Read Mail.app Database

README.md

If you wanted to dig into your emails and you use the native OSX Mail app, check out these queries.

Mail.app uses sqlite as its datastore, for messages, recipients, etc.

Launch sqlite on the sqlite database.

cd ~/Library/Mail/V5/MailData
sqlite3 "Envelope Index"

Looking around you can run a couple of interesting queries, some interesting ones:

--- Spammy Users in the last 180 days
SELECT address, count(sender) AS cnt FROM messages 
JOIN addresses ON sender = addresses.rowid 
WHERE date_sent > strftime('%s',date('now','start of month','-3 month'))
GROUP BY sender 
ORDER BY cnt DESC 
LIMIT 25;

--- Unique Domains from Known Address
SELECT r.name
     , count(*) AS cnt
FROM (
  SELECT 
    lower(
      substr(address,
      instr(address, '@')
      )
    ) name
  FROM addresses 
) r
GROUP BY r.name
ORDER BY cnt DESC;

mail_etl.py

"""
Plots the date last opened against date received
"""

import pandas as pd

from sqlalchemy import create_engine
from matplotlib import pyplot as plt


engine = create_engine('sqlite:///~/Library/Mail/V5/MailData/Envelope Index')

with engine.connect() as conn, conn.begin():
    msg = pd.read_sql_table('messages', conn)
    adr = pd.read_sql_table('addresses', conn)
    
for x in ['date_sent', 'date_received', 'date_created', 'date_last_viewed']:    
    msg[x.replace('date_', 'dt_')] = pd.to_datetime(msg[x], unit='s')
    
    
# Plot opened emails in the last 180 days
mask = msg['date_sent'] > 1508958377
msg[mask].plot(x='date_received', y='date_last_viewed', kind='scatter', figsize=(10,10))

plt.title(f'Re-opened emails in the last 180 days')
plt.show()

Output.png