guykhazma commented on code in PR #12931:
URL: https://github.com/apache/iceberg/pull/12931#discussion_r2067415111
##########
docs/docs/spark-configuration.md:
##########
@@ -145,6 +145,61 @@ Using those SQL commands requires adding Iceberg
extensions to your Spark enviro
## Runtime configuration
+### Precedence of Configuration Settings
+Iceberg allows configurations to be specified at different levels. The
effective configuration for a read or write operation is determined based on
the following order of precedence:
+
+1. DataSource API Read/Write Options – Explicitly passed to `.option(...)` in
a read/write operation.
+
+2. Spark Session Configuration - Set globally in Spark via
`spark.conf.set(...)`, `spark-defaults.conf`, or `--conf` in spark-submit.
+
+3. Table Properties – Defined on the Iceberg table via `ALTER TABLE SET
TBLPROPERTIES`.
+
+4. Default Value
+
+If a setting is not defined at a higher level, the next level is used as
fallback. This allows flexibility while enabling global defaults when needed.
+
+### Spark SQL Options
+
+Iceberg supports setting various global behaviors using Spark SQL
configuration options. These can be set via `spark.conf`, `SparkSession
settings`, or Spark submit arguments.
+For example:
+
+```scala
+// disabling vectorization
+val spark = SparkSession.builder()
+ .appName("IcebergExample")
+ .master("local[*]")
+ .config("spark.sql.catalog.my_catalog",
"org.apache.iceberg.spark.SparkCatalog")
+ .config("spark.sql.extensions",
"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
+ .config("spark.sql.iceberg.vectorization.enabled", "false")
+ .getOrCreate()
+```
+
+| Spark option | Default
| Description
|
+|--------------------------------------------------------|----------------------------------------------------------------|------------------------------------------------------------------------|
+| spark.sql.iceberg.vectorization.enabled | Table default
| Enables vectorized reads of data
files |
+| spark.sql.iceberg.parquet.reader-type | ICEBERG
| Sets Parquet reader implementation
(`ICEBERG`,`COMET`) |
+| spark.sql.iceberg.check-nullability | true
| Whether to perform the nullability
check during writes |
+| spark.sql.iceberg.check-ordering | true
| Whether to check the order of
fields during writes |
+| spark.sql.iceberg.planning.preserve-data-grouping | false
| Whether to preserve the existing
grouping of data while planning splits |
Review Comment:
I will specify it is for storage partitioned joins. And then it makes sense
to add a page describing SPJ and refer to it (as part of a separate PR).
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