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SubjectRe: [PATCH] bcache: consider the fragmentation when update the writeback rate
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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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