Spark是一个通用的并行计算框架，由UCBerkeley的AMP实验室开发。

Spark和Hadoop有什么不同呢?
Spark是基于map reduce算法实现的分布式计算，拥有Hadoop MapReduce所具有的优点;但不同于MapReduce的是Job中间输出和结果可以保存在内存中，从而不再需要读写HDFS，因此Spark能更好地适用于数据挖掘与机器学习等需要迭代的map reduce的算法。
 
Spark的适用场景
Spark是基于内存的迭代计算框架，适用于需要多次操作特定数据集的应用场合。需要反复操作的次数越多，所需读取的数据量越大，受益越大，数据量小但是计算密集度较大的场合，受益就相对较小
由于RDD的特性，Spark不适用那种异步细粒度更新状态的应用，例如web服务的存储或者是增量的web爬虫和索引。就是对于那种增量修改的应用模型不适合。
总的来说Spark的适用面比较广泛且比较通用。
运行模式
  • 本地模式
  • Standalone模式
  • Mesoes模式
  • yarn模式
 
我们来看看Standalone模式怎么运行。

1.下载安装

http://spark.apache.org/downloads.html

这里可以选择下载源码编译，或者下载已经编译好的程序（因为spark是运行在JVM上面，也可以说是跨平台的）,这里是直接下载可执行程序。

Chose a package type: Pre-built for Hadoop 2.4 and later 。

解压这个 spark-1.3.0-bin-hadoop2.4.tgz 即可。

PS：你需要安装java运行环境

~/project/spark-1.3.0-bin-hadoop2.4 $java -version

java version "1.8.0_25"

Java(TM) SE Runtime Environment (build 1.8.0_25-b17)

Java HotSpot(TM) 64-Bit Server VM (build 25.25-b02, mixed mode)

2.目录分布

sbin目录是各种启动命令

~/project/spark-1.3.0-bin-hadoop2.4 $tree sbin/

sbin/

├── slaves.sh

├── spark-config.sh

├── spark-daemon.sh

├── spark-daemons.sh

├── start-all.sh

├── start-history-server.sh

├── start-master.sh

├── start-slave.sh

├── start-slaves.sh

├── start-thriftserver.sh

├── stop-all.sh

├── stop-history-server.sh

├── stop-master.sh

├── stop-slaves.sh

└── stop-thriftserver.sh

conf目录是一些配置模板： 

~/project/spark-1.3.0-bin-hadoop2.4 $tree conf/

conf/

├── fairscheduler.xml.template

├── log4j.properties.template

├── metrics.properties.template

├── slaves.template

├── spark-defaults.conf.template

└── spark-env.sh.template

3.启动master 

~/project/spark-1.3.0-bin-hadoop2.4 $./sbin/start-master.sh

starting org.apache.spark.deploy.master.Master, logging to /Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/sbin/../logs/spark-qpzhang-org.apache.spark.deploy.master.Master-1-qpzhangdeMac-mini.local.out

没有进行任何配置时，采用的都是默认配置，可以看到日志文件的输出：

~/project/spark-1.3.0-bin-hadoop2.4 $cat logs/spark-qpzhang-org.apache.spark.deploy.master.Master-1-qpzhangdeMac-mini.local.out 

Spark assembly has been built with Hive, including Datanucleus jars on classpath

Spark Command: /Library/Java/JavaVirtualMachines/jdk1.8.0_25.jdk/Contents/Home/bin/java -cp :/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/sbin/../conf:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/lib/spark-assembly-1.3.0-hadoop2.4.0.jar:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/lib/datanucleus-api-jdo-3.2.6.jar:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/lib/datanucleus-core-3.2.10.jar:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/lib/datanucleus-rdbms-3.2.9.jar -Dspark.akka.logLifecycleEvents=true -Xms512m -Xmx512m org.apache.spark.deploy.master.Master --ip qpzhangdeMac-mini.local --port 7077 --webui-port 8080

========================================

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties

15/03/20 10:08:09 INFO Master: Registered signal handlers for [TERM, HUP, INT]

15/03/20 10:08:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

15/03/20 10:08:10 INFO SecurityManager: Changing view acls to: qpzhang

15/03/20 10:08:10 INFO SecurityManager: Changing modify acls to: qpzhang

15/03/20 10:08:10 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(qpzhang); users with modify permissions: Set(qpzhang)

15/03/20 10:08:10 INFO Slf4jLogger: Slf4jLogger started

15/03/20 10:08:10 INFO Remoting: Starting remoting

15/03/20 10:08:10 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkMaster@qpzhangdeMac-mini.local:7077]

15/03/20 10:08:10 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkMaster@qpzhangdeMac-mini.local:7077]

15/03/20 10:08:10 INFO Utils: Successfully started service 'sparkMaster' on port 7077.

15/03/20 10:08:11 INFO Server: jetty-8.y.z-SNAPSHOT

15/03/20 10:08:11 INFO AbstractConnector: Started SelectChannelConnector@qpzhangdeMac-mini.local:6066

15/03/20 10:08:11 INFO Utils: Successfully started service on port 6066.

15/03/20 10:08:11 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066

15/03/20 10:08:11 INFO Master: Starting Spark master at spark://qpzhangdeMac-mini.local:7077

15/03/20 10:08:11 INFO Master: Running Spark version 1.3.0

15/03/20 10:08:11 INFO Server: jetty-8.y.z-SNAPSHOT

15/03/20 10:08:11 INFO AbstractConnector: Started SelectChannelConnector@0.0.0.0:8080

15/03/20 10:08:11 INFO Utils: Successfully started service 'MasterUI' on port 8080.

15/03/20 10:08:11 INFO MasterWebUI: Started MasterWebUI at http://10.60.215.41:8080

15/03/20 10:08:11 INFO Master: I have been elected leader! New state: ALIVE

可以看到输出的几条重要的信息，service端口6066，spark端口 7077，ui端口8080等，并且当前node通过选举，确认自己为leader。

这个时候，我们可以通过 http://localhost:8080/ 来查看到当前master的总体状态。

4.附加一个worker到master

~/project/spark-1.3.0-bin-hadoop2.4 $./bin/spark-class org.apache.spark.deploy.worker.Worker spark://qpzhangdeMac-mini.local:7077

Spark assembly has been built with Hive, including Datanucleus jars on classpath

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties

15/03/20 10:33:49 INFO Worker: Registered signal handlers for [TERM, HUP, INT]

15/03/20 10:33:49 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

15/03/20 10:33:49 INFO SecurityManager: Changing view acls to: qpzhang

15/03/20 10:33:49 INFO SecurityManager: Changing modify acls to: qpzhang

15/03/20 10:33:49 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(qpzhang); users with modify permissions: Set(qpzhang)

15/03/20 10:33:50 INFO Slf4jLogger: Slf4jLogger started

15/03/20 10:33:50 INFO Remoting: Starting remoting

15/03/20 10:33:50 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkWorker@10.60.215.41:60994]

15/03/20 10:33:50 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkWorker@10.60.215.41:60994]

15/03/20 10:33:50 INFO Utils: Successfully started service 'sparkWorker' on port 60994.

15/03/20 10:33:50 INFO Worker: Starting Spark worker 10.60.215.41:60994 with 8 cores, 7.0 GB RAM

15/03/20 10:33:50 INFO Worker: Running Spark version 1.3.0

15/03/20 10:33:50 INFO Worker: Spark home: /Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4

15/03/20 10:33:50 INFO Server: jetty-8.y.z-SNAPSHOT

15/03/20 10:33:50 INFO AbstractConnector: Started SelectChannelConnector@0.0.0.0:8081

15/03/20 10:33:50 INFO Utils: Successfully started service 'WorkerUI' on port 8081.

15/03/20 10:33:50 INFO WorkerWebUI: Started WorkerWebUI at http://10.60.215.41:8081

15/03/20 10:33:50 INFO Worker: Connecting to master akka.tcp://sparkMaster@qpzhangdeMac-mini.local:7077/user/Master...

15/03/20 10:33:50 INFO Worker: Successfully registered with master spark://qpzhangdeMac-mini.local:7077

从日志输出可以看到, worker自己在60994端口工作，然后为自己也起了一个UI，端口是8081，可以通过 http://10.60.215.41:8081查看worker的工作状态，（不得不说，选择的分布式少不了UI监控状态这一块儿了）。

5.启动spark shell终端：

~/project/spark-1.3.0-bin-hadoop2.4 $./bin/spark-shell

Spark assembly has been built with Hive, including Datanucleus jars on classpath

log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).

log4j:WARN Please initialize the log4j system properly.

log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties

15/03/20 10:43:39 INFO SecurityManager: Changing view acls to: qpzhang

15/03/20 10:43:39 INFO SecurityManager: Changing modify acls to: qpzhang

15/03/20 10:43:39 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(qpzhang); users with modify permissions: Set(qpzhang)

15/03/20 10:43:39 INFO HttpServer: Starting HTTP Server

15/03/20 10:43:39 INFO Server: jetty-8.y.z-SNAPSHOT

15/03/20 10:43:39 INFO AbstractConnector: Started SocketConnector@0.0.0.0:61644

15/03/20 10:43:39 INFO Utils: Successfully started service 'HTTP class server' on port 61644.

Welcome to

      ____              __

     / __/__  ___ _____/ /__

    _\ \/ _ \/ _ `/ __/  '_/

   /___/ .__/\_,_/_/ /_/\_\   version 1.3.0

      /_/

Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_25)

Type in expressions to have them evaluated.

Type :help for more information.

15/03/20 10:43:43 INFO SparkContext: Running Spark version 1.3.0

15/03/20 10:43:43 INFO SecurityManager: Changing view acls to: qpzhang

15/03/20 10:43:43 INFO SecurityManager: Changing modify acls to: qpzhang

15/03/20 10:43:43 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(qpzhang); users with modify permissions: Set(qpzhang)

15/03/20 10:43:43 INFO Slf4jLogger: Slf4jLogger started

15/03/20 10:43:43 INFO Remoting: Starting remoting

15/03/20 10:43:43 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriver@10.60.215.41:61645]

15/03/20 10:43:43 INFO Utils: Successfully started service 'sparkDriver' on port 61645.

15/03/20 10:43:43 INFO SparkEnv: Registering MapOutputTracker

15/03/20 10:43:44 INFO SparkEnv: Registering BlockManagerMaster

15/03/20 10:43:44 INFO DiskBlockManager: Created local directory at /var/folders/2l/195zcc1n0sn2wjfjwf9hl9d80000gn/T/spark-5349b1ce-bd10-4f44-9571-da660c1a02a3/blockmgr-a519687e-0cc3-45e4-839a-f93ac8f1397b

15/03/20 10:43:44 INFO MemoryStore: MemoryStore started with capacity 265.1 MB

15/03/20 10:43:44 INFO HttpFileServer: HTTP File server directory is /var/folders/2l/195zcc1n0sn2wjfjwf9hl9d80000gn/T/spark-29d81b59-ec6a-4595-b2fb-81bf6b1d3b10/httpd-c572e4a5-ff85-44c9-a21f-71fb34b831e1

15/03/20 10:43:44 INFO HttpServer: Starting HTTP Server

15/03/20 10:43:44 INFO Server: jetty-8.y.z-SNAPSHOT

15/03/20 10:43:44 INFO AbstractConnector: Started SocketConnector@0.0.0.0:61646

15/03/20 10:43:44 INFO Utils: Successfully started service 'HTTP file server' on port 61646.

15/03/20 10:43:44 INFO SparkEnv: Registering OutputCommitCoordinator

15/03/20 10:43:44 INFO Server: jetty-8.y.z-SNAPSHOT

15/03/20 10:43:44 INFO AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040

15/03/20 10:43:44 INFO Utils: Successfully started service 'SparkUI' on port 4040.

15/03/20 10:43:44 INFO SparkUI: Started SparkUI at http://10.60.215.41:4040

15/03/20 10:43:44 INFO Executor: Starting executor ID <driver> on host localhost

15/03/20 10:43:44 INFO Executor: Using REPL class URI: http://10.60.215.41:61644

15/03/20 10:43:44 INFO AkkaUtils: Connecting to HeartbeatReceiver: akka.tcp://sparkDriver@10.60.215.41:61645/user/HeartbeatReceiver

15/03/20 10:43:44 INFO NettyBlockTransferService: Server created on 61651

15/03/20 10:43:44 INFO BlockManagerMaster: Trying to register BlockManager

15/03/20 10:43:44 INFO BlockManagerMasterActor: Registering block manager localhost:61651 with 265.1 MB RAM, BlockManagerId(<driver>, localhost, 61651)

15/03/20 10:43:44 INFO BlockManagerMaster: Registered BlockManager

15/03/20 10:43:44 INFO SparkILoop: Created spark context..

Spark context available as sc.

15/03/20 10:43:45 INFO SparkILoop: Created sql context (with Hive support)..

SQL context available as sqlContext.

scala>

从输出可以看到，又是一堆端口(各种service进行通信，没办法)，包含UI, driver等等。warning日志告诉你没有进行config，采用默认。如何进行config，后面再说，先用默认的跑起来玩玩。

6.通过shell下达命令

下面我们来执行几个官网上面overview中的几个命令来玩玩。

scala> val textFile = sc.textFile("README.md")  //加载数据文件，可以是本地路径，也是是HDFS路径或者其它

15/03/20 10:55:20 INFO MemoryStore: ensureFreeSpace(159118) called with curMem=0, maxMem=278019440

15/03/20 10:55:20 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 155.4 KB, free 265.0 MB)

15/03/20 10:55:20 INFO MemoryStore: ensureFreeSpace(22692) called with curMem=159118, maxMem=278019440

15/03/20 10:55:20 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 22.2 KB, free 265.0 MB)

15/03/20 10:55:20 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:61651 (size: 22.2 KB, free: 265.1 MB)

15/03/20 10:55:20 INFO BlockManagerMaster: Updated info of block broadcast_0_piece0

15/03/20 10:55:20 INFO SparkContext: Created broadcast 0 from textFile at <console>:21

textFile: org.apache.spark.rdd.RDD[String] = README.md MapPartitionsRDD[1] at textFile at <console>:21

scala> textFile.count() //列出文件行数

15/03/20 10:56:38 INFO FileInputFormat: Total input paths to process : 1

15/03/20 10:56:38 INFO SparkContext: Starting job: count at <console>:24

15/03/20 10:56:38 INFO DAGScheduler: Got job 0 (count at <console>:24) with 2 output partitions (allowLocal=false)

15/03/20 10:56:38 INFO DAGScheduler: Final stage: Stage 0(count at <console>:24)

15/03/20 10:56:38 INFO DAGScheduler: Parents of final stage: List()

15/03/20 10:56:38 INFO DAGScheduler: Missing parents: List()

15/03/20 10:56:38 INFO DAGScheduler: Submitting Stage 0 (README.md MapPartitionsRDD[1] at textFile at <console>:21), which has no missing parents

15/03/20 10:56:38 INFO MemoryStore: ensureFreeSpace(2632) called with curMem=181810, maxMem=278019440

15/03/20 10:56:38 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 2.6 KB, free 265.0 MB)

15/03/20 10:56:38 INFO MemoryStore: ensureFreeSpace(1923) called with curMem=184442, maxMem=278019440

15/03/20 10:56:38 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 1923.0 B, free 265.0 MB)

15/03/20 10:56:38 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on localhost:61651 (size: 1923.0 B, free: 265.1 MB)

15/03/20 10:56:38 INFO BlockManagerMaster: Updated info of block broadcast_1_piece0

15/03/20 10:56:38 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:839

15/03/20 10:56:38 INFO DAGScheduler: Submitting 2 missing tasks from Stage 0 (README.md MapPartitionsRDD[1] at textFile at <console>:21)

15/03/20 10:56:38 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks

15/03/20 10:56:38 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, PROCESS_LOCAL, 1327 bytes)

15/03/20 10:56:38 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, localhost, PROCESS_LOCAL, 1327 bytes)

15/03/20 10:56:38 INFO Executor: Running task 1.0 in stage 0.0 (TID 1)

15/03/20 10:56:38 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)

15/03/20 10:56:38 INFO HadoopRDD: Input split: file:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/README.md:0+1814

15/03/20 10:56:38 INFO HadoopRDD: Input split: file:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/README.md:1814+1815

15/03/20 10:56:38 INFO deprecation: mapred.tip.id is deprecated. Instead, use mapreduce.task.id

15/03/20 10:56:38 INFO deprecation: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id

15/03/20 10:56:38 INFO deprecation: mapred.task.is.map is deprecated. Instead, use mapreduce.task.ismap

15/03/20 10:56:38 INFO deprecation: mapred.task.partition is deprecated. Instead, use mapreduce.task.partition

15/03/20 10:56:38 INFO deprecation: mapred.job.id is deprecated. Instead, use mapreduce.job.id

15/03/20 10:56:38 INFO Executor: Finished task 1.0 in stage 0.0 (TID 1). 1830 bytes result sent to driver

15/03/20 10:56:38 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1830 bytes result sent to driver

15/03/20 10:56:38 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 120 ms on localhost (1/2)

15/03/20 10:56:38 INFO TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 111 ms on localhost (2/2)

15/03/20 10:56:38 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool

15/03/20 10:56:38 INFO DAGScheduler: Stage 0 (count at <console>:24) finished in 0.134 s

15/03/20 10:56:38 INFO DAGScheduler: Job 0 finished: count at <console>:24, took 0.254626 s

res0: Long = 98

scala> textFile.first() //输出第一个item, 也就是第一行内容

15/03/20 10:59:31 INFO SparkContext: Starting job: first at <console>:24

15/03/20 10:59:31 INFO DAGScheduler: Got job 1 (first at <console>:24) with 1 output partitions (allowLocal=true)

15/03/20 10:59:31 INFO DAGScheduler: Final stage: Stage 1(first at <console>:24)

15/03/20 10:59:31 INFO DAGScheduler: Parents of final stage: List()

15/03/20 10:59:31 INFO DAGScheduler: Missing parents: List()

15/03/20 10:59:31 INFO DAGScheduler: Submitting Stage 1 (README.md MapPartitionsRDD[1] at textFile at <console>:21), which has no missing parents

15/03/20 10:59:31 INFO MemoryStore: ensureFreeSpace(2656) called with curMem=186365, maxMem=278019440

15/03/20 10:59:31 INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 2.6 KB, free 265.0 MB)

15/03/20 10:59:31 INFO MemoryStore: ensureFreeSpace(1945) called with curMem=189021, maxMem=278019440

15/03/20 10:59:31 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 1945.0 B, free 265.0 MB)

15/03/20 10:59:31 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on localhost:61651 (size: 1945.0 B, free: 265.1 MB)

15/03/20 10:59:31 INFO BlockManagerMaster: Updated info of block broadcast_2_piece0

15/03/20 10:59:31 INFO SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:839

15/03/20 10:59:31 INFO DAGScheduler: Submitting 1 missing tasks from Stage 1 (README.md MapPartitionsRDD[1] at textFile at <console>:21)

15/03/20 10:59:31 INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks

15/03/20 10:59:31 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 2, localhost, PROCESS_LOCAL, 1327 bytes)

15/03/20 10:59:31 INFO Executor: Running task 0.0 in stage 1.0 (TID 2)

15/03/20 10:59:31 INFO HadoopRDD: Input split: file:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/README.md:0+1814

15/03/20 10:59:31 INFO Executor: Finished task 0.0 in stage 1.0 (TID 2). 1809 bytes result sent to driver

15/03/20 10:59:31 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 8 ms on localhost (1/1)

15/03/20 10:59:31 INFO DAGScheduler: Stage 1 (first at <console>:24) finished in 0.009 s

15/03/20 10:59:31 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool

15/03/20 10:59:31 INFO DAGScheduler: Job 1 finished: first at <console>:24, took 0.016292 s

res1: String = # Apache Spark

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) //定义一个filter,  这里定义的是包含Spark关键词的filter

linesWithSpark: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at <console>:23

scala> linesWithSpark.count() //输出filter中的结果数

15/03/20 11:00:28 INFO SparkContext: Starting job: count at <console>:26

15/03/20 11:00:28 INFO DAGScheduler: Got job 2 (count at <console>:26) with 2 output partitions (allowLocal=false)

15/03/20 11:00:28 INFO DAGScheduler: Final stage: Stage 2(count at <console>:26)

15/03/20 11:00:28 INFO DAGScheduler: Parents of final stage: List()

15/03/20 11:00:28 INFO DAGScheduler: Missing parents: List()

15/03/20 11:00:28 INFO DAGScheduler: Submitting Stage 2 (MapPartitionsRDD[2] at filter at <console>:23), which has no missing parents

15/03/20 11:00:28 INFO MemoryStore: ensureFreeSpace(2840) called with curMem=190966, maxMem=278019440

15/03/20 11:00:28 INFO MemoryStore: Block broadcast_3 stored as values in memory (estimated size 2.8 KB, free 265.0 MB)

15/03/20 11:00:28 INFO MemoryStore: ensureFreeSpace(2029) called with curMem=193806, maxMem=278019440

15/03/20 11:00:28 INFO MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated size 2029.0 B, free 265.0 MB)

15/03/20 11:00:28 INFO BlockManagerInfo: Added broadcast_3_piece0 in memory on localhost:61651 (size: 2029.0 B, free: 265.1 MB)

15/03/20 11:00:28 INFO BlockManagerMaster: Updated info of block broadcast_3_piece0

15/03/20 11:00:28 INFO SparkContext: Created broadcast 3 from broadcast at DAGScheduler.scala:839

15/03/20 11:00:28 INFO DAGScheduler: Submitting 2 missing tasks from Stage 2 (MapPartitionsRDD[2] at filter at <console>:23)

15/03/20 11:00:28 INFO TaskSchedulerImpl: Adding task set 2.0 with 2 tasks

15/03/20 11:00:28 INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 3, localhost, PROCESS_LOCAL, 1327 bytes)

15/03/20 11:00:28 INFO TaskSetManager: Starting task 1.0 in stage 2.0 (TID 4, localhost, PROCESS_LOCAL, 1327 bytes)

15/03/20 11:00:28 INFO Executor: Running task 0.0 in stage 2.0 (TID 3)

15/03/20 11:00:28 INFO Executor: Running task 1.0 in stage 2.0 (TID 4)

15/03/20 11:00:28 INFO HadoopRDD: Input split: file:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/README.md:1814+1815

15/03/20 11:00:28 INFO HadoopRDD: Input split: file:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/README.md:0+1814

15/03/20 11:00:28 INFO Executor: Finished task 1.0 in stage 2.0 (TID 4). 1830 bytes result sent to driver

15/03/20 11:00:28 INFO Executor: Finished task 0.0 in stage 2.0 (TID 3). 1830 bytes result sent to driver

15/03/20 11:00:28 INFO TaskSetManager: Finished task 1.0 in stage 2.0 (TID 4) in 9 ms on localhost (1/2)

15/03/20 11:00:28 INFO TaskSetManager: Finished task 0.0 in stage 2.0 (TID 3) in 11 ms on localhost (2/2)

15/03/20 11:00:28 INFO DAGScheduler: Stage 2 (count at <console>:26) finished in 0.011 s

15/03/20 11:00:28 INFO TaskSchedulerImpl: Removed TaskSet 2.0, whose tasks have all completed, from pool

15/03/20 11:00:28 INFO DAGScheduler: Job 2 finished: count at <console>:26, took 0.019407 s

res2: Long = 19  //可以看到有19行包含 Spark关键词

scala> linesWithSpark.first() //打印第一行数据

15/03/20 11:00:35 INFO SparkContext: Starting job: first at <console>:26

15/03/20 11:00:35 INFO DAGScheduler: Got job 3 (first at <console>:26) with 1 output partitions (allowLocal=true)

15/03/20 11:00:35 INFO DAGScheduler: Final stage: Stage 3(first at <console>:26)

15/03/20 11:00:35 INFO DAGScheduler: Parents of final stage: List()

15/03/20 11:00:35 INFO DAGScheduler: Missing parents: List()

15/03/20 11:00:35 INFO DAGScheduler: Submitting Stage 3 (MapPartitionsRDD[2] at filter at <console>:23), which has no missing parents

15/03/20 11:00:35 INFO MemoryStore: ensureFreeSpace(2864) called with curMem=195835, maxMem=278019440

15/03/20 11:00:35 INFO MemoryStore: Block broadcast_4 stored as values in memory (estimated size 2.8 KB, free 265.0 MB)

15/03/20 11:00:35 INFO MemoryStore: ensureFreeSpace(2048) called with curMem=198699, maxMem=278019440

15/03/20 11:00:35 INFO MemoryStore: Block broadcast_4_piece0 stored as bytes in memory (estimated size 2.0 KB, free 264.9 MB)

15/03/20 11:00:35 INFO BlockManagerInfo: Added broadcast_4_piece0 in memory on localhost:61651 (size: 2.0 KB, free: 265.1 MB)

15/03/20 11:00:35 INFO BlockManagerMaster: Updated info of block broadcast_4_piece0

15/03/20 11:00:35 INFO SparkContext: Created broadcast 4 from broadcast at DAGScheduler.scala:839

15/03/20 11:00:35 INFO DAGScheduler: Submitting 1 missing tasks from Stage 3 (MapPartitionsRDD[2] at filter at <console>:23)

15/03/20 11:00:35 INFO TaskSchedulerImpl: Adding task set 3.0 with 1 tasks

15/03/20 11:00:35 INFO TaskSetManager: Starting task 0.0 in stage 3.0 (TID 5, localhost, PROCESS_LOCAL, 1327 bytes)

15/03/20 11:00:35 INFO Executor: Running task 0.0 in stage 3.0 (TID 5)

15/03/20 11:00:35 INFO HadoopRDD: Input split: file:/Users/qpzhang/project/spark-1.3.0-bin-hadoop2.4/README.md:0+1814

15/03/20 11:00:35 INFO Executor: Finished task 0.0 in stage 3.0 (TID 5). 1809 bytes result sent to driver

15/03/20 11:00:35 INFO TaskSetManager: Finished task 0.0 in stage 3.0 (TID 5) in 10 ms on localhost (1/1)

15/03/20 11:00:35 INFO DAGScheduler: Stage 3 (first at <console>:26) finished in 0.010 s

15/03/20 11:00:35 INFO TaskSchedulerImpl: Removed TaskSet 3.0, whose tasks have all completed, from pool

15/03/20 11:00:35 INFO DAGScheduler: Job 3 finished: first at <console>:26, took 0.016494 s

res3: String = # Apache Spark

更多命令参考： https://spark.apache.org/docs/latest/quick-start.html

期间，我们可以通过UI看到job列表和状态：

跑起来先，第一步已经完成，那么spark架构是什么样的？运行原理？如何自定义配置？如何扩展到分布式？如何编程实现？我们后面再慢慢研究。

spark学习（二）的更多相关文章

  1. Spark Standalone Mode 多机启动 -- 分布式计算系统spark学习(二)（更新一键启动slavers）

    捣鼓了一下,先来个手动挡吧.自动挡要设置ssh无密码登陆啥的,后面开搞. 一.手动多台机链接master 手动链接master其实上篇已经用过. 这里有两台机器: 10.60.215.41 启动mas ...

  2. Spark 学习(二)

    继续学习spark 认真查看了一下${SPARK_HOME}/bin/pyspark 的脚本,原来开启spark 的python 交互挺简单的. 主要操作 export PYTHONPATH=${SP ...

  3. spark 学习(二) RDD及共享变量

    声明:本文基于spark的programming guide,并融合自己的相关理解整理而成      Spark应用程序总是包括着一个driver program(驱动程序),它运行着用户的main方 ...

  4. Spark学习笔记之SparkRDD

    Spark学习笔记之SparkRDD 一.   基本概念 RDD(resilient distributed datasets)弹性分布式数据集. 来自于两方面 ①   内存集合和外部存储系统 ②   ...

  5. [转]Spark学习之路 （三）Spark之RDD

    Spark学习之路 (三)Spark之RDD   https://www.cnblogs.com/qingyunzong/p/8899715.html 目录 一.RDD的概述 1.1 什么是RDD? ...

  6. Spark学习之基于MLlib的机器学习

    Spark学习之基于MLlib的机器学习 1. 机器学习算法尝试根据训练数据(training data)使得表示算法行为的数学目标最大化,并以此来进行预测或作出决定. 2. MLlib完成文本分类任 ...

  7. Spark学习之Spark调优与调试（7）

    Spark学习之Spark调优与调试(7) 1. 对Spark进行调优与调试通常需要修改Spark应用运行时配置的选项. 当创建一个SparkContext时就会创建一个SparkConf实例. 2. ...

  8. spark学习及环境配置

    http://dblab.xmu.edu.cn/blog/spark/ 厦大数据库实验室博客 总结.分享.收获 实验室主页 首页 大数据 数据库 数据挖掘 其他 子雨大数据之Spark入门教程  林子 ...

  9. Spark学习（4）----ScalaTest

    一.例子: 1.一个简单例子:https://www.jianshu.com/p/ceabf3437dd7 2.Funsuite例子:https://www.programcreek.com/scal ...

  10. Spark学习入门(让人看了想吐的话题)

    这是个老生常谈的话题,大家是不是看到这个文章标题就快吐了,本来想着手写一些有技术深度的东西,但是看到太多童鞋卡在入门的门槛上,所以还是打算总结一下入门经验.这种标题真的真的在哪里都可以看得到,度娘一搜 ...

随机推荐

  1. POJ 2255 Tree Recovery——二叉树的前序遍历、后序遍历、中序遍历规则（递归）

    1.前序遍历的规则:(根左右) (1)访问根节点 (2)前序遍历左子树 (3)前序遍历右子树 对于图中二叉树,前序遍历结果:ABDECF 2.中序遍历的规则:(左根右) (1)中序遍历左子树 (2)访 ...

  2. PAT 乙级 1037

    题目 题目地址:PAT 乙级 1037 题解 本题有两个版本的代码,初版因为种种问题写得比较繁琐,具体的分析见后文,更新的之后的版本相对来说要好很多,代码也比较清晰简洁. 初版的代码主要有如下几方面的 ...

  3. struts2命名空间与访问路径

    比如项目deom的struts.xml中有如下片段 Java代码 <package name="demo" extends="struts-default" ...

  4. spring，spring mvc，mybatis 常用注解

    文章来源:https://www.cnblogs.com/hello-tl/p/9209063.html  0.在spring,soring mvc, mybistis 中的常用注解有一下 <! ...

  5. ajax以及文件上传的几种方式

    方式一:通过form表单中,html input 标签的“file”完成 # 前端代码uoload.html <form method="post" action=" ...

  6. Scrapy爬取多层级网页内容的方式

    # -*- coding: utf-8 -*- import scrapy from Avv.items import AvvItem class AvSpider(scrapy.Spider): n ...

  7. as eclipse 对比

    eclipse目录结构 src:存放prj源码 gen:自动生成文件,R类存放资源文件的唯一id android+版本:项目jar包 assets:res存放资源文件,不会被gen生成资源id,同过a ...

  8. jquery 页面加载效果

    30个jquery 页面加载效果 30个jquery 页面加载效果   30 CSS Page Preload Animations   加载效果列表 Square Animations Demo 1 ...

  9. Sort a linked list in O(n log n) time using constant space complexity.

    因为题目要求复杂度为O(nlogn),故可以考虑归并排序的思想. 归并排序的一般步骤为: 1)将待排序数组(链表)取中点并一分为二: 2)递归地对左半部分进行归并排序: 3)递归地对右半部分进行归并排 ...

  10. oracle用户密码错误导致用户锁定

    解决方法:使用DBA用户将其解锁: SQL> alter user ecology account unlock; 用户已更改. 用户密码限制设置: 查看FAILED_LOGIN_ATTEMPT ...