With the evolution of modern applications serving increasing needs for real-time data processing and retrieval, scalability does, too. One such open-source, distributed search and analytics engine is Elasticsearch, which is very efficient at handling data in large sets and high-velocity queries. However, the process for effectively scaling Elasticsearch can be nuanced, since one needs a proper understanding of the architecture behind it and of performance tradeoffs.While Elasticsearch’s distributed nature lets it scale horizontally, that also introduces more complexities in how data is spread and queries served. One of the theoretical challenges associated with scaling Elasticsearch is its inherently distributed nature. In most practical scenarios, reads on a standalone node will always outperform reads in a sharded cluster. This is because, in a sharded cluster, data ownership is spread across multiple nodes. That means every query may have to shoot multiple requests to different nodes, aggregate the results back at the coordinating node, and return the result. This extra network overhead will easily result in increased latency compared to a single-node architecture where data access is straightforward.