How Aeca Simplifies Search Development
Introduction
Previously, we explored why adding a search function to an application is challenging.
Why Search Function Development is Difficult
This is because the search capabilities provided by databases are limited, and integrating a search engine requires significant development resources. So you might wonder: what if we combine the database and the search engine into one? With all data handled by a single system, synchronization issues disappear, and DevOps resources like monitoring are significantly reduced.
However, databases and search engines are fundamentally different technologies, and merging the two systems has been considered virtually impossible. Although search solutions like Elasticsearch have been in widespread use since the early 2010s, databases and search solutions are still used separately even after a decade.
But Aeca has achieved what was thought to be difficult. It has successfully implemented search engine functionality within a database. Aeca is an OLTP database that supports CRUD operations and is optimized for fast data recording. Writing data and reading or searching data are mutually exclusive processes. However, with Aeca, it's magically possible to perform real-time searches as soon as data is processed. Moreover, it integrates various features like OLAP functionalities such as Aggregation and Time to Live into a single database.
Now, proper Full-Text Search, which was only possible with search engines, can be used within a database. This marks the moment to break free from the pains of search development that have plagued CEOs, product planners, and developers. Let's delve into how Aeca can specifically help alleviate the challenges of search development.
Search Development Possible in Just Three Days
Without the need to integrate a search engine with the database, development time is drastically reduced. Since data can be searched immediately upon being stored in the database, you can offer excellent search functionality from the early stages of your service.
Complex development processes like data extraction for synchronization, preprocessing, indexing, and consistency maintenance are no longer necessary, making it possible to develop search features that usually take several months in just a few days.
Reduction in Server Costs
By managing all data within a single database without the need to store duplicate data in a search engine, additional data storage and operational resources become unnecessary. This leads to reduced cloud costs.
No More Need for Additional Solutions
Since the search engine itself is no longer needed, additional solutions for building synchronization mechanisms with the database are also unnecessary. You no longer need to use message queues like Apache Kafka. This not only reduces solution adoption costs but also dramatically improves operational efficiency due to backend simplification.
Search Results Without Size Limitations
One drawback of search engines like Elasticsearch is their inability to return large volumes of search results. When you need to extract key information from a vast set of search results that meet certain conditions, important results might be missed. Especially when handling massive training data for machine learning, you need to search and extract large datasets. Aeca is not affected by the size of search results; it can return over a million search results at once.
Beyond Full-Text Search
As demonstrated, Aeca provides excellent Full-Text Search within the database. Now, even early-stage companies can incorporate robust search functionalities into their services.
But that's not all. In addition to traditional Full-Text Search, it also offers vector search, which is gaining attention in AI search, and hybrid search that combines the strengths of Full-Text Search and vector search.
Read more
The Challenges of Developing Search Functionality
Search functionality is essential in modern applications like Coupang, Baemin, and Yanolja, where users search for various items. However, developing search functionality is not straightforward. Especially when users expect Google-level search quality, the technical requirements become complex. There are two primary approaches to developing search functionality: using database query capabilities and using a separate search engine. Database query capabilities are suitable for simple searches, but they have limitations for complex search requirements. On the other hand, using a search engine can provide high-quality search functionality but increases development complexity and maintenance challenges. Therefore, early-stage services need to consider how to effectively implement high-quality search functionality.
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Search in web and mobile applications is a core function that shapes a positive user experience. Particularly in commerce services, search goes beyond enhancing user experience and directly impacts company revenue. With the explosive growth of product information and content, search quality—providing timely information that matches the keywords entered by customers—has become a critical factor determining the success or failure of applications and websites. Generally, customers searching for products in commerce services are considered strong potential buyers with a high willingness to pay. It's observed that all actions users take when searching and reacting to search results reflect their purchase intentions, needs, and willingness to spend. Statistically, the conversion rate of users who perform searches is more than twice that of those who don't. Although only less than 20% of the total MAU use search, it's known that over half of the revenue comes from users who have performed a search at least once. Additionally, the churn rate is high for users who fail on their first search, and the conversion rate for customers who re-search is very low. In other words, search is not only a powerful tool to open customers' wallets but also a factor that greatly impacts the sustainability of the service. So, how does search specifically contribute to customer retention, revenue growth, and service improvement?
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