Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/15951#discussion_r88984227
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -84,30 +84,90 @@ case class DataSource(
private val caseInsensitiveOptions = new CaseInsensitiveMap(options)
/**
- * Infer the schema of the given FileFormat, returns a pair of schema
and partition column names.
+ * Get the schema of the given FileFormat, if provided by
`userSpecifiedSchema`, or try to infer
+ * it. In the read path, only Hive managed tables provide the partition
columns properly when
+ * initializing this class. All other file based data sources will try
to infer the partitioning,
+ * and then cast the inferred types to user specified dataTypes if the
partition columns exist
+ * inside `userSpecifiedSchema`, otherwise we can hit data corruption
bugs like SPARK-18510.
+ * This method will try to do the least amount of work given whether
`userSpecifiedSchema` and
+ * `partitionColumns` are provided. Here are some code paths that use
this method:
+ * 1. `spark.read` (no schema): Most amount of work. Infer both schema
and partitioning columns
+ * 2. `spark.read.schema(userSpecifiedSchema)`: Parse partitioning
columns, cast them to the
+ * dataTypes provided in `userSpecifiedSchema` if they exist or
fallback to inferred
+ * dataType if they don't.
+ * 3. `spark.readStream.schema(userSpecifiedSchema)`: For streaming
use cases, users have to
+ * provide the schema. Here, we also perform partition inference
like 2, and try to use
+ * dataTypes in `userSpecifiedSchema`. All subsequent triggers for
this stream will re-use
+ * this information, therefore calls to this method should be very
cheap, i.e. there won't
+ * be any further inference in any triggers.
+ * 4. `df.saveAsTable(tableThatExisted)`: In this case, we call this
method to resolve the
+ * existing table's partitioning scheme. This is achieved by not
providing
+ * `userSpecifiedSchema`. For this case, we add the boolean
`justPartitioning` for an early
+ * exit, if we don't care about the schema of the original table.
+ *
+ * Returns a pair of schema and partition column names.
*/
- private def inferFileFormatSchema(format: FileFormat): (StructType,
Seq[String]) = {
- userSpecifiedSchema.map(_ -> partitionColumns).orElse {
- val allPaths = caseInsensitiveOptions.get("path")
+ private def getOrInferFileFormatSchema(
--- End diff --
can you add param docs to define what `justPartitioning` means?
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