Interesting, I see 0 cores in the UI?
- *Cores:* 0 Total, 0 Used On Fri, Apr 3, 2015 at 2:55 PM, Tathagata Das <t...@databricks.com> wrote: > 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); >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
