Add information to the README how to query data

from staging or production database

Author: roschaefer

scripts/README.md

@@ -1,10 +1,15 @@
-# How to use the backup script
+# MongoDB scripts 
+
+This README explains how to directly access the production or staging database
+for backup or query purposes.
+
+## Backup script
 
 The backup script is intended to be used as a cron job or as a single command from your laptop.
 It uses SSH tunneling to a remote host and dumps the mongo database on your machine.
 Therefore, a public SSH key needs to be copied to the remote machine.
 
-# Usage
+### Usage
 
 All parameters must be supplied as environment variables:
 
@@ -28,7 +33,7 @@ After exporting these environment variables to your bash, run:
 ```
 
 
-# Import into your local mongo db (optional)
+### Import into your local mongo db (optional)
 
 Run (but change the file name accordingly):
 ```bash
@@ -40,3 +45,36 @@ If you previously encrypted your dump, run:
 gpg --decrypt human-connection-dump_2018-11-21.archive.gpg | mongorestore --gzip --archive
 ```
 
+
+## Query remote MongoDB
+
+In contrast to the backup script, querying the database is expected to be done
+interactively and on demand by the user. Therefore our suggestion is to use a
+tool like [MongoDB compass](https://www.mongodb.com/products/compass) to query
+the mongo db through an SSH tunnel. This tool can export a collection as .csv
+file and you can further do custom processing with a csv tool like
+[q](https://github.com/harelba/q).
+
+### Suggested workflow
+
+Read on the mongodb compass documentation how to connect to the remote mongo
+database [through SSH](https://docs.mongodb.com/compass/master/connect/). You
+will need all the credentials and a public SSH key on the server as for the
+backup script above.
+
+Once you have a connection, use the MongoDB Compass
+[query bar](https://docs.mongodb.com/compass/master/query-bar/) to query for the
+desired data. You can
+[export the result](https://docs.mongodb.com/compass/master/import-export/) as 
+.json or .csv.
+
+Once you have the .csv file on your machine, use standard SQL queries through
+the command line tool q to further process the data.
+
+For example
+```sh
+q "SELECT email FROM ./invites.csv INTERSECT SELECT email FROM ./emails.csv" -H --delimiter=,
+```
+
+[Q's website](http://harelba.github.io/q/usage.html) explains the usage fairly
+well.