以sql调用来分析：

[hadoop@10 ~]$ spark-sql --master local \

--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \

--conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \

--conf spark.sql.catalog.spark_catalog.type=hadoop \

--conf spark.sql.catalog.spark_catalog.warehouse=hdfs://ns1/user/wanghongbing/db

流程如下：

其中，spark包重要的类：

• org.apache.spark.sql.connector.catalog.CatalogManager

• org.apache.spark.sql.connector.catalog.Catalogs

iceberg包对应的类：

• org.apache.iceberg.spark.SparkSessionCatalog
• org.apache.iceberg.spark.SparkCatalog

# CatalogManager

def catalog(name: String): CatalogPlugin = synchronized {

  if (name.equalsIgnoreCase(SESSION_CATALOG_NAME)) {

    v2SessionCatalog

  } else {

    catalogs.getOrElseUpdate(name, Catalogs.load(name, conf))

  }

}

private[sql] object CatalogManager {

  val SESSION_CATALOG_NAME: String = "spark_catalog"

}



# SparkSessionCatalog

/**

 * A Spark catalog that can also load non-Iceberg tables.

 *

 * @param <T> CatalogPlugin class to avoid casting to TableCatalog and SupportsNamespaces.

 */

public class SparkSessionCatalog<T extends TableCatalog & SupportsNamespaces>

    extends BaseCatalog implements CatalogExtension {

这里 org.apache.iceberg.spark.SparkSessionCatalog 实现了 org.apache.spark.sql.connector.catalog.CatalogExtension 和 CatalogPlugin

小结：Spark包中定义了Catalog的接口，iceberg用于实现。