Open source search engines deliver speed of innovation, scalability and cost control
Why open source search engine?
For almost a decade, we are seeing large enterprises migrating to open source search engines from proprietary solutions to power their search and analytics capabilities. The tribute for this should without doubt go to a cornerstone Apache Lucene project which opened the world of information retrieval to developer community. Search platforms like SolrCloud and Elasticsearch soon followed, further democratizing search solutions and fueling immense community-driven innovation in the search area.
Enterprises migrating to open source search systems enjoy not only superb performance, scalability and cutting-edge features of those engines. They have easier access to the talent and skills. Solr and Elasticsearch were successfully deployed in thousands of enterprises, from small firms to Fortune-100 companies and there is a vivid community of developers and consultants working in this space. On top of that, better developers are generally attracted by open source platforms as they feel more in control of what's going on under the hood.
Having cut on commercial product license costs, enterprises can dial up and down the level of investment into the search area based on the current business needs.
An open source search engine gives developers independence from the commercial search vendor's roadmap. Developers can create custom solutions, innovate and differentiate the search space.
Experts in open source search
Grid Dynamics started in 2006 with the goal of making mission-critical systems scalable. Since then, performance and scalability engineering have been at the core of our company's DNA. As Solr and Lucene contributors, we have a deep understanding of search engine internals, and possess years of experience tuning the performance of search systems. For over 5 years, the search solutions that we have built for our retail customers have survived Black Friday traffic storms with flying colors, without a single outage or breach of SLAs. We have helped numerous customers solve their search performance issues, improving response times, online conversion rates from search and indexing speeds, sometimes at an exponential level.
Open source search engine migration process
Move search traffic to the new search engine
The first phase of migrating to an open source search engine is to get production search traffic onto the new search engine in the cloud without negatively affecting user experience or disrupting business KPIs. This process involves synching the catalog and other related data to the new service supporting legacy URLs for SEO reasons, and then testing the new service to ensure the relevancy is improved. Once that is done, Canary traffic is set up and monitored before diverting all production traffic to the new search engine.
Implementing merchandising tools on open source
During the next phase of the replatforming process, a merchandising tool can be implemented, allowing business users to switch from the legacy experience manager to the new tool. The reference architecture is shown above:
Adding in real-time and ML features
The last phase in migrating to open source search is to add advanced functionalities, like ML/AI, personalization, conversational search and visual search. These generally support features that are not possible in legacy systems, namely an omnichannel catalog, store-level inventory and near real-time inventory updates. These features can be implemented in the first phase of replatforming as a part of the core search engine driving immediate business value, or later in the migration process.