The download speed in terminal is very slow. (9-15 kb). But the speed in browser works fine. I am using shared internet through my hotspot from mobile. The ubuntu version 19.04
I’ve just fixed a production performance issue by dropping an index and recreate it. I suspect dropping the index also dropped executions plans that used it and one of them happen to be bad.
Arguments in favor of bad execution plan :
- Before dropping the indexe, I looked up the last update date for the statistics on the given table and they were up to date.
- My DBA has put in place Hallengren’s index and statistic maintenance solution
- The slow query was a select statement executed from
sp_executesqlwith dates parameters. Executing the same select statement without
sp_executesqlwas fast, but also didn’t use the same execution plan.
Arguments against bad execution plan :
- Before dropping the indexe, we went real wild and runned the forbidden
dbcc freeproccacheto clear any bad plan, but this didn’t fix or change the performance issue.
The slow query happen to be using a table indexed by date. However, there is wide differences in the amount of records for each date. In other word, any given date range from few records to more than 100k and it is pretty random.
The database is running under compatibility level 140 (SQL Server 2017)
Was the source of the problem a bad plan or a stale indexe?
This is my systemd-analyze blame
52.231s plymouth-quit-wait.service 15.176s snapd.service 15.051s dev-sda5.device 12.565s networkd-dispatcher.service 12.202s systemd-journal-flush.service 11.897s gpu-manager.service 10.950s ModemManager.service 10.698s NetworkManager-wait-online.service 9.294s udisks2.service 8.115s dev-loop1.device 6.849s accounts-daemon.service 6.706s NetworkManager.service 5.915s dev-loop11.device 5.579s dev-loop18.device 5.480s dev-loop2.device 5.299s dev-loop12.device 5.253s systemd-resolved.service 5.156s dev-loop19.device 5.029s dev-loop14.device 5.020s dev-loop16.device 4.924s dev-loop9.device 4.575s thermald.service 4.571s grub-common.service 4.447s apport.service 4.341s dev-loop7.device 3.968s systemd-logind.service 3.594s avahi-daemon.service 3.531s bluetooth.service 3.526s wpa_supplicant.service 3.489s fwupd.service 3.366s dev-loop8.device 2.919s rsyslog.service 2.760s dev-loop10.device 2.731s dev-loop6.device 2.656s dev-loop4.device 2.644s dev-loop5.device 2.390s systemd-fsck@dev-disk-by\x2duuid-90E0\x2d8818.service 2.371s apparmor.service 2.255s systemd-tmpfiles-setup.service 2.108s polkit.service 1.943s dev-loop3.device 1.870s dev-loop13.device 1.764s dev-loop0.device 1.727s systemd-udevd.service 1.641s dev-loop15.device 1.405s dev-loop17.device 1.391s systemd-sysctl.service 1.298s gdm.service 1.094s upower.service 874ms snap-gnome\x2dcalculator-406.mount 838ms snap-core18-1066.mount 837ms snap-gtk\x2dcommon\x2dthemes-1198.mount 836ms snap-gnome\x2dcharacters-254.mount 832ms grub-initrd-fallback.service 821ms snap-gnome\x2d3\x2d28\x2d1804-71.mount 821ms snap-gnome\x2dcharacters-296.mount 820ms snap-gnome\x2dsystem\x2dmonitor-100.mount 783ms systemd-backlight@backlight:intel_backlight.service 755ms snap-libreoffice-139.mount 754ms snap-core18-1074.mount 747ms systemd-modules-load.service 673ms snap-vlc-1049.mount 644ms pppd-dns.service 639ms snap-gtk\x2dcommon\x2dthemes-1313.mount 633ms snap-chromium-821.mount 583ms systemd-timesyncd.service 565ms snap-core-7270.mount 552ms systemd-tmpfiles-setup-dev.service 532ms systemd-sysusers.service 525ms keyboard-setup.service 497ms systemd-rfkill.service 482ms systemd-journald.service 466ms switcheroo-control.service 423ms snapd.seeded.service 383ms plymouth-start.service 347ms systemd-udev-trigger.service 332ms networking.service 323ms snap-gnome\x2d3\x2d28\x2d1804-67.mount 314ms colord.service 308ms snap-core-7396.mount 296ms systemd-user-sessions.service 289ms openvpn.service 278ms swapfile.swap 206ms snap-gimp-189.mount 182ms ifupdown-pre.service 181ms snap-chromium-817.mount 180ms nvidia-persistenced.service 172ms dns-clean.service 169ms sys-kernel-debug.mount 169ms dev-mqueue.mount 166ms dev-hugepages.mount 163ms boot-efi.mount 163ms plymouth-read-write.service 162ms snap-gnome\x2dsystem\x2dmonitor-77.mount 162ms snap-gnome\x2dlogs-61.mount 154ms rtkit-daemon.service 149ms snap-hw\x2dprobe-337.mount 137ms systemd-random-seed.service 132ms systemd-update-utmp.service 125ms ufw.service 121ms kmod-static-nodes.service 119ms setvtrgb.service 107ms kerneloops.service 99ms console-setup.service 97ms bolt.service 91ms systemd-remount-fs.service 65ms firstname.lastname@example.org 11ms email@example.com 9ms systemd-update-utmp-runlevel.service 3ms sys-fs-fuse-connections.mount 1ms sys-kernel-config.mount 1ms snapd.socket
I recently bought a Samsung Exernal SSD T5 for storing backups with Ubuntu’s built Deja Dup backup tool, and I’m finding it’s performance is terrible. Samsung advertises it as having “the T5 provides transfer speeds of up to 540 MB/s*, that’s up to 4.9x faster than external HDDs”, but the real world performance isn’t anywhere close to this.
Using the command provided in this answer, I’m monitoring the transfer progress of several large files. One file, called
duplicity-full-signatures.20190720T075111Z.sigtar.gz is 648 MB in size and the tool is saying the ETA for transfer completion is in 5 hours!
Am I missing something here? Shouldn’t a drive with transfer speeds up 540 MB/s be able to have a 648 MB file transferred to it in 648 / 540 = 1.2 seconds? I realize they said “up to” and other resource draws on my computer will cause the actual transfer speed to be well below that that…but not by 5 hours.
Other than Samsung being outright frauds, what would the reason be for these slow transfer times? I formatted the drive in Ext4 with encryption. Is there a different format I should use to speed things up? Are there any other system-wide changes I could make to speed up the deja-dup/duplicity process without making my system unusable during the backup?
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Like this,about 20kb/s: enter image description here
How can I install a snap fast?
Computer running verrry slow.. How may this be rectified? (19.04)
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 2880 root 20 0 262020 43240 34580 R 60.0 1.1 16:05.41 Xorg 4034 sayne 20 0 3567880 147212 52868 S 40.0 3.8 3:40.92 gnome-shell 3913 sayne 20 0 338420 6000 3168 S 20.0 0.2 0:00.92 ibus-extension- 5630 sayne 20 0 976408 44224 32576 S 20.0 1.1 0:00.87 gnome-terminal- 5709 sayne 20 0 12052 3752 3064 R 20.0 0.1 0:00.07 top
I wasn’t sure if I should post this on the machine learning board or this one, but I chose this one since my problem has more to do with optimization. I am trying to build a YOLO model from scratch in python, but each convolution operation takes 10 seconds. Clearly I am doing something wrong, as YOLO is supposed to be super fast (able to produce results real-time). I don’t need the network to run real-time, but it will be a nightmare trying to train it if it takes several hours to run on one image. Please help me!
Here is my convolution function:
def convolve(image, filter, stride, modifier): new_image = np.zeros ([image.shape, _round((image.shape-filter.shape)/stride)+1, _round((image.shape-filter.shape)/stride)+1], float) #convolve for channel in range (0, image.shape): filterPositionX = 0 filterPositionY = 0 while filterPositionX < image.shape-filter.shape+1: while filterPositionY < image.shape-filter.shape+1: sum = 0 for i in range(0,filter.shape): for j in range(0,filter.shape): if filterPositionX+i<image.shape and filterPositionY+j<image.shape: sum += image[channel][filterPositionX+i][filterPositionY+j]*filter[channel][i][j] new_image[channel][int(filterPositionX/stride)][int(filterPositionY/stride)] = sum*modifier filterPositionY += stride filterPositionX += stride filterPositionY = 0 #condense condensed_new_image = np.zeros ([new_image.shape, new_image.shape], float) for i in range(0, new_image.shape): for j in range(0, new_image.shape): sum = 0 for channel in range (0, new_image.shape): sum += new_image[channel][i][j] condensed_new_image[i][j] = sum condensed_new_image = np.clip (condensed_new_image, 0, 255) return condensed_new_image
Running the function on a 448×448 grayscale image with a 7×7 filter and a stride of 2 takes about 10 seconds. My computer has an i7 processor.
I working on some automation testing code and I’ve installed the Slow Cheetah extension and packages in order to be able to create multiple config files.
The idea is that I can then target these config files depending on where I want my code to run (e.g. locally, on a server, etc).
So I’ve a dummy file just to try the feature and when I was happy with it working, I deleted the file.
The feature allows you to generate the config files, and somehow, when I do that, it resurrects the deleted file.
So this is my issue, I’m haunted by a ghost file….
Things I’ve tried to exorcise it:
- deleted the file in Visual Studio
- deleted the local file manually
- deleted any trace of the file from the Configuration Manager
So yeah, I cleaned up all the slime and every time I click on Add Transform to generate new config file, the ghost one tags along…
…there is something strange in my neighborhood, who you gonna call?
I just did a fresh install of Ubuntu 19.04 on a Razer Blade 15 (2019) and am trying to install OpenRazer drivers so I can control the keyboard backlighting.
I go into terminal and run
sudo add-apt-repository ppa:openrazer/stable. When I hit enter, it sits there for 5-10 minutes and finally asks whether or not I want to add the repository.
I hit enter to confirm, wait another minute or so, and it says that it timed out trying to retrieve the gpg key.
- Restarting and running the command again
- Disabling IPv6
- Letting Software & Updates choose the best download mirror
- Using multiple different keyservers
- Removing all repositories with missing keys
Nothing seems to work. I get the same behaviour every time. Is there any way to fix this?