A Guide to Search Engine Database

What Is a Search Engine Database and How Does It Work?

Search engine databases are central to the search experience of web and app users today. User expectations for applications and websites keep rising in terms of performance. Even small companies are held to the same standards as tech giants when it comes to user experience. On your website or in your app, users expect to be able to get the information they need quickly and easily.

This guide is all about search engine databases and why they are an important tool for accomplishing the above goals, and how to get started using them to create a reliable and organized collection of data.

What is a search engine database?

A search engine database is a database built for indexing and querying information. These databases are optimized for searching through large volumes of stored data based on user queries and then return results ranked by relevance. They are generally considered to be a type of NoSQL or non-relational database and use indexes to categorize data to make searching faster and more efficient.

Search engine databases can handle both structured and unstructured data and provide functionality to handle things like full-text search, complex search expressions, and different ways to rank and structure returned search results.

Search engine database use cases

Full-text search

Allowing users to search through items or documents for text matching their input is common across many types of applications. While simple pattern matching can be useful, a search engine database excels at providing relevant results even if the user has typos or no exact matches for their search. Full-text search could also be used to generate autocomplete results for users as they type into a search bar. And thanks to proper indexing, this process can be performed much faster than standard pattern matching across large volumes of data.

Log analysis

Another strong use case for search engine databases would be log analysis for monitoring software applications. Search engine databases will have built-in support for indexing and storing logs based on factors that can be defined by developers. The NoSQL nature of modern search engine databases means that replication and scalability are built in features that developers don’t have to worry about. You could also break out certain data from your logs and store those metrics in a time series database where it makes sense.

Log-analysis

Geolocation search

Another common capability of search engine databases is geospatial or geolocation based searches. Think about the type of functionality you see when using Google Maps, where you can search for all types of a certain business within a defined radius. This type of search would be challenging to implement and inefficient with a standard database, but most search engine databases will provide this functionality out of the box.

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Search engine database features

In this section you will learn about some of the key features that define a search engine database.

Speed and efficiency

The primary reason for choosing a search engine database over a standard database would be due to improvements in performance such as:

    • Writes per second
    • Query response time
    • Reduced hardware cost due to more efficient workloads

Search algorithms - pre-built and custom

Another reason to choose a dedicated search engine database is the ability to customize how your data is ranked and results are returned. Search engine databases provide algorithms that will work well for most use cases out of the box and also provide the ability to fine-tune the settings on these algorithms.

For developers who want more control, you also have the option to use your own search algorithms. For example, many companies are now building and deploying their own machine learning models to provide more accurate search results using open source tools like Tensorflow. These models can be used to make it possible to search things like image galleries based on what types of objects are depicted in the images.

Developer experience and productivity

Developer experience and productivity might be the most important reason companies are choosing to use specialized search engine databases. By using these tools companies are able to focus on writing code that provides direct value to their user rather than worry about implementation details that aren’t their speciality. Some of the benefits provided by most search engine databases are:

    • Built-in tools for making common queries
    • Default support for data replication
    • Support for customized sharding and scaling configuration

How a search engine database works

There are a number of components that make up a search engine database and steps involved with returning results for a query.

The first step is storing data in the database, which obviously needs to happen before users can query anything. During ingestion the database will store each piece of data as a document, analyze the document, and add indexes that map to the documents. For example, an index could keep track of any document that contains the word “cat”.

Once the data is stored and indexed, users can begin querying and searching. Generally this would require some sort of UI. The query is split into tokens, and then the index is used to find all relevant documents related to the query.

Perhaps the biggest challenge then is determining the most relevant documents. For most users, returning every single possible result isn’t useful. As mentioned above, most search engine databases will allow developers to rank results with a custom algorithm or give the option to use a more generic algorithm. How to rank search results is a huge challenge and the prime example of this is Google, which became a company valued at over 1 Trillion dollars due to the success of their ability to return relevant search results for users.

Why search engine databases are important

Search engine databases are important for both developers and end users. For developers, they provide the benefit of making their jobs easier and allowing them to ship faster. For end users, they get a better experience, even if they don’t know the tool providing that experience behind the scenes.

Search engine database examples

There are a number of options available for search engine databases, including open source projects. Apache Lucene was originally released in 1999 and is still under active development. Lucene provides many low-level features needed for a search engine database like full-text indexing and searching. Lucene is technically a library written in Java and on its own wouldn’t provide the features needed for applications.

Lucene’s biggest impact has been seen by other projects building and extending the project to create databases that provide even more functionality. The most popular examples would be Elasticsearch and Apache Solr, both open source search engine databases. MongoDB also uses Lucene for their Atlas Search functionality.

Another option would be RedisSearch, which uses Redis as the underlying data store to provide features like text search, autocompletion, as well as querying and aggregation of data. Even more traditional relational databases have added support for things like full-text search, so there is some overlap in the functionality of many different types of databases. Whether it’s worth it to deploy and maintain a dedicated search engine database will depend on your use case and performance requirements.

Search engine database FAQ

Search engine databases vs relational databases

The main difference between a search engine database versus a relational database is the focus on reading vs writing data. As the name suggests, a search engine database is optimized for reading/querying data. Because of this writing data to a search engine database is generally less performant compared to a relational database, due to the cost of updating indexes as the new data is written. The tradeoff is that the queries are faster later due to those indexes improving query performance.

Lucene vs Solr

Lucene is a low-level library that provides functionality required for search engine databases. It could be used to make your own custom search engine database. Solr is a higher-level application that can be used out of the box as a search engine database. A simple metaphor would be that Lucene is like buying groceries where you can use the raw ingredients for whatever you want to make versus Solr which is like going to a restaurant and buying a meal.

Search engine vs database

A search engine is a tool for searching stored data. A database is a collection of information that has been stored and organized. Technically speaking you can’t have a search engine without a database that contains data to store in the first place.

Databases are generally associated with structured data like you would see in a relational database table while search engines are associated with more unstructured data like you would find on the web.

In the context of this article, a search engine database is generally an application that combines both of these technologies to some degree and provides a unified tool that abstracts away most of the complexity behind the scenes so you don’t have to worry about it as a developer. You simply store your data and then write queries to grab data.

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