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Real-Time Search - What It Is and When You Need It

ยท 6 min read

Google first introduced a real-time search function to its search engine over a decade ago (2010). Today, it is virtually impossible to imagine Google without it. Yet, strangely, real-time and live searches aren't as widespread as they should be. The only real players with comprehensive real-time search capabilities are social media platforms and search engines. App and web developers often avoid adding this functionality because it is either too difficult or they feel it's unnecessary. However, it can be highly useful for e-commerce apps and websites that consistently post fresh content (such as blogs). The last decade saw a shift in the software industry's priorities as greater emphasis has been placed on user experience (UX) and UI design. Knowing how and when to implement real-time search is a crucial skill to have as a modern developer. The following guide will explore what it is and where and when to use it.

Most website and app full-text search functions use indexes that are routinely updated by crawlers. However, this approach is unsuitable for social media sites like Twitter, where new content is published every second. There are at least 350,000 Tweets posted every second. Twitter manages popular and relevant content using (hashtag) trends and its real-time search feature.

Modern search engines connect you to websites that are continuously updating, so a real-time search function is a valuable feature. Initially, Google offered this function through its dedicated Realtime search website. However, the website was decommissioned in 2016, and many of its features were repurposed and refined for Google Trends . On the other hand, Bing uses real-time search functionality for its vertical searches (news and tiles) and keyword research tool to help identify trends.

Recently, there has been some discourse and debate on what real-time search is. While some believe that defining real-time search as a feature that finds content (real-time content) as it is published is sufficient, others believe that true real-time search finds content as it's being created, written, or updated.

Real-time or live search retrieves the latest relevant content. While this is possible with shared code repositories and SaaS products that allow multiple parties to collaborate on content creation (such as Google Docs), it's harder to achieve at large scales. Nevertheless, this distinction is ultimately inconsequential.

Real-Time Search vs. Autosuggestโ€‹

Many users confuse autocomplete or autosuggest with real-time search. While they're completely different, these two features can share a relationship. Autocomplete refers to a text box (typically a search box) that completes search phrases for you using an internal index. Autosuggest uses past searches and search trends to retrieve suggestions to help you complete your search phrase or select related content.

If autosuggest or autocomplete uses trends to populate its suggestions, it may use the real-time search functionality in the background. Likewise, autocomplete and autosuggest are often used in real-time search boxes.

Besides search engines and social media platforms, there are many scenarios where real-time search can be helpful, and the following are just a few examples:

In E-commerceโ€‹

Amazon is the king of e-commerce and will likely remain so for years. It operates in thirteen countries and has over nine million sellers worldwide. Amazon has an estimated twelve million products in its inventory. Amazon Marketplace has at least 350 million products on sale. Its inventory and stock are constantly changing.

With product and stock counts constantly in flux, live-search functionality is a strong advantage for e-commerce websites like Amazon.com. It could bring back results based on the latest promotions, the amount of stock left, written reviews, etc.

Amazon uses real-time search for its Amazon Live feature. Your e-commerce website could benefit from real-time search in the same way. It can be used to return the most popular items based on which ones are purchased, reviewed, or wish-listed the most. Real-time search functionality can be particularly useful for holiday shopping seasons - especially Black Friday and Cyber Monday, where stock availability can change abruptly. Real-time search can ensure that your site visitors get the most relevant results for product queries. This can translate to quicker conversions and boosted sales. Furthermore, it can minimize situations where sold-out stock is erroneously displayed as in stock because of a caching, late update, or UI issue.

Google Maps is famous for being one of the first mapping mobile apps to use real-time data to track traffic and determine the best routes for users to take. Location-based search functions on the same principle and can be used in autocomplete or autosuggestions to facilitate what is known as Geosearch. It can determine trends based on your location and then populate its full-text search suggestions with geographically relevant recommendations.

Uber Technologies, chiefly famous for its revolutionary ride-hailing service, uses real-time features in many of its services. The most obvious example is the Uber app - Request a Ride feature, which displays a map with all the available cars in your area in real-time. This real-time data determines which ride is best for your trip.

Recently, we've seen real-time location-based searches used in epidemiology to track and contain the spread of the Covid-19 virus. Mapping apps were created to inform people of virus hotspots and vaccination sites.

Alternatively, users can initiate these searches and access breaking news or relevant information about their surroundings. Location-based searches can provide real-time actionable insights if there is a terrorist threat, fire, demonstration, etc., nearby.

Of course, real-time search can also be used for recreation and leisure activities. Users can find the nearest shopping center or a restaurant and potentially the number of people there, allowing them to determine if there is a wait or not.

Almost all modern GUI applications have some form of search or find functionality. Search is a crucial software feature and should be seen as mandatory. Thus, the conversation isn't about whether you have search functionality; it's about how advanced and responsive it is.

But how should you go about integrating real-time search? Do you even have the infrastructure or budget to build a fully custom real-time search feature from scratch? This is where a service like Tigris comes in.

Real-Time Search with Tigrisโ€‹

One of the biggest obstacles that software developers face when building highly complex real-time search engines and functions is matching the right tools with their data infrastructure. Often, they'll find themselves trying to create processes that efficiently collect and compile information from a range of disparate data sources. Tigris helps developers forgo this practice and the headaches that come with it.

Tigris is a data platform built for developers. Tigris provides an embedded full-text search engine that gives developers a seamless and scalable experience for building rich search experiences in their applications. They can search across all the data stores automatically using full text or faceted search. And the embedded search engine eliminates the need to run a separate search system alongside your database.


Tigris is the data platform built for developers! Use it as a scalable, ACID transactional, real-time backend for your serverless applications. Build data-rich features without worrying about slow queries or missing indexes. Seamlessly implement search within your applications with its embedded search engine. Connect serverless functions with its event streams to build highly responsive applications that scale automatically.

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