Hi All,
I have a two node spark cluster, to which I'm connecting using IPython
notebook.
To see how data saving/loading works, I simply created a dataframe using
people.json using the Code below;
df = sqlContext.read.json("examples/src/main/resources/people.json")
Then called the following to save the dataframe as a parquet.
df.write.save("people.parquet")
Tried loading the saved dataframe using;
df2 = sqlContext.read.parquet('people.parquet');
But this simply fails giving the following exception
---------------------------------------------------------------------------Py4JJavaError
Traceback (most recent call
last)<ipython-input-97-35f91873c48f> in <module>()----> 1 df2 =
sqlContext.read.parquet('people.parquet2');
/srv/spark/python/pyspark/sql/readwriter.pyc in parquet(self, *path)
154 [('name', 'string'), ('year', 'int'), ('month', 'int'),
('day', 'int')] 155 """--> 156 return
self._df(self._jreader.parquet(_to_seq(self._sqlContext._sc, path)))
157 158 @since(1.4)
/srv/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
__call__(self, *args) 536 answer =
self.gateway_client.send_command(command) 537 return_value
= get_return_value(answer, self.gateway_client,--> 538
self.target_id, self.name) 539 540 for temp_arg in
temp_args:
/srv/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py in
get_return_value(answer, gateway_client, target_id, name) 298
raise Py4JJavaError( 299 'An error
occurred while calling {0}{1}{2}.\n'.--> 300
format(target_id, '.', name), value) 301 else: 302
raise Py4JError(
Py4JJavaError: An error occurred while calling o53840.parquet.
: java.lang.AssertionError: assertion failed: No schema defined, and
no Parquet data file or summary file found under
file:/home/ubuntu/ipython/people.parquet2.
at scala.Predef$.assert(Predef.scala:179)
at
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache.org$apache$spark$sql$parquet$ParquetRelation2$MetadataCache$$readSchema(newParquet.scala:429)
at
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$11.apply(newParquet.scala:369)
at
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$11.apply(newParquet.scala:369)
at scala.Option.orElse(Option.scala:257)
at
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache.refresh(newParquet.scala:369)
at
org.apache.spark.sql.parquet.ParquetRelation2.org$apache$spark$sql$parquet$ParquetRelation2$$metadataCache$lzycompute(newParquet.scala:126)
at
org.apache.spark.sql.parquet.ParquetRelation2.org$apache$spark$sql$parquet$ParquetRelation2$$metadataCache(newParquet.scala:124)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$dataSchema$1.apply(newParquet.scala:165)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$dataSchema$1.apply(newParquet.scala:165)
at scala.Option.getOrElse(Option.scala:120)
at
org.apache.spark.sql.parquet.ParquetRelation2.dataSchema(newParquet.scala:165)
at
org.apache.spark.sql.sources.HadoopFsRelation.schema$lzycompute(interfaces.scala:506)
at
org.apache.spark.sql.sources.HadoopFsRelation.schema(interfaces.scala:505)
at
org.apache.spark.sql.sources.LogicalRelation.<init>(LogicalRelation.scala:30)
at
org.apache.spark.sql.SQLContext.baseRelationToDataFrame(SQLContext.scala:438)
at
org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:264)
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:601)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:722)
I'm using spark-1.4.1-bin-hadoop2.6 with java 1.7.
Thanks
Amila
