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Say “search engine” and you probably think of the search box on Google’s main page, which can give you an organized collection of search results related to a certain subject area, right?
Or maybe you prefer to think of behind-the-scenes web crawlers doing a World Wide Web search using a web search engine like Bing or Yahoo to pull up certain types of information. Or maybe your favorite social-media search feature comes to mind. Or maybe you just think of SEO.
At any rate, you certainly don’t think of looking for full-text articles in an old-school library catalog or adding an asterisk to a boolean search term. You use search engines, so you’ve got this. You know how search engine software works.
But say “search engine database” (or “database search engine”) and uh-oh, you may draw a bit of a blank.
That’s because “search engine database” isn’t a commonly used phrase. (Heck, it’s not even defined in Wikipedia). However, one of these fancy-sounding things in quotation marks is a vital element of business website success, and something that substantively impacts the user search experience when they’re looking for certain information on a website.
In this blog post, we’ll look at what “search engine database” means, as well as consider the differences between search engines and databases, which are fairly subtle.
A search engine database is a database that’s equipped with search functionality. Search features help you quickly find information in the database. Plus, this search engine is optimized for large volumes of data (such as what you’d find if you’re doing an academic search in a research database) that is either semi-structured or unstructured.
A search engine database is also a type of NoSQL database or non-relational database.
A relational database (e.g., SQL database, MySQL) is a database that stores information in tables. These tables often have information that can be shared between them, making the information retrieval process “relational.”
Sample use case: A small business might use two simple tables of data sets to process customer orders. The first is a customer information table that contains the customer’s name, address, billing information, and contact phone number. The second is a customer order table that includes the order ID number, items ordered, quantity, size, and color.
These two tables have a common key, or ID number, which links them. With this number, the relational database can create a relationship between the two tables. When a customer places an order, information in the database can be pulled from each table into a user interface to create and submit the order.
A non-relational database stores database files in a non-tabular form, in structures such as documents. A document can contain large amounts of information and it has a more flexible structure. Non-relational databases are used when there’s a large quantity of complex and diverse data.
Example: A large business might have a database in which each customer is represented by a separate document. This document contains every piece of customer information, from their name and address to their order history and product information. Unlike in a relational database, despite the different formats of these pieces of data, they can all be stored in the same document.
Then, when an order is created on the business’s website, information can be pulled from just that single document, rather than from multiple tables. This makes a non-relational database perfect for storing data that changes frequently or takes different formats. It’s also faster to search than a relational database.
So, we’ve defined a search engine database. But what about the individual search terms?
A database can, in theory, exist without a search engine. However, the lack of a search engine makes finding information incredibly difficult, especially in a non-relational database that houses a larger quantity of data and complexities.
We are used to being able to easily do all types of search. We expect search engines like Google and Bing to give us high-quality, quick search results and send us to the right web pages.
Unfortunately, company databases aren’t always teamed with high-quality search tools. And not all search functions are created equal, either.
A search engine database is structured in a way that makes integrating high-quality search easy. Here are some features of search databases:
Consumers are used to being served up relevant information from content repositories based on machine-learning-facilitated algorithms when they enter a search query. But many databases still use a simple search pattern that requires exact wording in order to yield a result.
What is full-text search? Providing search results that contain some or all of the words in the searcher’s query. A search engine database uses full-text search, which provides relevant results even when you enter a typo or there’s no exact match. This differs from traditional search, which provides only results from exact matches.
Database search engines have in-built support for indexing and storing logs. You can pull out data from the logs for analysis, potentially gaining valuable insight and thereby improving your business decision making.
A search engine database has a feature called geolocation search. You can see this functionality in action if you use apps like Google Maps and search for something within a certain geographical radius. In a standard database, implementing geolocation search requires a plugin.
A search engine database is structured for speed and efficiency. It has a fast query response time and can handle full-text search faster than a relational database can. For example, when using database search, a searcher instantly receives autocompletes and suggestions.
Most websites and applications are built on top of databases. Many of these are still being used with simple search engines. Rudimentary search techniques don’t allow users to perform a full-text search (with autocomplete and suggestions) or get instantaneous results, or allow business teams to analyze the search data.
This is where Algolia’s advanced search API comes in.
Our search engine was built expressly to improve the search capacity within website databases. It provides instant search with real-time results, handles typos intelligently, and allows you to balance a search by both relevance and popularity. It accounts for natural language factors. It provides users with a fast, efficient, and reliable search experience, streamlining the search process and adding optimization value for your business, whether you need your users to be able to find ecommerce products, subject terms for academic journals, tutorials, healthcare data, primary sources for newspaper articles, or FAQs. It even accounts for synonyms.
You can use Algolia’s search capabilities across any data source, including a NoSQL or non-relational database. It has been designed to accommodate document search, Big Data search, and object search (unlike Elasticsearch, which does not support all three of these functionalities).
With Algolia, you can put your website users in good hands in terms of your search strategies and letting users quickly find the content they need. Looking to add the best search to your website database to improve your site metrics? Contact our team and let’s go!