Elasticsearch is introducing a new feature that incorporates proximity, both geographical and temporal, into its result ranking system to enhance relevance scoring. This feature, known as the distance feature query, allows for a more nuanced ranking by integrating proximity with traditional criteria such as word frequency, overcoming previous technical challenges related to normalization and performance. By using the Saturation normalization function, the proximity score is normalized, making it easier to combine with other scores, while performance improvements allow the ranking to consider a broader set of records. The main advantage is the ability to better rank records by considering both proximity and other relevance criteria without significant performance degradation, addressing the need for more relevant results in scenarios where location or time is crucial. This development emerged from community feedback and contributions, leading to new algorithms and APIs that facilitate more efficient top-hit retrievals, ultimately showcasing Elasticsearch's commitment to evolving and enhancing its search capabilities.