Merge pull request #30 from CurtLH/update_cli

Update CLI

prism/cli.py

@@ -34,7 +34,7 @@ def main(ctx, base_url, tenant_name, client_id, client_secret, refresh_token):
     """CLI for interacting with Workday’s Prism API"""
 
     # initialize the prism class with your credentials
-    p = prism.Prism(base_url, tenant_name, client_id, client_secret, refresh_token)
+    p = prism.Prism(base_url, tenant_name, client_id, client_secret, refresh_token, version="v2")
 
     # create the bearer token
     p.create_bearer_token()
@@ -44,64 +44,89 @@ def main(ctx, base_url, tenant_name, client_id, client_secret, refresh_token):
 
 
 @main.command()
-@click.option("--id", default=None, type=str, help="The ID of the dataset to obtain details about")
+@click.option("--name", default=None, type=str, help="The name of the table to obtain details about")
 @click.pass_context
-def list(ctx, id):
-    """List all datasets of type API"""
+def list(ctx, name):
+    """List all tables of type API"""
 
     # get the initialized prism class
     p = ctx.obj["p"]
 
-    # list the datasets
-    status = p.list_dataset(dataset_id=id)
+    # list the tables
+    status = p.list_table(table_name=name)
 
     # print message
     if id is None:
-        click.echo("There are {} API datasets".format(status["total"]))
+        click.echo("There are {} API tables".format(status["total"]))
         click.echo(json.dumps(status["data"], indent=2, sort_keys=True))
     else:
         click.echo(json.dumps(status, indent=2, sort_keys=True))
 
 
 @main.command()
+@click.argument("table_name", type=str)
+@click.argument("schema_path", type=click.Path())
+@click.pass_context
+def create(ctx, table_name, schema_path):
+    """Create a new Prism table TABLE_NAME with schema from SCHEMA_PATH
+
+    Example: prism create my_table /home/data/schema.json
+    """
+
+    # get the initialized prism class
+    p = ctx.obj["p"]
+
+    # read in your table schema
+    schema = prism.load_schema(schema_path)
+
+    # clean up the table name
+    table_name = table_name.replace(" ", "_")
+
+    # create an empty API table
+    table = p.create_table(table_name, schema=schema["fields"])
+
+    # print message
+    click.echo(json.dumps(table, indent=2, sort_keys=True))
+
+
+@main.command()
+@click.argument("gzip_file", type=click.Path())
+@click.argument("table_id", type=str)
 @click.option(
-    "--dataset_name",
-    type=str,
-    required=True,
-    help="The dataset name. The name must be unique and conform to the name validation rules",
+    "--operation",
+    type=click.Choice(["TruncateandInsert", "Insert", "Update", "Upsert", "Delete"]),
+    default="TruncateandInsert",
+    help="The Table load operation",
 )
-@click.option("--schema_path", type=click.Path(), required=True, help="The path to your schema file")
-@click.option("--data_path", type=click.Path(), required=True, help="The path to your gzip compressed data file")
 @click.pass_context
-def upload(ctx, dataset_name, schema_path, data_path):
-    """Upload a gzip CSV file"""
+def upload(ctx, gzip_file, table_id, operation):
+    """Upload GZIP_FILE to TABLE_ID
+
+    Example: prism upload /home/data/file.csv.gz bbab30e3018b01a723524ce18010811b
+    """
 
     # get the initialized prism class
     p = ctx.obj["p"]
 
-    # clean up the dataset name
-    dataset_name = dataset_name.replace(" ", "_")
+    # get the details about the newly created table
+    details = p.describe_table(table_id)
 
-    # create an empty API dataset
-    dataset = p.create_dataset(dataset_name)
-
-    # read in your dataset schema
-    schema = prism.load_schema(schema_path)
+    # convert the details to a bucket schema
+    bucket_schema = p.convert_describe_schema_to_bucket_schema(details)
 
     # create a new bucket to hold your file
-    bucket = p.create_bucket(schema, dataset["id"])
+    bucket = p.create_bucket(bucket_schema, table_id, operation=operation)
 
-    # add your file the bucket you just created
-    p.upload_file_to_bucket(bucket["id"], data_path)
+    # add your file to the bucket you just created
+    p.upload_file_to_bucket(bucket["id"], gzip_file)
 
     # complete the bucket and upload your file
     p.complete_bucket(bucket["id"])
 
-    # check the status of the dataset you just created
-    status = p.list_dataset(dataset["id"])
+    # check the status of the table you just created
+    status = p.list_table(table_id)
 
     # print message
-    click.echo("{} has successfully uploaded".format(dataset_name))
     click.echo(json.dumps(status["data"], indent=2, sort_keys=True))