Github user brkyvz commented on a diff in the pull request: https://github.com/apache/spark/pull/15951#discussion_r89030464 --- 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 + // maintain old behavior before SPARK-18510. If userSpecifiedSchema is empty used inferred + // partitioning + if (userSpecifiedSchema.isEmpty) { + inferredPartitions + } else { + val partitionFields = partitionColumns.map { partitionColumn => + userSpecifiedSchema.flatMap(_.find(_.name == partitionColumn)).orElse { + logDebug(s"Schema of partition column: $partitionColumn not found in specified " + + "schema. Falling back to inferred dataType if it exists.") + inferredPartitions.find(_.name == partitionColumn) + }.getOrElse { + throw new AnalysisException("Failed to resolve the schema for the partition column: " + + s"$partitionColumn. It must be specified manually.") + } + } + StructType(partitionFields) + } + } + if (justPartitioning) { + return new StructType() -> partitionSchema --- End diff -- done
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