Exploring subdomains in the whole of gov.au

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Most of the notebooks in this repository work with small slices of web archive data. In this notebook we'll scale things up a bit to try and find all of the subdomains that have existed in the gov.au domain. As in other notebooks, we'll obtain the data by querying the Internet Archive's CDX API. The only real difference is that it will take some hours to harvest all the data.

All we're interested in this time are unique domain names, so to minimise the amount of data we'll be harvesting we can make use of the CDX API's collapse parameter. By setting collapse=urlkey we can tell the CDX API to drop records with duplicate urlkey values – this should mean we only get one capture per page. However, this only works if the capture records are in adjacent rows, so there probably will still be some duplicates. We'll also use the fl to limit the fields returned, and the filter parameter to limit results by statuscode and mimetype. So the parameters we'll use are:

  • url=*.gov.au – all of the pages in all of the subdomains under gov.au
  • collapse=urlkey – as few captures per page as possible
  • filter=statuscode:200,mimetype:text/html – only successful captures of HTML pages
  • fl=urlkey,timestamp,original – only these fields

Even with these limits, the query will retrieve a LOT of data. To make the harvesting process easier to manage and more robust, I'm going to make use of the requests-cache module. This will capture the results of all requests, so that if things get interrupted and we have to restart, we can retrieve already harvested requests from the cache without downloading them again. We'll also write the harvested results directly to disk rather than consuming all our computer's memory. The file format will be the NDJSON (Newline Delineated JSON) format – because each line is a separate JSON object we can just write it a line at a time as the data is received.

For a general approach to harvesting domain-level information from the IA CDX API see Harvesting data about a domain using the IA CDX API

In [1]:
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from tqdm.auto import tqdm
import pandas as pd
import time
from requests_cache import CachedSession
import ndjson
from pathlib import Path
from slugify import slugify
import arrow
import json
import re
from newick import Node
import newick
from ete3 import Tree, TreeStyle
import ipywidgets as widgets
from IPython.display import display, HTML, FileLink

s = CachedSession()
retries = Retry(total=10, backoff_factor=1, status_forcelist=[ 502, 503, 504 ])
s.mount('https://', HTTPAdapter(max_retries=retries))
s.mount('http://', HTTPAdapter(max_retries=retries))
In [2]:
domain = 'gov.au'
In [3]:
def get_total_pages(params):
    Gets the total number of pages in a set of results.
    these_params = params.copy()
    these_params['showNumPages'] = 'true'
    response = s.get('http://web.archive.org/cdx/search/cdx', params=these_params, headers={'User-Agent': ''})
    return int(response.text)

def prepare_params(url, **kwargs):
    Prepare the parameters for a CDX API requests.
    Adds all supplied keyword arguments as parameters (changing from_ to from).
    Adds in a few necessary parameters.
    params = kwargs
    params['url'] = url
    params['output'] = 'json'
    # CDX accepts a 'from' parameter, but this is a reserved word in Python
    # Use 'from_' to pass the value to the function & here we'll change it back to 'from'.
    if 'from_' in params:
        params['from'] = params['from_']
    return params

def get_cdx_data(params):
    Make a request to the CDX API using the supplied parameters.
    Check the results for a resumption key, and return the key (if any) and the results.
    response = s.get('http://web.archive.org/cdx/search/cdx', params=params, headers={'User-Agent': ''})
    results = response.json()
    if not response.from_cache:
    return results

def convert_lists_to_dicts(results):
    if results:
        keys = results[0]
        results_as_dicts = [dict(zip(keys, v)) for v in results[1:]]
        results_as_dicts = results
    return results_as_dicts

def get_cdx_data_by_page(url, **kwargs):
    page = 0
    params = prepare_params(url, **kwargs)
    total_pages = get_total_pages(params)
    # We'll use a timestamp to distinguish between versions
    timestamp = arrow.now().format('YYYYMMDDHHmmss')
    file_path = Path(f'{slugify(domain)}-cdx-data-{timestamp}.ndjson')
    # Remove any old versions of the data file
    except FileNotFoundError:
    with tqdm(total=total_pages-page) as pbar1:
        with tqdm() as pbar2:
            while page < total_pages:
                params['page'] = page
                results = get_cdx_data(params)
                with file_path.open('a') as f:
                    writer = ndjson.writer(f, ensure_ascii=False)
                    for result in convert_lists_to_dicts(results):
                page += 1
                pbar2.update(len(results) - 1)
In [ ]:
# Note than harvesting a domain has the same number of pages (ie requests) no matter what filters are applied -- it's just that some pages will be empty.
# So repeating a domain harvest with different filters will mean less data, but the same number of requests.
# What's most efficient? I dunno.
get_cdx_data_by_page(f'*.{domain}', filter=['statuscode:200', 'mimetype:text/html'], collapse='urlkey', fl='urlkey,timestamp,original', pageSize=5)

Process the harvested data

After many hours, and many interruptions, the harvesting process finally finished. I ended up with a 65gb ndjson file. How many captures does it include?

In [4]:
count = 0
with open('gov-au-cdx-data.ndjson') as f:
    for line in f:
        count += 1
CPU times: user 1min 5s, sys: 27.8 s, total: 1min 33s
Wall time: 2min 26s

Find unique domains

Now let's get extract a list of unique domains from all of those page captures. In the code below we extract domains from the urlkey and add them to a list. After every 100,000 lines, we use set to remove duplicates from the list. This is an attempt to find a reasonable balance between speed and memory consumption.

In [5]:
# This is slow, but will avoid eating up memory
domains = []
with open('gov-au-cdx-data.ndjson') as f:
    count = 0
    with tqdm() as pbar:
        for line in f:
            capture = json.loads(line)
            # Split the urlkey on ) to separate domain from path
            domain = capture['urlkey'].split(')')[0]
            # Remove port numbers
            domain = re.sub(r'\:\d+', '', domain)
            count += 1
            # Remove duplicates after every 100,000 lines to conserve memory
            if count > 100000:
                domains = list(set(domains))
                count = 0
domains = list(set(domains))
CPU times: user 14min 58s, sys: 38.7 s, total: 15min 37s
Wall time: 15min 45s

How many unique domains are there?

In [6]:
In [7]:
df = pd.DataFrame(domains, columns=['urlkey'])
0 au,gov,consumersonline,maggie
1 au,gov,nsw,leeton,libero
2 au,gov,nsw,schools,burrenjunc-p
3 au,gov,nsw,schools,bringelly-p
4 au,gov,testcensus,stream0

Save the list of domains to a CSV file to save us having to extract them again.

In [8]:
df.to_csv('domains/gov-au-unique-domains.csv', index=False)

Reload the list of domains from the CSV if necessary.

In [4]:
domains = pd.read_csv('domains/gov-au-unique-domains.csv')['urlkey'].to_list()

Number of unique urls per subdomain

Now that we have a list of unique domains we can use this to generate a count of unique urls per subdomain. This won't be exact. As noted previously, even with collapse set to urlkey there are likely to be duplicate urls. Getting rid of all the duplicates in such a large file would require a fair bit of processing, and I'm not sure it's worth it at this point. We really just want a sense of how subdomains are actually used.

In [5]:
# Create a dictionary with the domains as keys and the values set to zero
domain_counts = dict(zip(domains, [0] * len(domains)))
In [ ]:
# As above we'll go though the file line by line
# but this time we'll extract the domain and increment the corresponding value in the dict.
with open('gov-au-cdx-data.ndjson') as f:
    count = 0
    with tqdm() as pbar:
        for line in f:
            capture = json.loads(line)
            # Split the urlkey on ) to separate domain from path
            domain = capture['urlkey'].split(')')[0]
            domain = re.sub(r'\:\d+', '', domain)
            # Increment domain count
            domain_counts[domain] += 1
            count += 1
            # This is just to update the progress bar
            if count > 100000:
                count = 0

Convert to a dataframe

We'll now convert the data to a dataframe and do a bit more processing.

In [7]:
# Reshape dict as a list of dicts
domain_counts_as_list = [{'urlkey': k, 'number_of_pages': v} for k, v in domain_counts.items()]

# Convert to dataframe
df_counts = pd.DataFrame(domain_counts_as_list)
urlkey number_of_pages
0 au,gov,consumersonline,maggie 144
1 au,gov,nsw,leeton,libero 1
2 au,gov,nsw,schools,burrenjunc-p 187
3 au,gov,nsw,schools,bringelly-p 197
4 au,gov,testcensus,stream0 9

Now we're going to split the urlkey into its separate subdomains.

In [8]:
# Split the urlkey on commas into separate columns -- this creates a new df
df_split = df_counts['urlkey'].str.split(',', expand=True)

# Merge the new df back with the original so we have both the urlkey and it's components
df_merged = pd.merge(df_counts, df_split, left_index=True, right_index=True)

Finally, we'll stich the subdomains back together in a traditional domain format just for readability.

In [9]:
def join_domain(x):
    parts = x.split(',')
    return '.'.join(parts)

df_merged['domain'] = df_merged['urlkey'].apply(join_domain)
urlkey number_of_pages 0 1 2 3 4 5 6 7 8 9 domain
0 au,gov,consumersonline,maggie 144 au gov consumersonline maggie None None None None None None maggie.consumersonline.gov.au
1 au,gov,nsw,leeton,libero 1 au gov nsw leeton libero None None None None None libero.leeton.nsw.gov.au
2 au,gov,nsw,schools,burrenjunc-p 187 au gov nsw schools burrenjunc-p None None None None None burrenjunc-p.schools.nsw.gov.au
3 au,gov,nsw,schools,bringelly-p 197 au gov nsw schools bringelly-p None None None None None bringelly-p.schools.nsw.gov.au
4 au,gov,testcensus,stream0 9 au gov testcensus stream0 None None None None None None stream0.testcensus.gov.au
In [10]:

Let's count things!

How many third level domains are there?

In [11]:

Which third level domains have the most subdomains?

In [12]:
nsw         7477
vic         3418
qld         2772
wa          2690
sa          1719
tas          957
nt           752
act          362
embassy      151
nla          138
govspace     111
deewr         77
ga            75
treasury      74
ato           73
health        73
dest          69
abs           61
govcms        60
bom           59
Name: 2, dtype: int64

Which domains have the most unique pages?

In [13]:
top_20 = df_merged[['domain', 'number_of_pages']].sort_values(by='number_of_pages', ascending=False)[:20]
top_20.style.format({'number_of_pages': '{:,}'})
domain number_of_pages
3487 trove.nla.gov.au 9,285,603
6159 nla.gov.au 2,592,182
8705 collectionsearch.nma.gov.au 2,422,514
8719 passwordreset.parliament.qld.gov.au 2,089,256
22057 parlinfo.aph.gov.au 1,882,646
23439 aph.gov.au 1,731,559
12100 bmcc.nsw.gov.au 1,414,711
4375 jobsearch.gov.au 1,293,760
17414 arpansa.gov.au 1,278,603
19663 abs.gov.au 961,526
15325 libero.gtcc.nsw.gov.au 959,490
15308 canterbury.nsw.gov.au 956,500
12439 library.campbelltown.nsw.gov.au 932,933
16309 defencejobs.gov.au 894,770
5804 webopac.gosford.nsw.gov.au 854,395
25031 library.lachlan.nsw.gov.au 838,972
22341 library.shoalhaven.nsw.gov.au 800,541
12284 catalogue.nla.gov.au 787,616
5886 library.bankstown.nsw.gov.au 767,550
1963 myagedcare.gov.au 759,384

Are there really domains made up of 10 levels?

In [14]:

Let's visualise things!

I thought it would be interesting to try and visualise all the subdomains as a circular dendrogram. After a bit of investigation I discovered the ETE Toolkit for the visualisation of phylogenetic trees – it seemed perfect. But to get data into ETE I first had to convert it into a Newick formatted string. Fortunately, there's a Python package for that.

Warning! While the code below will indeed generate circular dendrograms from a domain name hierarchy, if you have more than a few hundred domains you'll find that the image gets very big, very quickly. I successfully saved the whole of the gov.au domain as a 32mb SVG file, which you can (very slowly) view in a web browser or graphics program. But any attempt to save into another image format at a size that would make the text readable consumed huge amounts of memory and forced me to pull the plug.

In [15]:
def make_domain_tree(domains):
    Converts a list of urlkeys into a Newick tree via nodes.
    d_tree = Node()
    for domain in domains:
        domain = re.sub(r'\:\d+', '', domain)
        sds = domain.split(',')
        for i, sd in enumerate(sds):
            parent = '.'.join(reversed(sds[0:i])) if i > 0 else None
            label = '.'.join(reversed(sds[:i+1]))
            if not d_tree.get_node(label):
                if parent:
    return newick.dumps(d_tree)
In [16]:
# Convert domains to a Newick tree
full_tree = make_domain_tree(domains)
In [17]:
def save_dendrogram_to_file(tree, width, output_file):
    t = Tree(tree, format=1)
    circular_style = TreeStyle()
    circular_style.mode = "c" # draw tree in circular mode
    circular_style.optimal_scale_level = 'full'
    circular_style.root_opening_factor = 0
    circular_style.show_scale = False
    t.render(output_file, w=width, tree_style=circular_style)

First let's play safe by creating a PNG with a fixed width.

In [18]:
# Saving a PNG with a fixed width will work, but you won't be able to read any text
save_dendrogram_to_file(full_tree, 5000, 'images/govau-all-5000.png')

Here's the result!

Circular dendrogram of all gov.au domains

This will save a zoomable SVG version that allows you to read the labels, but it will be very slow to use, and difficult to convert into other formats.

In [19]:
# Here be dendrodragons!
# I don't think width does anything if you save to SVG
save_dendrogram_to_file(full_tree, 5000, 'govau-all.svg')

Let's try some third level domains.

In [20]:
def display_dendrogram(label, level=2, df=df_merged, width=300):
    domains = df.loc[df[2] == label]['urlkey'].to_list()
    tree = make_domain_tree(domains)
    save_dendrogram_to_file(tree, width, f'images/{label}-domains-{width}.png')
    return f'<div style="width: 300px; float: left; margin-right: 10px;"><img src="images/{label}-domains-{width}.png" style=""><p style="text-align: center;">{label.upper()}</p></div>'
In [21]:
# Create dendrograms for each state/territory
html = ''
for state in ['nsw', 'vic', 'qld', 'sa', 'wa', 'tas', 'nt', 'act']:
    html += display_dendrogram(state)










If there are fewer domains you can see more detail.

In [22]:
act = display_dendrogram(state, width=8000)


Created by Tim Sherratt for the GLAM Workbench. Support me by becoming a GitHub sponsor!

Work on this notebook was supported by the IIPC Discretionary Funding Programme 2019-2020