Cars24 Improves Search For 300 Million Users With MongoDB Atlas

Summary

Online car marketplace Cars24 previously operated a dual architecture where relational databases like Postgres synchronized data into separate search engines like Elasticsearch. As service offerings expanded globally for 300 million users, multiple engineering teams piping data into a single search index caused severe synchronization challenges, operational overhead, and developer friction. To simplify this infrastructure, Cars24 migrated to MongoDB Atlas hosted on Google Cloud, utilizing integrated Apache Lucene-powered MongoDB Atlas Search directly within the database layer. This architectural shift eliminated synchronization pipelines while accelerating search feature delivery through a unified API. Cars24 also replaced its ArangoDB geospatial search deployment with MongoDB Atlas, achieving robust multi-document ACID transactions across shards and cutting operational costs by 50%.

Context

Cars24 faced synchronization challenges, heavy operational overhead, and decreased developer productivity while syncing data from databases like Postgres to external search engines like Elasticsearch. Additionally, its geospatial search setup on ArangoDB suffered from performance bottlenecks, weak transaction guarantees, and limited ecosystem support.

Approach / What changed

Cars24 adopted MongoDB Atlas hosted on Google Cloud to integrate operational data storage and Apache Lucene-powered search within a single platform. It later replaced ArangoDB with MongoDB Atlas to consolidate geospatial search workloads and leverage multi-document ACID transactions across shards.

Takeaways

• Running embedded MongoDB Atlas Search powered by Apache Lucene eliminated the need for external data synchronization pipelines and separate indexing infrastructure.
• Migrating geospatial search workloads from ArangoDB to MongoDB Atlas delivered 50% cost savings alongside multi-document ACID transactions across shards.
• Consolidating database and search operations under a unified API reduced operational overhead and simplified developer onboarding across growing engineering teams.

Related reading

• MongoDB · Sep 25, 2025

  From Niche NoSQL to Enterprise Powerhouse: The Story of MongoDB's Evolution

  Early perceptions categorized MongoDB alongside eventually consistent NoSQL databases, creating doubts regarding its suitability for mission-critical enterprise workloads. Over fifteen years of development, the platform addressed enterprise requirements by introducing replica sets with Raft-style consensus, native horizontal sharding, tunable read and write concerns, and multi-document ACID transactions across sharded clusters. Security capabilities were expanded through Role-Based Access Control, Field-Level Encryption, and Queryable Encryption supporting prefix, suffix, and substring searches. Current initiatives focus on decoupling compute and storage scaling, developing AI-driven tooling within the Application Modernization Platform to translate legacy relational applications to microservices, and optimizing query execution for non-denormalized data models. MongoDB Atlas now incorporates Atlas Search, Atlas Vector Search, native Stream Processing, and embedding generation powered by Voyage AI models.

  Ashish Kumar

• MongoDB