docs updated

.gitignore

@@ -26,3 +26,5 @@ user_dbs/*
 tmp2
 chinook.sqlite
 model_data.json
+tst1
+tst1-journal

docs/features.rst

@@ -176,16 +176,6 @@ File Uploads
 
 Upload CSV or JSON files, or SQLite databases to immediately create connections for querying.
 
-The base name of the file and the ID of the uploaded is used as the database name, to prevent collisions from multiple
-users uploading a file with the same name. The base name of the file is also used as the table name (e.g. uploading
-customers.csv results in a database file named customers_1.db, with a table named 'customers').
-
-Of interest, you can also append uploaded files to previously uploaded data sources. For example, if you had a
-'customers.csv' file and an 'orders.csv' file, you could upload customers.csv and create a new data source. You can
-then go back and upload orders.csv with the 'Append' drop-down set to your newly-created customers database, and you
-will have a resulting SQLite database connection with both tables available to be queried together. If you were to
-upload a new 'orders.csv' and append it to customers, the table 'orders' would be *fully replaced* with the new file.
-
 **How it works**
 
 1. Your file is uploaded to the web server. For CSV files, the first row is assumed to be a header.
@@ -194,14 +184,24 @@ upload a new 'orders.csv' and append it to customers, the table 'orders' would b
 4. A customer parser runs type-detection on each column for richer typer information.
 5. The dataframe is coerced to these more accurate types.
 6. The dataframe is written to a SQLite file, which is present on the server, and uploaded to S3.
-7. The SQLite database is added as a new connection to SQL Explorer and is available for querying, just like any
+7. The SQLite database file will be named <filename>_<userid>.db to prevent conflicts if different users uploaded files
+   with the same name.
+8. The SQLite database is added as a new connection to SQL Explorer and is available for querying just like any
    other data source.
-8. If the SQLite file is not available locally, it will be pulled on-demand from S3 when needed.
-9. Local SQLite files are periodically cleaned up by a recurring task after (by default) 7 days of inactivity.
+9. If the SQLite file is not available locally, it will be pulled on-demand from S3 to the app server when needed.
+10. Local SQLite files are periodically cleaned up by a recurring task after (by default) 7 days of inactivity.
 
 Note that if the upload is a SQLite database, steps 2-5 are skipped and the database is simply uploaded to S3 and made
 available for querying.
 
+**Adding tables to uploads**
+
+You can also append uploaded files to previously uploaded data sources. For example, if you had a
+'customers.csv' file and an 'orders.csv' file, you could upload customers.csv and create a new data source. You can
+then go back and upload orders.csv with the 'Append' drop-down set to your newly-created customers database, and you
+will have a resulting SQLite database connection with both tables available to be queried together. If you were to
+upload a new 'orders.csv' and append it to customers, the table 'orders' would be *fully replaced* with the new file.
+
 **File formats**
 
 - Supports well-formed .csv, and .json files. Also supports .json files where each line of the file is a separate json

test_project/settings.py

@@ -105,7 +105,7 @@
 # Tasks disabled by default, but if you have celery installed
 # make sure the broker URL is set correctly
 EXPLORER_TASKS_ENABLED = False
-CELERY_BROKER_URL = os.environ.get('CELERY_BROKER_URL')
+CELERY_BROKER_URL = os.environ.get("CELERY_BROKER_URL")
 
 EXPLORER_S3_BUCKET = os.environ.get("EXPLORER_S3_BUCKET")
 EXPLORER_S3_ACCESS_KEY = os.environ.get("EXPLORER_S3_ACCESS_KEY")