Gfs google-file-system-13331

The Google File SystemTut Chi Io(Modified by Fengchang)

WHAT IS GFS

• Google FILE SYSTEM(GFS)• scalable distr ibuted fi le system (DFS) • falt tolerence• Reliabil i ty• Scalabil ity• availabil i ty and performance to large

networks and connected nodes.

WHAT IS GFS

• built from low-cost COMMODITY HARDWARE components

• optimized to accomodate Google's different data use and storage needs,

• capitalized on the strength of off-the-shelf servers while minimizing hardware weaknesses

Design Overview – Assumption

• Inexpensive commodity hardware• Large fi les: Multi-GB• Workloads

– Large streaming reads– Small random reads– Large, sequential appends

• Concurrent append to the same fi le• High Throughput > Low Latency

Design Overview – Interface

• Create• Delete• Open• Close• Read• Write

• Snapshot• Record Append

What does it look like

Design Overview – Architecture

• Single master, mult iple chunk servers, multiple clients– User-level process running on commodity

Linux machine– GFS client code l inked into each cl ient

application to communicate• File -> 64MB chunks -> Linux f i les

– on local disks of chunk servers– replicated on multiple chunk servers (3r)

• Cache metadata but not chunk on clients

Design Overview – Single Master

• Why centralization? Simplicity!• Global knowledge is needed for

– Chunk placement– Replication decisions

Design Overview – Chunk Size• 64MB – Much Larger than ordinary,

why?– Advantages

• Reduce client-master interaction• Reduce network overhead• Reduce the size of the metadata

– Disadvantages• Internal fragmentation

– Solution: lazy space al location• Hot Spots – many clients accessing a 1-chunk

f i le, e.g. executables– Solution:– Higher replication factor– Stagger application start t imes– Client-to-client communication

Design Overview – Metadata• File & chunk namespaces

– In master ’s memory– In master ’s and chunk servers ’ storage

• File-chunk mapping– In master ’s memory– In master ’s and chunk servers ’ storage

• Location of chunk replicas– In master ’s memory– Ask chunk servers when

• Master starts• Chunk server joins the cluster

– If persistent, master and chunk servers must be in sync

Design Overview – Metadata – In-memory DS

• Why in-memory data structure for the master? – Fast! For GC and LB

• Will i t pose a l imit on the number of chunks -> total capacity?– No, a 64MB chunk needs less than 64B

metadata (640TB needs less than 640MB)• Most chunks are full• Prefix compression on f i le names

Design Overview – Metadata – Log• The only persistent record of metadata• Defines the order of concurrent

operations• Crit ical

– Replicated on multiple remote machines– Respond to client only when log locally

and remotely• Fast recovery by using checkpoints

– Use a compact B-tree l ike form directly mapping into memory

– Switch to a new log, Create new checkpoints in a separate threads

Design Overview – Consistency Model

• Consistent– All cl ients wil l see the same data,

regardless of which replicas they read from

• Defined– Consistent, and clients wil l see what the

mutation writes in its entirety

Design Overview – Consistency Model

• After a sequence of success, a region is guaranteed to be defined– Same order on all replicas– Chunk version number to detect stale

replicas• Client cache stale chunk locations?

– Limited by cache entry ’s timeout– Most f i les are append-only

• A Stale replica return a premature end of chunk

System Interactions – Lease

• Minimized management overhead• Granted by the master to one of the

replicas to become the primary• Primary picks a serial order of

mutation and all replicas follow• 60 seconds timeout, can be extended• Can be revoked

System Interactions – Mutation OrderCurrent lease holder?

identity of primarylocation of replicas(cached by client)

3a. data

3b. data

3c. data

Write request

Primary assign s/n to mutationsApplies itForward write request

Operation completed

Operation completed

Operation completedor Error report

System Interactions – Data Flow• Decouple data flow and control f low• Control f low

– Master -> Primary -> Secondaries• Data flow

– Carefully picked chain of chunk servers• Forward to the closest f irst• Distances estimated from IP addresses

– Linear (not tree), to fully uti l ize outbound bandwidth (not divided among recipients)

– Pipelining, to exploit full-duplex l inks• Time to transfer B bytes to R replicas = B/T +

RL• T: network throughput, L: latency

System Interactions – Atomic Record Append

• Concurrent appends are serializable– Client specif ies only data– GFS appends at least once atomically– Return the offset to the client– Heavily used by Google to use fi les as

• mult iple-producer/single-consumer queues• Merged results from many dif ferent cl ients

– On failures, the cl ient retries the operation

– Data are defined, intervening regions are inconsistent

• A Reader can identify and discard extra padding and record fragments using the checksums

System Interactions – Snapshot• Makes a copy of a fi le or a directory

tree almost instantaneously• Use copy-on-write• Steps

– Revokes lease– Logs operations to disk– Duplicates metadata, pointing to the same

chunks• Create real duplicate locally

– Disks are 3 t imes as fast as 100 Mb Ethernet l inks

Master Operation – Namespace Management

• No per-directory data structure• No support for alias• Lock over regions of namespace to

ensure serialization• Lookup table mapping full pathnames

to metadata– Prefix compression -> In-Memory

Master Operation – Namespace Locking

• Each node (fi le/directory) has a read-write lock

• Scenario: prevent /home/user/foo from being created while /home/user is being snapshotted to /save/user– Snapshot

• Read locks on /home, /save• Write locks on /home/user, /save/user

– Create• Read locks on /home, /home/user• Write lock on /home/user/foo

Master Operation – Policies

• New chunks creation policy– New replicas on below-average disk

uti l ization– Limit # of “recent” creations on each chun

server– Spread replicas of a chunk across racks

• Re-replication priority– Far from replication goal f irst– Chunk that is blocking client f irst – Live f i les f irst (rather than deleted)

• Rebalance replicas periodically

Master Operation – Garbage Collection• Lazy reclamation

– Logs deletion immediately– Rename to a hidden name

• Remove 3 days later• Undelete by renaming back

• Regular scan for orphaned chunks– Not garbage:

• All references to chunks: f i le-chunk mapping• All chunk replicas: Linux fi les under designated

directory on each chunk server

– Erase metadata– HeartBeat message to tell chunk servers to

delete chunks

Master Operation – Garbage Collection

• Advantages– Simple & reliable

• Chunk creation may fai led• Deletion messages may be lost

– Uniform and dependable way to clean up unuseful replicas

– Done in batches and the cost is amortized– Done when the master is relatively free– Safety net against accidental, irreversible

deletion

Master Operation – Garbage Collection

• Disadvantage– Hard to f ine tune when storage is t ight

• Solution– Delete twice explicit ly -> expedite storage

reclamation– Different policies for different parts of the

namespace

• Stale Replica Detection– Master maintains a chunk version number

Fault Tolerance – High Availability

• Fast Recovery– Restore state and start in seconds– Do not distinguish normal and abnormal

termination• Chunk Replication

– Different replication levels for different parts of the f i le namespace

– Keep each chunk fully replicated as chunk servers go off l ine or detect corrupted replicas through checksum verif ication

Fault Tolerance – High Availability

• Master Replication– Log & checkpoints are replicated– Master failures?

• Monitoring infrastructure outside GFS starts a new master process

– “Shadow” masters• Read-only access to the f i le system when the

primary master is down• Enhance read availabil i ty• Reads a replica of the growing operation log

Fault Tolerance – Data Integrity• Use checksums to detect data corruption• A chunk(64MB) is broken up into 64KB

blocks with 32-bit checksum• Chunk server verif ies the checksum before

returning, no error propagation• Record append

– Incrementally update the checksum for the last block, error wil l be detected when read

• Random write– Read and verify the f irst and last block f irst– Perform write, compute new checksums

Conclusion

• GFS supports large-scale data processing using commodity hardware

• Reexamine traditional f i le system assumption– based on application workload and

technological environment– Treat component failures as the norm

rather than the exception– Optimize for huge fi les that are mostly

appended– Relax the stand fi le system interface

Conclusion

• Fault tolerance– Constant monitoring– Replicating crucial data– Fast and automatic recovery– Checksumming to detect data corruption

at the disk or IDE subsystem level• High aggregate throughput

– Decouple control and data transfer– Minimize operations by large chunk size

and by chunk lease

Reference

• Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung, “The Google File System”