Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/15951#discussion_r89030586
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala
---
@@ -84,30 +84,95 @@ 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 skip file scanning 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 the data schema (excluding partition columns) and
the schema of the partition
+ * columns.
+ *
+ * @param justPartitioning Whether to exit early and provide just the
schema partitioning. The
+ * data schema is incorrect in this case and
should not be used.
*/
- private def inferFileFormatSchema(format: FileFormat): (StructType,
Seq[String]) = {
- userSpecifiedSchema.map(_ -> partitionColumns).orElse {
- val allPaths = caseInsensitiveOptions.get("path")
+ private def getOrInferFileFormatSchema(
+ format: FileFormat,
+ justPartitioning: Boolean = false): (StructType, StructType) = {
+ // the operations below are expensive therefore try not to do them if
we don't need to
+ lazy val tempFileCatalog = {
+ val allPaths = caseInsensitiveOptions.get("path") ++ paths
val globbedPaths = allPaths.toSeq.flatMap { path =>
val hdfsPath = new Path(path)
val fs =
hdfsPath.getFileSystem(sparkSession.sessionState.newHadoopConf())
val qualified = hdfsPath.makeQualified(fs.getUri,
fs.getWorkingDirectory)
SparkHadoopUtil.get.globPathIfNecessary(qualified)
}.toArray
- val fileCatalog = new InMemoryFileIndex(sparkSession, globbedPaths,
options, None)
- val partitionSchema = fileCatalog.partitionSpec().partitionColumns
- val inferred = format.inferSchema(
+ new InMemoryFileIndex(sparkSession, globbedPaths, options, None)
+ }
+ val partitionSchema = if (partitionColumns.isEmpty &&
catalogTable.isEmpty) {
+ // Try to infer partitioning, because no DataSource in the read path
provides the partitioning
+ // columns properly unless it is a Hive DataSource
+ val resolved = tempFileCatalog.partitionSchema.map { partitionField
=>
+ val equality = sparkSession.sessionState.conf.resolver
+ // SPARK-18510: try to get schema from userSpecifiedSchema,
otherwise fallback to inferred
+ userSpecifiedSchema.flatMap(_.find(f => equality(f.name,
partitionField.name))).getOrElse(
+ partitionField)
+ }
+ StructType(resolved)
+ } else {
+ // in streaming mode, we have already inferred and registered
partition columns, we will
+ // never have to re-use this
+ lazy val inferredPartitions = tempFileCatalog.partitionSchema
--- End diff --
Since everything is already defined in `userSpecifiedSchema` once we create
the `FileStreamSource`, we will never have to materialize this variable,
because we will not use it.
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