Add descriptive message on successful deployment to Composer (#700)

* Add descriptive message on successful deployment to Composer * Add unit-test * Make test error_message more concise
googledatalab · Aug 18, 2018 · db83a9e · db83a9e
1 parent 3bcda7a
commit db83a9e
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Showing 2 changed files with 6 additions and 2 deletions.
diff --git a/google/datalab/bigquery/commands/_bigquery.py b/google/datalab/bigquery/commands/_bigquery.py
@@ -939,7 +939,8 @@ def _pipeline_cell(args, cell_body):
       try:
         airflow = google.datalab.contrib.pipeline.airflow.Airflow(gcs_dag_bucket, gcs_dag_file_path)
         airflow.deploy(name, airflow_spec)
-        error_message += "Pipeline successfully deployed! View Airflow dashboard for more details."
+        error_message += ("Airflow pipeline successfully deployed! View dashboard for more "
+                          "details.\n")
       except AttributeError:
         return "Perhaps you're missing: import google.datalab.contrib.pipeline.airflow"
 
@@ -950,6 +951,8 @@ def _pipeline_cell(args, cell_body):
       try:
         composer = google.datalab.contrib.pipeline.composer.Composer(location, environment)
         composer.deploy(name, airflow_spec)
+        error_message += ("Composer pipeline successfully deployed! View dashboard for more "
+                          "details.\n")
       except AttributeError:
         return "Perhaps you're missing: import google.datalab.contrib.pipeline.composer"
 

diff --git a/tests/bigquery/pipeline_tests.py b/tests/bigquery/pipeline_tests.py
@@ -650,7 +650,8 @@ def test_pipeline_cell_golden(self, mock_bucket_class, mock_get_table, mock_tabl
 
     output = google.datalab.bigquery.commands._bigquery._pipeline_cell(args, cell_body)
 
-    error_message = "Pipeline successfully deployed! View Airflow dashboard for more details."
+    error_message = ("Airflow pipeline successfully deployed! View dashboard for more details.\n"
+                     "Composer pipeline successfully deployed! View dashboard for more details.\n")
     airflow_spec_pattern = """
 import datetime
 from airflow import DAG