test: Add test cases for arrays and objects

In PostgreSQL, all boils down to the `jsonb[]` type, but arrays are
reflected as `sqlalchemy.dialects.postgresql.ARRAY` instead of
`sqlalchemy.dialects.postgresql.JSONB`.

In order to prepare for more advanced type mangling & validation, and to
better support databases pretending to be compatible with PostgreSQL,
the new test cases exercise arrays with different kinds of inner values,
because, on other databases, ARRAYs may need to have uniform content.

Along the lines, it adds a `verify_schema` utility function in the
spirit of the `verify_data` function, refactored and generalized from
the `test_anyof` test case.

Author: amotl

target_postgres/tests/data_files/array_boolean.singer

@@ -0,0 +1,5 @@
+{"type": "SCHEMA", "stream": "array_boolean", "key_properties": ["id"], "schema": {"required": ["id"], "type": "object", "properties": {"id": {"type": "integer"}, "value": {"type": "array", "items": {"type": "boolean"}}}}}
+{"type": "RECORD", "stream": "array_boolean", "record": {"id": 1, "value": [ true, false ]}}
+{"type": "RECORD", "stream": "array_boolean", "record": {"id": 2, "value": [ false ]}}
+{"type": "RECORD", "stream": "array_boolean", "record": {"id": 3, "value": [ false, true, true, false ]}}
+{"type": "STATE", "value": {"array_boolean": 3}}

target_postgres/tests/data_files/array_data.singer

@@ -0,0 +1,5 @@
+{"type": "SCHEMA", "stream": "array_float_vector", "key_properties": ["id"], "schema": {"required": ["id"], "type": "object", "properties": {"id": {"type": "integer"}, "value": {"type": "array", "items": {"type": "number"}, "sql": {"type": "vector", "dim": 4}}}}}
+{"type": "RECORD", "stream": "array_float_vector", "record": {"id": 1, "value": [ 1.0, 2.0, 1.0, 1.0 ]}}
+{"type": "RECORD", "stream": "array_float_vector", "record": {"id": 2, "value": [ 1.0, 1.0, 1.0, 2.0 ]}}
+{"type": "RECORD", "stream": "array_float_vector", "record": {"id": 3, "value": [ 2.0, 1.0, 1.0, 1.0 ]}}
+{"type": "STATE", "value": {"array_float_vector": 3}}

target_postgres/tests/data_files/array_float_vector.singer

@@ -0,0 +1,5 @@
+{"type": "SCHEMA", "stream": "array_number", "key_properties": ["id"], "schema": {"required": ["id"], "type": "object", "properties": {"id": {"type": "integer"}, "value": {"type": "array", "items": {"type": "number"}}}}}
+{"type": "RECORD", "stream": "array_number", "record": {"id": 1, "value": [ 42.42, 84.84, 23 ]}}
+{"type": "RECORD", "stream": "array_number", "record": {"id": 2, "value": [ 1.0 ]}}
+{"type": "RECORD", "stream": "array_number", "record": {"id": 3, "value": [ 1.11, 2.22, 3, 4, 5.55 ]}}
+{"type": "STATE", "value": {"array_number": 3}}

target_postgres/tests/data_files/array_number.singer

@@ -0,0 +1,6 @@
+{"type": "SCHEMA", "stream": "array_string", "key_properties": ["id"], "schema": {"required": ["id"], "type": "object", "properties": {"id": {"type": "integer"}, "value": {"type": "array","items": {"type": "string"}}}}}
+{"type": "RECORD", "stream": "array_string", "record": {"id": 1, "value": [ "apple", "orange", "pear" ]}}
+{"type": "RECORD", "stream": "array_string", "record": {"id": 2, "value": [ "banana", "apple" ]}}
+{"type": "RECORD", "stream": "array_string", "record": {"id": 3, "value": [ "pear" ]}}
+{"type": "RECORD", "stream": "array_string", "record": {"id": 4, "value": [ "orange", "banana", "apple", "pear" ]}}
+{"type": "STATE", "value": {"array_string": 4}}

target_postgres/tests/data_files/array_string.singer

@@ -0,0 +1,5 @@
+{"type": "SCHEMA", "stream": "array_timestamp", "key_properties": ["id"], "schema": {"required": ["id"], "type": "object", "properties": {"id": {"type": "integer"}, "value": {"type": "array", "items": {"type": "string", "format": "date-time"}}}}}
+{"type": "RECORD", "stream": "array_timestamp", "record": {"id": 1, "value": [ "2023-12-13T01:15:02", "2023-12-13T01:16:02" ]}}
+{"type": "RECORD", "stream": "array_timestamp", "record": {"id": 2, "value": [ "2023-12-13T01:15:02" ]}}
+{"type": "RECORD", "stream": "array_timestamp", "record": {"id": 3, "value": [ "2023-12-13T01:15:02", "2023-12-13T01:16:02", "2023-12-13T01:17:02" ]}}
+{"type": "STATE", "value": {"array_timestamp": 3}}

target_postgres/tests/data_files/array_timestamp.singer

@@ -0,0 +1,3 @@
+{"type": "SCHEMA", "stream": "object_mixed", "key_properties": ["id"], "schema": {"required": ["id"], "type": "object", "properties": {"id": {"type": "integer"}, "value": {"type": "object"}}}}
+{"type": "RECORD", "stream": "object_mixed", "record": {"id": 1, "value": {"string": "foo", "integer": 42, "float": 42.42, "timestamp": "2023-12-13T01:15:02", "array_boolean": [true, false], "array_float": [42.42, 84.84], "array_integer": [42, 84], "array_string": ["foo", "bar"], "nested_object": {"foo": "bar"}}}}
+{"type": "STATE", "value": {"object_mixed": 1}}

target_postgres/tests/data_files/object_mixed.singer

@@ -11,8 +11,8 @@
 import sqlalchemy
 from singer_sdk.exceptions import MissingKeyPropertiesError
 from singer_sdk.testing import get_target_test_class, sync_end_to_end
-from sqlalchemy.dialects.postgresql import ARRAY
-from sqlalchemy.types import TEXT, TIMESTAMP
+from sqlalchemy.dialects.postgresql import ARRAY, JSONB
+from sqlalchemy.types import BIGINT, TEXT, TIMESTAMP
 
 from target_postgres.connector import PostgresConnector
 from target_postgres.target import TargetPostgres
@@ -94,7 +94,7 @@ def verify_data(
 
     Args:
         target: The target to obtain a database connection from.
-        full_table_name: The schema and table name of the table to check data for.
+        table_name: The schema and table name of the table to check data for.
         primary_key: The primary key of the table.
         number_of_rows: The expected number of rows that should be in the table.
         check_data: A dictionary representing the full contents of the first row in the
@@ -134,6 +134,43 @@ def verify_data(
             assert result.first()[0] == number_of_rows
 
 
+def verify_schema(
+    target: TargetPostgres,
+    table_name: str,
+    check_columns: dict = None,
+):
+    """Checks whether the schema of a database table matches the provided column definitions.
+
+    Args:
+        target: The target to obtain a database connection from.
+        table_name: The schema and table name of the table to check data for.
+        check_columns: A dictionary mapping column names to their definitions. Currently,
+            it is all about the `type` attribute which is compared.
+    """
+    engine = create_engine(target)
+    schema = target.config["default_target_schema"]
+    with engine.connect() as connection:
+        meta = sqlalchemy.MetaData()
+        table = sqlalchemy.Table(
+            table_name, meta, schema=schema, autoload_with=connection
+        )
+        for column in table.c:
+            # Ignore `_sdc` columns for now.
+            if column.name.startswith("_sdc"):
+                continue
+            try:
+                column_type_expected = check_columns[column.name]["type"]
+            except KeyError:
+                raise ValueError(
+                    f"Invalid check_columns - missing definition for column: {column.name}"
+                )
+            if not isinstance(column.type, column_type_expected):
+                raise TypeError(
+                    f"Column '{column.name}' (with type '{column.type}') "
+                    f"does not match expected type: {column_type_expected}"
+                )
+
+
 def test_sqlalchemy_url_config(postgres_config_no_ssl):
     """Be sure that passing a sqlalchemy_url works
 
@@ -406,11 +443,111 @@ def test_duplicate_records(postgres_target):
     verify_data(postgres_target, "test_duplicate_records", 2, "id", row)
 
 
-def test_array_data(postgres_target):
-    file_name = "array_data.singer"
+def test_array_boolean(postgres_target):
+    file_name = "array_boolean.singer"
+    singer_file_to_target(file_name, postgres_target)
+    row = {"id": 1, "value": [True, False]}
+    verify_data(postgres_target, "array_boolean", 3, "id", row)
+    verify_schema(
+        postgres_target,
+        "array_boolean",
+        check_columns={
+            "id": {"type": BIGINT},
+            "value": {"type": ARRAY},
+        },
+    )
+
+
+@pytest.mark.skip("Needs pgvector support")
+def test_array_float_vector(postgres_target):
+    file_name = "array_float_vector.singer"
+    singer_file_to_target(file_name, postgres_target)
+    row = {
+        "id": 1,
+        "value": [Decimal("1.0"), Decimal("2.0"), Decimal("1.0"), Decimal("1.0")],
+    }
+    verify_data(postgres_target, "array_float_vector", 3, "id", row)
+    verify_schema(
+        postgres_target,
+        "array_float_vector",
+        check_columns={
+            "id": {"type": BIGINT},
+            "value": {"type": ARRAY},
+        },
+    )
+
+
+def test_array_number(postgres_target):
+    file_name = "array_number.singer"
+    singer_file_to_target(file_name, postgres_target)
+    row = {"id": 1, "value": [Decimal("42.42"), Decimal("84.84"), 23]}
+    verify_data(postgres_target, "array_number", 3, "id", row)
+    verify_schema(
+        postgres_target,
+        "array_number",
+        check_columns={
+            "id": {"type": BIGINT},
+            "value": {"type": ARRAY},
+        },
+    )
+
+
+def test_array_string(postgres_target):
+    file_name = "array_string.singer"
+    singer_file_to_target(file_name, postgres_target)
+    row = {"id": 1, "value": ["apple", "orange", "pear"]}
+    verify_data(postgres_target, "array_string", 4, "id", row)
+    verify_schema(
+        postgres_target,
+        "array_string",
+        check_columns={
+            "id": {"type": BIGINT},
+            "value": {"type": ARRAY},
+        },
+    )
+
+
+def test_array_timestamp(postgres_target):
+    file_name = "array_timestamp.singer"
     singer_file_to_target(file_name, postgres_target)
-    row = {"id": 1, "fruits": ["apple", "orange", "pear"]}
-    verify_data(postgres_target, "test_carts", 4, "id", row)
+    row = {"id": 1, "value": ["2023-12-13T01:15:02", "2023-12-13T01:16:02"]}
+    verify_data(postgres_target, "array_timestamp", 3, "id", row)
+    verify_schema(
+        postgres_target,
+        "array_timestamp",
+        check_columns={
+            "id": {"type": BIGINT},
+            "value": {"type": ARRAY},
+        },
+    )
+
+
+def test_object_mixed(postgres_target):
+    file_name = "object_mixed.singer"
+    singer_file_to_target(file_name, postgres_target)
+    row = {
+        "id": 1,
+        "value": {
+            "string": "foo",
+            "integer": 42,
+            "float": Decimal("42.42"),
+            "timestamp": "2023-12-13T01:15:02",
+            "array_boolean": [True, False],
+            "array_float": [Decimal("42.42"), Decimal("84.84")],
+            "array_integer": [42, 84],
+            "array_string": ["foo", "bar"],
+            "nested_object": {"foo": "bar"},
+        },
+    }
+    verify_data(postgres_target, "object_mixed", 1, "id", row)
+    verify_schema(
+        postgres_target,
+        "object_mixed",
+        check_columns={
+            "id": {"type": BIGINT},
+            "value": {"type": JSONB},
+        },
+    )
 
 
 def test_encoded_string_data(postgres_target):
@@ -456,41 +593,32 @@ def test_large_int(postgres_target):
 
 def test_anyof(postgres_target):
     """Test that anyOf is handled correctly"""
-    engine = create_engine(postgres_target)
     table_name = "commits"
     file_name = f"{table_name}.singer"
-    schema = postgres_target.config["default_target_schema"]
     singer_file_to_target(file_name, postgres_target)
-    with engine.connect() as connection:
-        meta = sqlalchemy.MetaData()
-        table = sqlalchemy.Table(
-            "commits", meta, schema=schema, autoload_with=connection
-        )
-        for column in table.c:
-            # {"type":"string"}
-            if column.name == "id":
-                assert isinstance(column.type, TEXT)
 
+    verify_schema(
+        postgres_target,
+        table_name,
+        check_columns={
+            # {"type":"string"}
+            "id": {"type": TEXT},
             # Any of nullable date-time.
             # Note that postgres timestamp is equivalent to jsonschema date-time.
             # {"anyOf":[{"type":"string","format":"date-time"},{"type":"null"}]}
-            if column.name in {"authored_date", "committed_date"}:
-                assert isinstance(column.type, TIMESTAMP)
-
+            "authored_date": {"type": TIMESTAMP},
+            "committed_date": {"type": TIMESTAMP},
             # Any of nullable array of strings or single string.
             # {"anyOf":[{"type":"array","items":{"type":["null","string"]}},{"type":"string"},{"type":"null"}]}
-            if column.name == "parent_ids":
-                assert isinstance(column.type, ARRAY)
-
+            "parent_ids": {"type": ARRAY},
             # Any of nullable string.
             # {"anyOf":[{"type":"string"},{"type":"null"}]}
-            if column.name == "commit_message":
-                assert isinstance(column.type, TEXT)
-
+            "commit_message": {"type": TEXT},
             # Any of nullable string or integer.
             # {"anyOf":[{"type":"string"},{"type":"integer"},{"type":"null"}]}
-            if column.name == "legacy_id":
-                assert isinstance(column.type, TEXT)
+            "legacy_id": {"type": TEXT},
+        },
+    )
 
 
 def test_new_array_column(postgres_target):