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What’s HDFS

HDFS is a distributed file system that is fault tolerant, scalable and extremely easy to expand.
HDFS is the primary distributed storage for Hadoop applications.
HDFS provides interfaces for applications to move themselves closer to data.
HDFS is designed to ‘just work’, however a working knowledge helps in diagnostics and improvements.

Introduction to HDFS

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Components of HDFS

There are two (and a half) types of machines in a HDFS cluster

NameNode :– is the heart of an HDFS filesystem, it maintains and manages the file system metadata. E.g; what blocks make up a file, and on which datanodes those blocks are stored.
DataNode :- where HDFS stores the actual data, there are usually quite a few of these.

Introduction to HDFS

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HDFS Architecture

Introduction to HDFS

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Unique features of HDFS

HDFS also has a bunch of unique features that make it ideal for distributed systems:

Failure tolerant - data is duplicated across multiple DataNodes to protect against machine failures. The default is a replication factor of 3 (every block is stored on three machines).
Scalability - data transfers happen directly with the DataNodes so your read/write capacity scales fairly well with the number of DataNodes
Space - need more disk space? Just add more DataNodes and re-balance
Industry standard - Other distributed applications are built on top of HDFS (HBase, Map-Reduce)

HDFS is designed to process large data sets with write-once-read-many semantics, it is not for low latency access

Introduction to HDFS

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HDFS – Data Organization

Each file written into HDFS is split into data blocks
Each block is stored on one or more nodes
Each copy of the block is called replica
Block placement policy

First replica is placed on the local node
Second replica is placed in a different rack
Third replica is placed in the same rack as the second replica

Introduction to HDFS

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Read Operation in HDFS

Introduction to HDFS

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Write Operation in HDFS

Introduction to HDFS

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HDFS Security

Authentication to Hadoop

Simple – insecure way of using OS username to determine hadoop identity
Kerberos – authentication using kerberos ticket
Set by hadoop.security.authentication=simple|kerberos

File and Directory permissions are same like in POSIX

read (r), write (w), and execute (x) permissions
also has an owner, group and mode
enabled by default (dfs.permissions.enabled=true)

ACLs are used for implemention permissions that differ from natural hierarchy of users and groups

enabled by dfs.namenode.acls.enabled=true

Introduction to HDFS

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HDFS Configuration

HDFS Defaults

Block Size – 64 MB
Replication Factor – 3
Web UI Port – 50070

HDFS conf file - /etc/hadoop/conf/hdfs-site.xml

<name>dfs.namenode.name.dir</name>

<value>file:///data1/cloudera/dfs/nn,file:///data2/cloudera/dfs/nn</value>

</property>

<name>dfs.blocksize</name>

</property>

<name>dfs.replication</name>

</property>

<name>dfs.namenode.http-address</name>

<value>itracXXX.cern.ch:50070</value>

</property>

Introduction to HDFS

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Interfaces to HDFS

Java API (DistributedFileSystem)
C wrapper (libhdfs)
HTTP protocol
WebDAV protocol
Shell Commands

However the command line is one of the simplest and most familiar

Introduction to HDFS

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HDFS – Shell Commands

There are two types of shell commands

User Commands

hdfs dfs – runs filesystem commands on the HDFS

hdfs fsck – runs a HDFS filesystem checking command

Administration Commands

hdfs dfsadmin – runs HDFS administration commands

Introduction to HDFS

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HDFS – User Commands (dfs)

List directory contents

Display the disk space used by files

Introduction to HDFS

hdfs dfs –ls

hdfs dfs -ls /

hdfs dfs -ls -R /var

hdfs dfs -du -h /

hdfs dfs -du /hbase/data/hbase/namespace/

hdfs dfs -du -h /hbase/data/hbase/namespace/

hdfs dfs -du -s /hbase/data/hbase/namespace/

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HDFS – User Commands (dfs)

Copy data to HDFS

Copy the file back to local filesystem

Introduction to HDFS

hdfs dfs -mkdir tdata

hdfs dfs -ls

hdfs dfs -copyFromLocal tutorials/data/geneva.csv tdata

hdfs dfs -ls –R

cd tutorials/data/

hdfs dfs –copyToLocal tdata/geneva.csv geneva.csv.hdfs

md5sum geneva.csv geneva.csv.hdfs

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HDFS – User Commands (acls)

List acl for a file

List the file statistics – (%r – replication factor)

Write to hdfs reading from stdin

Introduction to HDFS

hdfs dfs -getfacl tdata/geneva.csv

hdfs dfs -stat "%r" tdata/geneva.csv

echo "blah blah blah" | hdfs dfs -put - tdataset/tfile.txt

hdfs dfs -ls –R

hdfs dfs -cat tdataset/tfile.txt

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HDFS – User Commands (fsck)

Removing a file

List the blocks of a file and their locations

Print missing blocks and the files they belong to

Introduction to HDFS

hdfs dfs -rm tdataset/tfile.txt

hdfs dfs -ls –R

hdfs fsck /user/cloudera/tdata/geneva.csv -files -blocks –locations

hdfs fsck / -list-corruptfileblocks

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HDFS – Adminstration Commands

Comprehensive status report of HDFS cluster

Prints a tree of racks and their nodes

Get the information for a given datanode (like ping)

Introduction to HDFS

hdfs dfsadmin –report

hdfs dfsadmin –printTopology

hdfs dfsadmin -getDatanodeInfo localhost:50020

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HDFS – Advanced Commands

Get a list of namenodes in the Hadoop cluster

Dump the NameNode fsimage to XML file

The general command line syntax is

hdfs command [genericOptions] [commandOptions]

Introduction to HDFS

hdfs getconf –namenodes

cd /var/lib/hadoop-hdfs/cache/hdfs/dfs/name/current

hdfs oiv -i fsimage_0000000000000003388 -o /tmp/fsimage.xml -p XML

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Other Interfaces to HDFS

HTTP Interface

MountableHDFS – FUSE

Once mounted all operations on HDFS can be performed using standard Unix utilities such as 'ls', 'cd', 'cp', 'mkdir', 'find', 'grep',

Introduction to HDFS

http://quickstart.cloudera:50070

mkdir /home/cloudera/hdfs

sudo hadoop-fuse-dfs dfs://quickstart.cloudera:8020 /home/cloudera/hdfs

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Basic Features: HDFS

Highly fault-tolerant
High throughput
Suitable for applications with large data sets
Streaming access to file system data
Can be built out of commodity hardware

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Fault tolerance

Failure is the norm rather than exception
A HDFS instance may consist of thousands of server machines, each storing part of the file system’s data.
Since we have huge number of components and that each component has non-trivial probability of failure means that there is always some component that is non-functional.
Detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS.

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Data Characteristics

Streaming data access
Applications need streaming access to data
Batch processing rather than interactive user access.
Large data sets and files: gigabytes to terabytes size
High aggregate data bandwidth
Scale to hundreds of nodes in a cluster
Tens of millions of files in a single instance
Write-once-read-many: a file once created, written and closed need not be changed – this assumption simplifies coherency
A map-reduce application or web-crawler application fits perfectly with this model.

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MapReduce

Cat

Bat

Dog

Other

Words

(size:

TByte)

map

split

combine

reduce

part0

part1

part2

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Architecture

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Namenode and Datanodes

Master/slave architecture
HDFS cluster consists of a single Namenode, a master server that manages the file system namespace and regulates access to files by clients.
There are a number of DataNodes usually one per node in a cluster.
The DataNodes manage storage attached to the nodes that they run on.
HDFS exposes a file system namespace and allows user data to be stored in files.
A file is split into one or more blocks and set of blocks are stored in DataNodes.
DataNodes: serves read, write requests, performs block creation, deletion, and replication upon instruction from Namenode.

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HDFS Architecture

Namenode

replication

Rack1

Rack2

Client

Blocks

Datanodes

Client

Write

Read

Metadata ops

Metadata(Name, replicas..)

(/home/foo/data,6. ..

Block ops

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File system Namespace

Hierarchical file system with directories and files
Create, remove, move, rename etc.
Namenode maintains the file system
Any meta information changes to the file system recorded by the Namenode.
An application can specify the number of replicas of the file needed: replication factor of the file. This information is stored in the Namenode.

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Data Replication

HDFS is designed to store very large files across machines in a large cluster.
Each file is a sequence of blocks.
All blocks in the file except the last are of the same size.
Blocks are replicated for fault tolerance.
Block size and replicas are configurable per file.
The Namenode receives a Heartbeat and a BlockReport from each DataNode in the cluster.
BlockReport contains all the blocks on a Datanode.

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Replica Placement

The placement of the replicas is critical to HDFS reliability and performance.
Optimizing replica placement distinguishes HDFS from other distributed file systems.
Rack-aware replica placement:

Goal: improve reliability, availability and network bandwidth utilization
Research topic

Many racks, communication between racks are through switches.
Network bandwidth between machines on the same rack is greater than those in different racks.
Namenode determines the rack id for each DataNode.
Replicas are typically placed on unique racks

Simple but non-optimal
Writes are expensive
Replication factor is 3
Another research topic?

Replicas are placed: one on a node in a local rack, one on a different node in the local rack and one on a node in a different rack.
1/3 of the replica on a node, 2/3 on a rack and 1/3 distributed evenly across remaining racks.

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Replica Selection

Replica selection for READ operation: HDFS tries to minimize the bandwidth consumption and latency.
If there is a replica on the Reader node then that is preferred.
HDFS cluster may span multiple data centers: replica in the local data center is preferred over the remote one.

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Safemode Startup

On startup Namenode enters Safemode.
Replication of data blocks do not occur in Safemode.
Each DataNode checks in with Heartbeat and BlockReport.
Namenode verifies that each block has acceptable number of replicas
After a configurable percentage of safely replicated blocks check in with the Namenode, Namenode exits Safemode.
It then makes the list of blocks that need to be replicated.
Namenode then proceeds to replicate these blocks to other Datanodes.

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Filesystem Metadata

The HDFS namespace is stored by Namenode.
Namenode uses a transaction log called the EditLog to record every change that occurs to the filesystem meta data.

For example, creating a new file.
Change replication factor of a file
EditLog is stored in the Namenode’s local filesystem

Entire filesystem namespace including mapping of blocks to files and file system properties is stored in a file FsImage. Stored in Namenode’s local filesystem.

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Namenode

Keeps image of entire file system namespace and file Blockmap in memory.
4GB of local RAM is sufficient to support the above data structures that represent the huge number of files and directories.
When the Namenode starts up it gets the FsImage and Editlog from its local file system, update FsImage with EditLog information and then stores a copy of the FsImage on the filesytstem as a checkpoint.
Periodic checkpointing is done. So that the system can recover back to the last checkpointed state in case of a crash.

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Datanode

A Datanode stores data in files in its local file system.
Datanode has no knowledge about HDFS filesystem
It stores each block of HDFS data in a separate file.
Datanode does not create all files in the same directory.
It uses heuristics to determine optimal number of files per directory and creates directories appropriately:

Research issue?

When the filesystem starts up it generates a list of all HDFS blocks and send this report to Namenode: Blockreport.

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Protocol

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The Communication Protocol

All HDFS communication protocols are layered on top of the TCP/IP protocol
A client establishes a connection to a configurable TCP port on the Namenode machine. It talks ClientProtocol with the Namenode.
The Datanodes talk to the Namenode using Datanode protocol.
RPC abstraction wraps both ClientProtocol and Datanode protocol.
Namenode is simply a server and never initiates a request; it only responds to RPC requests issued by DataNodes or clients.

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Robustness

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Objectives

Primary objective of HDFS is to store data reliably in the presence of failures.
Three common failures are: Namenode failure, Datanode failure and network partition.

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DataNode failure and heartbeat

A network partition can cause a subset of Datanodes to lose connectivity with the Namenode.
Namenode detects this condition by the absence of a Heartbeat message.
Namenode marks Datanodes without Hearbeat and does not send any IO requests to them.
Any data registered to the failed Datanode is not available to the HDFS.
Also the death of a Datanode may cause replication factor of some of the blocks to fall below their specified value.

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Re-replication

The necessity for re-replication may arise due to:

A Datanode may become unavailable,
A replica may become corrupted,
A hard disk on a Datanode may fail, or
The replication factor on the block may be increased.

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Cluster Rebalancing

HDFS architecture is compatible with data rebalancing schemes.
A scheme might move data from one Datanode to another if the free space on a Datanode falls below a certain threshold.
In the event of a sudden high demand for a particular file, a scheme might dynamically create additional replicas and rebalance other data in the cluster.
These types of data rebalancing are not yet implemented: research issue.

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Data Integrity

Consider a situation: a block of data fetched from Datanode arrives corrupted.
This corruption may occur because of faults in a storage device, network faults, or buggy software.
A HDFS client creates the checksum of every block of its file and stores it in hidden files in the HDFS namespace.
When a clients retrieves the contents of file, it verifies that the corresponding checksums match.
If does not match, the client can retrieve the block from a replica.

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Metadata Disk Failure

FsImage and EditLog are central data structures of HDFS.
A corruption of these files can cause a HDFS instance to be non-functional.
For this reason, a Namenode can be configured to maintain multiple copies of the FsImage and EditLog.
Multiple copies of the FsImage and EditLog files are updated synchronously.
Meta-data is not data-intensive.
The Namenode could be single point failure: automatic failover is NOT supported! Another research topic.

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Data Organization

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Data Blocks

HDFS support write-once-read-many with reads at streaming speeds.
A typical block size is 64MB (or even 128 MB).
A file is chopped into 64MB chunks and stored.

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Staging

A client request to create a file does not reach Namenode immediately.
HDFS client caches the data into a temporary file. When the data reached a HDFS block size the client contacts the Namenode.
Namenode inserts the filename into its hierarchy and allocates a data block for it.
The Namenode responds to the client with the identity of the Datanode and the destination of the replicas (Datanodes) for the block.
Then the client flushes it from its local memory.

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Staging (contd.)

The client sends a message that the file is closed.
Namenode proceeds to commit the file for creation operation into the persistent store.
If the Namenode dies before file is closed, the file is lost.
This client side caching is required to avoid network congestion; also it has precedence is AFS (Andrew file system).

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Replication Pipelining

When the client receives response from Namenode, it flushes its block in small pieces (4K) to the first replica, that in turn copies it to the next replica and so on.
Thus data is pipelined from Datanode to the next.

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API (Accessibility)

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Application Programming Interface

HDFS provides Java API for application to use.
Python access is also used in many applications.
A C language wrapper for Java API is also available.
A HTTP browser can be used to browse the files of a HDFS instance.

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FS Shell, Admin and Browser Interface

HDFS organizes its data in files and directories.
It provides a command line interface called the FS shell that lets the user interact with data in the HDFS.
The syntax of the commands is similar to bash and csh.
Example: to create a directory /foodir

/bin/hadoop dfs –mkdir /foodir

There is also DFSAdmin interface available
Browser interface is also available to view the namespace.

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Space Reclamation

When a file is deleted by a client, HDFS renames file to a file in be the /trash directory for a configurable amount of time.
A client can request for an undelete in this allowed time.
After the specified time the file is deleted and the space is reclaimed.
When the replication factor is reduced, the Namenode selects excess replicas that can be deleted.
Next heartbeat(?) transfers this information to the Datanode that clears the blocks for use.

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Summary

We discussed the features of the Hadoop File System, a peta-scale file system to handle big-data sets.
What discussed: Architecture, Protocol, API, etc.
Missing element: Implementation

The Hadoop file system (internals)
An implementation of an instance of the HDFS (for use by applications such as web crawlers).