What does the Spark Standalone UI at port 8080 say about number of cores? On Fri, Apr 3, 2015 at 2:53 PM, Mohit Anchlia <mohitanch...@gmail.com> wrote:
> [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); >>>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >
