Where as i find many solutions for taking the back up of metadata in name node and would like to know how to take the back up of datanode? leaving replication factor aside but want to know the detail process to take the back up of data node in the production level for 20 node cluster.
distcp command in hadoop, can copy data from source clusster to target
for example :
hadoop distcp hftp://cdh57-namenode:50070/hbase hdfs://CDH59-nameservice/hbase
this command copy hbase folder from cdh57-namenode to CDH59-nameservice
more information can obtain from this link
https://www.cloudera.com/documentation/enterprise/5-5-x/topics/cdh_admin_distcp_data_cluster_migrate.html
Related
I'm a novice. I have a 3-Node Cluster. The Name Node, Job Tracker and Secondary Name Node are running in one node and two data nodes (HData1, HData2) in the other two cluster. If I store data from my local system to HDFS, how to find in which node it resides? Is there a way I can explicitly specify in which data node it has to be stored?
Thanks in advance!
Yes you can find it using hadoop fsck path
you can refer below links
how does hdfs choose a datanode to store
How to explicilty define datanodes to store a particular given file in HDFS?
Shall we copyFromlocal/put file to hdfs before processing map-reduce job? When I run mapreduce example I was taught to format hdfs in master node and copyFromLocal files to that hdfs space in master.
Then why some tutorials said master nodes just inform metadata to client.The laptop(client) will copy file blocks to data nodes not to master? e.g. http://www.youtube.com/watch?v=ziqx2hJY8Hg at 25:50. My understanding based on this tutorial is that the file (splitted by blocks) will be copied to slave nodes. so we do not need to copyFromlocal /put files to master nodes. I was so confused. Can anybody explain where will files copied/replicated to?
Blocks will not be copied to master node.
The master (Namenode) sends meta data to the client containing the data node locations
for placing each block by the client.
No actual block data is transferred to the NameNode.
I found this comic to be a good hdfs explanation
The master node (Namenode) in hadoop just deals with the Metadata (Datanode<->data information). It does not deal with the actual files. The actual files are instead stored only in the datanodes.
In Hadoop, if I decommission a node Hadoop will redistribute the files across the cluster so they are properly replicated. Will the data be deleted from the decomissioned node?
I am trying to balance the data across the disks on a particular node. I plan to do this by decomissioning the node and then recomissioning the node. Do I need to delete the data from that node after decomissioning is complete, or will it be enough to simply recomission it (remove it from the excludes file and run hadoop dfsadmin -refreshNodes)?
UPDATE: It worked for me to decomission a node, delete all the data on that node, and then recomission it.
AFAIK, data is not removed from a DataNode when you decommission it. Further writes on that DataNode will not be possible though. When you decommission a DataNode, the replicas held by that DataNode are marked as "decommissioned" replicas, which are still eligible for read access.
But why do you want to perform this decomissioning/recomissioning cycle?Why don't you just specify all the disks as a comma separated value to the dfs.data.dir property in your hdfs-site.xml and restart the DataNode daemon. Run the balancer after the restart.
Hadoop currently doesn't support doing this automatically. But there might be hacks around to do that automatically.
Decommissioning and then replication, will be slow in my opinion, then manually moving blocks across different disks.
You can do the balancing manually though across the disks, something like this -
1.Take down the HDFS or only the datanode you are targeting.
2.Use the UNIX mv command to move the individual blocks and meta pairs from one directory to another on the host machine. E.g. move pairs of blk data file and blk.meta files to accross the disks on the same host.
3.Restart the HDFS or the datanode
Reference link for the procedure
Addendum:
You need to probably move pairs of blk_* and blk_*.meta files to and from inside the dfs/current directory of each data disk. E.g. pair files - blk_3340211089776584759 and blk_3340211089776584759_1158.meta
If you don't want do this manually, you can probably write a custom script to detect how much is occupied in the dfs/current directory of the each of your data disks and re-balance them accordingly i.e. move pairs of blk_* and blk_*.meta from one to another.
As the title indicates, when a client requests to write a file to the hdfs, how does the HDFS or name node choose which datanode to store the file?
Does the hdfs try to store all the blocks of this file in the same node or some node in the same rack if it is too big?
Does the hdfs provide any APIs for applications to store the file in a certain datanode as he likes?
how does the HDFS or name node choose which datanode to store the file?
HDFS has a BlockPlacementPolicyDefault, check the API documentation for more details. It should be possible to extend BlockPlacementPolicy for a custom behavior.
Does the hdfs provide any APIs for applications to store the file in a certain datanode as he likes?
The placement behavior should not be specific to a particular datanode. That's what makes HDFS resilient to failure and also scalable.
The code for choosing datanode is in function ReplicationTargetChooser.chooseTarget().
The comment says that :
The replica placement strategy is that 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
the same rack as the first replica.
It doesn`t provide any API for applications to store the file in the datanode they want.
If someone prefers charts, here is a picture (source):
Now with Hadoop-385 patch, we can choose the block placement policy, so as to place all blocks of a file in the same node (and similarly for replicated nodes). Read this blog about this topic - look at the comments section.
You can see that when namenode instructs datanode to store data. The first replica is stored in the local machine and other two replicas are made on other rack and so on.
If any replica fails, data is stored from other replica. Chances of failing every replica is just like falling of fan on your head while you were sleeping :p i.e. there is very less chance for that.
I am new in hadoop so I have some doubts. If the master-node fails what happened the hadoop cluster? Can we recover that node without any loss? Is it possible to keep a secondary master-node to switch automatically to the master when the current one fails?
We have the backup of the namenode (Secondary namenode), so we can restore the namenode from Secondary namenode when it fails. Like this, How can we restore the data's in datanode when the datanode fails? The secondary namenode is the backup of namenode only not to datenode, right? If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node?
How can we restore the entire cluster data if anything happens?
And my final question, can we use C program in Mapreduce (For example, Bubble sort in mapreduce)?
Thanks in advance
Although, It is too late to answer your question but just It may help others..
First of all let me Introduce you with Secondary Name Node:
It Contains the name space image, edit log files' back up for past one
hour (configurable). And its work is to merge latest Name Node
NameSpaceImage and edit logs files to upload back to Name Node as
replacement of the old one. To have a Secondary NN in a cluster is not
mandatory.
Now coming to your concerns..
If the master-node fails what happened the hadoop cluster?
Supporting Frail's answer, Yes hadoop has single point of failure so
whole of your currently running task like Map-Reduce or any other that
is using the failed master node will stop. The whole cluster including
client will stop working.
Can we recover that node without any loss?
That is hypothetical, Without loss it is least possible, as all the
data (block reports) will lost which has sent by Data nodes to Name
node after last back up taken by secondary name node. Why I mentioned
least, because If name node fails just after a successful back up run
by secondary name node then it is in safe state.
Is it possible to keep a secondary master-node to switch automatically to the master when the current one fails?
It is staright possible by an Administrator (User). And to switch it
automatically you have to write a native code out of the cluster, Code
to moniter the cluster that will cofigure the secondary name node
smartly and restart the cluster with new name node address.
We have the backup of the namenode (Secondary namenode), so we can restore the namenode from Secondary namenode when it fails. Like this, How can we restore the data's in datanode when the datanode fails?
It is about replication factor, We have 3 (default as best practice,
configurable) replicas of each file block all in different data nodes.
So in case of failure for time being we have 2 back up data nodes.
Later Name node will create one more replica of the data that failed
data node contained.
The secondary namenode is the backup of namenode only not to datenode, right?
Right. It just contains all the metadata of data nodes like data node
address,properties including block report of each data node.
If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node?
HDFS will forcely try to continue the job. But again it depends on
replication factor, rack awareness and other configuration made by
admin. But if following Hadoop's best practices about HDFS then it
will not get failed. JobTracker will get replicated node address to
continnue.
How can we restore the entire cluster data if anything happens?
By Restarting it.
And my final question, can we use C program in Mapreduce (For example, Bubble sort in mapreduce)?
yes, you can use any programming language which support Standard file
read write operations.
I Just gave a try. Hope it will help you as well as others.
*Suggestions/Improvements are welcome.*
Currently hadoop cluster has a single point of failure which is namenode.
And about the secondary node isssue (from apache wiki) :
The term "secondary name-node" is somewhat misleading. It is not a
name-node in the sense that data-nodes cannot connect to the secondary
name-node, and in no event it can replace the primary name-node in
case of its failure.
The only purpose of the secondary name-node is to perform periodic
checkpoints. The secondary name-node periodically downloads current
name-node image and edits log files, joins them into new image and
uploads the new image back to the (primary and the only) name-node.
See User Guide.
So if the name-node fails and you can restart it on the same physical
node then there is no need to shutdown data-nodes, just the name-node
need to be restarted. If you cannot use the old node anymore you will
need to copy the latest image somewhere else. The latest image can be
found either on the node that used to be the primary before failure if
available; or on the secondary name-node. The latter will be the
latest checkpoint without subsequent edits logs, that is the most
recent name space modifications may be missing there. You will also
need to restart the whole cluster in this case.
There are tricky ways to overcome this single point of failure. If you are using cloudera distribution, one of the ways explained here. Mapr distribution has a different way to handle to this spof.
Finally, you can use every single programing language to write map reduce over hadoop streaming.
Although, It is too late to answer your question but just It may help others..firstly we will discuss role of Hadoop 1.X daemons and then your issues..
1. What is role of secondary name Node
it is not exactly a backup node. it reads a edit logs and create updated fsimage file for name node periodically. it get metadata from name node periodically and keep it and uses when name node fails.
2. what is role of name node
it is manager of all daemons. its master jvm proceess which run at master node. it interact with data nodes.
3. what is role of job tracker
it accepts job and distributes to task trackers for processing at data nodes. its called as map process
4. what is role of task trackers
it will execute program provided for processing on existing data at data node. that process is called as map.
limitations of hadoop 1.X
single point of failure
which is name node so we can maintain high quality hardware for the name node. if name node fails everything will be inaccessible
Solutions
solution to single point of failure is hadoop 2.X which provides high availability.
high availability with hadoop 2.X
now your topics ....
How can we restore the entire cluster data if anything happens?
if cluster fails we can restart it..
If a node is failed before completion of a job, so there is job pending in job tracker, is that job continue or restart from the first in the free node?
we have default 3 replicas of data(i mean blocks) to get high availability it depends upon admin that how much replicas he has set...so job trackers will continue with other copy of data on other data node
can we use C program in Mapreduce (For example, Bubble sort in mapreduce)?
basically mapreduce is execution engine which will solve or process big data problem in(storage plus processing) distributed manners. we are doing file handling and all other basic operations using mapreduce programming so we can use any language of where we can handle files as per the requirements.
hadoop 1.X architecture
hadoop 1.x has 4 basic daemons
I Just gave a try. Hope it will help you as well as others.
Suggestions/Improvements are welcome.