feat: Added FileSystem connection type

README.md

@@ -108,9 +108,15 @@ used to run powerful validations without writing any queries.
 
 There are many occasions where you need to explore a data source while running
 validations. To avoid the need to open and install a new client, the CLI allows
-you to run custom queries. `data-validation query --conn connection-name The
-named connection to be queried. --query, -q The Raw query to run against the
-supplied connection`
+you to run custom queries. 
+
+```
+data-validation query 
+  --conn or -c CONN
+          The connection name to be queried
+  --query or -q QUERY
+          The raw query to run against the supplied connection
+```
 
 ## Query Configurations
 

data_validation/__main__.py

@@ -127,7 +127,11 @@ def build_config_managers_from_args(args):
     target_client = clients.get_data_client(target_conn)
 
     threshold = args.threshold if args.threshold else 0.0
-    tables_list = cli_tools.get_tables_list(args.tables_list, default_value=[])
+
+    is_filesystem = True if source_conn["source_type"] == "FileSystem" else False
+    tables_list = cli_tools.get_tables_list(
+        args.tables_list, default_value=[], is_filesystem=is_filesystem
+    )
 
     for table_obj in tables_list:
         config_manager = ConfigManager.build_config_manager(

data_validation/cli_tools.py

@@ -113,6 +113,11 @@
         ["database_id", "ID of Spanner database (schema) to connect to"],
         ["google_service_account_key_path", "(Optional) GCP SA Key Path"],
     ],
+    "FileSystem": [
+        ["table_name", "Table name to use as reference for file data"],
+        ["file_path", "The local, s3, or GCS file path to the data"],
+        ["file_type", "The file type of the file.'csv' or 'json'"],
+    ],
 }
 
 
@@ -491,11 +496,12 @@ def get_arg_list(arg_value, default_value=None):
     return arg_list
 
 
-def get_tables_list(arg_tables, default_value=None):
+def get_tables_list(arg_tables, default_value=None, is_filesystem=False):
     """ Returns dictionary of tables. Backwards compatible for JSON input.
 
     arg_table (str): tables_list argument specified
     default_value (Any): A default value to supply when arg_value is empty.
+    is_filesystem (boolean): Boolean indicating whether source connection is a FileSystem. In this case, a schema is not required.
     """
     if not arg_tables:
         return default_value
@@ -506,17 +512,22 @@ def get_tables_list(arg_tables, default_value=None):
     except json.decoder.JSONDecodeError:
         tables_list = []
         tables_mapping = list(csv.reader([arg_tables]))[0]
+        source_schema_required = False if is_filesystem else True
 
         for mapping in tables_mapping:
             tables_map = mapping.split("=")
             if len(tables_map) == 1:
-                schema, table = split_table(tables_map)
+                schema, table = split_table(
+                    tables_map, schema_required=source_schema_required
+                )
                 table_dict = {
                     "schema_name": schema,
                     "table_name": table,
                 }
             elif len(tables_map) == 2:
-                src_schema, src_table = split_table([tables_map[0]])
+                src_schema, src_table = split_table(
+                    [tables_map[0]], schema_required=source_schema_required
+                )
 
                 table_dict = {
                     "schema_name": src_schema,

data_validation/clients.py

@@ -214,7 +214,7 @@ def get_data_client(connection_config):
     "Impala": impala_connect,
     "MySQL": MySQLClient,
     "Oracle": OracleClient,
-    "Pandas": get_pandas_client,
+    "FileSystem": get_pandas_client,
     "Postgres": PostgreSQLClient,
     "Redshift": PostgreSQLClient,
     "Teradata": TeradataClient,

data_validation/config_manager.py

@@ -133,7 +133,7 @@ def filters(self):
     @property
     def source_schema(self):
         """Return string value of source schema."""
-        return self._config[consts.CONFIG_SCHEMA_NAME]
+        return self._config.get(consts.CONFIG_SCHEMA_NAME, None)
 
     @property
     def source_table(self):
@@ -143,9 +143,7 @@ def source_table(self):
     @property
     def target_schema(self):
         """Return string value of target schema."""
-        return self._config.get(
-            consts.CONFIG_TARGET_SCHEMA_NAME, self._config[consts.CONFIG_SCHEMA_NAME]
-        )
+        return self._config.get(consts.CONFIG_TARGET_SCHEMA_NAME, self.source_schema)
 
     @property
     def target_table(self):
@@ -283,11 +281,7 @@ def build_config_manager(
             consts.CONFIG_TYPE: config_type,
             consts.CONFIG_SOURCE_CONN: source_conn,
             consts.CONFIG_TARGET_CONN: target_conn,
-            consts.CONFIG_SCHEMA_NAME: table_obj[consts.CONFIG_SCHEMA_NAME],
             consts.CONFIG_TABLE_NAME: table_obj[consts.CONFIG_TABLE_NAME],
-            consts.CONFIG_TARGET_SCHEMA_NAME: table_obj.get(
-                consts.CONFIG_TARGET_SCHEMA_NAME, table_obj[consts.CONFIG_SCHEMA_NAME]
-            ),
             consts.CONFIG_TARGET_TABLE_NAME: table_obj.get(
                 consts.CONFIG_TARGET_TABLE_NAME, table_obj[consts.CONFIG_TABLE_NAME]
             ),
@@ -297,6 +291,16 @@ def build_config_manager(
             consts.CONFIG_FILTERS: filter_config,
         }
 
+        # Only FileSystem connections do not require schemas
+        if source_conn["source_type"] != "FileSystem":
+            config[consts.CONFIG_SCHEMA_NAME] = table_obj[consts.CONFIG_SCHEMA_NAME]
+            config[consts.CONFIG_TARGET_SCHEMA_NAME] = table_obj.get(
+                consts.CONFIG_TARGET_SCHEMA_NAME, table_obj[consts.CONFIG_SCHEMA_NAME]
+            )
+        else:
+            config[consts.CONFIG_SCHEMA_NAME] = None
+            config[consts.CONFIG_TARGET_SCHEMA_NAME] = None
+
         return ConfigManager(config, source_client, target_client, verbose=verbose)
 
     def build_config_grouped_columns(self, grouped_columns):

docs/connections.md

@@ -9,17 +9,17 @@ These commands can be used to create connections:
 
 ## Command template to create a connection:
 ```
-data-validation connections add --connection-name my-conn-name source_type 
+data-validation connections add --connection-name CONN_NAME source_type 
 ```
 
 ## Create a sample BigQuery connection:
 ```
-data-validation connections add --connection-name MY-BQ-CONNECTION BigQuery --project-id MY-GCP-PROJECT
+data-validation connections add --connection-name MY_BQ_CONN BigQuery --project-id MY_GCP_PROJECT
 ```
 
 ## Create a sample Teradata connection:
 ```
-data-validation connections add --connection-name MY-TD-CONNECTION Teradata --host HOST_IP --port PORT --user_name USER_NAME --password PASSWORD
+data-validation connections add --connection-name MY_TD_CONN Teradata --host HOST_IP --port PORT --user_name USER_NAME --password PASSWORD
 ```
 
 ## List existing connections
@@ -42,13 +42,14 @@ The data validation tool supports the following connection types.
 * [Postgres](#postgres)
 * [MySQL](#mysql)
 * [Redshift](#redshift)
+* [FileSystem](#filesystem)
 
 As you see above, Teradata and BigQuery have different sets of custom arguments (for example project_id for BQ versus host for Teradata).
 
 Every connection type requires its own configuration for connectivity. To find out the parameters for each connection type, use the following command.
 
 ```
-data-validation connections add -c '<name>' <connection type> -h
+data-validation connections add -c CONN_NAME <connection type> -h
 ```
 
 Below is the expected configuration for each type.
@@ -91,7 +92,7 @@ Below is the expected configuration for each type.
     # Configuration Required for All Data Soures
     "source_type": "Spanner",
 
-    # GCP Project to use for Spanne
+    # GCP Project to use for Spanner
     "project_id": "my-project-name",
 
     # ID of Spanner instance to connect to
@@ -220,3 +221,19 @@ Then `pip install pyodbc`.
 }
 ```
 
+## FileSystem
+```
+{
+    # Configuration Required for All Data Soures
+    "source_type": "FileSystem",
+
+    # Table name to use as a reference for file data
+    "table_name": "my_table_name",
+
+    # The local, s3, or GCS file path to the data
+    "file_path": "gs://path/to/file",
+
+    # The file type. Either 'csv' or 'json
+    "file_type":"csv"
+}
+```

docs/examples.md

@@ -2,9 +2,9 @@
 This page describes some basic use cases of the tool.
 
 **PLEASE NOTE:** In below commands, my_bq_conn refers to the connection name for your BigQuery project. We are validating BigQuery tables that are
-available in BigQuery public datasets. 
+available in BigQuery public datasets. These examples validate a table agaist itself for example purposes.  
 
-Also, note that if no aggregation flag is provided, the tool will run a 'COUNT *' default aggregation.
+Also, note that if no aggregation flag is provided, the tool will run a 'COUNT *' as the default aggregation.
 
 #### Simple COUNT(*) on a table
 ````shell script
@@ -110,6 +110,17 @@ data-validation run -t GroupedColumn -sc my_bq_conn -tc my_bq_conn -tbls bigquer
 data-validation run -t Column -sc my_bq_conn -tc my_bq_conn -tbls bigquery-public-data.new_york_citibike.citibike_trips --count tripduration,start_station_name -l tag=test-run,owner=name
 ````
 
+#### Run validation on a file
+````shell script
+# Additional dependencies needed for GCS files
+pip install gcsfs
+pip install fsspec
+
+data-validation connections add --connection-name file_conn FileSystem --table-name my_local_file --file-path gs://path/to/file --file-type csv
+
+data-validation run -t Column -sc file_conn -tc file_conn -tbls my_local_file --count name
+````
+
 #### Run custom SQL 
 ````shell script
 data-validation query

tests/unit/test_clients.py

@@ -30,7 +30,7 @@
 SOURCE_TABLE_FILE_PATH = "source_table_data.json"
 JSON_DATA = """[{"col_a":0,"col_b":"a"},{"col_a":1,"col_b":"b"}]"""
 SOURCE_CONN_CONFIG = {
-    "source_type": "Pandas",
+    "source_type": "FileSystem",
     "table_name": "my_table",
     "file_path": SOURCE_TABLE_FILE_PATH,
     "file_type": "json",

tests/unit/test_config_manager.py

@@ -101,6 +101,16 @@ def test_config_property(module_under_test):
     assert config == config_manager._config
 
 
+def test_schema_property(module_under_test):
+    """Test getting schema."""
+    config_manager = module_under_test.ConfigManager(
+        SAMPLE_CONFIG, MockIbisClient(), MockIbisClient(), verbose=False
+    )
+
+    target_schema = config_manager.target_schema
+    assert target_schema == "bigquery-public-data.new_york_citibike"
+
+
 def test_filters_property(module_under_test):
     config_manager = module_under_test.ConfigManager(
         SAMPLE_CONFIG, MockIbisClient(), MockIbisClient(), verbose=False

tests/unit/test_data_validation.py

@@ -26,14 +26,14 @@
 TARGET_TABLE_FILE_PATH = "target_table_data.json"
 
 SOURCE_CONN_CONFIG = {
-    "source_type": "Pandas",
+    "source_type": "FileSystem",
     "table_name": "my_table",
     "file_path": SOURCE_TABLE_FILE_PATH,
     "file_type": "json",
 }
 
 TARGET_CONN_CONFIG = {
-    "source_type": "Pandas",
+    "source_type": "FileSystem",
     "table_name": "my_table",
     "file_path": TARGET_TABLE_FILE_PATH,
     "file_type": "json",

tests/unit/test_schema_validation.py

@@ -23,14 +23,14 @@
 TARGET_TABLE_FILE_PATH = "target_table_data.json"
 
 SOURCE_CONN_CONFIG = {
-    "source_type": "Pandas",
+    "source_type": "FileSystem",
     "table_name": "my_table",
     "file_path": SOURCE_TABLE_FILE_PATH,
     "file_type": "json",
 }
 
 TARGET_CONN_CONFIG = {
-    "source_type": "Pandas",
+    "source_type": "FileSystem",
     "table_name": "my_table",
     "file_path": TARGET_TABLE_FILE_PATH,
     "file_type": "json",