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42
Crash Consistency: FSCK and Journaling
As we’ve seen thus far, the file system manages a set of data structures to
implement the expected abstractions: files, directories, and all of the other
metadata needed to support the basic abstraction that we expect from a
file system. Unlike most data structures (for example, those found in
memory of a running program), file system data structures must persist,
i.e., they must survive over the long haul, stored on devices that retain
data despite power loss (such as hard disks or flash-based SSDs).
One major challenge faced by a file system is how to update persis-
tent data structures despite the presence of a power loss or system crash.
Specifically, what happens if, right in the middle of updating on-disk
structures, someone trips over the power cord and the machine loses
power? Or the operating system encounters a bug and crashes? Because
of power losses and crashes, updating a persistent data structure can be
quite tricky, and leads to a new and interesting problem in file system
implementation, known as the crash-consistency problem.
This problem is quite simple to understand. Imagine you have to up-
date two on-disk structures, A and B, in order to complete a particular
operation. Because the disk only services a single request at a time, one
of these requests will reach the disk first (either A or B). If the system
crashes or loses power after one write completes, the on-disk structure
will be left in an inconsistent state. And thus, we have a problem that all
file systems need to solve:
THE CRUX: HOW TO UPDATE THE DISK DESPITE CRASHES
The system may crash or lose power between any two writes, and
thus the on-disk state may only partially get updated. After the crash,
the system boots and wishes to mount the file system again (in order to
access files and such). Given that crashes can occur at arbitrary points
in time, how do we ensure the file system keeps the on-disk image in a
reasonable state?
1
2 CRASH CONSISTENCY: FSCK AND JOURNALING
In this chapter, we’ll describe this problem in more detail, and look
at some methods file systems have used to overcome it. We’ll begin by
examining the approach taken by older file systems, known as fsck or the
file system checker. We’ll then turn our attention to another approach,
known as journaling (also known as write-ahead logging), a technique
which adds a little bit of overhead to each write but recovers more quickly
from crashes or power losses. We will discuss the basic machinery of
journaling, including a few different flavors of journaling that Linux ext3
[T98,PAA05] (a relatively modern journaling file system) implements.
42.1 A Detailed Example
To kick off our investigation of journaling, let’s look at an example.
We’ll need to use a workload that updates on-disk structures in some
way. Assume here that the workload is simple: the append of a single
data block to an existing file. The append is accomplished by opening the
file, calling lseek() to move the file offset to the end of the file, and then
issuing a single 4KB write to the file before closing it.
Let’s also assume we are using standard simple file system structures
on the disk, similar to file systems we have seen before. This tiny example
includes an inode bitmap (with just 8 bits, one per inode), a data bitmap
(also 8 bits, one per data block), inodes (8 total, numbered 0 to 7, and
spread across four blocks), and data blocks (8 total, numbered 0 to 7).
Here is a diagram of this file system:
Bitmaps
Inode Data Inodes Data Blocks
I[
v
1
]
Da
0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7
If you look at the structures in the picture, you can see that a single inode
is allocated (inode number 2), which is marked in the inode bitmap, and a
single allocated data block (data block 4), also marked in the data bitmap.
The inode is denoted I[v1], as it is the first version of this inode; it will
soon be updated (due to the workload described above).
Let’s peek inside this simplified inode too. Inside of I[v1], we see:
owner : remzi
permissions : read-write
size : 1
pointer : 4
pointer : null
pointer : null
pointer : null
In this simplified inode, the size of the file is 1 (it has one block al-
located), the first direct pointer points to block 4 (the first data block of
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the file, Da), and all three other direct pointers are set to null (indicating
that they are not used). Of course, real inodes have many more fields; see
previous chapters for more information.
When we append to the file, we are adding a new data block to it, and
thus must update three on-disk structures: the inode (which must point
to the new block and record the new larger size due to the append), the
new data block Db, and a new version of the data bitmap (call it B[v2]) to
indicate that the new data block has been allocated.
Thus, in the memory of the system, we have three blocks which we
must write to disk. The updated inode (inode version 2, or I[v2] for short)
now looks like this:
owner : remzi
permissions : read-write
size : 2
pointer : 4
pointer : 5
pointer : null
pointer : null
The updated data bitmap (B[v2]) now looks like this: 00001100. Finally,
there is the data block (Db), which is just filled with whatever it is users
put into files. Stolen music perhaps?
What we would like is for the final on-disk image of the file system to
look like this:
Bitmaps
Inode Data Inodes Data Blocks
I[
v
2
]
Da Db
0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7
To achieve this transition, the file system must perform three sepa-
rate writes to the disk, one each for the inode (I[v2]), bitmap (B[v2]), and
data block (Db). Note that these writes usually don’t happen immedi-
ately when the user issues a write() system call; rather, the dirty in-
ode, bitmap, and new data will sit in main memory (in the page cache
or buffer cache) for some time first; then, when the file system finally
decides to write them to disk (after say 5 seconds or 30 seconds), the file
system will issue the requisite write requests to the disk. Unfortunately,
a crash may occur and thus interfere with these updates to the disk. In
particular, if a crash happens after one or two of these writes have taken
place, but not all three, the file system could be left in a funny state.
Crash Scenarios
To understand the problem better, let’s look at some example crash sce-
narios. Imagine only a single write succeeds; there are thus three possible
outcomes, which we list here:
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• Just the data block (Db) is written to disk. In this case, the data is
on disk, but there is no inode that points to it and no bitmap that
even says the block is allocated. Thus, it is as if the write never
occurred. This case is not a problem at all, from the perspective of
file-system crash consistency1.
• Just the updated inode (I[v2]) is written to disk. In this case, the
inode points to the disk address (5) where Db was about to be writ-
ten, but Db has not yet been written there. Thus, if we trust that
pointer, we will read garbage data from the disk (the old contents
of disk address 5).
Further, we have a new problem, which we call a file-system in-
consistency. The on-disk bitmap is telling us that data block 5 has
not been allocated, but the inode is saying that it has. The disagree-
ment between the bitmap and the inode is an inconsistency in the
data structures of the file system; to use the file system, we must
somehow resolve this problem (more on that below).
• Just the updated bitmap (B[v2]) is written to disk. In this case, the
bitmap indicates that block 5 is allocated, but there is no inode that
points to it. Thus the file system is inconsistent again; if left unre-
solved, this write would result in a space leak, as block 5 would
never be used by the file system.
There are also three more crash scenarios in this attempt to write three
blocks to disk. In these cases, two writes succeed and the last one fails:
• The inode (I[v2]) and bitmap (B[v2]) are written to disk, but not
data (Db). In this case, the file system metadata is completely con-
sistent: the inode has a pointer to block 5, the bitmap indicates that
5 is in use, and thus everything looks OK from the perspective of
the file system’s metadata. But there is one problem: 5 has garbage
in it again.
• The inode (I[v2]) and the data block (Db) are written, but not the
bitmap (B[v2]). In this case, we have the inode pointing to the cor-
rect data on disk, but again have an inconsistency between the in-
ode and the old version of the bitmap (B1). Thus, we once again
need to resolve the problem before using the file system.
• The bitmap (B[v2]) and data block (Db) are written, but not the
inode (I[v2]). In this case, we again have an inconsistency between
the inode and the data bitmap. However, even though the block
was written and the bitmap indicates its usage, we have no idea
which file it belongs to, as no inode points to the file.
1However, it might be a problem for the user, who just lost some data!
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The Crash Consistency Problem
Hopefully, from these crash scenarios, you can see the many problems
that can occur to our on-disk file system image because of crashes: we can
have inconsistency in file system data structures; we can have space leaks;
we can return garbage data to a user; and so forth. What we’d like to do
ideally is move the file system from one consistent state (e.g., before the
file got appended to) to another atomically (e.g., after the inode, bitmap,
and new data block have been written to disk). Unfortunately, we can’t
do this easily because the disk only commits one write at a time, and
crashes or power loss may occur between these updates. We call this
general problem the crash-consistency problem (we could also call it the
consistent-update problem).
42.2 Solution #1: The File System Checker
Early file systems took a simple approach to crash consistency. Basi-
cally, they decided to let inconsistencies happen and then fix them later
(when rebooting). A classic example of this lazy approach is found in a
tool that does this: fsck2. fsck is a UNIX tool for finding such incon-
sistencies and repairing them [MK96]; similar tools to check and repair
a disk partition exist on different systems. Note that such an approach
can’t fix all problems; consider, for example, the case above where the file
system looks consistent but the inode points to garbage data. The only
real goal is to make sure the file system metadata is internally consistent.
The tool fsck operates in a number of phases, as summarized in
McKusick and Kowalski’s paper [MK96]. It is run before the file system
is mounted and made available (fsck assumes that no other file-system
activity is on-going while it runs); once finished, the on-disk file system
should be consistent and thus can be made accessible to users.
Here is a basic summary of what fsck does:
• Superblock: fsck first checks if the superblock looks reasonable,
mostly doing sanity checks such as making sure the file system size
is greater than the number of blocks that have been allocated. Usu-
ally the goal of these sanity checks is to find a suspect (corrupt)
superblock; in this case, the system (or administrator) may decide
to use an alternate copy of the superblock.
• Free blocks: Next, fsck scans the inodes, indirect blocks, double
indirect blocks, etc., to build an understanding of which blocks are
currently allocated within the file system. It uses this knowledge
to produce a correct version of the allocation bitmaps; thus, if there
is any inconsistency between bitmaps and inodes, it is resolved by
trusting the information within the inodes. The same type of check
is performed for all the inodes, making sure that all inodes that look
like they are in use are marked as such in the inode bitmaps.
2Pronounced either “eff-ess-see-kay”, “eff-ess-check”, or, if you don’t like the tool, “eff-
suck”. Yes, serious professional people use this term.
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• Inode state: Each inode is checked for corruption or other prob-
lems. For example, fsck makes sure that each allocated inode has
a valid type field (e.g., regular file, directory, symbolic link, etc.). If
there are problems with the inode fields that are not easily fixed, the
inode is considered suspect and cleared by fsck; the inode bitmap
is correspondingly updated.
• Inode links: fsck also verifies the link count of each allocated in-
ode. As you may recall, the link count indicates the number of dif-
ferent directories that contain a reference (i.e., a link) to this par-
ticular file. To verify the link count, fsck scans through the en-
tire directory tree, starting at the root directory, and builds its own
link counts for every file and directory in the file system. If there
is a mismatch between the newly-calculated count and that found
within an inode, corrective action must be taken, usually by fixing
the count within the inode. If an allocated inode is discovered but
no directory refers to it, it is moved to the lost+found directory.
• Duplicates: fsck also checks for duplicate pointers, i.e., cases where
two different inodes refer to the same block. If one inode is obvi-
ously bad, it may be cleared. Alternately, the pointed-to block could
be copied, thus giving each inode its own copy as desired.
• Bad blocks: A check for bad block pointers is also performed while
scanning through the list of all pointers. A pointer is considered
“bad” if it obviously points to something outside its valid range,
e.g., it has an address that refers to a block greater than the parti-
tion size. In this case, fsck can’t do anything too intelligent; it just
removes (clears) the pointer from the inode or indirect block.
• Directory checks: fsck does not understand the contents of user
files; however, directories hold specifically formatted information
created by the file system itself. Thus, fsck performs additional
integrity checks on the contents of each directory, making sure that
“.” and “..” are the first entries, that each inode referred to in a
directory entry is allocated, and ensuring that no directory is linked
to more than once in the entire hierarchy.
As you can see, building a working fsck requires intricate knowledge
of the file system; making sure such a piece of code works correctly in all
cases can be challenging [G+08]. However, fsck (and similar approaches)
have a bigger and perhaps more fundamental problem: they are too slow.
With a very large disk volume, scanning the entire disk to find all the
allocated blocks and read the entire directory tree may take many minutes
or hours. Performance of fsck, as disks grew in capacity and RAIDs
grew in popularity, became prohibitive (despite recent advances [M+13]).
At a higher level, the basic premise of fsck seems just a tad irra-
tional. Consider our example above, where just three blocks are written
to the disk; it is incredibly expensive to scan the entire disk to fix prob-
lems that occurred during an update of just three blocks. This situation is
akin to dropping your keys on the floor in your bedroom, and then com-
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mencing a search-the-entire-house-for-keys recovery algorithm, starting in
the basement and working your way through every room. It works but is
wasteful. Thus, as disks (and RAIDs) grew, researchers and practitioners
started to look for other solutions.
42.3 Solution #2: Journaling (or Write-Ahead Logging)
Probably the most popular solution to the consistent update problem
is to steal an idea from the world of database management systems. That
idea, known as write-ahead logging, was invented to address exactly this
type of problem. In file systems, we usually call write-ahead logging jour-
naling for historical reasons. The first file system to do this was Cedar
[H87], though many modern file systems use the idea, including Linux
ext3 and ext4, reiserfs, IBM’s JFS, SGI’s XFS, and Windows NTFS.
The basic idea is as follows. When updating the disk, before over-
writing the structures in place, first write down a little note (somewhere
else on the disk, in a well-known location) describing what you are about
to do. Writing this note is the “write ahead” part, and we write it to a
structure that we organize as a “log”; hence, write-ahead logging.
By writing the note to disk, you are guaranteeing that if a crash takes
places during the update (overwrite) of the structures you are updating,
you can go back and look at the note you made and try again; thus, you
will know exactly what to fix (and how to fix it) after a crash, instead
of having to scan the entire disk. By design, journaling thus adds a bit
of work during updates to greatly reduce the amount of work required
during recovery.
We’ll now describe how Linux ext3, a popular journaling file system,
incorporates journaling into the file system. Most of the on-disk struc-
tures are identical to Linux ext2, e.g., the disk is divided into block groups,
and each block group contains an inode bitmap, data bitmap, inodes, and
data blocks. The new key structure is the journal itself, which occupies
some small amount of space within the partition or on another device.
Thus, an ext2 file system (without journaling) looks like this:
Super Group 0 Group 1 . . . Group N
Assuming the journal is placed within the same file system image
(though sometimes it is placed on a separate device, or as a file within
the file system), an ext3 file system with a journal looks like this:
Super Journal Group 0 Group 1 . . . Group N
The real difference is just the presence of the journal, and of course,
how it is used.
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Data Journaling
Let’s look at a simple example to understand how data journaling works.
Data journaling is available as a mode with the Linux ext3 file system,
from which much of this discussion is based.
Say we have our canonical update again, where we wish to write the
inode (I[v2]), bitmap (B[v2]), and data block (Db) to disk again. Before
writing them to their final disk locations, we are now first going to write
them to the log (a.k.a. journal). This is what this will look like in the log:
J
o
u
rn
a
l
TxB I[v2] B[v2] Db TxE
You can see we have written five blocks here. The transaction begin
(TxB) tells us about this update, including information about the pend-
ing update to the file system (e.g., the final addresses of the blocks I[v2],
B[v2], and Db), and some kind of transaction identifier (TID). The mid-
dle three blocks just contain the exact contents of the blocks themselves;
this is known as physical logging as we are putting the exact physical
contents of the update in the journal (an alternate idea, logical logging,
puts a more compact logical representation of the update in the journal,
e.g., “this update wishes to append data block Db to file X”, which is a
little more complex but can save space in the log and perhaps improve
performance). The final block (TxE) is a marker of the end of this transac-
tion, and will also contain the TID.
Once this transaction is safely on disk, we are ready to overwrite the
old structures in the file system; this process is called checkpointing.
Thus, to checkpoint the file system (i.e., bring it up to date with the pend-
ing update in the journal), we issue the writes I[v2], B[v2], and Db to
their disk locations as seen above; if these writes complete successfully,
we have successfully checkpointed the file system and are basically done.
Thus, our initial sequence of operations:
1. Journal write: Write the transaction, including a transaction-begin
block, all pending data and metadata updates, and a transaction-
end block, to the log; wait for these writes to complete.
2. Checkpoint: Write the pending metadata and data updates to their
final locations in the file system.
In our example, we would write TxB, I[v2], B[v2], Db, and TxE to the
journal first. When these writes complete, we would complete the update
by checkpointing I[v2], B[v2], and Db, to their final locations on disk.
Things get a little trickier when a crash occurs during the writes to
the journal. Here, we are trying to write the set of blocks in the transac-
tion (e.g., TxB, I[v2], B[v2], Db, TxE) to disk. One simple way to do this
would be to issue each one at a time, waiting for each to complete, and
then issuing the next. However, this is slow. Ideally, we’d like to issue
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ASIDE: FORCING WRITES TO DISK
To enforce ordering between two disk writes, modern file systems have
to take a few extra precautions. In olden times, forcing ordering between
two writes, A and B, was easy: just issue the write of A to the disk, wait
for the disk to interrupt the OS when the write is complete, and then issue
the write of B.
Things got slightly more complex due to the increased use of write caches
within disks. With write buffering enabled (sometimes called immediate
reporting), a disk will inform the OS the write is complete when it simply
has been placed in the disk’s memory cache, and has not yet reached
disk. If the OS then issues a subsequent write, it is not guaranteed to
reach the disk after previous writes; thus ordering between writes is not
preserved. One solution is to disable write buffering. However, more
modern systems take extra precautions and issue explicit write barriers;
such a barrier, when it completes, guarantees that all writes issued before
the barrier will reach disk before any writes issued after the barrier.
All of this machinery requires a great deal of trust in the correct oper-
ation of the disk. Unfortunately, recent research shows that some disk
manufacturers, in an effort to deliver “higher performing” disks, explic-
itly ignore write-barrier requests, thus making the disks seemingly run
faster but at the risk of incorrect operation [C+13, R+11]. As Kahan said,
the fast almost always beats out the slow, even if the fast is wrong.
all five block writes at once, as this would turn five writes into a single
sequential write and thus be faster. However, this is unsafe, for the fol-
lowing reason: given such a big write, the disk internally may perform
scheduling and complete small pieces of the big write in any order. Thus,
the disk internally may (1) write TxB, I[v2], B[v2], and TxE and only later
(2) write Db. Unfortunately, if the disk loses power between (1) and (2),
this is what ends up on disk:
J
o
u
rn
a
l
TxB
id=1
I[v2] B[v2] ?? TxE
id=1
Why is this a problem? Well, the transaction looks like a valid trans-
action (it has a begin and an end with matching sequence numbers). Fur-
ther, the file system can’t look at that fourth block and know it is wrong;
after all, it is arbitrary user data. Thus, if the system now reboots and
runs recovery, it will replay this transaction, and ignorantly copy the con-
tents of the garbage block ’??’ to the location where Db is supposed to
live. This is bad for arbitrary user data in a file; it is much worse if it hap-
pens to a critical piece of file system, such as the superblock, which could
render the file system unmountable.
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ASIDE: OPTIMIZING LOG WRITES
You may have noticed a particular inefficiency of writing to the log.
Namely, the file system first has to write out the transaction-begin block
and contents of the transaction; only after these writes complete can the
file system send the transaction-end block to disk. The performance im-
pact is clear, if you think about how a disk works: usually an extra rota-
tion is incurred (think about why).
One of our former graduate students, Vijayan Prabhakaran, had a simple
idea to fix this problem [P+05]. When writing a transaction to the journal,
include a checksum of the contents of the journal in the begin and end
blocks. Doing so enables the file system to write the entire transaction at
once, without incurring a wait; if, during recovery, the file system sees
a mismatch in the computed checksum versus the stored checksum in
the transaction, it can conclude that a crash occurred during the write
of the transaction and thus discard the file-system update. Thus, with a
small tweak in the write protocol and recovery system, a file system can
achieve faster common-case performance; on top of that, the system is
slightly more reliable, as any reads from the journal are now protected by
a checksum.
This simple fix was attractive enough to gain the notice of Linux file sys-
tem developers, who then incorporated it into the next generation Linux
file system, called (you guessed it!) Linux ext4. It now ships on mil-
lions of machines worldwide, including the Android handheld platform.
Thus, every time you write to disk on many Linux-based systems, a little
code developed at Wisconsin makes your system a little faster and more
reliable.
To avoid this problem, the file system issues the transactional write in
two steps. First, it writes all blocks except the TxE block to the journal,
issuing these writes all at once. When these writes complete, the journal
will look something like this (assuming our append workload again):
J
o
u
rn
a
l
TxB
id=1
I[v2] B[v2] Db
When those writes complete, the file system issues the write of the TxE
block, thus leaving the journal in this final, safe state:
J
o
u
rn
a
l
TxB
id=1
I[v2] B[v2] Db TxE
id=1
An important aspect of this process is the atomicity guarantee pro-
vided by the disk. It turns out that the disk guarantees that any 512-byte
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write will either happen or not (and never be half-written); thus, to make
sure the write of TxE is atomic, one should make it a single 512-byte block.
Thus, our current protocol to update the file system, with each of its three
phases labeled:
1. Journal write: Write the contents of the transaction (including TxB,
metadata, and data) to the log; wait for these writes to complete.
2. Journal commit: Write the transaction commit block (containing
TxE) to the log; wait for write to complete; transaction is said to be
committed.
3. Checkpoint: Write the contents of the update (metadata and data)
to their final on-disk locations.
Recovery
Let’s now understand how a file system can use the contents of the jour-
nal to recover from a crash. A crash may happen at any time during this
sequence of updates. If the crash happens before the transaction is writ-
ten safely to the log (i.e., before Step 2 above completes), then our job
is easy: the pending update is simply skipped. If the crash happens af-
ter the transaction has committed to the log, but before the checkpoint is
complete, the file system can recover the update as follows. When the
system boots, the file system recovery process will scan the log and look
for transactions that have committed to the disk; these transactions are
thus replayed (in order), with the file system again attempting to write
out the blocks in the transaction to their final on-disk locations. This form
of logging is one of the simplest forms there is, and is called redo logging.
By recovering the committed transactions in the journal, the file system
ensures that the on-disk structures are consistent, and thus can proceed
by mounting the file system and readying itself for new requests.
Note that it is fine for a crash to happen at any point during check-
pointing, even after some of the updates to the final locations of the blocks
have completed. In the worst case, some of these updates are simply per-
formed again during recovery. Because recovery is a rare operation (only
taking place after an unexpected system crash), a few redundant writes
are nothing to worry about3.
Batching Log Updates
You might have noticed that the basic protocol could add a lot of extra
disk traffic. For example, imagine we create two files in a row, called
file1 and file2, in the same directory. To create one file, one has
to update a number of on-disk structures, minimally including: the in-
ode bitmap (to allocate a new inode), the newly-created inode of the file,
3Unless you worry about everything, in which case we can’t help you. Stop worrying so
much, it is unhealthy! But now you’re probably worried about over-worrying.
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the data block of the parent directory containing the new directory en-
try, and the parent directory inode (which now has a new modification
time). With journaling, we logically commit all of this information to
the journal for each of our two file creations; because the files are in the
same directory, and assuming they even have inodes within the same in-
ode block, this means that if we’re not careful, we’ll end up writing these
same blocks over and over.
To remedy this problem, some file systems do not commit each update
to disk one at a time (e.g., Linux ext3); rather, one can buffer all updates
into a global transaction. In our example above, when the two files are
created, the file system just marks the in-memory inode bitmap, inodes
of the files, directory data, and directory inode as dirty, and adds them to
the list of blocks that form the current transaction. When it is finally time
to write these blocks to disk (say, after a timeout of 5 seconds), this single
global transaction is committed containing all of the updates described
above. Thus, by buffering updates, a file system can avoid excessive write
traffic to disk in many cases.
Making The Log Finite
We thus have arrived at a basic protocol for updating file-system on-disk
structures. The file system buffers updates in memory for some time;
when it is finally time to write to disk, the file system first carefully writes
out the details of the transaction to the journal (a.k.a. write-ahead log);
after the transaction is complete, the file system checkpoints those blocks
to their final locations on disk.
However, the log is of a finite size. If we keep adding transactions to
it (as in this figure), it will soon fill. What do you think happens then?
J
o
u
rn
a
l
Tx1 Tx2 Tx3 Tx4 Tx5 …
Two problems arise when the log becomes full. The first is simpler,
but less critical: the larger the log, the longer recovery will take, as the
recovery process must replay all the transactions within the log (in order)
to recover. The second is more of an issue: when the log is full (or nearly
full), no further transactions can be committed to the disk, thus making
the file system “less than useful” (i.e., useless).
To address these problems, journaling file systems treat the log as a
circular data structure, re-using it over and over; this is why the journal
is sometimes referred to as a circular log. To do so, the file system must
take action some time after a checkpoint. Specifically, once a transaction
has been checkpointed, the file system should free the space it was occu-
pying within the journal, allowing the log space to be reused. There are
many ways to achieve this end; for example, you could simply mark the
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oldest and newest non-checkpointed transactions in the log in a journal
superblock; all other space is free. Here is a graphical depiction:
J
o
u
rn
a
l
Journal
Super
Tx1 Tx2 Tx3 Tx4 Tx5 …
In the journal superblock (not to be confused with the main file system
superblock), the journaling system records enough information to know
which transactions have not yet been checkpointed, and thus reduces re-
covery time as well as enables re-use of the log in a circular fashion. And
thus we add another step to our basic protocol:
1. Journal write: Write the contents of the transaction (containing TxB
and the contents of the update) to the log; wait for these writes to
complete.
2. Journal commit: Write the transaction commit block (containing
TxE) to the log; wait for the write to complete; the transaction is
now committed.
3. Checkpoint: Write the contents of the update to their final locations
within the file system.
4. Free: Some time later, mark the transaction free in the journal by
updating the journal superblock.
Thus we have our final data journaling protocol. But there is still a
problem: we are writing each data block to the disk twice, which is a
heavy cost to pay, especially for something as rare as a system crash. Can
you figure out a way to retain consistency without writing data twice?
Metadata Journaling
Although recovery is now fast (scanning the journal and replaying a few
transactions as opposed to scanning the entire disk), normal operation
of the file system is slower than we might desire. In particular, for each
write to disk, we are now also writing to the journal first, thus doubling
write traffic; this doubling is especially painful during sequential write
workloads, which now will proceed at half the peak write bandwidth of
the drive. Further, between writes to the journal and writes to the main
file system, there is a costly seek, which adds noticeable overhead for
some workloads.
Because of the high cost of writing every data block to disk twice, peo-
ple have tried a few different things in order to speed up performance.
For example, the mode of journaling we described above is often called
data journaling (as in Linux ext3), as it journals all user data (in addition
to the metadata of the file system). A simpler (and more common) form
of journaling is sometimes called ordered journaling (or just metadata
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journaling), and it is nearly the same, except that user data is not writ-
ten to the journal. Thus, when performing the same update as above, the
following information would be written to the journal:
J
o
u
rn
a
l
TxB I[v2] B[v2] TxE
The data block Db, previously written to the log, would instead be
written to the file system proper, avoiding the extra write; given that most
I/O traffic to the disk is data, not writing data twice substantially reduces
the I/O load of journaling. The modification does raise an interesting
question, though: when should we write data blocks to disk?
Let’s again consider our example append of a file to understand the
problem better. The update consists of three blocks: I[v2], B[v2], and
Db. The first two are both metadata and will be logged and then check-
pointed; the latter will only be written once to the file system. When
should we write Db to disk? Does it matter?
As it turns out, the ordering of the data write does matter for metadata-
only journaling. For example, what if we write Db to disk after the trans-
action (containing I[v2] and B[v2]) completes? Unfortunately, this ap-
proach has a problem: the file system is consistent but I[v2] may end up
pointing to garbage data. Specifically, consider the case where I[v2] and
B[v2] are written but Db did not make it to disk. The file system will then
try to recover. Because Db is not in the log, the file system will replay
writes to I[v2] and B[v2], and produce a consistent file system (from the
perspective of file-system metadata). However, I[v2] will be pointing to
garbage data, i.e., at whatever was in the slot where Db was headed.
To ensure this situation does not arise, some file systems (e.g., Linux
ext3) write data blocks (of regular files) to the disk first, before related
metadata is written to disk. Specifically, the protocol is as follows:
1. Data write: Write data to final location; wait for completion
(the wait is optional; see below for details).
2. Journal metadata write: Write the begin block and metadata to the
log; wait for writes to complete.
3. Journal commit: Write the transaction commit block (containing
TxE) to the log; wait for the write to complete; the transaction (in-
cluding data) is now committed.
4. Checkpoint metadata: Write the contents of the metadata update
to their final locations within the file system.
5. Free: Later, mark the transaction free in journal superblock.
By forcing the data write first, a file system can guarantee that a pointer
will never point to garbage. Indeed, this rule of “write the pointed-to
object before the object that points to it” is at the core of crash consis-
tency, and is exploited even further by other crash consistency schemes
[GP94] (see below for details).
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In most systems, metadata journaling (akin to ordered journaling of
ext3) is more popular than full data journaling. For example, Windows
NTFS and SGI’s XFS both use some form of metadata journaling. Linux
ext3 gives you the option of choosing either data, ordered, or unordered
modes (in unordered mode, data can be written at any time). All of these
modes keep metadata consistent; they vary in their semantics for data.
Finally, note that forcing the data write to complete (Step 1) before is-
suing writes to the journal (Step 2) is not required for correctness, as indi-
cated in the protocol above. Specifically, it would be fine to concurrently
issue writes to data, the transaction-begin block, and journaled metadata;
the only real requirement is that Steps 1 and 2 complete before the issuing
of the journal commit block (Step 3).
Tricky Case: Block Reuse
There are some interesting corner cases that make journaling more tricky,
and thus are worth discussing. A number of them revolve around block
reuse; as Stephen Tweedie (one of the main forces behind ext3) said:
“What’s the hideous part of the entire system? … It’s deleting files.
Everything to do with delete is hairy. Everything to do with delete…
you have nightmares around what happens if blocks get deleted and
then reallocated.” [T00]
The particular example Tweedie gives is as follows. Suppose you are
using some form of metadata journaling (and thus data blocks for files
are not journaled). Let’s say you have a directory called foo. The user
adds an entry to foo (say by creating a file), and thus the contents of
foo (because directories are considered metadata) are written to the log;
assume the location of the foo directory data is block 1000. The log thus
contains something like this:
J
o
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rn
a
l
TxB
id=1
I[foo]
ptr:1000
D[foo]
[final addr:1000]
TxE
id=1
At this point, the user deletes everything in the directory and the di-
rectory itself, freeing up block 1000 for reuse. Finally, the user creates a
new file (say bar), which ends up reusing the same block (1000) that used
to belong to foo. The inode of bar is committed to disk, as is its data;
note, however, because metadata journaling is in use, only the inode of
bar is committed to the journal; the newly-written data in block 1000 in
the file bar is not journaled.
J
o
u
rn
a
l
TxB
id=1
I[foo]
ptr:1000
D[foo]
[final addr:1000]
TxE
id=1
TxB
id=2
I[bar]
ptr:1000
TxE
id=2
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Journal File System
TxB Contents TxE Metadata Data
(metadata) (data)
issue issue issue
complete
complete
complete
issue
complete
issue issue
complete
complete
Figure 42.1: Data Journaling Timeline
Now assume a crash occurs and all of this information is still in the
log. During replay, the recovery process simply replays everything in the
log, including the write of directory data in block 1000; the replay thus
overwrites the user data of current file bar with old directory contents!
Clearly this is not a correct recovery action, and certainly it will be a sur-
prise to the user when reading the file bar.
There are a number of solutions to this problem. One could, for ex-
ample, never reuse blocks until the delete of said blocks is checkpointed
out of the journal. What Linux ext3 does instead is to add a new type
of record to the journal, known as a revoke record. In the case above,
deleting the directory would cause a revoke record to be written to the
journal. When replaying the journal, the system first scans for such re-
voke records; any such revoked data is never replayed, thus avoiding the
problem mentioned above.
Wrapping Up Journaling: A Timeline
Before ending our discussion of journaling, we summarize the protocols
we have discussed with timelines depicting each of them. Figure 42.1
shows the protocol when journaling data and metadata, whereas Figure
42.2 shows the protocol when journaling only metadata.
In each figure, time increases in the downward direction, and each row
in the figure shows the logical time that a write can be issued or might
complete. For example, in the data journaling protocol (Figure 42.1), the
writes of the transaction begin block (TxB) and the contents of the trans-
action can logically be issued at the same time, and thus can be completed
in any order; however, the write to the transaction end block (TxE) must
not be issued until said previous writes complete. Similarly, the check-
pointing writes to data and metadata blocks cannot begin until the trans-
action end block has committed. Horizontal dashed lines show where
write-ordering requirements must be obeyed.
A similar timeline is shown for the metadata journaling protocol. Note
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Journal File System
TxB Contents TxE Metadata Data
(metadata)
issue issue issue
complete
complete
complete
issue
complete
issue
complete
Figure 42.2: Metadata Journaling Timeline
that the data write can logically be issued at the same time as the writes
to the transaction begin and the contents of the journal; however, it must
be issued and complete before the transaction end has been issued.
Finally, note that the time of completion marked for each write in the
timelines is arbitrary. In a real system, completion time is determined by
the I/O subsystem, which may reorder writes to improve performance.
The only guarantees about ordering that we have are those that must
be enforced for protocol correctness (and are shown via the horizontal
dashed lines in the figures).
42.4 Solution #3: Other Approaches
We’ve thus far described two options in keeping file system metadata
consistent: a lazy approach based on fsck, and a more active approach
known as journaling. However, these are not the only two approaches.
One such approach, known as Soft Updates [GP94], was introduced by
Ganger and Patt. This approach carefully orders all writes to the file sys-
tem to ensure that the on-disk structures are never left in an inconsis-
tent state. For example, by writing a pointed-to data block to disk before
the inode that points to it, we can ensure that the inode never points to
garbage; similar rules can be derived for all the structures of the file sys-
tem. Implementing Soft Updates can be a challenge, however; whereas
the journaling layer described above can be implemented with relatively
little knowledge of the exact file system structures, Soft Updates requires
intricate knowledge of each file system data structure and thus adds a fair
amount of complexity to the system.
Another approach is known as copy-on-write (yes, COW), and is used
in a number of popular file systems, including Sun’s ZFS [B07]. This tech-
nique never overwrites files or directories in place; rather, it places new
updates to previously unused locations on disk. After a number of up-
dates are completed, COW file systems flip the root structure of the file
system to include pointers to the newly updated structures. Doing so
makes keeping the file system consistent straightforward. We’ll be learn-
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ing more about this technique when we discuss the log-structured file
system (LFS) in a future chapter; LFS is an early example of a COW.
Another approach is one we just developed here at Wisconsin. In this
technique, entitled backpointer-based consistency (or BBC), no ordering
is enforced between writes. To achieve consistency, an additional back
pointer is added to every block in the system; for example, each data
block has a reference to the inode to which it belongs. When accessing
a file, the file system can determine if the file is consistent by checking if
the forward pointer (e.g., the address in the inode or direct block) points
to a block that refers back to it. If so, everything must have safely reached
disk and thus the file is consistent; if not, the file is inconsistent, and an
error is returned. By adding back pointers to the file system, a new form
of lazy crash consistency can be attained [C+12].
Finally, we also have explored techniques to reduce the number of
times a journal protocol has to wait for disk writes to complete. Entitled
optimistic crash consistency [C+13], this new approach issues as many
writes to disk as possible by using a generalized form of the transaction
checksum [P+05], and includes a few other techniques to detect incon-
sistencies should they arise. For some workloads, these optimistic tech-
niques can improve performance by an order of magnitude. However, to
truly function well, a slightly different disk interface is required [C+13].
42.5 Summary
We have introduced the problem of crash consistency, and discussed
various approaches to attacking this problem. The older approach of
building a file system checker works but is likely too slow to recover on
modern systems. Thus, many file systems now use journaling. Journaling
reduces recovery time from O(size-of-the-disk-volume) to O(size-of-the-
log), thus speeding recovery substantially after a crash and restart. For
this reason, many modern file systems use journaling. We have also seen
that journaling can come in many different forms; the most commonly
used is ordered metadata journaling, which reduces the amount of traffic
to the journal while still preserving reasonable consistency guarantees for
both file system metadata and user data. In the end, strong guarantees
on user data are probably one of the most important things to provide;
oddly enough, as recent research has shown, this area remains a work in
progress [P+14].
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References
[B07] “ZFS: The Last Word in File Systems” by Jeff Bonwick and Bill Moore. Available online:
http://www.ostep.org/Citations/zfs_last.pdf. ZFS uses copy-on-write and journal-
ing, actually, as in some cases, logging writes to disk will perform better.
[C+12] “Consistency Without Ordering” by Vijay Chidambaram, Tushar Sharma, Andrea C.
Arpaci-Dusseau, Remzi H. Arpaci-Dusseau. FAST ’12, San Jose, California. A recent paper of
ours about a new form of crash consistency based on back pointers. Read it for the exciting details!
[C+13] “Optimistic Crash Consistency” by Vijay Chidambaram, Thanu S. Pillai, Andrea C.
Arpaci-Dusseau, Remzi H. Arpaci-Dusseau . SOSP ’13, Nemacolin Woodlands Resort, PA,
November 2013. Our work on a more optimistic and higher performance journaling protocol. For
workloads that call fsync() a lot, performance can be greatly improved.
[GP94] “Metadata Update Performance in File Systems” by Gregory R. Ganger and Yale N.
Patt. OSDI ’94. A clever paper about using careful ordering of writes as the main way to achieve
consistency. Implemented later in BSD-based systems.
[G+08] “SQCK: A Declarative File System Checker” by Haryadi S. Gunawi, Abhishek Ra-
jimwale, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau. OSDI ’08, San Diego, Califor-
nia. Our own paper on a new and better way to build a file system checker using SQL queries. We also
show some problems with the existing checker, finding numerous bugs and odd behaviors, a direct result
of the complexity of fsck.
[H87] “Reimplementing the Cedar File System Using Logging and Group Commit” by Robert
Hagmann. SOSP ’87, Austin, Texas, November 1987. The first work (that we know of) that applied
write-ahead logging (a.k.a. journaling) to a file system.
[M+13] “ffsck: The Fast File System Checker” by Ao Ma, Chris Dragga, Andrea C. Arpaci-
Dusseau, Remzi H. Arpaci-Dusseau. FAST ’13, San Jose, California, February 2013. A recent
paper of ours detailing how to make fsck an order of magnitude faster. Some of the ideas have already
been incorporated into the BSD file system checker [MK96] and are deployed today.
[MK96] “Fsck – The UNIX File System Check Program” by Marshall Kirk McKusick and T. J.
Kowalski. Revised in 1996. Describes the first comprehensive file-system checking tool, the epony-
mous fsck. Written by some of the same people who brought you FFS.
[MJLF84] “A Fast File System for UNIX” by Marshall K. McKusick, William N. Joy, Sam J.
Leffler, Robert S. Fabry. ACM Transactions on Computing Systems, Volume 2:3, August 1984.
You already know enough about FFS, right? But come on, it is OK to re-reference important papers.
[P+14] “All File Systems Are Not Created Equal: On the Complexity of Crafting Crash-Consistent
Applications” by Thanumalayan Sankaranarayana Pillai, Vijay Chidambaram, Ramnatthan
Alagappan, Samer Al-Kiswany, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau. OSDI
’14, Broomfield, Colorado, October 2014. A paper in which we study what file systems guarantee
after crashes, and show that applications expect something different, leading to all sorts of interesting
problems.
[P+05] “IRON File Systems” by Vijayan Prabhakaran, Lakshmi N. Bairavasundaram, Nitin
Agrawal, Haryadi S. Gunawi, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau. SOSP
’05, Brighton, England, October 2005. A paper mostly focused on studying how file systems react
to disk failures. Towards the end, we introduce a transaction checksum to speed up logging, which was
eventually adopted into Linux ext4.
[PAA05] “Analysis and Evolution of Journaling File Systems” by Vijayan Prabhakaran, Andrea
C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau. USENIX ’05, Anaheim, California, April 2005.
An early paper we wrote analyzing how journaling file systems work.
[R+11] “Coerced Cache Eviction and Discreet-Mode Journaling” by Abhishek Rajimwale, Vijay
Chidambaram, Deepak Ramamurthi, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau.
DSN ’11, Hong Kong, China, June 2011. Our own paper on the problem of disks that buffer writes in
a memory cache instead of forcing them to disk, even when explicitly told not to do that! Our solution
to overcome this problem: if you want A to be written to disk before B, first write A, then send a lot of
“dummy” writes to disk, hopefully causing A to be forced to disk to make room for them in the cache. A
neat if impractical solution.
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[T98] “Journaling the Linux ext2fs File System” by Stephen C. Tweedie. The Fourth Annual
Linux Expo, May 1998. Tweedie did much of the heavy lifting in adding journaling to the Linux ext2
file system; the result, not surprisingly, is called ext3. Some nice design decisions include the strong
focus on backwards compatibility, e.g., you can just add a journaling file to an existing ext2 file system
and then mount it as an ext3 file system.
[T00] “EXT3, Journaling Filesystem” by Stephen Tweedie. Talk at the Ottawa Linux Sympo-
sium, July 2000. olstrans.sourceforge.net/release/OLS2000-ext3/OLS2000-ext3.html A tran-
script of a talk given by Tweedie on ext3.
[T01] “The Linux ext2 File System” by Theodore Ts’o, June, 2001.. Available online here:
http://e2fsprogs.sourceforge.net/ext2.html. A simple Linux file system based on
the ideas found in FFS. For a while it was quite heavily used; now it is really just in the kernel as an
example of a simple file system.
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Homework (Simulation)
This section introduces fsck.py, a simple simulator you can use to
better understand how file system corruptions can be detected (and po-
tentially repaired). Please see the associated README for details on how
to run the simulator.
Questions
1. First, run fsck.py -D; this flag turns off any corruption, and thus
you can use it to generate a random file system, and see if you can
determine which files and directories are in there. So, go ahead and
do that! Use the -p flag to see if you were right. Try this for a few
different randomly-generated file systems by setting the seed (-s)
to different values, like 1, 2, and 3.
2. Now, let’s introduce a corruption. Run fsck.py -S 1 to start.
Can you see what inconsistency is introduced? How would you fix
it in a real file system repair tool? Use -c to check if you were right.
3. Change the seed to -S 3 or -S 19; which inconsistency do you
see? Use -c to check your answer. What is different in these two
cases?
4. Change the seed to -S 5; which inconsistency do you see? How
hard would it be to fix this problem in an automatic way? Use -c to
check your answer. Then, introduce a similar inconsistency with -S
38; is this harder/possible to detect? Finally, use -S 642; is this
inconsistency detectable? If so, how would you fix the file system?
5. Change the seed to -S 6 or -S 13; which inconsistency do you
see? Use -c to check your answer. What is the difference across
these two cases? What should the repair tool do when encountering
such a situation?
6. Change the seed to -S 9; which inconsistency do you see? Use -c
to check your answer. Which piece of information should a check-
and-repair tool trust in this case?
7. Change the seed to -S 15; which inconsistency do you see? Use
-c to check your answer. What can a repair tool do in this case? If
no repair is possible, how much data is lost?
8. Change the seed to -S 10; which inconsistency do you see? Use
-c to check your answer. Is there redundancy in the file system
structure here that can help a repair?
9. Change the seed to -S 16 and -S 20; which inconsistency do you
see? Use -c to check your answer. How should the repair tool fix
the problem?
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