Spark学习之路 (二)Spark2.3 HA集群的分布式安装[转]
下载Spark安装包
从官网下载
http://spark.apache.org/downloads.html
从微软的镜像站下载
http://mirrors.hust.edu.cn/apache/
从清华的镜像站下载
https://mirrors.tuna.tsinghua.edu.cn/apache/
安装基础
- Java8安装成功
- zookeeper安装成功
- hadoop2.7.5 HA安装成功
- Scala安装成功(不安装进程也可以启动)
Spark安装过程
上传并解压缩
[hadoop@hadoop1 ~]$ ls
apps data exam inithive.conf movie spark-2.3.0-bin-hadoop2.7.tgz udf.jar
cookies data.txt executions json.txt projects student zookeeper.out
course emp hive.sql log sougou temp
[hadoop@hadoop1 ~]$ tar -zxvf spark-2.3.0-bin-hadoop2.7.tgz -C apps/
为安装包创建一个软连接
[hadoop@hadoop1 ~]$ cd apps/
[hadoop@hadoop1 apps]$ ls
hadoop-2.7.5 hbase-1.2.6 spark-2.3.0-bin-hadoop2.7 zookeeper-3.4.10 zookeeper.out
[hadoop@hadoop1 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark
[hadoop@hadoop1 apps]$ ll
总用量 36
drwxr-xr-x. 10 hadoop hadoop 4096 3月 23 20:29 hadoop-2.7.5
drwxrwxr-x. 7 hadoop hadoop 4096 3月 29 13:15 hbase-1.2.6
lrwxrwxrwx. 1 hadoop hadoop 26 4月 20 13:48 spark -> spark-2.3.0-bin-hadoop2.7/
drwxr-xr-x. 13 hadoop hadoop 4096 2月 23 03:42 spark-2.3.0-bin-hadoop2.7
drwxr-xr-x. 10 hadoop hadoop 4096 3月 23 2017 zookeeper-3.4.10
-rw-rw-r--. 1 hadoop hadoop 17559 3月 29 13:37 zookeeper.out
[hadoop@hadoop1 apps]$
进入spark/conf修改配置文件
(1)进入配置文件所在目录
[hadoop@hadoop1 ~]$ cd apps/spark/conf/
[hadoop@hadoop1 conf]$ ll
总用量 36
-rw-r--r--. 1 hadoop hadoop 996 2月 23 03:42 docker.properties.template
-rw-r--r--. 1 hadoop hadoop 1105 2月 23 03:42 fairscheduler.xml.template
-rw-r--r--. 1 hadoop hadoop 2025 2月 23 03:42 log4j.properties.template
-rw-r--r--. 1 hadoop hadoop 7801 2月 23 03:42 metrics.properties.template
-rw-r--r--. 1 hadoop hadoop 865 2月 23 03:42 slaves.template
-rw-r--r--. 1 hadoop hadoop 1292 2月 23 03:42 spark-defaults.conf.template
-rwxr-xr-x. 1 hadoop hadoop 4221 2月 23 03:42 spark-env.sh.template
[hadoop@hadoop1 conf]$
(2)复制spark-env.sh.template并重命名为spark-env.sh,并在文件最后添加配置内容
[hadoop@hadoop1 conf]$ cp spark-env.sh.template spark-env.sh
[hadoop@hadoop1 conf]$ vi spark-env.sh
export JAVA_HOME=/usr/local/jdk1.8.0_73
#export SCALA_HOME=/usr/share/scala
export HADOOP_HOME=/home/hadoop/apps/hadoop-2.7.5
export HADOOP_CONF_DIR=/home/hadoop/apps/hadoop-2.7.5/etc/hadoop
export SPARK_WORKER_MEMORY=500m
export SPARK_WORKER_CORES=1
export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop1:2181,hadoop2:2181,hadoop3:2181,hadoop4:2181 -Dspark.deploy.zookeeper.dir=/spark"
注:
export SPARK_MASTER_IP=hadoop1 这个配置要注释掉。
集群搭建时配置的spark参数可能和现在的不一样,主要是考虑个人电脑配置问题,如果memory配置太大,机器运行很慢。
说明:
-Dspark.deploy.recoveryMode=ZOOKEEPER #说明整个集群状态是通过zookeeper来维护的,整个集群状态的恢复也是通过zookeeper来维护的。就是说用zookeeper做了spark的HA配置,Master(Active)挂掉的话,Master(standby)要想变成Master(Active)的话,Master(Standby)就要像zookeeper读取整个集群状态信息,然后进行恢复所有Worker和Driver的状态信息,和所有的Application状态信息;
-Dspark.deploy.zookeeper.url=hadoop1:2181,hadoop2:2181,hadoop3:2181,hadoop4:2181#将所有配置了zookeeper,并且在这台机器上有可能做master(Active)的机器都配置进来;(我用了4台,就配置了4台)
-Dspark.deploy.zookeeper.dir=/spark
这里的dir和zookeeper配置文件zoo.cfg中的dataDir的区别???
-Dspark.deploy.zookeeper.dir是保存spark的元数据,保存了spark的作业运行状态;
zookeeper会保存spark集群的所有的状态信息,包括所有的Workers信息,所有的Applactions信息,所有的Driver信息。
(3)复制slaves.template成slaves
[hadoop@hadoop1 conf]$ cp slaves.template slaves
[hadoop@hadoop1 conf]$ vi slaves
添加如下内容
hadoop1
hadoop2
hadoop3
hadoop4
(4)将安装包分发给其他节点
[hadoop@hadoop1 ~]$ cd apps/
[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop2:$PWD
[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop3:$PWD
[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop4:$PWD
创建软连接
[hadoop@hadoop2 ~]$ cd apps/
[hadoop@hadoop2 apps]$ ls
hadoop-2.7.5 hbase-1.2.6 spark-2.3.0-bin-hadoop2.7 zookeeper-3.4.10
[hadoop@hadoop2 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark
[hadoop@hadoop2 apps]$ ll
总用量 16
drwxr-xr-x 10 hadoop hadoop 4096 3月 23 20:29 hadoop-2.7.5
drwxrwxr-x 7 hadoop hadoop 4096 3月 29 13:15 hbase-1.2.6
lrwxrwxrwx 1 hadoop hadoop 26 4月 20 19:26 spark -> spark-2.3.0-bin-hadoop2.7/
drwxr-xr-x 13 hadoop hadoop 4096 4月 20 19:24 spark-2.3.0-bin-hadoop2.7
drwxr-xr-x 10 hadoop hadoop 4096 3月 21 19:31 zookeeper-3.4.10
[hadoop@hadoop2 apps]$
配置环境变量
所有节点均要配置
[hadoop@hadoop1 spark]$ vi ~/.bashrc
#Spark
export SPARK_HOME=/home/hadoop/apps/spark
export PATH=$PATH:$SPARK_HOME/bin
保存并使其立即生效
[hadoop@hadoop1 spark]$ source ~/.bashrc
启动
先启动zookeeper集群
所有节点均要执行
[hadoop@hadoop1 ~]$ zkServer.sh start
ZooKeeper JMX enabled by default
Using config: /home/hadoop/apps/zookeeper-3.4.10/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
[hadoop@hadoop1 ~]$ zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /home/hadoop/apps/zookeeper-3.4.10/bin/../conf/zoo.cfg
Mode: follower
[hadoop@hadoop1 ~]$
在启动HDFS集群
在一个节点上执行
[hadoop@hadoop1 ~]$ cd apps/spark/sbin/
[hadoop@hadoop1 sbin]$ start-all.sh
查看进程
问题
查看进程发现spark集群只有hadoop1成功启动了Master进程,其他3个节点均没有启动成功,需要手动启动,进入到/home/hadoop/apps/spark/sbin目录下执行以下命令,3个节点都要执行
[hadoop@hadoop2 ~]$ cd ~/apps/spark/sbin/
[hadoop@hadoop2 sbin]$ start-master.sh
执行之后再次查看进程
Master进程和Worker进程都以启动成功
验证
查看Web界面Master状态
hadoop1是ALIVE状态,hadoop2、hadoop3和hadoop4均是STANDBY状态
hadoop1节点
hadoop2节点
hadoop3节点
hadoop4节点
验证HA的高可用
手动干掉hadoop1上面的Master进程,观察是否会自动进行切换
干掉hadoop1上的Master进程之后,再次查看web界面
hadoo1节点,由于Master进程被干掉,所以界面无法访问
hadoop2节点,Master被干掉之后,hadoop2节点上的Master成功篡位成功,成为ALIVE状态
hadoop3节点
hadoop4节点
执行Spark程序on standalone
执行第一个Spark程序
[hadoop@hadoop3 ~]$ /home/hadoop/apps/spark/bin/spark-submit \
> --class org.apache.spark.examples.SparkPi \
> --master spark://hadoop1:7077 \
> --executor-memory 500m \
> --total-executor-cores 1 \
> /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \
> 100
其中的spark://hadoop1:7077是下图中的地址
运行结果
启动spark shell
[hadoop@hadoop1 ~]$ /home/hadoop/apps/spark/bin/spark-shell \
> --master spark://hadoop1:7077 \
> --executor-memory 500m \
> --total-executor-cores 1
参数说明:
--master spark://hadoop1:7077 指定Master的地址
--executor-memory 500m:指定每个worker可用内存为500m
--total-executor-cores 1: 指定整个集群使用的cup核数为1个
注意:
如果启动spark shell时没有指定master地址,但是也可以正常启动spark shell和执行spark shell中的程序,其实是启动了spark的local模式,该模式仅在本机启动一个进程,没有与集群建立联系。
Spark Shell中已经默认将SparkContext类初始化为对象sc。用户代码如果需要用到,则直接应用sc即可
Spark Shell中已经默认将SparkSQl类初始化为对象spark。用户代码如果需要用到,则直接应用spark即可
在spark shell中编写WordCount程序
(1)编写一个hello.txt文件并上传到HDFS上的spark目录下
[hadoop@hadoop1 ~]$ vi hello.txt
[hadoop@hadoop1 ~]$ hadoop fs -mkdir -p /spark
[hadoop@hadoop1 ~]$ hadoop fs -put hello.txt /spark
hello.txt的内容如下
you,jump
i,jump
you,jump
i,jump
jump
(2)在spark shell中用scala语言编写spark程序
scala>
sc.textFile("/spark/hello.txt").flatMap(_.split(",")).map((_,1)).reduceByKey(_+_).saveAsTextFile("/spark/out")
说明:
sc是SparkContext对象,该对象是提交spark程序的入口
textFile("/spark/hello.txt")是hdfs中读取数据
flatMap(.split(" "))先map再压平
map((,1))将单词和1构成元组
reduceByKey(+)按照key进行reduce,并将value累加
saveAsTextFile("/spark/out")将结果写入到hdfs中
(3)使用hdfs命令查看结果
[hadoop@hadoop2 ~]$ hadoop fs -cat /spark/out/p*
(jump,5)
(you,2)
(i,2)
[hadoop@hadoop2 ~]$
执行Spark程序on YARN
前提
成功启动zookeeper集群、HDFS集群、YARN集群
启动Spark on YARN
[hadoop@hadoop1 bin]$ spark-shell --master yarn --deploy-mode client
报错如下:
报错原因:内存资源给的过小,yarn直接kill掉进程,则报rpc连接失败、ClosedChannelException等错误。
解决方法:
先停止YARN服务,然后修改yarn-site.xml,增加如下内容
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
<description>Whether virtual memory limits will be enforced for containers</description>
</property>
<property>
<name>yarn.nodemanager.vmem-pmem-ratio</name>
<value>4</value>
<description>Ratio between virtual memory to physical memory when setting memory limits for containers</description>
</property>
将新的yarn-site.xml文件分发到其他Hadoop节点对应的目录下,最后在重新启动YARN。
重新执行以下命令启动spark on yarn
[hadoop@hadoop1 hadoop]$ spark-shell --master yarn --deploy-mode client
启动成功
打开YARN的web界面
打开YARN WEB页面:http://hadoop4:8088
可以看到Spark shell应用程序正在运行
单击ID号链接,可以看到该应用程序的详细信息
单击“ApplicationMaster”链接
运行程序
scala> val array = Array(1,2,3,4,5)
array: Array[Int] = Array(1, 2, 3, 4, 5)
scala> val rdd = sc.makeRDD(array)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at makeRDD at <console>:26
scala> rdd.count
res0: Long = 5
scala>
再次查看YARN的web界面
查看executors
执行Spark自带的示例程序PI
[hadoop@hadoop1 ~]$ spark-submit --class org.apache.spark.examples.SparkPi \
> --master yarn \
> --deploy-mode cluster \
> --driver-memory 500m \
> --executor-memory 500m \
> --executor-cores 1 \
> /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \
> 10
执行过程
[hadoop@hadoop1 ~]$ spark-submit --class org.apache.spark.examples.SparkPi \
> --master yarn \
> --deploy-mode cluster \
> --driver-memory 500m \
> --executor-memory 500m \
> --executor-cores 1 \
> /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \
> 10
2018-04-21 17:57:32 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2018-04-21 17:57:34 INFO ConfiguredRMFailoverProxyProvider:100 - Failing over to rm2
2018-04-21 17:57:34 INFO Client:54 - Requesting a new application from cluster with 4 NodeManagers
2018-04-21 17:57:34 INFO Client:54 - Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
2018-04-21 17:57:34 INFO Client:54 - Will allocate AM container, with 884 MB memory including 384 MB overhead
2018-04-21 17:57:34 INFO Client:54 - Setting up container launch context for our AM
2018-04-21 17:57:34 INFO Client:54 - Setting up the launch environment for our AM container
2018-04-21 17:57:34 INFO Client:54 - Preparing resources for our AM container
2018-04-21 17:57:36 WARN Client:66 - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
2018-04-21 17:57:39 INFO Client:54 - Uploading resource file:/tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720/__spark_libs__8262081479435245591.zip -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/__spark_libs__8262081479435245591.zip
2018-04-21 17:57:44 INFO Client:54 - Uploading resource file:/home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/spark-examples_2.11-2.3.0.jar
2018-04-21 17:57:44 INFO Client:54 - Uploading resource file:/tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720/__spark_conf__2498510663663992254.zip -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/__spark_conf__.zip
2018-04-21 17:57:44 INFO SecurityManager:54 - Changing view acls to: hadoop
2018-04-21 17:57:44 INFO SecurityManager:54 - Changing modify acls to: hadoop
2018-04-21 17:57:44 INFO SecurityManager:54 - Changing view acls groups to:
2018-04-21 17:57:44 INFO SecurityManager:54 - Changing modify acls groups to:
2018-04-21 17:57:44 INFO SecurityManager:54 - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); groups with view permissions: Set(); users with modify permissions: Set(hadoop); groups with modify permissions: Set()
2018-04-21 17:57:44 INFO Client:54 - Submitting application application_1524303370510_0005 to ResourceManager
2018-04-21 17:57:44 INFO YarnClientImpl:273 - Submitted application application_1524303370510_0005
2018-04-21 17:57:45 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:45 INFO Client:54 -
client token: N/A
diagnostics: N/A
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: default
start time: 1524304664749
final status: UNDEFINED
tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/
user: hadoop
2018-04-21 17:57:46 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:47 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:48 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:49 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:50 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:51 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:52 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:53 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)
2018-04-21 17:57:54 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:57:54 INFO Client:54 -
client token: N/A
diagnostics: N/A
ApplicationMaster host: 192.168.123.104
ApplicationMaster RPC port: 0
queue: default
start time: 1524304664749
final status: UNDEFINED
tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/
user: hadoop
2018-04-21 17:57:55 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:57:56 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:57:57 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:57:58 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:57:59 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:00 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:01 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:02 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:03 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:04 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:05 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:06 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:07 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:08 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)
2018-04-21 17:58:09 INFO Client:54 - Application report for application_1524303370510_0005 (state: FINISHED)
2018-04-21 17:58:09 INFO Client:54 -
client token: N/A
diagnostics: N/A
ApplicationMaster host: 192.168.123.104
ApplicationMaster RPC port: 0
queue: default
start time: 1524304664749
final status: SUCCEEDED
tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/
user: hadoop
2018-04-21 17:58:09 INFO Client:54 - Deleted staging directory hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005
2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Shutdown hook called
2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Deleting directory /tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720
2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Deleting directory /tmp/spark-06de6905-8067-4f1e-a0a0-bc8a51daf535
[hadoop@hadoop1 ~]$
Spark学习之路 (二)Spark2.3 HA集群的分布式安装[转]的更多相关文章
- Spark学习之路 (二)Spark2.3 HA集群的分布式安装
一.下载Spark安装包 1.从官网下载 http://spark.apache.org/downloads.html 2.从微软的镜像站下载 http://mirrors.hust.edu.cn/a ...
- Spark学习(四)Spark2.3 HA集群的分布式安装
一.下载Spark安装包 1.从官网下载 http://spark.apache.org/downloads.html 2.从微软的镜像站下载 http://mirrors.hust.edu.cn/a ...
- Spark2.3 HA集群的分布式安装
一.下载Spark安装包 1.从官网下载 http://spark.apache.org/downloads.html 2.从微软的镜像站下载 http://mirrors.hust.edu.cn/a ...
- Hadoop学习之路(四)Hadoop集群搭建和简单应用
概念了解 主从结构:在一个集群中,会有部分节点充当主服务器的角色,其他服务器都是从服务器的角色,当前这种架构模式叫做主从结构. 主从结构分类: 1.一主多从 2.多主多从 Hadoop中的HDFS和Y ...
- Docker 与 K8S学习笔记(二十三)—— Kubernetes集群搭建
小伙伴们,好久不见,这几个月实在太忙,所以一直没有更新,今天刚好有空,咱们继续k8s的学习,由于我们后面需要深入学习Pod的调度,所以我们原先使用MiniKube搭建的实验环境就不能满足我们的需求了, ...
- Hadoop学习之路(五)Hadoop集群搭建模式和各模式问题
分布式集群的通用问题 当前的HDFS和YARN都是一主多从的分布式架构,主从节点---管理者和工作者 问题:如果主节点或是管理者宕机了.会出现什么问题? 群龙无首,整个集群不可用.所以在一主多从的架构 ...
- Storm 学习之路(四)—— Storm集群环境搭建
一.集群规划 这里搭建一个3节点的Storm集群:三台主机上均部署Supervisor和LogViewer服务.同时为了保证高可用,除了在hadoop001上部署主Nimbus服务外,还在hadoop ...
- HBase 学习之路(四)—— HBase集群环境配置
一.集群规划 这里搭建一个3节点的HBase集群,其中三台主机上均为Regin Server.同时为了保证高可用,除了在hadoop001上部署主Master服务外,还在hadoop002上部署备用的 ...
- Hadoop 学习之路(五)—— Hadoop集群环境搭建
一.集群规划 这里搭建一个3节点的Hadoop集群,其中三台主机均部署DataNode和NodeManager服务,但只有hadoop001上部署NameNode和ResourceManager服务. ...
随机推荐
- [MacOS]MacOS字体文件位置
$ cd /System/Library/Fonts $ ls Apple Braille Outline 6 Dot.ttf Noteworthy.ttc SFCompactText-Regular ...
- ID生成器之——别人家的方案and自家的方案
“叮咚,叮咚……”,微信提示音一声接一声,声音是那么的频繁,有妖气,待俺去看一看. 这天刚吃完午饭,打开微信,发现我们的技术讨论组里有 100 多条未读消息,心想,是不是系统出问题了?怎么消息那么频繁 ...
- POJ 1753 Flip Game 暴力 深搜
Flip Game Time Limit: 1000MS Memory Limit: 65536K Total Submissions: 59468 Accepted: 24750 Descr ...
- JS中new的实现原理及重写
提到new,肯定会和类和实例联系起来,如: function Func() { let x = 100; this.num = x + } let f = new Func(); 上面的代码,我们首先 ...
- win10安装两个不同版本的mysql(mysql5.7和mysql-8.0.19)
win10中安装mysql5.7后,安装mysql-8.0.19 在D:\mysql-8.0.19-winx64目录下创建一个my.ini文件 [mysqld] # 设置3307端口 port # 设 ...
- Git命令行操作方法
1.GitHub上创建一个Repositories(仓库),并复制仓库地址
- 回炉重造之重读Windows核心编程-006-线程
线程也是有两部分组成的: 线程的内核对象,操作系统用来管理线程和统计线程信息的地方. 线程堆栈,用于维护现场在执行代码的时候用到的所有函数参数和局部变量. 进程是线程的容器,如果进程中有一个以上的线程 ...
- 《HTML&CSS design and build websites》学习笔记(1)
Chapter 1: Structure Chapter 2: Text <head>表示标题,默认显示在第一行. <title>表示抬头,显示在浏览器的标签页,还有窗口栏. ...
- vim编辑超大文件
进入大文件(12g,250w+ lines),vim,耐心等待 有两种方法编辑删除冗余字段 1.set number ,可以通过:+数字组合跳到指定行,输入命令 ":100,200d&q ...
- matlab中的输出显示函数
matlab中的输出显示函数 在matlab中使用的显示函数有disp.sprintf.fprintf比较常用.下面来介绍一下他们的用法. 1.disp()函数: disp(x)主要是用来输出变量x的 ...