[
https://issues.apache.org/jira/browse/SPARK-13062?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vincent Warmerdam updated SPARK-13062:
--------------------------------------
Description:
Let's create two dataframes.
{code}
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()
{code}
Spark does a nice thing here, you can use different schemas consistently.
{code}
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|
+-----+----+
{code}
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.
{code}
sqlCtx.read.load('/tmp/ddf1', schema=ddf1.schema)\
.write.parquet('/tmp/ddf1_again', mode = 'overwrite')
{code}
Nothing seems to go wrong.
{code}
> sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema).show()
+----+-----+----+
|name|thing|time|
+----+-----+----+
|null| a| 1|
|null| b| 2|
|null| b| 3|
+----+-----+----+
{code}
But what happends when I rewrite the file with a new schema.
{code}
sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema)\
.write.parquet('/tmp/ddf1_again', mode = 'overwrite')
{code}
I get this big error.
{code}
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
...
{code}
The error message seems odd, it cannot find the file. Turns out that the file
existed before the command, but not after. Oh noes!
{code}
> sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema).show()
+----+-----+----+
|name|thing|time|
+----+-----+----+
+----+-----+----+
{code}
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.
was:
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.
> 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.
> {code}
> 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()
> {code}
> Spark does a nice thing here, you can use different schemas consistently.
> {code}
> 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|
> +-----+----+
> {code}
> 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.
> {code}
> sqlCtx.read.load('/tmp/ddf1', schema=ddf1.schema)\
> .write.parquet('/tmp/ddf1_again', mode = 'overwrite')
> {code}
> Nothing seems to go wrong.
> {code}
> > sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema).show()
> +----+-----+----+
> |name|thing|time|
> +----+-----+----+
> |null| a| 1|
> |null| b| 2|
> |null| b| 3|
> +----+-----+----+
> {code}
> But what happends when I rewrite the file with a new schema.
> {code}
> sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema)\
> .write.parquet('/tmp/ddf1_again', mode = 'overwrite')
> {code}
> I get this big error.
> {code}
> 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
> ...
> {code}
> The error message seems odd, it cannot find the file. Turns out that the file
> existed before the command, but not after. Oh noes!
> {code}
> > sqlCtx.read.load('/tmp/ddf1_again', schema=ddf2.schema).show()
> +----+-----+----+
> |name|thing|time|
> +----+-----+----+
> +----+-----+----+
> {code}
> 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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