Why does starting spark-shell fail with "we couldn't find any external IP address!" on Windows? - apache-spark

I am having trouble in starting spark-shell on my Windows computer now. The version of Spark I am using is 1.5.2 pre-built for Hadoop 2.4 or later. I think spark-shell.cmd could be run directly without any configuration since it is pre-built and I cannot figure out what is the problem that prevents me from starting Spark correctly.
Aside from the error message printed out I can still execute some basic scala command on the command line, but apparently something is going wrong here.
Here is the error log from cmd:
log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.li
b.MutableMetricsFactory).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more in
fo.
Using Spark's repl log4j profile: org/apache/spark/log4j-defaults-repl.propertie
s
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 1.8.0_25)
Type in expressions to have them evaluated.
Type :help for more information.
15/11/18 17:51:32 WARN MetricsSystem: Using default name DAGScheduler for source
because spark.app.id is not set.
Spark context available as sc.
15/11/18 17:51:39 WARN General: Plugin (Bundle) "org.datanucleus" is already reg
istered. Ensure you dont have multiple JAR versions of the same plugin in the cl
asspath. The URL "file:/C:/spark-1.5.2-bin-hadoop2.4/lib/datanucleus-core-3.2.10
.jar" is already registered, and you are trying to register an identical plugin
located at URL "file:/C:/spark-1.5.2-bin-hadoop2.4/bin/../lib/datanucleus-core-3
.2.10.jar."
15/11/18 17:51:39 WARN General: Plugin (Bundle) "org.datanucleus.store.rdbms" is
already registered. Ensure you dont have multiple JAR versions of the same plug
in in the classpath. The URL "file:/C:/spark-1.5.2-bin-hadoop2.4/lib/datanucleus
-rdbms-3.2.9.jar" is already registered, and you are trying to register an ident
ical plugin located at URL "file:/C:/spark-1.5.2-bin-hadoop2.4/bin/../lib/datanu
cleus-rdbms-3.2.9.jar."
15/11/18 17:51:39 WARN General: Plugin (Bundle) "org.datanucleus.api.jdo" is alr
eady registered. Ensure you dont have multiple JAR versions of the same plugin i
n the classpath. The URL "file:/C:/spark-1.5.2-bin-hadoop2.4/bin/../lib/datanucl
eus-api-jdo-3.2.6.jar" is already registered, and you are trying to register an
identical plugin located at URL "file:/C:/spark-1.5.2-bin-hadoop2.4/lib/datanucl
eus-api-jdo-3.2.6.jar."
15/11/18 17:51:39 WARN Connection: BoneCP specified but not present in CLASSPATH
(or one of dependencies)
15/11/18 17:51:40 WARN Connection: BoneCP specified but not present in CLASSPATH
(or one of dependencies)
15/11/18 17:51:46 WARN ObjectStore: Version information not found in metastore.
hive.metastore.schema.verification is not enabled so recording the schema versio
n 1.2.0
15/11/18 17:51:46 WARN ObjectStore: Failed to get database default, returning No
SuchObjectException
15/11/18 17:51:47 WARN : Your hostname, Lenovo-PC resolves to a loopback/non-reachab
le address: fe80:0:0:0:297a:e76d:828:59dc%wlan2, but we couldn't find any extern
al IP address!
java.lang.RuntimeException: java.lang.NullPointerException
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.jav
a:522)
at org.apache.spark.sql.hive.client.ClientWrapper.<init>(ClientWrapper.s
cala:171)
at org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveCo
ntext.scala:162)
at org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala
:160)
at org.apache.spark.sql.hive.HiveContext.<init>(HiveContext.scala:167)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstruct
orAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingC
onstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:408)
at org.apache.spark.repl.SparkILoop.createSQLContext(SparkILoop.scala:10
28)
at $iwC$$iwC.<init>(<console>:9)
at $iwC.<init>(<console>:18)
at <init>(<console>:20)
at .<init>(<console>:24)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.
java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAcces
sorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:
1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:
1340)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840
)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:8
57)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.sca
la:902)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply
(SparkILoopInit.scala:132)
at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply
(SparkILoopInit.scala:124)
at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:324)
at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoop
Init.scala:124)
at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:64)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$Spark
ILoop$$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:974)
at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.s
cala:159)
at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:64)
at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkIL
oopInit.scala:108)
at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:
64)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$Spark
ILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:991)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$Spark
ILoop$$process$1.apply(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$Spark
ILoop$$process$1.apply(SparkILoop.scala:945)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClass
Loader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$pr
ocess(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.
java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAcces
sorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSub
mit$$runMain(SparkSubmit.scala:674)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:18
0)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.NullPointerException
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1012)
at org.apache.hadoop.util.Shell.runCommand(Shell.java:445)
at org.apache.hadoop.util.Shell.run(Shell.java:418)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:
650)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:739)
at org.apache.hadoop.util.Shell.execCommand(Shell.java:722)
at org.apache.hadoop.fs.FileUtil.execCommand(FileUtil.java:1097)
at org.apache.hadoop.fs.RawLocalFileSystem$DeprecatedRawLocalFileStatus.
loadPermissionInfo(RawLocalFileSystem.java:559)
at org.apache.hadoop.fs.RawLocalFileSystem$DeprecatedRawLocalFileStatus.
getPermission(RawLocalFileSystem.java:534)
at org.apache.hadoop.hive.ql.session.SessionState.createRootHDFSDir(Sess
ionState.java:599)
at org.apache.hadoop.hive.ql.session.SessionState.createSessionDirs(Sess
ionState.java:554)
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.jav
a:508)
... 56 more
<console>:10: error: not found: value sqlContext
import sqlContext.implicits._
^
<console>:10: error: not found: value sqlContext
import sqlContext.sql
^

There are a couple of issues. You're on Windows and things are different on this OS comparing to other POSIX-compliant OSes.
Start by reading Problems running Hadoop on Windows document and see if "missing WINUTILS.EXE" is the issue. Make sure you run spark-shell in console with admin rights.
You may also want to read the answers to a similar question Why does starting spark-shell fail with NullPointerException on Windows?
Also, you may have started spark-shell inside bin subdirectory and hence the errors like:
15/11/18 17:51:39 WARN General: Plugin (Bundle)
"org.datanucleus.api.jdo" is already registered. Ensure you dont have
multiple JAR versions of the same plugin in the classpath. The URL
"file:/C:/spark-1.5.2-bin-hadoop2.4/bin/../lib/datanucleus-api-jdo-3.2.6.jar"
is already registered, and you are trying to register an identical
plugin located at URL
"file:/C:/spark-1.5.2-bin-hadoop2.4/lib/datanucleus-api-jdo-3.2.6.jar."
And the last issue:
15/11/18 17:51:47 WARN : Your hostname, Lenovo-PC resolves to a
loopback/non-reachable address: fe80:0:0:0:297a:e76d:828:59dc%wlan2,
but we couldn't find any external IP address!
One workaround is to set SPARK_LOCAL_HOSTNAME to some resolvable host name and be done with it.
SPARK_LOCAL_HOSTNAME is the custom host name that overrides any other candidates for hostname when driver, master, workers, and executors are created.
In your case, using spark-shell, just execute the following:
SPARK_LOCAL_HOSTNAME=localhost ./bin/spark-shell
You can also use:
./bin/spark-shell -c spark.driver.host=localhost
Refer also to Environment Variables in the official documentation of Spark.

Related

Spark error : java.lang.NoClassDefFoundError: org/apache/spark/sql/sources/v2/StreamWriteSupport

I am using Spark in Horton works, when i execute the below code i am getting exception. i also have a separate spark instance running in my system - same code is working fine in it.
I need to do anything different in Horton works to resolve the below error?.kindly assist me.
[root#sandbox-hdp bin]# ./spark-shell
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
18/08/31 11:36:44 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
Spark context Web UI available at http://172.17.0.2:4041
Spark context available as 'sc' (master = local[*], app id = local-1535715404685).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.2.0.2.6.3.0-235
/_/
Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_151)
Type in expressions to have them evaluated.
Type :help for more information.
scala> :paste
// Entering paste mode (ctrl-D to finish)
import org.apache.spark.sql.SQLContext
val sqlContext = new SQLContext(sc)
val df = sqlContext.read
.format("com.databricks.spark.csv")
.option("header", "true")
.option("inferSchema", "true")
.load("hdfs://sandbox-hdp.hortonworks.com:8020/city.csv")
df.show()
df.printSchema()
// Exiting paste mode, now interpreting.
warning: there was one deprecation warning; re-run with -deprecation for details
java.lang.NoClassDefFoundError: org/apache/spark/sql/sources/v2/StreamWriteSupport
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:763)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:467)
at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
at java.net.URLClassLoader$1.run(URLClassLoader.java:368)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
at java.lang.ClassLoader.loadClass(ClassLoader.java:411)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:370)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
at scala.collection.convert.Wrappers$JIteratorWrapper.next(Wrappers.scala:43)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.filterImpl(TraversableLike.scala:247)
at scala.collection.TraversableLike$class.filter(TraversableLike.scala:259)
at scala.collection.AbstractTraversable.filter(Traversable.scala:104)
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:533)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:89)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:89)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:304)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)
... 53 elided
Caused by: java.lang.ClassNotFoundException: org.apache.spark.sql.sources.v2.StreamWriteSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 86 more
All the CSV drivers are properly copied to jar folder inside spark. This is the same exception i am facing to connect to Excel, Hbase and apache phoenix.
I am facing this only in horton works.
After adding Latest Spark code Jar it worked
I had the same question.I found a mistake in my pom.xml that my spark version is diffrent with my environment. So, I changed the spark version and kept the same environment and solved this error.

JMeter 3.3 connect Spark 2.2.1 error: "Cannot create PoolableConnectionFactory (Method not supported)"

Use this list of jars, I can successfully connect SQuirrel SQL to Spark 2.2.1:
commons-logging-1.1.3.jar
hadoop-common-2.7.3.jar
hive-exec-1.2.1.spark2.jar
hive-jdbc-1.2.1.spark2.jar
hive-metastore-1.2.1.spark2.jar
http-client-1.0.4.jar
httpclient-4.5.2.jar
httpcore-4.4.4.jar
libfb303-0.9.3.jar
libthrift-0.9.3.jar
log4j-1.2.17.jar
slf4j-api-1.7.16.jar
slf4j-log4j12-1.7.16.jar
spark-hive-thriftserver_2.11-2.2.1.jar
spark-hive_2.11-2.2.1.jar
spark-network-common_2.11-2.2.1.jar
I think the above jars are more than necessary. But when trying to connect JMeter 3.3 to same Spark 2.2.1 ThriftServer with them, I got below error message
enter code here Cannot create PoolableConnectionFactory (Method not supported)
The JDBC configuration is here:
The full response at Jmeter is here:
Thread Name: test 1-1
Sample Start: 2018-04-03 13:34:43 CST
Load time: 511
Connect Time: 510
Latency: 0
Size in bytes: 62
Sent bytes:0
Headers size in bytes: 0
Body size in bytes: 62
Sample Count: 1
Error Count: 1
Data type ("text"|"bin"|""): text
Response code: null 0
Response message: java.sql.SQLException: Cannot create PoolableConnectionFactory (Method not supported)
Response headers:
SampleResult fields:
ContentType: text/plain
DataEncoding: UTF-8
I also try to use newer Hive JDBC driver 2.3.0, but it is obviously that is not worked with Spark 2.2.1 either on beeline or any others including Jmeter.
Error message when using beeline with Hive JDBC driver 2.3.0 is here:
$ beeline -u jdbc:hive2://<hostip>:10000/tpch_sf100_orc -n rxxxds
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/apache-hive-2.3.0-bin/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/apache-tez-0.9.0-bin/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/hadoop-2.9.0/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
Connecting to jdbc:hive2://<hostip>:10000/tpch_sf100_orc
18/04/03 13:41:58 [main]: ERROR jdbc.HiveConnection: Error opening session
org.apache.thrift.TApplicationException: Required field 'client_protocol' is unset! Struct:TOpenSessionReq(client_protocol:null, configuration:{set:hiveconf:hive.server2.thrift.resultset.default.fetch.size=1000, use:database=tpch_sf100_orc})
at org.apache.thrift.TApplicationException.read(TApplicationException.java:111) ~[hive-exec-2.3.0.jar:2.3.0]
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79) ~[hive-exec-2.3.0.jar:2.3.0]
at org.apache.hive.service.rpc.thrift.TCLIService$Client.recv_OpenSession(TCLIService.java:168) ~[hive-exec-2.3.0.jar:2.3.0]
at org.apache.hive.service.rpc.thrift.TCLIService$Client.OpenSession(TCLIService.java:155) ~[hive-exec-2.3.0.jar:2.3.0]
at org.apache.hive.jdbc.HiveConnection.openSession(HiveConnection.java:680) [hive-jdbc-2.3.0.jar:2.3.0]
at org.apache.hive.jdbc.HiveConnection.<init>(HiveConnection.java:200) [hive-jdbc-2.3.0.jar:2.3.0]
at org.apache.hive.jdbc.HiveDriver.connect(HiveDriver.java:107) [hive-jdbc-2.3.0.jar:2.3.0]
at java.sql.DriverManager.getConnection(DriverManager.java:664) [?:1.8.0_112]
at java.sql.DriverManager.getConnection(DriverManager.java:208) [?:1.8.0_112]
at org.apache.hive.beeline.DatabaseConnection.connect(DatabaseConnection.java:145) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.DatabaseConnection.getConnection(DatabaseConnection.java:209) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.Commands.connect(Commands.java:1641) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.Commands.connect(Commands.java:1536) [hive-beeline-2.3.0.jar:2.3.0]
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) ~[?:1.8.0_112]
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) ~[?:1.8.0_112]
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[?:1.8.0_112]
at java.lang.reflect.Method.invoke(Method.java:498) ~[?:1.8.0_112]
at org.apache.hive.beeline.ReflectiveCommandHandler.execute(ReflectiveCommandHandler.java:56) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.execCommandWithPrefix(BeeLine.java:1274) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.dispatch(BeeLine.java:1313) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.connectUsingArgs(BeeLine.java:867) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.initArgs(BeeLine.java:776) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.begin(BeeLine.java:1010) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.mainWithInputRedirection(BeeLine.java:519) [hive-beeline-2.3.0.jar:2.3.0]
at org.apache.hive.beeline.BeeLine.main(BeeLine.java:501) [hive-beeline-2.3.0.jar:2.3.0]
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) ~[?:1.8.0_112]
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) ~[?:1.8.0_112]
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[?:1.8.0_112]
at java.lang.reflect.Method.invoke(Method.java:498) ~[?:1.8.0_112]
at org.apache.hadoop.util.RunJar.run(RunJar.java:239) [hadoop-common-2.9.0.jar:?]
at org.apache.hadoop.util.RunJar.main(RunJar.java:153) [hadoop-common-2.9.0.jar:?]
18/04/03 13:41:58 [main]: WARN jdbc.HiveConnection: Failed to connect to <hostip>:10000
Error: Could not open client transport with JDBC Uri: jdbc:hive2://<hostip>:10000/tpch_sf100_orc: Could not establish connection to jdbc:hive2://<hostip>:10000/tpch_sf100_orc: Required field 'client_protocol' is unset! Struct:TOpenSessionReq(client_protocol:null, configuration:{set:hiveconf:hive.server2.thrift.resultset.default.fetch.size=1000, use:database=tpch_sf100_orc}) (state=08S01,code=0)
Beeline version 2.3.0 by Apache Hive
What else can be done to connect JMeter to Spark?
Most probably this is due to client and server libraries mismatch, you need to use the same version as on the server.
So identify which version of hive is running on your server and download appropriate hive-jdbc jar and all the dependencies (you can retrieve them using i.e. Maven Dependency Plugin) and update the ones in JMeter Classpath with the correct versions.
It might be better to use "clean" JMeter installation for this to avoid possible jar hell so it is a good time to upgrade to JMeter 4.0
You could also build your own "JDBC sampler" and add/adapt just few line of code from the original to make it works. I had to do that for Hive and Phoenix support 4 years ago and it worked. It required a Timeout method that was not implemented back then.
Here are the sources:
https://github.com/csalperwyck/JMeterJDBCSamplerWithOutTimeOut

How to integrate Ganglia for Spark 2.1 Job metrics, Spark ignoring Ganglia metrics

I am trying to integrate Spark 2.1 job's metrics to Ganglia.
My spark-default.conf looks like
*.sink.ganglia.class org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.name Name
*.sink.ganglia.host $MASTERIP
*.sink.ganglia.port $PORT
*.sink.ganglia.mode unicast
*.sink.ganglia.period 10
*.sink.ganglia.unit seconds
When i submit my job i can see the warn
Warning: Ignoring non-spark config property: *.sink.ganglia.host=host
Warning: Ignoring non-spark config property: *.sink.ganglia.name=Name
Warning: Ignoring non-spark config property: *.sink.ganglia.mode=unicast
Warning: Ignoring non-spark config property: *.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
Warning: Ignoring non-spark config property: *.sink.ganglia.period=10
Warning: Ignoring non-spark config property: *.sink.ganglia.port=8649
Warning: Ignoring non-spark config property: *.sink.ganglia.unit=seconds
My environment details are
Hadoop : Amazon 2.7.3 - emr-5.7.0
Spark : Spark 2.1.1,
Ganglia: 3.7.2
If you have any inputs or any other alternative of Ganglia please reply.
according to the spark docs
The metrics system is configured via a configuration file that Spark expects to be present at $SPARK_HOME/conf/metrics.properties. A custom file location can be specified via the spark.metrics.conf configuration property.
so instead of having these confs in spark-default.conf, move them to $SPARK_HOME/conf/metrics.properties
For EMR specifically, you'll need to put these settings in /etc/spark/conf/metrics.properties on the master node.
Spark on EMR does include the Ganglia library:
$ ls -l /usr/lib/spark/external/lib/spark-ganglia-lgpl_*
-rw-r--r-- 1 root root 28376 Mar 22 00:43 /usr/lib/spark/external/lib/spark-ganglia-lgpl_2.11-2.3.0.jar
In addition, your example is missing the equals sign (=) between the config names and values - unsure if that's an issue. Below is an example config that worked successfully for me.
*.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.name=AMZN-EMR
*.sink.ganglia.host=$MASTERIP
*.sink.ganglia.port=8649
*.sink.ganglia.mode=unicast
*.sink.ganglia.period=10
*.sink.ganglia.unit=seconds
From this page:
https://spark.apache.org/docs/latest/monitoring.html
Spark also supports a Ganglia sink which is not included in the default build due to licensing restrictions:
GangliaSink: Sends metrics to a Ganglia node or multicast group.
**To install the GangliaSink you’ll need to perform a custom build of Spark**. Note that by embedding this library you will include LGPL-licensed code in your Spark package. For sbt users, set the SPARK_GANGLIA_LGPL environment variable before building. For Maven users, enable the -Pspark-ganglia-lgpl profile. In addition to modifying the cluster’s Spark build user
I don't know if anyone still needs this. But you have to make the full Ganglia configurations:
# Ganglia conf
*.sink.ganglia.class=org.apache.spark.metrics.sink.GangliaSink
*.sink.ganglia.name=AMZN-EMR
*.sink.ganglia.host=$MASTERIP
*.sink.ganglia.port=8649
*.sink.ganglia.mode=unicast
*.sink.ganglia.period=10
*.sink.ganglia.unit=seconds
# Enable JvmSource for instance master, worker, driver and executor
master.source.jvm.class=org.apache.spark.metrics.source.JvmSource
worker.source.jvm.class=org.apache.spark.metrics.source.JvmSource
driver.source.jvm.class=org.apache.spark.metrics.source.JvmSource
executor.source.jvm.class=org.apache.spark.metrics.source.JvmSource
Even with the full configuration, I'm running into this issue from AWS EMR 5.33.0
21/05/26 14:18:20 ERROR org.apache.spark.metrics.MetricsSystem: Source class org.apache.spark.metrics.source.JvmSource cannot be instantiated
java.lang.ClassNotFoundException: org.apache.spark.metrics.source.JvmSource
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:239)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSources$1.apply(MetricsSystem.scala:184)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSources$1.apply(MetricsSystem.scala:181)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:130)
at org.apache.spark.metrics.MetricsSystem.registerSources(MetricsSystem.scala:181)
at org.apache.spark.metrics.MetricsSystem.start(MetricsSystem.scala:102)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:528)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
21/05/26 14:18:20 ERROR org.apache.spark.metrics.MetricsSystem: Sink class org.apache.spark.metrics.sink.GangliaSink cannot be instantiated
21/05/26 14:18:20 ERROR org.apache.spark.SparkContext: Error initializing SparkContext.
java.lang.ClassNotFoundException: org.apache.spark.metrics.sink.GangliaSink
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:239)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSinks$1.apply(MetricsSystem.scala:200)
at org.apache.spark.metrics.MetricsSystem$$anonfun$registerSinks$1.apply(MetricsSystem.scala:196)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:130)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:130)
at org.apache.spark.metrics.MetricsSystem.registerSinks(MetricsSystem.scala:196)
at org.apache.spark.metrics.MetricsSystem.start(MetricsSystem.scala:104)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:528)
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
It's weird because AWS EMR should provide this dependency (org.apache.spark:spark-core_2.11:2.4.7) and I hope that the Spark distribution with AWS EMR is compiled with the Ganglia option. Forcing this jar on --packages or --jars spark options doesn't help either.
If someone manages to get Ganglia working with Spark on AWS EMR with driver/executors jvm monitoring. Please do tell me how.

Why does spark-shell fail with "The root scratch dir: /tmp/hive on HDFS should be writable."?

I am a spark noob, and using windows 10, trying to get spark to work. I have set the environment variables correctly, and I also have winutils. When I go into spark/bin, and type spark-shell, it runs spark but it gives the following errors.
Also it doesn't show the spark context or spark session.
C:\Users\Akshay\Downloads\spark\bin>spark-shell
17/06/19 23:45:12 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/06/19 23:45:19 WARN General: Plugin (Bundle) "org.datanucleus.api.jdo" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/Users/Akshay/Downloads/spark/bin/../jars/datanucleus-api-jdo-3.2.6.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/Users/Akshay/Downloads/spark/jars/datanucleus-api-jdo-3.2.6.jar."
17/06/19 23:45:20 WARN General: Plugin (Bundle) "org.datanucleus" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/Users/Akshay/Downloads/spark/bin/../jars/datanucleus-core-3.2.10.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/Users/Akshay/Downloads/spark/jars/datanucleus-core-3.2.10.jar."
17/06/19 23:45:20 WARN General: Plugin (Bundle) "org.datanucleus.store.rdbms" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/Users/Akshay/Downloads/spark/bin/../jars/datanucleus-rdbms-3.2.9.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/Users/Akshay/Downloads/spark/jars/datanucleus-rdbms-3.2.9.jar."
java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionState':
at org.apache.spark.sql.SparkSession$.org$apache$spark$sql$SparkSession$$reflect(SparkSession.scala:981)
at org.apache.spark.sql.SparkSession.sessionState$lzycompute(SparkSession.scala:110)
at org.apache.spark.sql.SparkSession.sessionState(SparkSession.scala:109)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$getOrCreate$5.apply(SparkSession.scala:878)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$getOrCreate$5.apply(SparkSession.scala:878)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:99)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:878)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:96)
... 47 elided
Caused by: java.lang.reflect.InvocationTargetException: java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveExternalCatalog':
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at org.apache.spark.sql.SparkSession$.org$apache$spark$sql$SparkSession$$reflect(SparkSession.scala:978)
... 58 more
Caused by: java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveExternalCatalog':
at org.apache.spark.sql.internal.SharedState$.org$apache$spark$sql$internal$SharedState$$reflect(SharedState.scala:169)
at org.apache.spark.sql.internal.SharedState.<init>(SharedState.scala:86)
at org.apache.spark.sql.SparkSession$$anonfun$sharedState$1.apply(SparkSession.scala:101)
at org.apache.spark.sql.SparkSession$$anonfun$sharedState$1.apply(SparkSession.scala:101)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession.sharedState$lzycompute(SparkSession.scala:101)
at org.apache.spark.sql.SparkSession.sharedState(SparkSession.scala:100)
at org.apache.spark.sql.internal.SessionState.<init>(SessionState.scala:157)
at org.apache.spark.sql.hive.HiveSessionState.<init>(HiveSessionState.scala:32)
... 63 more
Caused by: java.lang.reflect.InvocationTargetException: java.lang.reflect.InvocationTargetException: java.lang.RuntimeException: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at org.apache.spark.sql.internal.SharedState$.org$apache$spark$sql$internal$SharedState$$reflect(SharedState.scala:166)
... 71 more
Caused by: java.lang.reflect.InvocationTargetException: java.lang.RuntimeException: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:264)
at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:358)
at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:262)
at org.apache.spark.sql.hive.HiveExternalCatalog.<init>(HiveExternalCatalog.scala:66)
... 76 more
Caused by: java.lang.RuntimeException: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
at org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:188)
... 84 more
Caused by: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at org.apache.hadoop.hive.ql.session.SessionState.createRootHDFSDir(SessionState.java:612)
at org.apache.hadoop.hive.ql.session.SessionState.createSessionDirs(SessionState.java:554)
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:508)
... 85 more
<console>:14: error: not found: value spark
import spark.implicits._
^
<console>:14: error: not found: value spark
import spark.sql
^
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.1.1
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_101)
Type in expressions to have them evaluated.
Type :help for more information.
scala>
How do I resolve this?
As per the error message:
Caused by: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: ---------
at org.apache.hadoop.hive.ql.session.SessionState.createRootHDFSDir(SessionState.java:612)
at org.apache.hadoop.hive.ql.session.SessionState.createSessionDirs(SessionState.java:554)
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:508)
... 85 more
You should change the permissions of C:\tmp\hive directory as follows:
winutils.exe chmod -R 777 C:\tmp\hive
Please refer to this article where was described how to run spark on windows 10 with hadoop support.
Spark on windows
This means that the hive root scratch directory doesn't have the required permission.
Follow below steps for resolution.
First change the permission using below command.
$>C:\Users\xxxx\winutils\winutils\bin\winutils.exe chmod 777 .\hive\
if the error exists even after this, check if the trust relationship between your workstation and the primary domain using below command.
$>C:\Users\xxxx\winutils\winutils\bin\winutils.exe ls .\hive\
if this gives "FindFileOwnerAndPermission error (1789): The trust relationship between this workstation and the primary domain failed.", it means that your computer domain controller is not reachable , possible reason could be you are not on same VPN as your system domain controller.Connect to VPN and try again.
upgrade Spark version 2.4 , issue is resolved in new version

spark-shell, dependency jars and class not found exception

I'm trying to run my spark app on spark shell. Here is what I tried and many more variants after hours of reading on this error...but none seem to work.
spark-shell --class my_home.myhome.RecommendMatch —jars /Users/anon/Documents/Works/sparkworkspace/myhome/target/myhome-0.0.1-SNAPSHOT.jar,/Users/anon/Documents/Works/sparkworkspace/myhome/target/original-myhome-0.0.1-SNAPSHOT.jar
What is get instead is
java.lang.ClassNotFoundException: my_home.myhome.RecommendMatch
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at org.apache.spark.util.Utils$.classForName(Utils.scala:229)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:695)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Any ideas please? Thanks!
UPDATE:
Found that the jars must be colon(:) separated and not comma(,) separated as described in several articles/docs
spark-shell --class my_home.myhome.RecommendMatch —jars /Users/anon/Documents/Works/sparkworkspace/myhome/target/myhome-0.0.1-SNAPSHOT.jar:/Users/anon/Documents/Works/sparkworkspace/myhome/target/original-myhome-0.0.1-SNAPSHOT.jar
However, now the errors have changed. Note ls -la finds the paths although the following lines complain that don't exit. Bizarre..
Warning: Local jar /Users/anon/Documents/Works/sparkworkspace/myhome/target/myhome-0.0.1-SNAPSHOT.jar:/Users/anon/Documents/Works/sparkworkspace/myhome/target/original-myhome-0.0.1-SNAPSHOT.jar does not exist, skipping.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
java.lang.SecurityException: Invalid signature file digest for Manifest main attributes
at sun.security.util.SignatureFileVerifier.processImpl(SignatureFileVerifier.java:314)
at sun.security.util.SignatureFileVerifier.process(SignatureFileVerifier.java:268)
UPDATE 2:
spark-shell —class my_home.myhome.RecommendMatch —-jars “/Users/anon/Documents/Works/sparkworkspace/myhome/target/myhome-0.0.1-SNAPSHOT.jar:/Users/anon/Documents/Works/sparkworkspace/myhome/target/original-myhome-0.0.1-SNAPSHOT.jar”
The above command yields the following on spark-shell.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/05/16 01:19:08 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/05/16 01:19:13 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.0.101:4040
Spark context available as 'sc' (master = local[*], app id = local-1494877749685).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.1.0
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_121)
Type in expressions to have them evaluated.
Type :help for more information.
scala> :load my_home.myhome.RecommendMatch
That file does not exist
scala> :load RecommendMatch
That file does not exist
scala> :load my_home.myhome.RecommendMatch.scala
That file does not exist
scala> :load RecommendMatch.scala
That file does not exist
The jars don't seem to be loaded :( based on what I see at http://localhost:4040/environment/
The URLs supplied to --jars must be separated by commas. Your first command is correct.
You also have to add the jar at last param to spark-submit. Lets say my_home.myhome.RecommendMatch is part of myhome-0.0.1-SNAPSHOT.jar jar file.
spark-submit --class my_home.myhome.RecommendMatch \
—jars "/Users/anon/Documents/Works/sparkworkspace/myhome/target/original-myhome-0.0.1-SNAPSHOT.jar" \
/Users/anon/Documents/Works/sparkworkspace/myhome/target/myhome-0.0.1-SNAPSHOT.jar

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