I am building a new Hadoop cluster (expanding number of nodes and extending capacity of current nodes) and need to back up all of the existing data. Right now I am just tar-ing everything and sending it to another server.
Is there a smarter way of doing this which will allow me to easily deploy once the new cluster is set up?
Edit: I should also point out that I don't store any data on the cluster. I bring data to the cluster, process it, and then send the processed data back to the original server. Any temporary data on the cluster is the deleted.
Use Distcp to transfer the HDFS data to other cluster or any cloud inorder to store the data.
If you want to schedule the Backup process you may avail OOZIE-DISTCP for backup process!!
Related
I’m trying to understand how hbase uses the hdfs.
so here is what I understand (please correct me if I'm wrong):
I know that hbase use hdfs to store data and that data is split into regions, and that each region server my serve many regions,so I guess that one region (exclusively) may communicate with many data node to get and put data, so If that is correct then if that region server fails then data stored in those data node, will not be accessible anymore
thank you in advance :)
In general, a Regionserver runs on a datanode.
Due to how HDFS works, the Regionserver will perform its reads and writes to the local datanode when possible, and then HDFS will ensure that the data is replicated onto two other random datanodes. So at all times, the data written by that regionserver is stored on 3 nodes in HDFS.
While a regionserver is serving a region, only it will read / write the data for that region, but if the regionserver process crashes, the HBase master will select another regionsever to serve that region. The data will be unavailable for a few minutes, but HBase will recover quickly.
If the entire host fails, then as HDFS ensured the data was written onto two other nodes, the scenario is the same - the master will select a new regionserver to open the failed region and the data not be lost.
I am learning hadoop. I want to understand how dataset/database is setup for environments like Dev, Test and Pre-prod.
Of course in PROD environment we will be dealing with Terabytes of data, but having the same replica of tera bytes of data to other environments, i dont think it is possible.
For other environments how the datasets are replicated? only certain portions of data will be loaded and used in these non prod environments? if so how it is done?
How it is replicated, basically the concept of hdfs relevant to namenodes and datanodrs should give you some research. When you create a new file it goes to name node which updated the metadata and give you a blank block id once you write it finds the nearest datanodes base on the rack location. It replicates to the first datanodes, once its done replicating. Datanode first will replicate it to the next second then thirds and so fourth. It basically just re0licate on the very first node and the hdfs framework will handle the next preceedi g replication
we have set up the hadoop cluster with 2 machines, we are trying to implement cluster in our real time projects , we need information in a multiple node cluster about uploading the data , suppose if i have 9 data nodes , which slave node we need to upload the data.can i can give choice to upload the data into 2 slave nodes , if i am uploading the data into hdfs is it replicated into another slave nodes?. As we observed curretnly hdfs using /tmp location incase if the /tmp is full which location HDFS will use.
Purpose of adding the more number of cluster is to enlarge the data storage..
Are you looking for secure the cluster, grant the privileges to some of the user shold upload the data in to the HDFS ?? right If means you can implement the KERBEROS principle or authorize the user to upload the data!
Data replication:
Yes once the data will be uploaded to the HDFS it will replicate the data in to the nodes, Once the decommission of data node occurs it ill take care the data itll moved form the decommissioned node into the other node.
Introduction
According to several documentation 1, 2, 3 HDFS' Location Awareness is about knowing the physical location of nodes and replicating data on different racks to reduce the impact of rack issues due to, e.g. power supply and/or switch issues.
Question
How does HDFS know the physical location of nodes and racks and subsequently decide to replicate data to nodes located on other racks?
Rack-awareness is configured when the cluster is set up. This can be done either manually for each node or through a script.
Each DataNode is given a network location which is simple a string, much like a file system path.
Example:
datacenter-1/rack-1/node1
datacenter-1/rack-1/node2
datacenter-1/rack-2/node3
The NameNode then builds a network topology (basically a tree structure) using the network locations of each DataNode. This topology is then used to determine block replica placement.
somebody needs to know where Data Nodes are located in the network topology and use that information to make an intelligent decision about where data replicas should exist in the cluster. That “somebody” is the Name Node.
The Name node stores this information and is the the namespace.
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.
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.