Scaling Enterprise Search for 3+ Million Users Using Firestore and Algolia

Overview

Adaptive Development helped a healthcare compliance platform overcome search limitations while supporting more than 3 million users and over 100 million records.

The platform relied on Google Firestore for real-time data synchronization and scalability. However, as the system grew, administrators required advanced search capabilities that Firestore alone could not efficiently provide.

By introducing Algolia as a dedicated search engine alongside Firestore, we delivered fast, flexible, and highly scalable search experiences across millions of records.

The Challenge

The platform's administrative users needed the ability to:

• Search users by name, email, or keywords.
• Perform partial-text searches.
• Combine search terms with multiple filters.
• Retrieve results quickly across large datasets.

Although Firestore handled transactional workloads exceptionally well, it presented challenges for advanced search scenarios.

No Partial Keyword Search

Administrators expected searches similar to:

              LIKE '%john%'
            

Firestore does not support this type of query natively.

Index Complexity

As more filters were introduced, Firestore required additional composite indexes for different query combinations. Managing these indexes became increasingly difficult and limited future scalability.

Limited Search Experience

Features such as:

• Typo tolerance.
• Search relevance ranking.
• Fast keyword matching.
• Flexible filtering.

were outside the scope of Firestore's native capabilities.

Our Solution

Rather than replacing Firestore, we designed a hybrid architecture that leveraged the strengths of both technologies.

Firestore

Continued serving as the platform's primary database and source of truth.

Algolia

Handled all search-related operations, including:

• Full-text search.
• Partial keyword matching.
• Search ranking.
• Advanced filtering.

Data from selected Firestore collections was synchronized to Algolia indexes in near real time.

Architecture

The search workflow was designed to be simple and efficient:

• Data is stored in Firestore.
• Searchable records are synchronized to Algolia.
• Users perform searches through Algolia.
• Matching record identifiers are returned instantly.
• Detailed record information is retrieved from Firestore.

This approach allowed us to maintain data consistency while providing enterprise-grade search performance.

Results

The implementation delivered significant improvements for both administrators and end users.

Business Benefits

• Faster user and record discovery.
• Improved administrator productivity.
• Better overall user experience.
• Positive feedback from enterprise customers.

Technical Benefits

• Supported more than 3 million users.
• Scaled across 100+ million records.
• Eliminated dependency on excessive Firestore indexes.
• Enabled advanced keyword and partial-text searching.
• Improved long-term scalability.

Performance Impact

Searches that previously required complex workarounds became near-instant, even across very large datasets.

Key Takeaway

Every technology has strengths and limitations.

Firestore provided outstanding scalability, reliability, and real-time capabilities. Algolia provided the advanced search functionality the platform required.

By combining both technologies, Adaptive Development delivered a scalable search architecture capable of supporting millions of users while maintaining excellent performance and user experience.

Technologies Used

• Google Firestore.
• Firebase Authentication.
• Firebase Cloud Functions.
• Algolia Search.
• Angular.
• TypeScript.
• Node.js.
• Google Cloud Platform.

Need to scale search for your platform?

Adaptive Development designs and builds scalable cloud solutions for SaaS, healthcare, enterprise, and data-intensive applications worldwide. Contact us to discuss your requirements.