X

The Wonders of ZFS Storage
Performance for your Data

  • ZFS
    February 20, 2015

ZIL Pipelining

Roch Bourbonnais
Principal Performance Engineer
The third topic on my list of improvements since 2010 is ZIL pipelining :

Allow the ZIL to carve up smaller units of

work for better pipelining and higher log device

utilization.
So let's remind ourselves of a few things about the ZIL and why it's so critical to ZFS. The ZIL stands for ZFS Intent Log and exists in order to speed up synchronous operations such as an O_DSYNC write or fsync(3C) calls. Since most Database operation involve synchronous writes it's easy to understand that having good ZIL performance is critical in many environments.

It is well understood that a ZFS pool updates it's global on-disk state at a set interval (5 seconds these days). The ZIL is actually what keeps information in between those transaction group (TXG). The ZIL records what is committed to stable storage from a users point of view. Basically the last committed TXG + replay of the ZIL is the valid storage state from a users perspective.

The on-disk ZIL is a linked list of records which is actually only useful in the event of a power outage or system crash. As part of a pool import, the on-disk ZIL is read and operations replayed such that the ZFS pool contains the exact information that had been committed before the disruption.

While we often think of the ZIL as it's on-disk representation (it's committed state), the ZIL is also an in-memory representation of every posix operation that needs to modify data. For example, a file creation even if that is an asynchronous operation needs to be tracked by the ZIL. This is because any asynchronous operation, may at any point in time require to be committed to disk; this is often due to an fsync(3C) call. At that moment, every pending operation on a given file needs to be packaged up and committed to the on-disk ZIL.

Where is the on-disk ZIL stored ?

Well that's also more complex than it sound. ZFS manages devices specifically geared to store ZIL blocks; those separate slog devices or slogs are very often flash SSD. However the ZIL is not constrained to only using blocks from slog devices; it can store data on main (non-slog) pool devices. When storing ZIL information into the non-slog pool devices, the ZIL has a choice of recording data inside zil blocks or recording full file records inside pool blocks and storing a reference to it inside the ZIL. This last method for storing ZIL blocks has the benefit of offloading work from the upcoming TXG sync at the expense of higher latency since the ZIL I/Os are being sent to rotating disks. This mode is the one used with logbias=throughput. More on that below.

Net net: the ZIL records data in stable storage in a link list and user applications have synchronization point in which they choose to wait on the ZIL to complete it's operation.

When things are not stressed, operations show up at the ZIL, wait a little bit while the ZIL does it's work, and are then released. Latency of the ZIL is then coherent with the underlying device used to capture the information. In this rosy picture we would not have done this train project.

At times though, the system can get stressed. The older mode of operation of the ZIL was to issue a ZIL transaction (implemented by ZFS function zil_commit_writer) and while that was going on, build up the next ZIL transaction with everything that showed up at the door. Under stress when a first operation would be serviced with a high latency, the next transaction would accumulate many operations, growing in size thus leading to a longer latency transaction and this would spiral out of control. The system would automatically divide into 2 ad-hoc sets of users; a set of operations which would commit together as a group, while all other threads in the system would form the next ZIL transaction and vice-versa.

This leads to bursty activity on the ZIL devices, which meant that, at times, they would go unused even though they were the critical resource. This 'convoy' effect also meant disruption of servers because when those large ZIL transaction do complete, 100s or 1000s of user threads might see their synchronous operation complete and all would end up flagged as 'runnable' at the same time. Often those would want to consume the same resource, run on the same CPU, of use the same lock etc. This led to thundering herds, a source of system inefficiency.

Thanks to the ZIL train project, we now have the ability to break down convoys into smaller units and dispatch them into smaller ZIL level transactions which are then pipelined through the entire data center.

With logbias set to throughput, the new code is attempting to group ZIL transactions in sets of approximately 40 operations which is a compromise between efficient use of ZIL and reduction of the convoy effect. For other types of synchronous operations we group them into sets representing about ~32K of data to sync. That means that a single sufficiently large operation may run by itself but more threads will group together if their individual commit size are small.

The ZIL train is thus expected to handle burst of synchronous activity with a lot less stress on the system.

The THROUGHPUT VS LATENCY debate.

As we just saw the ZIL provides 2 modes of operation. The throughput mode and the default latency mode. The throughput mode is named as such not so much because it favors throughput but more so because it doesn't care too much about individual operation latency. The implied corollary of throughput friendly workloads is that they are very highly concurrent (100s or 1000s of independent operations) and therefore are able to get to high throughput even when served at high latency. The goal of providing a ZIL throughput mode is to actually free up slog devices from having to handle such highly concurrent workloads and allow those slog devices to concentrate on serving other low-concurrency, but highly sensitive to latency operations.

For Oracle DB, we therefore recommend the use of logbias set to throughput for DB files which are subject to highly concurrent DB writer operations while we recommend the use of the default latency mode for handling other latency sensitive files such as the redo log. This separation is particularly important when redo log latency is very critical and when the slog device is itself subject to stress.

When using Oracle 12c with dnfs and OISP, this best practice is automatically put into place. In addition to proper logbias handling, DB data files are created with a ZFS recordsize matching the established best practice : ZFS recordsize matching DB blocksize for datafiles; ZFS recordsize of 128K for redo log.

When setting up a DB, with or without OISP, there is one thing that Storage Administrators must enforce : they must segregate redo log files into their own filesystems (also known as shares or datasets). The reason for this is that the ZIL is a single linked list of transactions maintained by each filesystem (other filesystems run their own ZIL independently). And while the ZIL train allows for multiple transaction to be in flight concurrently, there is a strong requirement for completion of the transaction and notification of waiters to be handled in order. If one were to mix data files and redo log files in the same ZIL, then some redo transaction would be linked behind some DB writer transactions. Those critical redo transaction committing in latency mode to a slog device would see their I/O complete quickly (100us timescale) but nevertheless have to wait for an antecedent DB writer transaction committing in throughput mode to regular spinning disk device (ms timescale). In order to avoid this situation, one must ensure that redo log files are stored in their own shares.

Let me stop here, I have a train to catch...

Be the first to comment

Comments ( 0 )
Please enter your name.Please provide a valid email address.Please enter a comment.CAPTCHA challenge response provided was incorrect. Please try again.Captcha