Add test for migrated tables

tests/conftest.py

@@ -2502,9 +2502,13 @@ def spark() -> "SparkSession":
     spark_version = ".".join(importlib.metadata.version("pyspark").split(".")[:2])
     scala_version = "2.12"
     iceberg_version = "1.9.2"
+    hadoop_version = "3.3.4"
+    aws_sdk_version = "1.12.753"
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = (
         f"--packages org.apache.iceberg:iceberg-spark-runtime-{spark_version}_{scala_version}:{iceberg_version},"
+        f"org.apache.hadoop:hadoop-aws:{hadoop_version},"
+        f"com.amazonaws:aws-java-sdk-bundle:{aws_sdk_version},"
         f"org.apache.iceberg:iceberg-aws-bundle:{iceberg_version} pyspark-shell"
     )
     os.environ["AWS_REGION"] = "us-east-1"
@@ -2526,14 +2530,21 @@ def spark() -> "SparkSession":
         .config("spark.sql.catalog.integration.warehouse", "s3://warehouse/wh/")
         .config("spark.sql.catalog.integration.s3.endpoint", "http://localhost:9000")
         .config("spark.sql.catalog.integration.s3.path-style-access", "true")
-        .config("spark.sql.defaultCatalog", "integration")
         .config("spark.sql.catalog.hive", "org.apache.iceberg.spark.SparkCatalog")
         .config("spark.sql.catalog.hive.type", "hive")
         .config("spark.sql.catalog.hive.uri", "http://localhost:9083")
         .config("spark.sql.catalog.hive.io-impl", "org.apache.iceberg.aws.s3.S3FileIO")
         .config("spark.sql.catalog.hive.warehouse", "s3://warehouse/hive/")
         .config("spark.sql.catalog.hive.s3.endpoint", "http://localhost:9000")
         .config("spark.sql.catalog.hive.s3.path-style-access", "true")
+        .config("spark.sql.catalog.spark_catalog", "org.apache.iceberg.spark.SparkSessionCatalog")
+        .config("spark.sql.catalog.spark_catalog.type", "hive")
+        .config("spark.sql.catalog.spark_catalog.uri", "http://localhost:9083")
+        .config("spark.sql.catalog.spark_catalog.warehouse", "s3://warehouse/hive/")
+        .config("spark.hadoop.fs.s3a.endpoint", "http://localhost:9000")
+        .config("spark.hadoop.fs.s3a.path.style.access", "true")
+        .config("spark.sql.catalogImplementation", "hive")
+        .config("spark.sql.defaultCatalog", "integration")
         .config("spark.sql.execution.arrow.pyspark.enabled", "true")
         .getOrCreate()
     )

tests/integration/test_hive_migration.py

@@ -0,0 +1,86 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import time
+
+import pytest
+from pyspark.sql import SparkSession
+
+from pyiceberg.catalog import Catalog
+
+
+@pytest.mark.integration
+def test_migrate_table(
+    session_catalog_hive: Catalog,
+    spark: SparkSession,
+) -> None:
+    """
+    Imported tables are an edge case since the partition column is not stored
+    in the Parquet files:
+
+    test_migrate_table_hive_1754486926/dt=2022-01-01/part-00000-30a9798b-7597-4027-86d9-79d7c529bc87.c000.snappy.parquet
+    {
+      "type" : "record",
+      "name" : "spark_schema",
+      "fields" : [ {
+        "name" : "number",
+        "type" : "int"
+      } ]
+    }
+
+    PyIceberg will project this column when the table is being read
+    """
+    # Create new tables to avoid complex cleanup
+    src_table_identifier = f"spark_catalog.default.test_migrate_table_hive_{int(time.time())}"
+    dst_table_identifier = f"default.test_migrate_table_{int(time.time())}"
+
+    spark.sql(f"""
+        CREATE TABLE {src_table_identifier} (
+            number INTEGER
+        )
+        PARTITIONED BY (dt date)
+        STORED AS parquet
+    """)
+
+    spark.sql(f"""
+        INSERT OVERWRITE TABLE {src_table_identifier}
+        PARTITION (dt='2022-01-01')
+        VALUES (1), (2), (3)
+    """)
+
+    spark.sql(f"""
+        INSERT OVERWRITE TABLE {src_table_identifier}
+        PARTITION (dt='2023-01-01')
+        VALUES (4), (5), (6)
+    """)
+
+    # Docs: https://iceberg.apache.org/docs/latest/hive-migration/#snapshot-hive-table-to-iceberg
+    spark.sql(f"""
+        CALL hive.system.snapshot('{src_table_identifier}', 'hive.{dst_table_identifier}')
+    """)
+
+    tbl = session_catalog_hive.load_table(dst_table_identifier)
+    assert tbl.schema().column_names == ["number", "dt"]
+
+    # TODO: Returns the primitive type (int), rather than the logical type
+    # assert set(tbl.scan().to_arrow().column(1).combine_chunks().tolist()) == {'2022-01-01', '2023-01-01'}
+
+    assert tbl.scan(row_filter="number > 3").to_arrow().column(0).combine_chunks().tolist() == [4, 5, 6]
+
+    assert tbl.scan(row_filter="dt == '2023-01-01'").to_arrow().column(0).combine_chunks().tolist() == [4, 5, 6]
+
+    # TODO: Issue with filtering the projected column
+    # assert tbl.scan(row_filter="dt == '2022-01-01'").to_arrow().column(0).combine_chunks().tolist() == [1, 2, 3]