I have followed this (http://sujitpal.blogspot.in/2012/09/learning-mahout-clustering.html) link and created initial clusters and k-means clusters as mentioned in below link but when i tried to dump the clusters to local system i am getting below error
hadoop binary is not in PATH,HADOOP_HOME/bin,HADOOP_PREFIX/bin, running locally
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/mahout/mahout-examples-0.8-job.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/mahout/lib/slf4j-jcl-1.7.5.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.slf4j.impl.JCLLoggerFactory]
Aug 5, 2013 1:21:51 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: Command line arguments: {--dictionary=[vectorfiles/dictionary.file-0], --dictionaryType=[seqfiles], --distanceMeasure=[org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure], --endPhase=[2147483647], --input=[kmeans-clusters], --numWords=[10], --output=[cluster.txt], --outputFormat=[TEXT], --startPhase=[0], --tempDir=[temp]}
Exception in thread "main" java.lang.IllegalArgumentException: Invalid dictionary format
at org.apache.mahout.utils.clustering.ClusterDumper.printClusters(ClusterDumper.java:169)
at org.apache.mahout.utils.clustering.ClusterDumper.run(ClusterDumper.java:156)
at org.apache.mahout.utils.clustering.ClusterDumper.main(ClusterDumper.java:100)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
at java.lang.reflect.Method.invoke(Method.java:597)
at org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68)
at org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139)
at org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:194)
command to dump clusers
mahout clusterdump -i kmeans-clusters -d vectorfiles/dictionary.file-* -dt seqfiles -n 10 -o cluster.txt
Please suggest me how to get this
Thanks
I used below command and its working
mahout clusterdump -dt sequencefile -d vectorfiles/dictionary.file-0 -i kmeans-clusters/clusters-1-final -o result.txt -b 10 -n 10
Related
I am trying to start a pyspark job using Amazon EMR Jupyter hub feature, as follow:
And with following code:
from pyspark import SparkSession
spark = SparkSession \
.builder \
.appName("My App") \
.getOrCreate()
But at the end, I always got:
The code failed because of a fatal error:
Session 0 unexpectedly reached final status 'dead'. See logs:
stdout:
stderr:
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/lib/spark/jars/slf4j-log4j12-1.7.30.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/share/aws/emr/emrfs/lib/slf4j-log4j12-1.7.12.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/share/aws/redshift/jdbc/redshift-jdbc42-1.2.37.1061.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.slf4j.impl.Log4jLoggerFactory]
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/usr/lib/spark/jars/spark-unsafe_2.12-3.1.2-amzn-1.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
22/05/04 13:21:11 INFO RSCDriver: Connecting to: ip-10-42-255-42.eu-west-1.compute.internal:10000
22/05/04 13:21:11 INFO RSCDriver: Starting RPC server...
22/05/04 13:21:11 INFO RpcServer: Connected to the port 10001
22/05/04 13:21:11 WARN RSCConf: Your hostname, ip-10-42-255-42.eu-west-1.compute.internal, resolves to a loopback address, but we couldn't find any external IP address!
22/05/04 13:21:11 WARN RSCConf: Set livy.rsc.rpc.server.address if you need to bind to another address.
Exception in thread "main" java.lang.IncompatibleClassChangeError: Inconsistent constant pool data in classfile for class org/apache/livy/shaded/json4s/DefaultFormats. Method 'java.text.SimpleDateFormat $anonfun$df$1(org.apache.livy.shaded.json4s.DefaultFormats)' at index 156 is CONSTANT_MethodRef and should be CONSTANT_InterfaceMethodRef
at org.apache.livy.shaded.json4s.DefaultFormats.$init$(Formats.scala:318)
at org.apache.livy.shaded.json4s.DefaultFormats$.<init>(Formats.scala:296)
at org.apache.livy.shaded.json4s.DefaultFormats$.<clinit>(Formats.scala)
at org.apache.livy.repl.Session.<init>(Session.scala:66)
at org.apache.livy.repl.ReplDriver.initializeSparkEntries(ReplDriver.scala:43)
at org.apache.livy.rsc.driver.RSCDriver.run(RSCDriver.java:337)
at org.apache.livy.rsc.driver.RSCDriverBootstrapper.main(RSCDriverBootstrapper.java:93)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:959)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1047)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1056)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
22/05/04 13:21:11 INFO ShutdownHookManager: Shutdown hook called
22/05/04 13:21:11 INFO ShutdownHookManager: Deleting directory /mnt/tmp/spark-2804f6ee-21f1-4773-98dc-8b3e3bd1924a
Seems the livy version is clahing with the livy version embedded with the apache shaded jar, so I tried to override the jar using a fat jar that contains all the spark jar I'm used to use, and use the following config to import it:
%%configure -f
{
"conf": {
"spark.jars": "s3://mybucket/myfatjar.jar"
}
}
But without any effect.
I am running a spark application using yarn in cloudera.
Spark version: 2.1
I get the following error:
SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found
binding in
[jar:file:/data/yarn/nm/filecache/13/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in
[jar:file:/opt/cloudera/parcels/CDH-5.10.2-1.cdh5.10.2.p0.5/jars/slf4j-log4j12-1.7.5.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.slf4j.impl.Log4jLoggerFactory] 18/04/14 22:20:57 INFO
util.SignalUtils: Registered signal handler for TERM 18/04/14 22:20:57
INFO util.SignalUtils: Registered signal handler for HUP 18/04/14
22:20:57 INFO util.SignalUtils: Registered signal handler for INT
Exception in thread "main" java.lang.ClassCastException:
org.apache.hadoop.conf.Configuration cannot be cast to
org.apache.hadoop.yarn.conf.YarnConfiguration at
org.apache.spark.deploy.yarn.ApplicationMaster.(ApplicationMaster.scala:60)
at
org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$main$1.apply$mcV$sp(ApplicationMaster.scala:764)
at
org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:67)
at
org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:66)
at java.security.AccessController.doPrivileged(Native Method) at
javax.security.auth.Subject.doAs(Subject.java:415) at
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1656)
at
org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:66)
at
org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:763)
at
org.apache.spark.deploy.yarn.ApplicationMaster.main(ApplicationMaster.scala)
I managed to solve it by verifyning that the spark version configured in SPARK_HOME variable matches the hadoop version installed in cloudera.
From the following link https://spark.apache.org/downloads.html you can download the suitable version for your required hadoop.
The haddop version in cloudera can by found by:
$ hadoop version
I encounter the same issue while trying to start a Spark job using Yarn Rest API.
And the reason was that the environment variable SPARK_YARN_MODE was missing. Adding this env var, everything works fine :
export SPARK_YARN_MODE=true
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
I am trying to create a log file while using pyspark, but I am getting the following error message:
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
I have added the jar files in my class-path, but I still get this error. Anyway I can sort this out. I even tried to pass the jar file as arguments in driver-class-path, but still I get the same error.
I am using HDP2.3 and trying to use Spark(1.3.1) as the execution engine for running hive queries.
spark-assembly jar is also available in the hive/lib folder.
I am able to run the query in spark-master: local but facing the below issue when using spark-master: yarn-cluster.
command run,
hive -e "set hive.execution.engine=spark; set
spark.master=yarn-cluster; select count(*) from db_name.table_name;"
output,
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/hdp/2.3.0.0-2557/hadoop/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/hdp/2.3.0.0-2557/hive/lib/spark-assembly-1.3.1-hadoop2.6.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/root/downloads/machine/spark/lib/spark-assembly-1.3.1-hadoop2.6.0.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.slf4j.impl.Log4jLoggerFactory]
WARNING: Use "yarn jar" to launch YARN applications.
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/hdp/2.3.0.0-2557/hadoop/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/hdp/2.3.0.0-2557/hive/lib/spark-assembly-1.3.1-hadoop2.6.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/root/downloads/machine/spark/lib/spark-assembly-1.3.1-hadoop2.6.0.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.slf4j.impl.Log4jLoggerFactory]
Logging initialized using configuration in file:/etc/hive/2.3.0.0-2557/0/hive-log4j.properties
Query ID = root_20150909201120_a67d5ca3-36df-43fe-894a-3645585eec7a
Total jobs = 1
Launching Job 1 out of 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Failed to execute spark task, with exception 'org.apache.hadoop.hive.ql.metadata.HiveException(Failed to create spark client.)'
FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.spark.SparkTask
yarn log of the application,
15/09/09 19:42:27 INFO yarn.ApplicationMaster: Waiting for spark context initialization ...
15/09/09 19:42:27 INFO client.RemoteDriver: Connecting to: sandbox.hortonworks.com:59941
15/09/09 19:42:27 ERROR yarn.ApplicationMaster: User class threw exception: SPARK_RPC_CLIENT_CONNECT_TIMEOUT
java.lang.NoSuchFieldError: SPARK_RPC_CLIENT_CONNECT_TIMEOUT
at org.apache.hive.spark.client.rpc.RpcConfiguration.<clinit>(RpcConfiguration.java:46)
at org.apache.hive.spark.client.RemoteDriver.<init>(RemoteDriver.java:146)
at org.apache.hive.spark.client.RemoteDriver.main(RemoteDriver.java:556)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:480)
15/09/09 19:42:27 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 15, (reason: User class threw exception: SPARK_RPC_CLIENT_CONNECT_TIMEOUT)
15/09/09 19:42:37 ERROR yarn.ApplicationMaster: SparkContext did not initialize after waiting for 100000 ms. Please check earlier log output for errors. Failing the application.
15/09/09 19:42:37 INFO yarn.ApplicationMaster: Unregistering ApplicationMaster with FAILED (diag message: User class threw exception: SPARK_RPC_CLIENT_CONNECT_TIMEOUT)
15/09/09 19:42:37 INFO yarn.ApplicationMaster: Deleting staging directory .sparkStaging/application_1441817597849_0008
Any help on debugging the issue is much appreciated.
I don't think queries can be executed in yarn-cluster mode.
You can run interactive queries in local and yarn-client mode only