Nicholas：hdfs what is new in hadoop 2

HDFS: What is New in Hadoop 2
Sze Tsz-Wo Nicholas
施子和
December 6, 2013

© Hortonworks Inc. 2013

Page 1

About Me
• 施子和 Sze Tsz-Wo Nicholas, Ph.D.
– Software Engineer at Hortonworks
– PMC Member at Apache Hadoop
– One of the most active contributors/committers of HDFS
• Started in 2007

– Used Hadoop to compute Pi at the two-quadrillionth (2x1015th) bit
• It is the current World Record.

= 3.141592654…

– Received Ph.D. from the University of Maryland, College Park
• Discovered a novel square root algorithm over finite field.

Architecting the Future of Big Data

Page 2

Agenda
• New HDFS features in Hadoop-2
– New appendable write-pipeline
– Multiple Namenode Federation
– Namenode HA
– File System Snapshots


Page 3

We have been hard at work…
• Progress is being made in many areas
– Scalability
– Performance
– Enterprise features
– Ongoing operability improvements
– Enhancements for other projects in the ecosystem

– Expand Hadoop ecosystem to more platforms and use cases

• 2192 commits in Hadoop in the last year
– Almost a million lines of changes
– ~150 contributors
– Lot of new contributors - ~80 with < 3 patches

• 350K lines of changes in HDFS and common

Page 4

Building on Rock-solid Foundation
• Original design choices - simple and robust
– Single Namenode metadata server – all state in memory
– Fault Tolerance: multiple replicas, active monitoring
– Storage: Rely on OS’s file system not raw disk

• Reliability
– Over 7 9’s of data reliability, less than 0.38 failures across 25 clusters

• Operability
– Small teams can manage large clusters
• An operator per 3K node cluster

– Fast Time to repair on node or disk failure
• Minutes to an hour Vs. RAID array repairs taking many long hours

• Scalable - proven by large scale deployments not bits
– > 100 PB storage, > 400 million files, > 4500 nodes in a single cluster
– ~ 100 K nodes of HDFS in deployment and use

Page 5

New Appendable
Write-Pipeline


Page 6

HDFS Write Pipeline
• The write pipeline has been improved dramatically
–
–
–
–

Better durability
Better visibility
Consistency guarantees
Appendable
data

Writer

data

DN1
ack


data

DN2
ack

DN3
ack

Page 7

New Feature in Write Pipeline
• Earlier versions of HDFS
– Files were immutable
– Write-once-read-many model

• New features in Hadoop 2
–
–
–
–

Files can be reopened for append
New primitives: hflush and hsync
Read consistency
Replace datanode on failure


Page 8

HDFS hflush and hsync
• Java flush (or C++ fflush)
– forces any buffered output bytes to be written out.

• HDFS hflush
– Flush data to all the datanodes in the write pipeline
– Guarantees the data is visible for reading
– The data may be in datanodes’ memory

• HDFS sync
– Hfush with local file system sync
– May also update the file length in Namenode


Page 9

Read Consistency
• A reader may read data during write
– It can read from any datanode in the pipeline
– and then failover to any other datanode to read the same data
data

Writer

ack

data

DN1

ack

data

DN2

ack

DN3

read

read

Reader


Page 10

In the past …
• When a datanode fails, the pipeline is reconstructed with
data
the remain datanodes
ack

data

Writer

DN1

DN2

DN3

ack

• When another datanode fails, only one datanode remains!
data

Writer

DN1

DN2

DN3

ack


Page 11

Replace Datanode on Failure
• Add new datanodes to the pipeline
data

ack
data

Writer

data

DN1

DN2

DN3

ack

DN4
ack

• User clients may choose the replacement policy
– Performance vs data reliability


Page 12

Multiple Namenode
Federation


Page 13

Namespace

HDFS Architecture
Persistent Namespace
Metadata & Journal

Hierarchal Namespace
File Name  BlockIDs

Namespace
State
Namenode

Block
Map

Block ID  Block Locations

Block Storage

Heartbeats & Block Reports

b2

b1

b3

b1

b3

b5

b3

Datanodes

b2

b5

b1

b2

b5

Block ID  Data
JBOD

JBOD

JBOD

JBOD

Horizontally Scale IO and Storage

14
Page 14

Single Namenode Limitations
• Namespace size is limited by the namenode memory size
– 64GB memory can support ~100m files and blocks
– Solution: Federation

• Single point of failure (SPOF)
– The service is down when the namenode is down
– Solution: HA


Page 15

Federation Cluster
• Multiple namenodes and namespace volumes in a cluster
–
–
–
–

The namenodes/namespaces are independent
Scalability by adding more namenodes/namespaces
Isolation – separating applications to their own namespaces
Client side mount tables/ViewFS for integrated views

• Block Storage as generic storage service
– Datanodes store blocks in block pools for all namespaces


Page 16

Namespace

Multiple Namenode Federation
Foreign
NS n

NS k

NS1

...

Pool 1

Block Storage

NN-n

NN-k

NN-1

...

Pool k

Pool n

Block Pools

DN 1

..

DN 2

..

DN m

..

Common Storage

Page 17

Namenode HA


Page 18

High Availability – No SPOF
• Support standby namenode and failover
– Planned downtime
– Unplanned downtime

• Release 1.1
– Cold standby
• Require reconstructing in-memory data structures during failure-over

– Uses NFS as shared storage
– Standard HA frameworks as failover controller
• Linux HA and VMWare VSphere

– Suitable for small clusters up to 500 nodes


Page 19

Hadoop Full Stack HA

Slave Nodes of Hadoop Cluster

jo
b

jo
b

jo
b

jo
b

jo
b

Apps
Running
Outside

Failover
JT into Safemode

NN

JT

Server

Server

NN

Server

HA Cluster for Master Daemons

Page 20

High Availability – Release 2.0
• Support for Hot Standby
– The standby namenode maintains in-memory data structures

• Supports manual and automatic failover
• Automatic failover with Failover Controller
– Active NN election and failure detection using ZooKeeper
– Periodic NN health check
– Failover on NN failure

• Removed shared storage dependency
– Quorum Journal Manager
• 3 to 5 Journal Nodes for storing editlog
• Edit must be written to quorum number of Journal Nodes

• Replay cache for correctness & transparent failovers

Page 21

Namenode HA in Hadoop 2
ZK

Heartbeat

ZK

ZK

Heartbeat

FailoverController
Active

FailoverController
Standby

Cmds

Monitor Health
of NN. OS, HW

JN

NN
Active

JN

JN

Shared NN state
through Quorum
of JournalNodes

NN
Standby

Monitor Health
of NN. OS, HW

Block Reports to Active & Standby
DN fencing: only obey commands
from active

DN

DN

DN

DN

Namenode HA has no external dependency

Page 22

File System Snapshots


Page 23

Before Snapshots…
• Deleted files cannot be restored
– Trash is buggy and not well understood
– Trash works only for CLI based deletion

• No point-in-time recovery

• No periodic snapshots to restore from
– No admin/user managed snapshots


Page 24

HDFS Snapshot

Point-in-time image of the file system
Read-only
Copy-on-write


Page 25

Use Cases

Protection against user errors
Backup
Experimental/Test setups


Page 26

Example: Periodic Snapshots for Backup
• A typical snapshot policy:
Take a snapshot in
– every 15 mins and
– every 1 hr,
– every 1 day,
– every 1 week,
– every 1 month,


keep it for 24 hrs
keep 2 days
keep 14 days
keep 3 months
keep 1 year

Page 27

Design Goal: Efficiency
• Storage efficiency
– No block data copying
– No metadata copying for unmodified files

• Processing efficiency
– No additional costs for processing current data

• Cheap snapshot creation
– Must be fast and lightweight
– Must support for a very large number of snapshots


Page 28

Design Goal: Features
• Read-only
– Files and directories in a snapshot are immutable
– Nothing can be added to or removed from directories

• Hierarchical snapshots
– Snapshots of the entire namespace
– Snapshots of subtrees

• User operation
– Users can take snapshots for their data
– Admins manage where users can take snapshots


Page 29

HDFS-2802: Snapshot Development
• Available in Hadoop 2 GA release (v2.2.0)
• Community-driven
– Special thanks to who have provided for the valuable discussion
and feedback on the feature requirements and the open questions

• 136 subtask JIRAs
– Mainly contributed by Hortonworks

• The merge patch has about 28k lines
• ~8 months of development


Page 30

Namenode Only Operation
• No complicated distributed mechanism
• Snapshot metadata stored in Namenode
• Datanodes have no knowledge of snapshots

• Block management layer also don’t know about
snapshots


Page 31

Fast Snapshot Creation
• Snapshot Creation: O(1)
– It just adds a record to an inode

/
d
1

f1


d
2

f2

S1

f3

Page 32

Low Memory Overhead
• NameNode memory usage: O(M)
– M is the number of modified files/directories
– Additional memory is used only when modifications are made
relative to a snapshot

/
d
1
f1

d
2
f4


f2

S1

Modifications:
1. rm f3
2. add f4

f3

Page 33

File Blocks Sharing
• Blocks in datanodes are not copied
– The snapshot files record the block list and the file size
– No data copying

/
d

blk0

S1

f'

f’’

S2

f

blk1

blk2

blk3
Page 34

Persistent Data Structures
• A well-known data structure for “time travel”
– Support querying previous version of the data

• Access slow down
– The additional time required for the data structure

• In traditional persistent data structures
– There is slow down on accessing current data and snapshot data

• In our implementation
– No slow down on accessing current data
– Slow down happens only on accessing snapshot data


Page 35

No Slow Down on Accessing Current Data
• The current data can be accessed directly
– Modifications are recorded in reverse chronological order

Snapshot data = Current data – Modifications

/
~ modifications

d
1
f1

d
2
f4

f2

S1

Modifications:
1. rm f3
2. add f4

f3
f2


d
2
f3
Page 36

Easy Management
• Snapshots can be taken on any directory
– Set the directory to be snapshottable

• Support 65,536 simultaneous snapshots
• No limit on the number of snapshottable directories
– Nested snapshottable directories are currently NOT allowed


Page 37

Admin Ops
• Allow snapshots on a directory
– hdfs dfsadmin –allowSnapshot <path>

• Reset a snapshottable directory
– hdfs dfsadmin –disallowSnapshot <path>

• Example


Page 38

User Ops
• Create/delete/rename snapshots
– hdfs dfs -createSnapshot <path> [<snapshotName>]
– hdfs dfs –deleteSnapshot <path> <snapshotName>
– hdfs dfs –renameSnapshot <path> <oldName> <newName>

• Get snapshottable directory listing
– hdfs lsSnapshottableDir

• Get snapshots difference report
– hdfs snapshotDiff <path> <from> <to>


Page 39

Use snapshot paths in CLI
• All regular commands and APIs can be used against
snapshot path
– /<snapshottableDir>/.snapshot/<snapshotName>/foo/bar

• List all the files in a snapshot
– ls /test/.snapshot/s4

• List all the snapshots under that path
– ls <path>/.snapshot


Page 40

Test Snapshot Functionalities
• ~100 unit tests
• ~1.4 million generated system tests
– Covering most combination of (snapshot + rename) operations

• Automated long-running tests for months


Page 41

NFS Support
and Other Features


Page 42

NFS Support
• NFS Gateway provides NFS access to HDFS
– File browsing, Data download/upload, Data streaming
– No client-side library
– Better alternative to Hadoop + Fuse based solution
• Better consistency guarantees

• Supports NFSv3
• Stateless Gateway
– Simpler design, easy to handle failures

• Future work
– High Availability for NFS Gateway
– NFSv4 support?


Page 43

Other Features
• Protobuf, wire compatibility
– Post 2.0 GA stronger wire compatibility

• Rolling upgrades
– With relaxed version checks

• Improvements for other projects
– Stale node to improve HBase MTTR

• Block placement enhancements
– Better support for other topologies such as VMs and Cloud

• On the wire encryption
– Both data and RPC

• Expanding ecosystem, platforms and applicability
– Native support for Windows


Page 44

Enterprise Readiness
• Storage fault-tolerance – built into HDFS 
– 100% data reliability

• High Availability 
• Standard Interfaces 
– WebHDFS(REST), Fuse, NFS, HttpFs, libwebhdfs and libhdfs

• Wire protocol compatibility 
– Protocol buffers

• Rolling upgrades 

• Snapshots 
• Disaster Recovery 
– Distcp for parallel and incremental copies across cluster
– Apache Ambari and HDP for automated management

Page 45

Work in Progress
• HDFS-2832: Heterogeneous storages
– Datanode abstraction from single storage to collection of storages
– Support different storage types: Disk and SSD

• HDFS-5535: Zero download rolling upgrade
– Namenodes and Datanodes can be upgraded independently
– No upgrade downtime

• HDFS-4685: ACLs
– More flexible than user-group-permission


Page 46

Future Works
• HDFS-5477: Block manager as a service
– Move block management out from Namenode
– Support different name service, e.g. key-value store

• HDFS-3154: Immutable files
– Write-once and then read-only

• HDFS-4704: Transient files
– Tmp files will not be recorded in snapshots


Page 47

Q&A
• Myths and misinformation of HDFS
–
–
–
–
–

Not reliable (was never true)
Namenode dies, all state is lost (was never true)
Does not support disaster recovery (distcp in Hadoop0.15)
Hard to operate for new comers
Performance improvements (always ongoing)
• Major improvements in 1.2 and 2.x

– Namenode is a single point of failure
– Needs shared NFS storage for HA
– Does not have point in time recovery

Thank You!

Page 48

Nicholas：hdfs what is new in hadoop 2

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