XFS Logging Design

Preamble

This document describes the design and algorithms that the XFS journalling subsystem is based on. This document describes the design and algorithms that the XFS journalling subsystem is based on so that readers may familiarize themselves with the general concepts of how transaction processing in XFS works.

We begin with an overview of transactions in XFS, followed by describing how transaction reservations are structured and accounted, and then move into how we guarantee forwards progress for long running transactions with finite initial reservations bounds. At this point we need to explain how relogging works. With the basic concepts covered, the design of the delayed logging mechanism is documented.

Introduction

XFS uses Write Ahead Logging for ensuring changes to the filesystem metadata are atomic and recoverable. For reasons of space and time efficiency, the logging mechanisms are varied and complex, combining intents, logical and physical logging mechanisms to provide the necessary recovery guarantees the filesystem requires.

Some objects, such as inodes and dquots, are logged in logical format where the details logged are made up of the changes to in-core structures rather than on-disk structures. Other objects - typically buffers - have their physical changes logged. Long running atomic modifications have individual changes chained together by intents, ensuring that journal recovery can restart and finish an operation that was only partially done when the system stopped functioning.

The reason for these differences is to keep the amount of log space and CPU time required to process objects being modified as small as possible and hence the logging overhead as low as possible. Some items are very frequently modified, and some parts of objects are more frequently modified than others, so keeping the overhead of metadata logging low is of prime importance.

The method used to log an item or chain modifications together isn’t particularly important in the scope of this document. It suffices to know that the method used for logging a particular object or chaining modifications together are different and are dependent on the object and/or modification being performed. The logging subsystem only cares that certain specific rules are followed to guarantee forwards progress and prevent deadlocks.

Transactions in XFS

XFS has two types of high level transactions, defined by the type of log space reservation they take. These are known as “one shot” and “permanent” transactions. Permanent transaction reservations can take reservations that span commit boundaries, whilst “one shot” transactions are for a single atomic modification.

The type and size of reservation must be matched to the modification taking place. This means that permanent transactions can be used for one-shot modifications, but one-shot reservations cannot be used for permanent transactions.

In the code, a one-shot transaction pattern looks somewhat like this:

tp = xfs_trans_alloc(<reservation>)
<lock items>
<join item to transaction>
<do modification>
xfs_trans_commit(tp);

As items are modified in the transaction, the dirty regions in those items are tracked via the transaction handle. Once the transaction is committed, all resources joined to it are released, along with the remaining unused reservation space that was taken at the transaction allocation time.

In contrast, a permanent transaction is made up of multiple linked individual transactions, and the pattern looks like this:

tp = xfs_trans_alloc(<reservation>)
xfs_ilock(ip, XFS_ILOCK_EXCL)

loop {
        xfs_trans_ijoin(tp, 0);
        <do modification>
        xfs_trans_log_inode(tp, ip);
        xfs_trans_roll(&tp);
}

xfs_trans_commit(tp);
xfs_iunlock(ip, XFS_ILOCK_EXCL);

While this might look similar to a one-shot transaction, there is an important difference: xfs_trans_roll() performs a specific operation that links two transactions together:

ntp = xfs_trans_dup(tp);
xfs_trans_commit(tp);
xfs_log_reserve(ntp);

This results in a series of “rolling transactions” where the inode is locked across the entire chain of transactions. Hence while this series of rolling transactions is running, nothing else can read from or write to the inode and this provides a mechanism for complex changes to appear atomic from an external observer’s point of view.

It is important to note that a series of rolling transactions in a permanent transaction does not form an atomic change in the journal. While each individual modification is atomic, the chain is not atomic. If we crash half way through, then recovery will only replay up to the last transactional modification the loop made that was committed to the journal.

This affects long running permanent transactions in that it is not possible to predict how much of a long running operation will actually be recovered because there is no guarantee of how much of the operation reached stale storage. Hence if a long running operation requires multiple transactions to fully complete, the high level operation must use intents and deferred operations to guarantee recovery can complete the operation once the first transactions is persisted in the on-disk journal.

Transactions are Asynchronous

In XFS, all high level transactions are asynchronous by default. This means that xfs_trans_commit() does not guarantee that the modification has been committed to stable storage when it returns. Hence when a system crashes, not all the completed transactions will be replayed during recovery.

However, the logging subsystem does provide global ordering guarantees, such that if a specific change is seen after recovery, all metadata modifications that were committed prior to that change will also be seen.

For single shot operations that need to reach stable storage immediately, or ensuring that a long running permanent transaction is fully committed once it is complete, we can explicitly tag a transaction as synchronous. This will trigger a “log force” to flush the outstanding committed transactions to stable storage in the journal and wait for that to complete.

Synchronous transactions are rarely used, however, because they limit logging throughput to the IO latency limitations of the underlying storage. Instead, we tend to use log forces to ensure modifications are on stable storage only when a user operation requires a synchronisation point to occur (e.g. fsync).

Transaction Reservations

It has been mentioned a number of times now that the logging subsystem needs to provide a forwards progress guarantee so that no modification ever stalls because it can’t be written to the journal due to a lack of space in the journal. This is achieved by the transaction reservations that are made when a transaction is first allocated. For permanent transactions, these reservations are maintained as part of the transaction rolling mechanism.

A transaction reservation provides a guarantee that there is physical log space available to write the modification into the journal before we start making modifications to objects and items. As such, the reservation needs to be large enough to take into account the amount of metadata that the change might need to log in the worst case. This means that if we are modifying a btree in the transaction, we have to reserve enough space to record a full leaf-to-root split of the btree. As such, the reservations are quite complex because we have to take into account all the hidden changes that might occur.

For example, a user data extent allocation involves allocating an extent from free space, which modifies the free space trees. That’s two btrees. Inserting the extent into the inode’s extent map might require a split of the extent map btree, which requires another allocation that can modify the free space trees again. Then we might have to update reverse mappings, which modifies yet another btree which might require more space. And so on. Hence the amount of metadata that a “simple” operation can modify can be quite large.

This “worst case” calculation provides us with the static “unit reservation” for the transaction that is calculated at mount time. We must guarantee that the log has this much space available before the transaction is allowed to proceed so that when we come to write the dirty metadata into the log we don’t run out of log space half way through the write.

For one-shot transactions, a single unit space reservation is all that is required for the transaction to proceed. For permanent transactions, however, we also have a “log count” that affects the size of the reservation that is to be made.

While a permanent transaction can get by with a single unit of space reservation, it is somewhat inefficient to do this as it requires the transaction rolling mechanism to re-reserve space on every transaction roll. We know from the implementation of the permanent transactions how many transaction rolls are likely for the common modifications that need to be made.

For example, and inode allocation is typically two transactions - one to physically allocate a free inode chunk on disk, and another to allocate an inode from an inode chunk that has free inodes in it. Hence for an inode allocation transaction, we might set the reservation log count to a value of 2 to indicate that the common/fast path transaction will commit two linked transactions in a chain. Each time a permanent transaction rolls, it consumes an entire unit reservation.

Hence when the permanent transaction is first allocated, the log space reservation is increases from a single unit reservation to multiple unit reservations. That multiple is defined by the reservation log count, and this means we can roll the transaction multiple times before we have to re-reserve log space when we roll the transaction. This ensures that the common modifications we make only need to reserve log space once.

If the log count for a permanent transaction reaches zero, then it needs to re-reserve physical space in the log. This is somewhat complex, and requires an understanding of how the log accounts for space that has been reserved.

Log Space Accounting

The position in the log is typically referred to as a Log Sequence Number (LSN). The log is circular, so the positions in the log are defined by the combination of a cycle number - the number of times the log has been overwritten - and the offset into the log. A LSN carries the cycle in the upper 32 bits and the offset in the lower 32 bits. The offset is in units of “basic blocks” (512 bytes). Hence we can do realtively simple LSN based math to keep track of available space in the log.

Log space accounting is done via a pair of constructs called “grant heads”. The position of the grant heads is an absolute value, so the amount of space available in the log is defined by the distance between the position of the grant head and the current log tail. That is, how much space can be reserved/consumed before the grant heads would fully wrap the log and overtake the tail position.

The first grant head is the “reserve” head. This tracks the byte count of the reservations currently held by active transactions. It is a purely in-memory accounting of the space reservation and, as such, actually tracks byte offsets into the log rather than basic blocks. Hence it technically isn’t using LSNs to represent the log position, but it is still treated like a split {cycle,offset} tuple for the purposes of tracking reservation space.

The reserve grant head is used to accurately account for exact transaction reservations amounts and the exact byte count that modifications actually make and need to write into the log. The reserve head is used to prevent new transactions from taking new reservations when the head reaches the current tail. It will block new reservations in a FIFO queue and as the log tail moves forward it will wake them in order once sufficient space is available. This FIFO mechanism ensures no transaction is starved of resources when log space shortages occur.

The other grant head is the “write” head. Unlike the reserve head, this grant head contains an LSN and it tracks the physical space usage in the log. While this might sound like it is accounting the same state as the reserve grant head - and it mostly does track exactly the same location as the reserve grant head - there are critical differences in behaviour between them that provides the forwards progress guarantees that rolling permanent transactions require.

These differences when a permanent transaction is rolled and the internal “log count” reaches zero and the initial set of unit reservations have been exhausted. At this point, we still require a log space reservation to continue the next transaction in the sequeunce, but we have none remaining. We cannot sleep during the transaction commit process waiting for new log space to become available, as we may end up on the end of the FIFO queue and the items we have locked while we sleep could end up pinning the tail of the log before there is enough free space in the log to fulfil all of the pending reservations and then wake up transaction commit in progress.

To take a new reservation without sleeping requires us to be able to take a reservation even if there is no reservation space currently available. That is, we need to be able to overcommit the log reservation space. As has already been detailed, we cannot overcommit physical log space. However, the reserve grant head does not track physical space - it only accounts for the amount of reservations we currently have outstanding. Hence if the reserve head passes over the tail of the log all it means is that new reservations will be throttled immediately and remain throttled until the log tail is moved forward far enough to remove the overcommit and start taking new reservations. In other words, we can overcommit the reserve head without violating the physical log head and tail rules.

As a result, permanent transactions only “regrant” reservation space during xfs_trans_commit() calls, while the physical log space reservation - tracked by the write head - is then reserved separately by a call to xfs_log_reserve() after the commit completes. Once the commit completes, we can sleep waiting for physical log space to be reserved from the write grant head, but only if one critical rule has been observed:

Code using permanent reservations must always log the items they hold
locked across each transaction they roll in the chain.

“Re-logging” the locked items on every transaction roll ensures that the items attached to the transaction chain being rolled are always relocated to the physical head of the log and so do not pin the tail of the log. If a locked item pins the tail of the log when we sleep on the write reservation, then we will deadlock the log as we cannot take the locks needed to write back that item and move the tail of the log forwards to free up write grant space. Re-logging the locked items avoids this deadlock and guarantees that the log reservation we are making cannot self-deadlock.

If all rolling transactions obey this rule, then they can all make forwards progress independently because nothing will block the progress of the log tail moving forwards and hence ensuring that write grant space is always (eventually) made available to permanent transactions no matter how many times they roll.

Re-logging Explained

XFS allows multiple separate modifications to a single object to be carried in the log at any given time. This allows the log to avoid needing to flush each change to disk before recording a new change to the object. XFS does this via a method called “re-logging”. Conceptually, this is quite simple - all it requires is that any new change to the object is recorded with a new copy of all the existing changes in the new transaction that is written to the log.

That is, if we have a sequence of changes A through to F, and the object was written to disk after change D, we would see in the log the following series of transactions, their contents and the log sequence number (LSN) of the transaction:

Transaction             Contents        LSN
   A                       A               X
   B                      A+B             X+n
   C                     A+B+C           X+n+m
   D                    A+B+C+D         X+n+m+o
    <object written to disk>
   E                       E               Y (> X+n+m+o)
   F                      E+F             Y+p

In other words, each time an object is relogged, the new transaction contains the aggregation of all the previous changes currently held only in the log.

This relogging technique allows objects to be moved forward in the log so that an object being relogged does not prevent the tail of the log from ever moving forward. This can be seen in the table above by the changing (increasing) LSN of each subsequent transaction, and it’s the technique that allows us to implement long-running, multiple-commit permanent transactions.

A typical example of a rolling transaction is the removal of extents from an inode which can only be done at a rate of two extents per transaction because of reservation size limitations. Hence a rolling extent removal transaction keeps relogging the inode and btree buffers as they get modified in each removal operation. This keeps them moving forward in the log as the operation progresses, ensuring that current operation never gets blocked by itself if the log wraps around.

Hence it can be seen that the relogging operation is fundamental to the correct working of the XFS journalling subsystem. From the above description, most people should be able to see why the XFS metadata operations writes so much to the log - repeated operations to the same objects write the same changes to the log over and over again. Worse is the fact that objects tend to get dirtier as they get relogged, so each subsequent transaction is writing more metadata into the log.

It should now also be obvious how relogging and asynchronous transactions go hand in hand. That is, transactions don’t get written to the physical journal until either a log buffer is filled (a log buffer can hold multiple transactions) or a synchronous operation forces the log buffers holding the transactions to disk. This means that XFS is doing aggregation of transactions in memory - batching them, if you like - to minimise the impact of the log IO on transaction throughput.

The limitation on asynchronous transaction throughput is the number and size of log buffers made available by the log manager. By default there are 8 log buffers available and the size of each is 32kB - the size can be increased up to 256kB by use of a mount option.

Effectively, this gives us the maximum bound of outstanding metadata changes that can be made to the filesystem at any point in time - if all the log buffers are full and under IO, then no more transactions can be committed until the current batch completes. It is now common for a single current CPU core to be to able to issue enough transactions to keep the log buffers full and under IO permanently. Hence the XFS journalling subsystem can be considered to be IO bound.

Delayed Logging: Concepts

The key thing to note about the asynchronous logging combined with the relogging technique XFS uses is that we can be relogging changed objects multiple times before they are committed to disk in the log buffers. If we return to the previous relogging example, it is entirely possible that transactions A through D are committed to disk in the same log buffer.

That is, a single log buffer may contain multiple copies of the same object, but only one of those copies needs to be there - the last one “D”, as it contains all the changes from the previous changes. In other words, we have one necessary copy in the log buffer, and three stale copies that are simply wasting space. When we are doing repeated operations on the same set of objects, these “stale objects” can be over 90% of the space used in the log buffers. It is clear that reducing the number of stale objects written to the log would greatly reduce the amount of metadata we write to the log, and this is the fundamental goal of delayed logging.

From a conceptual point of view, XFS is already doing relogging in memory (where memory == log buffer), only it is doing it extremely inefficiently. It is using logical to physical formatting to do the relogging because there is no infrastructure to keep track of logical changes in memory prior to physically formatting the changes in a transaction to the log buffer. Hence we cannot avoid accumulating stale objects in the log buffers.

Delayed logging is the name we’ve given to keeping and tracking transactional changes to objects in memory outside the log buffer infrastructure. Because of the relogging concept fundamental to the XFS journalling subsystem, this is actually relatively easy to do - all the changes to logged items are already tracked in the current infrastructure. The big problem is how to accumulate them and get them to the log in a consistent, recoverable manner. Describing the problems and how they have been solved is the focus of this document.

One of the key changes that delayed logging makes to the operation of the journalling subsystem is that it disassociates the amount of outstanding metadata changes from the size and number of log buffers available. In other words, instead of there only being a maximum of 2MB of transaction changes not written to the log at any point in time, there may be a much greater amount being accumulated in memory. Hence the potential for loss of metadata on a crash is much greater than for the existing logging mechanism.

It should be noted that this does not change the guarantee that log recovery will result in a consistent filesystem. What it does mean is that as far as the recovered filesystem is concerned, there may be many thousands of transactions that simply did not occur as a result of the crash. This makes it even more important that applications that care about their data use fsync() where they need to ensure application level data integrity is maintained.

It should be noted that delayed logging is not an innovative new concept that warrants rigorous proofs to determine whether it is correct or not. The method of accumulating changes in memory for some period before writing them to the log is used effectively in many filesystems including ext3 and ext4. Hence no time is spent in this document trying to convince the reader that the concept is sound. Instead it is simply considered a “solved problem” and as such implementing it in XFS is purely an exercise in software engineering.

The fundamental requirements for delayed logging in XFS are simple:

  1. Reduce the amount of metadata written to the log by at least an order of magnitude.
  2. Supply sufficient statistics to validate Requirement #1.
  3. Supply sufficient new tracing infrastructure to be able to debug problems with the new code.
  4. No on-disk format change (metadata or log format).
  5. Enable and disable with a mount option.
  6. No performance regressions for synchronous transaction workloads.

Delayed Logging: Design

Storing Changes

The problem with accumulating changes at a logical level (i.e. just using the existing log item dirty region tracking) is that when it comes to writing the changes to the log buffers, we need to ensure that the object we are formatting is not changing while we do this. This requires locking the object to prevent concurrent modification. Hence flushing the logical changes to the log would require us to lock every object, format them, and then unlock them again.

This introduces lots of scope for deadlocks with transactions that are already running. For example, a transaction has object A locked and modified, but needs the delayed logging tracking lock to commit the transaction. However, the flushing thread has the delayed logging tracking lock already held, and is trying to get the lock on object A to flush it to the log buffer. This appears to be an unsolvable deadlock condition, and it was solving this problem that was the barrier to implementing delayed logging for so long.

The solution is relatively simple - it just took a long time to recognise it. Put simply, the current logging code formats the changes to each item into an vector array that points to the changed regions in the item. The log write code simply copies the memory these vectors point to into the log buffer during transaction commit while the item is locked in the transaction. Instead of using the log buffer as the destination of the formatting code, we can use an allocated memory buffer big enough to fit the formatted vector.

If we then copy the vector into the memory buffer and rewrite the vector to point to the memory buffer rather than the object itself, we now have a copy of the changes in a format that is compatible with the log buffer writing code. that does not require us to lock the item to access. This formatting and rewriting can all be done while the object is locked during transaction commit, resulting in a vector that is transactionally consistent and can be accessed without needing to lock the owning item.

Hence we avoid the need to lock items when we need to flush outstanding asynchronous transactions to the log. The differences between the existing formatting method and the delayed logging formatting can be seen in the diagram below.

Current format log vector:

Object    +---------------------------------------------+
Vector 1      +----+
Vector 2                    +----+
Vector 3                                   +----------+

After formatting:

Log Buffer    +-V1-+-V2-+----V3----+

Delayed logging vector:

Object    +---------------------------------------------+
Vector 1      +----+
Vector 2                    +----+
Vector 3                                   +----------+

After formatting:

Memory Buffer +-V1-+-V2-+----V3----+
Vector 1      +----+
Vector 2           +----+
Vector 3                +----------+

The memory buffer and associated vector need to be passed as a single object, but still need to be associated with the parent object so if the object is relogged we can replace the current memory buffer with a new memory buffer that contains the latest changes.

The reason for keeping the vector around after we’ve formatted the memory buffer is to support splitting vectors across log buffer boundaries correctly. If we don’t keep the vector around, we do not know where the region boundaries are in the item, so we’d need a new encapsulation method for regions in the log buffer writing (i.e. double encapsulation). This would be an on-disk format change and as such is not desirable. It also means we’d have to write the log region headers in the formatting stage, which is problematic as there is per region state that needs to be placed into the headers during the log write.

Hence we need to keep the vector, but by attaching the memory buffer to it and rewriting the vector addresses to point at the memory buffer we end up with a self-describing object that can be passed to the log buffer write code to be handled in exactly the same manner as the existing log vectors are handled. Hence we avoid needing a new on-disk format to handle items that have been relogged in memory.

Tracking Changes

Now that we can record transactional changes in memory in a form that allows them to be used without limitations, we need to be able to track and accumulate them so that they can be written to the log at some later point in time. The log item is the natural place to store this vector and buffer, and also makes sense to be the object that is used to track committed objects as it will always exist once the object has been included in a transaction.

The log item is already used to track the log items that have been written to the log but not yet written to disk. Such log items are considered “active” and as such are stored in the Active Item List (AIL) which is a LSN-ordered double linked list. Items are inserted into this list during log buffer IO completion, after which they are unpinned and can be written to disk. An object that is in the AIL can be relogged, which causes the object to be pinned again and then moved forward in the AIL when the log buffer IO completes for that transaction.

Essentially, this shows that an item that is in the AIL can still be modified and relogged, so any tracking must be separate to the AIL infrastructure. As such, we cannot reuse the AIL list pointers for tracking committed items, nor can we store state in any field that is protected by the AIL lock. Hence the committed item tracking needs it’s own locks, lists and state fields in the log item.

Similar to the AIL, tracking of committed items is done through a new list called the Committed Item List (CIL). The list tracks log items that have been committed and have formatted memory buffers attached to them. It tracks objects in transaction commit order, so when an object is relogged it is removed from it’s place in the list and re-inserted at the tail. This is entirely arbitrary and done to make it easy for debugging - the last items in the list are the ones that are most recently modified. Ordering of the CIL is not necessary for transactional integrity (as discussed in the next section) so the ordering is done for convenience/sanity of the developers.

Delayed Logging: Checkpoints

When we have a log synchronisation event, commonly known as a “log force”, all the items in the CIL must be written into the log via the log buffers. We need to write these items in the order that they exist in the CIL, and they need to be written as an atomic transaction. The need for all the objects to be written as an atomic transaction comes from the requirements of relogging and log replay - all the changes in all the objects in a given transaction must either be completely replayed during log recovery, or not replayed at all. If a transaction is not replayed because it is not complete in the log, then no later transactions should be replayed, either.

To fulfill this requirement, we need to write the entire CIL in a single log transaction. Fortunately, the XFS log code has no fixed limit on the size of a transaction, nor does the log replay code. The only fundamental limit is that the transaction cannot be larger than just under half the size of the log. The reason for this limit is that to find the head and tail of the log, there must be at least one complete transaction in the log at any given time. If a transaction is larger than half the log, then there is the possibility that a crash during the write of a such a transaction could partially overwrite the only complete previous transaction in the log. This will result in a recovery failure and an inconsistent filesystem and hence we must enforce the maximum size of a checkpoint to be slightly less than a half the log.

Apart from this size requirement, a checkpoint transaction looks no different to any other transaction - it contains a transaction header, a series of formatted log items and a commit record at the tail. From a recovery perspective, the checkpoint transaction is also no different - just a lot bigger with a lot more items in it. The worst case effect of this is that we might need to tune the recovery transaction object hash size.

Because the checkpoint is just another transaction and all the changes to log items are stored as log vectors, we can use the existing log buffer writing code to write the changes into the log. To do this efficiently, we need to minimise the time we hold the CIL locked while writing the checkpoint transaction. The current log write code enables us to do this easily with the way it separates the writing of the transaction contents (the log vectors) from the transaction commit record, but tracking this requires us to have a per-checkpoint context that travels through the log write process through to checkpoint completion.

Hence a checkpoint has a context that tracks the state of the current checkpoint from initiation to checkpoint completion. A new context is initiated at the same time a checkpoint transaction is started. That is, when we remove all the current items from the CIL during a checkpoint operation, we move all those changes into the current checkpoint context. We then initialise a new context and attach that to the CIL for aggregation of new transactions.

This allows us to unlock the CIL immediately after transfer of all the committed items and effectively allow new transactions to be issued while we are formatting the checkpoint into the log. It also allows concurrent checkpoints to be written into the log buffers in the case of log force heavy workloads, just like the existing transaction commit code does. This, however, requires that we strictly order the commit records in the log so that checkpoint sequence order is maintained during log replay.

To ensure that we can be writing an item into a checkpoint transaction at the same time another transaction modifies the item and inserts the log item into the new CIL, then checkpoint transaction commit code cannot use log items to store the list of log vectors that need to be written into the transaction. Hence log vectors need to be able to be chained together to allow them to be detached from the log items. That is, when the CIL is flushed the memory buffer and log vector attached to each log item needs to be attached to the checkpoint context so that the log item can be released. In diagrammatic form, the CIL would look like this before the flush:

CIL Head
   |
   V
Log Item <-> log vector 1       -> memory buffer
   |                            -> vector array
   V
Log Item <-> log vector 2       -> memory buffer
   |                            -> vector array
   V
......
   |
   V
Log Item <-> log vector N-1     -> memory buffer
   |                            -> vector array
   V
Log Item <-> log vector N       -> memory buffer
                                -> vector array

And after the flush the CIL head is empty, and the checkpoint context log vector list would look like:

Checkpoint Context
   |
   V
log vector 1    -> memory buffer
   |            -> vector array
   |            -> Log Item
   V
log vector 2    -> memory buffer
   |            -> vector array
   |            -> Log Item
   V
......
   |
   V
log vector N-1  -> memory buffer
   |            -> vector array
   |            -> Log Item
   V
log vector N    -> memory buffer
                -> vector array
                -> Log Item

Once this transfer is done, the CIL can be unlocked and new transactions can start, while the checkpoint flush code works over the log vector chain to commit the checkpoint.

Once the checkpoint is written into the log buffers, the checkpoint context is attached to the log buffer that the commit record was written to along with a completion callback. Log IO completion will call that callback, which can then run transaction committed processing for the log items (i.e. insert into AIL and unpin) in the log vector chain and then free the log vector chain and checkpoint context.

Discussion Point: I am uncertain as to whether the log item is the most efficient way to track vectors, even though it seems like the natural way to do it. The fact that we walk the log items (in the CIL) just to chain the log vectors and break the link between the log item and the log vector means that we take a cache line hit for the log item list modification, then another for the log vector chaining. If we track by the log vectors, then we only need to break the link between the log item and the log vector, which means we should dirty only the log item cachelines. Normally I wouldn’t be concerned about one vs two dirty cachelines except for the fact I’ve seen upwards of 80,000 log vectors in one checkpoint transaction. I’d guess this is a “measure and compare” situation that can be done after a working and reviewed implementation is in the dev tree….

Delayed Logging: Checkpoint Sequencing

One of the key aspects of the XFS transaction subsystem is that it tags committed transactions with the log sequence number of the transaction commit. This allows transactions to be issued asynchronously even though there may be future operations that cannot be completed until that transaction is fully committed to the log. In the rare case that a dependent operation occurs (e.g. re-using a freed metadata extent for a data extent), a special, optimised log force can be issued to force the dependent transaction to disk immediately.

To do this, transactions need to record the LSN of the commit record of the transaction. This LSN comes directly from the log buffer the transaction is written into. While this works just fine for the existing transaction mechanism, it does not work for delayed logging because transactions are not written directly into the log buffers. Hence some other method of sequencing transactions is required.

As discussed in the checkpoint section, delayed logging uses per-checkpoint contexts, and as such it is simple to assign a sequence number to each checkpoint. Because the switching of checkpoint contexts must be done atomically, it is simple to ensure that each new context has a monotonically increasing sequence number assigned to it without the need for an external atomic counter - we can just take the current context sequence number and add one to it for the new context.

Then, instead of assigning a log buffer LSN to the transaction commit LSN during the commit, we can assign the current checkpoint sequence. This allows operations that track transactions that have not yet completed know what checkpoint sequence needs to be committed before they can continue. As a result, the code that forces the log to a specific LSN now needs to ensure that the log forces to a specific checkpoint.

To ensure that we can do this, we need to track all the checkpoint contexts that are currently committing to the log. When we flush a checkpoint, the context gets added to a “committing” list which can be searched. When a checkpoint commit completes, it is removed from the committing list. Because the checkpoint context records the LSN of the commit record for the checkpoint, we can also wait on the log buffer that contains the commit record, thereby using the existing log force mechanisms to execute synchronous forces.

It should be noted that the synchronous forces may need to be extended with mitigation algorithms similar to the current log buffer code to allow aggregation of multiple synchronous transactions if there are already synchronous transactions being flushed. Investigation of the performance of the current design is needed before making any decisions here.

The main concern with log forces is to ensure that all the previous checkpoints are also committed to disk before the one we need to wait for. Therefore we need to check that all the prior contexts in the committing list are also complete before waiting on the one we need to complete. We do this synchronisation in the log force code so that we don’t need to wait anywhere else for such serialisation - it only matters when we do a log force.

The only remaining complexity is that a log force now also has to handle the case where the forcing sequence number is the same as the current context. That is, we need to flush the CIL and potentially wait for it to complete. This is a simple addition to the existing log forcing code to check the sequence numbers and push if required. Indeed, placing the current sequence checkpoint flush in the log force code enables the current mechanism for issuing synchronous transactions to remain untouched (i.e. commit an asynchronous transaction, then force the log at the LSN of that transaction) and so the higher level code behaves the same regardless of whether delayed logging is being used or not.

Delayed Logging: Checkpoint Log Space Accounting

The big issue for a checkpoint transaction is the log space reservation for the transaction. We don’t know how big a checkpoint transaction is going to be ahead of time, nor how many log buffers it will take to write out, nor the number of split log vector regions are going to be used. We can track the amount of log space required as we add items to the commit item list, but we still need to reserve the space in the log for the checkpoint.

A typical transaction reserves enough space in the log for the worst case space usage of the transaction. The reservation accounts for log record headers, transaction and region headers, headers for split regions, buffer tail padding, etc. as well as the actual space for all the changed metadata in the transaction. While some of this is fixed overhead, much of it is dependent on the size of the transaction and the number of regions being logged (the number of log vectors in the transaction).

An example of the differences would be logging directory changes versus logging inode changes. If you modify lots of inode cores (e.g. chmod -R g+w *), then there are lots of transactions that only contain an inode core and an inode log format structure. That is, two vectors totaling roughly 150 bytes. If we modify 10,000 inodes, we have about 1.5MB of metadata to write in 20,000 vectors. Each vector is 12 bytes, so the total to be logged is approximately 1.75MB. In comparison, if we are logging full directory buffers, they are typically 4KB each, so we in 1.5MB of directory buffers we’d have roughly 400 buffers and a buffer format structure for each buffer - roughly 800 vectors or 1.51MB total space. From this, it should be obvious that a static log space reservation is not particularly flexible and is difficult to select the “optimal value” for all workloads.

Further, if we are going to use a static reservation, which bit of the entire reservation does it cover? We account for space used by the transaction reservation by tracking the space currently used by the object in the CIL and then calculating the increase or decrease in space used as the object is relogged. This allows for a checkpoint reservation to only have to account for log buffer metadata used such as log header records.

However, even using a static reservation for just the log metadata is problematic. Typically log record headers use at least 16KB of log space per 1MB of log space consumed (512 bytes per 32k) and the reservation needs to be large enough to handle arbitrary sized checkpoint transactions. This reservation needs to be made before the checkpoint is started, and we need to be able to reserve the space without sleeping. For a 8MB checkpoint, we need a reservation of around 150KB, which is a non-trivial amount of space.

A static reservation needs to manipulate the log grant counters - we can take a permanent reservation on the space, but we still need to make sure we refresh the write reservation (the actual space available to the transaction) after every checkpoint transaction completion. Unfortunately, if this space is not available when required, then the regrant code will sleep waiting for it.

The problem with this is that it can lead to deadlocks as we may need to commit checkpoints to be able to free up log space (refer back to the description of rolling transactions for an example of this). Hence we must always have space available in the log if we are to use static reservations, and that is very difficult and complex to arrange. It is possible to do, but there is a simpler way.

The simpler way of doing this is tracking the entire log space used by the items in the CIL and using this to dynamically calculate the amount of log space required by the log metadata. If this log metadata space changes as a result of a transaction commit inserting a new memory buffer into the CIL, then the difference in space required is removed from the transaction that causes the change. Transactions at this level will always have enough space available in their reservation for this as they have already reserved the maximal amount of log metadata space they require, and such a delta reservation will always be less than or equal to the maximal amount in the reservation.

Hence we can grow the checkpoint transaction reservation dynamically as items are added to the CIL and avoid the need for reserving and regranting log space up front. This avoids deadlocks and removes a blocking point from the checkpoint flush code.

As mentioned early, transactions can’t grow to more than half the size of the log. Hence as part of the reservation growing, we need to also check the size of the reservation against the maximum allowed transaction size. If we reach the maximum threshold, we need to push the CIL to the log. This is effectively a “background flush” and is done on demand. This is identical to a CIL push triggered by a log force, only that there is no waiting for the checkpoint commit to complete. This background push is checked and executed by transaction commit code.

If the transaction subsystem goes idle while we still have items in the CIL, they will be flushed by the periodic log force issued by the xfssyncd. This log force will push the CIL to disk, and if the transaction subsystem stays idle, allow the idle log to be covered (effectively marked clean) in exactly the same manner that is done for the existing logging method. A discussion point is whether this log force needs to be done more frequently than the current rate which is once every 30s.

Delayed Logging: Log Item Pinning

Currently log items are pinned during transaction commit while the items are still locked. This happens just after the items are formatted, though it could be done any time before the items are unlocked. The result of this mechanism is that items get pinned once for every transaction that is committed to the log buffers. Hence items that are relogged in the log buffers will have a pin count for every outstanding transaction they were dirtied in. When each of these transactions is completed, they will unpin the item once. As a result, the item only becomes unpinned when all the transactions complete and there are no pending transactions. Thus the pinning and unpinning of a log item is symmetric as there is a 1:1 relationship with transaction commit and log item completion.

For delayed logging, however, we have an asymmetric transaction commit to completion relationship. Every time an object is relogged in the CIL it goes through the commit process without a corresponding completion being registered. That is, we now have a many-to-one relationship between transaction commit and log item completion. The result of this is that pinning and unpinning of the log items becomes unbalanced if we retain the “pin on transaction commit, unpin on transaction completion” model.

To keep pin/unpin symmetry, the algorithm needs to change to a “pin on insertion into the CIL, unpin on checkpoint completion”. In other words, the pinning and unpinning becomes symmetric around a checkpoint context. We have to pin the object the first time it is inserted into the CIL - if it is already in the CIL during a transaction commit, then we do not pin it again. Because there can be multiple outstanding checkpoint contexts, we can still see elevated pin counts, but as each checkpoint completes the pin count will retain the correct value according to it’s context.

Just to make matters more slightly more complex, this checkpoint level context for the pin count means that the pinning of an item must take place under the CIL commit/flush lock. If we pin the object outside this lock, we cannot guarantee which context the pin count is associated with. This is because of the fact pinning the item is dependent on whether the item is present in the current CIL or not. If we don’t pin the CIL first before we check and pin the object, we have a race with CIL being flushed between the check and the pin (or not pinning, as the case may be). Hence we must hold the CIL flush/commit lock to guarantee that we pin the items correctly.

Delayed Logging: Concurrent Scalability

A fundamental requirement for the CIL is that accesses through transaction commits must scale to many concurrent commits. The current transaction commit code does not break down even when there are transactions coming from 2048 processors at once. The current transaction code does not go any faster than if there was only one CPU using it, but it does not slow down either.

As a result, the delayed logging transaction commit code needs to be designed for concurrency from the ground up. It is obvious that there are serialisation points in the design - the three important ones are:

  1. Locking out new transaction commits while flushing the CIL
  2. Adding items to the CIL and updating item space accounting
  3. Checkpoint commit ordering

Looking at the transaction commit and CIL flushing interactions, it is clear that we have a many-to-one interaction here. That is, the only restriction on the number of concurrent transactions that can be trying to commit at once is the amount of space available in the log for their reservations. The practical limit here is in the order of several hundred concurrent transactions for a 128MB log, which means that it is generally one per CPU in a machine.

The amount of time a transaction commit needs to hold out a flush is a relatively long period of time - the pinning of log items needs to be done while we are holding out a CIL flush, so at the moment that means it is held across the formatting of the objects into memory buffers (i.e. while memcpy()s are in progress). Ultimately a two pass algorithm where the formatting is done separately to the pinning of objects could be used to reduce the hold time of the transaction commit side.

Because of the number of potential transaction commit side holders, the lock really needs to be a sleeping lock - if the CIL flush takes the lock, we do not want every other CPU in the machine spinning on the CIL lock. Given that flushing the CIL could involve walking a list of tens of thousands of log items, it will get held for a significant time and so spin contention is a significant concern. Preventing lots of CPUs spinning doing nothing is the main reason for choosing a sleeping lock even though nothing in either the transaction commit or CIL flush side sleeps with the lock held.

It should also be noted that CIL flushing is also a relatively rare operation compared to transaction commit for asynchronous transaction workloads - only time will tell if using a read-write semaphore for exclusion will limit transaction commit concurrency due to cache line bouncing of the lock on the read side.

The second serialisation point is on the transaction commit side where items are inserted into the CIL. Because transactions can enter this code concurrently, the CIL needs to be protected separately from the above commit/flush exclusion. It also needs to be an exclusive lock but it is only held for a very short time and so a spin lock is appropriate here. It is possible that this lock will become a contention point, but given the short hold time once per transaction I think that contention is unlikely.

The final serialisation point is the checkpoint commit record ordering code that is run as part of the checkpoint commit and log force sequencing. The code path that triggers a CIL flush (i.e. whatever triggers the log force) will enter an ordering loop after writing all the log vectors into the log buffers but before writing the commit record. This loop walks the list of committing checkpoints and needs to block waiting for checkpoints to complete their commit record write. As a result it needs a lock and a wait variable. Log force sequencing also requires the same lock, list walk, and blocking mechanism to ensure completion of checkpoints.

These two sequencing operations can use the mechanism even though the events they are waiting for are different. The checkpoint commit record sequencing needs to wait until checkpoint contexts contain a commit LSN (obtained through completion of a commit record write) while log force sequencing needs to wait until previous checkpoint contexts are removed from the committing list (i.e. they’ve completed). A simple wait variable and broadcast wakeups (thundering herds) has been used to implement these two serialisation queues. They use the same lock as the CIL, too. If we see too much contention on the CIL lock, or too many context switches as a result of the broadcast wakeups these operations can be put under a new spinlock and given separate wait lists to reduce lock contention and the number of processes woken by the wrong event.

Lifecycle Changes

The existing log item life cycle is as follows:

1. Transaction allocate
2. Transaction reserve
3. Lock item
4. Join item to transaction
        If not already attached,
                Allocate log item
                Attach log item to owner item
        Attach log item to transaction
5. Modify item
        Record modifications in log item
6. Transaction commit
        Pin item in memory
        Format item into log buffer
        Write commit LSN into transaction
        Unlock item
        Attach transaction to log buffer

<log buffer IO dispatched>
<log buffer IO completes>

7. Transaction completion
        Mark log item committed
        Insert log item into AIL
                Write commit LSN into log item
        Unpin log item
8. AIL traversal
        Lock item
        Mark log item clean
        Flush item to disk

<item IO completion>

9. Log item removed from AIL
        Moves log tail
        Item unlocked

Essentially, steps 1-6 operate independently from step 7, which is also independent of steps 8-9. An item can be locked in steps 1-6 or steps 8-9 at the same time step 7 is occurring, but only steps 1-6 or 8-9 can occur at the same time. If the log item is in the AIL or between steps 6 and 7 and steps 1-6 are re-entered, then the item is relogged. Only when steps 8-9 are entered and completed is the object considered clean.

With delayed logging, there are new steps inserted into the life cycle:

1. Transaction allocate
2. Transaction reserve
3. Lock item
4. Join item to transaction
        If not already attached,
                Allocate log item
                Attach log item to owner item
        Attach log item to transaction
5. Modify item
        Record modifications in log item
6. Transaction commit
        Pin item in memory if not pinned in CIL
        Format item into log vector + buffer
        Attach log vector and buffer to log item
        Insert log item into CIL
        Write CIL context sequence into transaction
        Unlock item

<next log force>

7. CIL push
        lock CIL flush
        Chain log vectors and buffers together
        Remove items from CIL
        unlock CIL flush
        write log vectors into log
        sequence commit records
        attach checkpoint context to log buffer

<log buffer IO dispatched>
<log buffer IO completes>

8. Checkpoint completion
        Mark log item committed
        Insert item into AIL
                Write commit LSN into log item
        Unpin log item
9. AIL traversal
        Lock item
        Mark log item clean
        Flush item to disk
<item IO completion>
10. Log item removed from AIL
        Moves log tail
        Item unlocked

From this, it can be seen that the only life cycle differences between the two logging methods are in the middle of the life cycle - they still have the same beginning and end and execution constraints. The only differences are in the committing of the log items to the log itself and the completion processing. Hence delayed logging should not introduce any constraints on log item behaviour, allocation or freeing that don’t already exist.

As a result of this zero-impact “insertion” of delayed logging infrastructure and the design of the internal structures to avoid on disk format changes, we can basically switch between delayed logging and the existing mechanism with a mount option. Fundamentally, there is no reason why the log manager would not be able to swap methods automatically and transparently depending on load characteristics, but this should not be necessary if delayed logging works as designed.