[jira] [Created] (SPARK-13062) Using same file name with new schema destroys original file.

["Vincent Warmerdam (JIRA)" <ji...@apache.org>]

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. 



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