If I copy a set of files to HDFS in a Hadoop 7 node cluster, would HDFS take care of automatically balancing out the data across the 7 nodes, is there any way I can tell HDFS to constrain/force data to a particular node in the cluster?
NameNode is 'the' master who decides about where to put data blocks on different nodes in the cluster. In theory, you should not alter this behavior as it is not recommended. If you copy files to hadoop cluster, NameNode will automatically take care of distributing them almost equally on all the DataNodes.
If you want to force change this behaviour (not recommended), these posts could be useful:
How to put files to specific node?
How to explicilty define datanodes to store a particular given file in HDFS?
Related
I was going through Hadoop, I have doubt whether there is difference between Rack wareness and Name Node. Will Rack wareness and name node will remain on same box
As Aviral rightly said, the question has been quite vague. But just quoting for your understanding,
Namenode : The NameNode is the centerpiece of an HDFS file system. It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It does not store the data of these files itself.
Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives.
You can read in detail about this concept here.
Rack Awareness : In simple words rack awareness is the strategy namenode employs to choose the nearest datanode based on rack information. You can read details here
Further more, I would like to suggest this blog
Image credits Brad Hedlund
From Apache HDFS Users Guide
HDFS is the primary distributed storage used by Hadoop applications.
A HDFS cluster primarily consists of a NameNode that manages the file system metadata and DataNodes that store the actual data
Typically large Hadoop clusters are arranged in racks and network traffic between different nodes with in the same rack is much more desirable than network traffic across the racks. In addition NameNode tries to place replicas of block on multiple racks for improved fault tolerance.
From RackAwareness tutorial:
Hadoop components are rack-aware. For example, HDFS block placement will use rack awareness for fault tolerance by placing one block replica on a different rack. This provides data availability in the event of a network switch failure or partition within the cluster.
Let's see how Hadoop writes are implemented.
If the writer is on a datanode, the 1st replica is placed on the local machine, otherwise a random datanode.
The 2nd replica is placed on a datanode that is on a different rack.
The 3rd replica is placed on a datanode which is on a different node of the rack as the second replica.
Due to replication of data blocks on three different nodes across two different RACs, Hadoop read operations provides high availability of data blocks.
At least one replica is stored on different RAC. If one RAC is not accessible, still Hadoop can fetch data block from other RAC.
I am new to Qubole and wanted to know if data remains in HDFS after Hadoop cluster is down?
Any help is appreciated.
Thank you.
No data on HDFS is gone. We don't backup/restore HDFS. The model of computation on EC2/S3 is that the long-lived data always lives on S3 and HDFS is used only for intermediate and control data. We also use HDFS (and local disk), sometimes, as a cache.
That depends on what is down in the cluster. There are daemons in Hadoop, Namenode, data node, Resource manager, AppMaster and etc.
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.
If a Data node is down on a cluster (slave node), then you will not be able to access the data from this node, but by default data will be stored in 3 locations in the cluster for fault tolerance. So you can still access the data from other two nodes.
I am fairly new to Hadoop and I have following questions on Hadoop framework. Can anybody please guide on this?
Is DataNode and TaskTracker located physically on separate machines in a production environment?
When does Hadoop splits a file into blocks? Does this happen when you copy a file from local filesystem into HDFS?
Short answer
Most of the time, but not necessarily.
Yes.
Long Answer
1)
An installation of Hadoop on a cluster will have 2 main types of nodes:
Master Nodes
Data Nodes
Master Nodes typically run at least:
CLDB
Zookeeper
JobTracker
Data Nodes typically run at least:
TaskTracker
The DataNode service can run on a different node than the TaskTracker service. However, the Hadoop Docs for the DataNode service recommend to run DataNode and TaskTracker on the same nodes so that MapReduce operations are performed close to the data.
For the MapR distribution of Hadoop, the two server roles typically run:
MapR Control Node
ZooKeeper *
CLDB *
JobTracker *
HBaseMaster
NFS Gateway
Webserver
MapR Data Node
TaskTracker *
RegionServer (sometimes)
Zookeeper (sometimes)
2)
While most filesystems store data in blocks, HDFS distributes & replicates the blocks across DataNodes. When you first store data in HDFS, it will break it into blocks and store it across different nodes according to the specified replication factor. However, if you add new DataNodes to the cluster, it will not automatically rebalance old blocks across them unless the replication factor is not met.
(Thanks to #javadba for clarifying this!)
Given TrinitronX has already answered #1 - though the Short Answer should be NO - the datanode/task tracker MAY be on different physical machines, but it is uncommon. You are best to start off with "slave" machines being datanode plus task tracker.
So this is an answer to the second part of the question
2) When does Hadoop splits a file into blocks? Does this happen when you copy a file from local filesystem into HDFS?
Yes. The file is broken into blocks upon loading into HDFS.
Data-node and Job tracker can be run on different machines.
Hadoop always stores a files as a blocks during all operations on hadoop
Refer
1.Hadoop Job tracker and task tracker
2.Hadoop block size and Replication
I am new to Hadoop.
When I store Excel files using hadoop -fs put commoad, it is stored in HDFS.
Replication factor is 3.
My question is: Does it take 3 copies and store them into 3 nodes each?
Here is a comic for HDFS working.
https://docs.google.com/file/d/0B-zw6KHOtbT4MmRkZWJjYzEtYjI3Ni00NTFjLWE0OGItYTU5OGMxYjc0N2M1/edit?pli=1
Does it take 3 copies and store them into 3 nodes each.
answer is: NO
Replication is done in pipelining
that is it copies some part of file to datanode1 and then copies to datanode2 from datanode1 and to datanode3 from datanode1
http://hadoop.apache.org/docs/r1.2.1/hdfs_design.html#Replication+Pipelining
see here for Replication Pipelining
Your HDFS Client (hadoop fs in this case) will be given the block names and datanode locations (the first being the closest location if the NameNode can determine this from the rack awareness script) of where to store these files by the NameNode.
The client then copies the blocks to the closest Data node. The data node is then responsible for copying the block to a second datanode (preferably on another rack), where finally the second will copy to the third (on the same rack as the third).
So your client will only copy data to one of the data nodes, and the framework will take care of the replication between datanodes.
It will store the original file to one (or more in case of large files) blocks. These blocks will be replicated to two other nodes.
Edit: My answer applies to Hadoop 2.2.0. I have no experience with prior versions.
Yes it will be replicated in 3 nodes (maximum upto 3 nodes).
The Hadoop Client is going to break the data file into smaller “Blocks”, and place those blocks on different machines throughout the cluster. The more blocks you have, the more machines that will be able to work on this data in parallel. At the same time, these machines may be prone to failure, so it is safe to insure that every block of data is on multiple machines at once to avoid data loss.
So each block will be replicated in the cluster as its loaded. The standard setting for Hadoop is to have (3) copies of each block in the cluster. This can be configured with the dfs.replication parameter in the file hdfs-site.xml.
And replicating data is not a drawback of Hadoop at all, in fact it is an integral part of what makes Hadoop effective. Not only does it provide you with a good degree of fault tolerance, but it also helps in running your map tasks close to the data to avoid putting extra load on the network (read about data locality).
Yes it make n(replications factor) number copies in hdfs
use this command to find out the location of file, find #rack it is stored, what is the block name on all racks
hadoop fsck /path/to/your/directory -files -blocks -locations -racks
Use this command to load data into hdfs with replication
hadoop fs -Ddfs.replication=1 -put big.file /tmp/test1.file
and -Ddfs.replication=1 you can define number of replication copy will created while to loading data into hdfs
When I'm uploading a file to HDFS, if I set the replication factor to 1 then the file splits gonna reside on one single machine or the splits would be distributed to multiple machines across the network ?
hadoop fs -D dfs.replication=1 -copyFromLocal file.txt /user/ablimit
According to the Hadoop : Definitive Guide
Hadoop’s default strategy is to place the first replica on the same node as the client (for
clients running outside the cluster, a node is chosen at random, although the system
tries not to pick nodes that are too full or too busy). The second replica is placed on a
different rack from the first (off-rack), chosen at random. The third replica is placed on
the same rack as the second, but on a different node chosen at random. Further replicas
are placed on random nodes on the cluster, although the system tries to avoid placing
too many replicas on the same rack.
This logic makes sense as it decreases the network chatter between the different nodes. But, the book was published in 2009 and there had been a lot of changes in the Hadoop framework.
I think it depends on, whether the client is same as a Hadoop node or not. If the client is a Hadoop node then all the splits will be on the same node. This doesn't provide any better read/write throughput in-spite of having multiple nodes in the cluster. If the client is not same as the Hadoop node, then the node is chosen at random for each split, so the splits are spread across the nodes in a cluster. Now, this provides a better read/write throughput.
One advantage of writing to multiple nodes is that even if one of the node goes down, a couple of splits might be down, but at least some data can be recovered somehow from the remaining splits.
If you set replication to be 1, then the file will be present only on the client node, that is the node from where you are uploading the file.
If your cluster is single node then when you upload a file it will be spilled according to the blocksize and it remains in single machine.
If your cluster is Multi node then when you upload a file it will be spilled according to the blocksize and it will be distributed to different datanode in your cluster via pipeline and NameNode will decide where the data should be moved in the cluster.
HDFS replication factor is used to make a copy of the data (i.e) if your replicator factor is 2 then all the data which you upload to HDFS will have a copy.
If you set replication factor is 1 it means that the single node cluster. It has only one client node http://commandstech.com/replication-factor-in-hadoop/. Where you can upload files then use in a single node or client node.