1. What does ‘jps’ command do?
Ans: It gives the status of the deamons which run Hadoop cluster. It gives the output mentioning the status of namenode, datanode , secondary namenode, Jobtracker and Task tracker.
2. How to restart Namenode?
Ans: Step-1. Click on stop-all.sh and then click on start-all.sh OR
Step-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).
3. Which are the three modes in which Hadoop can be run?
Ans: The three modes in which Hadoop can be run are −standalone (local) modePseudo-distributed modeFully distributed mode
4. What does /etc /init.d do?
Ans: /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.
5. What if a Namenode has no data?
Ans: It cannot be part of the Hadoop cluster.
6. What happens to job tracker when Namenode is down?
Ans: When Namenode is down, your cluster is OFF, this is because Namenode is the single point of failure in HDFS.
7. What is Big Data?
Ans: 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.
8. What are the four characteristics of Big Data?
Ans: 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. Veracity: biases, noise and abnormality in dataHow is analysis of Big Data 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.
9. Why do we need Hadoop?
Ans: 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. The following link Why Hadoop gives a detailed explanation about why Hadoop is gaining so much popularity!
10. What is the basic difference between traditional RDBMS and Hadoop?
Ans: 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
11. What is Fault Tolerance?
Ans: 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 it 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 least 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?No, 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.
12. What is a Namenode?
Ans : 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.
13. Is Namenode also a commodity hardware?
Ans: No. Namenode can never be 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.
14 .What is a Datanode?
Ans: 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.
15. Why do we use HDFS for applications having large data sets and not when there are lot of small files?Ans: 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.
16. What is a job tracker?
Ans: 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.
17. What is a task tracker?
Ans: 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.
18. What is a heartbeat in HDFS?
Ans: 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.
19. What is a ‘block’ in HDFS?
Ans: 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.
20. What are the benefits of block transfer?
Ans: 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?
21. How indexing is done in HDFS?
Ans: 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.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.
22. What is the communication channel between client and namenode/datanode?
Ans: The mode of communication is SSH.
23. What is a rack?
Ans: 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.
24. What is a Secondary Namenode? Is it a substitute to the Namenode?
Ans: 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.Explain how do ‘map’ and ‘reduce’ works.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.
25. Why ‘Reading‘ is done in parallel and ‘Writing‘ is not in HDFS?
Ans: Through mapreduce program the file can be read by splitting its blocks when reading. But while writing as the incoming values are not yet known to the system mapreduce cannot be applied and no parallel writing is possible.Copy a directory from one node in the cluster to anotherUse ‘-distcp’ command to copy,Default replication factor to a file is 3.Use ‘-setrep’ command to change replication factor of a file to 2.hadoop fs -setrep -w 2 apache_hadoop/sample.txt
Ans: Step-1. Click on stop-all.sh and then click on start-all.sh OR
Step-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).
3. Which are the three modes in which Hadoop can be run?
Ans: The three modes in which Hadoop can be run are −standalone (local) modePseudo-distributed modeFully distributed mode
4. What does /etc /init.d do?
Ans: /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.
5. What if a Namenode has no data?
Ans: It cannot be part of the Hadoop cluster.
6. What happens to job tracker when Namenode is down?
Ans: When Namenode is down, your cluster is OFF, this is because Namenode is the single point of failure in HDFS.
7. What is Big Data?
Ans: 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.
8. What are the four characteristics of Big Data?
Ans: 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. Veracity: biases, noise and abnormality in dataHow is analysis of Big Data 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.
9. Why do we need Hadoop?
Ans: 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. The following link Why Hadoop gives a detailed explanation about why Hadoop is gaining so much popularity!
10. What is the basic difference between traditional RDBMS and Hadoop?
Ans: 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
11. What is Fault Tolerance?
Ans: 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 it 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 least 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?No, 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.
12. What is a Namenode?
Ans : 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.
13. Is Namenode also a commodity hardware?
Ans: No. Namenode can never be 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.
14 .What is a Datanode?
Ans: 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.
15. Why do we use HDFS for applications having large data sets and not when there are lot of small files?Ans: 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.
16. What is a job tracker?
Ans: 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.
17. What is a task tracker?
Ans: 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.
18. What is a heartbeat in HDFS?
Ans: 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.
19. What is a ‘block’ in HDFS?
Ans: 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.
20. What are the benefits of block transfer?
Ans: 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?
21. How indexing is done in HDFS?
Ans: 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.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.
22. What is the communication channel between client and namenode/datanode?
Ans: The mode of communication is SSH.
23. What is a rack?
Ans: 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.
24. What is a Secondary Namenode? Is it a substitute to the Namenode?
Ans: 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.Explain how do ‘map’ and ‘reduce’ works.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.
25. Why ‘Reading‘ is done in parallel and ‘Writing‘ is not in HDFS?
Ans: Through mapreduce program the file can be read by splitting its blocks when reading. But while writing as the incoming values are not yet known to the system mapreduce cannot be applied and no parallel writing is possible.Copy a directory from one node in the cluster to anotherUse ‘-distcp’ command to copy,Default replication factor to a file is 3.Use ‘-setrep’ command to change replication factor of a file to 2.hadoop fs -setrep -w 2 apache_hadoop/sample.txt
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