Blog::Sniffer

It allows you to fetch information from all engineering blog posts from this list and save it in a database for further analysis.

Why?

I was just building a small fuzzy finder to get urls from blog content to easily access content while I'm learning about Timescale.

I'd like to have a fuzzy finder to easy find posts or docs from timescale. Validating if all internal links are working and so on...

Then, I thought...

I'm getting more deep on technical writing and my objective is better understand the industry language and the jargons. I'd like to play with this data and have fun with statistics related to text writing.
I'm also very interested in data science and I'd like to explore all the data in different ways.
I'm benchmarking the smallest instance that forge can offer 😉.

Installation

It's not a rubygem yet, so you need to clone and install locally:

git clone [email protected]:jonatas/blog-sniffer.git
cd blog-sniffer
bundle install

Setup your micro instance on Timescale for free. Then you can configure your PG_URI through your favorite manner to have access to the environment variable.

export PG_URI="postgres://<user>:<password>@<host>:<port>/tsdb?sslmode=require"

I try to use direnv make my life easier and safe. So, I can drop my secrets into a .envrc and it will be available only in this directory:

echo 'export PG_URI="postgres://<user>:<password>@<host>:<port>/tsdb?sslmode=require"' >> .envrc
direnv allow

Then you can use bin/blog-sniffer *<urls> to start crawling or simply take over the world!

bin/blog-sniffer

It will sniff all websites over lib/blog/target_sites that is fetching everything from this repo. To run over all repo list it took ~19 hours to me. Around 200k pages.

You can open and run parallel process and it will keep expanding and fetching more URLs. The modem of my house got stuck after hours parallelizing 10 process, but it uses a low band and memory.

A few tabs in parallel is enough. In 10+ hours I got 32k pages downloaded.

So, let's explain this journey using SQL, so, you can better understand the results.

State of art

I downloaded 228849 pages in 19 hours from my home :)

 SELECT MIN(time) AS start,
   MAX(time) AS end,
   COUNT(1) AS total_pages,
   EXTRACT(EPOCH FROM(MAX(time) - MIN(time)) / 3600)::INTEGER AS hours_of_adventure
   FROM pages;
            start             |              end              | total_pages | hours_of_adventure
------------------------------+-------------------------------+-------------+--------------------
 2021-05-05 19:05:43.54879+00 | 2021-05-06 14:05:51.156821+00 |      228849 |                 19

But, I haven't worked all the day and have paused it for several hours, so, let's have a look on the total pages downloaded per hour.

I'll use time_bucket function.

 SELECT time_bucket('1 hour', time) AS hour,
   COUNT(1) AS total_pages,
   pg_size_pretty(SUM(html_size)) AS bandwidth
   FROM pages
   GROUP BY 1;
          hour          | total_pages | bandwidth
------------------------+-------------+-----------
 2021-05-05 19:00:00+00 |        8154 | 1083 MB
 2021-05-05 20:00:00+00 |         890 | 154 MB
 2021-05-05 21:00:00+00 |        7818 | 1039 MB
 2021-05-05 22:00:00+00 |           8 | 164 kB
 2021-05-06 00:00:00+00 |         340 | 19 MB
 2021-05-06 01:00:00+00 |         500 | 26 MB
 2021-05-06 02:00:00+00 |        1828 | 358 MB
 2021-05-06 03:00:00+00 |        8773 | 808 MB
 2021-05-06 04:00:00+00 |       15926 | 1971 MB
 2021-05-06 05:00:00+00 |       18978 | 2543 MB
 2021-05-06 06:00:00+00 |       19905 | 2485 MB
 2021-05-06 07:00:00+00 |       22292 | 3111 MB
 2021-05-06 08:00:00+00 |       22141 | 3429 MB
 2021-05-06 09:00:00+00 |       21836 | 1962 MB
 2021-05-06 10:00:00+00 |       18465 | 2026 MB
 2021-05-06 11:00:00+00 |       24662 | 1549 MB
 2021-05-06 12:00:00+00 |       19350 | 1935 MB
 2021-05-06 13:00:00+00 |       16653 | 1722 MB
 2021-05-06 14:00:00+00 |         330 | 12 MB

If you clicked in the link before, maybe you would like to get to know more about time_bucket and build an amazing query for it:

SELECT url FROM pages WHERE title ~ 'time_bucket';
                                               url
--------------------------------------------------------------------------------------------------
 https://docs.timescale.com/api/latest/analytics/time_bucket/
 https://docs.timescale.com/api/latest/analytics/time_bucket_gapfill/
 https://blog.timescale.com/blog/simplified-time-series-analytics-using-the-time_bucket-function/
(3 rows)

Where the information comes from?

Counting pages per host. Extracting domain would be a bit more complicated, but here we can have a good overview of the most richful sources.

SELECT SPLIT_PART(url,'/',3) as host, COUNT(1) FROM pages GROUP BY 1 ORDER BY 2 DESC LIMIT 10;
       host              | count
-------------------------+-------
 medium.com              | 22132
 tech.lendinghome.com    |  2000
 www.stackabuse.com      |  1998
 blog.codinghorror.com   |  1992
 www.drivenbycode.com    |  1989
 engblog.nextdoor.com    |  1988
 sitepoint.com           |  1986
 snyk.io                 |  1942
 engineroom.teamwork.com |  1859
 blog.fedecarg.com       |  1842
(10 rows)

Exploring dynamic content

What if I reload the page and the title of the website changed? Let's discover who is doing that:

SELECT url, ARRAY_AGG(title)
FROM pages
GROUP BY 1 HAVING COUNT(DISTINCT title) > 1
ORDER BY COUNT(DISTINCT title) DESC LIMIT 10;
                     url                      |                                                        array_agg
----------------------------------------------+-------------------------------------------------------------------------------------------------------------------------
 http://blog.honeybadger.io/                  | {"Honeybadger Developer Blog","Exception and Uptime Monitoring for Application Developers - Honeybadger"}
 http://blog.mandrill.com/                    | {"All-In-One Integrated Marketing Platform for Small Business","What is Transactional Email?"}
 http://devblog.coolblue.nl/                  | {"DEV Community 👩‍💻👨‍💻","Coolblue - DEV Community"}
 http://facebook.github.io/react-native/blog/ | {"React Native · Learn once, write anywhere","Blog · React Native"}
 http://jakewharton.com/blog                  | {"Jake Wharton","Posts - Jake Wharton"}
 http://jlongster.com/archive                 | {"James Long","All Posts"}
 http://multithreaded.stitchfix.com/blog/     | {"Stitch Fix Technology – Multithreaded","Blog | Stitch Fix Technology – Multithreaded"}
 http://rocksdb.org/blog                      | {"GitHub Documentation","Blog | RocksDB"}
 http://www.boxever.com/blog/                 | {"Boxever Digital Optimisation Platform","Boxever Insights","Boxever Digital Optimisation Platform","Boxever Insights"}
 http://blog.faraday.io/                      | {"AI for B2C growth | Faraday AI","The Faraday Blog"}
(10 rows)

Funny, right? 😄

Getting familiar with the Postgresql Text Search Controls

Getting a ranked titles using text search controls.

Let's start with to_tsvector:

SELECT title, to_tsvector(title) FROM pages WHERE url ~ 'hypertable' LIMIT 5;
                  title                   |                  to_tsvector
------------------------------------------+-----------------------------------------------
 Distributed Hypertables | Timescale Docs | 'distribut':1 'doc':4 'hypert':2 'timescal':3
 ALTER | Timescale Docs                   | 'alter':1 'doc':3 'timescal':2
 DROP | Timescale Docs                    | 'doc':3 'drop':1 'timescal':2
 CREATE | Timescale Docs                  | 'creat':1 'doc':3 'timescal':2
 Hypertables & Chunks | Timescale Docs    | 'chunk':2 'doc':4 'hypert':1 'timescal':3
(5 rows)

Now, let's use to_tsquery with @@ operator to combine queries over vectors:

SELECT title, ts_rank_cd(to_tsvector(title), query) AS rank
FROM pages, to_tsquery('petabyte+scale') query
WHERE query @@ to_tsvector(title)
ORDER BY rank DESC
LIMIT 10;

Now let's wrap it on a function, that was one of my objectives:

CREATE TYPE ranked_post AS (title text, url text, rank real);

CREATE FUNCTION get_ranked_posts(text, integer default 5) RETURNS setof ranked_post
  AS $$
    SELECT title, url, ts_rank_cd(to_tsvector(title), query) AS rank
    FROM pages, to_tsquery($1) query
    WHERE query @@ to_tsvector(title)
    ORDER BY rank DESC
    LIMIT $2;
$$
LANGUAGE SQL;

Trying it:

select rank, title from get_ranked_posts('postgresql+scaling');
 rank |                   title
------+--------------------------------------------
  0.1 | How to Scale PostgreSQL 10
 0.05 | Upgrading PostgreSQL At Scale - 5 min read
 ...

Testing a different subject with another limit:

select rank, url from get_ranked_posts('Analytical+Platform',7) ;
 rank |                                                             url
------+------------------------------------------------------------------------------------------------------------------------------
  0.1 | https://eng.uber.com/logging/#respond
  0.1 | https://blog.timescale.com/blog/promscale-analytical-platform-long-term-store-for-prometheus-combined-sql-promql-postgresql/
  0.1 | https://blogs.nvidia.com/blog/2020/06/24/apache-spark-gpu-acceleration/?nv_excludes=45965,45983
  0.1 | https://blog.timescale.com/blog/promscale-analytical-platform-long-term-store-for-prometheus-combined-sql-promql-postgresql/
  0.1 | https://eng.uber.com/logging/
  0.1 | https://eng.uber.com/athenax/
  0.1 | https://blog.cloudera.com/why-an-integrated-analytics-platform-is-the-right-choice/
(7 rows)

Now, achieving one of my challenges to get the top posts I want from Timescale blog or docs:

CREATE OR REPLACE FUNCTION get_ts_url_for(text, integer default 5) RETURNS setof ranked_post
  AS $$
    SELECT title, url,
    ts_rank_cd(to_tsvector(title), query) + ts_rank_cd(to_tsvector(url), query) AS rank
    FROM pages, plainto_tsquery('english', $1) query
    WHERE url ~ '^https://(blog|docs).timescale.com'
      AND (query @@ to_tsvector(title) OR query @@ to_tsvector(url))
    ORDER BY rank DESC
    LIMIT $2;
$$
LANGUAGE SQL;

Running it, the timing is not very satisfatory:

\timing
tsdb=> select distinct url from get_ts_url_for('hypertable', 10);
...
Time: 2200.911 ms (00:02.201)

Let's create a materialized view to only get timescale content preprocessing the vector we're going to use for the search and limiting the scope to the domain we want:

DROP MATERIALIZED VIEW timescale_content;
 CREATE MATERIALIZED VIEW timescale_content AS
    SELECT title, url,
      to_tsvector(regexp_replace(url, '[^\w]+', ' ', 'gi') || title) AS search_vector
    FROM pages
    WHERE url ~ '^https://(blog|docs).timescale.com';
SELECT 960
Time: 2817.970 ms (00:02.818)

Now, let's rewrite the get_ts_url_for function to use the previous view that will limit the search and use pre-processed ts_vector combining url, title and headers.

CREATE OR REPLACE FUNCTION get_ts_url_for(text, integer default 5) RETURNS setof ranked_post
  AS $$
    SELECT title, url,
    ts_rank_cd(search_vector, query) AS rank
    FROM timescale_content, plainto_tsquery('english', $1) query
    WHERE query @@ search_vector
    ORDER BY rank DESC
    LIMIT $2;
$$
LANGUAGE SQL;
CREATE FUNCTION
Time: 388.378 ms

Now, let's see the timing for the same query:

select distinct url from get_ts_url_for('hypertable', 10);        https://docs.timescale.com/timescaledb/latest/overview/release-notes/changes-in-timescaledb-2/#caggs
... 
Time: 297.890 ms

7 times faster 🎉

And I use fish shell, so, I'll write a short script to easily get it from the command line:

set -gx docs_uri "postgres://<user>:<pass>@<host>:<port>/<dbname>?sslmode=require"

function ts_url --description "Get URL searching on timescale docs"
  set query "select distinct url from get_ts_url_for('$argv', 10);"
  psql $docs_uri -c "$query"
end

And, loading it in my fish sources, I can use:

ts_url hypertable
                                              url
-----------------------------------------------------------------------------------------------
 https://docs.timescale.com/timescaledb/latest/overview/core-concepts/distributed-hypertables/
 https://docs.timescale.com/timescaledb/latest/getting-started/create-hypertable/
 https://docs.timescale.com/timescaledb/latest/how-to-guides/schema-management/alter/
 https://docs.timescale.com/timescaledb/latest/how-to-guides/hypertables/
 https://docs.timescale.com/api/latest/distributed-hypertables/create_distributed_hypertable/
 https://docs.timescale.com/timescaledb/latest/overview/core-concepts/hypertables-and-chunks/
 https://docs.timescale.com/api/latest/hypertable/
 https://docs.timescale.com/api/latest/hypertable/hypertable_size/
 https://docs.timescale.com/timescaledb/latest/how-to-guides/distributed-hypertables/
 https://docs.timescale.com/api/latest/hypertable/create_hypertable/

Now, let's explore a bit of the power of the ts search:

ts_url hypertable drop
                                            url
-------------------------------------------------------------------------------------------
 https://docs.timescale.com/timescaledb/latest/how-to-guides/hypertables/drop/
 https://docs.timescale.com/timescaledb/latest/how-to-guides/distributed-hypertables/drop/
 https://docs.timescale.com/api/latest/hypertable/drop_chunks/
(3 rows)

Let's get deep and select only drop+chunks:

⋊> ~ ts_url hypertable drop chunk                                                                                               11:25:39
                              url
---------------------------------------------------------------
 https://docs.timescale.com/api/latest/hypertable/drop_chunks/
(1 row)

If we revert the order of the words it should still work:

⋊> ~ ts_url hypertable chunk drop                                                                                               11:25:53
                              url
---------------------------------------------------------------
 https://docs.timescale.com/api/latest/hypertable/drop_chunks/
(1 row)

Reverting the order of all words in the content:

⋊> ~ ts_url chunk drop  hypertable                                                                                              11:26:03
                              url
---------------------------------------------------------------
 https://docs.timescale.com/api/latest/hypertable/drop_chunks/
(1 row)

It's really working 👯

Now, let's create one more shortcut to also copy the top ranked link to the clipboard:

function ccc_url --description "Copy top URL searching on timescale docs"
  set query "select distinct url from get_ts_url_for('$argv', 1);"
  psql $docs_uri -c "$query"  | tail -n 3 | head -n 1| pbcopy
end

Vector Headline

When you match some specific term, you can also highlight the part of the text that contains something interesting as a headline with ts_headline.

In the next query, let's find all the "about" pages from companies and search for their "our mission is to ..." and discover the companies missions 🚀

WITH mission_statement AS (
  SELECT SPLIT_PART(url,'/',3) as host,
  ts_headline('english',
    body::text,
    to_tsquery('our+mission+is+to')
  ) AS mission_search
  FROM pages
  WHERE
  url !~ '/@' AND
  url ~ '/(about|company|about|about[_\-]?(us)?)\.?(html?)?/?$' AND
  to_tsquery('our+mission+is+to') @@ to_tsvector(body::text)
)
SELECT host, mission_search FROM mission_statement LIMIT 5;
───────────────────────────────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│             host             │                                                         mission_search                                                          │
├──────────────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ www.igvita.com               │ <b>mission</b> to supercharge commerce and empower entrepreneurs around the world.                                              │
│ blog.hypriot.com             │ <b>mission</b> was to make container technology a first class citizen on ARM and IoT devices                                    │
│ www.twilio.com               │ <b>mission</b> — to figure out how the brain works and then apply it to create more                                             │
│ blog.takipi.com              │ <b>mission</b> is to help fellow developer and operation teams ship the best products they can, faster                          │
│ blog.faraday.io              │ <b>mission</b> Faraday leverages ethical AI to predict consumer behavior at scale and power growth for innovative               │
│ engineering.mixmax.com       │ <b>mission</b> at Mixmax. Check out the product, give us your feedback, and join us on this                                     │
│ blog.mandrill.com            │ <b>mission</b> to empower the underdog.","Learn more about our Co-founders.","Mailchimp strives to create                       │
│ kinvolk.io                   │ <b>mission</b> to enhance open source cloud native technologies with the latest Linux capabilities.","Kinvolk is derived        │
│ blog.honeybadger.io          │ <b>mission</b> has been to make developer’s lives better, and we’ve had a blast                                                 │
│ codeascraft.com              │ <b>mission</b> is to enable people to make a living making things. The engineers who make                                       │
│ blog.gojekengineering.com    │ <b>mission</b> to improve the livelihoods of local ojeks (motorcycle taxis). Every customer was 'matched' manually              │
│ databricks.com               │ <b>mission</b>-critical for solving the biggest problems our world faces. From healthcare to sustainability to transportation   │
│ drivy.engineering            │ <b>mission</b> to disrupt car ownership and make cities better.","It’s an exciting time to be building                          │
│ blog.digitalocean.com        │ <b>mission</b> is to simplify cloud computing so developers and businesses can spend more time creating                         │
└──────────────────────────────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Most common words

We can use ts_stat to get the top most used words:

SELECT *
FROM ts_stat($$SELECT to_tsvector('english', title) FROM pages$$)
ORDER BY ndoc DESC
LIMIT 10;
   word   | ndoc  | nentry
----------+-------+--------
 blog     | 34361 |  35065
 medium   | 26995 |  27182
 engin    | 23974 |  25833
 facebook | 14594 |  16400
 page     | 12717 |  12740
 develop  | 11512 |  12237
 use      |  6374 |   6522
 tech     |  6373 |   6617
 code     |  6353 |   6602
 archiv   |  5442 |   5568
(10 rows)

And also reuse our timescale_content view to check the statistics only of timescale content:

SELECT *
FROM ts_stat($$SELECT search_vector FROM timescale_content$$)
ORDER BY ndoc DESC
LIMIT 10;
    word     | ndoc | nentry
-------------+------+--------
 https       |  960 |    960
 timescal    |  960 |   1761
 com         |  957 |    957
 blog        |  486 |   1046
 timescaledb |  483 |    671
 doc         |  474 |    947
 latest      |  472 |    473
 data        |  204 |    343
 guid        |  196 |    198
 time        |  192 |    347
(10 rows)

Some results are still repeated as I didn't have the proper time to normalize all the urls before fetch it. Feel free to contribute 🙌

Worst Scenarios fetching data

Let's check the average time to download a page grouped by domain:

SELECT SPLIT_PART(url,'/',3) AS domain, AVG(time_to_fetch) FROM pages GROUP BY 1 ORDER BY 2 DESC LIMIT 10;
               domain                |        avg
-------------------------------------+--------------------
 michaelcrump.net                    |  64.85552978515625
 adventuresinautomation.blogspot.com |  8.075942993164062
 www.billthelizard.com               |  5.909573554992676
 www.confluent.io                    |  4.935941823245434
 blog.lerner.co.il                   |  4.742450714111328
 haptik.ai                           |  4.613358974456787
 www.raizlabs.com                    |  4.457476615905762
 code.mixpanel.com                   | 4.2434492111206055
 www.future-processing.pl            |  4.072421073913574
 blog.blundellapps.co.uk             | 3.5754189491271973
(10 rows)

Slowest websites

Note that these are the slowest websites considering I did it from my home 🇧🇷

SELECT SPLIT_PART(url,'/',3) AS domain, COUNT(1) as total_pages,
  AVG(time_to_fetch) AS avg_time_to_fetch,
  SUM(time_to_fetch) AS total_time,
  PG_SIZE_PRETTY(SUM(html_size)) AS bandwidth
FROM pages
GROUP BY 1 ORDER BY 3 DESC LIMIT 10;
               domain                | total_pages | avg_time_to_fetch  | total_time | bandwidth
-------------------------------------+-------------+--------------------+------------+-----------
 michaelcrump.net                    |           1 |  64.85552978515625 |   64.85553 | 16 kB
 adventuresinautomation.blogspot.com |           1 |  8.075942993164062 |   8.075943 | 1203 kB
 www.billthelizard.com               |           1 |  5.909573554992676 |  5.9095736 | 319 kB
 www.confluent.io                    |          89 |  4.935941823245434 |  439.29883 | 63 MB
 blog.lerner.co.il                   |           1 |  4.742450714111328 |  4.7424507 | 92 kB
 haptik.ai                           |           1 |  4.613358974456787 |   4.613359 | 33 kB
 www.raizlabs.com                    |           1 |  4.457476615905762 |  4.4574766 | 40 kB
 code.mixpanel.com                   |           1 | 4.2434492111206055 |   4.243449 | 298 kB
 www.future-processing.pl            |           1 |  4.072421073913574 |   4.072421 | 87 kB
 blog.blundellapps.co.uk             |           1 | 3.5754189491271973 |   3.575419 | 17 kB

Where the crawler spent the time

SELECT SPLIT_PART(url,'/',3) AS domain, COUNT(1) AS total_pages,
  AVG(time_to_fetch) AS avg_time_to_fetch,
  SUM(time_to_fetch) AS total_time,
  PG_SIZE_PRETTY(SUM(html_size)) AS bandwidth
FROM pages
GROUP BY 1 ORDER BY 4 DESC LIMIT 10;

        domain        | total_pages | avg_time_to_fetch  | total_time | bandwidth
----------------------+-------------+--------------------+------------+-----------
 medium.com           |       22132 | 0.7525404742870937 |   16655.22 | 4201 MB
 www.stackabuse.com   |        1998 |  1.159109460268248 |  2315.9016 | 247 MB
 auth0.com            |         988 |  2.012826143126739 |  1988.6725 | 130 MB
 lambda.grofers.com   |         989 |  1.795852951238382 |  1776.0988 | 196 MB
 engblog.nextdoor.com |        1988 | 0.8649827361601579 |  1719.5862 | 297 MB
 www.adamtuliper.com  |         965 | 1.7380711382689253 |  1677.2385 | 192 MB
 www.cimgf.com        |         856 |  1.952296660429685 |  1671.1658 | 32 MB
 sitepoint.com        |        1986 | 0.8376746205228484 |  1663.6222 | 254 MB
 team.goodeggs.com    |         891 | 1.7691494242594432 |   1576.312 | 117 MB
 snyk.io              |        1942 | 0.7906604430357227 |  1535.4626 | 146 MB

How many pages got duplicated with links redirection?

I was trying to understand several pages generates '----' in the URL, so I decided to inspect it a bit:

select url from pages where url ~ '-------' limit 5;
                                                                                      url
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 https://...8d7f8425882?source=collection_home---4------0-----------------------
 https://...821168da?source=collection_home---4------1-----------------------
 https://...apache-flink-723ce072b7d2?source=collection_home---4------2-----------------------
 https://...g-data-hub-ba2605558883?source=collection_home---4------3-----------------------
 https://...1f3d87def1?source=collection_home---4------4-----------------------

I just reduced the urls to make it more visible what I mean.

So I thought about double checking how many duplications I got just because of the extra params I have, so let's understand the picture:

select count(url) from pages where url ~ '-------' ;
 count
-------
 33296
(1 row)

Wow, almost 15% of the data.

Now, let's count how many full duplicates we have:

tsdb=> select count(distinct url) from pages where url ~ '-------' ;
 count
-------
 30668
(1 row)

Normalizing the urls ignoring all params to check how many real sources we have:

select count(distinct split_part(url,'?',1)) from pages where url ~ '-------';
 count
-------
 12173
(1 row)

Now, confirming how many of this pages are the root pages of this 30k cases:

WITH normalized_urls AS (
  SELECT DISTINCT SPLIT_PART(url,'?',1) AS url
  FROM pages
  WHERE url ~ '-------' 
)
SELECT COUNT(1) FROM pages WHERE url IN (SELECT url FROM normalized_urls);

Interesting: 10% of the pages are fully URLs with different parameters! :iseewhatyoudidthere:.

What are the most used words in the titles?

WITH words AS ( SELECT REGEXP_SPLIT_TO_TABLE(title, E'\\W+') AS word FROM pages)
SELECT word, count(*) FROM words
WHERE LENGTH(word) > 5 -- Just to skip in, on, at, etc
GROUP BY 1 ORDER BY 2 DESC limit 10;
    word     | count
-------------+-------
 Medium      | 27117
 Engineering | 21363
 Facebook    | 16398
 Archives    |  4287
 Developer   |  4180
 Cloudflare  |  3976
 Google      |  3545
 Software    |  3282
 Product     |  3157
 Protection  |  3138

What are the most common call to action in the bottom of tutorial pages?

SELECT headers[array_length(headers,1)], count(1)
FROM pages
WHERE title ~ 'tutorial'
AND ARRAY_LENGTH(headers,1) > 1
GROUP BY 1 ORDER BY 2 DESC LIMIT 5;
       headers        | count
----------------------+-------
 Share your thinking. |    25
 Popular posts        |    18
 Tags                 |    12
 Books I've written   |     9
 Other                |     7
(5 rows)

What are the most common call to action in the bottom of any page?

 select headers[array_length(headers,1)], count(1) from pages where array_length(headers,1) > 1 group by 1 order by 2 desc limit 10;
          headers           | count
----------------------------+-------
 Share your thinking.       | 15790
 RELATED CATEGORIES         |  8473
 Resources                  |  3212
 Connect                    |  3067
 Meta                       |  3064
 Tags                       |  2815
 Post navigation            |  2753
 Categories                 |  2745
 Leave a Reply Cancel reply |  2718
 RSS                        |  2246
(10 rows)

Most common words

Let's explore the most common words in the Timescale domain:

WITH words AS ( SELECT REGEXP_SPLIT_TO_TABLE(title, E'\\W+') AS word FROM pages WHERE url ~ 'timescale.com')
SELECT word, count(*) FROM words where LENGTH(word) > 5 GROUP BY 1 ORDER BY 2 DESC limit 10;
    word     | count
-------------+-------
 Timescale   |   583
 TimescaleDB |   130
 series      |   111
 PostgreSQL  |    76
 database    |    56
 Series      |    42
 Create      |    41
 Grafana     |    37
 Database    |    33
 Building    |    29

As you can see we're not normalizing the words, so, let's LOWER them to unify the duplicated words:

tsdb=> WITH words AS ( SELECT REGEXP_SPLIT_TO_TABLE(LOWER(title), E'\\W+') AS word FROM pages WHERE url ~ 'timescale.com')
SELECT word, count(*) FROM words where LENGTH(word) > 5 GROUP BY 1 ORDER BY 2 DESC limit 10;
   word     | count
-------------+-------
timescale   |   583
series      |   153
timescaledb |   134
database    |    89
postgresql  |    76
create      |    45
grafana     |    37
building    |    32
continuous  |    28
aggregates  |    26
(10 rows)

Some common words from facebook engineering headers:

WITH words AS ( SELECT REGEXP_SPLIT_TO_TABLE(headers::text, E'\\W+') AS word FROM pages WHERE url ~ 'engineering.fb.com')
SELECT word, count(*) FROM words where LENGTH(word) > 5 GROUP BY 1 ORDER BY 2 DESC limit 10 offset 30;
    word     | count
-------------+-------
 production  |    50
 center      |    50
 better      |    47
 Networking  |    46
 efficient   |    46
 software    |    46
 analysis    |    45
 approach    |    45
 Introducing |    43
 hardware    |    43
(10 rows)

If you reached the end of the analyzes with me, please, go ahead and try it yourself! Contribute with any insights you have :)

Ranking search

I often need to search for Timescale content and I build this snippet which allows me to mix text search with similarity using levenshtein.

create extension pg_trgm;
drop materialized view timescale_content  cascade;
create materialized view timescale_content as SELECT pages.title,
    pages.url,
    to_tsvector(regexp_replace(pages.url, '[^\w]+'::text, ' '::text, 'gi'::text) || pages.title) AS search_vector
   FROM pages
  WHERE pages.url ~ '^https://(((blog|docs|www)\.)?timescale\.com|github.com/timescale)'::text;

CREATE OR REPLACE FUNCTION get_ts_url_for(text, integer default 5) RETURNS setof ranked_post
  AS $$
    SELECT title, url,
    ts_rank_cd(search_vector, query) + similarity(url, $1) AS rank
    FROM timescale_content, plainto_tsquery('english', $1) query
    WHERE query @@ search_vector OR similarity(url, $1) > 0.0
    ORDER BY rank DESC NULLS LAST
    LIMIT $2;
$$
LANGUAGE SQL;

Then you can get searches mixing both methods:

select distinct url from get_ts_url_for('gh tsdb tkit issues', 1);
select distinct url from get_ts_url_for('ts /community', 1);

Usage

Check bin/blog-sniffer to get more details in a massive crawling system, but the basics are:

Run bin/console to get the classes loaded into a pry session:

[1] pry(main)> spider = Blog::Sniffer::EngineeringDocs.new(root: "https://blog.timescale.com")
=> #<Blog::Sniffer::EngineeringDocs:0x00007fcdbc26dae0 @root="https://blog.timescale.com">
[2] pry(main)> spider.results.lazy.take(1).first
Handling  : "https://blog.timescale.com"
=> {:title=>"Timescale Blog",
 :headers=>
  ["$40 million to help developers measure everything that matters",
   "Timescale Newsletter Roundup: March 2021 Edition", ....],
 :links=>
  {["Products"]=>"https://www.timescale.com/products",
   ["Docs"]=>"https://docs.timescale.com",
   ["Blog"]=>"https://blog.timescale.com/",
   ["Log into Timescale Cloud"]=>"https://portal.timescale.cloud/login",
   ["Log into Timescale Forge"]=>"https://console.forge.timescale.com/",
   ["Try for free"]=>"https://www.timescale.com/timescale-signup", ...}
 :body=>
  [ "We're excited to announce that we've a raised $40M Series B, ...", ...]
 :html_size=>135963,
 :time_to_fetch=>2.063971000025049,
 :url=>"https://blog.timescale.com"}

Development

After checking out the repo, run bin/setup to install dependencies.

It does not contain any spec as I wrote it as a POC. Feel free to contribute and add them ;)

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/jonatas/blog-sniffer. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

License

The gem is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the Blog::Sniffer project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
bin		bin
lib/blog		lib/blog
spec		spec
.gitignore		.gitignore
.rspec		.rspec
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
Gemfile		Gemfile
LICENSE.txt		LICENSE.txt
README.md		README.md
Rakefile		Rakefile
blog-sniffer.gemspec		blog-sniffer.gemspec

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What are the most common call to action in the bottom of tutorial pages?

What are the most common call to action in the bottom of any page?

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