[ec2-user@ip-10-241-251-232 s_lib]$ cat /proc/cpuinfo |grep process processor : 0 processor : 1 processor : 2 processor : 3 processor : 4 processor : 5 processor : 6 processor : 7
On Fri, Apr 3, 2015 at 2:33 PM, Tathagata Das <t...@databricks.com> wrote: > How many cores are present in the works allocated to the standalone > cluster spark://ip-10-241-251-232:7077 ? > > > On Fri, Apr 3, 2015 at 2:18 PM, Mohit Anchlia <mohitanch...@gmail.com> > wrote: > >> If I use local[2] instead of *URL:* spark://ip-10-241-251-232:7077 this >> seems to work. I don't understand why though because when I >> give spark://ip-10-241-251-232:7077 application seem to bootstrap >> successfully, just doesn't create a socket on port 9999? >> >> >> On Fri, Mar 27, 2015 at 10:55 AM, Mohit Anchlia <mohitanch...@gmail.com> >> wrote: >> >>> I checked the ports using netstat and don't see any connections >>> established on that port. Logs show only this: >>> >>> 15/03/27 13:50:48 INFO Master: Registering app NetworkWordCount >>> 15/03/27 13:50:48 INFO Master: Registered app NetworkWordCount with ID >>> app-20150327135048-0002 >>> >>> Spark ui shows: >>> >>> Running Applications >>> IDNameCoresMemory per NodeSubmitted TimeUserStateDuration >>> app-20150327135048-0002 >>> <http://54.69.225.94:8080/app?appId=app-20150327135048-0002> >>> NetworkWordCount >>> <http://ip-10-241-251-232.us-west-2.compute.internal:4040/>0512.0 >>> MB2015/03/27 >>> 13:50:48ec2-userWAITING33 s >>> Code looks like is being executed: >>> >>> java -cp .:* org.spark.test.WordCount spark://ip-10-241-251-232:7077 >>> >>> *public* *static* *void* doWork(String masterUrl){ >>> >>> SparkConf conf = *new* SparkConf().setMaster(masterUrl).setAppName( >>> "NetworkWordCount"); >>> >>> JavaStreamingContext *jssc* = *new* JavaStreamingContext(conf, >>> Durations.*seconds*(1)); >>> >>> JavaReceiverInputDStream<String> lines = jssc.socketTextStream( >>> "localhost", 9999); >>> >>> System.*out*.println("Successfully created connection"); >>> >>> *mapAndReduce*(lines); >>> >>> jssc.start(); // Start the computation >>> >>> jssc.awaitTermination(); // Wait for the computation to terminate >>> >>> } >>> >>> *public* *static* *void* main(String ...args){ >>> >>> *doWork*(args[0]); >>> >>> } >>> And output of the java program after submitting the task: >>> >>> java -cp .:* org.spark.test.WordCount spark://ip-10-241-251-232:7077 >>> Using Spark's default log4j profile: >>> org/apache/spark/log4j-defaults.properties >>> 15/03/27 13:50:46 INFO SecurityManager: Changing view acls to: ec2-user >>> 15/03/27 13:50:46 INFO SecurityManager: Changing modify acls to: ec2-user >>> 15/03/27 13:50:46 INFO SecurityManager: SecurityManager: authentication >>> disabled; ui acls disabled; users with view permissions: Set(ec2-user); >>> users with modify permissions: Set(ec2-user) >>> 15/03/27 13:50:46 INFO Slf4jLogger: Slf4jLogger started >>> 15/03/27 13:50:46 INFO Remoting: Starting remoting >>> 15/03/27 13:50:47 INFO Remoting: Remoting started; listening on >>> addresses >>> :[akka.tcp://sparkdri...@ip-10-241-251-232.us-west-2.compute.internal >>> :60184] >>> 15/03/27 13:50:47 INFO Utils: Successfully started service 'sparkDriver' >>> on port 60184. >>> 15/03/27 13:50:47 INFO SparkEnv: Registering MapOutputTracker >>> 15/03/27 13:50:47 INFO SparkEnv: Registering BlockManagerMaster >>> 15/03/27 13:50:47 INFO DiskBlockManager: Created local directory at >>> /tmp/spark-local-20150327135047-5399 >>> 15/03/27 13:50:47 INFO MemoryStore: MemoryStore started with capacity >>> 3.5 GB >>> 15/03/27 13:50:47 WARN NativeCodeLoader: Unable to load native-hadoop >>> library for your platform... using builtin-java classes where applicable >>> 15/03/27 13:50:47 INFO HttpFileServer: HTTP File server directory is >>> /tmp/spark-7e26df49-1520-4c77-b411-c837da59fa5b >>> 15/03/27 13:50:47 INFO HttpServer: Starting HTTP Server >>> 15/03/27 13:50:47 INFO Utils: Successfully started service 'HTTP file >>> server' on port 57955. >>> 15/03/27 13:50:47 INFO Utils: Successfully started service 'SparkUI' on >>> port 4040. >>> 15/03/27 13:50:47 INFO SparkUI: Started SparkUI at >>> http://ip-10-241-251-232.us-west-2.compute.internal:4040 >>> 15/03/27 13:50:47 INFO AppClient$ClientActor: Connecting to master >>> spark://ip-10-241-251-232:7077... >>> 15/03/27 13:50:48 INFO SparkDeploySchedulerBackend: Connected to Spark >>> cluster with app ID app-20150327135048-0002 >>> 15/03/27 13:50:48 INFO NettyBlockTransferService: Server created on 58358 >>> 15/03/27 13:50:48 INFO BlockManagerMaster: Trying to register >>> BlockManager >>> 15/03/27 13:50:48 INFO BlockManagerMasterActor: Registering block >>> manager ip-10-241-251-232.us-west-2.compute.internal:58358 with 3.5 GB RAM, >>> BlockManagerId(<driver>, ip-10-241-251-232.us-west-2.compute.internal, >>> 58358) >>> 15/03/27 13:50:48 INFO BlockManagerMaster: Registered BlockManager >>> 15/03/27 13:50:48 INFO SparkDeploySchedulerBackend: SchedulerBackend is >>> ready for scheduling beginning after reached minRegisteredResourcesRatio: >>> 0.0 >>> 15/03/27 13:50:48 INFO ReceiverTracker: ReceiverTracker started >>> 15/03/27 13:50:48 INFO ForEachDStream: metadataCleanupDelay = -1 >>> 15/03/27 13:50:48 INFO ShuffledDStream: metadataCleanupDelay = -1 >>> 15/03/27 13:50:48 INFO MappedDStream: metadataCleanupDelay = -1 >>> 15/03/27 13:50:48 INFO FlatMappedDStream: metadataCleanupDelay = -1 >>> 15/03/27 13:50:48 INFO SocketInputDStream: metadataCleanupDelay = -1 >>> 15/03/27 13:50:48 INFO SocketInputDStream: Slide time = 1000 ms >>> 15/03/27 13:50:48 INFO SocketInputDStream: Storage level = >>> StorageLevel(false, false, false, false, 1) >>> 15/03/27 13:50:48 INFO SocketInputDStream: Checkpoint interval = null >>> 15/03/27 13:50:48 INFO SocketInputDStream: Remember duration = 1000 ms >>> 15/03/27 13:50:48 INFO SocketInputDStream: Initialized and validated >>> org.apache.spark.streaming.dstream.SocketInputDStream@75efa13d >>> 15/03/27 13:50:48 INFO FlatMappedDStream: Slide time = 1000 ms >>> 15/03/27 13:50:48 INFO FlatMappedDStream: Storage level = >>> StorageLevel(false, false, false, false, 1) >>> 15/03/27 13:50:48 INFO FlatMappedDStream: Checkpoint interval = null >>> 15/03/27 13:50:48 INFO FlatMappedDStream: Remember duration = 1000 ms >>> 15/03/27 13:50:48 INFO FlatMappedDStream: Initialized and validated >>> org.apache.spark.streaming.dstream.FlatMappedDStream@65ce9dc5 >>> 15/03/27 13:50:48 INFO MappedDStream: Slide time = 1000 ms >>> 15/03/27 13:50:48 INFO MappedDStream: Storage level = >>> StorageLevel(false, false, false, false, 1) >>> 15/03/27 13:50:48 INFO MappedDStream: Checkpoint interval = null >>> 15/03/27 13:50:48 INFO MappedDStream: Remember duration = 1000 ms >>> 15/03/27 13:50:48 INFO MappedDStream: Initialized and validated >>> org.apache.spark.streaming.dstream.MappedDStream@5ae2740f >>> 15/03/27 13:50:48 INFO ShuffledDStream: Slide time = 1000 ms >>> 15/03/27 13:50:48 INFO ShuffledDStream: Storage level = >>> StorageLevel(false, false, false, false, 1) >>> 15/03/27 13:50:48 INFO ShuffledDStream: Checkpoint interval = null >>> 15/03/27 13:50:48 INFO ShuffledDStream: Remember duration = 1000 ms >>> 15/03/27 13:50:48 INFO ShuffledDStream: Initialized and validated >>> org.apache.spark.streaming.dstream.ShuffledDStream@4931b366 >>> 15/03/27 13:50:48 INFO ForEachDStream: Slide time = 1000 ms >>> 15/03/27 13:50:48 INFO ForEachDStream: Storage level = >>> StorageLevel(false, false, false, false, 1) >>> 15/03/27 13:50:48 INFO ForEachDStream: Checkpoint interval = null >>> 15/03/27 13:50:48 INFO ForEachDStream: Remember duration = 1000 ms >>> 15/03/27 13:50:48 INFO ForEachDStream: Initialized and validated >>> org.apache.spark.streaming.dstream.ForEachDStream@5df91314 >>> 15/03/27 13:50:48 INFO SparkContext: Starting job: start at >>> WordCount.java:26 >>> 15/03/27 13:50:48 INFO RecurringTimer: Started timer for JobGenerator at >>> time 1427478649000 >>> 15/03/27 13:50:48 INFO JobGenerator: Started JobGenerator at >>> 1427478649000 ms >>> 15/03/27 13:50:48 INFO JobScheduler: Started JobScheduler >>> 15/03/27 13:50:48 INFO DAGScheduler: Registering RDD 2 (start at >>> WordCount.java:26) >>> 15/03/27 13:50:48 INFO DAGScheduler: Got job 0 (start at >>> WordCount.java:26) with 20 output partitions (allowLocal=false) >>> 15/03/27 13:50:48 INFO DAGScheduler: Final stage: Stage 1(start at >>> WordCount.java:26) >>> 15/03/27 13:50:48 INFO DAGScheduler: Parents of final stage: List(Stage >>> 0) >>> 15/03/27 13:50:48 INFO DAGScheduler: Missing parents: List(Stage 0) >>> 15/03/27 13:50:48 INFO DAGScheduler: Submitting Stage 0 (MappedRDD[2] at >>> start at WordCount.java:26), which has no missing parents >>> 15/03/27 13:50:48 INFO MemoryStore: ensureFreeSpace(2720) called with >>> curMem=0, maxMem=3771948072 >>> 15/03/27 13:50:48 INFO MemoryStore: Block broadcast_0 stored as values >>> in memory (estimated size 2.7 KB, free 3.5 GB) >>> 15/03/27 13:50:48 INFO MemoryStore: ensureFreeSpace(1943) called with >>> curMem=2720, maxMem=3771948072 >>> 15/03/27 13:50:48 INFO MemoryStore: Block broadcast_0_piece0 stored as >>> bytes in memory (estimated size 1943.0 B, free 3.5 GB) >>> 15/03/27 13:50:48 INFO BlockManagerInfo: Added broadcast_0_piece0 in >>> memory on ip-10-241-251-232.us-west-2.compute.internal:58358 (size: 1943.0 >>> B, free: 3.5 GB) >>> 15/03/27 13:50:48 INFO BlockManagerMaster: Updated info of block >>> broadcast_0_piece0 >>> 15/03/27 13:50:48 INFO SparkContext: Created broadcast 0 from broadcast >>> at DAGScheduler.scala:838 >>> 15/03/27 13:50:48 INFO DAGScheduler: Submitting 50 missing tasks from >>> Stage 0 (MappedRDD[2] at start at WordCount.java:26) >>> 15/03/27 13:50:48 INFO TaskSchedulerImpl: Adding task set 0.0 with 50 >>> tasks >>> 15/03/27 13:50:49 INFO JobScheduler: Added jobs for time 1427478649000 ms >>> 15/03/27 13:50:49 INFO JobScheduler: Starting job streaming job >>> 1427478649000 ms.0 from job set of time 1427478649000 ms >>> 15/03/27 13:50:49 INFO SparkContext: Starting job: print at >>> WordCount.java:53 >>> 15/03/27 13:50:49 INFO DAGScheduler: Registering RDD 6 (mapToPair at >>> WordCount.java:39) >>> 15/03/27 13:50:49 INFO DAGScheduler: Got job 1 (print at >>> WordCount.java:53) with 1 output partitions (allowLocal=true) >>> 15/03/27 13:50:49 INFO DAGScheduler: Final stage: Stage 3(print at >>> WordCount.java:53) >>> 15/03/27 13:50:49 INFO DAGScheduler: Parents of final stage: List(Stage >>> 2) >>> 15/03/27 13:50:49 INFO DAGScheduler: Missing parents: List() >>> 15/03/27 13:50:49 INFO DAGScheduler: Submitting Stage 3 (ShuffledRDD[7] >>> at reduceByKey at WordCount.java:46), which has no missing parents >>> 15/03/27 13:50:49 INFO MemoryStore: ensureFreeSpace(2264) called with >>> curMem=4663, maxMem=3771948072 >>> 15/03/27 13:50:49 INFO MemoryStore: Block broadcast_1 stored as values >>> in memory (estimated size 2.2 KB, free 3.5 GB) >>> 15/03/27 13:50:49 INFO MemoryStore: ensureFreeSpace(1688) called with >>> curMem=6927, maxMem=3771948072 >>> 15/03/27 13:50:49 INFO MemoryStore: Block broadcast_1_piece0 stored as >>> bytes in memory (estimated size 1688.0 B, free 3.5 GB) >>> 15/03/27 13:50:49 INFO BlockManagerInfo: Added broadcast_1_piece0 in >>> memory on ip-10-241-251-232.us-west-2.compute.internal:58358 (size: 1688.0 >>> B, free: 3.5 GB) >>> 15/03/27 13:50:49 INFO BlockManagerMaster: Updated info of block >>> broadcast_1_piece0 >>> 15/03/27 13:50:49 INFO SparkContext: Created broadcast 1 from broadcast >>> at DAGScheduler.scala:838 >>> 15/03/27 13:50:49 INFO DAGScheduler: Submitting 1 missing tasks from >>> Stage 3 (ShuffledRDD[7] at reduceByKey at WordCount.java:46) >>> 15/03/27 13:50:49 INFO TaskSchedulerImpl: Adding task set 3.0 with 1 >>> tasks >>> 15/03/27 13:50:50 INFO JobScheduler: Added jobs for time 1427478650000 ms >>> 15/03/27 13:50:51 INFO JobScheduler: Added jobs for time 1427478651000 ms >>> 15/03/27 13:50:52 INFO JobScheduler: Added jobs for time 1427478652000 ms >>> 15/03/27 13:50:53 IN >>> >>> >>> >>> On Thu, Mar 26, 2015 at 6:50 PM, Saisai Shao <sai.sai.s...@gmail.com> >>> wrote: >>> >>>> Hi, >>>> >>>> Did you run the word count example in Spark local mode or other mode, >>>> in local mode you have to set Local[n], where n >=2. For other mode, make >>>> sure available cores larger than 1. Because the receiver inside Spark >>>> Streaming wraps as a long-running task, which will at least occupy one >>>> core. >>>> >>>> Besides using lsof -p <pid> or netstat to make sure Spark executor >>>> backend is connected to the nc process. Also grep the executor's log to see >>>> if there's log like "Connecting to <host> <port>" and "Connected to <host> >>>> <port>" which shows that receiver is correctly connected to nc process. >>>> >>>> Thanks >>>> Jerry >>>> >>>> 2015-03-27 8:45 GMT+08:00 Mohit Anchlia <mohitanch...@gmail.com>: >>>> >>>>> What's the best way to troubleshoot inside spark to see why Spark is >>>>> not connecting to nc on port 9999? I don't see any errors either. >>>>> >>>>> On Thu, Mar 26, 2015 at 2:38 PM, Mohit Anchlia <mohitanch...@gmail.com >>>>> > wrote: >>>>> >>>>>> I am trying to run the word count example but for some reason it's >>>>>> not working as expected. I start "nc" server on port 9999 and then submit >>>>>> the spark job to the cluster. Spark job gets successfully submitting but >>>>>> I >>>>>> never see any connection from spark getting established. I also tried to >>>>>> type words on the console where "nc" is listening and waiting on the >>>>>> prompt, however I don't see any output. I also don't see any errors. >>>>>> >>>>>> Here is the conf: >>>>>> >>>>>> SparkConf conf = *new* SparkConf().setMaster(masterUrl).setAppName( >>>>>> "NetworkWordCount"); >>>>>> >>>>>> JavaStreamingContext *jssc* = *new* JavaStreamingContext(conf, >>>>>> Durations.*seconds*(1)); >>>>>> >>>>>> JavaReceiverInputDStream<String> lines = jssc.socketTextStream( >>>>>> "localhost", 9999); >>>>>> >>>>> >>>>> >>>> >>> >> >
