Search engines are absolutely ubiquitous today. They function as springboards for our online journeys, as well as tools to find products and content on online stores and media websites. Regardless of their purpose, however, all search engines that aim to find results quickly have to rely on something called an index.
What exactly is an index, though?
Well, typically, the data that a search engine needs to retrieve results from is stored within one or more databases, but simply scanning the contents of a database is an inefficient and slow way to look for items. Thus, what happens behind the scenes is that a search engine first goes through the database(s), parses the records in it, and stores the data in them in an easy-to-search form within a structure called an index.
Thus, when a search engine needs to process a query and return some search results, what it actually goes through is an index. Searching through an index is much faster than searching through an equivalent amount of raw data: this is why indices are so important for search engines to function.
But what if you have multiple databases or multiple indices behind the scenes for your ecommerce store? How can your site search engine deal with that? The most common way is through something called federated search. Let’s learn more about it, and the various scenarios where it’s commonly used.
What is federated search, and how is it useful?
Federated search is a technique that makes it possible for multiple sources of data to be searched through with a single query and a single user interface. In other words, even if your business requires you to create and maintain multiple databases and/or indices, you can still ensure that your site visitors can search through all your content through a single search bar.
There are several common kinds of contexts where federated search is essential. For instance, enterprise search typically has to work in a federated manner, given that companies will typically have several different databases (customers, business partners, employees, products, payroll, etc.), each of which will contain data in different formats.
Another important domain where federated search is often vital is ecommerce and retail. As a simple example, suppose that several products in an online store have documentation, blog articles, FAQs or demo videos associated with them; moreover, suppose that each such content type is stored in a different database, and that different content types need to be presented in a distinct way in the search results so as to keep things clear for users. In such a scenario, federated search would be the way to go.
Two more scenarios where federated search is useful are:
- Aggregator sites that enable people to carry out bookings for flights/hotels/events.
- Media streaming platforms or content websites that provide one-stop access to media/content from various music companies/artists/publishers.
In both the above cases, the search interface on the site in question will likely need to access various different indices/databases and then present the results produced using a unified interface.
How does federated search work behind the scenes? Well, there are four main approaches to implementing federated search:
- Search-time merging: With this approach, you maintain separate indices for each of your various data sources. When a search query is entered, each index is searched for that query separately. The results obtained from each index are then aggregated and sorted to produce the final list of search results.
- Index-time merging: This approach involves creating a central index that taps into all your data sources, and then simply using that sole index for retrieving search results.
- Hybrid federated search: As the name suggests, this approach combines elements from the above two approaches. Wherever possible, data sources are grouped together and represented by a single index, with sufficiently disparate data sources (e.g. due to extremely different data formats) being represented by different indices. Thus, this approach minimizes the number of indices that need to be parsed.
- A federated search interface: This approach fundamentally involves search-time merging, but instead of combining all the results obtained from the various indices, several different lists of results are presented to the user, one for each content type or index.
Let’s look at some use cases of federated search in ecommerce and retail.
3 use cases for federated search in ecommerce and retail
- Disparate product categories
Many online stores specialize in a particular niche and sell a relatively small number of different product types. For instance, a store might only sell t-shirts, shoes, or swimwear. For such stores, storing all of their product data in a single database is not a problem.
However, it’s also quite common for online stores to sell many different kinds of products. In such cases, the sorts of details that are relevant to each kind of product can be different enough to warrant the use of a separate database for each product type (e.g. think of the difference between the relevant properties of bags and shoes, or of headphones and mobile chargers). Given this, it makes sense to use federated search in such a scenario.
Similarly, if an ecommerce business aims to expand and add new product lines, brands or categories, federated search can enable it to do so without having to worry about breaking the store’s site search
- Minimizing the security risks that come with multiple search engines
If your online store has multiple databases/indices but does not use federated search, then the only realistic option before you is to incorporate different instances of a search engine (or different search engines altogether) on different pages.
This is not just a cumbersome situation to manage, but can actually put your business and your customers at risk. Search engines can be an entry point for hackers to gain unauthorized access to information regarding your products or possibly even your site visitors.
By using federated search, you can make the attack surface of your store smaller, thus leading to greater security and peace of mind.
- Offering seamless omnichannel experiences
For a business that operates a brick-and-mortar store as well as a corresponding online store, it may make sense to keep the brick-and-mortar product database separate from the online store’s product database. However, when a customer looks for a product in the online store, you ideally want the search results to query both databases at once, since a product can usually be picked up from the physical store and dispatched to the buyer.
Once again, in such a multi-database scenario, federated search is exactly what you need to ensure a smooth experience for your customers and maximize sales for your business.
Federated search: a trusty solution for common ecommerce quandaries
Thus, many kinds of growing pains that ecommerce stores might experience can be relatively easily mitigated by making use of federated search. By providing your customers with a single, easy-to-use search interface, you can keep them insulated from index-related messiness happening under the hood while giving them a great search experience.
If you’re looking for a powerful search engine for your ecommerce store, look no further than Zevi. Zevi uses Natural Language Processing (NLP) to understand users’ search intent and produce highly relevant results. Try our Shopify App, or reach out to us today for a free demo!