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Tag: GeorgeWilson

I’ve continued to explore ZFS as I try to understand performance pathologies, and improve performance. A particular point of interest has been the ZFS write throttle, the mechanism ZFS uses to avoid filling all of system memory with modified data. I’m eager to write about the strides we’re making in that regard at Delphix, but it’s hard to appreciate without an understanding of how ZFS batches data. Unfortunately that explanation is literally nowhere to be found. Back in 2001 I had not yet started working on DTrace, and was talking to Matt and Jeff, the authors of ZFS, about joining them. They had only been at it for a few months; I was fortunate to be in a conference with them as the ideas around transaction groups formulated. Transaction groups are how ZFS batches up chunks of data to be written to disk (“groups” of “transactions”). Jeff stood at the whiteboard and drew the progression of states for transaction groups, from open, accepting new transactions, to quiescing, allowing transactions to complete, to syncing, writing data out to disk. As far as I can tell, that was both the first time that picture had been drawn and the last. If you search for information on ZFS transaction groups you’ll find mention of those states… and not much else. The header comment in usr/src/uts/common/fs/zfs/txg.c isn’t particularly helpful:

/*
 * Pool-wide transaction groups.
 */

I set out to write a proper description of ZFS transaction groups. I’m posting it here first, and I’ll be offering it as a submission to illumos. Many thanks to Matt Ahrens, George Wilson, and Max Bruning for their feedback.

ZFS Transaction Groups

ZFS transaction groups are, as the name implies, groups of transactions that act on persistent state. ZFS asserts consistency at the granularity of these transaction groups. Each successive transaction group (txg) is assigned a 64-bit consecutive identifier. There are three active transaction group states: open, quiescing, or syncing. At any given time, there may be an active txg associated with each state; each active txg may either be processing, or blocked waiting to enter the next state. There may be up to three active txgs, and there is always a txg in the open state (though it may be blocked waiting to enter the quiescing state). In broad strokes, transactions — operations that change in-memory structures — are accepted into the txg in the open state, and are completed while the txg is in the open or quiescing states. The accumulated changes are written to disk in the syncing state.

Open

When a new txg becomes active, it first enters the open state. New transactions — updates to in-memory structures — are assigned to the currently open txg. There is always a txg in the open state so that ZFS can accept new changes (though the txg may refuse new changes if it has hit some limit). ZFS advances the open txg to the next state for a variety of reasons such as it hitting a time or size threshold, or the execution of an administrative action that must be completed in the syncing state.

Quiescing

After a txg exits the open state, it enters the quiescing state. The quiescing state is intended to provide a buffer between accepting new transactions in the open state and writing them out to stable storage in the syncing state. While quiescing, transactions can continue their operation without delaying either of the other states. Typically, a txg is in the quiescing state very briefly since the operations are bounded by software latencies rather than, say, slower I/O latencies. After all transactions complete, the txg is ready to enter the next state.

Syncing

In the syncing state, the in-memory state built up during the open and (to a lesser degree) the quiescing states is written to stable storage. The process of writing out modified data can, in turn modify more data. For example when we write new blocks, we need to allocate space for them; those allocations modify metadata (space maps)… which themselves must be written to stable storage. During the sync state, ZFS iterates, writing out data until it converges and all in-memory changes have been written out. The first such pass is the largest as it encompasses all the modified user data (as opposed to filesystem metadata). Subsequent passes typically have far less data to write as they consist exclusively of filesystem metadata.

To ensure convergence, after a certain number of passes ZFS begins overwriting locations on stable storage that had been allocated earlier in the syncing state (and subsequently freed). ZFS usually allocates new blocks to optimize for large, continuous, writes. For the syncing state to converge however it must complete a pass where no new blocks are allocated since each allocation requires a modification of persistent metadata. Further, to hasten convergence, after a prescribed number of passes, ZFS also defers frees, and stops compressing.

In addition to writing out user data, we must also execute synctasks during the syncing context. A synctask is the mechanism by which some administrative activities work such as creating and destroying snapshots or datasets. Note that when a synctask is initiated it enters the open txg, and ZFS then pushes that txg as quickly as possible to completion of the syncing state in order to reduce the latency of the administrative activity. To complete the syncing state, ZFS writes out a new uberblock, the root of the tree of blocks that comprise all state stored on the ZFS pool. Finally, if there is a quiesced txg waiting, we signal that it can now transition to the syncing state.

What else?

Please let me know if you have suggestions for how to improve the descriptions above. There’s more to be written on the specifics of the implementation, transactions, the DMU, and, well, ZFS in general. One thing that I’d note is that Matt mentioned to me recently that were he starting from scratch, he might eliminate the quiescing state. I didn’t understand fully until I researched the subsystem. Typically transactions take a very brief amount of time to “complete”, time measured by CPU latency as opposed, say, to I/O latency. Had the quiescing phase been merged into the syncing phase, the design would be slightly simpler, and it would eliminate the mostly idle intermediate phase where a bunch of dirty data can sit in memory relatively idle.

Next I’ll write about the ZFS write throttle, it’s various brokenness, and our ideas for how to fix it.

Lately, I’ve been rooting around in the bowels of ZFS as we’ve explored some long-standing performance pathologies. To that end I’ve been fortunate to learn at the feet of Matt Ahrens who was half of the ZFS founding team and George Wilson who has forgotten more about ZFS than most people will ever know. I wanted to start sharing some of the interesting details I’ve unearthed.

For allocation purposes, ZFS carves vdevs (disks) into a number of “metaslabs” — simply smaller, more manageable chunks of the whole. How many metaslabs? Around 200:

void
vdev_metaslab_set_size(vdev_t *vd)
{
        /*
         * Aim for roughly 200 metaslabs per vdev.
         */
        vd->vdev_ms_shift = highbit(vd->vdev_asize / 200);
        vd->vdev_ms_shift = MAX(vd->vdev_ms_shift, SPA_MAXBLOCKSHIFT);
}

http://src.illumos.org/source/xref/illumos-gate/usr/src/uts/common/fs/zfs/vdev.c#1553

Why 200? Well, that just kinda worked and was never revisited. Is it optimal? Almost certainly not. Should there be more or less? Should metaslab size be independent of vdev size? How much better could we do? All completely unknown.

The space in the vdev is allotted proportionally, and contiguously to those metaslabs. But what happens when a vdev is expanded? This can happen when a disk is replaced by a larger disk or if an administrator grows a SAN-based LUN. It turns out that ZFS simply creates more metaslabs — an answer whose simplicity was only obvious in retrospect.

For example, let’s say we start with a 2T disk; then we’ll have 200 metaslabs of 10G each. If we then grow the LUN to 4TB then we’ll have 400 metaslabs. If we started instead from a 200GB LUN that we eventually grew to 4TB we’d end up with 4,000 metaslabs (each 1G). Further, if we started with a 40TB LUN (why not) and grew it by 100G ZFS would not have enough space to allocate a full metaslab and we’d therefore not be able to use that additional space.

At Delphix our metaslabs can become highly fragmented because most of our datasets use a 8K record size (read up on space maps to understand how metaslabs are managed — truly fascinating), and our customers often expand LUNs as a mechanism for adding more space. It’s not clear how much room there is for improvement, but these are curious phenomena that we intend to investigate along with the structure of space maps, the idiosyncrasies of the allocation path, and other aspects of ZFS as we continue to understand and improve performance. Stay tuned.

For the second time in as many quadrennial dtrace.confs, I was impressed at how well the unconference format worked out. Sharing coffee with the DTrace community, it was great to see some of the oldest friends of DTrace — Jarod Jenson, Stephen O’Grady, Jonathan Adams to name a few — and to put faces to names — Scott Fritchie, Dustin Sallings, Blake Irvin, etc — of the many new additions to the DTrace community. You can see all the slides and videos; these are my thoughts and notes on the day.

Bryan provided a typically eloquent review of the state of the community. DTrace development is alive and well — after a lull while Oracle’s acquisition of Sun settled in — with new support for a variety of languages and runtimes, and new products that rely heavily on DTrace as a secret sauce. Bryan laid out some important development goals, areas where many have started straying from the edges of the completed DTrace features into the partially complete or starkly missing. We all then set to work hammering out a loose schedule for the day; I’ll admit that at first I was worried that we’d have too many listeners and not enough presenters, but the schedule quickly filled — and with more topics than we’d end up having time to cover.

User-land CTF and Dynamic Translators

DTrace, from its inception, has been a systemic analysis tool, but the earliest development focused on kernel observability — not a surprise since Bryan, Mike, and I developed it while working in the Solaris kernel development. After its use spread (quickly) beyond the kernel team, use shifted more and more to features focused on understanding C and C++ applications in user-land, and then to applications written in a variety of higher-level languages — Java, Ruby, Perl, Javascript, Erlang, etc. User-land Statically Defined Tracing (USDT) is the DTrace facility that enables rich tracing of higher-level languages. It was a relatively late addition to DTrace (integrated in 2004, well after the initial integration in 2003), and since then we’ve learned a lot about what we got right, what we got wrong, and where it’s rough — in some cases very rough — around the edges.

In his opening remarks, Bryan identified USDT improvements as a key area for the community’s focus. In DTrace development we tried to focus on making the impossible possible rather than making the possible easier. In its current form, some things are still impossible with DTrace, namely consumption of type structures from user-land programs; stable, non-privileged use of DTrace; and support for different runtime versions. Dave Pacheco and I took the first  slot on the schedule and spoke (at length — sorry) about solutions to these problems.

While others had the benefit of a bit more time to prepare, I did have the advantage of spending many years idly contemplating the problem space and possible solutions. On the subject of user-land type information (in the form of CTF), I identified the key parts of the code that would would need some work. For the USDT enhancements, we discussed dynamic translators — D code that would be linked and executed at runtime, contrasted with today’s static translators that are compiled into a D program — how they would address the problem, and how these ideas could be extended to the kernel (for once, user-land is actually a bit ahead).

I’ll go into the details of our off the cuff proposals, and delve into the code to firm up those ideas in a future blog post. Beyond the extensive implementation work we laid out, the next step is to gather the most complicated, extant USDT providers and proposals for other providers, and figure out what they should look like in the new, dynamic translator world.

The D Language

Next up, my long-time colleague, DTrace contributor, Eric Schrock led the discussion on D language additions. The format of a D program is heavily tied to DTrace’s implementation: all clauses must trace a fixed amount of data, and infinite loops are forbidden. For this reason, D lacks the backward branches needed for traditional looping, subroutines for common code, and if/else clauses for control flow. Each of these has a work-alike — unrolled loops, macros, and predicates or the ternary operator — but their absence renders D confusing to some — especially those unaware of the motivation. Further, the D language need not necessarily hold the underlying implementation so central.

Eric discussed some proposals for how each might be addressed, and I noted that it would be possible to create a prototype environment where we could try out these “D++” features by compiling into D work-alikes. The next step is to identify the most complicated D scripts, and see what they might look like for various incarnations of those language features.

Work with DTrace

The next few sessions focused not on changes to DTrace, but interesting work done using DTrace:

John Thompson of Sony talked about their port of DTrace to the Playstation Vita (!). Sony developers are given access to DTrace, but found it to be unfamiliar and unapproachable. John spoke his attempts to remedy this by replacing D with a C++-like interface which he implemented by replacing the D compiler with Clang.

My Fishworks colleague, Brendan Gregg, showed some of beautiful visualizations they’ve been developing at Joyent, and talked about the analyses those visualizations enabled. As always, it was fascinating stuff. If you don’t read Brendan’s blog, you really should. Long-time DTrace advocate, Theo Schlossnagle, talked about the visualizations they’re doing in Circonus — also fascinating stuff for anyone thinking about how to present system activity in comprehensible ways. Richard Elling showed the DTrace-based visualizations Nexenta used at VMworld to rave reviews.

Mark Cavage presented Joyent’s work bringing DTrace to node.js; Scott Fritchie talked about DTrace for Erlang. Both were useful sources of ideas for how we could improve USDT.

Ryan Stone presented the state of DTrace on FreeBSD. That DTrace is not enabled in the build by default remains a key obstacle for adoption. I hope that Ryan et al. are able to persuade the FreeBSD leadership that their licensing fears are misguided.

DTrace for OEL

I was delighted that Kris van Hees was able to attend to present the Oracle port to Linux. DTrace for OEL was announced at Oracle Open World 2011, but the initial beta didn’t live up to its billing at OOW. As is often the case, this was more a failure of messaging than of engineering. Kris and his team are making steady progress. While it’s not yet in the public beta, they have the kernel function boundary tracing provider (fbt) implemented. Most heartening of all, Oracle intends to keep DTrace for OEL moving forward as the community evolves and improves DTrace — rather than forking it. How that plays out, and what that means for DTrace on Oracle Solaris will be interesting to see, but it’s great to hear that Kris sees the value of DTrace ubiquity and DTrace compatibility.

As was remarked several times, having DTrace available on the fastest growing deployment platform will be the single most significant accelerator for DTrace adoption. The work Kris and his team at Oracle are doing is probably the most important in the DTrace ecosystem, and I think that I speak for the entire DTrace community in offering to assist in any way possible.

A ZFS DTrace Provider

Matt Ahrens and George Wilson — respectively the co-inventor of ZFS, and the preeminent SPA developer — presented a proposal for a DTrace provider for ZFS. ZFS is a highly sophisticated filesystem, but one that is also difficult to understand. Building in rich instrumentation is going to be a tremendous step forward for anyone using ZFS (for example, our mutual employer, Delphix).

Whither DTrace?

Jarod Jenson — the first DTrace user outside of Sun — took the stage in the final session to talk about DTrace adoption. Jarod has made DTrace a significant part of his business for many years. What continues to amazing him, despite numerous presentations, demonstrations, and lessons, is the relatively low level of DTrace adoption. DTrace is a tool that comes alive in the hands of a skilled, scientific, incisive practitioner — and in all of those, Jarod is superlative — but it can have a high bar of entry. There were many concrete suggestions for how to improve DTrace adoption. Most of them didn’t hold water for me — different avenue for education, further documentation, community outreach, higher level tools, visualizations, etc. — but two were quite compelling: DTrace for Linux, and DTrace on stackoverflow.com (and the like). I don’t know how much room there is to participate in the former, but by all means if there are DTrace one-liners that solve problems (on Mac OS X for example), post them, and get people covertly using DTrace.

The core DTrace community is growing. It was great to see old friends like Steve Peters who worked on porting DTrace to Mac OS X in the same room as Kris van Hees as he spoke about his port to Linux. It was inspiring to see so many new members of the community, eager to use, build and improve DTrace. And personally it inspired me to get back into the code to finish up some projects I had in flight, and to chart out the course for some of the projects we discussed.

Thanks to everyone who attended dtrace.conf in person or online. And thanks especially to Deirdre Straughan who made it happen.

This week it was my pleasure to welcome my former Sun colleague Matt Ahrens and George Wilson to Delphix. Matt and I studied computer science together at Brown and then joined Sun in 2001. Matt joined Jeff Bonwick to start ZFS while I worked on DTrace. George joined Sun in 1996, and worked in a variety of roles, joining the ZFS team in 2006 (just as I was leaving to help start Fishworks).

George and Matt bring an amazing knowledge of ZFS — the lower, and upper halves respectively — and are also just great engineers who are already contributing tangibly to the the success of Delphix. You can take a look at Matt’s old blog, or watch George in a bunch of videos (including one of him being interviewed by a muppet).

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