aokolnychyi commented on a change in pull request #2210:
URL: https://github.com/apache/iceberg/pull/2210#discussion_r571191846
##########
File path: spark3/src/main/java/org/apache/iceberg/spark/Spark3Util.java
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@@ -790,4 +804,53 @@ public Identifier identifier() {
public static TableIdentifier identifierToTableIdentifier(Identifier
identifier) {
return TableIdentifier.of(Namespace.of(identifier.namespace()),
identifier.name());
}
+
+ /**
+ * Use Spark to list all partitions in the table.
+ *
+ * @param spark a Spark session
+ * @param rootPath a table identifier
+ * @param format format of the file
+ * @return all table's partitions
+ */
+ public static List<SparkTableUtil.SparkPartition> getPartitions(SparkSession
spark, Path rootPath, String format) {
+ FileStatusCache fileStatusCache = FileStatusCache.getOrCreate(spark);
+ Map<String, String> emptyMap = Collections.emptyMap();
+
+ InMemoryFileIndex fileIndex = new InMemoryFileIndex(
Review comment:
Could we use reflection for this? Seems we had a similar issue in
`HiveClientPool`.
I understand `InMemoryFileIndex` is a low-level API and it may not be the
best idea to rely on it. I'm ok with this as the first step. We may build our
own logic if we need it in other query engines.
I'd be also alright to always require a specific partition to import and not
support importing all partitions in a given path, which should avoid the need
to use Spark's `InMemoryFileIndex` to infer the underlying partitioning. As I
wrote before, I think the primary use case for this is CREATE TABLE LIKE and
then calling `add_files` for a couple of partitions to try reads/writes or
migrating partition by partition if a table has too many partitions to migrate
them at once. In both cases, there is a specific partition we have to import.
If there is a need to import files in a given location, one can create an
external Spark table pointing to the location, call `MSCK REPAIR TABLE` to
infer partitions and add them to the metastore and then call either snapshot or
migrate procedure.
What are your thoughts, @RussellSpitzer @rdblue?
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