rdblue commented on a change in pull request #24798: [SPARK-27724][SQL]
Implement REPLACE TABLE and REPLACE TABLE AS SELECT with V2
URL: https://github.com/apache/spark/pull/24798#discussion_r305479389
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/WriteToDataSourceV2Exec.scala
##########
@@ -89,15 +92,145 @@ case class CreateTableAsSelectExec(
case _ =>
// table does not support writes
- throw new SparkException(s"Table implementation does not support
writes: ${ident.quoted}")
+ throw new SparkException(
+ s"Table implementation does not support writes: ${ident.quoted}")
}
+ })(catchBlock = {
+ catalog.dropTable(ident)
+ })
+ }
+}
+
+/**
+ * Physical plan node for v2 create table as select, when the catalog is
determined to support
+ * staging table creation.
+ *
+ * A new table will be created using the schema of the query, and rows from
the query are appended.
+ * The CTAS operation is atomic. The creation of the table is staged and the
commit of the write
+ * should bundle the commitment of the metadata and the table contents in a
single unit. If the
+ * write fails, the table is instructed to roll back all staged changes.
+ */
+case class AtomicCreateTableAsSelectExec(
+ catalog: StagingTableCatalog,
+ ident: Identifier,
+ partitioning: Seq[Transform],
+ query: SparkPlan,
+ properties: Map[String, String],
+ writeOptions: CaseInsensitiveStringMap,
+ ifNotExists: Boolean) extends AtomicTableWriteExec {
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ if (catalog.tableExists(ident)) {
+ if (ifNotExists) {
+ return sparkContext.parallelize(Seq.empty, 1)
+ }
+
+ throw new TableAlreadyExistsException(ident)
+ }
+ val stagedTable = catalog.stageCreate(
+ ident, query.schema, partitioning.toArray, properties.asJava)
+ writeToStagedTable(stagedTable, writeOptions, ident)
+ }
+}
+
+/**
+ * Physical plan node for v2 replace table as select when the catalog does not
support staging
+ * table replacement.
+ *
+ * A new table will be created using the schema of the query, and rows from
the query are appended.
+ * If the table exists, its contents and schema should be replaced with the
schema and the contents
+ * of the query. This is a non-atomic implementation that drops the table and
then runs non-atomic
+ * CTAS. For an atomic implementation for catalogs with the appropriate
support, see
+ * ReplaceTableAsSelectStagingExec.
+ */
+case class ReplaceTableAsSelectExec(
+ catalog: TableCatalog,
+ ident: Identifier,
+ partitioning: Seq[Transform],
+ query: SparkPlan,
+ properties: Map[String, String],
+ writeOptions: CaseInsensitiveStringMap,
+ orCreate: Boolean) extends AtomicTableWriteExec {
+
+ import org.apache.spark.sql.catalog.v2.CatalogV2Implicits.IdentifierHelper
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ // Note that this operation is potentially unsafe, but these are the
strict semantics of
+ // RTAS if the catalog does not support atomic operations.
+ //
+ // There are numerous cases we concede to where the table will be dropped
and irrecoverable:
+ //
+ // 1. Creating the new table fails,
+ // 2. Writing to the new table fails,
+ // 3. The table returned by catalog.createTable doesn't support writing.
+ if (catalog.tableExists(ident)) {
+ catalog.dropTable(ident)
+ } else if (!orCreate) {
+ throw new CannotReplaceMissingTableException(ident)
+ }
+ val createdTable = catalog.createTable(
+ ident, query.schema, partitioning.toArray, properties.asJava)
+ Utils.tryWithSafeFinallyAndFailureCallbacks({
+ createdTable match {
+ case table: SupportsWrite =>
+ val batchWrite = table.newWriteBuilder(writeOptions)
+ .withInputDataSchema(query.schema)
+ .withQueryId(UUID.randomUUID().toString)
+ .buildForBatch()
+
+ doWrite(batchWrite)
+ case _ =>
+ // table does not support writes
+ throw new SparkException(
+ s"Table implementation does not support writes: ${ident.quoted}")
+ }
})(catchBlock = {
catalog.dropTable(ident)
})
}
}
+/**
+ *
+ * Physical plan node for v2 replace table as select when the catalog supports
staging
+ * table replacement.
+ *
+ * A new table will be created using the schema of the query, and rows from
the query are appended.
+ * If the table exists, its contents and schema should be replaced with the
schema and the contents
+ * of the query. This implementation is atomic. The table replacement is
staged, and the commit
+ * operation at the end should perform tne replacement of the table's metadata
and contents. If the
+ * write fails, the table is instructed to roll back staged changes and any
previously written table
+ * is left untouched.
+ */
+case class AtomicReplaceTableAsSelectExec(
+ catalog: StagingTableCatalog,
+ ident: Identifier,
+ partitioning: Seq[Transform],
+ query: SparkPlan,
+ properties: Map[String, String],
+ writeOptions: CaseInsensitiveStringMap,
+ orCreate: Boolean) extends AtomicTableWriteExec {
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ val stagedTable = if (catalog.tableExists(ident)) {
Review comment:
I disagree that there is no need to check whether the table exists. We had a
similar discussion on CREATE TABLE. Spark should check existence to ensure that
the error is consistently thrown. If the table does not exist and `orCreate` is
false, then Spark should thrown an exception and not rely on the source to do
it.
That said, I think it would be simpler to update the logic a little:
```scala
if (orCreate) {
catalog.stageCreateOrReplace(
ident, query.schema, partitioning.toArray, properties.asJava)
} else if (catalog.tableExists(ident) {
catalog.stageReplace(
ident, query.schema, partitioning.toArray, properties.asJava)
} else {
throw new CannotReplaceMissingTableException(ident)
}
```
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