def test_pd_df_load(): dataset = get_dataset() table = "%s.%s" % (dataset, "df") test_df = pd.DataFrame({"num1": [1, 3], "num2": [2, 4]}) create_solid = bq_create_dataset.alias("create_solid") load_solid = import_df_to_bq.alias("load_solid") query_solid = bq_solid_for_queries(["SELECT num1, num2 FROM %s" % table]).alias("query_solid") delete_solid = bq_delete_dataset.alias("delete_solid") @solid( input_defs=[InputDefinition("success", Nothing)], output_defs=[OutputDefinition(DataFrame)] ) def return_df(_context): # pylint: disable=unused-argument return test_df config = { "solids": { "create_solid": {"config": {"dataset": dataset, "exists_ok": True}}, "load_solid": {"config": {"destination": table}}, "delete_solid": {"config": {"dataset": dataset, "delete_contents": True}}, } } @pipeline(mode_defs=bq_modes()) def bq_pipeline(): delete_solid(query_solid(load_solid(return_df(create_solid())))) result = execute_pipeline(bq_pipeline, config) assert result.success values = result.result_for_solid("query_solid").output_value() assert values[0].to_dict() == test_df.to_dict() # BQ loads should throw an exception if pyarrow and fastparquet aren't available with mock.patch.dict(sys.modules, {"pyarrow": None, "fastparquet": None}): with pytest.raises(DagsterExecutionStepExecutionError) as exc_info: result = execute_pipeline(bq_pipeline, config) assert ( "loading data to BigQuery from pandas DataFrames requires either pyarrow or fastparquet" " to be installed" in str(exc_info.value.user_exception) ) cleanup_config = { "solids": {"delete_solid": {"config": {"dataset": dataset, "delete_contents": True}}} } @pipeline(mode_defs=bq_modes()) def cleanup(): delete_solid() assert execute_pipeline(cleanup, cleanup_config).success assert not dataset_exists(dataset)
def test_pd_df_load(): dataset = get_dataset() table = '%s.%s' % (dataset, 'df') test_df = pd.DataFrame({'num1': [1, 3], 'num2': [2, 4]}) create_solid = bq_create_dataset.alias('create_solid') load_solid = import_df_to_bq.alias('load_solid') query_solid = bq_solid_for_queries(['SELECT num1, num2 FROM %s' % table]).alias('query_solid') delete_solid = bq_delete_dataset.alias('delete_solid') @solid( input_defs=[InputDefinition('success', Nothing)], output_defs=[OutputDefinition(DataFrame)] ) def return_df(_context): # pylint: disable=unused-argument return test_df config = { 'solids': { 'create_solid': {'config': {'dataset': dataset, 'exists_ok': True}}, 'load_solid': {'config': {'destination': table}}, 'delete_solid': {'config': {'dataset': dataset, 'delete_contents': True}}, } } @pipeline(mode_defs=bq_modes()) def bq_pipeline(): delete_solid(query_solid(load_solid(return_df(create_solid())))) result = execute_pipeline(bq_pipeline, config) assert result.success values = result.result_for_solid('query_solid').output_value() assert values[0].to_dict() == test_df.to_dict() # BQ loads should throw an exception if pyarrow and fastparquet aren't available with mock.patch.dict(sys.modules, {'pyarrow': None, 'fastparquet': None}): with pytest.raises(DagsterExecutionStepExecutionError) as exc_info: result = execute_pipeline(bq_pipeline, config) assert ( 'loading data to BigQuery from pandas DataFrames requires either pyarrow or fastparquet' ' to be installed' in str(exc_info.value.user_exception) ) cleanup_config = { 'solids': {'delete_solid': {'config': {'dataset': dataset, 'delete_contents': True}}} } @pipeline(mode_defs=bq_modes()) def cleanup(): delete_solid() assert execute_pipeline(cleanup, cleanup_config).success assert not dataset_exists(dataset)
def test_gcs_load(): dataset = get_dataset() table = '%s.%s' % (dataset, 'df') create_solid = bq_create_dataset.alias('create_solid') query_solid = bq_solid_for_queries( [ 'SELECT string_field_0, string_field_1 FROM %s ORDER BY string_field_0 ASC LIMIT 1' % table ] ).alias('query_solid') delete_solid = bq_delete_dataset.alias('delete_solid') @solid( input_defs=[InputDefinition('success', Nothing)], output_defs=[OutputDefinition(List[Path])] ) def return_gcs_uri(_context): # pylint: disable=unused-argument return ["gs://cloud-samples-data/bigquery/us-states/us-states.csv"] config = { 'solids': { 'create_solid': {'config': {'dataset': dataset, 'exists_ok': True}}, 'import_gcs_paths_to_bq': { 'config': { 'destination': table, 'load_job_config': { 'autodetect': True, 'skip_leading_rows': 1, 'source_format': 'CSV', 'write_disposition': 'WRITE_TRUNCATE', }, } }, 'delete_solid': {'config': {'dataset': dataset, 'delete_contents': True}}, } } @pipeline(mode_defs=bq_modes()) def bq_pipeline(): delete_solid(query_solid(import_gcs_paths_to_bq(return_gcs_uri(create_solid())))) result = execute_pipeline(bq_pipeline, config) assert result.success values = result.result_for_solid('query_solid').output_value() assert values[0].to_dict() == {'string_field_0': {0: 'Alabama'}, 'string_field_1': {0: 'AL'}} assert not dataset_exists(dataset)
def test_gcs_load(): dataset = get_dataset() table = "%s.%s" % (dataset, "df") create_solid = bq_create_dataset.alias("create_solid") query_solid = bq_solid_for_queries( [ "SELECT string_field_0, string_field_1 FROM %s ORDER BY string_field_0 ASC LIMIT 1" % table ] ).alias("query_solid") delete_solid = bq_delete_dataset.alias("delete_solid") @solid( input_defs=[InputDefinition("success", Nothing)], output_defs=[OutputDefinition(List[str])] ) def return_gcs_uri(_context): # pylint: disable=unused-argument return ["gs://cloud-samples-data/bigquery/us-states/us-states.csv"] config = { "solids": { "create_solid": {"config": {"dataset": dataset, "exists_ok": True}}, "import_gcs_paths_to_bq": { "config": { "destination": table, "load_job_config": { "autodetect": True, "skip_leading_rows": 1, "source_format": "CSV", "write_disposition": "WRITE_TRUNCATE", }, } }, "delete_solid": {"config": {"dataset": dataset, "delete_contents": True}}, } } @pipeline(mode_defs=bq_modes()) def bq_pipeline(): delete_solid(query_solid(import_gcs_paths_to_bq(return_gcs_uri(create_solid())))) result = execute_pipeline(bq_pipeline, config) assert result.success values = result.result_for_solid("query_solid").output_value() assert values[0].to_dict() == {"string_field_0": {0: "Alabama"}, "string_field_1": {0: "AL"}} assert not dataset_exists(dataset)
def create_pipeline(): bq_create_dataset.alias('create_solid')()
def create_pipeline(): bq_create_dataset.alias("create_solid")()
def test_pd_df_load(): dataset = get_dataset() table = "%s.%s" % (dataset, "df") test_df = pd.DataFrame({"num1": [1, 3], "num2": [2, 4]}) create_op = bq_create_dataset.alias("create_op") load_op = import_df_to_bq.alias("load_op") query_op = bq_op_for_queries(["SELECT num1, num2 FROM %s" % table]).alias("query_op") delete_op = bq_delete_dataset.alias("delete_op") @op(input_defs=[InputDefinition("success", Nothing)], output_defs=[OutputDefinition(DataFrame)]) def return_df(_context): # pylint: disable=unused-argument return test_df @job(resource_defs={"bigquery": bigquery_resource}) def bq_circle_of_life(): delete_op(query_op(load_op(return_df(create_op())))) result = bq_circle_of_life.execute_in_process( run_config={ "ops": { "create_op": {"config": {"dataset": dataset, "exists_ok": True}}, "load_op": {"config": {"destination": table}}, "delete_op": {"config": {"dataset": dataset, "delete_contents": True}}, } } ) assert result.success values = result.output_for_node("query_op") assert values[0].to_dict() == test_df.to_dict() # BQ loads should throw an exception if pyarrow and fastparquet aren't available with mock.patch.dict(sys.modules, {"pyarrow": None, "fastparquet": None}): with pytest.raises(DagsterExecutionStepExecutionError) as exc_info: bq_circle_of_life.execute_in_process( run_config={ "ops": { "create_op": {"config": {"dataset": dataset, "exists_ok": True}}, "load_op": {"config": {"destination": table}}, "delete_op": {"config": {"dataset": dataset, "delete_contents": True}}, } } ) assert ( "loading data to BigQuery from pandas DataFrames requires either pyarrow or fastparquet" " to be installed" in str(exc_info.value.user_exception) ) @job(resource_defs={"bigquery": bigquery_resource}) def cleanup_bq(): delete_op() result = cleanup_bq.execute_in_process( run_config={ "ops": {"delete_op": {"config": {"dataset": dataset, "delete_contents": True}}} } ) assert result.success assert not dataset_exists(dataset)
def create_dataset(): bq_create_dataset.alias("create_op")()