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#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""Summarise Pyzor database.
Generate a summary of the current state of a Pyzor database.
This currently only works with a MySQL (or compatible) database.
This can currently only output to a Slack channel.
There are extra requirements for this script:
* click
* requests
"""
import os
import datetime
import ConfigParser
import MySQLdb
import requests
import click
@click.command()
@click.option("--config", default=None)
@click.argument("hook")
def summarise(config, hook):
"""Generate a summary of a Pyzor database."""
if config is None:
config = os.path.expanduser("~/.pyzor/config")
conf = ConfigParser.ConfigParser()
conf.read(config)
(host, user, password, db_name, table) = conf.get("server", "DigestDB").split(",")
db = MySQLdb.connect(
host=host,
user=user,
db=db_name,
passwd=password,
)
c = db.cursor()
# TODO: With a newer Python, this could use f-strings.
data = {}
c.execute("SELECT COUNT(*) FROM `%s`" % table)
data["total"] = c.fetchone()[0]
c.execute(
"SELECT MIN(wl_entered), MIN(wl_updated), "
"MIN(r_entered), MIN(r_updated), MAX(wl_entered), MAX(wl_updated), "
"MAX(r_entered), MAX(r_updated) from `%s`" % table
)
(
data["oldest_white"],
data["oldest_white_update"],
data["oldest_spam"],
data["oldest_spam_update"],
data["newest_white"],
data["newest_white_update"],
data["newest_spam"],
data["newest_spam_update"],
) = c.fetchone()
c.execute("SELECT MAX(r_count), MAX(wl_count) FROM `%s`" % table)
data["max_spam"], data["max_white"] = c.fetchone()
# Frequency table for counts.
for column in ("r_count", "wl_count"):
buckets = []
for bucket in range(10):
low = bucket * 100
high = (bucket + 1) * 100
c.execute(
"SELECT COUNT(*) FROM `%s` WHERE %s BETWEEN %%s AND %%s"
% (table, column),
(low, high),
)
buckets.append(c.fetchone()[0])
data[column] = buckets
# Frequency table for age.
for column in ("r_updated", "wl_updated"):
buckets = []
for bucket in range(10):
now = datetime.datetime.now()
low = now - datetime.timedelta(days=(bucket + 1) * 7)
high = now - datetime.timedelta(days=bucket * 7)
c.execute(
"SELECT COUNT(*) FROM `%s` WHERE %s BETWEEN %%s AND %%s"
% (table, column),
(low, high),
)
buckets.append(c.fetchone()[0])
data[column] = buckets
data["table"] = table
notify_slack(hook, data)
c.close()
db.close()
# Borrowed from https://raw.githubusercontent.com/kennethreitz/spark.py/master/spark.py
def spark_string(ints, fit_min=False):
"""Returns a spark string from given iterable of ints.
Keyword Arguments:
fit_min: Matches the range of the sparkline to the input integers
rather than the default of zero. Useful for large numbers with
relatively small differences between the positions
"""
ticks = " ▁▂▃▄▅▆▇█"
min_range = min(ints) if fit_min else 0
step_range = max(ints) - min_range
step = (step_range / float(len(ticks) - 1)) or 1
return "".join(ticks[int(round((i - min_range) / step))] for i in ints)
def notify_slack(hook, data):
"""Send a notification containing a summary of a Pyzor database to a
Slack channel."""
text = "Pyzor summary for _%(table)s_ (%(total)s digests)" % data
format = "%d %b %Y"
if data["max_spam"] < 100:
spam_colour = "danger"
else:
spam_colour = "good"
if data["max_white"] < 100:
white_colour = "danger"
else:
white_colour = "good"
if (datetime.datetime.now() - data["newest_spam_update"]).days > 2:
spam_age_colour = "danger"
else:
spam_age_colour = "good"
if (datetime.datetime.now() - data["newest_white_update"]).days > 2:
white_age_colour = "danger"
else:
white_age_colour = "good"
attachments = [
{
"title": "Spam Reports",
"text": spark_string(data["r_count"], fit_min=True),
"fields": [
{
"title": "Most common count",
"value": data["max_spam"],
"short": True,
},
],
"color": spam_colour,
},
{
"title": "Whitelist Reports",
"text": spark_string(data["wl_count"], fit_min=True),
"fields": [
{
"title": "Most common count",
"value": data["max_white"],
"short": True,
},
],
"color": white_colour,
},
{
"title": "Spam Age",
"text": spark_string(data["r_updated"], fit_min=True),
"fields": [
{
"title": "Oldest",
"value": data["oldest_spam"].strftime(format),
"short": True,
},
{
"title": "Oldest Update",
"value": data["oldest_spam_update"].strftime(format),
"short": True,
},
{
"title": "Latest",
"value": data["newest_spam"].strftime(format),
"short": True,
},
{
"title": "Latest Update",
"value": data["newest_spam_update"].strftime(format),
"short": True,
},
],
"color": spam_age_colour,
},
{
"title": "Whitelist Age",
"text": spark_string(data["wl_updated"], fit_min=True),
"fields": [
{
"title": "Oldest",
"value": data["oldest_white"].strftime(format),
"short": True,
},
{
"title": "Oldest Update",
"value": data["oldest_white_update"].strftime(format),
"short": True,
},
{
"title": "Latest",
"value": data["newest_white"].strftime(format),
"short": True,
},
{
"title": "Latest Update",
"value": data["newest_white_update"].strftime(format),
"short": True,
},
],
"color": white_age_colour,
},
]
response = requests.post(hook, json={"text": text, "attachments": attachments})
if __name__ == "__main__":
summarise()
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