The plugin will not create an exact mapping of
1 page = 1 record. Instead, it
will split all pages into smaller chunks, and each chunk will be saved as a
record. Splitting into small chunks is key to offer the best relevance of
The default chunking mechanism is to create one chunk per paragraph of content. So, for a blog post that is made up of 10 paragraphs of text, it will create 10 records.
Estimating the number of records you will need can be tricky as it depends on both the number of pages you have, and on the average length of them. Some configuration options (such as nodes_to_index) can also influence the final result.
The following table should give you a ballpark estimate of what to expect. All calculations were done with an average of 15 paragraphs per page, on a timeline of one year.
|update frequency||# of new pages||# of records||Algolia plan|
|~1 new page per week||~50||~750||Community|
|~1 new page per day||~400||~6.000||Community|
|~2 new pages per day||~700||~10.500||Essential|
|~1 new page per hour||~8800||~132.000||Essential|
|~1 new page every 5 minutes||~105.000||~1.575.000||Plus|
If you get an error about one of your records being too big, be sure to update the plugin to the latest version. We keep improving the plugin with ways of making the records smaller.
If you’re still having an error, you should check the
.json log file that has
been created in your source directory. This will contain the content of the
record that has been rejected. It might give you hints about what went wrong.
A common cause for this issue often lies in the page HTML. Maybe the HTML is malformed (a tag is not closed for example), or instead of several small paragraphs there is only one large paragraph. This can cause the parser to take the whole page content (instead of small chunks of it) to create the records.
If you don’t find where the issue is coming from, feel free to open a GitHub issue with a copy of the log file and we’ll be happy to help you.
The plugin default configuration will rank results based on their textual relevance. You can adapt the ranking to fit your needs by using a combination of front-matter and Algolia index settings.
For example, if you know some of your blog posts are popular, you might want to
give them a boost in their ranking. To do so, add a
popular: true entry to the
front-matter of such posts. Any custom key added to a front-matter is
automatically pushed to their corresponding records.
Then, you would have to edit your
_config.yml file to pass a custom
settings.customRanking value. The
customRanking is one of the way ranking
can be configured in an Algolia index and follows a tie-breaking algorithm. You
can find more information about the way it works either in the official
documentation or in this video.
customRanking used by the plugin is defined here and use the
date, weight of the header and position in the page by default. You can
overwrite it to also take the
popular flag into account like:
algolia settings customRanking desc(popular) desc(date) desc(custom_ranking.heading) asc(custom_ranking.position)
This will rank popular posts matching your keywords before other posts. You can
use either boolean or numeric values for the