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Posted to issues@spark.apache.org by "Andrew Davidson (JIRA)" <ji...@apache.org> on 2015/12/02 23:47:10 UTC
[jira] [Created] (SPARK-12110) spark-1.5.1-bin-hadoop2.6;
pyspark.ml.feature Exception: ("You must build Spark with Hive
Andrew Davidson created SPARK-12110:
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Summary: 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: ML, PySpark, SQL
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.<init>(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.<init>(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)
<ipython-input-1-0beb490d573c> in <module>()
5 (1, "I wish Java could use case classes"),
6 (2, "Logistic,regression,models,are,neat")
----> 7 ], ["label", "sentence"])
8 tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
9 wordsDataFrame = tokenizer.transform(sentenceDataFrame)
/root/spark/python/pyspark/sql/context.py in createDataFrame(self, data, schema, samplingRatio)
406 rdd, schema = self._createFromLocal(data, schema)
407 jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
--> 408 jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
409 df = DataFrame(jdf, self)
410 df._schema = schema
/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))
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