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© 2012 IBM Corporation

IBM Security Systems

1© 2013 IBM Corporation

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Big Data - Storage

Kalapriya KannanIBM Research LabsJuly, 2013




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What are we going to understand

Why distributed computingIs it something new?HDFS - basics




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What are the things we do with data

+Store+Perform some basic operations (add, substract.. ) typically called Transformations.

+Build functions to analyze the data – trends, prediction, forecasting etc.,




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What is required?

Not just parallel program. - not Sufficient–Data has to be parallelized – for parallel access.

Function(){Read a,b from db;Compute c= a+b;Compute d= a*b;Compute c+d;}

c d

Step 1: Parallelize this program?

a,b

c da1,b1a0,b0




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What is common to all?

Manage files–Booking keeping

•Where it is located? What is the name of it ? What is the block size?•Is there a lock on it?•What kind of access control is there?•Who are the people who can read/write?•Is there a fragmentation within the file.•When was it last modified.

File system




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Distributed file system?

DEFINITIONS: • A Distributed File System ( DFS ) is simply a classical model of a file system ( as discussed before )

distributed across multiple machines. The purpose is to promote sharing of dispersed files.

• This is an area of active research interest today.

• The resources on a particular machine are local to itself. Resources on other machines are remote.

• A file system provides a service for clients. The server interface is the normal set of file operations: create, read, etc. on files.

Classic Example: NFS




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Lets Revisit the parameters

Requirements. NFS Comments

Parallelize access to same data NO Multiple clients connecting to the same machine can cause server bottleneck

Consistency YES

Split data into different machines for parallel access-Transparently.

NO Single machine logical volume

Store unlimited data NO Can store only as much as that machine can store

Reliability NO If the machine that is storing the data goes down, data is unavailable.

Parallel access NO Will still be directed to the single server




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The popular Hadoop

HDFS–Why is it so popular

• Simplicity• Easy to add new machines• Cheap storage (no new investments).




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HDFS is good for………….

Precisely does what we require.–Parallelize the data so that accesses to it can be parallelized.

Appears as single disk–Runs on top of a native file system eg.,Ext3, ext4, XFS…..

Based on Google File System (GFS) Fault tolerant

–Can handle disk crashes, machine crashes etc., “Cheap” commodity server Hardware

–Cost of a 3 terabyte – file server costs nearly Rs. 150,000/-–Cheapest commodity machine available Rs. 20,000/-

Storing large files–Terabytes, petabytes etc., Million rather than billion of files

Streaming data–Write once and read multiple times–Optimized for streaming reads rather than random reads.




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HDFS is not good for…

Low latency reads

–High throughput rather than low latency for small chunks of data

–Hbase addresses this issue Better for millions of large files rather than billions of small files Multiple writers

–Single writer per file

–Writes only at the end of the file, no support for arbitrary offset.




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HDFS world

+Three terms - name node, Data node and secondary node

+Name node

+Book keeping -1 to limited number of nodes

+Data node – all nodes

+Secondary node – 1 node as Name node




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HDFS Organization

File system cluster is managed by three types of processes–Namenode

• Manages the file system’s namespace/meta-data/file blocks• Run on 1 machine to several machines

–Data node• Stores and retrieves data blocks• Reports to Name node• Runs on many machines

–Secondary Name node• Performs house keeping work• Requires similar hardware as Namenode machine• Not used for high-availability –not a backup for namenode.




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HDFS Features

Files are split into Blocks (single unit of Storage)+Managed by Namenode, stored by DataNode+Transparent to the user

Replicated across machines at load time+Same block is stored on multiple machines+Good for Fault-Tolerant and access+Default replication is 3.




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HDFS Blocks

Block sizes are traditionally either 64 MB or 128MB+Default is 64 MB

The motivation is to minimize the cost of seeks as compared to transfer rate

+‘Time to Transfer’ > ‘Time to Seek’For example, lets say

+Seek time = 10ms+Transfer rate = 100MB/s

To achieve seek time of 1% transfer rate+Block size will need to be = 100 MB.




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Block Replication

Namenode determines replica placementReplica placement are rack aware

–Balance between reliability and performance+Attempts to reduce bandwidth+Attempts to improve reliability by putting replicas on multiple racks.

–Default replication is 3+1st replica on the local disk+2nd replica on the local rack but different machine+3rd replica on the different rack

–This policy may change/improve in the future.




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Client, Name node and Data nodes

Name node do NOT directly write or read data+One of the reasons for HDFS Scalability

Client interacts with Namenode to update Name node’s HDFS namespace and retrieve block locations for writing and reading

Clients interact with Datanode to read/write data




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Recap

Two kinds of programs

–Computing should be more–Data access should be parallelized




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Some traditional paradigms

Performance

Perhaps Good Performance

MachineIO Bottleneck

On the machine

n/w bottleneck + io bottleneck

Distributed through network




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How HDFS works

file

1 2 3 4 5Split files

2 1 4 5 3Replicate to machines

code

1 2 3 4 5

Move to machines

1 2 3 4 5

codecode code code code

codecode code code code




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HDFS functionality




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HDFS File Write




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HDFS File Read




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Name node considerations…

»For fast access Namenode keeps all the metadata in-memory

–The bigger the cluster – the more RAM is required.•Best for millions of large files than billions•Will work well for clusters of 100s machines

–Changing block size will affect how much space a cluster can host•64MB to 128MB will reduce the number of blocks and

significantly increase how much space the Namenode will be able to support.

•Example:–Lets say we are storing 200 Terabytes = 209,715,200 MB–With 64 MB block size that equates to 3,276,800 blocks–With 128 MB block size that equates to 1,638,400 blocks–




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Name node’s fault- tolerance

Name node daemon process must be running at all times

+If process crashes then cluster is down.Name node is a single point of failure

+Host on a machine with reliable hardware (ex. Sustain a disk-failure).

+Usually not an issue.




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Simple Exercise to get the machines ready for the cluster

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