Spotify Technology Most Frequently Asked Latest Hadoop Interview Questions Answers
What are active and passive “NameNodes”?
In HA (High Availability) architecture, we have two NameNodes – Active “NameNode” and Passive “NameNode”.
Active “NameNode” is the “NameNode” which works and runs in the cluster.
Passive “NameNode” is a standby “NameNode”, which has similar data as active “NameNode”.
When the active “NameNode” fails, the passive “NameNode” replaces the active “NameNode” in the cluster. Hence, the cluster is never without a “NameNode” and so it never fails.
Why does one remove or add nodes in a Hadoop cluster frequently?
One of the most attractive features of the Hadoop framework is its utilization of commodity hardware. However, this leads to frequent “DataNode” crashes in a Hadoop cluster. Another striking feature of Hadoop Framework is the ease of scale in accordance with the rapid growth in data volume. Because of these two reasons, one of the most common task of a Hadoop administrator is to commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster.
What happens when two clients try to access the same file in the HDFS?
HDFS supports exclusive writes only.
When the first client contacts the “NameNode” to open the file for writing, the “NameNode” grants a lease to the client to create this file. When the second client tries to open the same file for writing, the “NameNode” will notice that the lease for the file is already granted to another client, and will reject the open request for the second client.
How does NameNode tackle DataNode failures?
NameNode periodically receives a Heartbeat (signal) from each of the DataNode in the cluster, which implies DataNode is functioning properly.
A block report contains a list of all the blocks on a DataNode. If a DataNode fails to send a heartbeat message, after a specific period of time it is marked dead.
The NameNode replicates the blocks of dead node to another DataNode using the replicas created earlier.
What will you do when NameNode is down?
The NameNode recovery process involves the following steps to make the Hadoop cluster up and running:
Use the file system metadata replica (FsImage) to start a new NameNode.
Then, configure the DataNodes and clients so that they can acknowledge this new NameNode, that is started.
Now the new NameNode will start serving the client after it has completed loading the last checkpoint FsImage (for metadata information) and received enough block reports from the DataNodes.
Name a few companies that use Zookeeper.
Yahoo, Solr, Helprace, Neo4j, Rackspace
What is the role of Zookeeper in HBase architecture?
In HBase architecture, ZooKeeper is the monitoring server that provides different services like –tracking server failure and network partitions, maintaining the configuration information, establishing communication between the clients and region servers, usability of ephemeral nodes to identify the available servers in the cluster.
Explain about ZooKeeper in Kafka
Apache Kafka uses ZooKeeper to be a highly distributed and scalable system. Zookeeper is used by Kafka to store various configurations and use them across the hadoop cluster in a distributed manner. To achieve distributed-ness, configurations are distributed and replicated throughout the leader and follower nodes in the ZooKeeper ensemble. We cannot directly connect to Kafka by bye-passing ZooKeeper because if the ZooKeeper is down it will not be able to serve the client request.
Explain how Zookeeper works
ZooKeeper is referred to as the King of Coordination and distributed applications use ZooKeeper to store and facilitate important configuration information updates. ZooKeeper works by coordinating the processes of distributed applications. ZooKeeper is a robust replicated synchronization service with eventual consistency. A set of nodes is known as an ensemble and persisted data is distributed between multiple nodes.
3 or more independent servers collectively form a ZooKeeper cluster and elect a master. One client connects to any of the specific server and migrates if a particular node fails. The ensemble of ZooKeeper nodes is alive till the majority of nods are working. The master node in ZooKeeper is dynamically selected by the consensus within the ensemble so if the master node fails then the role of master node will migrate to another node which is selected dynamically. Writes are linear and reads are concurrent in ZooKeeper.
List some examples of Zookeeper use cases.
Found by Elastic uses Zookeeper comprehensively for resource allocation, leader election, high priority notifications and discovery. The entire service of Found built up of various systems that read and write to Zookeeper.
Apache Kafka that depends on ZooKeeper is used by LinkedIn
Storm that relies on ZooKeeper is used by popular companies like Groupon and Twitter.
How to use Apache Zookeeper command line interface?
ZooKeeper has a command line client support for interactive use. The command line interface of ZooKeeper is similar to the file and shell system of UNIX. Data in ZooKeeper is stored in a hierarchy of Znodes where each znode can contain data just similar to a file. Each znode can also have children just like directories in the UNIX file system.
Zookeeper-client command is used to launch the command line client. If the initial prompt is hidden by the log messages after entering the command, users can just hit ENTER to view the prompt.
What are the different types of Znodes?
There are 2 types of Znodes namely- Ephemeral and Sequential Znodes.
The Znodes that get destroyed as soon as the client that created it disconnects are referred to as Ephemeral Znodes.
Sequential Znode is the one in which sequential number is chosen by the ZooKeeper ensemble and is pre-fixed when the client assigns name to the znode.
What are watches?
Client disconnection might be troublesome problem especially when we need to keep a track on the state of Znodes at regular intervals. ZooKeeper has an event system referred to as watch which can be set on Znode to trigger an event whenever it is removed, altered or any new children are created below it.
What is a checkpoint?
In brief, “Checkpointing” is a process that takes an FsImage, edit log and compacts them into a new FsImage. Thus, instead of replaying an edit log, the NameNode can load the final in-memory state directly from the FsImage. This is a far more efficient operation and reduces NameNode startup time. Checkpointing is performed by Secondary NameNode.
How is HDFS fault tolerant?
When data is stored over HDFS, NameNode replicates the data to several DataNode. The default replication factor is 3. You can change the configuration factor as per your need. If a DataNode goes down, the NameNode will automatically copy the data to another node from the replicas and make the data available. This provides fault tolerance in HDFS.
Can NameNode and DataNode be a commodity hardware?
The smart answer to this question would be, DataNodes are commodity hardware like personal computers and laptops as it stores data and are required in a large number. But from your experience, you can tell that, NameNode is the master node and it stores metadata about all the blocks stored in HDFS. It requires high memory (RAM) space, so NameNode needs to be a high-end machine with good memory space.
Why do we use HDFS for applications having large data sets and not when there are a lot of small files?
HDFS is more suitable for large amounts of data sets in a single file as compared to small amount of data spread across multiple files. As you know, the NameNode stores the metadata information regarding the file system in the RAM. Therefore, the amount of memory produces a limit to the number of files in my HDFS file system. In other words, too many files will lead to the generation of too much metadata. And, storing these metadata in the RAM will become a challenge. As a thumb rule, metadata for a file, block or directory takes 150 bytes.
How do you define “block” in HDFS? What is the default block size in Hadoop 1 and in Hadoop 2? Can it be changed?
Blocks are the nothing but the smallest continuous location on your hard drive where data is stored. HDFS stores each as blocks, and distribute it across the Hadoop cluster. Files in HDFS are broken down into block-sized chunks, which are stored as independent units.
Hadoop 1 default block size: 64 MB
Hadoop 2 default block size: 128 MB
Yes, blocks can be configured. The dfs.block.size parameter can be used in the hdfs-site.xml file to set the size of a block in a Hadoop environment.
What does ‘jps’ command do?
The ‘jps’ command helps us to check if the Hadoop daemons are running or not. It shows all the Hadoop daemons i.e namenode, datanode, resourcemanager, nodemanager etc. that are running on the machine.
How do you define “Rack Awareness” in Hadoop?
Rack Awareness is the algorithm in which the “NameNode” decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between “DataNodes” within the same rack. Let’s say we consider replication factor 3 (default), the policy is that “for every block of data, two copies will exist in one rack, third copy in a different rack”. This rule is known as the “Replica Placement Policy”.
What is “speculative execution” in Hadoop?
If a node appears to be executing a task slower, the master node can redundantly execute another instance of the same task on another node. Then, the task which finishes first will be accepted and the other one is killed. This process is called “speculative execution”.
What are active and passive “NameNodes”?
In HA (High Availability) architecture, we have two NameNodes – Active “NameNode” and Passive “NameNode”.
Active “NameNode” is the “NameNode” which works and runs in the cluster.
Passive “NameNode” is a standby “NameNode”, which has similar data as active “NameNode”.
When the active “NameNode” fails, the passive “NameNode” replaces the active “NameNode” in the cluster. Hence, the cluster is never without a “NameNode” and so it never fails.
Why does one remove or add nodes in a Hadoop cluster frequently?
One of the most attractive features of the Hadoop framework is its utilization of commodity hardware. However, this leads to frequent “DataNode” crashes in a Hadoop cluster. Another striking feature of Hadoop Framework is the ease of scale in accordance with the rapid growth in data volume. Because of these two reasons, one of the most common task of a Hadoop administrator is to commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster.
What happens when two clients try to access the same file in the HDFS?
HDFS supports exclusive writes only.
When the first client contacts the “NameNode” to open the file for writing, the “NameNode” grants a lease to the client to create this file. When the second client tries to open the same file for writing, the “NameNode” will notice that the lease for the file is already granted to another client, and will reject the open request for the second client.
 |
| Spotify Technology Most Frequently Asked Latest Hadoop Interview Questions Answers |
How does NameNode tackle DataNode failures?
NameNode periodically receives a Heartbeat (signal) from each of the DataNode in the cluster, which implies DataNode is functioning properly.
A block report contains a list of all the blocks on a DataNode. If a DataNode fails to send a heartbeat message, after a specific period of time it is marked dead.
The NameNode replicates the blocks of dead node to another DataNode using the replicas created earlier.
What will you do when NameNode is down?
The NameNode recovery process involves the following steps to make the Hadoop cluster up and running:
Use the file system metadata replica (FsImage) to start a new NameNode.
Then, configure the DataNodes and clients so that they can acknowledge this new NameNode, that is started.
Now the new NameNode will start serving the client after it has completed loading the last checkpoint FsImage (for metadata information) and received enough block reports from the DataNodes.
Name a few companies that use Zookeeper.
Yahoo, Solr, Helprace, Neo4j, Rackspace
What is the role of Zookeeper in HBase architecture?
In HBase architecture, ZooKeeper is the monitoring server that provides different services like –tracking server failure and network partitions, maintaining the configuration information, establishing communication between the clients and region servers, usability of ephemeral nodes to identify the available servers in the cluster.
Explain about ZooKeeper in Kafka
Apache Kafka uses ZooKeeper to be a highly distributed and scalable system. Zookeeper is used by Kafka to store various configurations and use them across the hadoop cluster in a distributed manner. To achieve distributed-ness, configurations are distributed and replicated throughout the leader and follower nodes in the ZooKeeper ensemble. We cannot directly connect to Kafka by bye-passing ZooKeeper because if the ZooKeeper is down it will not be able to serve the client request.
Explain how Zookeeper works
ZooKeeper is referred to as the King of Coordination and distributed applications use ZooKeeper to store and facilitate important configuration information updates. ZooKeeper works by coordinating the processes of distributed applications. ZooKeeper is a robust replicated synchronization service with eventual consistency. A set of nodes is known as an ensemble and persisted data is distributed between multiple nodes.
3 or more independent servers collectively form a ZooKeeper cluster and elect a master. One client connects to any of the specific server and migrates if a particular node fails. The ensemble of ZooKeeper nodes is alive till the majority of nods are working. The master node in ZooKeeper is dynamically selected by the consensus within the ensemble so if the master node fails then the role of master node will migrate to another node which is selected dynamically. Writes are linear and reads are concurrent in ZooKeeper.
List some examples of Zookeeper use cases.
Found by Elastic uses Zookeeper comprehensively for resource allocation, leader election, high priority notifications and discovery. The entire service of Found built up of various systems that read and write to Zookeeper.
Apache Kafka that depends on ZooKeeper is used by LinkedIn
Storm that relies on ZooKeeper is used by popular companies like Groupon and Twitter.
How to use Apache Zookeeper command line interface?
ZooKeeper has a command line client support for interactive use. The command line interface of ZooKeeper is similar to the file and shell system of UNIX. Data in ZooKeeper is stored in a hierarchy of Znodes where each znode can contain data just similar to a file. Each znode can also have children just like directories in the UNIX file system.
Zookeeper-client command is used to launch the command line client. If the initial prompt is hidden by the log messages after entering the command, users can just hit ENTER to view the prompt.
What are the different types of Znodes?
There are 2 types of Znodes namely- Ephemeral and Sequential Znodes.
The Znodes that get destroyed as soon as the client that created it disconnects are referred to as Ephemeral Znodes.
Sequential Znode is the one in which sequential number is chosen by the ZooKeeper ensemble and is pre-fixed when the client assigns name to the znode.
What are watches?
Client disconnection might be troublesome problem especially when we need to keep a track on the state of Znodes at regular intervals. ZooKeeper has an event system referred to as watch which can be set on Znode to trigger an event whenever it is removed, altered or any new children are created below it.
In brief, “Checkpointing” is a process that takes an FsImage, edit log and compacts them into a new FsImage. Thus, instead of replaying an edit log, the NameNode can load the final in-memory state directly from the FsImage. This is a far more efficient operation and reduces NameNode startup time. Checkpointing is performed by Secondary NameNode.
How is HDFS fault tolerant?
When data is stored over HDFS, NameNode replicates the data to several DataNode. The default replication factor is 3. You can change the configuration factor as per your need. If a DataNode goes down, the NameNode will automatically copy the data to another node from the replicas and make the data available. This provides fault tolerance in HDFS.
Can NameNode and DataNode be a commodity hardware?
The smart answer to this question would be, DataNodes are commodity hardware like personal computers and laptops as it stores data and are required in a large number. But from your experience, you can tell that, NameNode is the master node and it stores metadata about all the blocks stored in HDFS. It requires high memory (RAM) space, so NameNode needs to be a high-end machine with good memory space.
Why do we use HDFS for applications having large data sets and not when there are a lot of small files?
HDFS is more suitable for large amounts of data sets in a single file as compared to small amount of data spread across multiple files. As you know, the NameNode stores the metadata information regarding the file system in the RAM. Therefore, the amount of memory produces a limit to the number of files in my HDFS file system. In other words, too many files will lead to the generation of too much metadata. And, storing these metadata in the RAM will become a challenge. As a thumb rule, metadata for a file, block or directory takes 150 bytes.
How do you define “block” in HDFS? What is the default block size in Hadoop 1 and in Hadoop 2? Can it be changed?
Blocks are the nothing but the smallest continuous location on your hard drive where data is stored. HDFS stores each as blocks, and distribute it across the Hadoop cluster. Files in HDFS are broken down into block-sized chunks, which are stored as independent units.
Hadoop 1 default block size: 64 MB
Hadoop 2 default block size: 128 MB
Yes, blocks can be configured. The dfs.block.size parameter can be used in the hdfs-site.xml file to set the size of a block in a Hadoop environment.
What does ‘jps’ command do?
The ‘jps’ command helps us to check if the Hadoop daemons are running or not. It shows all the Hadoop daemons i.e namenode, datanode, resourcemanager, nodemanager etc. that are running on the machine.
How do you define “Rack Awareness” in Hadoop?
Rack Awareness is the algorithm in which the “NameNode” decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between “DataNodes” within the same rack. Let’s say we consider replication factor 3 (default), the policy is that “for every block of data, two copies will exist in one rack, third copy in a different rack”. This rule is known as the “Replica Placement Policy”.
What is “speculative execution” in Hadoop?
If a node appears to be executing a task slower, the master node can redundantly execute another instance of the same task on another node. Then, the task which finishes first will be accepted and the other one is killed. This process is called “speculative execution”.
Post a Comment