zachdisc commented on code in PR #12840:
URL: https://github.com/apache/iceberg/pull/12840#discussion_r2054894652
##########
spark/v3.5/spark/src/main/java/org/apache/iceberg/spark/actions/RewriteManifestsSparkAction.java:
##########
@@ -251,12 +282,40 @@ private List<ManifestFile> writeUnpartitionedManifests(
private List<ManifestFile> writePartitionedManifests(
ManifestContent content, Dataset<Row> manifestEntryDF, int numManifests)
{
+ // Extract desired clustering criteria into a dedicated column
+ Dataset<Row> clusteredManifestEntryDF;
+
+ if (partitionFieldClustering != null) {
+ LOG.info(
+ "Clustering manifests for specId {} by partition columns by {} ",
+ spec.specId(),
+ partitionFieldClustering);
+
+ // Map the top level partition column names to the column name
referenced within the manifest
+ // entry dataframe
+ Column[] actualPartitionColumns =
+ partitionFieldClustering.stream()
+ .map(p -> col("data_file.partition." + p))
+ .toArray(Column[]::new);
+
+ // Form a new temporary column to cluster manifests on, based on the
custom clustering columns
+ // order provided
+ clusteredManifestEntryDF =
+ manifestEntryDF.withColumn(
+ CUSTOM_CLUSTERING_COLUMN_NAME,
functions.struct(actualPartitionColumns));
+ } else {
+ clusteredManifestEntryDF =
+ manifestEntryDF.withColumn(CUSTOM_CLUSTERING_COLUMN_NAME,
col("data_file.partition"));
+ }
+
return withReusableDS(
- manifestEntryDF,
+ clusteredManifestEntryDF,
df -> {
WriteManifests<?> writeFunc = newWriteManifestsFunc(content,
df.schema());
- Column partitionColumn = df.col("data_file.partition");
- Dataset<Row> transformedDF = repartitionAndSort(df, partitionColumn,
numManifests);
+ Column partitionColumn = df.col(CUSTOM_CLUSTERING_COLUMN_NAME);
Review Comment:
That'd work, can do
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