- What data is stored in NameNode?
- What are the different schedulers available in Hadoop?
- What is the difference between a NameNode and a secondary NameNode?
- What happens when NameNode goes down?
- What happens when a data node fails?
- Which of the following has the largest Hadoop cluster?
- How is Hdfs tolerant?
- How do I disable secondary NameNode in Hadoop?
- What happens when a data node fails in Hadoop environment?
- How files are stored in HDFS?
- What is DataNode in Hadoop?
- What is InputSplit in Hadoop?
- When Namenode fails which node takes the responsibility of active node?
- What is the use of secondary NameNode?
- What happens if secondary NameNode fails?
- How do you recover NameNode if it is down?
- Can you access cluster and data if NameNode is down?
- Is it possible to provide multiple inputs to Hadoop?
What data is stored in NameNode?
NameNode is the centerpiece of HDFS.
NameNode only stores the metadata of HDFS – the directory tree of all files in the file system, and tracks the files across the cluster.
NameNode does not store the actual data or the dataset.
The data itself is actually stored in the DataNodes..
What are the different schedulers available in Hadoop?
There are mainly 3 types of Schedulers in Hadoop: FIFO (First In First Out) Scheduler. Capacity Scheduler. Fair Scheduler.
What is the difference between a NameNode and a secondary NameNode?
Secondary namenode is just a helper for Namenode. It gets the edit logs from the namenode in regular intervals and applies to fsimage. Once it has new fsimage, it copies back to namenode. Namenode will use this fsimage for the next restart, which will reduce the startup time.
What happens when NameNode goes down?
When the NameNode goes down, the file system goes offline. There is an optional SecondaryNameNode that can be hosted on a separate machine. It only creates checkpoints of the namespace by merging the edits file into the fsimage file and does not provide any real redundancy.
What happens when a data node fails?
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.
Which of the following has the largest Hadoop cluster?
FacebookFacebook has the world’s largest Hadoop Cluster. Facebook is using Hadoop for data warehousing and they are having the largest Hadoop storage cluster in the world. Some of the properties of the HDFS cluster of Facebook is: HDFS cluster of 21 PB storage capacity.
How is Hdfs tolerant?
HDFS is highly fault-tolerant. … It creates a replica of users’ data on different machines in the HDFS cluster. So whenever if any machine in the cluster goes down, then data is accessible from other machines in which the same copy of data was created.
How do I disable secondary NameNode in Hadoop?
InstructionsExecute this command on all NodeManagers: su -l yarn -c “/usr/hdp/current/hadoop-yarn-nodemanager/sbin/yarn-daemon.sh stop nodemanager”Execute this command on the History Server host machine: su -l mapred -c “/usr/hdp/current/hadoop-mapreduce-historyserver/sbin/mr-jobhistory-daemon.sh stop historyserver”More items…
What happens when a data node fails in Hadoop environment?
If Namenode gets down then the whole Hadoop cluster is inaccessible and considered dead. Datanode stores actual data and works as instructed by Namenode. A Hadoop file system can have multiple data nodes but only one active Namenode.
How files are stored in HDFS?
HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories.
What is DataNode in Hadoop?
DataNode: DataNodes are the slave nodes in HDFS. Unlike NameNode, DataNode is a commodity hardware, that is, a non-expensive system which is not of high quality or high-availability. The DataNode is a block server that stores the data in the local file ext3 or ext4.
What is InputSplit in Hadoop?
InputSplit in Hadoop MapReduce is the logical representation of data. It describes a unit of work that contains a single map task in a MapReduce program. Hadoop InputSplit represents the data which is processed by an individual Mapper. The split is divided into records.
When Namenode fails which node takes the responsibility of active node?
Passive nodeWhen active node fails, then Passive node takes the responsibility of active node. Passive node is also called standby namenode that takes the responsibility to remove the problem of single point of failure (SPOF).
What is the use of secondary NameNode?
The secondary NameNode merges the fsimage and the edits log files periodically and keeps edits log size within a limit. It is usually run on a different machine than the primary NameNode since its memory requirements are on the same order as the primary NameNode.
What happens if secondary NameNode fails?
If NameNode is failed, File System metadata can be recovered from the last saved FsImage on the Secondary NameNode but Secondary NameNode can’t take the primary NameNode’s functionality.
How do you recover NameNode if it is down?
Recover Hadoop NameNode FailureStart the namenode in a different host with a empty dfs. name. dir.Point the dfs. name. … Use –importCheckpoint option while starting namenode after pointing fs. checkpoint. … Change the fs.default.name to the backup host name URI and restart the cluster with all the slave IP’s in slaves file.
Can you access cluster and data if NameNode is down?
So if Namenode is down (Master node), then the data remains as is in the cluster, BUT you will not be able to access it at all. Because, Name node holds the meta data of the data nodes. … So you can still access the data from other two nodes.
Is it possible to provide multiple inputs to Hadoop?
Here Hadoop development experts will make you understand the concept of multiple input files required in Hadoop MapReduce. As a mapper extracts its input from the input file, if there are multiple input files, developers will require the same amount of mapper to read records from input files.