In today’s fast-paced digital era, food delivery platforms must provide seamless and intuitive user experiences. A popular food ordering site approached us at InheTech with a significant challenge: their mobile app lacked the ability to search for specific food items and return restaurants that offered those dishes. This limitation frustrated users, leading to missed opportunities and a potential loss of market share.

We solved this by implementing Elasticsearch with advanced search capabilities, including typo tolerance and phonetic matching. By also integrating Logstash for efficient indexing of data from their MySQL database, we ensured the platform could deliver accurate and lightning-fast search results. This case study details our approach, the challenges faced, and how our expert solutions turned their app into a more user-friendly and competitive platform.

The Problem

The client’s mobile app, though functional, had a major drawback. Users could only search for restaurants by name or cuisine, not by specific dishes. For instance, a user craving “pepperoni pizza” had no way to search for it directly and find restaurants offering that item. This gap not only affected user satisfaction but also restaurant visibility on the platform.

The client needed a solution that could:

1. Enable users to search for specific food items.
2. Tolerate typos and variations in search queries.
3. Deliver results swiftly and accurately.
4. Ensure scalability for a growing user base and dataset.

Our Solution

At InheTech, we proposed leveraging Elasticsearch for its powerful full-text search and analytics capabilities. Here’s how we tackled the project step-by-step:

1. Implementing Elasticsearch

Elasticsearch became the backbone of the search functionality. Its inverted index structure provided the speed and relevance required to handle thousands of queries simultaneously. We designed the schema to index food items and associated restaurant data, ensuring optimal query performance.

2. Wildcard Search with Phonetic Analyzers

We enhanced the search experience by incorporating wildcard searches and phonetic analyzers. This approach ensured users would still get relevant results even if they made typographical errors or used phonetically similar words. For instance:

• A search for “chikn biryani” would return results for “chicken biryani.”
• Queries like “pitza” would match “pizza.”

3. Logstash Integration for Data Indexing

To keep Elasticsearch updated with the latest restaurant menus and food items, we implemented Logstash. Logstash periodically extracted data from the client’s MySQL database and indexed it into Elasticsearch. This ensured that the search results remained current and accurate, even as restaurants updated their menus.

• Pipeline Design: We created Logstash pipelines to transform and normalize the data for Elasticsearch indexing. This process included formatting, deduplication, and mapping fields to the appropriate Elasticsearch schema.
• Scheduling Updates: A schedule was set for incremental updates, minimizing load on both the MySQL database and Elasticsearch cluster.

4. Advanced Query Design

We designed queries that combined full-text search, filtering, and scoring mechanisms. For example:

• Boosting restaurants with higher ratings.
• Prioritizing results from nearby locations using geospatial data.

Challenges and How We Overcame Them

1. Handling Typographical Errors

Challenge: Typos in user search queries could result in no results or irrelevant matches.

Solution: We implemented Elasticsearch’s “fuzzy matching” along with phonetic analyzers. This allowed queries with minor spelling errors or variations to return relevant results without compromising performance.

2. Data Volume and Update Frequency

Challenge: With hundreds of restaurants and thousands of menu items, indexing and updating the Elasticsearch cluster was a potential bottleneck.

Solution: Using Logstash’s incremental indexing capabilities, we ensured that only updated data was re-indexed. This minimized the processing load and kept the system responsive.

3. Optimizing Search Relevance

Challenge: Balancing relevance with query performance was critical. Users expected the best matches at the top of their search results.

Solution: We fine-tuned Elasticsearch’s scoring algorithms, leveraging custom analyzers and boosting factors to prioritize:

• Restaurants with higher user ratings.
• Frequently ordered dishes.
• Geographical proximity to the user.

4. Scalability and Resilience

Challenge: The client anticipated significant growth in user base and data volume, requiring a solution that could scale seamlessly.

Solution: We implemented Elasticsearch’s horizontal scaling features. By adding additional nodes and replicas, we ensured that the system could handle increasing traffic and provide high availability.

Results

The implementation of Elasticsearch transformed the client’s search functionality. Key achievements included:

1. Enhanced User Experience: Users could now search for specific dishes, with results displaying all restaurants offering those items. Typo tolerance and phonetic matching further improved usability.

2. Increased Restaurant Visibility: Restaurants saw a noticeable uptick in orders as users discovered more options through specific food item searches.

3. Scalability Achieved: The platform was now capable of handling high search volumes without performance degradation.

4. Seamless Data Syncing: Logstash ensured that Elasticsearch stayed up-to-date with minimal manual intervention, even as restaurants updated their menus.

Why they choose inhetech?

This project highlights InheTech’s expertise in designing and implementing robust search solutions for modern digital platforms. Whether it’s leveraging Elasticsearch, integrating complex data pipelines with Logstash, or optimizing for scalability, our team has the skills and experience to deliver transformative results.

If your business is looking to enhance its search capabilities, improve user experience, or handle large-scale data efficiently, InheTech is here to help. Contact us today to learn how we can take your platform to the next level.

About US

inhetech is a leading IT consulting agency specializing in search technologies, big data solutions, and cloud infrastructure. With a proven track record of delivering high-impact solutions, we help businesses achieve their digital transformation goals. Reach out to us at [email protected] for a free consultation.