Improving Metadata Performance By Reducing Journal Overhead

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Improving Metadata Performance By Reducing Journal Overhead

XFS currently uses asynchronous write-ahead logging to ensure that changes to the filesystem structure are preserved on crash. It does this by logging detailed records of the changes being made to each object on disk during a transaction. Every byte that is modified needs to be recorded in the journal.

There are two issues with this approach. The first is that transactions can modify a *lot* of metadata to complete a single operation. Worse is the fact that the average size of a transactions grows as structures get larger and deeper, so performance on larger, fuller filesystem drops off as log bandwidth is consumed by fewer, larger transactions.

The second is that we re-log previous changes that are active in the journal if the object is modified again. hence if an object is modified repeatedly, the dirty parts of the object get rewritten over and over again. in the worst case, frequently logged buffers will be entirely dirty and so even if we only change a single byte in the buffer we'll log the entire buffer.

The problem can be approached along two different axes:

- reduce the amount we log in a given transaction - change the way we re-log objects.

Both of these things give the same end result - we require less bandwidth to the journal to log changes that are happening in the filesystem.

Asynchronous Transaction Aggregation

Status: Done, known as delayed logging.

Experimental in 2.6.35, stable for production in 2.6.37, planned for default in 2.6.39.

Design documentation can be found here:

http://git.kernel.org/?p=linux/kernel/git/torvalds/linux-2.6.git;a=blob;f=Documentation/filesystems/xfs-delayed-logging-design.txt

Atomic Multi-Transaction Operations

A feature asynchronous transaction aggregation makes possible is atomic multi-transaction operations. On the first transaction we hold the queue in memory, preventing it from being committed. We can then do further transactions that will end up in the same commit record, and on the final transaction we unlock the async transaction queue. This will allow all those transaction to be applied atomically. This is far simpler than any other method I've been looking at to do this.

After a bit of reflection, I think this feature may be necessary for correct implementation of existing logging techniques. The way we currently implement rolling transactions (with permanent log reservations and rolling dup/commit/re-reserve sequences) would seem to require all the commits in a rolling transaction to be including in a single commit record. If I understand history and the original design correctly, these rolling transactions were implemented so that large, complex transactions would not pin the tail of the log as they progressed. IOWs, they implicitly use re-logging to keep the tail of the log moving forward as they progress and continue to modify items in the transaction.

Given we are using asynchronous transaction aggregation as a method of reducing re-logging, it would make sense to prevent these sorts of transactions from pinning the tail of the log at all. Further, because we are effectively disturbing the concept of unique transactions, I don't think that allowing a rolling transaction to span aggregated commits is valid as we are going to be ignoring the transaction IDs that are used to identify individual transactions.

Hence I think it is a good idea to simply replace rolling transactions with atomic multi-transaction operations. This may also allow us to split some of the large compound transactions into smaller, more self contained transactions. This would reduce reservation pressure on log space in the common case where all the corner cases in the transactions are not taken. In terms of implementation, I think we can initially augment the permanent transaction reservation/release interface to acheive this. With a working implementation, we can then look to changing to a more explicit interface and slowly work to remove the 'permanent log transaction' concept entirely. This shold simplify the log code somewhat....

Note: This asynchronous transaction aggregation is originally based on a concept floated by Nathan Scott called 'Delayed Logging' after observing how ext3 implemented journalling. This never passed more than a concept description phase....


Operation Based Logging

The second approach to reducing log traffic is to change exactly what we log in the transactions. At the moment, what we log is the exact change to the item that is being made. For things like inodes and dquots, this isn't particularly expensive because it is already a very compact form. The issue comes with changes that are logged in buffers.

The prime example of this is a btree modification that involves either removing or inserting a record into a buffer. The records are kept in compact form, so an insert or remove will also move other records around in the buffer. In the worst case, a single insert or remove of a 16 byte record can dirty an entire block (4k generally, but could be up to 64k). In this case, if we were to log the btree operation (e.g. insert {record, index}) rather than the resultant change on the buffer the overhead of a btree operation is fixed. Such logging also allows us to avoid needing to log the changes due to splits and merges - we just replay the operation and subsequent splits/merges get done as part of replay.

The result of this is that complex transactions no longer need as much log space as all possible change they can cause - we only log the basic operations that are occurring and their result. Hence transaction end up being much smaller, vary less in size between empty and full filesystems, etc. An example set of operations describing all the changes made by an extent allocation on an inode would be:

- inode X intent to allocate extent {off, len} - AGCNT btree update record in AG X {old rec} {new rec values} - AGBNO btree delete record in AG X {block, len} - inode X BMBT btree insert record {off, block, len} - inode X delta

This comes down to a relatively small, bound amount of space which is close the minimun and existing allocation transaction would consume. However, with this method of logging the transaction size does not increase with the size of structures or the amount of updates necessary to complete the operations.

A major difference to the existing transaction system is that re-logging of items doesn't fit very neatly with operation based logging.

There are three main disadvantages to this approach:

- recovery becomes more complex - it will need to change substantially to accomodate operation replay rather than just reading from disk and applying deltas. - we have to create a whole new set of item types and add the necessary hooks into the code to log all the operations correctly. - re-logging is probably not possible, and that introduces differences to the way we'll need to track objects for flushing. It may, in fact, require transaction IDs in all objects to allow us to determine what the last transaction that modified the item on disk was during recovery.

Changing the logging strategy as described is a much more fundamental change to XFS than asynchronous transaction aggregation. It will be difficult to change to such a model in an evolutionary manner; it is more of a 'flag day' style change where then entire functionality needs to be added in one hit. Given that we will also still have to support the old log format, it doesn't enable us to remove any code, either.

Given that we are likely to see major benefits in the problem workloads as a result of asynchronous transaction aggregation, it may not be necessary to completely rework the transaction subsystem. Combining aggregation with an ongoing process of targeted reduction of transaction size will provide benefits out to at least the medium term. It is unclear whether this direction will be sufficient in the long run until we can measure the benefit that aggregation will provide.


Reducing Transaction Overhead

Per iclog callback list locks: Done

AIL tail pushing in it's own thread: Done

Bulk AIL insert and delete operations: Done

Log grant lock split-up: Done

Lock free transaction reserve path: Done

Moving all of the log interfacing out of the direct transaction commit path may provide similar benefits to moving the AIL pushing into it's own thread. This will mean that there will typically only be a single thread formatting and writing to iclog buffers. This will remove much of the parallelism that puts excessive pressure on many of these locks.

Reducing Recovery Time

With 2GB logs, recovery can take an awfully long time due to the need to read each object synchronously as we process the journal. An obvious way to avoid this is to add another pass to the processing to do asynchronous readahead of all the objects in the log before doing the processing passes. This will populate the cache as quickly as possible and hide any read latency that could occur as we process commit records.

A logical extension to this is to sort the objects in ascending offset order before issuing I/O on them. That will further optimise the readahead I/O to reduce seeking and hence should speed up the read phase of recovery further.

ToDo

Further investigation of recovery for future optimisation.

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