Elasticsearch is a highly scalable, fast and accurate open-source search and analytics engine for all types of data.

Who uses this technology?

General usage

Users of on-site search features have almost double the conversion rate compared to overall site conversion rates. It thus makes sense to invest in a fast, accurate internal search engine for your ecommerce application, especially data-intensive platforms like online marketplaces.

Elasticsearch is the go-to choice for storing, searching and analysing huge volumes of data quickly. This is an important consideration when you have lots of vendors who are selling multiple products to many customers who want fast transactions

It can be used in a wide range of applications, since it can handle most types of data, whether textual, numerical, geospatial, structured, or unstructured.

NoSQL distributed databases are used to deliver near instant search results. Autocomplete suggestions can further speed up the search process.

The relevance of search results can be improved in a number of ways:

  • Web crawlers can be configured to index and synchronise selected content at chosen intervals. 
  • Search algorithms can be fine-tuned with bigram matching, stemming, provision for typos and synonyms, and customised with page ranking and weight.
  • Faceted search (filters) can be added to refine search results by price, category, location and other product attributes.

A real-time analytics dashboard is available for logs and search metrics via Elastic’s customisable Kibana user interface. This can provide valuable insights that can be used for product line optimisation.

Other pluspoints include quick implementation via a few lines of code and resilience due to its distributed database backups.


Elasticsearch is supported by a large developer community (1650+ contributors on GitHub) and is the most popular Search as a Service tool on Sharestack by far.

Marketplace benefits

Elasticsearch is not generally used for early-stage marketplaces, but relatively easy to add once the platform’s growth demands it. It can be scaled very easily by adding additional servers to increase capacity.

We used Elasticsearch to build a robust search feature for MobyPark’s parking marketplace. It’s ability to deliver regularly updated near real-time search results was important since parking space bookings could be for as short as fifteen minutes. Subsequently, search failures decreaseded five-fold. The improved user experience contributed to a 4x growth in repeat purchases.

SoShop is a neobank with a searchable loyalty reward platform that handles more than 50,000 transactions per day. We used Elastic’s Kibana UI to visualise their search data, which provided them with valuab;le actionable insights.

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