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is returned: case-sensitive, must use letters or $ or _, must be preceded by; Common methods for 2.String types: indexOf (Str,index): Finds the position of the first occurrence of a specified character in a string Charat (index): Returns the character at the specified position toLowerCase (): Converted to lowercase touppercase (): Convert to uppercase substring (INDEX1,INDEX2): Intercepts the string at the specified position, split before the package (): Split the string into an array definitio

? ? ? ?? 28dtraefik-conf? ? ? ? ? ? ? ? ? ? ? ?? 1? ? ? ?? 12s[[emailprotected] config]#``6. Deploy Traefik, which is primarily to correlate the created secret and Configmap, and mount the corresponding host directory.Back up the original file[[emailprotected] k8s]# cp traefik-deployment.yaml traefik-deployment.yaml.bk[[emailprotected] k8s]# llA well-configured reference is shown below:[[emailprotected] k8s]# cat traefik-deployment.yaml---apiVersion:v

multicore, scheme one wins if four tasks are I /O intensive, no more cores can not solve i/# Conclusion: Now the computer is basically multicore, Python for compute-intensive tasks open multi-threading efficiency does not bring much performance improvement, It's not even as serial (without a lot of switching), but there's a significant increase in efficiency for IO-intensive tasks. Five multi-threading performance testing fromMultiprocessingImportProcess fromThreadingImportThreadImportOs,timede

#创建secretkubectl create -f secrets.yml#查看secret运行状态kubectl get secret --all-namespacesNAMESPACE NAME TYPE DATA AGEdefault mysecret Opaque 2 10m#使用secret##创建好secret之后，有两种方式来使用它：（1）以Volume方式（2）以环境变量方式 Mount the secret to the volume#创建nginx.yamlapiVersion: v1kind: Podmetadata: labels: name: wtf name: mypodspec:

computer is basically multicore, Python for compute-intensive task open multi-threading efficiency does not bring much performance improvement, or even better than serial ( Without a lot of switching), however, there is a significant increase in efficiency for IO-intensive tasks. Five multi-threading performance testing fromMultiprocessingImportProcess fromThreadingImportThreadImportOs,timedefWork (): Res=0 forIinchRange (100000000): Res*=Iif __name__=='__main__': L=[] Print(Os.cpu_count ())

Fifth: Copy Code code as follows: Select W2.N, w1.* from ARTICLE W1, (Select up 50030 row_number () over (order by year DESC, id DESC) n, IDs from ARTICLE ) W2 WHERE w1.id = w2.id and W2.N > 50000 order by W2.N ASC Average Enquiry 100 Time: 15S Query 第1000-1030条 Records First scenario: Copy Code code as follows: Select Top * from ARTICLE WHERE ID. (select Top 1000 ID to ARTICLE order by year DESC, id DESC) Sc,id DESC Average Enquiry 100 Time

"-------------------------------------------------------------------" echo "NAME PHONE JA n| feb| mar| Total danated "echo"-------------------------------------------------------------------"awk-f: ' {printf ( "%-20s%12s%5d%5d%5d\t% 5d\n", $1,$2,$3,$4,$5,$3+$4+$5)} ' linux2.txt echo '---------------------------------------- ---------------------------"echo" SUMMARY "echo" "-------------------------------------------------------------------" awk-f

installation Installation Docker is an operating system-level virtualization tool that automates the deployment of applications in containers Install Docker [root@linuxprobe~]# yum-y Install Docker [root@linuxprobe~]# systemctl start Docker [root@linuxprobe~]# Systemctl Enable Docker [Root@linuxprobe ~]# systemctl Status Docker # View Docker status Docker.service-docker application Container Engine Loaded: loaded (/usr/lib/systemd/system/docker.service; vendor preset:disab

) Text To convert a timestamp into a string To_char (Current_timestamp, ' HH12:MI:SS ') To_char (interval, text) Text Convert time intervals to strings To_char (interval ' 15h 2m 12s ', ' HH24:MI:SS ') To_char (int, text) Text Converts integers to strings To_char (125, ' 999 ') To_char (double precision, text) Text Converts a real/double number to a string To_char (125

of all layers to find the results of the average merge. can see that (1) For each individual layer, the final layer of the feature fusion results are the best. (2) to average the results of all layers, the final accuracy rate will be improved. During the training process, the edge detection is actually a 2 classification task for each pixel. Most of the pixels are not edges, only a few pixels are marginal, and in order to balance the positive and negative samples, the authors introduce class-b

because of the out of memory. IO (Reference) # docker run-it--rm Dbyin/tlinux:1.2/bin/bash bash-4.1# Time $ (dd If=/dev/zero of=f1.txt bs=1024 count=500000 Sync) 500000+0 Records in 500000+0 records out 512000000 bytes (MB) copied, 1.28334 s, 399 MB/s Real 0m12.091s user 0m0.056s sys 0m1.237s can see, write 512M data, share 12s, average 42m/s. Limit IO bandwidth to 10m/s: # echo "253:

Install Perl modules to a non-custom directory How does I get Makemaker and Module::build to install to the same place? Module::build, as of 0.28, supports, ways to install to the same location as Makemaker. 1) Use Install_base/--install_base makemaker (as of 6.31) and Module::build (as of 0.28) both can INSTALL to the same lo Cations using the "install_base" concept. See "Install_base" in Extutils::makemaker for details. To get MM and MB to install to the same location simply set install_base i