Date:   Fri, 8 Jan 2021 16:47:55 +0800
From:   Dongdong Tao <dongdong.tao@...onical.com>
To:     Coly Li <colyli@...e.de>
Cc:     Kent Overstreet <kent.overstreet@...il.com>,
        "open list:BCACHE (BLOCK LAYER CACHE)" <linux-bcache@...r.kernel.org>,
        open list <linux-kernel@...r.kernel.org>,
        Gavin Guo <gavin.guo@...onical.com>,
        Gerald Yang <gerald.yang@...onical.com>,
        Trent Lloyd <trent.lloyd@...onical.com>,
        Dominique Poulain <dominique.poulain@...onical.com>,
        Dongsheng Yang <dongsheng.yang@...ystack.cn>
Subject: Re: [PATCH] bcache: consider the fragmentation when update the
 writeback rate

Yeap, I will scale the testing for multiple threads with larger IO
depth, thanks for the suggestion!

On Fri, Jan 8, 2021 at 4:40 PM Coly Li <colyli@...e.de> wrote:
>
> On 1/8/21 4:30 PM, Dongdong Tao wrote:
> > Hi Coly,
> >
> > They are captured with the same time length, the meaning of the
> > timestamp and the time unit on the x-axis are different.
> > (Sorry, I should have clarified this right after the chart)
> >
> > For the latency chart:
> > The timestamp is the relative time since the beginning of the
> > benchmark, so the start timestamp is 0 and the unit is based on
> > millisecond
> >
> > For the dirty data and cache available percent chart:
> > The timestamp is the UNIX timestamp, the time unit is based on second,
> > I capture the stats every 5 seconds with the below script:
> > ---
> > #!/bin/sh
> > while true; do echo "`date +%s`, `cat
> > /sys/block/bcache0/bcache/dirty_data`, `cat
> > /sys/block/bcache0/bcache/cache/cache_available_percent`, `cat
> > /sys/block/bcache0/bcache/writeback_rate`" >> $1; sleep 5; done;
> > ---
> >
> > Unfortunately, I can't easily make them using the same timestamp, but
> > I guess I can try to convert the UNIX timestamp to the relative time
> > like the first one.
> > But If we ignore the value of the X-axis,  we can still roughly
> > compare them by using the length of the X-axis since they have the
> > same time length,
> > and we can see that the Master's write start hitting the backing
> > device when the cache_available_percent dropped to around 30.
>
> Copied, thanks for the explanation. The chart for single thread with io
> depth 1 is convinced IMHO :-)
>
> One more question, the benchmark is about a single I/O thread with io
> depth 1, which is not typical condition for real workload. Do you have
> plan to test the latency and IOPS for multiple threads with larger I/O
> depth ?
>
>
> Thanks.
>
>
> Coly Li
>
>
> >
> > On Fri, Jan 8, 2021 at 12:06 PM Coly Li <colyli@...e.de> wrote:
> >>
> >> 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
> >>>
> >>
> >> Hi Dongdong,
> >>
> >> Nice charts :-)
> >>
> >>> Steps:
> >>>
> >>>  1.
> >>>
> >>>     make-bcache -B <HDD> -C <NVME> --writeback
> >>>
> >>>  2.
> >>>
> >>>     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.
> >>
> >>
> >> Thanks.
> >>
> >>
> >> Coly Li
> >>
> >>
> >>
> >>
> >>
> >>>
> >>> Let’s have a look at the write latency first:
> >>>
> >>> Master:
> >>>
> >>>
> >>>
> >>> 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:
> >>>
> >>> 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.
> >>>
> >>>
> >>> Regards,
> >>> Dongdong
> >>>
> >>> On Tue, Jan 5, 2021 at 12:33 PM Coly Li <colyli@...e.de
> >>> <mailto:colyli@...e.de>> 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
> >>>     first
> >>>     > 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
> >>>     tries
> >>>     > 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
> >>>     fragmentation
> >>>     > to a fine stage, then the next stage should increase the
> >>>     aggressiveness
> >>>     > 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
> >>>     first
> >>>     > strategy controls the initial writeback rate of each stage, while this
> >>>     > one increases the rate based on the initial rate, which is
> >>>     initial_rate
> >>>     > * (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
> >>>     adjust
> >>>     > 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
> >>>     without
> >>>     > giving too much stress to the backing devices, but it might still
> >>>     enter
> >>>     > the third stage if the bucket consumption is very aggressive.
> >>>     >
> >>>     > This patch use (dirty / dirty_buckets) * fp_term to calculate the
> >>>     rate,
> >>>     > 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
> >>>     reclaim
> >>>     > 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
> >>>     unless
> >>>     > 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
> >>>     dirty
> >>>     > data is big, this is mostly true for the large cache devices. The
> >>>     basic
> >>>     > 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
> >>>     amount
> >>>     > 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
> >>>     certain
> >>>     > 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.
> >>>     [snipped]
> >>>
> >>>     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
> >>>     configuration.
> >>>
> >>>     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.
> >>>
> >>>     Thanks.
> >>>
> >>>     Coly Li
> >>>
> >>
>

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