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From Dasun Hegoda <dasunheg...@gmail.com>
Subject Re: Hive on Spark - Hadoop 2 - Installation - Ubuntu
Date Fri, 27 Nov 2015 17:54:29 GMT
I found the solution. Use the IP instead of the hostname in
spark-default.conf

spark.master            spark://192.168.7.87:7077

On Fri, Nov 27, 2015 at 10:01 PM, Dasun Hegoda <dasunhegoda@gmail.com>
wrote:

> Thanks for the input. Let's see whether someone can help us
>
> On Fri, Nov 27, 2015 at 9:50 PM, Mich Talebzadeh <mich@peridale.co.uk>
> wrote:
>
>> Hi,
>>
>>
>>
>> I download the latest version of Hive 1.2.1 few days ago and upgraded
>> from Hive 0.14.0 to Hive 1.2.1. Pretty straight forward and minimal changes
>> to Metastore schema (mine is on Oracle).
>>
>>
>>
>> Now I have no problem making Spark work with Hive when a pre-compiled
>> version of Spark like 1.5.2 is downloaded. For example you can create in
>> Spark via Scala and that will be seen through Hive.
>>
>>
>>
>> However, that is not my primary concern. I don’t want to run Spark
>> standalone or as an application with Hive.
>>
>>
>>
>> My prime interest is to see if I can make Hive to use Spark as its
>> execution engine, as opposed to the long established MapReduce engine that
>> Hive uses.
>>
>>
>>
>> As of now I have not succeeded to get it working. I have downloaded Spark
>> source code for versions 1.5.1, 1.4, 1.3 etc and created projects using mvn
>> and tar files. However, I have not succeeded even starting spark master
>> (start-master.sh). It just crashes with errors I have reported before. The
>> reason seem to be that a jar file in $SPARK_HOME/lib (sorry I cannot recall
>> itd name now) works fine in the pre-built but is much smaller in lib
>> directory when spark is built from source. Indeed if you copy the original
>> one from the pre-built lib directory you will be able to start master node.
>> However, that is not a solution.
>>
>>
>>
>> I am sure someone in this forum with much better knowledge of Java should
>> be able to come up with some solution.
>>
>>
>>
>> HTH,
>>
>>
>>
>> Mich
>>
>>
>>
>>
>>
>> *From:* Dasun Hegoda [mailto:dasunhegoda@gmail.com]
>> *Sent:* 27 November 2015 15:13
>> *To:* user@hive.apache.org
>> *Subject:* Re: Hive on Spark - Hadoop 2 - Installation - Ubuntu
>>
>>
>>
>> Hey!
>>
>>
>>
>> Thanks for the clarification. I have been to struggling to deploy hive on
>> spark for 3 weeks now. Still no luck. I can't believe that even Hive
>> experts here don't know about it. I'm wondering what to do.
>>
>>
>>
>> Any guesses???
>>
>>
>>
>> On Fri, Nov 27, 2015 at 3:52 PM, Mich Talebzadeh <mich@peridale.co.uk>
>> wrote:
>>
>> This should work as long as $SPARK_HOME has been setup and your CLASSPATH
>> includes spark jars.
>>
>>
>>
>> Also bear in mind that this will work OK BUT crucially Hive will not be
>> able to use Spark engine with pre-built Spark binary downloads
>>
>>
>>
>> Example
>>
>>
>>
>> *spark-shell --master spark://rhes564:7077*
>>
>>
>>
>> /11/27 10:19:25 INFO spark.SecurityManager: Changing view acls to: hduser
>>
>> 15/11/27 10:19:25 INFO spark.SecurityManager: Changing modify acls to:
>> hduser
>>
>> 15/11/27 10:19:25 INFO spark.SecurityManager: SecurityManager:
>> authentication disabled; ui acls disabled; users with view permissions:
>> Set(hduser); users with modify permissions: Set(hduser)
>>
>> 15/11/27 10:19:25 INFO spark.HttpServer: Starting HTTP Server
>>
>> 15/11/27 10:19:25 INFO server.Server: jetty-8.y.z-SNAPSHOT
>>
>> 15/11/27 10:19:25 INFO server.AbstractConnector: Started
>> SocketConnector@0.0.0.0:22613
>>
>> 15/11/27 10:19:25 INFO util.Utils: Successfully started service 'HTTP
>> class server' on port 22613.
>>
>> Welcome to
>>
>>       ____              __
>>
>>      / __/__  ___ _____/ /__
>>
>>     _\ \/ _ \/ _ `/ __/  '_/
>>
>>    /___/ .__/\_,_/_/ /_/\_\   version 1.5.2
>>
>>       /_/
>>
>>
>>
>> Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java
>> 1.7.0_25)
>>
>> Type in expressions to have them evaluated.
>>
>> Type :help for more information.
>>
>> 15/11/27 10:19:29 WARN util.Utils: Your hostname, rhes564 resolves to a
>> loopback address: 127.0.0.1; using 50.140.197.217 instead (on interface
>> eth0)
>>
>> 15/11/27 10:19:29 WARN util.Utils: Set SPARK_LOCAL_IP if you need to bind
>> to another address
>>
>> 15/11/27 10:19:29 INFO spark.SparkContext: Running Spark version 1.5.2
>>
>> 15/11/27 10:19:29 INFO spark.SecurityManager: Changing view acls to:
>> hduser
>>
>> 15/11/27 10:19:29 INFO spark.SecurityManager: Changing modify acls to:
>> hduser
>>
>> 15/11/27 10:19:29 INFO spark.SecurityManager: SecurityManager:
>> authentication disabled; ui acls disabled; users with view permissions:
>> Set(hduser); users with modify permissions: Set(hduser)
>>
>> 15/11/27 10:19:30 INFO slf4j.Slf4jLogger: Slf4jLogger started
>>
>> 15/11/27 10:19:30 INFO Remoting: Starting remoting
>>
>> 15/11/27 10:19:30 INFO Remoting: Remoting started; listening on addresses
>> :[akka.tcp://sparkDriver@50.140.197.217:61620]
>>
>> 15/11/27 10:19:30 INFO util.Utils: Successfully started service
>> 'sparkDriver' on port 61620.
>>
>> 15/11/27 10:19:30 INFO spark.SparkEnv: Registering MapOutputTracker
>>
>> 15/11/27 10:19:30 INFO spark.SparkEnv: Registering BlockManagerMaster
>>
>> 15/11/27 10:19:30 INFO storage.DiskBlockManager: Created local directory
>> at /tmp/blockmgr-eae28f3e-f878-4591-85f0-e8a66c6acb02
>>
>> 15/11/27 10:19:30 INFO storage.MemoryStore: MemoryStore started with
>> capacity 529.9 MB
>>
>> 15/11/27 10:19:30 INFO spark.HttpFileServer: HTTP File server directory
>> is
>> /tmp/spark-75cd7444-5cf7-4175-a15b-6c3882c9d146/httpd-8dc465d5-664d-4cef-86a8-d4e8b34f4146
>>
>> 15/11/27 10:19:30 INFO spark.HttpServer: Starting HTTP Server
>>
>> 15/11/27 10:19:30 INFO server.Server: jetty-8.y.z-SNAPSHOT
>>
>> 15/11/27 10:19:30 INFO server.AbstractConnector: Started
>> SocketConnector@0.0.0.0:44656
>>
>> 15/11/27 10:19:30 INFO util.Utils: Successfully started service 'HTTP
>> file server' on port 44656.
>>
>> 15/11/27 10:19:30 INFO spark.SparkEnv: Registering OutputCommitCoordinator
>>
>> 15/11/27 10:19:30 INFO server.Server: jetty-8.y.z-SNAPSHOT
>>
>> 15/11/27 10:19:30 INFO server.AbstractConnector: Started
>> SelectChannelConnector@0.0.0.0:4040
>>
>> 15/11/27 10:19:30 INFO util.Utils: Successfully started service 'SparkUI'
>> on port 4040.
>>
>> 15/11/27 10:19:30 INFO ui.SparkUI: Started SparkUI at
>> http://50.140.197.217:4040
>>
>> 15/11/27 10:19:30 WARN metrics.MetricsSystem: Using default name
>> DAGScheduler for source because spark.app.id is not set.
>>
>> 15/11/27 10:19:30 INFO client.AppClient$ClientEndpoint: Connecting to
>> master spark://rhes564:7077...
>>
>> 15/11/27 10:19:31 INFO cluster.SparkDeploySchedulerBackend: Connected to
>> Spark cluster with app ID app-20151127101931-0001
>>
>> 15/11/27 10:19:31 INFO client.AppClient$ClientEndpoint: Executor added:
>> app-20151127101931-0001/0 on worker-20151127100137-50.140.197.217-38428 (
>> 50.140.197.217:38428) with 12 cores
>>
>> 15/11/27 10:19:31 INFO cluster.SparkDeploySchedulerBackend: Granted
>> executor ID app-20151127101931-0001/0 on hostPort 50.140.197.217:38428
>> with 12 cores, 1024.0 MB RAM
>>
>> 15/11/27 10:19:31 INFO client.AppClient$ClientEndpoint: Executor updated:
>> app-20151127101931-0001/0 is now LOADING
>>
>> 15/11/27 10:19:31 INFO client.AppClient$ClientEndpoint: Executor updated:
>> app-20151127101931-0001/0 is now RUNNING
>>
>> 15/11/27 10:19:31 INFO util.Utils: Successfully started service
>> 'org.apache.spark.network.netty.NettyBlockTransferService' on port 19761.
>>
>> 15/11/27 10:19:31 INFO netty.NettyBlockTransferService: Server created on
>> 19761
>>
>> 15/11/27 10:19:31 INFO storage.BlockManagerMaster: Trying to register
>> BlockManager
>>
>> 15/11/27 10:19:31 INFO storage.BlockManagerMasterEndpoint: Registering
>> block manager 50.140.197.217:19761 with 529.9 MB RAM,
>> BlockManagerId(driver, 50.140.197.217, 19761)
>>
>> 15/11/27 10:19:31 INFO storage.BlockManagerMaster: Registered BlockManager
>>
>> 15/11/27 10:19:31 INFO cluster.SparkDeploySchedulerBackend:
>> SchedulerBackend is ready for scheduling beginning after reached
>> minRegisteredResourcesRatio: 0.0
>>
>> 15/11/27 10:19:31 INFO repl.SparkILoop: Created spark context..
>>
>> Spark context available as sc.
>>
>> 15/11/27 10:19:31 INFO hive.HiveContext: Initializing execution hive,
>> version 1.2.1
>>
>> 15/11/27 10:19:31 INFO client.ClientWrapper: Inspected Hadoop version:
>> 2.6.0
>>
>> 15/11/27 10:19:31 INFO client.ClientWrapper: Loaded
>> org.apache.hadoop.hive.shims.Hadoop23Shims for Hadoop version 2.6.0
>>
>> 15/11/27 10:19:32 INFO hive.metastore: Trying to connect to metastore
>> with URI thrift://localhost:9083
>>
>> 15/11/27 10:19:32 INFO hive.metastore: Connected to metastore.
>>
>> 15/11/27 10:19:32 INFO session.SessionState: Created local directory:
>> /tmp/hive/b8bba1a1-646b-4734-bad3-4c1d6cb9344d_resources
>>
>> 15/11/27 10:19:32 INFO session.SessionState: Created HDFS directory:
>> /tmp/hive/hduser/b8bba1a1-646b-4734-bad3-4c1d6cb9344d
>>
>> 15/11/27 10:19:32 INFO session.SessionState: Created local directory:
>> /tmp/hive/b8bba1a1-646b-4734-bad3-4c1d6cb9344d
>>
>> 15/11/27 10:19:32 INFO session.SessionState: Created HDFS directory:
>> /tmp/hive/hduser/b8bba1a1-646b-4734-bad3-4c1d6cb9344d/_tmp_space.db
>>
>> 15/11/27 10:19:32 INFO hive.HiveContext: default warehouse location is
>> /user/hive/warehouse
>>
>> 15/11/27 10:19:32 INFO hive.HiveContext: Initializing
>> HiveMetastoreConnection version 1.2.1 using Spark classes.
>>
>> 15/11/27 10:19:32 INFO client.ClientWrapper: Inspected Hadoop version:
>> 2.6.0
>>
>> 15/11/27 10:19:33 INFO client.ClientWrapper: Loaded
>> org.apache.hadoop.hive.shims.Hadoop23Shims for Hadoop version 2.6.0
>>
>> 15/11/27 10:19:33 INFO cluster.SparkDeploySchedulerBackend: Registered
>> executor: AkkaRpcEndpointRef(Actor[akka.tcp://
>> sparkExecutor@50.140.197.217:55017/user/Executor#1724631850]) with ID 0
>>
>> 15/11/27 10:19:33 INFO storage.BlockManagerMasterEndpoint: Registering
>> block manager 50.140.197.217:25122 with 529.9 MB RAM, BlockManagerId(0,
>> 50.140.197.217, 25122)
>>
>> 15/11/27 10:19:33 WARN util.NativeCodeLoader: Unable to load
>> native-hadoop library for your platform... using builtin-java classes where
>> applicable
>>
>> 15/11/27 10:19:33 INFO hive.metastore: Trying to connect to metastore
>> with URI thrift://localhost:9083
>>
>> 15/11/27 10:19:33 INFO hive.metastore: Connected to metastore.
>>
>> 15/11/27 10:19:34 INFO session.SessionState: Created local directory:
>> /tmp/hive/d2b5c2bd-3989-4a72-a99f-885356f02f8b_resources
>>
>> 15/11/27 10:19:34 INFO session.SessionState: Created HDFS directory:
>> /tmp/hive/hduser/d2b5c2bd-3989-4a72-a99f-885356f02f8b
>>
>> 15/11/27 10:19:34 INFO session.SessionState: Created local directory:
>> /tmp/hive/d2b5c2bd-3989-4a72-a99f-885356f02f8b
>>
>> 15/11/27 10:19:34 INFO session.SessionState: Created HDFS directory:
>> /tmp/hive/hduser/d2b5c2bd-3989-4a72-a99f-885356f02f8b/_tmp_space.db
>>
>> 15/11/27 10:19:34 INFO repl.SparkILoop: Created sql context (with Hive
>> support)..
>>
>> SQL context available as sqlContext.
>>
>>
>>
>> *scala>*
>>
>>
>>
>> However, that is of little use to me cause I want to use Spark as Hive
>> engine for faster performance compared to MapReduce engine. *Spark as a
>> fully built application does not work as an engine alone*! For that I
>> need to build Spark WITHOUT HAVE Jars and use it as engine as opposed to
>> standalone application.
>>
>>
>>
>> HTH
>>
>>
>>
>>
>>
>> Mich Talebzadeh
>>
>>
>>
>> *Sybase ASE 15 Gold Medal Award 2008*
>>
>> A Winning Strategy: Running the most Critical Financial Data on ASE 15
>>
>>
>> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
>>
>> Author of the books* "A Practitioner’s Guide to Upgrading to Sybase ASE
>> 15", ISBN 978-0-9563693-0-7*.
>>
>> co-author *"Sybase Transact SQL Guidelines Best Practices", ISBN
>> 978-0-9759693-0-4*
>>
>> *Publications due shortly:*
>>
>> *Complex Event Processing in Heterogeneous Environments*, ISBN:
>> 978-0-9563693-3-8
>>
>> *Oracle and Sybase, Concepts and Contrasts*, ISBN: 978-0-9563693-1-4, volume
>> one out shortly
>>
>>
>>
>> http://talebzadehmich.wordpress.com
>>
>>
>>
>> NOTE: The information in this email is proprietary and confidential. This
>> message is for the designated recipient only, if you are not the intended
>> recipient, you should destroy it immediately. Any information in this
>> message shall not be understood as given or endorsed by Peridale Technology
>> Ltd, its subsidiaries or their employees, unless expressly so stated. It is
>> the responsibility of the recipient to ensure that this email is virus
>> free, therefore neither Peridale Ltd, its subsidiaries nor their employees
>> accept any responsibility.
>>
>>
>>
>> *From:* Dasun Hegoda [mailto:dasunhegoda@gmail.com]
>> *Sent:* 27 November 2015 05:11
>> *To:* user@hive.apache.org
>> *Subject:* Re: Hive on Spark - Hadoop 2 - Installation - Ubuntu
>>
>>
>>
>> This works fine for me
>>
>>
>>
>> spark-shell --master yarn-client
>>
>>
>>
>> On Tue, Nov 24, 2015 at 11:43 AM, Dasun Hegoda <dasunhegoda@gmail.com>
>> wrote:
>>
>> Hey floks,
>>
>>
>>
>> Any updates?
>>
>>
>>
>> On Mon, Nov 23, 2015 at 5:15 PM, Dasun Hegoda <dasunhegoda@gmail.com>
>> wrote:
>>
>> Do you have any clue how to get his fixed?
>>
>>
>>
>> On Mon, Nov 23, 2015 at 4:27 PM, Dasun Hegoda <dasunhegoda@gmail.com>
>> wrote:
>>
>> I get this now. It's different than what you get
>>
>>
>>
>> hduser@master:~/spark-1.5.1-bin-hadoop2.6/bin$ ./spark-shell
>>
>> 15/11/23 05:56:13 INFO spark.SecurityManager: Changing view acls to:
>> hduser
>>
>> 15/11/23 05:56:13 INFO spark.SecurityManager: Changing modify acls to:
>> hduser
>>
>> 15/11/23 05:56:13 INFO spark.SecurityManager: SecurityManager:
>> authentication disabled; ui acls disabled; users with view permissions:
>> Set(hduser); users with modify permissions: Set(hduser)
>>
>> 15/11/23 05:56:13 INFO spark.HttpServer: Starting HTTP Server
>>
>> 15/11/23 05:56:13 INFO server.Server: jetty-8.y.z-SNAPSHOT
>>
>> 15/11/23 05:56:13 INFO server.AbstractConnector: Started
>> SocketConnector@0.0.0.0:34334
>>
>> 15/11/23 05:56:13 INFO util.Utils: Successfully started service 'HTTP
>> class server' on port 34334.
>>
>> Welcome to
>>
>>       ____              __
>>
>>      / __/__  ___ _____/ /__
>>
>>     _\ \/ _ \/ _ `/ __/  '_/
>>
>>    /___/ .__/\_,_/_/ /_/\_\   version 1.5.1
>>
>>       /_/
>>
>>
>>
>> Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java
>> 1.7.0_55)
>>
>> Type in expressions to have them evaluated.
>>
>> Type :help for more information.
>>
>> 15/11/23 05:56:17 INFO spark.SparkContext: Running Spark version 1.5.1
>>
>> 15/11/23 05:56:17 WARN spark.SparkConf:
>>
>> SPARK_JAVA_OPTS was detected (set to '-Dspark.driver.port=53411').
>>
>> This is deprecated in Spark 1.0+.
>>
>>
>>
>> Please instead use:
>>
>>  - ./spark-submit with conf/spark-defaults.conf to set defaults for an
>> application
>>
>>  - ./spark-submit with --driver-java-options to set -X options for a
>> driver
>>
>>  - spark.executor.extraJavaOptions to set -X options for executors
>>
>>  - SPARK_DAEMON_JAVA_OPTS to set java options for standalone daemons
>> (master or worker)
>>
>>
>>
>> 15/11/23 05:56:17 WARN spark.SparkConf: Setting
>> 'spark.executor.extraJavaOptions' to '-Dspark.driver.port=53411' as a
>> work-around.
>>
>> 15/11/23 05:56:17 WARN spark.SparkConf: Setting
>> 'spark.driver.extraJavaOptions' to '-Dspark.driver.port=53411' as a
>> work-around.
>>
>> 15/11/23 05:56:17 INFO spark.SecurityManager: Changing view acls to:
>> hduser
>>
>> 15/11/23 05:56:17 INFO spark.SecurityManager: Changing modify acls to:
>> hduser
>>
>> 15/11/23 05:56:17 INFO spark.SecurityManager: SecurityManager:
>> authentication disabled; ui acls disabled; users with view permissions:
>> Set(hduser); users with modify permissions: Set(hduser)
>>
>> 15/11/23 05:56:18 INFO slf4j.Slf4jLogger: Slf4jLogger started
>>
>> 15/11/23 05:56:18 INFO Remoting: Starting remoting
>>
>> 15/11/23 05:56:18 INFO Remoting: Remoting started; listening on addresses
>> :[akka.tcp://sparkDriver@192.168.7.87:53411]
>>
>> 15/11/23 05:56:18 INFO util.Utils: Successfully started service
>> 'sparkDriver' on port 53411.
>>
>> 15/11/23 05:56:18 INFO spark.SparkEnv: Registering MapOutputTracker
>>
>> 15/11/23 05:56:18 INFO spark.SparkEnv: Registering BlockManagerMaster
>>
>> 15/11/23 05:56:18 INFO storage.DiskBlockManager: Created local directory
>> at /tmp/blockmgr-0232975c-c76b-444d-b7f7-1ef2f28e388c
>>
>> 15/11/23 05:56:18 INFO storage.MemoryStore: MemoryStore started with
>> capacity 530.3 MB
>>
>> 15/11/23 05:56:18 INFO spark.HttpFileServer: HTTP File server directory
>> is
>> /tmp/spark-2413b536-c845-4964-a96d-973e5ec02593/httpd-311975ea-ac22-493d-8fd5-0f48b562a9a5
>>
>> 15/11/23 05:56:18 INFO spark.HttpServer: Starting HTTP Server
>>
>> 15/11/23 05:56:18 INFO server.Server: jetty-8.y.z-SNAPSHOT
>>
>> 15/11/23 05:56:18 INFO server.AbstractConnector: Started
>> SocketConnector@0.0.0.0:60477
>>
>> 15/11/23 05:56:18 INFO util.Utils: Successfully started service 'HTTP
>> file server' on port 60477.
>>
>> 15/11/23 05:56:18 INFO spark.SparkEnv: Registering OutputCommitCoordinator
>>
>> 15/11/23 05:56:18 INFO server.Server: jetty-8.y.z-SNAPSHOT
>>
>> 15/11/23 05:56:18 INFO server.AbstractConnector: Started
>> SelectChannelConnector@0.0.0.0:4040
>>
>> 15/11/23 05:56:18 INFO util.Utils: Successfully started service 'SparkUI'
>> on port 4040.
>>
>> 15/11/23 05:56:18 INFO ui.SparkUI: Started SparkUI at
>> http://192.168.7.87:4040
>>
>> 15/11/23 05:56:18 WARN metrics.MetricsSystem: Using default name
>> DAGScheduler for source because spark.app.id is not set.
>>
>> 15/11/23 05:56:18 INFO client.AppClient$ClientEndpoint: Connecting to
>> master spark://master:7077...
>>
>> 15/11/23 05:56:38 ERROR util.SparkUncaughtExceptionHandler: Uncaught
>> exception in thread Thread[appclient-registration-retry-thread,5,main]
>>
>> java.util.concurrent.RejectedExecutionException: Task
>> java.util.concurrent.FutureTask@236f0e3a rejected from
>> java.util.concurrent.ThreadPoolExecutor@500f1402[Running, pool size = 1,
>> active threads = 0, queued tasks = 0, completed tasks = 1]
>>
>> at
>> java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2048)
>>
>> at
>> java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:821)
>>
>> at
>> java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1372)
>>
>> at
>> java.util.concurrent.AbstractExecutorService.submit(AbstractExecutorService.java:110)
>>
>> at
>> org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$tryRegisterAllMasters$1.apply(AppClient.scala:96)
>>
>> at
>> org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$tryRegisterAllMasters$1.apply(AppClient.scala:95)
>>
>> at
>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>
>> at
>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>
>> at
>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>
>> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>
>> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>
>> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>
>> at
>> org.apache.spark.deploy.client.AppClient$ClientEndpoint.tryRegisterAllMasters(AppClient.scala:95)
>>
>> at
>> org.apache.spark.deploy.client.AppClient$ClientEndpoint.org$apache$spark$deploy$client$AppClient$ClientEndpoint$$registerWithMaster(AppClient.scala:121)
>>
>> at
>> org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2$$anonfun$run$1.apply$mcV$sp(AppClient.scala:132)
>>
>> at org.apache.spark.util.Utils$.tryOrExit(Utils.scala:1119)
>>
>> at
>> org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anon$2.run(AppClient.scala:124)
>>
>> at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
>>
>> at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:304)
>>
>> at
>> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:178)
>>
>> at
>> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
>>
>> at
>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>>
>> at
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>
>> at java.lang.Thread.run(Thread.java:745)
>>
>> 15/11/23 05:56:38 INFO storage.DiskBlockManager: Shutdown hook called
>>
>> 15/11/23 05:56:38 INFO util.ShutdownHookManager: Shutdown hook called
>>
>> 15/11/23 05:56:38 INFO util.ShutdownHookManager: Deleting directory
>> /tmp/spark-2413b536-c845-4964-a96d-973e5ec02593/httpd-311975ea-ac22-493d-8fd5-0f48b562a9a5
>>
>> 15/11/23 05:56:38 INFO util.ShutdownHookManager: Deleting directory
>> /tmp/spark-8fefb39a-09b5-443c-b7b4-9c54bce6e245
>>
>> 15/11/23 05:56:38 INFO util.ShutdownHookManager: Deleting directory
>> /tmp/spark-2413b536-c845-4964-a96d-973e5ec02593/userFiles-b593fc93-c23a-4a9e-aede-ed051f149fcb
>>
>> 15/11/23 05:56:38 INFO util.ShutdownHookManager: Deleting directory
>> /tmp/spark-2413b536-c845-4964-a96d-973e5ec02593
>>
>>
>>
>> On Mon, Nov 23, 2015 at 4:19 PM, Mich Talebzadeh <mich@peridale.co.uk>
>> wrote:
>>
>> As example shows all set in hive-core.xml
>>
>>
>>
>> <property>
>>
>>     <name>hive.execution.engine</name>
>>
>>     *<value>spark</value>*
>>
>>     <description>
>>
>>       Expects one of [mr, tez, spark].
>>
>>       Chooses execution engine. Options are: mr (Map reduce, default) or
>> tez (hadoop 2 only)
>>
>>     </description>
>>
>>   </property>
>>
>>
>>
>> <property>
>>
>>     <name> spark.eventLog.enabled</name>
>>
>>     *<value>true</value>*
>>
>>     <description>
>>
>>            Spark event log setting
>>
>>     </description>
>>
>>   </property>
>>
>>
>>
>>
>>
>> Mich Talebzadeh
>>
>>
>>
>> *Sybase ASE 15 Gold Medal Award 2008*
>>
>> A Winning Strategy: Running the most Critical Financial Data on ASE 15
>>
>>
>> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
>>
>> Author of the books* "A Practitioner’s Guide to Upgrading to Sybase ASE
>> 15", ISBN 978-0-9563693-0-7*.
>>
>> co-author *"Sybase Transact SQL Guidelines Best Practices", ISBN
>> 978-0-9759693-0-4*
>>
>> *Publications due shortly:*
>>
>> *Complex Event Processing in Heterogeneous Environments*, ISBN:
>> 978-0-9563693-3-8
>>
>> *Oracle and Sybase, Concepts and Contrasts*, ISBN: 978-0-9563693-1-4, volume
>> one out shortly
>>
>>
>>
>> http://talebzadehmich.wordpress.com
>>
>>
>>
>> NOTE: The information in this email is proprietary and confidential. This
>> message is for the designated recipient only, if you are not the intended
>> recipient, you should destroy it immediately. Any information in this
>> message shall not be understood as given or endorsed by Peridale Technology
>> Ltd, its subsidiaries or their employees, unless expressly so stated. It is
>> the responsibility of the recipient to ensure that this email is virus
>> free, therefore neither Peridale Ltd, its subsidiaries nor their employees
>> accept any responsibility.
>>
>>
>>
>> *From:* Dasun Hegoda [mailto:dasunhegoda@gmail.com]
>> *Sent:* 23 November 2015 10:40
>>
>>
>> *To:* user@hive.apache.org
>> *Subject:* Re: Hive on Spark - Hadoop 2 - Installation - Ubuntu
>>
>>
>>
>> Thank you very much. This is very informative. Do you know how to set
>> these in hive-site.xml?
>>
>>
>>
>> hive> set spark.master=<Spark Master URL>
>>
>> hive> set spark.eventLog.enabled=true;
>>
>> hive> set spark.eventLog.dir=<Spark event log folder (must exist)>
>>
>> hive> set spark.executor.memory=512m;
>>
>> hive> set spark.serializer=org.apache.spark.serializer.KryoSerializer;
>>
>>
>>
>> If these set these in hive-site I think we will be able to get through
>>
>>
>>
>> On Mon, Nov 23, 2015 at 3:05 PM, Mich Talebzadeh <mich@peridale.co.uk>
>> wrote:
>>
>> Hi,
>>
>>
>>
>> I am looking at the set up here
>>
>>
>>
>>
>> https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started
>> .
>>
>>
>>
>> First this is about configuration of Hive to work with Spark. These are
>> my understanding
>>
>>
>>
>> 1.    Hive uses Yarn as its resource manager regardless
>>
>> 2.    Hive uses MapReduce as its execution engine by default
>>
>> 3.    Changing the execution engine to that of Spark at the
>> configuration level. If you look at Hive configuration file ->
>>  $HIVE_HOME/conf/hive-site.xml, you will see that default is mr MapReduce
>>
>> <property>
>>
>>     <name>hive.execution.engine</name>
>>
>>     *<value>mr</value>*
>>
>>     <description>
>>
>>       Expects one of [mr, tez].
>>
>>       Chooses execution engine. Options are: mr (Map reduce, default) or
>> tez (hadoop 2 only)
>>
>>     </description>
>>
>>   </property>
>>
>>
>>
>> 4.    If you change that to *spark and restart Hive, *you will force
>> Hive to use spark as its engine. So the choice is either do it at the
>> configuration level or session level (i.e set set
>> hive.execution.engine=spark;). For the rest of parameters you can do the
>> same. i.e. at hive-core.xml or at session level. Personally I would still
>> want hive to use MR engine so I will create spark-defaults.conf as
>> mentioned.
>>
>> 5.    I then start spark as standalone that works fine
>>
>> *hduser@rhes564::/usr/lib/spark> ./sbin/start-master.sh*
>>
>> starting org.apache.spark.deploy.master.Master, logging to
>> /usr/lib/spark/sbin/../logs/spark-hduser-org.apache.spark.deploy.master.Master-1-rhes564.out
>>
>> hduser@rhes564::/usr/lib/spark> more
>> /usr/lib/spark/sbin/../logs/spark-hduser-org.apache.spark.deploy.master.Master-1-rhes564.out
>>
>> Spark Command: /usr/java/latest/bin/java -cp
>> /usr/lib/spark/sbin/../conf/:/usr/lib/spark/lib/spark-assembly-1.5.2-hadoop2.6.0.jar:/usr/lib/spark/lib/datanucleus-core-3.2.10.jar:/usr/lib/spark/lib/datanucleus-ap
>>
>> i-jdo-3.2.6.jar:/usr/lib/spark/lib/datanucleus-rdbms-3.2.9.jar -Xms1g
>> -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.master.Master --ip
>> rhes564 --port 7077 --webui-port 8080
>>
>> ========================================
>>
>> Using Spark's default log4j profile:
>> org/apache/spark/log4j-defaults.properties
>>
>> 15/11/21 21:41:58 INFO Master: Registered signal handlers for [TERM, HUP,
>> INT]
>>
>> 15/11/21 21:41:58 WARN Utils: Your hostname, rhes564 resolves to a
>> loopback address: 127.0.0.1; using 50.140.197.217 instead (on interface
>> eth0)
>>
>> 15/11/21 21:41:58 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to
>> another address
>>
>> 15/11/21 21:41:59 WARN NativeCodeLoader: Unable to load native-hadoop
>> library for your platform... using builtin-java classes where applicable
>>
>> 15/11/21 21:41:59 INFO SecurityManager: Changing view acls to: hduser
>>
>> 15/11/21 21:41:59 INFO SecurityManager: Changing modify acls to: hduser
>>
>> 15/11/21 21:41:59 INFO SecurityManager: SecurityManager: authentication
>> disabled; ui acls disabled; users with view permissions: Set(hduser); users
>> with modify permissions: Set(hduser)
>>
>> 15/11/21 21:41:59 INFO Slf4jLogger: Slf4jLogger started
>>
>> 15/11/21 21:42:00 INFO Remoting: Starting remoting
>>
>> 15/11/21 21:42:00 INFO Remoting: Remoting started; listening on addresses
>> :[akka.tcp://sparkMaster@rhes564:7077]
>>
>> 15/11/21 21:42:00 INFO Utils: Successfully started service 'sparkMaster'
>> on port 7077.
>>
>> 15/11/21 21:42:00 INFO Master: Starting Spark master at
>> spark://rhes564:7077
>>
>> 15/11/21 21:42:00 INFO Master: Running Spark version 1.5.2
>>
>> 15/11/21 21:42:00 INFO Utils: Successfully started service 'MasterUI' on
>> port 8080.
>>
>> 15/11/21 21:42:00 INFO MasterWebUI: Started MasterWebUI at
>> http://50.140.197.217:8080
>>
>> 15/11/21 21:42:00 INFO Utils: Successfully started service on port 6066.
>>
>> 15/11/21 21:42:00 INFO StandaloneRestServer: Started REST server for
>> submitting applications on port 6066
>>
>> 15/11/21 21:42:00 INFO Master: I have been elected leader! New state:
>> ALIVE
>>
>> 6.    Then I try to start interactive spark-shell and it fails with an
>> error that I reported before
>>
>> *hduser@rhes564::/usr/lib/spark/bin> ./spark-shell --master
>> spark://rhes564:7077*
>>
>> 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 repl log4j profile:
>> org/apache/spark/log4j-defaults-repl.properties
>>
>> To adjust logging level use sc.setLogLevel("INFO")
>>
>> Welcome to
>>
>>       ____              __
>>
>>      / __/__  ___ _____/ /__
>>
>>     _\ \/ _ \/ _ `/ __/  '_/
>>
>>    /___/ .__/\_,_/_/ /_/\_\   version 1.5.2
>>
>>       /_/
>>
>>
>>
>> Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java
>> 1.7.0_25)
>>
>> Type in expressions to have them evaluated.
>>
>> Type :help for more information.
>>
>> 15/11/23 09:33:56 WARN Utils: Your hostname, rhes564 resolves to a
>> loopback address: 127.0.0.1; using 50.140.197.217 instead (on interface
>> eth0)
>>
>> 15/11/23 09:33:56 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to
>> another address
>>
>> 15/11/23 09:33:57 WARN MetricsSystem: Using default name DAGScheduler for
>> source because spark.app.id is not set.
>>
>> Spark context available as sc.
>>
>> 15/11/23 09:34:00 WARN HiveConf: HiveConf of name
>> hive.server2.thrift.http.min.worker.threads does not exist
>>
>> 15/11/23 09:34:00 WARN HiveConf: HiveConf of name
>> hive.mapjoin.optimized.keys does not exist
>>
>> 15/11/23 09:34:00 WARN HiveConf: HiveConf of name
>> hive.mapjoin.lazy.hashtable does not exist
>>
>> 15/11/23 09:34:00 WARN HiveConf: HiveConf of name
>> hive.server2.thrift.http.max.worker.threads does not exist
>>
>> 15/11/23 09:34:00 WARN HiveConf: HiveConf of name
>> hive.server2.logging.operation.verbose does not exist
>>
>> 15/11/23 09:34:00 WARN HiveConf: HiveConf of name
>> hive.optimize.multigroupby.common.distincts does not exist
>>
>> *java.lang.RuntimeException: java.lang.RuntimeException: The root scratch
>> dir: /tmp/hive on HDFS should be writable. Current permissions are:
>> rwx------*
>>
>>
>>
>> That is where I am now and I have reported this spark user group but no
>> luck yet.
>>
>>
>>
>>
>>
>> Mich Talebzadeh
>>
>>
>>
>> *Sybase ASE 15 Gold Medal Award 2008*
>>
>> A Winning Strategy: Running the most Critical Financial Data on ASE 15
>>
>>
>> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
>>
>> Author of the books* "A Practitioner’s Guide to Upgrading to Sybase ASE
>> 15", ISBN 978-0-9563693-0-7*.
>>
>> co-author *"Sybase Transact SQL Guidelines Best Practices", ISBN
>> 978-0-9759693-0-4*
>>
>> *Publications due shortly:*
>>
>> *Complex Event Processing in Heterogeneous Environments*, ISBN:
>> 978-0-9563693-3-8
>>
>> *Oracle and Sybase, Concepts and Contrasts*, ISBN: 978-0-9563693-1-4, volume
>> one out shortly
>>
>>
>>
>> http://talebzadehmich.wordpress.com
>>
>>
>>
>> NOTE: The information in this email is proprietary and confidential. This
>> message is for the designated recipient only, if you are not the intended
>> recipient, you should destroy it immediately. Any information in this
>> message shall not be understood as given or endorsed by Peridale Technology
>> Ltd, its subsidiaries or their employees, unless expressly so stated. It is
>> the responsibility of the recipient to ensure that this email is virus
>> free, therefore neither Peridale Ltd, its subsidiaries nor their employees
>> accept any responsibility.
>>
>>
>>
>> *From:* Dasun Hegoda [mailto:dasunhegoda@gmail.com]
>> *Sent:* 23 November 2015 07:05
>> *To:* user@hive.apache.org
>> *Subject:* Re: Hive on Spark - Hadoop 2 - Installation - Ubuntu
>>
>>
>>
>> Anyone????
>>
>>
>>
>> On Sat, Nov 21, 2015 at 1:32 PM, Dasun Hegoda <dasunhegoda@gmail.com>
>> wrote:
>>
>> Thank you very much but I would like to do the integration of these
>> components myself rather than using a packaged distribution. I think I have
>> come to right place. Can you please kindly tell me the configuration
>> steps run Hive on Spark?
>>
>>
>>
>> At least someone please elaborate these steps.
>>
>>
>> https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started
>> .
>>
>>
>>
>> Because at the latter part of the guide configurations are set in the
>> Hive runtime shell which is not permanent according to my knowledge.
>>
>>
>>
>> Please help me to get this done. Also I'm planning write a detailed guide
>> with configuration steps to run Hive on Spark. So others can benefited from
>> it and not troubled like me.
>>
>>
>>
>> Can someone please kindly tell me the configuration steps run Hive on
>> Spark?
>>
>>
>>
>>
>>
>> On Sat, Nov 21, 2015 at 12:28 PM, Sai Gopalakrishnan <
>> sai.gopalakrishnan@aspiresys.com> wrote:
>>
>> Hi everyone,
>>
>>
>>
>> Thank you for your responses. I think Mich's suggestion is a great one,
>> will go with it. As Alan suggested, using compactor in Hive should help out
>> with managing the delta files.
>>
>>
>>
>> @Dasun, pardon me for deviating from the topic. Regarding configuration,
>> you could try a packaged distribution (Hortonworks , Cloudera or MapR)
>> like  Jörn Franke said. I use Hortonworks, its open-source and compatible
>> with Linux and Windows, provides detailed documentation for installation
>> and can be installed in less than a day provided you're all set with the
>> hardware. http://hortonworks.com/hdp/downloads/
>>
>> [image: Image removed by sender.] <http://hortonworks.com/hdp/downloads/>
>>
>> Download Hadoop - Hortonworks
>>
>> Download Apache Hadoop for the enterprise with Hortonworks Data Platform.
>> Data access, storage, governance, security and operations across Linux and
>> Windows
>>
>> Read more... <http://hortonworks.com/hdp/downloads/>
>>
>>
>>
>>
>>
>> Regards,
>>
>> Sai
>>
>>
>> ------------------------------
>>
>> *From:* Dasun Hegoda <dasunhegoda@gmail.com>
>> *Sent:* Saturday, November 21, 2015 8:00 AM
>> *To:* user@hive.apache.org
>> *Subject:* Re: Hive on Spark - Hadoop 2 - Installation - Ubuntu
>>
>>
>>
>> Hi Mich, Hi Sai, Hi Jorn,
>>
>> Thank you very much for the information. I think we are deviating from
>> the original question. Hive on Spark on Ubuntu. Can you please kindly tell
>> me the configuration steps?
>>
>>
>>
>>
>>
>>
>>
>> On Fri, Nov 20, 2015 at 11:10 PM, Jörn Franke <jornfranke@gmail.com>
>> wrote:
>>
>> I think the most recent versions of cloudera or Hortonworks should
>> include all these components - try their Sandboxes.
>>
>>
>> On 20 Nov 2015, at 12:54, Dasun Hegoda <dasunhegoda@gmail.com> wrote:
>>
>> Where can I get a Hadoop distribution containing these technologies?
>> Link?
>>
>>
>>
>> On Fri, Nov 20, 2015 at 5:22 PM, Jörn Franke <jornfranke@gmail.com>
>> wrote:
>>
>> I recommend to use a Hadoop distribution containing these technologies. I
>> think you get also other useful tools for your scenario, such as Auditing
>> using sentry or ranger.
>>
>>
>> On 20 Nov 2015, at 10:48, Mich Talebzadeh <mich@peridale.co.uk> wrote:
>>
>> Well
>>
>>
>>
>> “I'm planning to deploy Hive on Spark but I can't find the installation
>> steps. I tried to read the official '[Hive on Spark][1]' guide but it has
>> problems. As an example it says under 'Configuring Yarn'
>> `yarn.resourcemanager.scheduler.class=org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler`
>> but does not imply where should I do it. Also as per the guide
>> configurations are set in the Hive runtime shell which is not permanent
>> according to my knowledge.”
>>
>>
>>
>> You can do that in yarn-site.xml file which is normally under
>> $HADOOP_HOME/etc/hadoop.
>>
>>
>>
>>
>>
>> HTH
>>
>>
>>
>>
>>
>>
>>
>> Mich Talebzadeh
>>
>>
>>
>> *Sybase ASE 15 Gold Medal Award 2008*
>>
>> A Winning Strategy: Running the most Critical Financial Data on ASE 15
>>
>>
>> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf
>>
>> Author of the books* "A Practitioner’s Guide to Upgrading to Sybase ASE
>> 15", ISBN 978-0-9563693-0-7*.
>>
>> co-author *"Sybase Transact SQL Guidelines Best Practices", ISBN
>> 978-0-9759693-0-4*
>>
>> *Publications due shortly:*
>>
>> *Complex Event Processing in Heterogeneous Environments*, ISBN:
>> 978-0-9563693-3-8
>>
>> *Oracle and Sybase, Concepts and Contrasts*, ISBN: 978-0-9563693-1-4,
>> volume one out shortly
>>
>>
>>
>> http://talebzadehmich.wordpress.com
>>
>>
>>
>> NOTE: The information in this email is proprietary and confidential. This
>> message is for the designated recipient only, if you are not the intended
>> recipient, you should destroy it immediately. Any information in this
>> message shall not be understood as given or endorsed by Peridale Technology
>> Ltd, its subsidiaries or their employees, unless expressly so stated. It is
>> the responsibility of the recipient to ensure that this email is virus
>> free, therefore neither Peridale Ltd, its subsidiaries nor their employees
>> accept any responsibility.
>>
>>
>>
>> *From:* Dasun Hegoda [mailto:dasunhegoda@gmail.com
>> <dasunhegoda@gmail.com>]
>> *Sent:* 20 November 2015 09:36
>> *To:* user@hive.apache.org
>> *Subject:* Hive on Spark - Hadoop 2 - Installation - Ubuntu
>>
>>
>>
>> Hi,
>>
>>
>>
>> What I'm planning to do is develop a reporting platform using existing
>> data. I have an existing RDBMS which has large number of records. So I'm
>> using. (
>> http://stackoverflow.com/questions/33635234/hadoop-2-7-spark-hive-jasperreports-scoop-architecuture
>> )
>>
>>
>>
>>  - Scoop - Extract data from RDBMS to Hadoop
>>
>>  - Hadoop - Storage platform -> *Deployment Completed*
>>
>>  - Hive - Datawarehouse
>>
>>  - Spark - Read time processing -> *Deployment Completed*
>>
>>
>>
>> I'm planning to deploy Hive on Spark but I can't find the installation
>> steps. I tried to read the official '[Hive on Spark][1]' guide but it has
>> problems. As an example it says under 'Configuring Yarn'
>> `yarn.resourcemanager.scheduler.class=org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler`
>> but does not imply where should I do it. Also as per the guide
>> configurations are set in the Hive runtime shell which is not permanent
>> according to my knowledge.
>>
>>
>>
>> Given that I read [this][2] but it does not have any steps.
>>
>>
>>
>> Please provide me the steps to run Hive on Spark on Ubuntu as a
>> production system?
>>
>>
>>
>>
>>
>>   [1]:
>> https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started
>>
>>   [2]:
>> http://stackoverflow.com/questions/26018306/how-to-configure-hive-to-use-spark
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>> [image: Image removed by sender. Aspire Systems]
>>
>> This e-mail message and any attachments are for the sole use of the
>> intended recipient(s) and may contain proprietary, confidential, trade
>> secret or privileged information. Any unauthorized review, use, disclosure
>> or distribution is prohibited and may be a violation of law. If you are not
>> the intended recipient, please contact the sender by reply e-mail and
>> destroy all copies of the original message.
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>>
>>
>>
>>
>> --
>>
>> Regards,
>>
>> Dasun Hegoda, Software Engineer
>> www.dasunhegoda.com | dasunhegoda@gmail.com
>>
>
>
>
> --
> Regards,
> Dasun Hegoda, Software Engineer
> www.dasunhegoda.com | dasunhegoda@gmail.com
>



-- 
Regards,
Dasun Hegoda, Software Engineer
www.dasunhegoda.com | dasunhegoda@gmail.com

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