They're separate in my code, how can I combine them? Here's what I have:
val sparkConf = new SparkConf()
val ssc = new StreamingContext(sparkConf, Seconds(bucketSecs))
val sc = new SparkContext()
On Tue, Feb 10, 2015 at 1:02 PM, Sandy Ryza <sandy.r...@cloudera.com> wrote:
> Is the SparkContext you're using the same one that the StreamingContext
> wraps? If not, I don't think using two is supported.
>
> -Sandy
>
> On Tue, Feb 10, 2015 at 9:58 AM, Jon Gregg <jonrgr...@gmail.com> wrote:
>
>> I'm still getting an error. Here's my code, which works successfully
>> when tested using spark-shell:
>>
>> val badIPs = sc.textFile("/user/sb/badfullIPs.csv").collect
>> val badIpSet = badIPs.toSet
>> val badIPsBC = sc.broadcast(badIpSet)
>>
>>
>> The job looks OK from my end:
>>
>> 15/02/07 18:59:58 INFO Client: Application report from ASM:
>>
>> application identifier: application_1423081782629_3861
>>
>> appId: 3861
>>
>> * clientToAMToken: Token { kind: YARN_CLIENT_TOKEN, service: }*
>>
>> appDiagnostics:
>>
>> appMasterHost: phd40010008.na.com
>>
>> appQueue: root.default
>>
>> appMasterRpcPort: 0
>>
>> appStartTime: 1423353581140
>>
>> * yarnAppState: RUNNING*
>>
>> distributedFinalState: UNDEFINED
>>
>>
>> But the streaming process never actually begins. The full log is below,
>> scroll to the end for the repeated warning "WARN YarnClusterScheduler:
>> Initial job has not accepted any resources; check your cluster UI to ensure
>> that workers are registered and have sufficient memory".
>>
>> I'll note that I have a different Spark Streaming app called "dqd"
>> working successfully for a different job that uses only a StreamingContext
>> and not an additional SparkContext. But this app (called "sbStreamingTv")
>> uses both a SparkContext and a StreamingContext for grabbing a lookup file
>> in HDFS for IP filtering. * The references to line #198 from the log
>> below refers to the "val badIPs =
>> sc.textFile("/user/sb/badfullIPs.csv").collect" line shown above, and it
>> looks like Spark doesn't get beyond that point in the code.*
>>
>> Also, this job ("sbStreamingTv") does work successfully using
>> yarn-client, even with both a SparkContext and StreamingContext. It looks
>> to me that in yarn-cluster mode it's grabbing resources for the
>> StreamingContext but not for the SparkContext.
>>
>> Any ideas?
>>
>> Jon
>>
>>
>> 15/02/10 12:06:16 INFO MemoryStore: MemoryStore started with capacity
>> 1177.8 MB.
>> 15/02/10 12:06:16 INFO ConnectionManager: Bound socket to port 30129 with
>> id = ConnectionManagerId(phd40010008.na.com,30129)
>> 15/02/10 12:06:16 INFO BlockManagerMaster: Trying to register BlockManager
>> 15/02/10 12:06:16 INFO BlockManagerInfo: Registering block manager
>> phd40010008.na.com:30129 with 1177.8 MB RAM
>> 15/02/10 12:06:16 INFO BlockManagerMaster: Registered BlockManager
>> 15/02/10 12:06:16 INFO HttpServer: Starting HTTP Server
>> 15/02/10 12:06:16 INFO HttpBroadcast: Broadcast server started at
>> http://10.229.16.108:35183
>> 15/02/10 12:06:16 INFO HttpFileServer: HTTP File server directory is
>> /hdata/12/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/container_1423081782629_7370_01_000001/tmp/spark-b73a964b-4d91-4af3-8246-48da420c1cec
>> 15/02/10 12:06:16 INFO HttpServer: Starting HTTP Server
>> 15/02/10 12:06:16 INFO JettyUtils: Adding filter:
>> org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
>> 15/02/10 12:06:16 INFO SparkUI: Started SparkUI at
>> http://phd40010008.na.com:25869
>> 15/02/10 12:06:17 INFO EventLoggingListener: Logging events to
>> /user/spark/applicationHistory/com.na.scalaspark.sbstreamingtv-1423587976801
>> 15/02/10 12:06:17 INFO YarnClusterScheduler: Created YarnClusterScheduler
>> 15/02/10 12:06:17 INFO ApplicationMaster$$anon$1: Adding shutdown hook
>> for context org.apache.spark.SparkContext@7f38095d
>> 15/02/10 12:06:17 INFO ApplicationMaster: Registering the
>> ApplicationMaster
>> 15/02/10 12:06:17 INFO ApplicationMaster: Allocating 3 executors.
>> 15/02/10 12:06:17 INFO YarnAllocationHandler: Will Allocate 3 executor
>> containers, each with 2432 memory
>> 15/02/10 12:06:17 INFO YarnAllocationHandler: Container request (host:
>> Any, priority: 1, capability: <memory:2432, vCores:1>
>> 15/02/10 12:06:17 INFO YarnAllocationHandler: Container request (host:
>> Any, priority: 1, capability: <memory:2432, vCores:1>
>> 15/02/10 12:06:17 INFO YarnAllocationHandler: Container request (host:
>> Any, priority: 1, capability: <memory:2432, vCores:1>
>> 15/02/10 12:06:20 INFO YarnClusterScheduler:
>> YarnClusterScheduler.postStartHook done
>> 15/02/10 12:06:20 WARN SparkConf: In Spark 1.0 and later spark.local.dir
>> will be overridden by the value set by the cluster manager (via
>> SPARK_LOCAL_DIRS in mesos/standalone and LOCAL_DIRS in YARN).
>> 15/02/10 12:06:20 INFO SecurityManager: Changing view acls to: jg
>> 15/02/10 12:06:20 INFO SecurityManager: SecurityManager: authentication
>> disabled; ui acls disabled; users with view permissions: Set(jg)
>> 15/02/10 12:06:20 INFO Slf4jLogger: Slf4jLogger started
>> 15/02/10 12:06:20 INFO Remoting: Starting remoting
>> 15/02/10 12:06:20 INFO Remoting: Remoting started; listening on addresses
>> :[akka.tcp://sp...@phd40010008.na.com:43340]
>> 15/02/10 12:06:20 INFO Remoting: Remoting now listens on addresses:
>> [akka.tcp://sp...@phd40010008.na.com:43340]
>> 15/02/10 12:06:20 INFO SparkEnv: Registering MapOutputTracker
>> 15/02/10 12:06:20 INFO SparkEnv: Registering BlockManagerMaster
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/1/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-f6e1
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/10/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-583d
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/11/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-0b66
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/12/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-bc8f
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/2/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-17e4
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/3/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-c01e
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/4/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-915c
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/5/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-38ff
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/6/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-c92f
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/7/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-b67a
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/8/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-46fb
>> 15/02/10 12:06:20 INFO DiskBlockManager: Created local directory at
>> /hdata/9/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/spark-local-20150210120620-9d11
>> 15/02/10 12:06:20 INFO MemoryStore: MemoryStore started with capacity
>> 1177.8 MB.
>> 15/02/10 12:06:20 INFO ConnectionManager: Bound socket to port 55944 with
>> id = ConnectionManagerId(phd40010008.na.com,55944)
>> 15/02/10 12:06:20 INFO BlockManagerMaster: Trying to register BlockManager
>> 15/02/10 12:06:20 INFO BlockManagerInfo: Registering block manager
>> phd40010008.na.com:55944 with 1177.8 MB RAM
>> 15/02/10 12:06:20 INFO BlockManagerMaster: Registered BlockManager
>> 15/02/10 12:06:20 INFO HttpFileServer: HTTP File server directory is
>> /hdata/12/yarn/nm/usercache/jg/appcache/application_1423081782629_7370/container_1423081782629_7370_01_000001/tmp/spark-b3daba9d-f743-4738-b6c2-f56e56813edd
>> 15/02/10 12:06:20 INFO HttpServer: Starting HTTP Server
>> 15/02/10 12:06:20 INFO JettyUtils: Adding filter:
>> org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
>> 15/02/10 12:06:20 INFO SparkUI: Started SparkUI at
>> http://phd40010008.na.com:10612
>> 15/02/10 12:06:20 INFO EventLoggingListener: Logging events to
>> /user/spark/applicationHistory/com.na.scalaspark.sbstreamingtv-1423587980782
>> 15/02/10 12:06:20 INFO YarnClusterScheduler: Created YarnClusterScheduler
>> 15/02/10 12:06:20 INFO YarnClusterScheduler:
>> YarnClusterScheduler.postStartHook done
>> 15/02/10 12:06:21 INFO MemoryStore: ensureFreeSpace(253715) called with
>> curMem=0, maxMem=1235012812
>> 15/02/10 12:06:21 INFO MemoryStore: Block broadcast_0 stored as values to
>> memory (estimated size 247.8 KB, free 1177.6 MB)
>> 15/02/10 12:06:21 INFO FileInputFormat: Total input paths to process : 1
>> 15/02/10 12:06:21 INFO SparkContext: Starting job: collect at
>> sbStreamingTv.scala:198
>> 15/02/10 12:06:21 INFO DAGScheduler: Got job 0 (collect at
>> sbStreamingTv.scala:198) with 2 output partitions (allowLocal=false)
>> 15/02/10 12:06:21 INFO DAGScheduler: Final stage: Stage 0(*collect at
>> sbStreamingTv.scala:198*)
>> 15/02/10 12:06:21 INFO DAGScheduler: Parents of final stage: List()
>> 15/02/10 12:06:21 INFO DAGScheduler: Missing parents: List()
>> 15/02/10 12:06:21 INFO DAGScheduler: Submitting Stage 0 (*MappedRDD[1]
>> at textFile at sbStreamingTv.scala:198*), which has no missing parents
>> 15/02/10 12:06:21 INFO DAGScheduler: Submitting 2 missing tasks from
>> Stage 0 (*MappedRDD[1] at textFile at sbStreamingTv.scala:198*)
>> 15/02/10 12:06:21 INFO YarnClusterScheduler: Adding task set 0.0 with 2
>> tasks
>> 15/02/10 12:06:21 INFO AMRMClientImpl: Received new token for :
>> phd40010024.na.com:8041
>> 15/02/10 12:06:21 INFO AMRMClientImpl: Received new token for :
>> phd40010002.na.com:8041
>> 15/02/10 12:06:21 INFO AMRMClientImpl: Received new token for :
>> phd40010022.na.com:8041
>> 15/02/10 12:06:21 INFO RackResolver: Resolved phd40010002.na.com to
>> /sdc/c4h5
>> 15/02/10 12:06:21 INFO RackResolver: Resolved phd40010022.na.com to
>> /sdc/c4h5
>> 15/02/10 12:06:21 INFO RackResolver: Resolved phd40010024.na.com to
>> /sdc/c4h1
>> 15/02/10 12:06:21 INFO YarnAllocationHandler: Launching container
>> container_1423081782629_7370_01_000003 for on host phd40010002.na.com
>> 15/02/10 12:06:21 INFO YarnAllocationHandler: Launching ExecutorRunnable.
>> driverUrl: akka.tcp://
>> sp...@phd40010008.na.com:58240/user/CoarseGrainedScheduler,
>> executorHostname: phd40010002.na.com
>> 15/02/10 12:06:21 INFO YarnAllocationHandler: Launching container
>> container_1423081782629_7370_01_000004 for on host phd40010022.na.com
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Starting Executor Container
>> 15/02/10 12:06:21 INFO YarnAllocationHandler: Launching ExecutorRunnable.
>> driverUrl: akka.tcp://
>> sp...@phd40010008.na.com:58240/user/CoarseGrainedScheduler,
>> executorHostname: phd40010022.na.com
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Starting Executor Container
>> 15/02/10 12:06:21 INFO YarnAllocationHandler: Launching container
>> container_1423081782629_7370_01_000002 for on host phd40010024.na.com
>> 15/02/10 12:06:21 INFO YarnAllocationHandler: Launching ExecutorRunnable.
>> driverUrl: akka.tcp://
>> sp...@phd40010008.na.com:58240/user/CoarseGrainedScheduler,
>> executorHostname: phd40010024.na.com
>> 15/02/10 12:06:21 INFO ContainerManagementProtocolProxy:
>> yarn.client.max-nodemanagers-proxies : 500
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Starting Executor Container
>> 15/02/10 12:06:21 INFO ContainerManagementProtocolProxy:
>> yarn.client.max-nodemanagers-proxies : 500
>> 15/02/10 12:06:21 INFO ContainerManagementProtocolProxy:
>> yarn.client.max-nodemanagers-proxies : 500
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Setting up ContainerLaunchContext
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Setting up ContainerLaunchContext
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Setting up ContainerLaunchContext
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Preparing Local resources
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Preparing Local resources
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Preparing Local resources
>> 15/02/10 12:06:21 INFO ApplicationMaster: All executors have launched.
>> 15/02/10 12:06:21 INFO ApplicationMaster: Started progress reporter
>> thread - sleep time : 5000
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Prepared Local resources
>> Map(__spark__.jar -> resource { scheme: "hdfs" host: "nameservice1" port:
>> -1 file:
>> "/user/jg/.sparkStaging/application_1423081782629_7370/spark-assembly-1.0.0-cdh5.1.3-hadoop2.3.0-cdh5.1.3.jar"
>> } size: 93542713 timestamp: 1423587960750 type: FILE visibility: PRIVATE,
>> __app__.jar -> resource { scheme: "hdfs" host: "nameservice1" port: -1
>> file:
>> "/user/jg/.sparkStaging/application_1423081782629_7370/sbStreamingTv-0.0.1-SNAPSHOT-jar-with-dependencies.jar"
>> } size: 95950353 timestamp: 1423587960370 type: FILE visibility: PRIVATE)
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Prepared Local resources
>> Map(__spark__.jar -> resource { scheme: "hdfs" host: "nameservice1" port:
>> -1 file:
>> "/user/jg/.sparkStaging/application_1423081782629_7370/spark-assembly-1.0.0-cdh5.1.3-hadoop2.3.0-cdh5.1.3.jar"
>> } size: 93542713 timestamp: 1423587960750 type: FILE visibility: PRIVATE,
>> __app__.jar -> resource { scheme: "hdfs" host: "nameservice1" port: -1
>> file:
>> "/user/jg/.sparkStaging/application_1423081782629_7370/sbStreamingTv-0.0.1-SNAPSHOT-jar-with-dependencies.jar"
>> } size: 95950353 timestamp: 1423587960370 type: FILE visibility: PRIVATE)
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Prepared Local resources
>> Map(__spark__.jar -> resource { scheme: "hdfs" host: "nameservice1" port:
>> -1 file:
>> "/user/jg/.sparkStaging/application_1423081782629_7370/spark-assembly-1.0.0-cdh5.1.3-hadoop2.3.0-cdh5.1.3.jar"
>> } size: 93542713 timestamp: 1423587960750 type: FILE visibility: PRIVATE,
>> __app__.jar -> resource { scheme: "hdfs" host: "nameservice1" port: -1
>> file:
>> "/user/jg/.sparkStaging/application_1423081782629_7370/sbStreamingTv-0.0.1-SNAPSHOT-jar-with-dependencies.jar"
>> } size: 95950353 timestamp: 1423587960370 type: FILE visibility: PRIVATE)
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Setting up executor with
>> commands: List($JAVA_HOME/bin/java, -server, -XX:OnOutOfMemoryError='kill
>> %p', -Xms2048m -Xmx2048m , -Djava.io.tmpdir=$PWD/tmp,
>> -Dlog4j.configuration=log4j-spark-container.properties,
>> org.apache.spark.executor.CoarseGrainedExecutorBackend, akka.tcp://
>> sp...@phd40010008.na.com:58240/user/CoarseGrainedScheduler, 1,
>> phd40010002.na.com, 1, 1>, <LOG_DIR>/stdout, 2>, <LOG_DIR>/stderr)
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Setting up executor with
>> commands: List($JAVA_HOME/bin/java, -server, -XX:OnOutOfMemoryError='kill
>> %p', -Xms2048m -Xmx2048m , -Djava.io.tmpdir=$PWD/tmp,
>> -Dlog4j.configuration=log4j-spark-container.properties,
>> org.apache.spark.executor.CoarseGrainedExecutorBackend, akka.tcp://
>> sp...@phd40010008.na.com:58240/user/CoarseGrainedScheduler, 3,
>> phd40010024.na.com, 1, 1>, <LOG_DIR>/stdout, 2>, <LOG_DIR>/stderr)
>> 15/02/10 12:06:21 INFO ExecutorRunnable: Setting up executor with
>> commands: List($JAVA_HOME/bin/java, -server, -XX:OnOutOfMemoryError='kill
>> %p', -Xms2048m -Xmx2048m , -Djava.io.tmpdir=$PWD/tmp,
>> -Dlog4j.configuration=log4j-spark-container.properties,
>> org.apache.spark.executor.CoarseGrainedExecutorBackend, akka.tcp://
>> sp...@phd40010008.na.com:58240/user/CoarseGrainedScheduler, 2,
>> phd40010022.na.com, 1, 1>, <LOG_DIR>/stdout, 2>, <LOG_DIR>/stderr)
>> 15/02/10 12:06:21 INFO ContainerManagementProtocolProxy: Opening proxy :
>> phd40010022.na.com:8041
>> 15/02/10 12:06:21 INFO ContainerManagementProtocolProxy: Opening proxy :
>> phd40010024.na.com:8041
>> 15/02/10 12:06:21 INFO ContainerManagementProtocolProxy: Opening proxy :
>> phd40010002.na.com:8041
>> 15/02/10 12:06:26 INFO CoarseGrainedSchedulerBackend: Registered
>> executor: Actor[akka.tcp://
>> sparkexecu...@phd40010022.na.com:29369/user/Executor#43651774] with ID 2
>> 15/02/10 12:06:26 INFO CoarseGrainedSchedulerBackend: Registered
>> executor: Actor[akka.tcp://
>> sparkexecu...@phd40010024.na.com:12969/user/Executor#1711844295] with ID
>> 3
>> 15/02/10 12:06:26 INFO BlockManagerInfo: Registering block manager
>> phd40010022.na.com:14119 with 1178.1 MB RAM
>> 15/02/10 12:06:26 INFO BlockManagerInfo: Registering block manager
>> phd40010024.na.com:53284 with 1178.1 MB RAM
>> 15/02/10 12:06:29 INFO CoarseGrainedSchedulerBackend: Registered
>> executor: Actor[akka.tcp://
>> sparkexecu...@phd40010002.na.com:35547/user/Executor#-1690254909] with
>> ID 1
>> 15/02/10 12:06:29 INFO BlockManagerInfo: Registering block manager
>> phd40010002.na.com:62754 with 1178.1 MB RAM
>> 15/02/10 12:06:36 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:06:51 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:07:06 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:07:21 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:07:36 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:07:51 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:08:06 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:08:21 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:08:36 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:08:51 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:09:06 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:09:21 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:09:36 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:09:51 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:10:06 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:10:21 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:10:36 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>> 15/02/10 12:10:51 WARN YarnClusterScheduler: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient memory
>>
>> On Fri, Feb 6, 2015 at 3:24 PM, Sandy Ryza <sandy.r...@cloudera.com>
>> wrote:
>>
>>> You can call collect() to pull in the contents of an RDD into the driver:
>>>
>>> val badIPsLines = badIPs.collect()
>>>
>>> On Fri, Feb 6, 2015 at 12:19 PM, Jon Gregg <jonrgr...@gmail.com> wrote:
>>>
>>>> OK I tried that, but how do I convert an RDD to a Set that I can then
>>>> broadcast and cache?
>>>>
>>>> val badIPs = sc.textFile("hdfs:///user/jon/"+ "badfullIPs.csv")
>>>> val badIPsLines = badIPs.getLines
>>>> val badIpSet = badIPsLines.toSet
>>>> val badIPsBC = sc.broadcast(badIpSet)
>>>>
>>>> produces the error "value getLines is not a member of
>>>> org.apache.spark.rdd.RDD[String]".
>>>>
>>>> Leaving it as an RDD and then constantly joining I think will be too
>>>> slow for a streaming job.
>>>>
>>>> On Thu, Feb 5, 2015 at 8:06 PM, Sandy Ryza <sandy.r...@cloudera.com>
>>>> wrote:
>>>>
>>>>> Hi Jon,
>>>>>
>>>>> You'll need to put the file on HDFS (or whatever distributed
>>>>> filesystem you're running on) and load it from there.
>>>>>
>>>>> -Sandy
>>>>>
>>>>> On Thu, Feb 5, 2015 at 3:18 PM, YaoPau <jonrgr...@gmail.com> wrote:
>>>>>
>>>>>> I have a file "badFullIPs.csv" of bad IP addresses used for
>>>>>> filtering. In
>>>>>> yarn-client mode, I simply read it off the edge node, transform it,
>>>>>> and then
>>>>>> broadcast it:
>>>>>>
>>>>>> val badIPs = fromFile(edgeDir + "badfullIPs.csv")
>>>>>> val badIPsLines = badIPs.getLines
>>>>>> val badIpSet = badIPsLines.toSet
>>>>>> val badIPsBC = sc.broadcast(badIpSet)
>>>>>> badIPs.close
>>>>>>
>>>>>> How can I accomplish this in yarn-cluster mode?
>>>>>>
>>>>>> Jon
>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> View this message in context:
>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-broadcast-a-variable-read-from-a-file-in-yarn-cluster-mode-tp21524.html
>>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>>> Nabble.com.
>>>>>>
>>>>>> ---------------------------------------------------------------------
>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>>>>>> For additional commands, e-mail: user-h...@spark.apache.org
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
