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Date:   Fri, 8 Jan 2021 16:39:55 +0800
From:   Coly Li <colyli@...e.de>
To:     Dongdong Tao <dongdong.tao@...onical.com>
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

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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