Disabling edge tiling on GNOME 3.26

Edge tiling?

It’s that thing where when you drag a window so it hits the edge of the screen, GNOME offers to maximise the window. Generally the number of times I will knowingly want to maximise a window by dragging it to the top of the screen is 0, while the number of times it happens accidentally is over 9,000 by lunch time.

Things that work

$ dconf write /org/gnome/mutter/edge-tiling false

It should take effect immediately.

If you like a pointy clicky way to do it then install dconf-editor package and run dconf-editor, but really all you will do is click down the tree orggnomemutter and then toggle edge-tiling so I don’t really see the point.

Things that people on the Internet say work, but don’t – a non-exhaustive list

These suggestions silently fail to do anything, as far as I can see. They may have been correct for earlier versions of GNOME, but I am using GNOME on Ubuntu 17.10 and they didn’t work for me.

dconf write /org/gnome/shell/extensions/classic-overrides/edge-tiling false
gsettings set org.gnome.shell.overrides edge-tiling false
dconf write /org/gnome/shell/overrides/edge-tiling false

Giving Cinema Paradiso a try

Farewell, LoveFiLM

I’ve been a customer of LoveFiLM for something like 12 years—since before they were owned by Amazon. In their original incarnation they were great: very cheap, and titles very often arrived in exactly the order you specified, i.e. they often managed to send the thing from the very top of the list.

In 2011 they got bought by Amazon and I was initially a bit concerned, but to be honest Amazon have run it well. The single list disappeared and was replaced by three priority lists; high, normal and low, and then a list of things that haven’t yet been released. New rentals were supposed to almost always come from the high priority list (as long as you had enough titles on there) but in a completely unpredictable order. Though of course they would keep multi-disc box sets together, and send lower-numbered seasons before later seasons.

Amazon have now announced that they’re shutting LoveFiLM by Post down at the end of October which I think is a shame, as it was a service I still enjoy.

It was inevitable I suppose due to the increasing popularity of streaming and downloads, and although I’m perfectly able to do the streaming and download thing, receiving discs by post still works for me.

I am used to receiving mockery for consuming some of my entertainment on little plastic discs that a human being has to physically transport to my residence, but LoveFiLM’s service was still cheap, the selection was very good, things could be rented as soon as they were available on disc, and the passive nature of just making a list and having the things sent to me worked well for me.

Cinema Paradiso

My first thought was that that was it for the disc-by-post rental model in the UK. That progress had left it behind. But very quickly people pointed me to Cinema Paradiso. After a quick look around I’ve decided to give it a try and so here are my initial thoughts.

Pricing

At a casual glance the pricing is slightly worse than LoveFiLM’s. I was paying £6.99 a month for 2 discs at home, unlimited rental per month. £6.98 at Cinema Paradiso gets you 2 discs at home but only 4 rentals per month.

I went back through my LoveFiLM rental history for the last year and found there were only 2 months where I managed to rent more than 4 discs, and those times I rented 5 and 6 discs respectively. Realistically it doesn’t seem like 4 discs per month will be much of a restriction to me.

Annoyingly, Cinema Paradiso have a 2 week trial period but only if you sign up to the £9.98 subscription (6 discs a month). You’d have to remember to downgrade to the cheaper subscriptions after 2 weeks, if that’s all you wanted.

Selection

I was pleasantly surprised at how good the selection is at Cinema Paradiso. Not only did they have every title that is currently on my LoveFiLM rental list (96 titles), but they also had a few things that LoveFiLM thinks haven’t been released yet.

I’m not going to claim that my tastes are particularly niche, but there are a few foreign language films and some anime in there, and release dates range from the 70s to 2017.

Manual approval

It seems that new Cinema Paradiso signups need to be manually approved, and this happens only on week days between 8am and mid day. I’ve signed up on a Saturday evening so nothing will get sent out until Monday I suppose.

It’s probably not a big deal as we’re talking about the postal service here so even with LoveFiLM nothing would get posted out until Monday anyway. It is a little jarring after moving away from the behemoth that is Amazon though, and serves as a reminder that Cinema Paradiso is a much smaller company.

Searching for titles

The search feature is okay. It provides suggestions as you type but if your title is obscure then it may not appear in the list of suggestions at all you and need to submit the search box and look through the longer list that appears.

A slight niggle is that if you have moused over any of the initial suggestions it replaces your text with that, so if your title isn’t amongst the suggestions you now have to re-type it.

I like that it shows a rating from Rotten Tomatoes as well as from their own site’s users. LoveFiLM shows IMDB ratings which I don’t trust very much, and also Amazon ratings, which I don’t trust at all for movies or TV. Seeing some of the shockingly-low Rotten Tomatoes scores for some of my LoveFiLM titles resulted in my Cinema Paradiso list shrinking to 83 titles!

Rental list mechanics

It’s hard to tell for sure at this stage because I haven’t yet got my account approved and had any rentals, but it looks to me like the rental list mechanics are a bit clunky compared to LoveFiLM’s.

At LoveFiLM at the point of adding a new title you would choose which of the three “buckets” to put a rental in; high priority, normal priority, or low priority. Every title in those buckets were of equal priority to every other item in the same bucket. So, when adding a new title all you had to consider was whether it was high, medium or low.

Cinema Paradiso has a single big list of rentals. In some ways this might appeal because you can fine-tune what order you would like things in. But I would suggest that very few people want to put that much effort into ordering their list. Personally, when I add a new title I can cope with:

  • “I want to see this soon”
  • “I want to see this some time”
  • “I want to see this, but I’m not bothered when”

Cinema Paradiso appears to want me to say:

  • “Put this at the top, I want it immediately!”
  • “This belongs at #11, just after the 6th season of American Horror Story, but before Capitalism: A love Story
  • “Just stick it at the end”

I can’t find any explanation anywhere on their site as to how the selection actually works, so the logical assumption is that they go down your list from top to bottom until they find a title that you want that they have available right now. Without the three buckets to put titles in, it seems to me then that every addition will have to involve some list management unless I either want to see that title really soon, or probably never.

I’ll have to give it a go but this mechanism seems a bit more awkward than LoveFiLM’s approach and needlessly so, because LoveFiLM’s way doesn’t make any promises about which of the titles in each bucket will come next either, nor even that it will be anything from the high priority bucket at all. Although I cannot remember a time when something has come that wasn’t from the high priority bucket.

Cinema Paradiso does let you have more than one list, and you can divide your disc allocation between lists, but I don’t think I could emulate the high/normal/low with that. Having a 2 disc allocation I’d always be getting one disc from the “high” list and one disc from the “normal” priority, which isn’t how I’d want that to work.

Let’s see how it goes.

Referral

I did not know when I signed up that there was a referral scheme which is a shame because I do know some people already using Cinema Paradiso. If you’re going to sign up then please use my referral link. I will get a ⅙ reduction in rental fees for each person that does.

Tricky issues when upgrading to the GoCardless “Pro” API

Background

Since 2012 BitFolk has been using GoCardless as a Direct Debit payment provider. On the whole it has been a pleasant experience:

  • Their API is a pleasure to integrate against, having excellent documentation
  • Their support is responsive and knowledgeable
  • Really good sandbox environment with plenty of testing tools
  • The fees, being 1% capped at £2.00, are pretty good for any kind of payment provider (much less than PayPal, Stripe, etc.)

Of course, if I was submitting Direct Debits myself there would be no charge at all, but BitFolk is too small and my bank (Barclays) are not interested in talking to me about that.

The “Pro” API

In September 2014 GoCardless came out with a new version of their API called the “Pro API”. It made a few things nicer but didn’t come with any real new features applicable to BitFolk, and also added a minimum fee of £0.20.

The original API I’d integrated against has a 1% fee capped at £2.00, and as BitFolk’s smallest plan is £10.79 including VAT the fee would generally be £0.11. Having a £0.20 fee on these payments would represent nearly a doubling of fees for many of my payments.

So, no compelling reason to use the Pro API.

Over the years, GoCardless made more noise about their Pro API and started calling their original API the “legacy API”. I could see the way things were going. Sure enough, eventually they announced that the legacy API would be disabled on 31 October 2017. No choice but to move to the Pro API now.

Payment caps

There aren’t normally any limits on Direct Debit payments. When you let your energy supplier or council or whatever do a Direct Debit, they can empty your bank account if they like.

The Direct Debit Guarantee has very strong provisions in it for protecting the payee and essentially if you dispute anything, any time, you get your money back without question and the supplier has to pursue you for the money by other means if they still think the charge was correct. A company that repeatedly gets Direct Debit chargebacks is going to be kicked off the service by their bank or payment provider.

The original GoCardless API had the ability to set caps on the mandate which would be enforced their side. A simple “X amount per Y time period”. I thought that this would provide some comfort to customers who may not be otherwise familiar with authorising Direct Debits from small companies like BitFolk, so I made use of that feature by default.

This turned out to be a bad decision.

The main problem with this was that there was no way to change the cap. If a customer upgraded their service then I’d have to cancel their Direct Debit mandate and ask them to authorise a new one because it would cease being possible to charge them the correct amount. Authorising a new mandate was not difficult—about the same amount of work as making any sort of online payment—but asking people to do things is always a pain point.

There was a long-standing feature request with GoCardless to implement some sort of “follow this link to authorise the change” feature, but it never happened.

Payment caps and the new API

The Pro API does not support mandates with a capped amount per interval. Given that I’d already established that it was a mistake to do that, I wasn’t too bothered about that.

I’ve since discovered however that the Pro API not only does not support setting the caps, it does not have any way to query them either. This is bad because I need to use the Pro API with mandates that were created in the legacy API. And all of those have caps.

Here’s the flow I had using the legacy API.

Legacy payment process

This way if the charge was coming a little too early, I could give some latitude and let it wait a couple of days until it could be charged. I’d also know if the problem was that the cap was too low. In that case there would be no choice but to cancel the customer’s mandate and ask them to authorise another one, but at least I would know exactly what the problem was.

With the Pro API, there is no way to check timings and charge caps. All I can do is make the charge, and then if it’s too soon or too much I get the same error message:

“Validation failed / exceeds mandate cap”

That’s it. It doesn’t tell me what the cap is, it doesn’t tell me if it’s because I’m charging too soon, nor if I’m charging too much. There is no way to distinguish between those situations.

Backwards compatible – sort of

GoCardless talk about the Pro API being backwards compatible to the legacy API, so that once switched I would still be able to create payments against mandates that were created using the legacy API. I would not need to get customers to re-authorise.

This is true to a point, but my use of caps per interval in the legacy API has severely restricted how compatible things are, and that’s something I wasn’t aware of. Sure, their “Guide to upgrading” does briefly mention that caps would continue to be enforced:

“Pre-authorisation mandates are not restricted, but the maximum amount and interval that you originally specified will still apply.”

That is the only mention of this issue in that entire document, and that statement would be fine by me, if there would have continued to be a way to tell which failure mode would be encountered.

Thinking that I was just misunderstanding, I asked GoCardless support about this. Their reply:

Thanks for emailing.

I’m afraid the limits aren’t exposed within the new API. The only solution as you suggest, is to try a payment and check for failure.

Apologies for the inconvenience caused here and if you have any further queries please don’t hesitate to let us know.

What now?

I am not yet sure of the best way to handle this.

The nuclear option would be to cancel all mandates and ask customers to authorise them again. I would like to avoid this if possible.

I am thinking that most customers continue to be fine on the “amount per interval” legacy mandates as long as they don’t upgrade, so I can leave them as they are until that happens. If they upgrade, or if a DD payment ever fails with “exceeds mandate cap” then I will have to cancel their mandate and ask them to authorise again. I can see if their mandate was created before ~today and advise them on the web site to cancel it and authorise it again.

Conclusion

I’m a little disappointed that GoCardless didn’t think that there would need to be a way to query mandate caps even though creating new mandates with those limits is no longer possible.

I can’t really accept that there is a good level of backwards compatibility here if there is a feature that you can’t even tell is in use until it causes a payment to fail, and even then you can’t tell which details of that feature cause the failure.

I understand why they haven’t just stopped honouring the caps: it wouldn’t be in line with the consumer-focused spirit of the Direct Debit Guarantee to alter things against customer expectations, and even sending out a notification to the customer might not be enough. I think they should have gone the other way and allowed querying of things that they are going to continue to enforce, though.

Could I have tested for this? Well, the difficulty there is that the GoCardless sandbox environment for the Pro API starts off clean with no access to any of your legacy activity neither from live nor from legacy sandbox. So I couldn’t do something like the following:

  1. Create legacy mandate in legacy sandbox, with amount per interval caps
  2. Try to charge against the legacy mandate from the Pro API sandbox, exceeding the cap
  3. Observe that it fails but with no way to tell why

I did note that there didn’t seem to be attributes of the mandate endpoint that would let me know when it could be charged and what the amount left to charge was, but it didn’t set off any alarm bells. Perhaps it should have.

Also I will admit I’ve had years to switch to Pro API and am only doing it now when forced. Perhaps if I had made a start on this years ago, I’d have noted what I consider to be a deficiency, asked them to remedy it and they might have had time to do so. I don’t actually think it’s likely they would bump the API version for that though. In my defence, as I mentioned, there is nothing attractive about the Pro API for my use, and it does cost more, so no surprise I’ve been reluctant to explore it.

So, if you are scrambling to update your GoCardless integration before 31 October, do check that you are prepared for payments against capped mandates to fail.

When is a 64-bit counter not a 64-bit counter?

…when you run a Xen device backend (commonly dom0) on a kernel version earlier than 4.10, e.g. Debian stable.

TL;DR

Xen netback devices used 32-bit counters until that bug was fixed and released in kernel version 4.10.

On a kernel with that bug you will see counter wraps much sooner than you would expect, and if the interface is doing enough traffic for there to be multiple wraps in 5 minutes, your monitoring will no longer be accurate.

The problem

A high-bandwidth VPS customer reported that the bandwidth figures presented by BitFolk’s monitoring bore no resemblance to their own statistics gathered from inside their VPS. Their figures were a lot higher.

About octet counters

The Linux kernel maintains byte/octet counters for its network interfaces. You can view them in /sys/class/net/<interface>/statistics/*_bytes.

They’re a simple count of bytes transferred, and so the count always goes up. Typically these are 64-bit unsigned integers so their maximum value would be 18,446,744,073,709,551,615 (264-1).

When you’re monitoring bandwidth use the monitoring system records the value and the timestamp. The difference in value over a known period allows the monitoring system to work out the rate.

Wrapping

Monitoring of network devices is often done using SNMP. SNMP has 32-bit and 64-bit counters.

The maximum value that can be held in a 32-bit counter is 4,294,967,295. As that is a byte count, that represents 34,359,738,368 bits or 34,359.74 megabits. Divide that by 300 (seconds in 5 minutes) and you get 114.5. Therefore if the average bandwidth is above 114.5Mbit/s for 5 minutes, you will overflow a 32-bit counter. When the counter overflows it wraps back through zero.

Wrapping a counter once is fine. We have to expect that a counter will wrap eventually, and as counters never decrease, if a new value is smaller than the previous one then we know it has wrapped and can still work out what the rate should be.

The problem comes when the counter wraps more than once. There is no way to tell how many times it has wrapped so the monitoring system will have to assume the answer is once. Once traffic reaches ~229Mbit/s the counters will be wrapping at least twice in 5 minutes and the statistics become meaningless.

64-bit counters to the rescue

For that reason, network traffic is normally monitored using 64-bit counters. You would have to have a traffic rate of almost 492 Petabit/s to wrap a 64-bit byte counter in 5 minutes.

The thing is, I was already using 64-bit SNMP counters.

Examining the sysfs files

I decided to remove SNMP from the equation by going to the source of the data that SNMP uses: the kernel on the device being monitored.

As mentioned, the kernel’s interface byte counters are exposed in sysfs at /sys/class/net/<interface>/statistics/*_bytes. I dumped out those values every 10 seconds and watched them scroll in a terminal session.

What I observed was that these counters, for that particular customer, were wrapping every couple of minutes. I never observed a value greater than 8,469,862,875. That’s larger than a 32-bit counter would hold, but very close to what a 33 bit counter would hold (8,589,934,591).

64-bit counters not to the rescue

Once I realised that the kernel’s own counters were wrapping every couple of minutes inside the kernel it became clear that using 64-bit counters in SNMP was not going to help at all, and multiple wraps would be seen in 5 minutes.

What a difference a minute makes

To test the hypothesis I switched to 1-minute polling. Here’s what 12 hours of real data looks like under both 5- and 1-minute polling.

As you can see that is a pretty dramatic difference.

The bug

By this point, I’d realised that there must be a bug in Xen’s netback driver (the thing that makes virtual network interfaces in dom0).

I went searching through the source of the kernel and found that the counters had changed from an unsigned long in kernel version 4.9 to a u64 in kernel version 4.10.

Of course, once I knew what to search for it was easy to unearth a previous bug report. If I’d found that at the time of the initial report that would have saved 2 days of investigation!

Even so, the fix for this was only committed in February of this year so, unfortunately, is not present in the kernel in use by the current Debian stable. Nor in many other current distributions.

For Xen set-ups on Debian the bug could be avoided by using a backports kernel or packaging an upstream kernel.

Or you could do 1-minute polling as that would only wrap one time at an average bandwidth of ~572Mbit/s and should be safe from multiple wraps up to ~1.1Gbit/s.

Inside the VPS the counters are 64-bit so it isn’t an issue for guest administrators.

A slightly more realistic look at lvmcache

Recap

And then…

I decided to perform some slightly more realistic benchmarks against lvmcache.

The problem with the initial benchmark was that it only covered 4GiB of data with a 4GiB cache device. Naturally once lvmcache was working correctly its performance was awesome – the entire dataset was in the cache. But clearly if you have enough fast block device available to fit all your data then you don’t need to cache it at all and may as well just use the fast device directly.

I decided to perform some fio tests with varying data sizes, some of which were larger than the cache device.

Test methodology

Once again I used a Zipf distribution with a factor of 1.2, which should have caused about 90% of the hits to come from just 10% of the data. I kept the cache device at 4GiB but varied the data size. The following data sizes were tested:

  • 1GiB
  • 2GiB
  • 4GiB
  • 8GiB
  • 16GiB
  • 32GiB
  • 48GiB

With the 48GiB test I expected to see lvmcache struggling, as the hot 10% (~4.8GiB) would no longer fit within the 4GiB cache device.

A similar fio job spec to those from the earlier articles was used:

[cachesize-1g]
size=512m
ioengine=libaio
direct=1
iodepth=8
numjobs=2
readwrite=randread
random_distribution=zipf:1.2
bs=4k
unlink=1
runtime=30m
time_based=1
per_job_logs=1
log_avg_msec=500
write_iops_log=/var/tmp/fio-${FIOTEST}

…the only difference being that several different job files were used each with a different size= directive. Note that as there are two jobs, the size= is half the desired total data size: each job lays out a data file of the specified size.

For each data size I took care to fill the cache with data first before doing a test run, as unreproducible performance is still seen against a completely empty cache device. This produced IOPS logs and a completion latency histogram. Test were also run against SSD and HDD to provide baseline figures.

Results

IOPS graphs

All-in-one

Immediately we can see that for data sizes 4GiB and below performance converges quite quickly to near-SSD levels. That is very much what we would expect when the cache device is 4GiB, so big enough to completely cache everything.

Let’s just have a look at the lower-performing configurations.

Low-end performers

For 8, 16 and 32GiB data sizes performance clearly gets progressively worse, but it is still much better than baseline HDD. The 10% of hot data still fits within the cache device, so plenty of acceleration is still happening.

For the 48GiB data size it is a little bit of a different story. Performance is still better (on average) than baseline HDD, but there are periodic dips back down to roughly HDD figures. This is because not all of the 10% hot data fits into the cache device any more. Cache misses cause reads from HDD and consequently end up with HDD levels of performance for those reads.

The results no longer look quite so impressive, with even the 8GiB data set achieving only a few thousand IOPS on average. Are things as bad as they seem? Well no, I don’t think they are, and to see why we will have to look at the completion latency histograms.

Completion latency histograms

The above graphs are generated by fitting a Bezier curve to a scatter of data points each of which represents a 500ms average of IOPS achieved. The problem there is the word average.

It’s important to understand what effect averaging the figures gives. We’ve already seen that HDDs are really slow. Even if only a few percent of IOs end up missing cache and going to HDD, the massive latency of those requests will pull the average for the whole 500ms window way down.

Presumably we have a cache because we suspect we have hot spots of data, and we’ve been trying to evaluate that by doing most of the reads from only 10% of the data. Do we care what the average performance is then? Well it’s a useful metric but it’s not going to say much about the performance of reads from the hot data.

The histogram of completion latencies can be more useful. This shows how long it took between issuing the IO and completing the read for a certain percentage of issued IOs. Below I have focused on the 50% to 99% latency buckets, with the times for each bucket averaged between the two jobs. In the interests of being able to see anything at all I’ve had to double the height of the graph and still cut off the y axis for the three worst performers.

A couple of observations:

  • Somewhere between 70% and 80% of IOs complete with a latency that’s so close to SSD performance as to be near-indistinguishable, no matter what the data size. So what I think I am proving is that:

    you can cache a 48GiB slow backing device with 4GiB of fast SSD and if you have 10% hot data then you can expect it to be served up at near-SSD latencies 70%–80% of the time. If your hot spots are larger (not so hot) then you won’t achieve that. If your fast device is larger than 1/12th the backing device then you should do better than 70%–80%.

  • If the cache were perfect then we should expect the 90th percentile to be near SSD performance even for the 32GiB data set, as the 10% hot spot of ~3.2GiB fits inside the 4GiB cache. For whatever reason this is not achieved, but for that data size the 90th percentile latency is still about half that of HDD.
  • When the backing device is many times larger (32GiB+) than the cache device, the 99th percentile latencies can be slightly worse than for baseline HDD.

    I hesitate to suggest there is a problem here as there are going to be very few samples in the top 1%, so it could just be showing close to HDD performance.

Conclusion

Assuming you are okay with using a 4.12..x kernel, and assuming you are already comfortable using LVM, then at the moment it looks fairly harmless to deploy lvmcache.

Getting a decent performance boost out of it though will require you to check that your data really does have hot spots and size your cache appropriately.

Measuring your existing workload with something like blktrace is probably advisable, and these days you can feed the output of blktrace back into fio to see what performance might be like in a difference configuration.

Full test output

You probably want to stop reading here unless the complete output of all the fio runs is of interest to you.
Continue reading “A slightly more realistic look at lvmcache”

Tracking down the lvmcache fix

Background

In the previous article I covered how, in order to get decent performance out of lvmcache with a packaged Debian kernel, you’d have to use the 4.12.2-1~exp1 kernel from experimental. The kernels packaged in sid, testing (buster) and stable (stretch) aren’t new enough.

I decided to bisect the Linux kernel upstream git repository to find out exactly which commit(s) fixed things.

Results

Here’s a graph showing the IOPS over time for baseline SSD and lvmcache with a full cache under several different kernel versions. As in previous articles, the lines are actually Bezier curves fitted to the data which is scattered all over the place from 500ms averages.

What we can see here is that performance starts to improve with commit 4d44ec5ab751 authored by Joe Thornber:

dm cache policy smq: put newly promoted entries at the top of the multiqueue

This stops entries bouncing in and out of the cache quickly.

This is part of a set of commits authored by Joe Thornber on the drivers/md/dm-cache-policy-smq.c file and committed on 2017-05-14. By the time we reach commit 6cf4cc8f8b3b we have the long-term good performance that we were looking for.

The first of Joe Thornber’s commits on that day in the dm-cache area was 072792dcdfc8 and stepping back to the commit immediately prior to that one (2ea659a9ef48) we get a kernel representing the moment that Linus designated the v4.12-rc1 tag. Joe’s commits went into -rc1, and without them the performance of lvmcache under these test conditions isn’t much better than baseline HDD.

It seems like some of Joe’s changes helped a lot and then the last one really provided the long term performance.

git bisect procedure

Normally when you do a git bisect you’re starting with something that works and you’re looking for the commit that introduced a bug. In this case I was starting off with a known-good state and was interested in which commit(s) got me there. The normal bisect key words of “good” and “bad” in this case would be backwards to what I wanted. Dominic gave me the tip that I could alias the terms in order to reduce my confusion:

$ git bisect start --term-old broken --term-new fixed

From here on, when I encountered a test run that produced poor results I would issue:

$ git bisect broken

and when I had a test run with good results I would issue:

$ git bisect fixed

As I knew that the tag v4.13-rc1 produced a good run and v4.11 was bad, I could start off with:

$ git bisect reset v4.13-rc1
$ git bisect fixed
$ git bisect broken v4.11

git would then keep bisecting the search space of commits until I would find the one(s) that resulted in the high performance I was looking for.

Good and bad?

As before I’m using fio to conduct the testing, with the same job specification:

ioengine=libaio
direct=1
iodepth=8
numjobs=2
readwrite=randread
random_distribution=zipf:1.2
bs=4k
size=2g
unlink=1
runtime=15m
time_based=1
per_job_logs=1
log_avg_msec=500
write_iops_log=/var/tmp/fio-${FIOTEST}

The only difference from the other articles was that the run time was reduced to 15 minutes as all of the interesting behaviour happened within the first 11 minutes.

To recap, this fio job specification lays out two 2GiB files of random data and then starts two processes that perform 4kiB-sized reads against the files. Direct IO is used, in order to bypass the page cache.

A Zipfian distribution with a factor of 1.2 is used; this gives a 90/10 split where about 90% of the reads should come from about 10% of the data. The purpose of this is to simulate the hot spots of popular data that occur in real life. If the access pattern were to be perfectly and uniformly random then caching would not be effective.

In previous tests we had observed that dramatically different performance would be seen on the first run against an empty cache device compared to all other subsequent runs against what would be a full cache device. In the tests using kernels with the fix present the IOPS achieved would converge towards baseline SSD performance, whereas in kernels without the fix the performance would remain down near the level of baseline HDD. Therefore the fio tests were carried out twice.

Where to next?

I think I am going to see what happens when the cache device is pretty small in comparison to the working data.

All of the tests so far have used a 4GiB cache with 4GiB of data, so if everything got promoted it would entirely fit in cache. Not only that but the Zipf distribution makes most of the hits come from 10% of the data, so it’s actually just ~400MiB of hot data. I think it would be interesting to see what happens when the hot 10% is bigger than the cache device.

git bisect progress and test output

Unless you are particularly interested in the fio output and why I considered each one to be either fixed or broken, you probably want to stop reading now.

Continue reading “Tracking down the lvmcache fix”

lvmcache with a 4.12.3 kernel

Background

In the previous two articles I had discovered that lvmcache had amazing performance on an empty cache but then on every run after that (i.e. when the cache device was full of junk) went scarcely better than baseline HDD.

A few days ago I happened across an email on the linux-lvm list where Mike Snitzer advised:

the [CentOS] 7.4 dm-cache will be much more performant than the 7.3 cache you appear to be using.

…and…

It could be that your workload isn’t accessing the data enough to warrant promotion to the cache. dm-cache is a “hotspot” cache. If you aren’t accessing the data repeatedly then you won’t see much benefit (particularly with the 7.3 and earlier releases).

Just to get a feel, you could try the latest upstream 4.12 kernel to see how effective the 7.4 dm-cache will be for your setup.

I don’t know what kernel version CentOS 7.3 uses, but the VM I’m testing with is Debian testing (buster), so some version of 4.11.x plus backported patches.

That seemed pretty new, but Mike is suggesting 4.12.x so I thought I’d re-test lvmcache with the latest stable upstream kernel, which at the time of writing is version 4.12.3.

Test methodology

This time around I only focused on fio tests, using the same settings as before:

[partial]
ioengine=libaio
direct=1
iodepth=8
numjobs=2
readwrite=randread
random_distribution=zipf:1.2
bs=4k
size=2g
unlink=1
runtime=20m
time_based=1
per_job_logs=1
log_avg_msec=500
write_iops_log=/var/tmp/fio-${FIOTEST}

The only changes were:

  1. to reduce the run time to 20 minutes from 30 minutes, since all the interesting things happened within the first 20 minutes before.
  2. to write an IOPS log entry every 500ms instead of ever 1000ms, as the log files were quite small and some higher resolution might help smooth graphs out.

Last time there was a dramatic difference between the initial run with an empty cache and subsequent runs with a cache volume full of junk, so I did a test for each of those conditions, as well as tests for the baseline SSD and HDD.

The virtual machine had been upgraded from Debian 9 (stretch) to testing (buster), so it still had packaged kernel versions 4.9.30-2 and 4.11.6-1 laying around to test things with. In addition I compiled up version 4.12.3 by copying the .config from 4.11.6-1 then doing make oldconfig accepting all defaults.

Results

Although the fio job spec was essentially the same as in the previous article, I have since worked out how to merge the IOPS logs from both jobs so the graphs will seem to show about double the IOPS as they did before.

All-in-one

Well that’s an interesting set of graphs but rather hard to distinguish. Let’s try that by kernel version.

Baseline SSD by kernel version

A couple of weird things here:

  1. 4.12.3 and 4.11.6-1 are actually fairly consistent, but 4.9.30-2 varies rather a lot.
  2. All kernels show a sharp dip a few minutes in. I don’t know what that is about.

Although these lines do look quite far apart, bear in mind that this graph’s y axis starts at 92k IOPS. The average IOPS didn’t vary that much:

Average IOPS by kernel version
4.9.30-2 4.11.6-1 4.12.3
102,325 102,742 104,352

So there was actually only a 1.9% difference between the worst performer and the best.

Baseline HDD by kernel version

4.9.30-2 and 4.12.3 are close enough here to probably be within the margin of error, but there is something weird going on with 4.11.6-1.

Its average IOPS across the 20 minute test were only 56% of those for 4.12.3 and 53% of those for 4.9.30-2, which is quite a big disparity. I re-ran these tests 5 times to check it wasn’t some anomaly, but no, it’s reproducible.

Maybe something to look into another day.

lvmcache by kernel version

Dragging things back to the point of this article: previously we discovered that lvmcache worked great the first time through, when its cache volume was completely empty, but then subsequent runs all absolutely sucked. They didn’t perform significantly better than HDD baseline.

Let’s graph all the lvmcache results for each kernel version against the SSD baseline for that kernel to see if things changed at all.

lvmcache 4.9.30-2

This is the similar to what we saw before: an empty cache volume produces decent results of around 47k IOPS. Although it’s interesting that the second job is down around 1k IOPS. Again the results on a full cache are poor. In fact the results for the second job of the empty cache are about the same as the results for both jobs on a full cache.

lvmcache 4.11.6-1

Same story again here, although the performance is a little higher. Again the first job on an empty cache is getting the big results of almost 60k IOPS while the second job—and both jobs on a full cache—get only around 1k IOPS.

lvmcache 4.12.3

Wow. Something dramatic has been fixed. The performance on an empty cache is still better, but crucially the performance on a full cache pretty quickly becomes very close to baseline SSD.

Also the runs against both the empty and full cache device result in both jobs getting roughly the same IOPS performance rather than the first job being great and all others very poor.

What’s next?

It’s really encouraging that the performance is so much better with 4.12.3. It’s changed lvmcache from a “hmm, maybe” option to one that I would strongly consider using anywhere I could.

It’s a shame though that such a new kernel is required. The kernel version in Debian testing (buster) is currently 4.11.6-1. Debian experimental’s linux-image-4.12.0-trunk-amd64 package currently has version 4.12.2-1 so I should test if that is new enough I tested to see if that was new enough.

Failing that I think I should git bisect or similar in order to find out exactly which changeset fixed this, so I could have some chance of knowing when it hits a packaged version.

Continue reading “lvmcache with a 4.12.3 kernel”

12 hours of lvmcache

In the previous post I noted that the performance of lvmcache was still increasing and it might be worth testing it for longer than 3 hours.

Here’a a 12 hour test

$ cd /srv/cache/fio && FIOTEST=lvmcache-12h fio ~/lvmcache-12h.fio
partial: (g=0): rw=randread, bs=4K-4K/4K-4K/4K-4K, ioengine=libaio, iodepth=8
...
fio-2.16
Starting 2 processes
partial: Laying out IO file(s) (1 file(s) / 2048MB)
partial: Laying out IO file(s) (1 file(s) / 2048MB)
Jobs: 2 (f=2): [r(2)] [100.0% done] [6272KB/0KB/0KB /s] [1568/0/0 iops] [eta 00m:00s]
partial: (groupid=0, jobs=1): err= 0: pid=11130: Fri Jul 21 16:37:30 2017
  read : io=136145MB, bw=3227.2KB/s, iops=806, runt=43200062msec
    slat (usec): min=3, max=586402, avg=14.27, stdev=619.54
    clat (usec): min=2, max=1517.9K, avg=9897.80, stdev=29334.14
     lat (usec): min=71, max=1517.9K, avg=9912.72, stdev=29344.74
    clat percentiles (usec):
     |  1.00th=[  103],  5.00th=[  110], 10.00th=[  113], 20.00th=[  119],
     | 30.00th=[  124], 40.00th=[  129], 50.00th=[  133], 60.00th=[  143],
     | 70.00th=[  157], 80.00th=[11840], 90.00th=[30848], 95.00th=[56576],
     | 99.00th=[136192], 99.50th=[179200], 99.90th=[309248], 99.95th=[382976],
     | 99.99th=[577536]
    lat (usec) : 4=0.01%, 10=0.01%, 20=0.01%, 50=0.01%, 100=0.03%
    lat (usec) : 250=76.84%, 500=0.26%, 750=0.13%, 1000=0.13%
    lat (msec) : 2=0.25%, 4=0.02%, 10=1.19%, 20=6.67%, 50=8.59%
    lat (msec) : 100=3.93%, 250=1.77%, 500=0.18%, 750=0.02%, 1000=0.01%
    lat (msec) : 2000=0.01%
  cpu          : usr=0.44%, sys=1.63%, ctx=34524570, majf=0, minf=17
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=100.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.1%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=34853153/w=0/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
     latency   : target=0, window=0, percentile=100.00%, depth=8
partial: (groupid=0, jobs=1): err= 0: pid=11131: Fri Jul 21 16:37:30 2017
  read : io=134521MB, bw=3188.7KB/s, iops=797, runt=43200050msec
    slat (usec): min=3, max=588479, avg=14.35, stdev=613.38
    clat (usec): min=2, max=1530.3K, avg=10017.42, stdev=29196.28
     lat (usec): min=70, max=1530.3K, avg=10032.43, stdev=29207.06
    clat percentiles (usec):
     |  1.00th=[  103],  5.00th=[  109], 10.00th=[  112], 20.00th=[  118],
     | 30.00th=[  124], 40.00th=[  127], 50.00th=[  133], 60.00th=[  143],
     | 70.00th=[  157], 80.00th=[12352], 90.00th=[31360], 95.00th=[57600],
     | 99.00th=[138240], 99.50th=[179200], 99.90th=[301056], 99.95th=[370688],
     | 99.99th=[561152]
    lat (usec) : 4=0.01%, 20=0.01%, 50=0.01%, 100=0.04%, 250=76.56%
    lat (usec) : 500=0.26%, 750=0.12%, 1000=0.13%
    lat (msec) : 2=0.26%, 4=0.02%, 10=1.20%, 20=6.75%, 50=8.65%
    lat (msec) : 100=4.01%, 250=1.82%, 500=0.17%, 750=0.01%, 1000=0.01%
    lat (msec) : 2000=0.01%
  cpu          : usr=0.45%, sys=1.60%, ctx=34118324, majf=0, minf=15
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=100.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.1%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=34437257/w=0/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
     latency   : target=0, window=0, percentile=100.00%, depth=8
 
Run status group 0 (all jobs):
   READ: io=270666MB, aggrb=6415KB/s, minb=3188KB/s, maxb=3227KB/s, mint=43200050msec, maxt=43200062msec
 
Disk stats (read/write):
    dm-2: ios=69290239/1883, merge=0/0, ticks=690078104/246240, in_queue=690329868, util=100.00%, aggrios=23098543/27863, aggrmerge=0/0, aggrticks=229728200/637782, aggrin_queue=230366965, aggrutil=100.00%
    dm-1: ios=247/64985, merge=0/0, ticks=36/15464, in_queue=15504, util=0.02%, aggrios=53025553/63449, aggrmerge=0/7939, aggrticks=7413340/14760, aggrin_queue=7427028, aggrutil=16.42%
  xvdc: ios=53025553/63449, merge=0/7939, ticks=7413340/14760, in_queue=7427028, util=16.42%
  dm-0: ios=53025306/6403, merge=0/0, ticks=7417028/1852, in_queue=7419784, util=16.42%
    dm-3: ios=16270078/12201, merge=0/0, ticks=681767536/1896032, in_queue=683665608, util=100.00%, aggrios=16270077/12200, aggrmerge=1/1, aggrticks=681637744/1813744, aggrin_queue=683453224, aggrutil=100.00%
  xvdd: ios=16270077/12200, merge=1/1, ticks=681637744/1813744, in_queue=683453224, util=100.00%

It’s still going up, slowly. The cache hit rate was 76.53%. In the 30 minute test the hit rate was 73.64%.

Over 30 minutes the average IOPS was 1,484.

Over 12 hours the average IOPS was 1,603.

I was kind of hoping to reach the point where the hit rate is so high that it just takes off like bcache does and we start to see tens of thousands of IOPS, but it wasn’t to be.

24 hours of lvmcache

…so I went ahead and ran the same thing for 24 hours.

I’ve skipped the first 2 hours of results since we know what they look like. It appears to still be going up, although the results past 20 hours leave some doubt there.

Here’s the full fio output.

$ cd /srv/cache/fio && FIOTEST=lvmcache-24h fio ~/lvmcache-24h.fio
partial: (g=0): rw=randread, bs=4K-4K/4K-4K/4K-4K, ioengine=libaio, iodepth=8
...
fio-2.16
Starting 2 processes
partial: Laying out IO file(s) (1 file(s) / 2048MB)
partial: Laying out IO file(s) (1 file(s) / 2048MB)
Jobs: 2 (f=2): [r(2)] [100.0% done] [7152KB/0KB/0KB /s] [1788/0/0 iops] [eta 00m:00s]
partial: (groupid=0, jobs=1): err= 0: pid=14676: Sat Jul 22 21:34:12 2017
  read : io=278655MB, bw=3302.6KB/s, iops=825, runt=86400091msec
    slat (usec): min=3, max=326, avg=12.43, stdev= 6.97
    clat (usec): min=1, max=1524.1K, avg=9673.02, stdev=28748.45
     lat (usec): min=71, max=1525.7K, avg=9686.11, stdev=28748.87
    clat percentiles (usec):
     |  1.00th=[  103],  5.00th=[  106], 10.00th=[  111], 20.00th=[  116],
     | 30.00th=[  119], 40.00th=[  125], 50.00th=[  131], 60.00th=[  139],
     | 70.00th=[  155], 80.00th=[11456], 90.00th=[30336], 95.00th=[55552],
     | 99.00th=[134144], 99.50th=[177152], 99.90th=[305152], 99.95th=[374784],
     | 99.99th=[569344]
    lat (usec) : 2=0.01%, 4=0.01%, 10=0.01%, 20=0.01%, 50=0.01%
    lat (usec) : 100=0.03%, 250=77.16%, 500=0.22%, 750=0.12%, 1000=0.12%
    lat (msec) : 2=0.23%, 4=0.02%, 10=1.18%, 20=6.65%, 50=8.54%
    lat (msec) : 100=3.84%, 250=1.70%, 500=0.17%, 750=0.01%, 1000=0.01%
    lat (msec) : 2000=0.01%
  cpu          : usr=0.47%, sys=1.64%, ctx=70653446, majf=0, minf=17
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=100.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.1%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=71335660/w=0/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
     latency   : target=0, window=0, percentile=100.00%, depth=8
partial: (groupid=0, jobs=1): err= 0: pid=14677: Sat Jul 22 21:34:12 2017
  read : io=283280MB, bw=3357.4KB/s, iops=839, runt=86400074msec
    slat (usec): min=3, max=330, avg=12.44, stdev= 6.98
    clat (usec): min=2, max=1515.9K, avg=9514.83, stdev=28128.86
     lat (usec): min=71, max=1515.2K, avg=9527.92, stdev=28129.29
    clat percentiles (usec):
     |  1.00th=[  103],  5.00th=[  109], 10.00th=[  112], 20.00th=[  118],
     | 30.00th=[  123], 40.00th=[  126], 50.00th=[  133], 60.00th=[  141],
     | 70.00th=[  157], 80.00th=[11328], 90.00th=[29824], 95.00th=[55040],
     | 99.00th=[132096], 99.50th=[173056], 99.90th=[292864], 99.95th=[362496],
     | 99.99th=[544768]
    lat (usec) : 4=0.01%, 10=0.01%, 20=0.01%, 50=0.01%, 100=0.03%
    lat (usec) : 250=77.29%, 500=0.23%, 750=0.11%, 1000=0.12%
    lat (msec) : 2=0.23%, 4=0.02%, 10=1.18%, 20=6.65%, 50=8.49%
    lat (msec) : 100=3.81%, 250=1.66%, 500=0.15%, 750=0.01%, 1000=0.01%
    lat (msec) : 2000=0.01%
  cpu          : usr=0.47%, sys=1.67%, ctx=71794214, majf=0, minf=15
  IO depths    : 1=0.1%, 2=0.1%, 4=0.1%, 8=100.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.1%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued    : total=r=72519640/w=0/d=0, short=r=0/w=0/d=0, drop=r=0/w=0/d=0
     latency   : target=0, window=0, percentile=100.00%, depth=8
 
Run status group 0 (all jobs):
   READ: io=561935MB, aggrb=6659KB/s, minb=3302KB/s, maxb=3357KB/s, mint=86400074msec, maxt=86400091msec
 
Disk stats (read/write):
    dm-2: ios=143855123/29, merge=0/0, ticks=1380761492/1976, in_queue=1380772508, util=100.00%, aggrios=47953627/25157, aggrmerge=0/0, aggrticks=459927326/7329, aggrin_queue=459937080, aggrutil=100.00%
    dm-1: ios=314/70172, merge=0/0, ticks=40/15968, in_queue=16008, util=0.01%, aggrios=110839338/72760, aggrmerge=0/2691, aggrticks=15300392/17100, aggrin_queue=15315432, aggrutil=16.92%
  xvdc: ios=110839338/72760, merge=0/2691, ticks=15300392/17100, in_queue=15315432, util=16.92%
  dm-0: ios=110839024/5279, merge=0/0, ticks=15308540/1768, in_queue=15312588, util=16.93%
    dm-3: ios=33021544/20, merge=0/0, ticks=1364473400/4252, in_queue=1364482644, util=100.00%, aggrios=33021544/19, aggrmerge=0/1, aggrticks=1364468920/4076, aggrin_queue=1364476064, aggrutil=100.00%
  xvdd: ios=33021544/19, merge=0/1, ticks=1364468920/4076, in_queue=1364476064, util=100.00%

So, 1,664 average IOPS (825 + 839), 77.05% (110,839,338 / (71,335,660 + 72,519,640)*100) cache hit rate.

Not sure I can be bothered to run a multi-day test on this now!

bcache and lvmcache

Background

Over at BitFolk we offer both SSD-backed storage and HDD-backed archive storage. The SSDs we use are really nice and basically have removed all IO performance problems we have ever encountered in the past. I can’t begin to describe how pleasant it is to just never have to think about that whole class of problems.

The main downside of course is that SSD capacity is still really expensive. That’s why we introduced the HDD-backed archive storage: for bulk storage of things that didn’t need to have high performance.

You’d really think though that by now there would be some kind of commodity tiered storage that would allow a relatively small amount of fast SSD storage to accelerate a much larger pool of HDDs. Of course there are various enterprise solutions, and there is also ZFS where SSDs could be used for the ZIL and L2ARC while HDDs are used for the pool.

ZFS is quite appealing but I’m not ready to switch everything to that yet, and I’m certainly not going to buy some enterprise storage solution. I also don’t necessarily want to commit to putting all storage behind such a system.

I decided to explore Linux’s own block device caching solutions.

Scope

I’ve restricted the scope to the two solutions which are part of the mainline Linux kernel as of July 2017, these being bcache and lvmcache.

lvmcache is based upon dm-cache which has been included with the mainline kernel since April 2013. It’s quite conservative, and having been around for quite a while is considered stable. It has the advantage that it can work with any LVM logical volume no matter what the contents. That brings the disadvantage that you do need to run LVM.

bcache has been around for a little longer but is a much more ambitious project. Being completely dedicated to accelerating slow block devices with fast ones it is claimed to be able to achieve higher performance than other caching solutions, but as it’s much more complicated than dm-cache there are still bugs being found. Also it requires you format your block devices as bcache before you use them for anything.

Test environment

I’m testing this on a Debian testing (buster) Xen virtual machine with a 20GiB xvda virtual disk containing the main operating system. That disk is backed by a software (md) RAID-10 composed of two Samsung sm863 SSDs. It was also used for testing the baseline SSD performance from the directory /srv/ssd.

The virtual machine had 1GiB of memory but the pagecache was cleared between each test run in an attempt to prevent anything being cached in memory.

A 5GiB xvdc virtual disk was provided, backed again on the SSD RAID. This was used for the cache role both in bcache and lvmcache.

A 50GiB xvdd virtual disk was provided, backed by a pair of Seagate ST4000LM016-1N2170 HDDs in software RAID-1. This was used for the HDD backing store in each of the caching implementations. The resulting cache device was mounted at /srv/cache.

Finally a 50GiB xvde virtual disk also backed on HDD was used to test baseline HDD performance, mounted at /srv/slow.

The filesystem in use in all cases was ext4 with default options. In dom0, deadline scheduler was used in all cases.

TL;DR, I just want graphs

In case you can’t be bothered to read the rest of this article, here’s just the graphs with some attempt at interpreting them. Down at the tests section you’ll find details of the actual testing process and more commentary on why certain graphs were produced.

git test graphs

Times to git clone and git grep.

fio IOPS graphs

These are graphs of IOPS across the 30 minutes of testing. There’s two important things to note about these graphs:

  1. They’re a Bezier curve fitted to the data points which are one per second. The actual data points are all over the place, because achieved IOPS depends on how many cache hits/misses there were, which is statistical.
  2. Only the IOPS for the first job is graphed. Even when using the per_job_logs=0 setting my copy of fio writes a set of results for each job. I couldn’t work out how to easily combine these so I’ve shown only the results for the first job.

    For all tests except bcache (sequential_cutoff=0) you just have to bear in mind that there is a second job working in parallel doing pretty much the same amount of IOPS. Strangely for that second bcache test the second job only managed a fraction of the IOPS (though still more than 10k IOPS) and I don’t know why.

IOPS over time for all tests

Well, those results are so extreme that it kind of makes it hard to distinguish between the low-end results.

A couple of observations:

  • SSD is incredibly and consistently fast.
  • For everything else there is a short steep section at the beginning which is likely to be the effect of HDD drive cache.
  • With sequential_cutoff set to 0, bcache very quickly reaches near-SSD performance for this workload (4k reads, 90% hitting 10% of data that fits entirely in the bcache). This is probably because the initial write put data straight into cache as it’s set to writeback.
  • When starting with a completely empty cache, lvmcache is no slouch either. It’s not quite as performant as bcache but that is still up near the 48k IOPS per process region, and very predictable.
  • When sequential_cutoff is left at its default of 4M, bcache performs much worse though still blazing compared to an HDD on its own. At the end of this 30 minute test performance was still increasing so it might be worth performing a longer test
  • The performance of lvmcache when starting with a cache already full of junk data seems to be not that much better than HDD baseline.
IOPS over time for low-end results

Leaving the high-performers out to see if there is anything interesting going on near the bottom of the previous graph.

Apart from the initial spike, HDD results are flat as expected.

Although the lvmcache (full cache) results in the previous graph seemed flat too, looking closer we can see that performance is still increasing, just very slowly. It may be interesting to test for longer to see if performance does continue to increase.

Both HDD and lvmcache have a very similar spike at the start of the test so let’s look closer at that.

IOPS for first 30 seconds

For all the lower-end performers the first 19 seconds are steeper and I can only think this is the effect of HDD drive cache. Once that is filled, HDD remains basically flat, lvmcache (full cache) increases performance more slowly and bcache with the default sequential_cutoff starts to take off.

SSDs don’t have the same sort of cache and bcache with no sequential_cutoff spikes up too quickly to really be noticeable at this scale.

3-hour lvmcache test

Since it seemed like lvmcache with a full cache device was still slowly increasing in performance I did a 3-hour testing on that one.

Skipping the first 20 minutes which show stronger growth, even after 3 hours there is still some performance increase happening. It seems like even a full cache would eventually promote read hot spots, but it could take a very very long time.

Continue reading “bcache and lvmcache”