OS Spring’04

File Systems:Design and Implementation

Operating SystemsSpring 2004

OS Spring’04

What is it all about? File system is a service which

supports an abstract representation of the secondary storage

Supported by OS

Why is a file system needed?What is so special about the secondary storage (as opposed to the main memory)?

OS Spring’04

Memory Hierarchy

Typical capacity

Main memory

SecondaryStorage: Disks

Off-line Storage:Tapes, CDs, etc

OS Spring’04

Main memory vs. Secondary storage

Small (MB/GB) ExpensiveFast (10-6/10-7 sec) VolatileDirectly accessible

by CPU Interface: (virtual)

memory address

Large (GB/TB)Cheap Slow (10-2/10-3 sec)Persistent Cannot be directly

accessed by CPUData should be first brought into the main memory

OS Spring’04

Some numbers… 1GB=230 ~109 Bytes 1TB=240 ~1012 (terabyte) 1PB=250 ~1015 (petabyte) 1EB=260 ~1018 (exabyte)

232 ~ 4 x 109: Genome base pairs 264 ~ 16 x 1018: Brain electrons 2256 ~ 65,536 x 1072: Particles in

Universe

OS Spring’04

Secondary storage structure A number of disks directly attached

to the computer Network attached disks accessible

through a fast networkStorage Area Network (SAN)

Simple disks Smart disks

OS Spring’04

Internal disk structure

OS Spring’04

Data Access Sector size is the minimum

read/write unit of data (usually 1KB)Access: (#surface, #track, #sector)

Smart disk drives hide out the internal disk layout

Access: (#sector)

Moving arm assembly (Seek) is expensive

Sequential access is x100 times faster than the random access

OS Spring’04

Overview File system services

What user applications see

File system implementationWhat the data on disk looks like, bit by bitThe runtime support of FS operations

The FS service and its implementation are deeply intertwined

Performance is the paramount issue for the file system implementation

OS Spring’04

File System services File system is a layer between the

secondary storage and the application

Presents the secondary storage as a collection of persistent objects with unique names, called files

Provides mechanisms for mapping the data between the secondary storage and the main memory

OS Spring’04

What is a file (קובץ) File is a named persistent collection of

data Unstructured, sequential (UNIX)

Data is accessed by specifying the offset Collection of records (database

systems)Supports associative access give me all records with “Name=Yossi”

Attributes: owner, permissions, modification time, size, etc…

OS Spring’04

File system interface File data access

READ: Bring a specified chunk of data from file into the process virtual address spaceWRITE: Write a specified chunk of data from the process virtual address space to the file

CREATE, DELETE, SEEK, TRUNCATE open, close, set_attributes Many semantical issues:

Automatic size-extensionHolesPersistence of open filesMore …

OS Spring’04

Accessing File Data: File Control Block

A control structure, File Control Block (FCB), is associated with each file in the file system

Each FCB has a unique identifier (FCB ID)UNIX: i-node, identified by i-node number

FCB structure: File attributesA data structure for accessing the file’s data

OS Spring’04

Accessing File Data Given the file name Get to the file’s FCB using the file

system catalog Use the FCB to get to the desired

offset within the file data

OS Spring’04

Accessing File Data: Catalog The catalog maps a file name to the FCB

Checks permissions This can be done for each file data access

Inefficient: Do this once when the file is first referenced

file_handle=open(file_name): search the catalog and bring FCB into the memoryUNIX: in-memory FCB: in-core i-node

close(file_handle): release FCB from memory

OS Spring’04

The Catalog Organization FCBs are stored in predefined

locations on the diskUNIX: i-node list

Hierarchical structure:Some FCBs are just a list of pointers to other FCBs Directories UNIX: directory is a file whose data is an

array of (file_name, i-node#) pairs

Recursive mapping

OS Spring’04

Directories Provide name to file mapping May provide additional attributes per

file Different from regular files

Support operations like create, delete, listPrevent duplicate namesMay be organized as a hash table for efficient searching

Mostly common structure: hierarchySupports hierarchical pathnames

OS Spring’04

Searching the UNIX catalog /a/b/c => i-node of /a/b/c Get the root i-node:

The i-node number of ‘/’ is pre-defined (2) Use the root i-node to get to the ‘/’ data Search (a, i-node#) in the root’s data Get the a’s i-node Get to the a’s data and search for (b, i-

node#) Get the b’s i-node Etc… Permissions are checked all along the way

Each dir in the path must be (at least) executable

OS Spring’04

Extending the directory hierarchy

Multiple volumesUnix: Mount/un-mount volume on directoryTransparent pathname traversal: in-core mount table, in-core i-node of mount point and or mounted root.

Remote volumesDistributed file systems: Sun NFS, AFS/Coda, etc.

OS Spring’04

NFS Collection of remote file service

protocols VFS: Virtual file system layer

Client: system call -> VFS -> local FS/NFS clientServer: system call/remote invocation -> VFS -> local FS

Compatible with most local FS implementations

OS Spring’04

VFS model Unix-like file system services: files,

directories, links, .. Fhandle provides working-file

capability, as well as file attributes Remote mount provides a seamless

name space Lookup(path) instead of open

Lookup does not cross mount points (version 3)

OS Spring’04

RPC communication Support for heterogeneous clients Stateless server No client caching, write-thru policy No authenticated sessions No persistence

fhandle must be unique

File locking handled separately by a lock manager

No server-failure recovery needed

OS Spring’04

NFS: Advanced issues File sharing by multiple clients Caching Locking and fault tolerance Security and access control

OS Spring’04

Sharing Unix single machine: writes take

immediate effectFile persistence on open

NFS version 3:Write thru in principleSession semantics in practice

File lockingRead/write lock, per file range of bytesWait queue with no callbacks

Share reservationSupported to facilitate NFS on Windows clients

OS Spring’04

Fault Tolerance RPC

Retransmit on timeoutsSuppress duplicates via duplicate-cacheReturn cached-response on duplicate request

File lockingVersion 4 issues leases with expiration and renewalIntroduce problems of clock synchronization, and renewal reliability

OS Spring’04

Allocating disk blocks to file data

Assume unstructured filesArray of bytes

Efficient offset -> disk block mapping Efficient disk access for both

sequential and random patternsMinimizing number of long seeks

Efficient space utilizationMinimizing external/internal fragmentation

OS Spring’04

Static Contiguous Allocation Allocate each file a fixed number of blocks

at the creation time#blocks is pre-defined or supplied as an argument

Efficient offset lookupOnly the block # of the offset 0 is needed

Efficient disk access Inefficient space utilization

Internal, external fragmentation

No support for dynamic extension

OS Spring’04

Static Contiguous Allocation

Catalog

OS Spring’04

Extent-based allocation File gets blocks in contiguous

chunks called extentsMultiple contiguous allocations

For large files, B-tree is used for efficient offset lookup

OS Spring’04

Extent-based allocation

0 1 2 3

4 5 6 7

8 9 10 11

12 13 14 15

16 17 18 19

foo.c bar.c

core.666

foo.c (0,3) (7,2) (16,2)bar.c (3,1) (12,4)

core.666 (8,3) (18,1)

Catalog

OS Spring’04

Extent-based allocation Efficient offset lookup and disk access Support for dynamic growth/shrink Dynamic memory allocation

techniques are used (e.g., first-fit) External/internal fragmentation may

be a problemDepending on the implementation, requirements, etc…

OS Spring’04

Single-block allocation Extent-based allocation with a

fixed extent size of one disk block

File blocks are scattered anywhere on the diskInefficient sequential access

UNIX block allocation Linked allocation

MS-DOS File Allocation Table (FAT)

OS Spring’04

Block Allocation in UNIX 10 direct pointers 1 single indirect pointer: points to a

block of N pointers to data blocks 1 double indirect pointer: points to a

block of N pointers each of which points to a block of N pointers to data blocks

1 triple indirect pointer… Overall addresses 10+N+N2+N3 disk

blocks

OS Spring’04

Block Allocation in UNIX

Direct 1Direct 2

...

Direct 10Indirect

Double indirectTriple indirect

1

2

...

10

11

...

N

N+1

2N

...

...

Ind 1

Dbl 1

Ind 1

Ind N

...

Trpl

Dbl 2

Dbl N

Ind N+1

...

Ind N+1

OS Spring’04

Block Allocation in UNIX Optimized for small files

Outdated empirical studies indicate that 98% of all files are under 80 KB

Poor performance for random access of large files (redirections)

No external fragmentation Wasted space in pointer blocks for large

sparse files Modern UNIX implementations use the

extent-based allocation

OS Spring’04

Linked Allocation Each file is a linked list of disk blocks Offset lookup:

Efficient for sequential accessInefficient for random access

Access to large files may be inefficient as the blocks are scattered

Solution: block clustering

No fragmentation, wasted space for pointers in each block

OS Spring’04

Linked AllocationCatalog

OS Spring’04

File Allocation Table (FAT) A section at the beginning of the disk

is set aside to contain the tableIndexed by the block numbers on diskAn entry for each disk block (or for a cluster thereof)

FAT Entries corresponding to blocks belonging to the same file are chained

The last file block, unused blocks and bad blocks have special markings

OS Spring’04

FATCatalog entry

OS Spring’04

FAT Pros and Cons Improved random access

just search a small table instead of the whole disk

Inefficient sequential accessSeek back to the table and forth to the block for each file block!

Block allocation is easyjust find the first 0 marked block

OS Spring’04

Free space management Disk bitmap: represent the disk

block allocation as an array of bitsBit for each disk block: 1 - non-allocated block, 0 - allocated block Simple and efficient in finding free blocksWastes space on disk

Linked list of free blocks (UNIX)Efficient for finding a single free block

OS Spring’04

File I/O CPU cannot access the file data directly Must be first brought to the main memory Problem:

Scenario 1: user process reads a block, meanwhile the process gets swapped out of memoryScenario 2: user process reads/writes 1 byte in blockScenario 3: user process continuously reads/writes a fileScenario 4: two processes access the same block

Solution: Read/Write mapping using buffer cache Memory mapped files

OS Spring’04

Read/Write Mapping File data is made available to

applications via a pre-allocated main memory region

Buffer cache The file systems transfers data

between the buffer cache and disk in granularity of disk blocks

The data is explicitly copied from/to buffer cache to/from the application address space

OS Spring’04

Read/Write Mapping

Buffer Cache

Main Memory

File A

File B

File C

Kernel

OS Spring’04

Reading data (Disk block=1K)

User

Buffer Cache

File C

Kernel

Buf

ptr

UNSIGNED CHAR BUF[8192];

UNSIGNED CHAR *PTR=BUF+126;

FD = OPEN(“C”,…);

SEEK(FD,1324); // 1324=1024+300

READ(FD,PTR,1848); // 724+1024+100=1848

1324

3172

OS Spring’04

Writing data (Disk block=1K)

User

Buffer Cache

File C

Kernel

Buf

ptr

UNSIGNED CHAR BUF[8192];

UNSIGNED CHAR *PTR=BUF+126;

FD = OPEN(“C”,…);

SEEK(FD,1324); // 1324=1024+300

WRITE(FD,PTR,1848); // 724+1024+100=1848

1324

3172 Unallocated

region

OS Spring’04

Buffer Cache management All disk I/O goes through the buffer

cacheBoth user data and control data (e.g., i-node) are cached

LRU replacement Dirty (modified) marker to indicate

whether write-back is needed

OS Spring’04

Advantages Strict separation of concerns

Hiding disk access peculiarities from the user Block size, memory alignment, memory

allocation in multiples of the block size, etc…

Disk blocks are cachedAggregation for small transfers (locality)Block re-use across processesTransient data might be never written to disk

OS Spring’04

Disadvantages Extra copying

Disk->buffer cache->user space Vulnerability to failures

Does not care about the user data blocksThe control data blocks (metadata) is the real problem E.g., i-nodes, pointer blocks can be in cache

when a failure occurs As a result the file system internal state

might be corrupted

OS Spring’04

Memory mapped files A file (or a portion thereof) is

mapped into a contiguous region of the process virtual memory

UNIX: mmap system call

Mapping operation is very efficient:just marking

The access to file is governed by the virtual memory subsystem

OS Spring’04

Mmapped files: Pros and Cons Advantages:

reduce copyingno need for a pre-allocated buffer cache in the main memory

Disadvantages: less or no control over the actual disk writing: the file data becomes volatileA mapped area must fit the virtual address space

OS Spring’04

Reliability and Recovery File system data consists of

Control data (metadata), user data

Failures can cause data loss and corruption

Cached dataPower failure during the sector write may corrupt physically the data stored in the sector

OS Spring’04

Metadata vs. User data Lost or corruption of the metadata

might lead to a massive user data loss

File systems must care about the metadataFile systems usually do not care much about the user data Operation semantics? Users must care about their data themselves

(e.g., backups)

OS Spring’04

Reliability and caching Caching affects the WRITE semantics

The write operation returnsIs it guaranteed that the requested data is indeed written on disk?What if some data blocks in cache are the metadata blocks?

Solutionswrite-through: writes bypass cachewrite-back: dirty blocks are written asynchronously

OS Spring’04

User data reliability in UNIX Based on write-back policy

User data is written back to disk periodicallyPOSIX compatible semanticsCommands like sync and fsync are used for forced write of the dirty blocks

OS Spring’04

Metadata reliability Based on write-through policy

updates are written to disk immediately

Some data is not written in-placeCan go back to the last consistent version

Some data is replicated UNIX superblock

File system goes through consistency check/repair cycle at the boot time

fsck, ScanDisk

OS Spring’04

Metadata reliability using logging

Write-through negatively affects performance

Think about random access

Solution: maintain a sequential log of metadata updates: Journal

IBM’s Journal File System (JFS)

OS Spring’04

Journal File System (JFS) Operations logged (journaled):

create,link,mkdir,truncate,allocating write, …Each operation may involve several metadata updates (transaction)

Once operation is logged it returnswrite ahead logging

The disk writes are performed asynchronously

aggregation possible

OS Spring’04

JFS: Journal maintenance A cursor (pointer) is maintained The cursor is advanced once the

updated blocks associated with the transaction are written to disk (hardened)

hardened transaction records can be deleted from the journal

Upon recovery: Re-do all the operations starting from the last cursor position

OS Spring’04

JFS: Pros and Cons Advantages:

Asynchronous metadata writeFast recovery: depends on the Journal size and not on the file-system size

Disadvantagesextra writespace wasted by journal (insignificant)

OS Spring’04

Log Structured File System Ousterhout & Douglis (1992) Caching is enough for good read

performance Writes is the real performance

bottleneckwriting-back cached user blocks may require many random disk accesseswrite-through for reliability denies optimizations logging solves the problem for metadata

OS Spring’04

Log Structured File System The idea: everything is log Each write - both data and control -

is appended to the sequential log The problem: how to locate files and

data efficiently for random access by Reads

The solution: use a floating file map

OS Spring’04

Log structured file systemsupermap

supermap

supermap

Before

After block change

After block addition