What is Hadoop?



Apache Hadoop is an open-source software framework that

  • Stores big data in a distributed manner

  • Processes big data parallelly

  • Builds on large clusters of commodity hardware

Based on Google’s papers on Google File System (2003) and MapReduce (2004)


Hadoop is:

  • Scalable to Petabytes or more easily(Volume)

  • Offering parallel data processing(Velocity)

  • Storing all kinds of data(Variety)


Hadoop offers:

  • Redundant, Fault-tolerant data storage (HDFS)

  • Parallel computation framework (MapReduce)

  • Job coordination/scheduling (YARN)


Programmers no longer need to worry about:

  • Where the file is located?

  • How to handle failures & data lost?

  • How to divide computation?

  • How to program for scaling?



Hadoop Ecosystem


Core of Hadoop:

  • Hadoop distributed file system (

  • MapReduce

  • YARN (Yet Another Resource Negotiator) (from Hadoop v2.0)

Additional software packages:

  • Pig

  • Hive

  • Spark

  • HBase

  • ........


The Master-Slave Architecture of Hadoop




Hadoop Distributed File Systems(HDFS)

HDFS is a file system that

  • follows master slave architecture

  • allows us to store data over multiple nodes(machines)

  • allows multiple users to access data.

  • just like file systems in your PC


HDFS supports

  • distributed storage

  • distributed computation

  • horizontal scalability


Vertical Scaling vs. Horizontal Scaling


HDFS Architecture




NameNode

NameNode maintains and manages the blocks in the DataNodes (slave nodes).

  • Master node


Functions:

  • records the metadata of all the files

  • FsImage: file system namespace since our name node is started

It records all changes

  • EditLogs: all the recent modifications e.g for the past 1 hour

  • records each change to the metadata

  • regularly checks the status of data nodes

  • keeps a record of all the blocks in HDFS

  • if the DataNode fails, handle data recovery


DataNode

A commodity hardware stores the data

  • Slave node

Functions

  • stores actual data

  • performs the read and write requests

  • reports the health to NameNode (heartbeat)


NameNode vs. DataNode


If NameNode failed…

All the files on HDFS will be lost

  • there’s no way to reconstruct the files from the blocks in DataNodes without the metadata in NameNode


In order to make NameNode resilient to failure

  • back up metadata in NameNode (with a remote NFS mount)

  • Secondary NameNode



Secondary NameNode

Take checkpoints of the file system metadata present on NameNode

  • It is not a backup NameNode!

Functions:

  • Stores a copy of FsImage file and Editlogs

  • Periodically applies Editlogs to FsImage and refreshes the Editlogs

  • If NameNode is failed, File System metadata can be recovered from the last saved FsImage on the Secondary NameNode


NameNode vs. Secondary NameNode


Blocks

Block is a sequence of bytes that stores data

  • Data stores as a set of blocks in HDFS

  • Default block size is 128M eta B ytes (Hadoop 2.x and 3.x) x), the default size in hadoob is 64 metab bytes

  • A file is spitted into multiple blocks


Why Large Block Size?

  • HDFS stores huge datasets

  • If block size is small (e.g., 4KB in Linux), then the number of blocks is large:

  • too much metadata for NameNode

  • too many seeks affect the read speed

  1. read speed=seek time+transfer time

  2. tranfer time=total size of file/transportation speed

  • harm the performance of MapReduce too

  • We don’t recommend using HDFS for small files due to similar reasons.


If DataNode Failed…

  • Commodity hardware fails

  • If NameNode hasn’t heard from a DataNode for 10mins, The DataNode is considered dead…

  • HDFS guarantees data reliability by generating multiple replications of data

  • each block has 3 replications by default

  • replications will be stored on different DataNodes

  • if blocks were lost due to the failure of a DataNode, they can be recovered from other replications

  • the total consumed space is 3 times the data size

  • It also helps to maintain data integrity (whether The data stored is correct or not


File, Block and Replica

  • A file contains one or more blocks

  • Blocks are different

  • Depends on the file size and block size

  • A block has multiple replicas

  • Replicas are the same

  • Depends on the preset replication factor


Replication Management

  • Each block is replicated 3 times and stored on different DataNodes



Why default replication factor 3?

  • If 1 replicate

  • DataNode fails, block lost

  • Assume

  • # of nodes N = 4000

  • # of blocks R = 1,000,000

  • Node failure rate FPD = 1 per day u expect to see 1 machine fail per day

  • If one node fails, then R/N = 250 blocks are lost

  • E(# of losing blocks in one day) 250

  • Let the number of losing blocks follows Poisson distribution, then

  • Pr[# of losing blocks in one day >= 250] = 0.508


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