1. local模式
$SPARK_HOME/bin/spark-shell --master local import org.apache.log4j.{Level,Logger} // 导入java log4j的日志相关类 Logger.getLogger("org.apache.spark").setLevel(Level.WARN) //设置日志级别 val data= sc.textFile("file:///home/workspace/software/spark-2.0.0/README.md") data.collect // 显示所有内容 data.first // 显示第一条记录内容，本例中是String data.take(0) // 显示第一条内容，但是是Array val filterdData=data.filter(line=>line.contains("Spark")) //对数据集进行过滤 filterdData.collect //如果数据量比较大的话，该方法可能会占用很大的内存，要慎用 filterdData.count //行数统计 filterdData.map(line=>line.split(" ").size).reduce((a,b)=>if (a>b) a else b) //获取单词数最多的行 filterdData.map(line=>line.split(" ").size).reduce((a,b)=>Math.max(a,b)) //获取单词数最多的行 val countResult=data.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey((a,b)=>a+b) //单词个数统计 countResult.collect // 显示统计结果，可能比较消耗内存哦 data.map(line=>line.split(" ").size).top(3) //获取单词个数排名前三的统计结果 data.count //cache前的行数统计 data.cache //设置data为内存缓存，注意RDD.cache()与RDD.persist()的区别，可用RDD.unpersist()取消缓存的rdd data.count //cache后的行数统计 data.saveAsTextFile("file:///home/workspace/countresult") //保存结果到文件系统/home/workspace/countresult文件夹下面
2. Spark standalone模式
先上传文件到hdfs
hdfs dfs -put /home/workspace/software/spark-2.0.0/README.md /
spark操作
$SPARK_HOME/bin/spark-shell --master spark://192.168.1.193:7077 import org.apache.log4j.{Level,Logger} // 导入java log4j的日志相关类 Logger.getLogger("org.apache.spark").setLevel(Level.WARN) //设置日志级别 val data= sc.textFile("/README.md") data.collect // 显示所有内容 data.first // 显示第一条记录内容，本例中是String data.take(0) // 显示第一条内容，但是是Array val filterdData=data.filter(line=>line.contains("Spark")) //对数据集进行过滤 filterdData.collect //如果数据量比较大的话，该方法可能会占用很大的内存，要慎用 filterdData.count //行数统计 filterdData.map(line=>line.split(" ").size).reduce((a,b)=>if (a>b) a else b) //获取单词数最多的行 filterdData.map(line=>line.split(" ").size).reduce((a,b)=>Math.max(a,b)) //获取单词数最多的行 val countResult=data.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey((a,b)=>a+b) //单词个数统计 countResult.collect // 显示统计结果，可能比较消耗内存哦 data.map(line=>line.split(" ").size).top(3) //获取单词个数排名前三的统计结果 data.count //cache前的行数统计 data.cache //设置data为内存缓存，注意RDD.cache()与RDD.persist()的区别，可用RDD.unpersist取消缓存的RDD data.count //cache后的行数统计 data.saveAsTextFile("/countresult") //保存结果到hdfs的/countresult文件夹下面
最后保存的结果为:
3. yarn模式
$SPARK_HOME/bin/spark-shell --master yarn import org.apache.log4j.{Level,Logger} // 导入java log4j的日志相关类 Logger.getLogger("org.apache.spark").setLevel(Level.WARN) //设置日志级别 val data= sc.textFile("/README.md") data.collect // 显示所有内容 data.first // 显示第一条记录内容，本例中是String data.take(0) // 显示第一条内容，但是是Array val filterdData=data.filter(line=>line.contains("Spark")) //对数据集进行过滤 filterdData.collect //如果数据量比较大的话，该方法可能会占用很大的内存，要慎用 filterdData.count //行数统计 filterdData.map(line=>line.split(" ").size).reduce((a,b)=>if (a>b) a else b) //获取单词数最多的行 filterdData.map(line=>line.split(" ").size).reduce((a,b)=>Math.max(a,b)) //获取单词数最多的行 val countResult=data.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey((a,b)=>a+b) //单词个数统计 countResult.collect // 显示统计结果，可能比较消耗内存哦 data.map(line=>line.split(" ").size).top(3) //获取单词个数排名前三的统计结果 data.count //cache前的行数统计 data.cache //设置data为内存缓存，注意RDD.cache()与RDD.persist()的区别 data.count //cache后的行数统计 data.saveAsTextFile("/countresult") //保存结果到hdfs的/countresult文件夹下面
还有一种mesos部署模式，因为环境没有做部署，没有做测试。
spark-shell参数列表:
[druid@palo101 workspace]$ spark-shell --help Usage: ./bin/spark-shell [options] Options: --master MASTER_URL spark://host:port, mesos://host:port, yarn, or local. --deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or on one of the worker machines inside the cluster ("cluster") (Default: client). --class CLASS_NAME Your application's main class (for Java / Scala apps). --name NAME A name of your application. --jars JARS Comma-separated list of local jars to include on the driver and executor classpaths. --packages Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths. Will search the local maven repo, then maven central and any additional remote repositories given by --repositories. The format for the coordinates should be groupId:artifactId:version. --exclude-packages Comma-separated list of groupId:artifactId, to exclude while resolving the dependencies provided in --packages to avoid dependency conflicts. --repositories Comma-separated list of additional remote repositories to search for the maven coordinates given with --packages. --py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place on the PYTHONPATH for Python apps. --files FILES Comma-separated list of files to be placed in the working directory of each executor. --conf PROP=VALUE Arbitrary Spark configuration property. --properties-file FILE Path to a file from which to load extra properties. If not specified, this will look for conf/spark-defaults.conf. --driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M). --driver-java-options Extra Java options to pass to the driver. --driver-library-path Extra library path entries to pass to the driver. --driver-class-path Extra class path entries to pass to the driver. Note that jars added with --jars are automatically included in the classpath. --executor-memory MEM Memory per executor (e.g. 1000M, 2G) (Default: 1G). --proxy-user NAME User to impersonate when submitting the application. This argument does not work with --principal / --keytab. --help, -h Show this help message and exit. --verbose, -v Print additional debug output. --version, Print the version of current Spark. Spark standalone with cluster deploy mode only: --driver-cores NUM Cores for driver (Default: 1). Spark standalone or Mesos with cluster deploy mode only: --supervise If given, restarts the driver on failure. --kill SUBMISSION_ID If given, kills the driver specified. --status SUBMISSION_ID If given, requests the status of the driver specified. Spark standalone and Mesos only: --total-executor-cores NUM Total cores for all executors. Spark standalone and YARN only: --executor-cores NUM Number of cores per executor. (Default: 1 in YARN mode, or all available cores on the worker in standalone mode) YARN-only: --driver-cores NUM Number of cores used by the driver, only in cluster mode (Default: 1). --queue QUEUE_NAME The YARN queue to submit to (Default: "default"). --num-executors NUM Number of executors to launch (Default: 2). If dynamic allocation is enabled, the initial number of executors will be at least NUM. --archives ARCHIVES Comma separated list of archives to be extracted into the working directory of each executor. --principal PRINCIPAL Principal to be used to login to KDC, while running on secure HDFS. --keytab KEYTAB The full path to the file that contains the keytab for the principal specified above. This keytab will be copied to the node running the Application Master via the Secure Distributed Cache, for renewing the login tickets and the delegation tokens periodically.