Re: [PATCH] bcache: consider the fragmentation when update the writeback rate
From: Coly Li
Date: Thu Jan 07 2021 - 23:06:47 EST
On 1/7/21 10:55 PM, Dongdong Tao wrote:
> Hi Coly,
> Thanks for the reminder, I understand that the rate is only a hint of
> the throughput, it’s a value to calculate the sleep time between each
> round of keys writeback, the higher the rate, the shorter the sleep
> time, most of the time this means the more dirty keys it can writeback
> in a certain amount of time before the hard disk running out of speed.
> Here is the testing data that run on a 400GB NVME + 1TB NVME HDD
Nice charts :-)
> make-bcache -B <HDD> -C <NVME> --writeback
> sudo fio --name=random-writers --filename=/dev/bcache0
> --ioengine=libaio --iodepth=1 --rw=randrw --blocksize=64k,8k
> --direct=1 --numjobs=1 --write_lat_log=mix --log_avg_msec=10
> > The fio benchmark commands ran for about 20 hours.
The time lengths of first 3 charts are 7.000e+7, rested are 1.60930e+9.
I guess the time length of the I/O latency chart is 1/100 of the rested.
Can you also post the latency charts for 1.60930e+9 seconds? Then I can
compare the latency with dirty data and available cache charts.
> Let’s have a look at the write latency first:
> Master+the patch:
> Combine them together:
> Again, the latency (y-axis) is based on nano-second, x-axis is the
> timestamp based on milli-second, as we can see the master latency is
> obviously much higher than the one with my patch when the master bcache
> hit the cutoff writeback sync, the master isn’t going to get out of this
> cutoff writeback sync situation, This graph showed it already stuck at
> the cutoff writeback sync for about 4 hours before I finish the testing,
> it may still needs to stuck for days before it can get out this
> situation itself.
> Note that there are 1 million points for each , red represents master,
> green represents mater+my patch. Most of them are overlapped with each
> other, so it may look like this graph has more red points then green
> after it hitting the cutoff, but simply it’s because the latency has
> scaled to a bigger range which represents the HDD latency.
> Let’s also have a look at the bcache’s cache available percent and dirty
> data percent.
> Master+this patch:
> As you can see, this patch can avoid it hitting the cutoff writeback sync.
> As to say the improvement for this patch against the first one, let’s
> take a look at the writeback rate changing during the run.
> patch V1:
> Patch V2:
> The Y-axis is the value of rate, the V1 is very aggressive as it jumps
> instantly from a minimum 8 to around 10 million. And the patch V2 can
> control the rate under 5000 during the run, and after the first round of
> writeback, it can stay even under 2500, so this proves we don’t need to
> be as aggressive as V1 to get out of the high fragment situation which
> eventually causes all writes hitting the backing device. This looks very
> reasonable for me now.
> Note that the fio command that I used is consuming the bucket quite
> aggressively, so it had to hit the third stage which has the highest
> aggressiveness, but I believe this is not true in a real production env,
> real production env won’t consume buckets that aggressively, so I expect
> stage 3 may not very often be needed to hit.
> As discussed, I'll run multiple block size testing on at least 1TB NVME
> device later.
> But it might take some time.
> On Tue, Jan 5, 2021 at 12:33 PM Coly Li <colyli@xxxxxxx
> <mailto:colyli@xxxxxxx>> wrote:
> On 1/5/21 11:44 AM, Dongdong Tao wrote:
> > Hey Coly,
> > This is the second version of the patch, please allow me to explain a
> > bit for this patch:
> > We accelerate the rate in 3 stages with different aggressiveness, the
> > first stage starts when dirty buckets percent reach above
> > BCH_WRITEBACK_FRAGMENT_THRESHOLD_LOW(50), the second is
> > BCH_WRITEBACK_FRAGMENT_THRESHOLD_MID(57) and the third is
> > BCH_WRITEBACK_FRAGMENT_THRESHOLD_HIGH(64). By default the first stage
> > tries to writeback the amount of dirty data in one bucket (on average)
> > in (1 / (dirty_buckets_percent - 50)) second, the second stage
> tries to
> > writeback the amount of dirty data in one bucket in (1 /
> > (dirty_buckets_percent - 57)) * 200 millisecond. The third stage tries
> > to writeback the amount of dirty data in one bucket in (1 /
> > (dirty_buckets_percent - 64)) * 20 millisecond.
> > As we can see, there are two writeback aggressiveness increasing
> > strategies, one strategy is with the increasing of the stage, the
> > stage is the easy-going phase whose initial rate is trying to
> write back
> > dirty data of one bucket in 1 second, the second stage is a bit more
> > aggressive, the initial rate tries to writeback the dirty data of one
> > bucket in 200 ms, the last stage is even more, whose initial rate
> > to writeback the dirty data of one bucket in 20 ms. This makes sense,
> > one reason is that if the preceding stage couldn’t get the
> > to a fine stage, then the next stage should increase the
> > properly, also it is because the later stage is closer to the
> > bch_cutoff_writeback_sync. Another aggressiveness increasing
> strategy is
> > with the increasing of dirty bucket percent within each stage, the
> > strategy controls the initial writeback rate of each stage, while this
> > one increases the rate based on the initial rate, which is
> > * (dirty bucket percent - BCH_WRITEBACK_FRAGMENT_THRESHOLD_X).
> > The initial rate can be controlled by 3 parameters
> > writeback_rate_fp_term_low, writeback_rate_fp_term_mid,
> > writeback_rate_fp_term_high, they are default 1, 5, 50, users can
> > them based on their needs.
> > The reason that I choose 50, 57, 64 as the threshold value is because
> > the GC must be triggered at least once during each stage due to the
> > “sectors_to_gc” being set to 1/16 (6.25 %) of the total cache
> size. So,
> > the hope is that the first and second stage can get us back to good
> > shape in most situations by smoothly writing back the dirty data
> > giving too much stress to the backing devices, but it might still
> > the third stage if the bucket consumption is very aggressive.
> > This patch use (dirty / dirty_buckets) * fp_term to calculate the
> > this formula means that we want to writeback (dirty /
> dirty_buckets) in
> > 1/fp_term second, fp_term is calculated by above aggressiveness
> > controller, “dirty” is the current dirty sectors, “dirty_buckets”
> is the
> > current dirty buckets, so (dirty / dirty_buckets) means the average
> > dirty sectors in one bucket, the value is between 0 to 1024 for the
> > default setting, so this formula basically gives a hint that to
> > one bucket in 1/fp_term second. By using this semantic, we can have a
> > lower writeback rate when the amount of dirty data is decreasing and
> > overcome the fact that dirty buckets number is always increasing
> > GC happens.
> > *Compare to the first patch:
> > *The first patch is trying to write back all the data in 40 seconds,
> > this will result in a very high writeback rate when the amount of
> > data is big, this is mostly true for the large cache devices. The
> > problem is that the semantic of this patch is not ideal, because we
> > don’t really need to writeback all dirty data in order to solve this
> > issue, and the instant large increase of the rate is something I
> feel we
> > should better avoid (I like things to be smoothly changed unless no
> > choice: )).
> > Before I get to this new patch(which I believe should be optimal
> for me
> > atm), there have been many tuning/testing iterations, eg. I’ve
> tried to
> > tune the algorithm to writeback ⅓ of the dirty data in a certain
> > of seconds, writeback 1/fragment of the dirty data in a certain amount
> > of seconds, writeback all the dirty data only in those error_buckets
> > (error buckets = dirty buckets - 50% of the total buckets) in a
> > amount of time. However, those all turn out not to be ideal, only the
> > semantic of the patch makes much sense for me and allows me to control
> > the rate in a more precise way.
> > *Testing data:
> > *I'll provide the visualized testing data in the next couple of days
> > with 1TB NVME devices cache but with HDD as backing device since it's
> > what we mostly used in production env.
> > I have the data for 400GB NVME, let me prepare it and take it for
> you to
> > review.
> Hi Dongdong,
> Thanks for the update and continuous effort on this idea.
> Please keep in mind the writeback rate is just a advice rate for the
> writeback throughput, in real workload changing the writeback rate
> number does not change writeback throughput obviously.
> Currently I feel this is an interesting and promising idea for your
> patch, but I am not able to say whether it may take effect in real
> workload, so we do need convinced performance data on real workload and
> Of course I may also help on the benchmark, but my to-do list is long
> enough and it may take a very long delay time.
> Coly Li