I have long TCL script . I want measure exact cpu time the process is taking in TCL
I tried the time command in TCL but I am not sure it is correct way to measure cpu time taken by complete TCL script
Tcl itself doesn't provide this information. You want to use the TclX package's times command.
package require Tclx
set pre [times]
# do CPU intensive operation here; for example calculating the length of a number with many digits...
string length [expr {13**12345}]
set post [times]
# the current process's non-OS CPU time is the first element; it's in milliseconds
set cpuSecondsTaken [expr {([lindex $post 0] - [lindex $pre 0]) / 1000.0}]
# The other elements are: system-cpu-time, subprocess-user-cpu-time, subprocess-system-time
You are, of course, dependent on the OS measuring this information correctly. It's not portable to non-POSIX systems (e.g., Windows) though there might be something logically similar in the TWAPI package.
Related
I have two folders each contains about 8,000 small csv files. One with an aggregated size of around 2GB and another with aggregated size of around 200GB.
These files are stored like this to better update them in a daily basis. However, when I conduct EDA, I would like them to be assigned to a single variable. For example.
path = "some random path"
df = pd.concat([pd.read_csv(f"{path}//{files}") for files in os.listdir(path)])
It would take much less time for me to read the dataset with 2GB in total size than reading it on the super computer cluster. And it is impossible to read the 200GB dataset on the local machine unless using some sort of scaling Pandas solutions. The situation does not seem to improve on the cluster even using the popular open-source tools like Dask and Modin.
Is there an effective way that enables to read those csv files effectively with given situation?
Q :"Is there an effective way that enables to read those csv files effectively ... ?"
A :Oh, sure, there is :
CSV format ( standard attempts in RFC4180 ) is not unambiguous and is not obeyed under all circumstances ( commas inside fields, header present or not ), so some caution & care is needed here. Given you are your own data curator, you shall be able to decide plausible steps for handling your own data properly.
So, the as-is state is :
# in <_folder_1_>
:::::::: # 8000 CSV-files ~ 2GB in total
||||||||||||||||||||||||||||||||||||||||||| # 8000 CSV-files ~ 200GB in total
# in <_folder_2_>
Speaking efficiency, O/S coreutils provide the best, stable, proven and most efficient (as system tool used to be since ever ) tools for the phase of merging thousands and thousands of plain CSV-files' content :
###################### if need be,
###################### use an in-place remove of all CSV-file headers first :
for F in $( ls *.csv ); do sed -i '1d' $F; done
this helps for case we cannot avoid headers on the CSV-exporter side. Works like this :
(base):~$ cat ?.csv
HEADER
1
2
3
HEADER
4
5
6
HEADER
7
8
9
(base):~$ for i in $( ls ?.csv ); do sed -i '1d' $i; done
(base):~$ cat ?.csv
1
2
3
4
5
6
7
8
9
Now, the merging phase :
###################### join
cat *.csv > __all_CSVs_JOINED.csv
Given the nature of the said file storage policy, performance can be boosted by using more processes for independent taking small files and large files separately, as defined above, having put the logic inside a pair of conversion_script_?.sh shell-scripts :
parallel --jobs 2 conversion_script_{1}.sh ::: $( seq -f "%1g" 1 2 )
As the transformation is a "just"-[CONCURRENT] flow of processing for a sake of removing the CSV-headers, but a pure-[SERIAL] ( for larger number of files, there might become interesting to use a multi-staged tree of trees - using several stages of [SERIAL]-collections of [CONCURRENT]-ly pre-processed leaves, yet for just 8000 files, not knowing the actual file-system details, the latency-masking from a just-[CONCURRENT] processing both of the directories just independently will be fine to start with )
Last but not least, the final pair of ___all_CSVs_JOINED.csv are safe to get opened using in a way, that prevents moving all disk-stored date into RAM at once ( using chunk-size-fused file-reading-iterator, avoiding RAM-spillovers by using mmaped-mode as a context manager ) :
with pandas.read_csv( "<_folder_1_>//___all_CSVs_JOINED.csv",
sep = NoDefault.no_default,
delimiter = None,
...
chunksize = SAFE_CHUNK_SIZE,
...
memory_map = True,
...
) \
as df_reader_MMAPer_CtxMGR:
...
When tweaking for ultimate performance, details matter and depend on physical hardware bottlenecks ( disk-I/O-wise, filesystem-wise, RAM-I/O-wise ), so due care may take further improvement for minimising the repetitive performed end-to-end processing times ( sometimes even turning data into a compressed/zipped form, in cases, where CPU/RAM resources permit sufficient performance advantages over limited performance of disk-I/O throughput - moving less bytes is so faster, that CPU/RAM-decompression costs are still lower, than moving 200+ [GB]s of uncompressed plain text data.
Details matter,tweak options,benchmark,tweak options,benchmark,tweak options,benchmark
would be nice to post your progress on testing the performanceend-2-end duration of strategy ... [s] AS-IS nowend-2-end duration of strategy ... [s] with parallel --jobs 2 ...end-2-end duration of strategy ... [s] with parallel --jobs 4 ...end-2-end duration of strategy ... [s] with parallel --jobs N ... + compression ... keep us posted
I've been working on rop recently. When using perf to count hardware information, I want to measure the number of return instructions executed by a given piece of code. But the perf interface only provides branch instructions.
If you're only x86 with a recent Intel CPU:
perf list on my Skylake shows there's a hardware counter for br_inst_retired.near_return. That will count only ret instructions, not other branches. But see erratum SKL091 for branch-instruction counters.
perf stat -e instructions,br_inst_retired.near_return,... ./a.out may be what you're looking for. Or maybe attaching perf stat to an already-running program, or maybe -I 1000 to print accumulated counts over intervals.
But note that if you're looking for ROP gadgets, you can find a C3 opcode inside what normally decodes as some other instruction. So restricting yourself only to ret instructions that actually run during the target program's normal execution is more limiting than it needs to be.
e.g. a 4-byte immediate might usefully decode as something + ret if you jump to the immediate.
I have this script that I created for a battery indicator widget that will show on my (Macbook pro) touch bar and have it set to re-run every 3 secs (to update the text and logo), but it is eating a lot of energy just running in the background and I was wondering if maybe there was a way to make it more efficient in some way in order to decrease battery consumption.
Here is the script:
(Keep in mind that the images for the icon are in base64 because I plan on sharing this widget with people who might know nothing about computers and therefor I can't have them download and save an image in a specific directory etc..)
set bat_empty_icon to "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"
set bat_low_icon to "iVBORw0KGgoAAAANSUhEUgAAAQAAAAEACAYAAABccqhmAAAACXBIWXMAAA3XAAAN1wFCKJt4AAAGAGlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4gPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iQWRvYmUgWE1QIENvcmUgNS42LWMxNDUgNzkuMTYzNDk5LCAyMDE4LzA4LzEzLTE2OjQwOjIyICAgICAgICAiPiA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPiA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyIgeG1sbnM6cGhvdG9zaG9wPSJodHRwOi8vbnMuYWRvYmUuY29tL3Bob3Rvc2hvcC8xLjAvIiB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIgeG1sbnM6c3RFdnQ9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZUV2ZW50IyIgeG1wOkNyZWF0b3JUb29sPSJBZG9iZSBQaG90b3Nob3AgQ0MgMjAxOSAoTWFjaW50b3NoKSIgeG1wOkNyZWF0ZURhdGU9IjIwMTgtMTItMTJUMDA6MDk6NTcrMDE6MDAiIHhtcDpNb2RpZnlEYXRlPSIyMDE4LTEyLTEyVDAwOjI0OjA1KzAxOjAwIiB4bXA6TWV0YWRhdGFEYXRlPSIyMDE4LTEyLTEyVDAwOjI0OjA1KzAxOjAwIiBkYzpmb3JtYXQ9ImltYWdlL3BuZyIgcGhvdG9zaG9wOkNvbG9yTW9kZT0iMyIgcGhvdG9zaG9wOklDQ1Byb2ZpbGU9InNSR0IgSUVDNjE5NjYtMi4xIiB4bXBNTTpJbnN0YW5jZUlEPSJ4bXAuaWlkOjJmOGJiM2NlLTYxOTYtNDg2ZC1hNTAxLTNhZDVlNGZlZDlkNSIgeG1wTU06RG9jdW1lbnRJRD0iYWRvYmU6ZG9jaWQ6cGhvdG9zaG9wOmM4N2E0ZWM5LTRhOGQtYzM0Yy05MjU0LTA2Y2FhOGUzYjAxMSIgeG1wTU06T3JpZ2luYWxEb2N1bWVudElEPSJ4bXAuZGlkOjBlYjhjMWMxLWI0MmEtNGUyOC04ODM0LTAzYWQyZGEwN2YwZSI+IDx4bXBNTTpIaXN0b3J5PiA8cmRmOlNlcT4gPHJkZjpsaSBzdEV2dDphY3Rpb249ImNyZWF0ZWQiIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6MGViOGMxYzEtYjQyYS00ZTI4LTg4MzQtMDNhZDJkYTA3ZjBlIiBzdEV2dDp3aGVuPSIyMDE4LTEyLTEyVDAwOjA5OjU3KzAxOjAwIiBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZG9iZSBQaG90b3Nob3AgQ0MgMjAxOSAoTWFjaW50b3NoKSIvPiA8cmRmOmxpIHN0RXZ0OmFjdGlvbj0ic2F2ZWQiIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6MmY4YmIzY2UtNjE5Ni00ODZkLWE1MDEtM2FkNWU0ZmVkOWQ1IiBzdEV2dDp3aGVuPSIyMDE4LTEyLTEyVDAwOjI0OjA1KzAxOjAwIiBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZG9iZSBQaG90b3Nob3AgQ0MgMjAxOSAoTWFjaW50b3NoKSIgc3RFdnQ6Y2hhbmdlZD0iLyIvPiA8L3JkZjpTZXE+IDwveG1wTU06SGlzdG9yeT4gPC9yZGY6RGVzY3JpcHRpb24+IDwvcmRmOlJERj4gPC94OnhtcG1ldGE+IDw/eHBhY2tldCBlbmQ9InIiPz471XDCAAAI4klEQVR4nO3dvYsd1wGG8Wd3ReLVSpWKaOUULkwgRapYCgFDwMaNUCyTLlETYhP/ASZdYpsQ3KgwbpIqbpyPVnLhxlgQECRYSuVCGFyYIGnVqNHqkxBtiqMrr1ar1d09c++Zmff5wbD6YK/OFfc8e2bmztyFjY0NJGVabD0ASe0YACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCGQApmAGQghkAKZgBkIIZACmYAZCCLXT0OMvAy8CLwLPAkU1fD3b0b0gtrANXgSubvp4HPgPuNBxXJ2oCcAg4CbwKvALs72RE0jDcBj4FPgbOAtfbDmd+VoC3KWXccHNzY50yJ1YYsSXg18Aa7f/D3dz6uK1R5sgSAzHtLsAR4AxwdHZDkUbjAvAa5ZhBr00TgGOUyb8626FIo7JGicDnjcexo6edBjwF/AMnv7Rbq5S5c6r1QHay077KKeAvwL45jUUam33Az4CvgC8aj2VbT9oFOEap1zNzHIs0VneBn9DD3YHtAnAEuIjLfqlLa8AL9OzA4NZjAEt4wE+ahVXK3OrVKcKtAXgdT/VJs3KUMsd6Y/MuwArlYMXhRmORElwDngdutR4IPLoCeAsnvzRrhylzrRcmK4BDwNfAgXZDkWLcBJ6jBxcQTVYAJ3HyS/NygDLneuMM3V0Q8SXwLnACzyZo+FYpr+V3KK/trubJmTk+hx0tUw5I1D6h+8D7Dx5PGqNlymv8PvXz5RY9mSsn6GbyH5/3wKVGjtNNBE7Me+BbLVJu41XrA+CTDh5HGoJPKK/5Wl3MvWofUb/P34uljDRHy9QfE/ho7qPeYpHy3v8af2MEN0eUdukO8PfKx6ide9UWKXfvrfHvLgYiDdDFyu+vnXuduEHdMsZTfUq1St3cuTH/IT9q4cFAah9DSlU7f2rdpFxqvAZcotyi/Bxwb9oHqD2VISWrnT+z2G4AfwS+M48nICVrPdl32taB3wHfmuUTkJK1nuTTbOfZYTVgAKS9az25p93+A/xg6+A9CCjVGdIPwcuUuxJdm/yBHw8u5fgu5SrEh3f7NgBSlh8Bv538pvkuwJ3fPNagLpZUzXdLlk/fbz0EzceQdgEmblPuS7jWpxVAlwcVPUApPdl+yseZ92YXYFaT1QhI2zsFfLsvAZglIyA97iDwUh8CMI8JagSkx53sQwDmxQhIj/p+UgDACEibraYFQNI3Vve1HkEDG/TgfQLSA7WvxZpV7QFXAFKw1AB4LEAiNwCSyA6AqwDFSw6AFC89AK4CFC09AFI0A+AqQMEMgBTMABSuAhTJAEjBDMA3XAUojgGQghmAR7kKUBQDIAUzAI9zFaAYBkAKZgC25ypAEQyAFMwAPJmrAI2eAZCCGYCduQrQqBkAKZgBeDpXARotAyAFMwDTcRWgUTIAUjADMD1XARodAyAFMwC74ypAo2IApGAGYPdcBWg0DIAUzADsjasAjYIBkIIZgL1zFaDBMwBSMANQx1WABs0ASMEMQD1XARosAyAFMwDdcBWgQTIAUjAD0B1XARocAyAFMwDdchWgQTEAUrB9rQcwQhvAQutBaDBarhpvugKQcq0ZACmXAZgRDwZqCC71IQDuL0ttnO1DAMbIqKnv1oFzfQmAE0aar78C9/oSACgRGEMIxvAcNG63gd9PfrNRuUnJaudPi+0PXT4BKVnrybzb7V/AM5PBL1A/iV3yKtmQfgheBo4C1yZ/0KdjAJJm5zJwnE2Tf8JdAGnvWi/pp132H57VE5CStZ7cO223KAf8Hu7zz+IJSMlaT/LtthvAn4DVpw3ey4GlYbsJrD3YLgFngXPAvWkf4AZ1tXlqZaSRWqX+J3VTi8DVysf4YRcDkQao9rVfO/eqLQJXKh/DACjVC5XfXzv3qnWxAvgFsNzBWKQhWQZ+XvkYo1gBfA94r4OxSEPyHuW1X6P5CgDgBPWnHe5T3mUkJThOec3XzpsT8x74dpYpbxjoIgLv4+6AxmuZ8hrvYvLfokdz5QzdvQnhS+BdSt08RaihW6W8lt+hvLa7midn5vgcnmhyJd+vgD+3HIgU5nXgw9aDmATgEPA1cKDdUKQYN4HngOuNx/HwcuDrwOmWA5GCnKYHkx8evZnHCvAVO1w2KKnaNeB5ykHA5pY2/fq/lFsF/7TRWKQEb1Guz++FrbfzWgL+SbltkKRuXQB+DPyv9UAmtruf3xHgIp7Ck7q0Rrl2oPnbfzfb7p6AV4HXgLvzHYo0Wncpc6pXkx+efFPQz4E35jkQacTeoMyp3lna4e++oJwVOI53DpL24i7wS8rHcPXSNPf0P0Z526LHBKTprVGW/b38yT8x7Yd6HKFEwLMD0tNdoKf7/FtN+8EgVymnL95kmw8WkASUufEmZa70fvLv1QrwNuVNQ11dGeXmNuRtnTInVhiYms/1OwScBF4FXgH2dzIiaRhuA58CH1Nuxd2L9/bvVlcf7LkMvAy8CDxLOWYw+Xqwo39DamGdspy/sunreeAz4E7DcXViYWNjo/UYJDXipwNLwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMEMgBTMAEjBDIAUzABIwQyAFMwASMH+D7UGZfpxuvd3AAAAAElFTkSuQmCC"
set bat_half_icon to "iVBORw0KGgoAAAANSUhEUgAAAQAAAAEACAQAAAD2e2DtAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAAmJLR0QAAKqNIzIAAAAJcEhZcwAADdcAAA3XAUIom3gAAAAHdElNRQfiDAwACgoCREMEAAAHIUlEQVR42u3dT2gUZxzG8SeJtK6JJw91NxU8BKGHnppYBDX+wUND1NBb46FFRW/twUIptNGCeBEq0lJ7sRerPfSykdaLKAihlRh7kVaEHKSYbKD1kk3806LpIY1JzCbvJPPO/mbm/X7mFDLM+77zPvubmd2ZXQkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMTVEGGdgnZrq1pVUqtKWmvd5ZyralQjGtWIBnRNj5NubukArNN+7dMerbHeK4F6pKu6rH49tGi8WX2qaorFfKmqT831nfwmHVHFfOAss0tFR9SUxFTXOgSUVFZHfROHCG6pR6O+N7owAJtVVtF6rKipoh4N+t1k40t/H9ANpj+1irqhA343Of+4ckDfa5X1KLGEVXpXw7rjb4NzDwGbdUOrrUcIpyfq9HcgmA1ASUMU/4yoqN3X6eDMOUATp34ZUlTZ10XhTAAOceGXKR065GdD04eAZg1rvfWYsCxjatNk/M1MV4BjTH/mrNcxH5tpkLRO99ViPR4s24Q2xv+YqFHSfqY/k1q038+GypE+jrinE+rmSiFxRXXruO5FmpOyjwYLmnQ29FxnVLDeM0Ep6IyeO+dl0sesdEeY/i7r/RGkrggR6I7bSKO2Otc5qyvW+yJIV3TWuY579pwuOI/9FH8rBee5wIW4TTSq5FjjUvI3JmIRj/WDYw3X7Dk1qtWxxm3rvRC0Icf/XbMXwbijyHDhZ6nomJ3xuA00aMq5Biy55sdlQhVVdFf9uq6ntRtYeoEtf3cWj+sbvbb8BmDL7+3lVX2uV5bXAGz5DcCUpjQwvw4QgHTzH4Ap/ak3ZzbPSWDaJfMSfKAOjUkLnwtAGF5XefoOcAIQqrf1meTzENCmXrWrve5vHP2lP/S7ftGP+qfOLddHcmdhj9Smip+TwAZ9GOGugmSXB/o4l19ekeQ+OxelgSjT/5Px5M8sf2uP9XxlKgDjetVHAD40n/jZ5Zk+sZ6xDAVgSu/ED0CbefF/eTlvPWcZCsC38a8CelP3DUIH9b51FzLjjfgBaLceQw1fq826CxlRzGcAWqbPb+FUjP8+QFo/LdimAesueJHw/OT3ncAT1h3IhvxWgLzUACrAip2w7kAW5LkC5KMGUAFi+MK6A+mX7wDs0jbrLqRdvgPAeYBT3gNADXDIewCoAQ75DwA1YEn5DwA1YEkhBGCXtlt3Ib1CCAA1YAlhBGAnNWAxYQSAGrCoUAJADVhEKAGgBiwinADsVKd1F9IonABQA2oKKQA7qAELhRQAakANYQWAGrBAWAGgBiwQWgB2aId1F9IltABQA14SXgA6qQFzhRcAasA8IQaAGjBHiAGgBswRZgA6tdO6C2kRZgCoAS+EGoDt1IBpoQaAGvC/cANADZAUcgCoAZLCDsB27bLugr2QA0ANUOgB2EYNCDsA1IDgAxB8DQg9AMHXAAIQeA3I99fERTOQ6q+Q4GviErdVu627YIcKIKW7BlAB6iDgGkAFmJbeGpD6ClCp6+5ISnZrQLzffJqIH4Ah6z3gSa91B0xUCMCM/P3iYBQeAnBJj6xH4cUGbbDugoG78QMwrE+tR+HFuEatu2Cg38dl4Ff62XocHtzQM+su1F1V130EYEp79VHGDwS/6ah1Fwxc1NPs/3x8XBXd1qC+1KR1RxaRiZ+PR3KS+93gk9EagK2kpv+mVks+3gpGspJ5CT5Qh8YkPgwK0wN1TU+/xCEg7ZIo/uuX0wBs+Z38SZ2cPvZHbwC2/E3+uM4tvEhfZT0+JGxCFVV0V/26rqe1Vhh35CZrb+zkS9H5qo6p0fkRyFvW+yBorr0f+wOsRo3E7AKS1O74v2v2nNwVoFcF670QrILec6xRhwqwSaes90OwTmmTY43YFUDqdl4+PFeX9Z4IUpeeO+emO34zBU1GiMAZDgR1VdCZCNM/6WdWypHeRrinE+rmojBxRXXruO5FmpNy/OYaJB3UeetRY0UO6bu4m2iQtE731WI9FizbhDbqYdyNNEp6qNPWY8EKnI4//TO3ezRreP5HhEi9MbX5uI+xSZL0r6raaz0iLMsx3fSxmZkbvpr0qzqsx4TIbmmLn+cYZu/4K2mIi7yMqKjd13NMs/cEjqpHT6xHhgieqMffY2xzbwod1GHrsSGCwxr0t7GmeX/d0bC6uEsoxZ7oA130ucGFd/1vVplzgZSqqMfnq1+q/dhHSWWuCFLols9j/4xaD4aMaouOzj44gBQY01Ftqe83GDSrT1XPd6WzrGSpqk/NSU3z0k/+rdN+7dMeraln8vDCI13VZfX7eM9/MVEe/Sxot7aqVSW1qqS11nsl56oa1YhGNaIBXdNj6+4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAC3/wBc7KYe8OjvLgAAACV0RVh0ZGF0ZTpjcmVhdGUAMjAxOC0xMi0xMlQwMDoxMDoxMCswMTowMGd3EmMAAAAldEVYdGRhdGU6bW9kaWZ5ADIwMTgtMTItMTJUMDA6MTA6MTArMDE6MDAWKqrfAAAAGXRFWHRTb2Z0d2FyZQB3d3cuaW5rc2NhcGUub3Jnm+48GgAAAABJRU5ErkJggg=="
set bat_three_quarters_icon to "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"
set bat_full_icon to "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"
set bat_charging_icon to "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"
set chargeState to do shell script "pmset -g batt | awk '{printf \"%s %s\\n\", $4,$5;exit}'"
set percentLeft to do shell script "pmset -g batt | egrep -ow '([0-9]{1,3})[%]'" as text
set percentcheck to (item 1 of words of percentLeft as integer)
on new_icon(base64Code)
set percentLeft to do shell script "pmset -g batt | egrep -ow '([0-9]{1,3})[%]'" as text
tell application "BetterTouchTool"
update_touch_bar_widget "482F2657-A978-4D25-B9D6-EABFAD7E3B0A" text percentLeft icon_data base64Code
end tell
end new_icon
if (chargeState contains "AC") then
my new_icon(bat_charging_icon)
else if percentcheck ≥ 85 then
my new_icon(bat_full_icon)
else if (percentcheck < 85) and (percentcheck ≥ 65) then
my new_icon(bat_three_quarters_icon)
else if (percentcheck < 65) and (percentcheck ≥ 31) then
my new_icon(bat_three_quarters_icon)
else if percentcheck < 31 then
my new_icon(bat_low_icon)
else if percentcheck ≤ 10 then
my new_icon(bat_empty_icon)
end if
Your first step should be to increase the intervals at which you run the script. There is no need to have it run every 3 seconds (!). Even once a minute would be sufficient but I, personally, would set it to something such as once every ten minutes, since the battery level doesn't tends to only change once within a ten minute window. Even if it changed twice (e.g. dropping from 97% to 96% then to 95%), there would be no negative consequences from your script registering the level going from 97% straight down to 95% in a ten minute interval.
The next thing you can do is store those base 64 strings in properties rather than variables. Properties are set once and stored between subsequent runs of a script. If the values of the properties aren't altered, then the script doesn't need to read a new set of values each time it executes. As they are set at compile time, the overhead is reduced at run time.
The next thing you can do is get rid of the do shell script commands. They incur a lot of overhead. Instead, you can use AppleScriptObjC calls to obtain the information you need. There is still a slight overhead in implementing AppleScriptObjC compared to vanilla AppleScript, but it won't be as great as the cost of creating multiple shell processes:
property bat_empty_icon : "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"
property bat_low_icon : "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"
property bat_half_icon : "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"
property bat_three_quarters_icon : "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"
property bat_full_icon : "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"
property bat_charging_icon : "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"
global percentage
set [percentage, powersource] to [|current capacity|, ¬
|power source state|] of battery's info()
if powersource contains "AC" then
new_icon(bat_charging_icon)
else if percentage ≥ 85 then
new_icon(bat_full_icon)
else if percentage ≥ 65 then
new_icon(bat_three_quarters_icon)
else if percentage ≥ 31 then
new_icon(bat_three_quarters_icon)
else if percentage ≥ 11 then
new_icon(bat_low_icon)
else
new_icon(bat_empty_icon)
end if
on new_icon(base64Code)
global percentage
tell application "BetterTouchTool"
update_touch_bar_widget ¬
"482F2657-A978-4D25-B9D6-EABFAD7E3B0A" text ¬
"" & percentage & "%" icon_data base64Code
end tell
end new_icon
script battery
use framework "IOKit"
on info()
current application's IOPSCopyPowerSourcesInfo() as record
end info
end script
Additionally, I tidied up the if...then...else block so it doesn't do more number comparisons than necessary.
Are there any specific problems with running Microsoft's BCP utility (on CentOS 7, https://learn.microsoft.com/en-us/sql/linux/sql-server-linux-migrate-bcp?view=sql-server-2017) on multiple threads? Googling could not find much, but am looking at a problem that seems to be related to just that.
Copying a set of large TSV files from HDFS to a remote MSSQL Server with some code of the form
bcpexport() {
filename=$1
TO_SERVER_ODBCDSN=$2
DB=$3
TABLE=$4
USER=$5
PASSWORD=$6
RECOMMEDED_IMPORT_MODE=$7
DELIMITER=$8
echo -e "\nRemoving header from TSV file $filename"
echo -e "Current head:\n"
echo $(head -n 1 $filename)
echo "$(tail -n +2 $filename)" > $filename
echo "First line of file is now..."
echo $(head -n 1 $filename)
# temp. workaround safeguard for NFS latency
#sleep 5 #FIXME: appears to sometimes cause script to hang, workaround implemented below, throws error if timeout reached
timeout 30 sleep 5
echo -e "\nReplacing null literal values with empty chars"
NULL_WITH_TAB="null\t" # WARN: assumes the first field is prime-key so never null
TAB="\t"
sed -i -e "s/$NULL_WITH_TAB/$TAB/g" $filename
echo -e "Lines containing null (expect zero): $(grep -c "\tnull\t" $filename)"
# temp. workaround safeguard for NFS latency
#sleep 5 #FIXME: appears to sometimes cause script to hang, workaround implemented below
timeout 30 sleep 5
/opt/mssql-tools/bin/bcp "$TABLE" in "$filename" \
$TO_SERVER_ODBCDSN \
-U $USER -P $PASSWORD \
-d $DB \
$RECOMMEDED_IMPORT_MODE \
-t "\t" \
-e ${filename}.bcperror.log
}
export -f bcpexport
parallel -q -j 7 bcpexport {} "$TO_SERVER_ODBCDSN" $DB $TABLE $USER $PASSWORD $RECOMMEDED_IMPORT_MODE $DELIMITER \
::: $DATAFILES/$TARGET_GLOB
where $DATAFILES/$TARGET_GLOB constructs a glob that lists a set of files in a directory.
When running this code for a set of TSV files, finding that sometimes some (but not all) of the parallel BCP threads fail, ie. some files successfully copy to MSSQL Server
Starting copy...
5397376 rows copied.
Network packet size (bytes): 4096
Clock Time (ms.) Total : 154902 Average : (34843.8 rows per sec.)
while others output error message
Starting copy...
BCP copy in failed
Usually, see this pattern: a few successful BCP copy-in operations in the first few threads returned, then a bunch of failing threads return their output until run out of files (GNU Parallel returns output only when whole thread done to appear same as if sequential).
Notice in the code there is the -e option to produce an error file for each BCP copy-in operation (see https://learn.microsoft.com/en-us/sql/tools/bcp-utility?view=sql-server-2017#e). When examining the files after observing these failing behaviors, all are blank, no error messages.
Only have seen this with the number of threads >= 10 (and only for certain sets of data (assuming has something to do with total number of files are files sizes, and yet...)), no errors seen so far when using ~7 threads, which further makes me suspect this has something to do with multi-threading.
Monitoring system resources (via free -mh) shows that generally ~13GB or RAM is always available.
May be helpful to note that the data bcp is trying to copy-in may be ~500000-1000000 records long with an upper limit of ~100 columns per record.
Does anyone have any idea what could be going on here? Note, am pretty new to using BCP as well as GNU Parallel and multi-threading.
No, no issues specific to the BCP program being run in multiple threads. You seem to be on the track of what I would say your issue is, system resources. Have you monitored system resources while increasing the number of threads? If anything, there is likely an issue with BCP executing properly when memory/cpu/network resources are low. Regarding the "-e" option, it is meant to output data errors. login errors, bad table names... many other errros are not reported in the file created with the -e option. When you get output using the "-e" option, you'll see info like "value truncated" and such... will give you line numbers and sample data that was at issue.
TLDR: Adding more threads to run concurrently to have bcp copy-in files of data seems to have the affect of overwhelming the endpoint MSSQL Server with write instructions, causing the bcp threads to fail (maybe timeing out?). When the number of threads becomes too many seems to depend on the size of the files getting copy-in'ed by bcp (ie. both the number of records in the file as well as the width of each record (ie. number of columns)).
Long version (more reasons for my theory):
1.
When running a larger number of bcp threads and looking at the processes started on the machine (https://clustershell.readthedocs.io/en/latest/tools/clush.html)
ps -aux | grep bcp
seeing a bunch of sleeping processes (notice the S, see https://askubuntu.com/a/360253/760862) as shown below (added newlines for readability)
me 135296 14.5 0.0 77596 6940 ? S 00:32 0:01
/opt/mssql-tools/bin/bcp TABLENAME in /path/to/tsv/1_16_0.tsv -D -S MyMSSQLServer -U myusername -P -d myDB -c -t \t -e /path/to/logfile
These threads appear to sleep for very long time. Further debugging into why these threads are sleeping suggests that they may in fact be doing their intended job (which would further imply that the problem may be coming from BCP itself (see https://stackoverflow.com/a/52748660/8236733)). From https://unix.stackexchange.com/a/47259/260742 and https://unix.stackexchange.com/a/36200/260742)
A process in S state is usually in a blocking system call, such as reading or writing to a file or the network, or waiting for another called program to finish.
(eg. writing to the MSSQL Server endpoint destination given to bcp in the ODBCDSN)
Your process will be in S state when it is doing reads and possibly writes that are blocking. Can also happen while waiting on semaphores or other synchronization primitives... This is all normal and expected, and not usually a problem... you don't want it to waste CPU while it's waiting for user input.
2. When running different sets of files of varying record-amount-per-file (eg. ranges of 500000 - 1000000 rows/file) and record-width-per-file (~10 - 100 columns/row), found that in cases with either very large data width or amounts, running a fixed set of bcp threads would fail.
Eg. for a set of ~33 TSVs with ~500000 rows each, each row being ~100 columns wide, a set of 30 threads would write the first few OK, but then all the rest would start returning failure messages. Incorporating a bit from #jamie's answer, the fact the the failure messages returned are "BCP copy in failed" errors does not necessarily mean it has do do with the content of the data in question. Having no actual content being written into the -e errorlog files from my process, #jamie's post says this
Regarding the "-e" option, it is meant to output data errors. login errors, bad table names... many other errros are not reported in the file created with the -e option. When you get output using the "-e" option, you'll see info like "value truncated" and such... will give you line numbers and sample data that was at issue.
Meanwhile, a set of ~33 TSVs with ~500000 rows each, each row being ~100 wide, and still using 30 bcp threads would complete quickly and without error (also would be faster when reducing the number of threads or file set). The only difference here being the overall size of the data being bcp copy-in'ed to the MSSQL Server.
All this while
free -mh
still showed that the machine running the threads still had ~15GB of free RAM remaining in each case (which is again why I suspect that the problem has to do with the remote MSSQL Server endpoint rather than with the code or local machine itself).
3. When running some of the tests from (2), found that manually killing the parallel process (via CTL+C) and then trying to remotely truncate the testing table being written to with /opt/mssql-tools/bin/sqlcmd -Q "truncate table mytable" on the local machine would take a very long time (as opposed to manually logging into the MSSQL Server and executing a truncate mytable in the DB). Again this makes me think that this has something to do with the MSSQL Server having too many connections and just being overwhelmed.
** Anyone with any MSSQL Mgmt Studio experience reading this (I have basically none), if you see anything here that makes you think that my theory is incorrect please let me know your thoughts.
Is there a Mono profiler mode similar to Java -Xloggc?
I would like to see a human readable GC report while my application is running. Currently Mono can be run with --profile=log option but the output is in binary format and every time I need to run mprof-report to read it. The output file also contains a lot of info which is not interesting for me.
I tried to reduce the file size by specifying heapshot=14400000ms to collect statistics every few hours but it didn't help a lot. In a week I had few gigabytes log.
I also tried to use "sample" profiler but the overhead was too much.
You can use Mono's trace filters for this. Just set the MONO_LOG_MASK to gc and lower the MONO_LOG_LEVEL. Then run your app normally and you will get the human-readable GC statistics while your app is running:
$ export MONO_LOG_MASK=gc
$ export MONO_LOG_LEVEL=debug
$ mono ... # run your application normally ..
...
# notice the human readable GC output
mono: GC_MAJOR: (LOS overflow) pause 26.00ms, total 26.06ms, bridge 0.00ms major 31472K/0K los 1575K/0K
Mono: GC_MINOR: (Nursery full) pause 2.30ms, total 2.35ms, bridge 0.00ms promoted 31456K major 31456K los 5135K
Mono: GC_MINOR: (Nursery full) pause 2.43ms, total 2.45ms, bridge 0.00ms promoted 31456K major 31456K los 8097K
Mono: GC_MINOR: (Nursery full) pause 1.80ms, total 1.82ms, bridge 0.00ms promoted 31472K major 31472K los 11425K