The Google File SystemPublished By:

Sanjay Ghemawat, Howard Gobioff, Shun-Tak LeungGoogle

Presented By:

Manoj Samaraweera (138231B) Azeem Mumtaz (138218R)

University of Moratuwa

Contents• Distributed File Systems• Introducing Google File System• Design Overview• System Interaction• Master Operation• Fault Tolerance and Diagnosis• Measurements and Benchmarks• Experience• Related Works• Conclusion• Reference

Distributed File Systems

•Enables programs to store and access remote files exactly as they do local ones

•New modes of data organization on disk or across multiple servers

•Goals▫Performance▫Scalability▫Reliability▫Availability

Introducing Google File System•Growing demand for Google data processing•Properties

▫A scalable distributed file system▫For large distributed data intensive

applications▫Fault tolerance ▫Inexpensive commodity hardware ▫High aggregated performance

•Design is driven by observation of workload and technological environment

Design Assumptions• Component failures are the norm

▫ Commodity Hardware• Files are huge by traditional standard

▫ Multi-GB files▫ Small files also must be supported,

Not optimized• Read Workloads

▫ Large streaming reads▫ Small random reads

• Write Workloads▫ Large, sequential writes that append data to file

• Multiple clients concurrently append to one file▫ Consistency Semantics▫ Files are used as producer-consumer queues or many way merging

• High sustained bandwidth is more important than low latency

Design Interface

•Typical File System Interface•Hierarchical Directory Organization•Files are identified as pathnames•Operations

▫Create, delete, open, close, read, write

Architecture (1/2)• Files are divided into chunks• Fixed-size chunks (64MB)• Unique 64-bit chunk handles

▫ Immutable and globally unique• Chunks as Linux files• Replicated over chunkservers, called replicas

▫ 3 replicas by default▫ Different replication for different region of file namespace

• Single master• Multiple chunkservers

▫ Grouped into Racks▫ Connected through switches

• Multiple clients• Master/chunkserver coordination

▫ HeartBeat Messages

Architecture (2/2)

Single Master

•Maintains Metadata•Controls System Wide Activities

▫Chunk lease management▫Garbage collection▫Chunk migration▫Replication

Chunk Size (1/2)•64 MB•Stored as plain Linux file on a chunkserver•Advantages

▫Reduces client’s interaction with single master▫Clients most likely to perform many operations

on a large chunk Reduce network overhead by keeping a persistent

TCP connection with the chunkserver▫Reduces the size of the metadata

Keep metadata in memory▫Lazy Space Allocation

Chunk Size (1/2)

•Disadvantages▫Small files consisting of small chunks may

become hot spots▫Solutions

Higher replication factor Stagger application start time Allow clients to read from other clients

Metadata (1/5)

•3 Major Types▫The file and chunk namespace▫File-to-chunk mappings▫The location of each chunk replicas

•Namespaces and mappings▫Persisted by logging mutation to an

operation log stored on master▫Operation log is replicated

Metadata (2/5)•Metadata are stored in the memory

▫Improves the performance master▫Easier to scan the entire state of metadata

periodically Chunk garbage collection Re-replication in the presence of chunkserver failure Chunk migration to balance load and disk space

•64 bytes of metadata for 64 MB chunk•File namespace data requires < 64 bytes per file

▫Prefix compression

Metadata (3/5)

•Chunk location information ▫Polled at master startup

Chunkservers join and leave the cluster▫Keeps up-to-date with chunkserver with

HeartBeat messages

Metadata (4/5)

•Operation Logs▫Historical record of critical metadata

changes▫Logical timeline that defines the order of

concurrent operations ▫Not visible to client

Until it is replicated and flushed the logs to the disk

▫Flushing and replication in batch Reduces impact on system throughput

Metadata (5/5)• Operation Logs

▫By replaying operation logs master recover its file system state

▫Checkpoints To avoid the growth of the operation logs beyond

the threshold avoids interfering other mutations by working in a

separate thread▫Compact B-tree like structure

Directly mapped into the memory and used for namespace lookup

No extra parsing

Consistency Model (1/3)

• Guarantees by GFS▫ File namespace mutations (i.e. File Creation) are atomic

Namespace management and locking guarantees atomicity and correctness

The master’s operation log▫ After a sequence of successful mutations, the mutated file is

guaranteed to be defined and contain the data written by the last mutation. This is obtained by Applying the same mutation in order to all replicas Using chunk version numbers to detect stale replica

Consistency Model (2/3)• Relaxed consistency model• Two types of mutations

▫ Writes Cause data to be written at an application-specified file offset

▫ Record Appends Cause data to be appended atomically at least once Offset chosen by GFS, not by the client

• States of a file region after a mutation▫ Consistent

All clients see the same data, regardless which replicas they read from▫ Inconsistent

Clients see different data at different times▫ Defined

consistent and all clients see what the mutation writes in its entirety▫ Undefined

consistent but it may not reflect what any mutation has written

Consistency Model (3/3)

•Implication for Applications▫Relying on appends rather on overwrites▫Checkpointing

to verify how much data has been successfully written

▫Writing self-validating records Checksums to detect and remove padding

▫Writing Self-identifying records Unique Identifiers to identify and discard

duplicates

Lease & Mutation Order

•Master uses leases to maintain a consistent mutation order among replicas

•Primary is the chunkserver who is granted a chunk lease▫Master delegates the authority of mutation ▫All others are secondary replicas

•Primary defines a mutation order between mutations▫Secondary replicas follows this order

Writes (1/7)

• Step 1▫ Which chunkserver

holds the current lease for the chunk?

▫ The location of secondary replicas

Writes (2/7)

• Step 2▫ Identities of primary

and secondary replicas

▫ Client cache this data for future mutation, until

Primary is unreachable

Primary no longer holds the lease

Writes (3/7)

• Step 3▫ Client pushes the data

to all replicas▫ Chunkserver stores

the data in an internal LRU buffer cache

Writes (4/7)

• Step 4▫ Client sends a write

request to the primary

▫ Primary assigns a consecutive serial numbers to mutations

Serialization ▫ Primary applies

mutations to its own state

Writes (5/7)

• Step 5▫ Forward the writes to

all secondary replicas ▫ Follows the mutation

order

Writes (6/7)

• Step 6▫ Secondary replicas

inform primary after completing the mutation

Writes (7/7)

• Step 7▫ Primary replies to the

client▫ Retries from step 3 to

7 in case of errors

Data Flow (1/2)

•Decoupled control flow and data flow•Data is pushed linearly along a chain of

chunkservers in a pipelined fashion▫Utilize inbound bandwidth

•Distance is accurately estimated from IP addresses

•Minimize latency by pipelining the data transmission over TCP

Data Flow (2/2)

•Ideal elapsed time for transmitting B bytes to R replicas:

T – Network Throughput L – Latency between 2 machines

•At Google: T = 100 Mbps L <= 1 ms 1000 replicas 1 MB distributed in 80 ms

RLΒ/Τ

Record Append• In traditional writes

▫Clients specifies offset where the data to be written▫Concurrent write to the same region is not serialized

• In record append▫Client specifies only the data▫Similar to writes ▫GFS appends data to the file at least once atomically

The chunk is padded if appending the record exceeds the maximum size

If a record append fails at any replica, the client retries the operation - record duplicates

File region may be defined but inconsistent

Snapshot (1/2)• Goals

▫To quickly create branch copies of huge data sets▫To easily checkpoint the current state

• Copy-on-write technique▫Master receive snapshot request,▫Revokes outstanding leases on chunks in the file▫Master logs the operation to the disk▫Applies this log to its in-memory state by

duplicating the metadata for the source file or directory tree

▫New snapshot file

Snapshot (2/2)

•After the snapshot operation ▫Clients sends a request to master to find

the current lease holder of a “chunk C”▫Reference count for chunk C is > 1▫Master pick a new chunk handle C▫Master asks chunkserver to create a new

chunk C▫Master grants one of the replicas a lease on

the new chunk C and replies to the client

Content

• Distributed File Systems• Introducing Google File System• Design Overview• System Interaction• Master Operation• Fault Tolerance and Diagnosis• Measurements and Benchmarks• Experience• Related Works• Conclusion• Reference

Master Operation

•Namespace Management and Locking•Replica Placement•Creation, Re-replication, Rebalancing•Garbage Collection•Stale Replica Detection

Namespace Management and Locking

•Each master operation acquires a set of locks before it runs

•Creating /home/user/foo while /home/user is snapshotted to /save/user

Replica Placement

•Chunk replica placement policy serves two purposes:▫Maximize data reliability and availability.▫Maximize network bandwidth utilization

Creation, Re-replication, Rebalancing• Creation

▫Want to place new replicas on chunkservers with below-average disk space utilization

▫Limit the number of “recent” creations on each chunkserver

▫Spread replicas of a chunk across racks.• Re-replication

▫As soon as # of replicas go below user specified goal

• Rebalancing▫Moves replicas for better disk space and load

balancing

Garbage Collection

•Mechanism▫Master logs the deletion immediately.▫File is just renamed to a hidden name.▫Removes any such hidden files if they have

existed for more than three days.▫In a regular scan of the chunk namespace,

master identifies orphaned chunks and erases the metadata for those chunks.

Stale Replica Detection

•Chunk version number to distinguish between up-to-date and stale replicas.

•Master removes stale replicas in its regular garbage collection.

Fault Tolerance and Diagnosis

• High Availability▫Fast Recovery

Master and the chunkserver are designed to restore their state and start in seconds.

▫Chunk Replication master clones existing replicas as needed to keep

each chunk fully replicated▫Master Replication

The master state is replicated for reliability Operation log and checkpoints are replicated on

multiple machines “Shadow master” read-only access to the FS even

when the primary master is down

Fault Tolerance and Diagnosis (2)

•Data Integrity▫Each chunkserver uses checksumming to

detect corruption of stored data.▫Chunk is broken up into 64 KB blocks. Each

has a corresponding 32 bit checksum▫Checksum computation is heavily optimized

for writes that append to the end of a chunk

Fault Tolerance and Diagnosis (3)

•Diagnostic Tools▫Extensive and detailed diagnostic logging

for in problem isolation, debugging, and performance analysis

▫GFS servers generate diagnostic logs that record many significant events and all RPC requests and replies

Measurements and BenchmarksMicro-benchmarks

GFS cluster consisting of one master, two master replicas, 16 chunkservers, and 16 clients

Measurements and Benchmarks (2)•Real World Clusters

• Cluster A is used regularly for research and development

• Cluster B is primarily used for production data processing

Measurements and Benchmarks (3)

Experience

•Biggest problems were disk and Linux related.▫Many of disks claimed to the Linux driver that

they supported a range of IDE protocol versions but in fact responded reliably only to the more recent ones.

▫Despite occasional problems, the availability of Linux code has helped to explore and understand system behavior.

Related Works (1/3)• Both GFS & AFS provides a location

independent namespace▫data to be moved transparently for load balance▫ fault tolerance

• Unlike AFS, GFS spreads a file’s data across storage servers in a way more akin to xFS and Swift in order to deliver aggregate performance and increased fault tolerance

• GFS currently uses replication for redundancy and consumes more raw storage than xFS or Swift.

Related Works (2/3)• In contrast to systems like AFS, xFS, Frangipani,

and Intermezzo, GFS does not provide any caching below the file system interface.

• GFS uses a centralized approach in order to simplify the design, increase its reliability, and gain flexibility▫unlike Frangipani, xFS, Minnesota’s GFS and

GPFS▫Makes it easier to implement sophisticated chunk

placement and replication policies since the master already has most of the relevant information and controls how it changes.

Related Works (3/3)• GFS delivers aggregated performance by focusing on

the needs of our applications rather than building a POSIX-compliant file system, unlike in Lustre

• NASD architecture is based on network-attached disk drives, similarly GFS uses commodity machines as chunkservers

• GFS chunkservers use lazily allocated fixed-size chunks, whereas NASD uses variable-length objects

• The producer-consumer queues enabled by atomic record appends address a similar problem as the distributed queues in River▫River uses memory-based queues distributed across

machines

Conclusion•GFS demonstrates the qualities essential

for supporting large-scale data processing workloads on commodity hardware.

•Provides fault tolerance by constant monitoring, replicating crucial data, and fast and automatic recovery

•Delivers high aggregate throughput to many concurrent readers and writers performing a variety of tasks

Reference

•Ghemawat. S., Gobioff. H., Leung. S., 2003. The Google file system. In Proceedings of the nineteenth ACM symposium on Operating systems principles (SOSP '03). ACM, New York, NY, USA, 29-43.

•Coulouris. G., Dollimore. J., Kindberg. T. 2005. Distributed Systems: Concepts and Design (4th Edition). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.

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