Imran Rashid created SPARK-22109:
------------------------------------
Summary: Reading tables partitioned by columns that look like
timestamps has inconsistent schema inference
Key: SPARK-22109
URL: https://issues.apache.org/jira/browse/SPARK-22109
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.2.0
Reporter: Imran Rashid
Priority: Minor
If you try to read a partitioned json table, spark automatically tries to read
figure out if the partition column is a timestamp based on the first value it
sees. So if you really partitioned by a string, and the first value happens to
look like a timestamp, then you'll run into errors. Even if you specify a
schema, the schema is ignored, and spark still tries to infer a timestamp type
for the partition column.
This is particularly weird because schema-inference does *not* work for regular
timestamp columns in a flat table. You have to manually specify the schema to
get the column interpreted as a timestamp.
This problem does not appear to be present for other types. Eg., if I
partition by a string column, and the first value happens to look like an int,
schema inference is still fine.
Here's a small example:
{noformat}
val df = Seq(
(1, "2015-01-01 00:00:00", Timestamp.valueOf("2015-01-01 00:00:00")),
(2, "2014-01-01 00:00:00", Timestamp.valueOf("2014-01-01 00:00:00")),
(3, "blah", Timestamp.valueOf("2016-01-01 00:00:00"))).toDF("i", "str", "t")
df.write.partitionBy("str").json("partition_by_str")
df.write.partitionBy("t").json("partition_by_t")
df.write.json("flat")
val readStr = spark.read.json("partition_by_str")/*
java.util.NoSuchElementException: None.get
at scala.None$.get(Option.scala:347)
at scala.None$.get(Option.scala:345)
at
org.apache.spark.sql.catalyst.expressions.TimeZoneAwareExpression$class.timeZone(datetimeExpressions.scala:46)
at
org.apache.spark.sql.catalyst.expressions.Cast.timeZone$lzycompute(Cast.scala:172)
at org.apache.spark.sql.catalyst.expressions.Cast.timeZone(Cast.scala:172)
at
org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToString$3$$anonfun$apply$16.apply(Cast.scala:208)
at
org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToString$3$$anonfun$apply$16.apply(Cast.scala:208)
at
org.apache.spark.sql.catalyst.expressions.Cast.org$apache$spark$sql$catalyst$expressions$Cast$$buildCast(Cast.scala:201)
at
org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToString$3.apply(Cast.scala:207)
at org.apache.spark.sql.catalyst.expressions.Cast.nullSafeEval(Cast.scala:533)
at
org.apache.spark.sql.catalyst.expressions.UnaryExpression.eval(Expression.scala:327)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$org$apache$spark$sql$execution$datasources$PartitioningUtils$$resolveTypeConflicts$1.apply(PartitioningUtils.scala:485)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$org$apache$spark$sql$execution$datasources$PartitioningUtils$$resolveTypeConflicts$1.apply(PartitioningUtils.scala:484)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.org$apache$spark$sql$execution$datasources$PartitioningUtils$$resolveTypeConflicts(PartitioningUtils.scala:484)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$15.apply(PartitioningUtils.scala:340)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$$anonfun$15.apply(PartitioningUtils.scala:339)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.Range.foreach(Range.scala:160)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.resolvePartitions(PartitioningUtils.scala:339)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.parsePartitions(PartitioningUtils.scala:141)
at
org.apache.spark.sql.execution.datasources.PartitioningUtils$.parsePartitions(PartitioningUtils.scala:97)
at
org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.inferPartitioning(PartitioningAwareFileIndex.scala:153)
at
org.apache.spark.sql.execution.datasources.InMemoryFileIndex.partitionSpec(InMemoryFileIndex.scala:70)
at
org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.partitionSchema(PartitioningAwareFileIndex.scala:50)
at
org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:133)
at
org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:333)
at org.apache.spark.sql.DataFrameReader.json(DataFrameReader.scala:279)
... 48 elided
*/
val readStr = spark.read.schema(df.schema).json("partition_by_str")
/*
same exception
*/
val readT = spark.read.json("partition_by_t") // OK
val readT = spark.read.schema(df.schema).json("partition_by_t") // OK
val readFlat = spark.read.json("flat") // NO error, by timestamp column is read
a String
val readFlat = spark.read.schema(df.schema).json("flat") // OK
{noformat}
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