The search_ignore_accents site setting can be used to make the search
indexer remove the accents before indexing the content. The unaccent
function from PostgreSQL is better than Ruby's unicode_normalize(:nfkd).
Random strings can result into much longer tsvectors. For example
parsing a Base64 string of ~600kb can result in a tsvector of over 1MB,
which is the maximum size of a tsvector.
Follow-up-to: 823c3f09d4
This commit fixes a bug where we our `HTMLScrubber` was only searching
for emoji img tags which contains only the "emoji" class. However, our emoji image tags
may contain more than just the "emoji" class like "only-emoji" when an
emoji exists by itself on a single line.
Long posts may have `cooked` fields that produce tsvectors longer than
the maximum size of 1MiB (1,048,576 bytes). This commit uses just the
first million characters of the scrubbed cooked text for indexing.
Reducing the size to exactly 1MB (1_048_576) is not sufficient because
sometimes the output tsvector may be longer than the input and this
gives us some breathing room.
Over the years we accrued many spelling mistakes in the code base.
This PR attempts to fix spelling mistakes and typos in all areas of the code that are extremely safe to change
- comments
- test descriptions
- other low risk areas
When the admin creates a new custom field they can specify if that field should be searchable or not.
That setting is taken into consideration for quick search results.
In the near future, we will be swtiching to PG headlines to generate the
search blurb. As such, we need to replace audio and video links in the
raw data used for headline generation. This also means that we avoid
replacing links each time we need to generate the blurb.
We do prefix matching in search so there is no need to inject the extra
terms.
Before:
```
"'discourse':10,11 'discourse.org':10,11 'org':10,11 'test':8A,10,11 'test.discourse.org':10,11 'titl':4A 'uncategor':9B"
```
After:
```
"'discourse.org':10,11 'org':10,11 'test':8A 'test.discourse.org':10,11 'titl':4A 'uncategor':9B"
```
```
discourse_development=# SELECT alias, lexemes FROM TS_DEBUG('www.discourse.org');
alias | lexemes
-------+---------------------
host | {www.discourse.org}
discourse_development=# SELECT TO_TSVECTOR('www.discourse.org');
to_tsvector
-----------------------
'www.discourse.org':1
```
Given the above lexeme, we will inject additional lexeme by splitting
the host on `.`. The actual tsvector stored will look something like
```
tsvector
---------------------------------------
'discourse':1 'discourse.org':1 'org':1 'www':1 'www.discourse.org':1
```
There is a feature in search where we take over from the tokenizer
in postgres and attempt to inject more words into search.
So for example: sam.i.am will inject the words i and am.
This is not ideal cause there are many edge cases and this can
cause extreme index bloat.
This is an opening move commit to make it configurable, over the
next few weeks we will evaluate and decide if we disable this by
default or simply remove.
This feature adds the ability to define synonyms for tags, and the ability to merge one tag into another while keeping it as a synonym. For example, tags named "js" and "java-script" can be synonyms of "javascript". When searching and creating topics using synonyms, they will be mapped to the base tag.
Along with this change is a new UI found on each tag's page (for example, `/tags/javascript`) where more information about the tag can be shown. It will list the synonyms, which categories it's restricted to (if any), and which tag groups it belongs to (if tag group names are public on the `/tags` page by enabling the "tags listed by group" setting). Staff users will be able to manage tags in this UI, merge tags, and add/remove synonyms.
Zeitwerk simplifies working with dependencies in dev and makes it easier reloading class chains.
We no longer need to use Rails "require_dependency" anywhere and instead can just use standard
Ruby patterns to require files.
This is a far reaching change and we expect some followups here.
Previous to this fix is a post had the test www.test.com/abc it would fail
to index.
This also simplifies the rules to avoid full url parsing which can be
expensive
This commit fixes the follow quality issue with `PostSearchData#raw_data`:
1. URLs are being tokenized and links with similar href and characters
are being duplicated in the raw data.
`Post#cooked`:
```
<p><a href=\"https://meta.discourse.org/some.png\" class=\"onebox\" target=\"_blank\" rel=\"nofollow noopener\">https://meta.discourse.org/some.png</a></p>
```
`PostSearchData#raw_data` Before:
```
This is a test topic 0 Uncategorized https://meta.discourse.org/some.png discourse org/some png https://meta.discourse.org/some.png discourse org/some png
```
`PostSearchData#raw_data` After:
```
This is a test topic 0 Uncategorized https://meta.discourse.org/some.png meta discourse org
```
2. Ligthbox being included in search pollutes the
`PostSearchData#raw_data` unncessarily.
From 28 March 2018 to 28 March 2019, searches for the term `image` on
`meta.discourse.org` had a click through rate of 2.1%. Non-lightboxed images are not included in indexing for search yet we were indexing content within a lightbox. Also, search for terms like `image` was affected we were using `Pasted image` as the filename for
uploads that were pasted.
`Post#cooked`
```
<p>Let me see how I can fix this image<br>\n<div class=\"lightbox-wrapper\"><a class=\"lightbox\" href=\"https://meta.discourse.org/some.png\" title=\"some.png\" rel=\"nofollow noopener\"><img src=\"https://meta.discourse.org/some.png\" width=\"275\" height=\"299\"><div class=\"meta\">\n<svg class=\"fa d-icon d-icon-far-image svg-icon\" aria-hidden=\"true\"><use xlink:href=\"#far-image\"></use></svg><span class=\"filename\">some.png</span><span class=\"informations\">1750×2000</span><svg class=\"fa d-icon d-icon-discourse-expand svg-icon\" aria-hidden=\"true\"><use xlink:href=\"#discourse-expand\"></use></svg>\n</div></a></div></p>
```
`PostSearchData#raw_data` Before:
```
This is a test topic 0 Uncategorized Let me see how I can fix this image some.png png https://meta.discourse.org/some.png discourse org/some png some.png png 1750×2000
```
`PostSearchData#raw_data` After:
```
This is a test topic 0 Uncategorized Let me see how I can fix this image
```
In terms of indexing performance, we now have to parse the given HTML
through nokogiri twice. However performance is not a huge worry here since a string length of 194170 takes only 30ms
to scrub plus the indexing takes place in a background job.
* This is causing certain posts to appear in searches incorrectly as `PostSearchData#raw_data` contains the outdated title, category name and tag names.
4481836 introduced accent stipping in search_indexer,
but we need to strip it from the query itself as well
TODO in search with diacritics:
- Still need to fix excerpts on search page
- need to support accent stripping in in_topic search
- need to make sure that in:title works correctly
- need to fix "word boldening" in titles
- By default, behaviour is not changed: tags are made lowercase upon creation and edit.
- If force_lowercase_tags is disabled, then mixed case tags are allowed.
- Tags must remain case-insensitively unique. This is enforced by ActiveRecord and Postgres.
- A migration is added to provide a `UNIQUE` index on `lower(name)`. Migration includes a safety to correct any current tags that do not meet the criteria.
- A `where_name` scope is added to `models/tag.rb`, to allow easy case-insensitive lookups. This is used instead of `Tag.where(name: "blah")`.
- URLs remain lowercase. Mixed case URLs are functional, but have the lowercase equivalent as the canonical.
Introduce new patterns for direct sql that are safe and fast.
MiniSql is not prone to memory bloat that can happen with direct PG usage.
It also has an extremely fast materializer and very a convenient API
- DB.exec(sql, *params) => runs sql returns row count
- DB.query(sql, *params) => runs sql returns usable objects (not a hash)
- DB.query_hash(sql, *params) => runs sql returns an array of hashes
- DB.query_single(sql, *params) => runs sql and returns a flat one dimensional array
- DB.build(sql) => returns a sql builder
See more at: https://github.com/discourse/mini_sql
