rdblue commented on a change in pull request #1523:
URL: https://github.com/apache/iceberg/pull/1523#discussion_r496257864
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File path: site/docs/spark.md
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@@ -519,6 +519,59 @@ data.writeTo("prod.db.table")
.createOrReplace()
```
+## Writing against partitioned table
+
+Iceberg requires the data to be sorted according to the partition spec in
prior to write against partitioned table.
+This applies both Writing with SQL and Writing with DataFrames.
+
+Assuming we would like to write the data against below sample table:
+
+```sql
+CREATE TABLE prod.db.sample (
+ id bigint,
+ data string,
+ category string,
+ ts timestamp)
+USING iceberg
+PARTITIONED BY (bucket(16, id), days(ts), category)
Review comment:
Should this example be a little simpler?
I think it makes sense to have an example for bucketing, but the other two
don't require creating a UDF with the Iceberg transform. Splitting this into
two examples might make sense: one with `days(ts)` and `category` to show how
to add the sort with a SQL `ORDER BY` and using `sortWithinPartitions`, and
then a more complicated one for bucketing.
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