Vincent Warmerdam created SPARK-13062: -----------------------------------------
Summary: Using same file name with new schema destroys original file. Key: SPARK-13062 URL: https://issues.apache.org/jira/browse/SPARK-13062 Project: Spark Issue Type: Bug Components: PySpark Affects Versions: 1.5.2 Reporter: Vincent Warmerdam Let's create two dataframes. ``` ddf1 = sqlCtx.createDataFrame(pd.DataFrame({'time':[1,2,3], 'thing':['a','b','b']})) ddf2 = sqlCtx.createDataFrame(pd.DataFrame({'time':[4,5,6,7], 'thing':['a','b','a','b'], 'name':['pi', 'ca', 'chu', '!']})) ddf1.printSchema() ddf2.printSchema() ddf1.write.parquet('/tmp/ddf1', mode = 'overwrite') ddf2.write.parquet('/tmp/ddf2', mode = 'overwrite') sqlCtx.read.load('/tmp/ddf1', schema=ddf2.schema).show() sqlCtx.read.load('/tmp/ddf2', schema=ddf1.schema).show() ``` Spark does a nice thing here, you can use different schemas consistently. ``` root |-- thing: string (nullable = true) |-- time: long (nullable = true) root |-- name: string (nullable = true) |-- thing: string (nullable = true) |-- time: long (nullable = true) +----+-----+----+ |name|thing|time| +----+-----+----+ |null| a| 1| |null| b| 3| |null| b| 2| +----+-----+----+ +-----+----+ |thing|time| +-----+----+ | b| 7| | b| 5| | a| 4| | a| 6| +-----+----+ ``` But here comes something naughty. Imagine that I want to update `ddf1` with the new schema and save this on the HDFS filesystem. I'll first write it to a new filename. ``` sqlCtx.read.load('/tmp/ddf1', schema=ddf1.schema)\ .write.parquet('/tmp/ddf1_again', mode = 'overwrite') ``` Nothing seems to go wrong. ``` > sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema).show() +----+-----+----+ |name|thing|time| +----+-----+----+ |null| a| 1| |null| b| 2| |null| b| 3| +----+-----+----+ ``` But what happends when I rewrite the file with a new schema. ``` sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema)\ .write.parquet('/tmp/ddf1_again', mode = 'overwrite') ``` I get this big error. ``` Py4JJavaError: An error occurred while calling o97.parquet. : org.apache.spark.SparkException: Job aborted. at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:156) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108) at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57) at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57) at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138) at org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933) at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933) at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197) at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146) at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137) at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:304) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) 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:745) Caused by: java.io.FileNotFoundException: File does not exist: /tmp/ddf1_again/part-r-00000-052bae31-47bf-487e-873e-2753248ab7eb.gz.parquet ... ``` But most scary of all, it removes the original data. Oh noes! ``` > sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema).show() +----+-----+----+ |name|thing|time| +----+-----+----+ +----+-----+----+ ``` The schema still seems to be intact, but the data has been removed without prompting the user. I'm assuming something is going awry with the metadata when I am rewriting a parquet file with the same filename but with a different schema, but I would expect the thrown error to be an indecation that nothing happened to the files on HDFS. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org