[jira] [Updated] (SPARK-12110) spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build Spark with Hive
[
https://issues.apache.org/jira/browse/SPARK-12110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Andrew Davidson updated SPARK-12110:
Attachment: launchCluster.sh.out
launchCluster.sh
launchCluster.sh is a wrapper around spark-ec2 script
launchCluster.sh is the output from when I ran this script on nov 5th 2015
> spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build
> Spark with Hive
>
>
> Key: SPARK-12110
> URL: https://issues.apache.org/jira/browse/SPARK-12110
> Project: Spark
> Issue Type: Bug
> Components: EC2
>Affects Versions: 1.5.1
> Environment: cluster created using
> spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2
>Reporter: Andrew Davidson
> Attachments: launchCluster.sh, launchCluster.sh.out
>
>
> I am using spark-1.5.1-bin-hadoop2.6. I used
> spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2 to create a cluster and configured
> spark-env to use python3. I can not run the tokenizer sample code. Is there a
> work around?
> Kind regards
> Andy
> {code}
> /root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
> 658 raise Exception("You must build Spark with Hive. "
> 659 "Export 'SPARK_HIVE=true' and run "
> --> 660 "build/sbt assembly", e)
> 661
> 662 def _get_hive_ctx(self):
> Exception: ("You must build Spark with Hive. Export 'SPARK_HIVE=true' and run
> build/sbt assembly", Py4JJavaError('An error occurred while calling
> None.org.apache.spark.sql.hive.HiveContext.\n', JavaObject id=o38))
> http://spark.apache.org/docs/latest/ml-features.html#tokenizer
> from pyspark.ml.feature import Tokenizer, RegexTokenizer
> sentenceDataFrame = sqlContext.createDataFrame([
> (0, "Hi I heard about Spark"),
> (1, "I wish Java could use case classes"),
> (2, "Logistic,regression,models,are,neat")
> ], ["label", "sentence"])
> tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
> wordsDataFrame = tokenizer.transform(sentenceDataFrame)
> for words_label in wordsDataFrame.select("words", "label").take(3):
> print(words_label)
> ---
> Py4JJavaError Traceback (most recent call last)
> /root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
> 654 if not hasattr(self, '_scala_HiveContext'):
> --> 655 self._scala_HiveContext = self._get_hive_ctx()
> 656 return self._scala_HiveContext
> /root/spark/python/pyspark/sql/context.py in _get_hive_ctx(self)
> 662 def _get_hive_ctx(self):
> --> 663 return self._jvm.HiveContext(self._jsc.sc())
> 664
> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
> __call__(self, *args)
> 700 return_value = get_return_value(answer, self._gateway_client,
> None,
> --> 701 self._fqn)
> 702
> /root/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
> 35 try:
> ---> 36 return f(*a, **kw)
> 37 except py4j.protocol.Py4JJavaError as e:
> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in
> get_return_value(answer, gateway_client, target_id, name)
> 299 'An error occurred while calling {0}{1}{2}.\n'.
> --> 300 format(target_id, '.', name), value)
> 301 else:
> Py4JJavaError: An error occurred while calling
> None.org.apache.spark.sql.hive.HiveContext.
> : java.lang.RuntimeException: java.io.IOException: Filesystem closed
> at
> org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
> at
> org.apache.spark.sql.hive.client.ClientWrapper.(ClientWrapper.scala:171)
> at
> org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:162)
> at
> org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:160)
> at org.apache.spark.sql.hive.HiveContext.(HiveContext.scala:167)
> 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:422)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
> at py4j.Gateway.invoke(Gateway.java:214)
> at
> py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
> at
[jira] [Updated] (SPARK-12110) spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build Spark with Hive
[
https://issues.apache.org/jira/browse/SPARK-12110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Andrew Davidson updated SPARK-12110:
Attachment: launchingSparkCluster.md
> spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build
> Spark with Hive
>
>
> Key: SPARK-12110
> URL: https://issues.apache.org/jira/browse/SPARK-12110
> Project: Spark
> Issue Type: Bug
> Components: EC2
>Affects Versions: 1.5.1
> Environment: cluster created using
> spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2
>Reporter: Andrew Davidson
> Attachments: launchCluster.sh, launchCluster.sh.out,
> launchingSparkCluster.md
>
>
> I am using spark-1.5.1-bin-hadoop2.6. I used
> spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2 to create a cluster and configured
> spark-env to use python3. I can not run the tokenizer sample code. Is there a
> work around?
> Kind regards
> Andy
> {code}
> /root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
> 658 raise Exception("You must build Spark with Hive. "
> 659 "Export 'SPARK_HIVE=true' and run "
> --> 660 "build/sbt assembly", e)
> 661
> 662 def _get_hive_ctx(self):
> Exception: ("You must build Spark with Hive. Export 'SPARK_HIVE=true' and run
> build/sbt assembly", Py4JJavaError('An error occurred while calling
> None.org.apache.spark.sql.hive.HiveContext.\n', JavaObject id=o38))
> http://spark.apache.org/docs/latest/ml-features.html#tokenizer
> from pyspark.ml.feature import Tokenizer, RegexTokenizer
> sentenceDataFrame = sqlContext.createDataFrame([
> (0, "Hi I heard about Spark"),
> (1, "I wish Java could use case classes"),
> (2, "Logistic,regression,models,are,neat")
> ], ["label", "sentence"])
> tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
> wordsDataFrame = tokenizer.transform(sentenceDataFrame)
> for words_label in wordsDataFrame.select("words", "label").take(3):
> print(words_label)
> ---
> Py4JJavaError Traceback (most recent call last)
> /root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
> 654 if not hasattr(self, '_scala_HiveContext'):
> --> 655 self._scala_HiveContext = self._get_hive_ctx()
> 656 return self._scala_HiveContext
> /root/spark/python/pyspark/sql/context.py in _get_hive_ctx(self)
> 662 def _get_hive_ctx(self):
> --> 663 return self._jvm.HiveContext(self._jsc.sc())
> 664
> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
> __call__(self, *args)
> 700 return_value = get_return_value(answer, self._gateway_client,
> None,
> --> 701 self._fqn)
> 702
> /root/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
> 35 try:
> ---> 36 return f(*a, **kw)
> 37 except py4j.protocol.Py4JJavaError as e:
> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in
> get_return_value(answer, gateway_client, target_id, name)
> 299 'An error occurred while calling {0}{1}{2}.\n'.
> --> 300 format(target_id, '.', name), value)
> 301 else:
> Py4JJavaError: An error occurred while calling
> None.org.apache.spark.sql.hive.HiveContext.
> : java.lang.RuntimeException: java.io.IOException: Filesystem closed
> at
> org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
> at
> org.apache.spark.sql.hive.client.ClientWrapper.(ClientWrapper.scala:171)
> at
> org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:162)
> at
> org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:160)
> at org.apache.spark.sql.hive.HiveContext.(HiveContext.scala:167)
> 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:422)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
> at py4j.Gateway.invoke(Gateway.java:214)
> at
> py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
> at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:68)
> at py4j.GatewayConnection.run(GatewayConnection.java:207)
> at
[jira] [Updated] (SPARK-12110) spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build Spark with Hive
[
https://issues.apache.org/jira/browse/SPARK-12110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Patrick Wendell updated SPARK-12110:
Description:
I am using spark-1.5.1-bin-hadoop2.6. I used
spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2 to create a cluster and configured
spark-env to use python3. I can not run the tokenizer sample code. Is there a
work around?
Kind regards
Andy
{code}
/root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
658 raise Exception("You must build Spark with Hive. "
659 "Export 'SPARK_HIVE=true' and run "
--> 660 "build/sbt assembly", e)
661
662 def _get_hive_ctx(self):
Exception: ("You must build Spark with Hive. Export 'SPARK_HIVE=true' and run
build/sbt assembly", Py4JJavaError('An error occurred while calling
None.org.apache.spark.sql.hive.HiveContext.\n', JavaObject id=o38))
http://spark.apache.org/docs/latest/ml-features.html#tokenizer
from pyspark.ml.feature import Tokenizer, RegexTokenizer
sentenceDataFrame = sqlContext.createDataFrame([
(0, "Hi I heard about Spark"),
(1, "I wish Java could use case classes"),
(2, "Logistic,regression,models,are,neat")
], ["label", "sentence"])
tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
wordsDataFrame = tokenizer.transform(sentenceDataFrame)
for words_label in wordsDataFrame.select("words", "label").take(3):
print(words_label)
---
Py4JJavaError Traceback (most recent call last)
/root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
654 if not hasattr(self, '_scala_HiveContext'):
--> 655 self._scala_HiveContext = self._get_hive_ctx()
656 return self._scala_HiveContext
/root/spark/python/pyspark/sql/context.py in _get_hive_ctx(self)
662 def _get_hive_ctx(self):
--> 663 return self._jvm.HiveContext(self._jsc.sc())
664
/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
__call__(self, *args)
700 return_value = get_return_value(answer, self._gateway_client,
None,
--> 701 self._fqn)
702
/root/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
35 try:
---> 36 return f(*a, **kw)
37 except py4j.protocol.Py4JJavaError as e:
/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in
get_return_value(answer, gateway_client, target_id, name)
299 'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
Py4JJavaError: An error occurred while calling
None.org.apache.spark.sql.hive.HiveContext.
: java.lang.RuntimeException: java.io.IOException: Filesystem closed
at
org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
at
org.apache.spark.sql.hive.client.ClientWrapper.(ClientWrapper.scala:171)
at
org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:162)
at
org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:160)
at org.apache.spark.sql.hive.HiveContext.(HiveContext.scala:167)
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:422)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:214)
at
py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:68)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.io.IOException: Filesystem closed
at org.apache.hadoop.hdfs.DFSClient.checkOpen(DFSClient.java:323)
at org.apache.hadoop.hdfs.DFSClient.getFileInfo(DFSClient.java:1057)
at
org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:554)
at
org.apache.hadoop.hive.ql.session.SessionState.createRootHDFSDir(SessionState.java:599)
at
org.apache.hadoop.hive.ql.session.SessionState.createSessionDirs(SessionState.java:554)
at
org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:508)
... 15 more
During handling of the above exception, another exception occurred:
Exception Traceback (most recent call last)
in
[jira] [Updated] (SPARK-12110) spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build Spark with Hive
[
https://issues.apache.org/jira/browse/SPARK-12110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Patrick Wendell updated SPARK-12110:
Component/s: (was: ML)
(was: SQL)
(was: PySpark)
EC2
> spark-1.5.1-bin-hadoop2.6; pyspark.ml.feature Exception: ("You must build
> Spark with Hive
>
>
> Key: SPARK-12110
> URL: https://issues.apache.org/jira/browse/SPARK-12110
> Project: Spark
> Issue Type: Bug
> Components: EC2
>Affects Versions: 1.5.1
> Environment: cluster created using
> spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2
>Reporter: Andrew Davidson
>
> I am using spark-1.5.1-bin-hadoop2.6. I used
> spark-1.5.1-bin-hadoop2.6/ec2/spark-ec2 to create a cluster and configured
> spark-env to use python3. I can not run the tokenizer sample code. Is there a
> work around?
> Kind regards
> Andy
> /root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
> 658 raise Exception("You must build Spark with Hive. "
> 659 "Export 'SPARK_HIVE=true' and run "
> --> 660 "build/sbt assembly", e)
> 661
> 662 def _get_hive_ctx(self):
> Exception: ("You must build Spark with Hive. Export 'SPARK_HIVE=true' and run
> build/sbt assembly", Py4JJavaError('An error occurred while calling
> None.org.apache.spark.sql.hive.HiveContext.\n', JavaObject id=o38))
> http://spark.apache.org/docs/latest/ml-features.html#tokenizer
> from pyspark.ml.feature import Tokenizer, RegexTokenizer
> sentenceDataFrame = sqlContext.createDataFrame([
> (0, "Hi I heard about Spark"),
> (1, "I wish Java could use case classes"),
> (2, "Logistic,regression,models,are,neat")
> ], ["label", "sentence"])
> tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
> wordsDataFrame = tokenizer.transform(sentenceDataFrame)
> for words_label in wordsDataFrame.select("words", "label").take(3):
> print(words_label)
> ---
> Py4JJavaError Traceback (most recent call last)
> /root/spark/python/pyspark/sql/context.py in _ssql_ctx(self)
> 654 if not hasattr(self, '_scala_HiveContext'):
> --> 655 self._scala_HiveContext = self._get_hive_ctx()
> 656 return self._scala_HiveContext
> /root/spark/python/pyspark/sql/context.py in _get_hive_ctx(self)
> 662 def _get_hive_ctx(self):
> --> 663 return self._jvm.HiveContext(self._jsc.sc())
> 664
> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
> __call__(self, *args)
> 700 return_value = get_return_value(answer, self._gateway_client,
> None,
> --> 701 self._fqn)
> 702
> /root/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
> 35 try:
> ---> 36 return f(*a, **kw)
> 37 except py4j.protocol.Py4JJavaError as e:
> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in
> get_return_value(answer, gateway_client, target_id, name)
> 299 'An error occurred while calling {0}{1}{2}.\n'.
> --> 300 format(target_id, '.', name), value)
> 301 else:
> Py4JJavaError: An error occurred while calling
> None.org.apache.spark.sql.hive.HiveContext.
> : java.lang.RuntimeException: java.io.IOException: Filesystem closed
> at
> org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
> at
> org.apache.spark.sql.hive.client.ClientWrapper.(ClientWrapper.scala:171)
> at
> org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:162)
> at
> org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:160)
> at org.apache.spark.sql.hive.HiveContext.(HiveContext.scala:167)
> 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:422)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
> at py4j.Gateway.invoke(Gateway.java:214)
> at
> py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
> at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:68)
> at py4j.GatewayConnection.run(GatewayConnection.java:207)
> at java.lang.Thread.run(Thread.java:745)
> Caused by:
