted. It receives heartbeat from task
tracker based on which Job tracker decides whether the assigned task is
completed or not.
What is a task tracker?
Task tracker is also a
daemon that runs on datanodes. Task Trackers manage the execution of individual
tasks on slave node. When a client submits a job, the job tracker will
initialize the job and divide the work and assign them to different task
trackers to perform MapReduce tasks. While performing this action, the task
tracker will be simultaneously communicating with job tracker by sending
heartbeat. If the job tracker does not receive heartbeat from task tracker
within specified time, then it will assume that task tracker has crashed and
assign that task to another task tracker in the cluster.
Is Namenode machine same as datanode machine as in terms of
hardware?
It depends upon the
cluster you are trying to create. The Hadoop VM can be there on the same
machine or on another machine. For instance, in a single node cluster, there is
only one machine, whereas in the development or in a testing environment,
Namenode and datanodes are on different machines.
What is a heartbeat in HDFS?
A heartbeat is a
signal indicating that it is alive. A datanode sends heartbeat to Namenode and
task tracker will send its heart beat to job tracker. If the Namenode or job
tracker does not receive heart beat then they will decide that there is some
problem in datanode or task tracker is unable to perform the assigned task.
Are Namenode and job tracker on the same host?
No, in
practical environment, Namenode is on a separate host and job tracker is
on a separate host.
What is a ‘block’ in HDFS?
A ‘block’ is the
minimum amount of data that can be read or written. In HDFS, the default block
size is 64 MB as contrast to the block size of 8192 bytes in Unix/Linux. Files
in HDFS are broken down into block-sized chunks, which are stored as
independent units. HDFS blocks are large as compared to disk blocks,
particularly to minimize the cost of seeks.
If a particular file is 50 mb, will the HDFS block still consume
64 mb as the default size?
No, not at all! 64 mb
is just a unit where the data will be stored. In this particular situation,
only 50 mb will be consumed by an HDFS block and 14 mb will be free to store
something else. It is the MasterNode that does data allocation in an efficient
manner.
What are the benefits of block transfer?
A file can be larger
than any single disk in the network. There’s nothing that requires the blocks
from a file to be stored on the same disk, so they can take advantage of any of
the disks in the cluster. Making the unit of abstraction a block rather
than a file simplifies the storage subsystem. Blocks provide fault
tolerance and availability. To insure against corrupted blocks and disk and
machine failure, each block is replicated to a small number of physically
separate machines (typically three). If a block becomes unavailable, a copy can
be read from another location in a way that is transparent to the client.
If we want to copy 10 blocks from one machine to
another, but another machine can copy only 8.5 blocks, can the blocks be broken
at the time of replication?
In HDFS, blocks cannot
be broken down. Before copying the blocks from one machine to another, the
Master node will figure out what is the actual amount of space required, how
many block are being used, how much space is available, and it will allocate
the blocks accordingly.
How indexing is done in HDFS?
Hadoop has its own way
of indexing. Depending upon the block size, once the data is stored, HDFS will
keep on storing the last part of the data which will say where the next part of
the data will be. In fact, this is the base of HDFS.
If a data Node is full how it’s identified?
When data is stored in
datanode, then the metadata of that data will be stored in the Namenode. So
Namenode will identify if the data node is full.
If datanodes increase, then do we need to
upgrade Namenode?
While installing the
Hadoop system, Namenode is determined based on the size of the clusters. Most
of the time, we do not need to upgrade the Namenode because it does not store
the actual data, but just the metadata, so such a requirement rarely arise.
Are job tracker and task trackers present in
separate machines?
Yes, job tracker and
task tracker are present in different machines. The reason is job tracker is a
single point of failure for the Hadoop MapReduce service. If it goes down, all
running jobs are halted.
When we send a data to a node, do we allow
settling in time, before sending another data to that node?
Yes, we do.
Does hadoop always require digital data to
process?
Yes. Hadoop
always require digital data to be processed.
On what basis Namenode will decide which
datanode to write on?
As the Namenode has
the metadata (information) related to all the data nodes, it knows which
datanode is free.
Doesn’t Google have its very own version of DFS?
Yes, Google owns a DFS known as “Google File System (GFS)”
developed by Google Inc. for its own use.
Who is a ‘user’ in HDFS?
A user is like you or
me, who has some query or who needs some kind of data.
Is client the end user in HDFS?
No, Client is an
application which runs on your machine, which is used to interact with the
Namenode (job tracker) or datanode (task tracker).
What is the communication channel between client
and namenode/datanode?
The mode of
communication is SSH.
What is a rack?
Rack is a storage area
with all the datanodes put together. These datanodes can be physically located
at different places. Rack is a physical collection of datanodes which are
stored at a single location. There can be multiple racks in a single location.
On what basis data will be stored on a rack?
When the client is ready to load a file into the cluster, the
content of the file will be divided into blocks. Now the client consults the
Namenode and gets 3 datanodes for every block of the file which indicates where
the block should be stored. While placing the datanodes, the key rule followed
is “for every block of data, two copies will exist in one rack, third
copy in a different rack“. This rule is known as “Replica Placement Policy“.
Do we need to place 2nd and 3rd data in rack 2
only?
Yes, this is to avoid
datanode failure.
What if rack 2 and datanode fails?
If both rack2 and
datanode present in rack 1 fails then there is no chance of getting data from
it. In order to avoid such situations, we need to replicate that data more
number of times instead of replicating only thrice. This can be done by
changing the value in replication factor which is set to 3 by default.
What is a Secondary Namenode? Is it a substitute
to the Namenode?
The secondary Namenode
constantly reads the data from the RAM of the Namenode and writes it into the
hard disk or the file system. It is not a substitute to the Namenode, so if the
Namenode fails, the entire Hadoop system goes down.
What is the difference between Gen1 and Gen2
Hadoop with regards to the Namenode?
In Gen 1 Hadoop,
Namenode is the single point of failure. In Gen 2 Hadoop, we have what is known
as Active and Passive Namenodes kind of a structure. If the active Namenode
fails, passive Namenode takes over the charge.
What is MapReduce?
Map Reduce is the ‘heart‘ of Hadoop that
consists of two parts – ‘map’ and ‘reduce’. Maps and reduces are programs for
processing data. ‘Map’ processes the data first to give some intermediate
output which is further processed by ‘Reduce’ to generate the final output.
Thus, MapReduce allows for distributed processing of the map and reduction
operations.
Can you explain how do ‘map’ and ‘reduce’ work?
Namenode takes the
input and divide it into parts and assign them to data nodes. These datanodes
process the tasks assigned to them and make a key-value pair and returns the
intermediate output to the Reducer. The reducer collects this key value pairs
of all the datanodes and combines them and generates the final output.
What is ‘Key value pair’ in HDFS?
Key value pair is
the intermediate data generated by maps and sent to reduces for generating the
final output.
What is the difference between MapReduce engine
and HDFS cluster?
HDFS cluster is the
name given to the whole configuration of master and slaves where data is
stored. Map Reduce Engine is the programming module which is used to retrieve
and analyze data.
Is map like a pointer?
No, Map is not like a
pointer.
Do we require two servers for the Namenode and
the datanodes?
Yes, we need two
different servers for the Namenode and the datanodes. This is because Namenode
requires highly configurable system as it stores information about the location
details of all the files stored in different datanodes and on the other hand,
datanodes require low configuration system.
Why are the number of splits equal to the number
of maps?
The number of maps is
equal to the number of input splits because we want the key and value pairs of
all the input splits.
Is a job split into maps?
No, a job is not split
into maps. Spilt is created for the file. The file is placed on datanodes in
blocks. For each split, a map is needed.
Which are the two types of ‘writes’ in HDFS?
There are two types of writes in HDFS: posted and non-posted write. Posted Write is when we write
it and forget about it, without worrying about the acknowledgement. It is
similar to our traditional Indian post. In a Non-posted Write, we wait for
the acknowledgement. It is similar to the today’s courier services. Naturally,
non-posted write is more expensive than the posted write. It is much more
expensive, though both writes are asynchronous.
Why ‘Reading‘ is done in
parallel and ‘Writing‘ is not in HDFS?
Reading is done in
parallel because by doing so we can access the data fast. But we do not perform
the write operation in parallel. The reason is that if we
perform the write operation in parallel, then it might result in
data inconsistency. For example, you have a file and two nodes are trying to
write data into the file in parallel, then the first node does not know what
the second node has written and vice-versa. So, this makes it confusing which
data to be stored and accessed.
Can Hadoop be compared to NOSQL database like
Cassandra?
Though NOSQL is the closet technology that can be compared
to Hadoop, it has its own pros and cons. There is no DFS in NOSQL. Hadoop is
not a database. It’s a filesystem (HDFS) and distributed programming framework
(MapReduce).
How can I install Cloudera VM in my system?
When you enrol for the hadoop course at Edureka, you can download
the Hadoop Installation steps.pdf file from our dropbox.
This will be shared with you by an e-mail.
Which are the three modes in which Hadoop can be run?
The
three modes in which Hadoop can be run are:
1.
standalone (local) mode
2. Pseudo-distributed mode
3. Fully distributed mode
2. Pseudo-distributed mode
3. Fully distributed mode
What are the features of Stand alone (local) mode?
In
stand-alone mode there are no daemons, everything runs on a single JVM. It has
no DFS and utilizes the local file system. Stand-alone mode is suitable
only for running MapReduce programs during development. It is one of the most
least used environments.
What are the features of Pseudo mode?
Pseudo
mode is used both for development and in the QA environment. In the Pseudo mode
all the daemons run on the same machine.
Can we call VMs as pseudos?
No, VMs
are not pseudos because VM is something different and pesudo is very specific
to Hadoop.
What are the features of Fully Distributed mode?
Fully
Distributed mode is used in the production environment, where we have ‘n’
number of machines forming a Hadoop cluster. Hadoop daemons run on a cluster of
machines. There is one host onto which Namenode is running and another host on
which datanode is running and then there are machines on which task tracker is
running. We have separate masters and separate slaves in this distribution.
Does Hadoop follows the UNIX pattern?
Yes, Hadoop closely follows the UNIX pattern. Hadoop also has
the ‘conf‘
directory as in the case of UNIX.
In which directory Hadoop is installed?
Cloudera
and Apache has the same directory structure. Hadoop is installed in cd /usr/lib/hadoop-0.20/.
What are the port numbers of Namenode, job tracker and task tracker?
The
port number for Namenode is ’70′, for job tracker is ’30′ and for task tracker
is ’60′.
What is the Hadoop-core configuration?
Hadoop
core is configured by two xml files:
1. hadoop-default.xml which was renamed to 2. hadoop-site.xml.
These files are written in xml format. We have certain properties in these xml files, which consist of name and value. But these files do not exist now.
1. hadoop-default.xml which was renamed to 2. hadoop-site.xml.
These files are written in xml format. We have certain properties in these xml files, which consist of name and value. But these files do not exist now.
What are the Hadoop configuration files at present?
There
are 3 configuration files in Hadoop:
1.
core-site.xml
2.
hdfs-site.xml
3.
mapred-site.xml
These files are located in the conf/ subdirectory.
How to exit the Vi editor?
To exit
the Vi Editor, press ESC and type :q and then press enter.
What is a spill factor with respect to the RAM?
Spill
factor is the size after which your files move to the temp file. Hadoop-temp
directory is used for this.
Is fs.mapr.working.dir a single directory?
Yes, fs.mapr.working.dir it
is just one directory.
Which are the three main hdfs-site.xml properties?
The
three main hdfs-site.xml properties are:
1. dfs.name.dir which
gives you the location on which metadata will be stored and where DFS is
located – on disk or onto the remote.
2. dfs.data.dir which
gives you the location where the data is going to be stored.
3. fs.checkpoint.dir
which is for secondary Namenode.
How to come out of the insert mode?
To come
out of the insert mode, press ESC, type :q (if you have not written anything)
OR type :wq (if you have written anything in the file) and then press ENTER.
What is Cloudera and why it is used?
Cloudera
is the distribution of Hadoop. It is a user created on VM by default. Cloudera
belongs to Apache and is used for data processing.
What happens if you get a ‘connection refused java exception’ when you
type hadoop fsck /?
It
could mean that the Namenode is not working on your VM.
We are using Ubuntu operating system with Cloudera, but from where we
can download Hadoop or does it come by default with Ubuntu?
This is
a default configuration of Hadoop that you have to download from Cloudera or
from Edureka’s dropbox and the run it on your systems. You can also proceed
with your own configuration but you need a Linux box, be it Ubuntu or Red hat.
There are installation steps present at the Cloudera location or in Edureka’s
Drop box. You can go either ways.
What does ‘jps’ command do?
This
command checks whether your Namenode, datanode, task tracker, job tracker, etc
are working or not.
How can I restart Namenode?
1. Click on stop-all.sh and
then click on start-all.sh OR
2. Write sudo hdfs (press enter), su-hdfs (press enter), /etc/init.d/ha (press enter) and then /etc/init.d/hadoop-0.20-namenode start (press enter).
2. Write sudo hdfs (press enter), su-hdfs (press enter), /etc/init.d/ha (press enter) and then /etc/init.d/hadoop-0.20-namenode start (press enter).
What is the full form of fsck?
Full form of fsck is File
System Check.
How can we check whether Namenode is working or not?
To
check whether Namenode is working or not, use the command /etc/init.d/hadoop-0.20-namenode
status or as simple as jps.
What does the command mapred.job.tracker do?
The
command mapred.job.tracker lists
out which of your nodes is acting as a job tracker.
What does /etc /init.d do?
/etc
/init.d specifies where daemons (services) are placed or to see
the status of these daemons. It is very LINUX specific, and nothing to do with
Hadoop.
How can we look for the Namenode in the browser?
If you have to look for Namenode in the browser, you don’t have
to give localhost:8021, the port number to look for Namenode in the brower
is 50070.
How to change from SU to Cloudera?
To
change from SU to Cloudera just type exit.
Which files are used by the startup and shutdown commands?
Slaves and Masters are used by the startup and the shutdown commands.
What do slaves consist of?
Slaves
consist of a list of hosts, one per line, that host datanode and task tracker
servers.
What do masters consist of?
Masters
contain a list of hosts, one per line, that are to host secondary namenode
servers.
What does hadoop-env.sh do?
hadoop-env.sh provides
the environment for Hadoop to run. JAVA_HOME is set over here.
Can we have multiple entries in the master files?
Yes, we
can have multiple entries in the Master files.
Where is hadoop-env.sh file present?
hadoop-env.sh file
is present in the conf location.
In Hadoop_PID_DIR, what does PID stands for?
PID
stands for ‘Process ID’.
What does /var/hadoop/pids do?
It
stores the PID.
What does hadoop-metrics.properties file do?
hadoop-metrics.properties is
used for ‘Reporting‘
purposes. It controls the reporting for Hadoop. The default status is ‘not to report‘.
What are the network requirements for Hadoop?
The Hadoop core uses Shell (SSH) to launch the server processes
on the slave nodes. It requires password-less SSH
connection between the master and all the slaves and the secondary machines.
Why do we need a password-less SSH in Fully Distributed environment?
We need a password-less SSH
in a Fully-Distributed environment because when the cluster is LIVE and running
in Fully
Distributed environment, the communication is too frequent. The job tracker should be able to send a task to task tracker quickly.
Distributed environment, the communication is too frequent. The job tracker should be able to send a task to task tracker quickly.
Does this lead to security issues?
No, not
at all. Hadoop cluster is an isolated cluster. And generally it has nothing to
do with an internet. It has a different kind of a configuration. We needn’t
worry about that kind of a security breach, for instance, someone hacking
through the internet, and so on. Hadoop has a very secured way to connect to
other machines to fetch and to process data.
On which port does SSH work?
SSH
works on Port No. 22, though
it can be configured. 22 is
the default Port number.
Can you tell us more about SSH?
SSH is
nothing but a secure shell communication, it is a kind of a protocol that works
on a Port No. 22, and when you do an SSH, what you really require is a
password.
Why password is needed in SSH localhost?
Password is required in SSH for security and in a situation
where password-less communication
is not set.
Do we need to give a password, even if the key is added in SSH?
Yes,
password is still required even if the key is added in SSH.
What if a Namenode has no data?
If a
Namenode has no data it is not a Namenode. Practically, Namenode will have some
data.
What happens to job tracker when Namenode is down?
When
Namenode is down, your cluster is OFF, this is because Namenode is the single
point of failure in HDFS.
What happens to a Namenode, when job tracker is down?
When a
job tracker is down, it will not be functional but Namenode will be present.
So, cluster is accessible if Namenode is working, even if the job tracker is
not working.
Can you give us some more details about SSH communication between
Masters and the Slaves?
SSH is
a password-less secure communication where data packets are sent across the
slave. It has some format into which data is sent across. SSH is not only
between masters and slaves but also between two hosts.
What is formatting of the DFS?
Just
like we do for Windows, DFS is formatted for proper structuring. It is not
usually done as it formats the Namenode too.
Does the HDFS client decide the input split or Namenode?
No, the
Client does not decide. It is already specified in one of the configurations
through which input split is already configured.
In Cloudera there is already a cluster, but if I want to form a cluster
on Ubuntu can we do it?
Yes,
you can go ahead with this! There are installation steps for creating a new
cluster. You can uninstall your present cluster and install the new cluster.
Can we create a Hadoop cluster from scratch?
Yes we
can do that also once we are familiar with the Hadoop environment.
Can we use Windows for Hadoop?
Actually, Red
Hat Linux or Ubuntu are
the best Operating Systems for Hadoop. Windows is not used frequently for
installing Hadoop as there are many support problems attached with Windows.
Thus, Windows is not a preferred environment for Hadoop.