Best practices

Traditional search technology only works with lists of individual words. It will match each word, no matter how long or short the list is. Indx takes a different approach. It looks at the entire searchable pattern, whether it is one word or many words. To make this work well, the search data may need to be optimized.

When working with categorized data, the indexed pattern will be identified and judged based on the built-in Relevancy Ranking of Indx Search. This means that repeated patterns (like a shop category named "winter sports") will be lower in relevancy, but less frequent patterns such as "fischer" and "cross-country skis" will be higher in relevancy in the indexing.

If you want to create a search that can match across categories and product names, here are some suggestions:

Often we find that regular search engines push us to include a lot of data in their index, which can lead to multiple copies of our datasets and more work to maintain them.

Indx uses RAM for ultra fast performance, instead of databases. To keep memory usage low, only store necessary search patterns in the index. This includes text like names, titles, and categories, and skip non-searchable data like pricing or Urls. To retrieve non-searchable info, use the foreign key in the Document class to fetch from another database.

This way, you can keep the search fast and responsive, while keeping maintenance tasks manageable.

Let’s look at an example of a pair of Nike running shoes in a store.

For the need of personalization, using user-specific data, these will usually be able to be ready shortly after a user logs in. This is because the system indexes very quickly.

This method can work without overloading your server with hundreds of users logged in at the same time, and if you have a massive user base (Hello 👋🏻) the memory and CPU usage will scale linearly. Technically this method can be done with a keyfilter or running multiple indexes. See section below for when to choose approach.

Aliases to a Document can be added simply by using the same key for multiple Documents.

Here’s an example:

Refer to the API docs to see how to do this in practice

Indx is currently the only search engine that allows for exclusive filtering, meaning that the user can choose to include all properties except the chosen ones, instead of just including.

Exclusive filtering can be a good option when there are many options to filter through. For example, if a user is searching for an apartment in London, they might be presented with 33 areas to filter through. Using exclusive filtering, they can quickly narrow down their search by selecting the areas they don't want to see results from. This can be much more efficient than having to select all the areas they do want to see results from.

We encourage thinking about this when structuring your data.

When you search, it's helpful to narrow down the search as much as possible. This is called the 'context of the search'. With a faceted search, you have two options. You can either set up a separate instance for each category, or set up one instance that includes all categories with a filter for each category.

If you have multiple indexes, you can combine them for a single search.

The best way to decide if you should use filters or multiple indexes depends on how much data you have. For a small amount of data, it won't make much difference. Most people use filters.

However, if you have a lot of data, you may benefit by dividing these into multiple indexes:

It's hard to say exactly how much data is too much, as it depends on the data itself. Indx has tools to analyse the data, and are available to help customers find the best solution.