StoreHippo slashes storage consumption by 50%, reduces latency by 30% and lowers DevOps overhead by 10% with MongoDB Atlas and Atlas Search
Launched in 2014, StoreHippo is a fully managed SaaS-based ecommerce platform. Built by Hippo Innovations, StoreHippo provides comprehensive turnkey solutions for B2C and B2B enterprises to build, scale, manage and market their online stores and multi-seller marketplaces. StoreHippo removes unnecessary IT burden from the shoulders of merchants, allowing them to focus on their core business.
StoreHippo has grown to become the leading Indian ecommerce SaaS platform focused on medium to large enterprises. It counts thousands of businesses as customers across more than 40 different industry sectors in 25+ Asia Pacific, European and Middle Eastern markets. The platform serves over 500 million secure API calls per month with less than 50 milliseconds average response times.
The company’s engineers originally built out its StoreHippo ecommerce platform with MongoDB community edition providing database services, and bolted-on external search engines to provide product catalog and content search.
To support the company’s rapidly scaling business and to increase the velocity of new feature releases, it migrated to the fully managed MongoDB Atlas platform and started offering Atlas Search as an alternative to Algolia and Azure Cognitive Search. As a result, the company has accelerated its speed in delivering new ecommerce services to market while further improving customer experience and reducing operational costs.
Fig. 1: Beautiful, engaging, and high converting ecommerce storefronts using powerful search with autocomplete, built on the StoreHippo platform and MongoDB Atlas
Self-managing databases and bolting-on external search engines
Fig. 2: StoreHippo’s MACH architecture with MongoDB powering the microservices’ data persistence layer
MongoDB was selected as the underlying database layer for StoreHippo because of its ease of integration with the Javascript-based MEAN Stack (MongoDB, Express, Angular, and Node). MongoDB’s flexible document data model allowed StoreHippo’s developers to handle the massive variability in product catalog and content attributes, along with associated metadata, media assets, and user reviews. Built on a distributed architecture, MongoDB provided the engineering teams with cloud-native resilience and scale-out to handle peak loads, especially over busy holiday seasons.
Rajiv Kumar Aggarwal, CEO and Founder, Hippo Innovations
MongoDB powers StoreHippo’s entire data persistence layer – from product catalog to order, inventory and customer management, through to payment processing. For security and performance isolation, each merchant is provisioned with their own single tenant MongoDB database and search engine.
The StoreHippo team initially opted to self-manage MongoDB on top of Microsoft Azure. However, self-managing MongoDB added operational overhead in running the systems, with the company’s engineers handling all patches, upgrades, scaling, backups, and performance optimizations.
Beyond the database, fast and relevant search is critical to help merchants’ customers quickly discover and buy products online. The StoreHippo platform offers customers a choice of using the Algolia or Azure Cognitive Search engines. However this makes the technology stack complex and costly. By bolting Algolia or Azure Cognitive Search onto their MongoDB database, the StoreHippo team has to grapple with:
Fig. 3: Technology sprawl with separate systems creating architectural complexity
Rajiv Kumar Aggarwal, CEO and Founder, Hippo Innovations
Before embarking on its migration to MongoDB Atlas, the StoreHippo team evaluated Azure CosmosDB, but quickly disqualified it because of a host of technical limitations:
Kriti Aggarwal, CMO and Co-Founder, Hippo Innovations
By moving to MongoDB Atlas, StoreHippo has also been able to take advantage of Atlas Search, offering it as a native search solution alongside the existing bolt-on Algolia and Azure Cognitive Search services.
By using Atlas Search the StoreHippo engineering team has streamlined its technology estate. Atlas Search is built on the industry standard Apache Lucene library. Therefore developers are able to deliver the key capabilities demanded for fast and relevant catalog and content search:
StoreHippo’s developers are more productive as they now work with a unified API and single MongoDB Node.js driver across both database and search operations. This integrated experience eliminates them having to context switch between different MongoDB and custom search engine query languages as they code, and removes unnecessary build dependencies.
Atlas Search is integrated into the MongoDB aggregation pipeline, therefore developers can combine multiple aggregation stages and operators to build more sophisticated queries. For example, the product catalog can store multiple descriptions for each product in different languages. Fast and dynamic filtering in the MongoDB query API allows the correct description to be returned, based on the customer’s language-specific search term.
Improving data consistency, Atlas automatically synchronizes all changes made to the catalog database with the Atlas search index. This avoids the engineering team having to manually build and maintain their own fragile custom replication mechanism and transformation logic.
Engineering cycles spent on building and maintaining the previously separate database, search engine and sync mechanism are now invested back into building new features and applications for the business.
Fig. 4: Dramatic architectural simplification with fully integrated database, sync, and search in MongoDB Atlas
Rajiv Kumar Aggarwal, CEO and Founder, Hippo Innovations
Moving its native search functionality to Atlas Search has also unlocked benefits in time to market and user experience:
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If you are bolting external search engines onto your databases today, we can help you simplify your application estate. Through the experiences gained by working with customers who have migrated, we have put together a repeatable 5-step methodology to replacing bolt-on search engines with MongoDB Atlas Search.