I am new to big data and hadoop. I would like to know are name node, data node, secondary name node, job tracker, task tracker different systems ? If i want to process 1000 PB data, How data is divided and who is doing that task and where should i input 1000 PB data.
Yes namenode, dataNode, secondaryNameNode, jobTracker, taskTracker are all different virtual machines (JVMs you can call them). You can start them all in one physical machine (pseudo/local mode) or you can start them on different physical machines (distributed mode). These are all in Hadoop1.
Hadoop2 has introduced containers with YARN in which jobTracker and taskTracer are removed with more efficient resourceManager, applicationManager, nodeManager etc. You can find more info hadoop-yarn-site
Data are stored in HDFS (Hadoop Distributed File System) and are stored in blocks, default to 64MB. When data is loaded to hdfs, hadoop distributes the data equally in the cluster with the defined block size. When a job is run code is distributed to the nodes in cluster so that each processing occurs where the data is residing except in shuffle and sorting cases.
I hope you must have general idea of how hadoop and hdfs works. Followings are some links for you to start with
Map Reduce programming
cluster setup
hadoop commands
Related
I started to learn hbase and I don't understand how it scales linearly.
The problem is that before you install hbase you have to have an hdfs cluster. The HDFS cluster have a master node which can be only one in the whole cluster, so it is a bottleneck. Ofcourse we can run 1 more master node (it is possible to run only 1 more master node) but it will be in the standby state.
As I understand hbase uses the HDFS cluster to store data. So, for me it is logically that it have no sense to run more than one Hmaster because all requests will go to the hdfs active master which performance can suffer if we have too much requests.
Also I don't understand properly do we need to install hbase on the same nodes with hdfs or separately. What are the benefits if we run hbase separately from HDFS.
As for me it is logically to install hbase cluster on the same nodes with hdfs as in the following example:
HDFS active master - HMaster
HDFS standby master - HMaster backup
HDFS Data node - HRegion server
for me it is the most logically structure because if we separate hdfs master from hmaster then probability to loose hbase cluster will be two times bigger.
I will be very happy if someone can share information about all these stuff. Because I really don't understand how hbase can linearly scales and how it works with hdfs.
First if you want you can install HBase over any supported file system. It is not mandatory to use it over Hdfs but using it with Hdfs give advantage to it like
Fault taulrence , Data replication, checksums etc.
That's why it is recommended to use HBase over hdfs
Moreover although there is a bottleneck of namenode in hdfs but it does not effect HBase efficiency because it is not that every operation internal working is dependent on namenode of hdfs for instance Region servers serve data for reads and writes. When accessing data, clients communicate with HBase RegionServers directly while Region assignment, DDL (create, delete tables) operations are handled by the HBase Master process. Which means that reading and writing of data is independent of creating and deleting of table.
You can refer https://www.mapr.com/blog/in-depth-look-hbase-architecture for more details about hdfs.
Also see this webinar on HBase by lars george. https://m.youtube.com/watch?v=_HLoH_PgrLk
Hope this will clear your doubts.
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
Does the worker nodes in a hadoop cluster need hadoop installed on each one ?
What if I need only the computing power of some PCs can I use only map-reduce without installing HDFS on each node ?
When you say worker nodes it includes both DataNodes and TaskTracker. So in that sense you need them on each machine if you wish to run MR jobs.
But the main point here is what would you do with MR alone. I mean running MR jobs on data stored in local FS is not gonna be of much use as you can't harness the power of distributed data storage and parallelism provided by Hadoop in that situation.
To use computing power of node you need to run TaskTracker on that node. Hence, Hadoop must be installed.
If you don't need HDFS, you can run only TaskTracker and don't start DataNode.
I have a doubht in HDFS architecture..
Is there any difference between Name Node and Job tracker? and Data Node and Task tracker?
both are same or each has some specific functionality?
I came to know that Name is considered to the master node . It has namespace in RAM that has all information about the metadata.
Is there any difference between Name Node and Job tracker?
Two unrelated components. Namenode is part of the HDFS, while Jobtracker is part of mapreduce. Apples and oranges. Ditto for datanode (HDFS) and tasktracker (mapreduce).
Hadoop core consists of two systems: the HDFS filesystem and the mapreduce components. HDFS is file system, it consists at minimum of one namenode (the central catalog) and several datanodes (the actual storage). Mapreduce consists of the job tracker (central 'brain' of mapreduce) and several tasktrackers (executors).
While deployed together, and getting +synergy from how they interact (data locality for compute), they are distinct. There is no point in asking what is common or different between them.
I have a Hadoop cluster that have 8 machines and all the 8 machines are data nodes.
There's a program running on one machine(say machine A) that will create sequence files ( each of the file is about 1GB) in HDFS continuously.
Here's the problem: All of the 8 machines are the same hardware and has the same capacity. When other machines still have about 50% free space on the disks for HDFS, machine A has only 5% left.
I checked the block info and found that almost every block has one replica on machine A.
Is there any way to balance the replicas?
Thanks.
This is the default placement policy. It works well for the typical M/R pattern, where each HDFS node is also a compute node and the writer machines are uniformly distributed.
If you don't like it, then there is HDFS-385 Design a pluggable interface to place replicas of blocks in HDFS. You need to write a class that implements BlockPlacementPolicy interface, and then set this class in as the dfs.block.replicator.classname in hdfs-site.xml.
There is a way. you can use hadoop command line balancer tool.
HDFS data might not always be be placed uniformly across the DataNode.To spread HDFS data uniformly across the DataNodes in the cluster, this can be used.
hadoop balancer [-threshold <threshold>]
where, threshold is Percentage of disk capacity
see the following links for details:
http://hadoop.apache.org/docs/r1.0.4/commands_manual.html
http://hadoop.apache.org/docs/r1.0.4/hdfs_user_guide.html#Rebalancer