Modify tests after code change (#20399)

Signed-off-by: NBinbin Lv <binbin.lv@zilliz.com>
Signed-off-by: NBinbin Lv <binbin.lv@zilliz.com>

tests/python_client/requirements.txt

@@ -12,7 +12,7 @@ allure-pytest==2.7.0
 pytest-print==0.2.1
 pytest-level==0.1.1
 pytest-xdist==2.5.0
-pymilvus==2.2.0.dev71
+pymilvus==2.2.0.dev72
 pytest-rerunfailures==9.1.1
 git+https://github.com/Projectplace/pytest-tags
 ndg-httpsclient

tests/python_client/testcases/test_insert.py

@@ -81,7 +81,7 @@ class TestInsertParams(TestcaseBase):
         """
         c_name = cf.gen_unique_str(prefix)
         collection_w = self.init_collection_wrap(name=c_name)
-        error = {ct.err_code: 0, ct.err_msg: "Data type is not support"}
+        error = {ct.err_code: 1, ct.err_msg: "The type of data should be list or pandas.DataFrame"}
         collection_w.insert(data=get_non_data_type, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -94,7 +94,8 @@ class TestInsertParams(TestcaseBase):
         """
         c_name = cf.gen_unique_str(prefix)
         collection_w = self.init_collection_wrap(name=c_name)
-        error = {ct.err_code: 0, ct.err_msg: "The data fields number is not match with schema"}
+        error = {ct.err_code: 1, ct.err_msg: "The fields don't match with schema fields, "
+                                             "expected: ['int64', 'float', 'varchar', 'float_vector'], got %s" % data}
         collection_w.insert(data=data, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -122,7 +123,7 @@ class TestInsertParams(TestcaseBase):
         collection_w = self.init_collection_wrap(name=c_name)
         df = cf.gen_default_dataframe_data(10)
         df.rename(columns={ct.default_int64_field_name: ' '}, inplace=True)
-        error = {ct.err_code: 0, ct.err_msg: "The types of schema and data do not match"}
+        error = {ct.err_code: 1, ct.err_msg: "The name of field don't match, expected: int64, got "}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -136,7 +137,7 @@ class TestInsertParams(TestcaseBase):
         collection_w = self.init_collection_wrap(name=c_name)
         df = cf.gen_default_dataframe_data(10)
         df.rename(columns={ct.default_int64_field_name: get_invalid_field_name}, inplace=True)
-        error = {ct.err_code: 0, ct.err_msg: "The types of schema and data do not match"}
+        error = {ct.err_code: 1, ct.err_msg: "The name of field don't match, expected: int64, got %s" % get_invalid_field_name}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     def test_insert_dataframe_index(self):
@@ -260,7 +261,7 @@ class TestInsertParams(TestcaseBase):
         collection_w = self.init_collection_wrap(name=c_name)
         df = cf.gen_default_dataframe_data(10)
         df.rename(columns={ct.default_float_field_name: "int"}, inplace=True)
-        error = {ct.err_code: 0, ct.err_msg: 'The types of schema and data do not match'}
+        error = {ct.err_code: 1, ct.err_msg: "The name of field don't match, expected: float, got int"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -325,7 +326,9 @@ class TestInsertParams(TestcaseBase):
         df = cf.gen_default_dataframe_data(ct.default_nb)
         new_values = [i for i in range(ct.default_nb)]
         df.insert(3, 'new', new_values)
-        error = {ct.err_code: 0, ct.err_msg: 'The data fields number is not match with schema.'}
+        error = {ct.err_code: 1, ct.err_msg: "The fields don't match with schema fields, "
+                                             "expected: ['int64', 'float', 'varchar', 'float_vector'], "
+                                             "got ['int64', 'float', 'varchar', 'new', 'float_vector']"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -339,7 +342,9 @@ class TestInsertParams(TestcaseBase):
         collection_w = self.init_collection_wrap(name=c_name)
         df = cf.gen_default_dataframe_data(ct.default_nb)
         df.drop(ct.default_float_vec_field_name, axis=1, inplace=True)
-        error = {ct.err_code: 0, ct.err_msg: 'The data fields number is not match with schema.'}
+        error = {ct.err_code: 1, ct.err_msg: "The fields don't match with schema fields, "
+                                             "expected: ['int64', 'float', 'varchar', 'float_vector'], "
+                                             "got ['int64', 'float', 'varchar']"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -486,7 +491,7 @@ class TestInsertOperation(TestcaseBase):
         """
         collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix))
         df = cf.gen_collection_schema_all_datatype
-        error = {ct.err_code: 0, ct.err_msg: "Data type is not support"}
+        error = {ct.err_code: 1, ct.err_msg: "The type of data should be list or pandas.DataFrame"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -528,7 +533,7 @@ class TestInsertOperation(TestcaseBase):
         field_one = cf.gen_int64_field(is_primary=True)
         field_two = cf.gen_int64_field()
         df = [field_one, field_two, vec_field]
-        error = {ct.err_code: 0, ct.err_msg: "Data type is not support."}
+        error = {ct.err_code: 1, ct.err_msg: "data should be a list of list"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L1)
@@ -686,7 +691,7 @@ class TestInsertOperation(TestcaseBase):
         schema = cf.gen_default_collection_schema(auto_id=True)
         collection_w = self.init_collection_wrap(name=c_name, schema=schema)
         df = cf.gen_default_dataframe_data(nb=100)
-        error = {ct.err_code: 0, ct.err_msg: 'Auto_id is True, primary field should not have data'}
+        error = {ct.err_code: 1, ct.err_msg: "Please don't provide data for auto_id primary field: int64"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
         assert collection_w.is_empty
 
@@ -701,7 +706,8 @@ class TestInsertOperation(TestcaseBase):
         schema = cf.gen_default_collection_schema(auto_id=True)
         collection_w = self.init_collection_wrap(name=c_name, schema=schema)
         data = cf.gen_default_list_data(nb=100)
-        error = {ct.err_code: 0, ct.err_msg: 'The data fields number is not match with schema'}
+        error = {ct.err_code: 1, ct.err_msg: "The fields don't match with schema fields, "
+                                             "expected: ['float', 'varchar', 'float_vector'], got ['', '', '', '']"}
         collection_w.insert(data=data, check_task=CheckTasks.err_res, check_items=error)
         assert collection_w.is_empty
 
@@ -1058,7 +1064,7 @@ class TestInsertInvalidBinary(TestcaseBase):
         vec_field, _ = self.field_schema_wrap.init_field_schema(name=ct.default_binary_vec_field_name,
                                                                 dtype=DataType.BINARY_VECTOR)
         df = [field_one, field_two, vec_field]
-        error = {ct.err_code: 0, ct.err_msg: "Data type is not support."}
+        error = {ct.err_code: 1, ct.err_msg: "data should be a list of list"}
         mutation_res, _ = collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L2)
@@ -1132,7 +1138,7 @@ class TestInsertString(TestcaseBase):
         df = cf.gen_default_dataframe_data(nb)
         new_float_value = pd.Series(data=[float(i) for i in range(nb)], dtype="float64")
         df.iloc[:, 2] = new_float_value
-        error = {ct.err_code: 0, ct.err_msg: 'The types of schema and data do not match'}
+        error = {ct.err_code: 1, ct.err_msg: "The data type of field varchar doesn't match, expected: VARCHAR, got DOUBLE"}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L0)
@@ -1146,7 +1152,7 @@ class TestInsertString(TestcaseBase):
         c_name = cf.gen_unique_str(prefix)
         collection_w = self.init_collection_wrap(name=c_name)
         df = [cf.gen_int64_field(), cf.gen_string_field(name=ct.get_invalid_strs), cf.gen_float_vec_field()]
-        error = {ct.err_code: 0, ct.err_msg: 'Data type is not support.'}
+        error = {ct.err_code: 1, ct.err_msg: 'data should be a list of list'}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L0)
@@ -1165,7 +1171,7 @@ class TestInsertString(TestcaseBase):
         field_three = cf.gen_string_field(max_length=nums)
         vec_field = cf.gen_float_vec_field()
         df = [field_one, field_two, field_three, vec_field]
-        error = {ct.err_code: 0, ct.err_msg: 'Data type is not support.'}
+        error = {ct.err_code: 1, ct.err_msg: 'data should be a list of list'}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L1)
@@ -1182,7 +1188,7 @@ class TestInsertString(TestcaseBase):
         int_field = cf.gen_int64_field(is_primary=True)
         vec_field = cf.gen_float_vec_field()
         df = [string_field, int_field, vec_field]
-        error = {ct.err_code: 0, ct.err_msg: 'Data type is not support.'}
+        error = {ct.err_code: 1, ct.err_msg: 'data should be a list of list'}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L1)
@@ -1199,7 +1205,7 @@ class TestInsertString(TestcaseBase):
         vec_field = cf.gen_float_vec_field()
         string_field = cf.gen_string_field(is_primary=True, auto_id=True)
         df = [int_field, string_field, vec_field]
-        error = {ct.err_code: 0, ct.err_msg: 'Data type is not support.'}
+        error = {ct.err_code: 1, ct.err_msg: 'data should be a list of list'}
         collection_w.insert(data=df, check_task=CheckTasks.err_res, check_items=error)
 
     @pytest.mark.tags(CaseLabel.L1)