1. Cloud Computing Platform as a Service The Google Filesystem
Cs 595
Lecture 10

2. Paper to Read Read the paper on GFS: Evolution on Fast-Forward
Also a link to a longer paper on GFS – original paper from 2003
I assume you are reading papers as specified in the class schedule

3. During the lecture, you should point out problems with GFS design decisions

4. Common Goals of GFS and most Distributed File Systems
Performance
Reliability
Scalability
Availability

5. GFS Design Considerations
Component failures are the norm rather than the exception.
File System consists of hundreds or even thousands of storage machines built from inexpensive commodity parts.
Files are Huge. Multi-GB Files are common.
Each file typically contains many application objects such as web documents.
Append, Append, Append.
Most files are mutated by appending new data rather than overwriting existing data.
Co-Designing
Co-designing applications and file system API benefits overall system by increasing flexibility

6. GFS Why assume hardware failure is the norm?
The amount of layers in a distributed system mean failure on any could contribute to data corruption.
Application OS
Memory
Disk
Network
Physical connections
Power
It is cheaper to assume common failure on poor hardware and account for it, rather than invest in expensive hardware and still experience occasional failure.
Quantity and quality of components guarantee some are not functional at any given time.
Constant monitoring, error detection, fault tolerance, and automatic recovery must exist in GFS

7. Initial Assumptions
System built from inexpensive commodity components that fail
Modest number of files:
few million 100MB or larger files.
Multi-GB files are common.
Didn’t optimize for smaller files
2 kinds of reads:
large streaming read (1MB)
small random reads (batch and sort)
Well-defined semantics:
Master/slave, producer/ consumer and many-way merge. 1 producer per machine append to file.
Atomic R/W with low overhead
High sustained bandwidth chosen over low latency (difference?)

8. High bandwidth versus low latency
Example:
An airplane flying across the country filled with backup drives has very high bandwidth because it gets all data at destination faster than any existing network
However – each individual piece of data had high latency

9. Interface GFS – familiar file system interface
Files organized hierarchically in directories and identified by names
Operations include:
Create, delete, open, close, read, write
Snapshot and record append
Snapshot creates a copy of a file or directory at low cost/overhead
Record append allows multiple users to append data to the same file concurrently.

10. Master/ChunkServers
Single master, multiple chunkservers, all running Linux
Each file divided into fixed-size chunks of 64 MB
Typical file system block size: 4KB
Chunks stored by chunkservers on local disks as Linux files
Immutable and globally unique 64 bit chunk handle (name or number) assigned at creation
Size of namespace: 264
Size of storage capacity: 264 x 64 MB
Regulated by storage capacity of the master

12. Master/chunkServers
Read or write chunk data specified by chunk handle and byte range
Each chunk replicated on multiple chunkservers – default is 3
Why?

13. Master/ChunkServers Master maintains all file system metadata
Namespace, access control info, mapping from files to chunks, location of chunks
Controls garbage collection of chunks
Reclaim physical storage when files are deleted
Communicates with each chunkserver through HeartBeat messages
Instructions and collect state
Clients interact with master for metadata, chunksevers do the rest, e.g. R/W on behalf of applications
No caching
For client working sets too large, simplified coherence
For chunkserver – chunks already stored as local files, Linux caches MFU in memory

14. Heartbeats What do we gain from Heartbeats?
Not only do we get the new state of a remote system, this also updates the master regarding failures.
Any system that fails to respond to a Heartbeat message is assumed dead.
This information allows the master to update his metadata accordingly.
This also queues the Master to create more replicas of the lost data.

15. Client Client translates offset in file into chunk index within file
Send master request with file name/chunk index
Master replies with chunk handle and location of replicas
Client caches info using file name/chunk index as key
Client sends request to one of the replicas (closest)
Further reads of same chunk require no interaction
Can ask for multiple chunks in same request

16. Master Operations Master executes all namespace operations
Manages chunk replicas
Makes placement decision
Creates new chunks (and replicas)
Coordinates various system-wide activities to keep chunks fully replicated
Balance load
Reclaim unused storage

17. Master - Justification
Single Master –
Simplifies design
Placement, replication decisions made with global knowledge
Doesn’t R/W, so not a bottleneck
Client asks master which chunkservers to contact
Clients then connect directly to chunkservers to access/manipulate data

18. Client – Simple read request
Interactions for a simple read:
The client translates the file name and byte offset of the data into a chunk index.
The client then sends the master a request containing the filename and chunk index.
The master replies with the chunk handle and location of replicas.
The client sends a request to the corresponding chunkserver using the data gathered from the master.

19. Chunk Size - Justification
64 MB, larger than typical file system block size
Replica stored as plain Linux file, extended as needed
Reduces interaction of client with master
R/W on same chunk only 1 request to master
Mostly R/W large sequential files
Likely to perform many operations on given chunk (keep persistent TCP connection)
Reduces size of metadata stored on master

20. Chunk problems But – If small file – one chunk may be hot spot
Can fix this with replication, stagger batch application start times

21. Metadata Three types: File and chunk namespaces
Mapping from files to chunks
Location of each chunk’s replicas
All metadata in memory of master
First two types stored in logs for persistence
on master local disk and replicated remotely

22. Metadata Instead of keeping track of chunk location info
Poll – which chunkserver has which replica
Master controls all chunk placement/replacement
Disks may go bad, chunkserver errors, etc.

23. Metadata - Justification
In memory – fast
Periodically scans state
garbage collect if needed
Re-replication if chunkserver failure
Migration to load balance
Master maintains < 64 B data for each 64 MB chunk
File namespace < 64B
To support larger file systems:
Add extra RAM/Disks to the master (cheap)

24. Chunk size (again)- Justification
64 MB is large – think of typical size of
Why Large Files?
METADATA!
Every file in the system adds to the total overhead metadata that the system must store.
More individual data means more data about the data is needed.

25. Operation Log Historical record of critical metadata changes
Provides logical time line of concurrent ops
Log replicated on remote machines
Flush record to disk locally and remotely
Log kept small – checkpoint when > size
Checkpoint in B-tree form
New checkpoint built without delaying mutations
takes about 1 min for 2 M files
Only keep latest checkpoint and subsequent logs

26. Snapshot Snapshot makes copy of file
Used to create checkpoint or copies of huge data sets
Newly created snapshot points to same chunks as source file
After snapshot, client sends request to master to find lease holder

27. Shadow Master Master Replication Replicated for reliability
Not mirrors, so may lag primary slightly (fractions of second)
Shadow master reads operation log, applies same sequence of changes to data structures as the primary does

28. Shadow Master If Master fails: Start shadow instantly
Read-only access to file systems even when primary master down
If machine or disk fails, monitor outside GFS starts new master with replicated log
Clients only use canonical name of master

29. Creation, Re-replication, Rebalancing
Master creates chunk
Place replicas on chunkservers with below-average disk utilization
Limit number of recent creates per chunkserver
New chunks may be hot
Spread replicas across racks
Re-replicate
When number of replicas falls below goal
Chunkserver unavailable, corrupted, etc.
Replicate based on priority (fewest replicas)
Master limits number of active clone operations

30. Creation, Re-replication, Rebalancing
Rebalance
Periodically moves replicas for better disk space and load balancing
Gradually fills up new chunkserver
Removes replicas from chunkservers with below-average free space

31. Consistency Model Why Append Only?
Overwriting existing data is not state safe.
We cannot read data while it is being modified.
A customized ("Atomized") append is implemented by the system that allows for concurrent read/write, write/write, and read/write/write events.

32. Data Integrity Checksumming to detect corruption of stored data
Impractical to compare replicas across chunkservers to detect corruption
Chunk divided into 64KB blocks, each with 32 bit checksums
Checksums stored in memory and persistently with logging

33. Data Integrity Before read, checksum
If problem, return error to requestor and reports to master
Requestor reads from replica, master clones chunk from other replica, delete bad replica
Most reads span multiple blocks, checksum small part of it
Checksum lookups done without I/O
Checksum computation optimized for appends
If partial corrupted, will detect with next read
During idle, chunkservers scan and verify inactive chunks

34. Garbage Collection
When a file is deleted, the file is renamed to a hidden name including the delete timestamp
During regular scan of file namespace
hidden files removed if existed > 3 days
Until then can be undeleted
When removed, in-memory metadata erased
Orphaned chunks identified and erased
HeartBeat message:
chunkserver/master exchange info about files, master tells chunkserver about files it can delete

35. Garbage Collection Easy in GFS Everything else is garbage
All chunks exist in file-to-chunk mappings of the master
All chunk replicas are Linux files under designated directories on each chunkserver
Everything else is garbage

36. Conclusions
GFS – qualities essential for large-scale data processing on commodity hardware
Component failures the norm rather than exception
Optimize for huge files appended to
Fault tolerance by constant monitoring, replication, fast/automatic recovery
High aggregate throughput
Separate file system control
Large file size

37. GFS In the Wild - 2003 Has Google made any adjustments?
Google currently has multiple GFS clusters deployed for different purposes.
The largest currently implemented systems have over 1000 storage nodes and over 300 TB of disk storage.
These clusters are heavily accessed by hundreds of clients on distinct machines.
Has Google made any adjustments?