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 file system (HDFS) and distributed programming framework (MapReduce).
How Many Daemon Processes Run On A Hadoop System?
Hadoop is comprised of five separate daemons. Each of these daemon run in its own JVM.Following 3 Daemons run on Master nodes
NameNode : This daemon stores and maintains the metadata for HDFS.
Secondary NameNode : Performs housekeeping functions for the NameNode.
JobTracker : Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker.
Following 2 Daemons run on each Slave nodes
DataNode : Stores actual HDFS data blocks.
TaskTracker : Responsible for instantiating and monitoring individual Map and Reduce tasks.
What Is Configuration Of A Typical Slave Node On Hadoop Cluster? How Many Jvms Run On A Slave Node?
Single instance of a Task Tracker is run on each Slave node. Task tracker is run as a separate JVM process.
Single instance of a DataNode daemon is run on each Slave node. DataNode daemon is run as a separate JVM process.
One or Multiple instances of Task Instance is run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.
What Is The Difference Between Hdfs And Nas ?
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.
Following are differences between HDFS and NAS
In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
HDFS is designed to work with MapReduce System, since computation are moved to data. NAS is not suitable for MapReduce since data is stored separately from the computations.
HDFS runs on a cluster of machines and provides redundancy using a replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.
How Namenode Handles Data Node Failures?
NameNode periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not recieved a hearbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead datanode. The NameNode Orchestrates the replication of data blocks from one datanode to another. The replication data transfer happens directly between datanodes and the data never passes through the namenode.
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.
How Jobtracker Schedules A Task?
The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.
What Is A Task Tracker In Hadoop? How Many Instances Of Tasktracker Run On A Hadoop Cluster
A TaskTracker is a slave node daemon in the cluster that accepts tasks (Map, Reduce and Shuffle operations) from a JobTracker. There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on its own JVM process. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it can accept. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. The TaskTracker monitors these task instances, capturing the output and exit codes. When the Task instances finish, successfully or not, the task tracker notifies the JobTracker. The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.
What Is A Task Instance In Hadoop? Where Does It Run?
Task instances are the actual MapReduce jobs which are run on each slave node. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a task.
Does Mapreduce Programming Model Provide A Way For Reducers To Communicate With Each Other? In A Mapreduce Job Can A Reducer Communicate With Another Reducer?
Nope, MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.
Can I Set The Number Of Reducers To Zero?
Yes, Setting the number of reducers to zero is a valid configuration in Hadoop. When you set the reducers to zero no reducers will be executed, and the output of each mapper will be stored to a separate file on HDFS. [This is different from the condition when reducers are set to a number greater than zero and the Mappers output (intermediate data) is written to the Local file system(NOT HDFS) of each mappter slave node.]
Where Is The Mapper Output (intermediate Kay-value Data) Stored ?
The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.
Do you have any Big Data experience? If so, please share it with us.
How to Approach: There is no specific answer to the question as it is a subjective question and the answer depends on your previous experience. Asking this question during a big data interview, the interviewer wants to understand your previous experience and is also trying to evaluate if you are fit for the project requirement.
So, how will you approach the question? If you have previous experience, start with your duties in your past position and slowly add details to the conversation. Tell them about your contributions that made the project successful. This question is generally, the 2nd or 3rd question asked in an interview. The later questions are based on this question, so answer it carefully. You should also take care not to go overboard with a single aspect of your previous job. Keep it simple and to the point.
Do you prefer good data or good models? Why?
How to Approach: This is a tricky question but generally asked in the big data interview. It asks you to choose between good data or good models. As a candidate, you should try to answer it from your experience. Many companies want to follow a strict process of evaluating data, means they have already selected data models. In this case, having good data can be game-changing. The other way around also works as a model is chosen based on good data.
As we already mentioned, answer it from your experience. However, don’t say that having both good data and good models is important as it is hard to have both in real life projects.
What do you understand by Rack Awareness in Hadoop?
It is an algorithm applied to the NameNode to decide how blocks and its replicas are placed. Depending on rack definitions network traffic is minimized between DataNodes within the same rack. For example, if we consider replication factor as 3, two copies will be placed on one rack whereas the third copy in a separate rack.
What is the difference between “HDFS Block” and “Input Split”?
The HDFS divides the input data physically into blocks for processing which is known as HDFS Block.
Input Split is a logical division of data by mapper for mapping operation.
What are the common input formats in Hadoop?
Answer: Below are the common input formats in Hadoop –
Text Input Format – The default input format defined in Hadoop is the Text Input Format.
Sequence File Input Format – To read files in a sequence, Sequence File Input Format is used.
Key Value Input Format – The input format used for plain text files (files broken into lines) is the Key Value Input Format.
Explain some important features of Hadoop.
Answer: Hadoop supports the storage and processing of big data. It is the best solution for handling big data challenges. Some important features of Hadoop are –
Open Source – Hadoop is an open source framework which means it is available free of cost. Also, the users are allowed to change the source code as per their requirements.
Distributed Processing – Hadoop supports distributed processing of data i.e. faster processing. The data in Hadoop HDFS is stored in a distributed manner and MapReduce is responsible for the parallel processing of data.
Fault Tolerance – Hadoop is highly fault-tolerant. It creates three replicas for each block at different nodes, by default. This number can be changed according to the requirement. So, we can recover the data from another node if one node fails. The detection of node failure and recovery of data is done automatically.
Reliability – Hadoop stores data on the cluster in a reliable manner that is independent of machine. So, the data stored in Hadoop environment is not affected by the failure of the machine.
Scalability – Another important feature of Hadoop is the scalability. It is compatible with the other hardware and we can easily ass the new hardware to the nodes.
High Availability – The data stored in Hadoop is available to access even after the hardware failure. In case of hardware failure, the data can be accessed from another path.
Explain the different modes in which Hadoop run.
Answer: Apache Hadoop runs in the following three modes –
Standalone (Local) Mode – By default, Hadoop runs in a local mode i.e. on a non-distributed, single node. This mode uses the local file system to perform input and output operation. This mode does not support the use of HDFS, so it is used for debugging. No custom configuration is needed for configuration files in this mode.
Pseudo-Distributed Mode – In the pseudo-distributed mode, Hadoop runs on a single node just like the Standalone mode. In this mode, each daemon runs in a separate Java process. As all the daemons run on a single node, there is the same node for both the Master and Slave nodes.
Fully – Distributed Mode – In the fully-distributed mode, all the daemons run on separate individual nodes and thus forms a multi-node cluster. There are different nodes for Master and Slave nodes.
What are the configuration parameters in a “MapReduce” program?
The main configuration parameters in “MapReduce” framework are:
Input locations of Jobs in the distributed file system
Output location of Jobs in the distributed file system
The input format of data
The output format of data
The class which contains the map function
The class which contains the reduce function
JAR file which contains the mapper, reducer and the driver classes
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.
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| Facebook Most Frequently Asked Latest Hadoop Interview Questions Answers |
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 file system (HDFS) and distributed programming framework (MapReduce).
How Many Daemon Processes Run On A Hadoop System?
Hadoop is comprised of five separate daemons. Each of these daemon run in its own JVM.Following 3 Daemons run on Master nodes
NameNode : This daemon stores and maintains the metadata for HDFS.
Secondary NameNode : Performs housekeeping functions for the NameNode.
JobTracker : Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker.
Following 2 Daemons run on each Slave nodes
DataNode : Stores actual HDFS data blocks.
TaskTracker : Responsible for instantiating and monitoring individual Map and Reduce tasks.
What Is Configuration Of A Typical Slave Node On Hadoop Cluster? How Many Jvms Run On A Slave Node?
Single instance of a Task Tracker is run on each Slave node. Task tracker is run as a separate JVM process.
Single instance of a DataNode daemon is run on each Slave node. DataNode daemon is run as a separate JVM process.
One or Multiple instances of Task Instance is run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.
What Is The Difference Between Hdfs And Nas ?
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.
Following are differences between HDFS and NAS
In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
HDFS is designed to work with MapReduce System, since computation are moved to data. NAS is not suitable for MapReduce since data is stored separately from the computations.
HDFS runs on a cluster of machines and provides redundancy using a replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.
How Namenode Handles Data Node Failures?
NameNode periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not recieved a hearbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead datanode. The NameNode Orchestrates the replication of data blocks from one datanode to another. The replication data transfer happens directly between datanodes and the data never passes through the namenode.
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.
How Jobtracker Schedules A Task?
The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.
What Is A Task Tracker In Hadoop? How Many Instances Of Tasktracker Run On A Hadoop Cluster
A TaskTracker is a slave node daemon in the cluster that accepts tasks (Map, Reduce and Shuffle operations) from a JobTracker. There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on its own JVM process. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it can accept. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. The TaskTracker monitors these task instances, capturing the output and exit codes. When the Task instances finish, successfully or not, the task tracker notifies the JobTracker. The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.
What Is A Task Instance In Hadoop? Where Does It Run?
Task instances are the actual MapReduce jobs which are run on each slave node. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a task.
Nope, MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.
Can I Set The Number Of Reducers To Zero?
Yes, Setting the number of reducers to zero is a valid configuration in Hadoop. When you set the reducers to zero no reducers will be executed, and the output of each mapper will be stored to a separate file on HDFS. [This is different from the condition when reducers are set to a number greater than zero and the Mappers output (intermediate data) is written to the Local file system(NOT HDFS) of each mappter slave node.]
Where Is The Mapper Output (intermediate Kay-value Data) Stored ?
The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.
Do you have any Big Data experience? If so, please share it with us.
How to Approach: There is no specific answer to the question as it is a subjective question and the answer depends on your previous experience. Asking this question during a big data interview, the interviewer wants to understand your previous experience and is also trying to evaluate if you are fit for the project requirement.
So, how will you approach the question? If you have previous experience, start with your duties in your past position and slowly add details to the conversation. Tell them about your contributions that made the project successful. This question is generally, the 2nd or 3rd question asked in an interview. The later questions are based on this question, so answer it carefully. You should also take care not to go overboard with a single aspect of your previous job. Keep it simple and to the point.
Do you prefer good data or good models? Why?
How to Approach: This is a tricky question but generally asked in the big data interview. It asks you to choose between good data or good models. As a candidate, you should try to answer it from your experience. Many companies want to follow a strict process of evaluating data, means they have already selected data models. In this case, having good data can be game-changing. The other way around also works as a model is chosen based on good data.
As we already mentioned, answer it from your experience. However, don’t say that having both good data and good models is important as it is hard to have both in real life projects.
What do you understand by Rack Awareness in Hadoop?
It is an algorithm applied to the NameNode to decide how blocks and its replicas are placed. Depending on rack definitions network traffic is minimized between DataNodes within the same rack. For example, if we consider replication factor as 3, two copies will be placed on one rack whereas the third copy in a separate rack.
What is the difference between “HDFS Block” and “Input Split”?
The HDFS divides the input data physically into blocks for processing which is known as HDFS Block.
Input Split is a logical division of data by mapper for mapping operation.
What are the common input formats in Hadoop?
Answer: Below are the common input formats in Hadoop –
Text Input Format – The default input format defined in Hadoop is the Text Input Format.
Sequence File Input Format – To read files in a sequence, Sequence File Input Format is used.
Key Value Input Format – The input format used for plain text files (files broken into lines) is the Key Value Input Format.
Explain some important features of Hadoop.
Answer: Hadoop supports the storage and processing of big data. It is the best solution for handling big data challenges. Some important features of Hadoop are –
Open Source – Hadoop is an open source framework which means it is available free of cost. Also, the users are allowed to change the source code as per their requirements.
Distributed Processing – Hadoop supports distributed processing of data i.e. faster processing. The data in Hadoop HDFS is stored in a distributed manner and MapReduce is responsible for the parallel processing of data.
Fault Tolerance – Hadoop is highly fault-tolerant. It creates three replicas for each block at different nodes, by default. This number can be changed according to the requirement. So, we can recover the data from another node if one node fails. The detection of node failure and recovery of data is done automatically.
Reliability – Hadoop stores data on the cluster in a reliable manner that is independent of machine. So, the data stored in Hadoop environment is not affected by the failure of the machine.
Scalability – Another important feature of Hadoop is the scalability. It is compatible with the other hardware and we can easily ass the new hardware to the nodes.
High Availability – The data stored in Hadoop is available to access even after the hardware failure. In case of hardware failure, the data can be accessed from another path.
Explain the different modes in which Hadoop run.
Answer: Apache Hadoop runs in the following three modes –
Standalone (Local) Mode – By default, Hadoop runs in a local mode i.e. on a non-distributed, single node. This mode uses the local file system to perform input and output operation. This mode does not support the use of HDFS, so it is used for debugging. No custom configuration is needed for configuration files in this mode.
Pseudo-Distributed Mode – In the pseudo-distributed mode, Hadoop runs on a single node just like the Standalone mode. In this mode, each daemon runs in a separate Java process. As all the daemons run on a single node, there is the same node for both the Master and Slave nodes.
Fully – Distributed Mode – In the fully-distributed mode, all the daemons run on separate individual nodes and thus forms a multi-node cluster. There are different nodes for Master and Slave nodes.
What are the configuration parameters in a “MapReduce” program?
The main configuration parameters in “MapReduce” framework are:
Input locations of Jobs in the distributed file system
Output location of Jobs in the distributed file system
The input format of data
The output format of data
The class which contains the map function
The class which contains the reduce function
JAR file which contains the mapper, reducer and the driver classes