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    SubjectRe: [PATCH] bcache: consider the fragmentation when update the writeback rate
    [Share the google doc here to avoid SPAM detection]

    Here is the new testing result with multiple threads fio testing:

    https://docs.google.com/document/d/1AmbIEa_2MhB9bqhC3rfga9tp7n9YX9PLn0jSUxscVW0/edit?usp=sharing


    On Fri, Jan 8, 2021 at 4:47 PM Dongdong Tao <dongdong.tao@canonical.com> wrote:
    >
    > 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@suse.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@suse.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@suse.de
    > > >>> <mailto:colyli@suse.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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