namrathamyske opened a new issue, #9737:
URL: https://github.com/apache/iceberg/issues/9737
### Apache Iceberg version
main (development)
### Query engine
None
### Please describe the bug 🐞
regarding this PR: https://github.com/apache/iceberg/pull/9131 - the change
reads as: Schema for a branch should return table schema
Shouldn't the Schema of a branch be the same as when the branch was created
- as opposed to the above change - ie., to move it to a future state of schema
change on the table? isn't the concept of branching to create a baseline based
on the state of data and metadata of the table - as to - when it was branched?
can you pl. help me understand the rationale behind this change?
Please consider this example:
```
-- create a table with a single column and insert a value
spark-sql (default)> create table t (s string);
spark-sql (default)> insert into t values ('foo');
-- create a branch, the schema is the same as the original table
spark-sql (default)> alter table t create branch b1;
```
Describe and Query the table & branch:
```
spark-sql (default)> describe default.t;
s string
spark-sql (default)> select * from default.t;
s
foo
spark-sql (default)> describe default.t.branch_b1;
s string
spark-sql (default)> select * from default.t.branch_b1;
s
foo
```
Alter the table - using the below statement to diverge the definition of the
table:
```
spark-sql (default)> alter table t add column i int;
spark-sql (default)> alter table t del column s;
spark-sql (default)> insert into t values (111);
```
Behavior before the above PR: [Please NOTE that the changes in the main
branch - DID NOT IMPACT the data and metadata on the branch - which lookslike
is the desirable behavior for any branching concept]
```
spark-sql (default)> describe default.t;
i int
spark-sql (default)> select * from default.t;
i
111
spark-sql (default)> describe default.t.branch_b1;
s string
spark-sql (default)> select * from default.t.branch_b1;
s
foo
```
Behavior after the above PR: [Please NOTE that a schema change in the main
branch - IMPACTED the data and metadata available on the branch - this feels
like an undesirable behavior;]
```
spark-sql (default)> describe default.t;
i int
spark-sql (default)> select * from default.t;
i
111
spark-sql (default)> describe default.t.branch_b1;
i int
spark-sql (default)> select * from default.t.branch_b1;
i
--no-data--
```
Unit test to replicate the issue:
```
@Test
public void testSchemaChange() throws Exception {
Assume.assumeFalse("Avro does not support metadata delete",
fileFormat.equals("avro"));
createAndInitUnpartitionedTable();
sql("INSERT INTO TABLE %s VALUES (1, 'hr'), (2, 'hardware'), (null,
'hr')", tableName);
createBranchIfNeeded();
String sql = String.format("SELECT * FROM %s ORDER BY id",
selectTarget());
spark.sql(sql).show();
/**
* +----+--------+
* | id| dep|
* +----+--------+
* |NULL| hr|
* | 1| hr|
* | 2|hardware|
* +----+--------+
*/
// Metadata Delete
Table table = Spark3Util.loadIcebergTable(spark, tableName);
table.refresh();
table.updateSchema().deleteColumn("dep").commit();
sql = String.format("SELECT * FROM %s ORDER BY id", selectTarget());
spark.sql(sql).show();
/**
* Data loss in branch, impacted as we consume schema from table schema
* +----+
* | id|
* +----+
* |NULL|
* | 1|
* | 2|
* +----+
*/
sql = String.format("SELECT * FROM %s ORDER BY id", tableName);
spark.sql(sql).show();
/**
* +----+
* | id|
* +----+
* |NULL|
* | 1|
* | 2|
* +----+
*/
}
```
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