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mac os 配置Android开发环境

1.安装Eclipse 下载地址：http://www.eclipse.org/downloads/ 用uname-a看一下当前的系统内核是多少位的，选择相应的版本下载，解压完即完成了安装 2.安装SDK 下载地址：http://developer.android.com/sdk/index.html 下载之后解压，移动到一个比较合适的位置，一般放在用户目录下 3.安装ADT 下载地址：http://developer.android.com/sdk/installing/installing-adt.html 该页面中有一个表格，里面有具体的下载地址，下载之后不用解压 4.配置环境 现在要将SDK，ADT都集成到Eclipse中 在Eclipse->help->InstallNewSoftware中点击Add按钮，再点archive，然后选择下载好的ADT压缩包后确定。 在下方的列表中会出现Developmenttools等项目，勾选所有选项后，点next，等待安装完成即可。 在Eclipse->偏好设定->android中，在右侧的SDKLocation栏中浏览之前解压前移动后的SDK目录，点击OK即可. 选择菜单中的Eclipse->Window->AndroidSDKManager， 在左上角的Tools->Options中勾选上Forcehttps://...,然后close，再次打开AndroidSDKManager，这样就能顺利的下载更新列表了，选择new和update之后，点击install按钮，一路安装即可 本文转自 ustb80 51CTO博客，原文链接：http://blog.51cto.com/ustb80/1030996，如需转载请自行联系原作者
2017-11-28
优秀的个人博客,低调大师

mac os 下安装hadoop-2.7.3+hive-2.1.1＋sqoop-1.99.3

hadoop 安装 安装jdk vim ~/.bash_profile export JAVA_HOME="YOUR_JAVA_HOME" export PATH=$PATH:$JAVA_HOME/bin 配置完成后，运行 java -version -------------- java version "1.8.0_121" Java(TM) SE Runtime Environment (build 1.8.0_121-b13) Java HotSpot(TM) 64-Bit Server VM (build 25.121-b13, mixed mode) ssh免密登入 ssh-keygen -t rsa cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys ssh localhost # 验证 配置hadoop 下载hadoop，解压到指定目录，这里是/opt配置系统变量 vim ~/.bash_profile export HADOOP_HOME=/opt/hadoop-2.7.3 export HADOOP_PREFIX=$HADOOP_HOME export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH 修改 /etc/hadoop/hadoop-env.sh export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_121.jdk/Contents/Home export HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop export HADOOP_OPTS="$HADOOP_OPTS -Djava.security.krb5.realm=OX.AC.UK -Djava.security.krb5.kdc=kdc0.ox.ac.uk:kdc1.ox.ac.uk" 修改/etc/hadoop/core-site.xml <property> <name>fs.defaultFS</name> <value>hdfs://localhost:9000</value> <description>The name of the default file system.</description> </property> <property> <name>hadoop.tmp.dir</name> <value>/Users/micmiu/tmp/hadoop</value> <description>A base for other temporary directories.</description> </property> <property> <name>io.native.lib.available</name> <value>false</value> <description>default value is true:Should native hadoop libraries, if present, be used.</description> </property> 修改hdfs-site.xml <property> <name>dfs.replication</name> <value>1</value> <!--如果是单节点配置为1，如果是集群根据实际集群数量配置 --> </property> 修改yarn-site.xml <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> 修改mapred-site.xml cp mapred-site.xml.template mapred-site.xml. <property> <name>mapreduce.framework.name</name> <value>yarn</value> <final>true</final> </property> 格式化namenode hadoop namenode -format 启动hdfs和yarn start-dfs.sh start-yarn.sh 查看守护进程是否开启 jps 6917 DataNode 6838 NameNode 2810 Launcher 7130 ResourceManager 7019 SecondaryNameNode 7772 Jps 7215 NodeManager wordcount示例 hdfs dfs -mkdir -p /user/jjzhu/wordcount/in hdfs dfs -put xxxxx.txt /user/jjzhu/wordcount/in hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /user/jjzhu/wordcount/in /user/jjzhu/wordcount/out 运行过程 17/04/07 13:04:10 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 17/04/07 13:04:10 INFO input.FileInputFormat: Total input paths to process : 1 17/04/07 13:04:10 INFO mapreduce.JobSubmitter: number of splits:1 17/04/07 13:04:11 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1491532908338_0004 17/04/07 13:04:11 INFO impl.YarnClientImpl: Submitted application application_1491532908338_0004 17/04/07 13:04:11 INFO mapreduce.Job: The url to track the job: http://jjzhu:8088/proxy/application_1491532908338_0004/ 17/04/07 13:04:11 INFO mapreduce.Job: Running job: job_1491532908338_0004 17/04/07 13:04:18 INFO mapreduce.Job: Job job_1491532908338_0004 running in uber mode : false 17/04/07 13:04:18 INFO mapreduce.Job: map 0% reduce 0% 17/04/07 13:04:23 INFO mapreduce.Job: map 100% reduce 0% 17/04/07 13:04:29 INFO mapreduce.Job: map 100% reduce 100% 17/04/07 13:04:29 INFO mapreduce.Job: Job job_1491532908338_0004 completed successfully 17/04/07 13:04:29 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=1141 FILE: Number of bytes written=239913 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=869 HDFS: Number of bytes written=779 HDFS: Number of read operations=6 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=2859 Total time spent by all reduces in occupied slots (ms)=2527 Total time spent by all map tasks (ms)=2859 Total time spent by all reduce tasks (ms)=2527 Total vcore-milliseconds taken by all map tasks=2859 Total vcore-milliseconds taken by all reduce tasks=2527 Total megabyte-milliseconds taken by all map tasks=2927616 Total megabyte-milliseconds taken by all reduce tasks=2587648 Map-Reduce Framework Map input records=1 Map output records=118 Map output bytes=1219 Map output materialized bytes=1141 Input split bytes=122 Combine input records=118 Combine output records=89 Reduce input groups=89 Reduce shuffle bytes=1141 Reduce input records=89 Reduce output records=89 Spilled Records=178 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=103 CPU time spent (ms)=0 Physical memory (bytes) snapshot=0 Virtual memory (bytes) snapshot=0 Total committed heap usage (bytes)=329252864 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=747 File Output Format Counters Bytes Written=779 查看结果 hdfs dfs -ls /user/jjzhu/wordcount/out -rw-r--r-- 1 didi supergroup 0 2017-04-07 13:04 /user/jjzhu/wordcount/out/_SUCCESS -rw-r--r-- 1 didi supergroup 779 2017-04-07 13:04 /user/jjzhu/wordcount/out/part-r-00000 hdfs dfs -cat /user/jjzhu/wordcount/out/part-r-00000 A 1 Other 1 Others 1 Some 2 There 1 a 1 access 2 access); 1 according 1 adding 1 allowing 1 ...... 关闭hadoop stop-hdfs.sh stop-yarn.sh 安装hive 下载解压配置环境变量export HIVE_HOME=/opt/hive-2.1.1export PATH=$HIVE_HOME/bin:$PATH 配置hive cd /opt/hive/conf cp hive-env.sh.template hive-env.sh cp hive-default.xml.template hive-site.xml vim hive-env.sh HADOOP_HOME=/opt/hadoop-2.7.3 export HIVE_CONF_DIR=/opt/hive-2.1.1/conf export HIVE_AUX_JARS_PATH=/opt/hive-2.1.1//lib 下载mysql-connector-xx.xx.xx.jar 到lib下 vim hive-site.xml将所有${system:java.io.tmpdir} 和 ${system:user.name}替换并配置mysql数据库连接信息 <property> <name>javax.jdo.option.ConnectionURL</name> <value>jdbc:mysql://localhost:3306/hive?createDatabaseIfNotExist=true&amp;characterEncoding=UTF-8&amp;useSSL=false</value> </property> <property> <name>javax.jdo.option.ConnectionDriverName</name> <value>com.mysql.jdbc.Driver</value> </property> <property> <name>javax.jdo.option.ConnectionUserName</name> <value>root</value> </property> <property> <name>javax.jdo.option.ConnectionPassword</name> <value>123456</value> </property> 为hive创建HDFS目录 hdfs dfs -mkdir -p /usr/hive/warehouse hdfs dfs -mkdir -p /usr/hive/tmp hdfs dfs -mkdir -p /usr/hive/log hdfs dfs -chmod -R 777 /usr/hive 初始化数据库 ./bin/schematool -initSchema -dbType mysql mysql> show databases; +--------------------+ | Database | +--------------------+ | information_schema | | hive | | mysql | | performance_schema | | sys | +--------------------+ mysql> use hive; Database changed mysql> show tables; +---------------------------+ | Tables_in_hive | +---------------------------+ | AUX_TABLE | | BUCKETING_COLS | | SORT_COLS | | TABLE_PARAMS | | TAB_COL_STATS | | TBLS | | TBL_COL_PRIVS | | TBL_PRIVS | | TXNS | | TXN_COMPONENTS | | TYPES | | TYPE_FIELDS | | VERSION | | WRITE_SET | +---------------------------+ 启动hive jjzhu:opt didi$ hive SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/opt/hive-2.1.1/lib/log4j-slf4j-impl-2.4.1.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/opt/hadoop-2.7.3/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory] Logging initialized using configuration in jar:file:/opt/hive-2.1.1/lib/hive-common-2.1.1.jar!/hive-log4j2.properties Async: true Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases. hive> 安装sqoop 下载解压配置环境变量 export $SQOOP_HOME=/opt/sqoop-1.99.7 export SQOOP_SERVER_EXTRA_LIB=$SQOOP_HOME/extra export PATH=$SQOOP_HOME/bin:$PATH 修改sqoop配置 在conf目录下的两个主要配置文件sqoop.properties和sqoop_bootstrap.properties主要修改sqoop.properties org.apache.sqoop.submission.engine.mapreduce.configuration.directory=/opt/hadoop-2.7.3/etc/hadoop org.apache.sqoop.security.authentication.type=SIMPLE org.apache.sqoop.security.authentication.handler=org.apache.sqoop.security.authentication.SimpleAuthenticationHandler org.apache.sqoop.security.authentication.anonymous=true 验证配置是否有效 jzhu:bin didi$ ./sqoop2-tool verify Setting conf dir: /opt/sqoop-1.99.7/bin/../conf Sqoop home directory: /opt/sqoop-1.99.7 Sqoop tool executor: Version: 1.99.7 Revision: 435d5e61b922a32d7bce567fe5fb1a9c0d9b1bbb Compiled on Tue Jul 19 16:08:27 PDT 2016 by abefine Running tool: class org.apache.sqoop.tools.tool.VerifyTool 0 [main] INFO org.apache.sqoop.core.SqoopServer - Initializing Sqoop server. 12 [main] INFO org.apache.sqoop.core.PropertiesConfigurationProvider - Starting config file poller thread SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/opt/hadoop-2.7.3/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/opt/hive-2.1.1/lib/log4j-slf4j-impl-2.4.1.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. Verification was successful. Tool class org.apache.sqoop.tools.tool.VerifyTool has finished correctly. jjzhu:bin didi$ 开启服务器 ./bin/sqoop2-server start jps 9505 SqoopJettyServer ....
2017-04-06
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那个最可怕的 iOS 漏洞，同样也可以攻击 mac OS

全宇宙最厉害的存在，不是神马 CEO，也不是 UFO，而是 NSO。 这个名为 NSO Group 的以色列“间谍公司”不久前刚刚被曝光，使用一组逆天的 iOS 漏洞攻击了一位社会名流。这显然不是他们第一次“作案”。 为了封堵这组漏洞，苹果紧急发布了 iOS 9.3.5 升级补丁。而当全世界的黑客对苹果发布的漏洞详情仔细研究之后才发现，这些漏洞已经难以用“逆天”来形容了。使用这些漏洞，NSO 居然可以实现远程越狱任何一部手机，进而监控手机上的所有隐私。 【受害者曼苏尔收到的两条短信，点击链接手机就会被控制】 NSO 简直就是代替了上帝的功能。 为了纪念这三个“上帝漏洞”，安全研究员给他们命名为：三叉戟漏洞。 简单科普一下，三叉戟漏洞的“三叉”分别为： 1、远程突破 iOS 的 Safari 浏览器漏洞； 2、使内核信息泄露的漏洞； 3、用于
2017-08-08

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