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new ddce65c Update Spark section on inspecting tables. (#66)
ddce65c is described below
commit ddce65c483deae73d2c10e01bf3c249983025a1c
Author: Wing Yew Poon <[email protected]>
AuthorDate: Tue Jun 7 08:19:47 2022 -0700
Update Spark section on inspecting tables. (#66)
Using the SparkSessionCatalog to query metadata tables is possible in Spark
3.2.
---
docs/content/docs/spark/spark-queries.md | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/docs/content/docs/spark/spark-queries.md
b/docs/content/docs/spark/spark-queries.md
index e2960bf..ab80362 100644
--- a/docs/content/docs/spark/spark-queries.md
+++ b/docs/content/docs/spark/spark-queries.md
@@ -168,7 +168,9 @@ To inspect a table's history, snapshots, and other
metadata, Iceberg supports me
Metadata tables are identified by adding the metadata table name after the
original table name. For example, history for `db.table` is read using
`db.table.history`.
{{< hint info >}}
-As of Spark 3.0, the format of the table name for inspection
(`catalog.database.table.metadata`) doesn't work with Spark's default catalog
(`spark_catalog`). If you've replaced the default catalog, you may want to use
`DataFrameReader` API to inspect the table.
+For Spark 2.4, use the `DataFrameReader` API to [inspect
tables](#inspecting-with-dataframes).
+
+For Spark 3, prior to 3.2, the Spark [session
catalog](../spark-configuration#replacing-the-session-catalog) does not support
table names with multipart identifiers such as
`catalog.database.table.metadata`. As a workaround, configure an
`org.apache.iceberg.spark.SparkCatalog`, or use the Spark `DataFrameReader` API.
{{< /hint >}}
### History
