Alex Cercel, Antonios Giannopoulos, and Pedro Albuquerque presents Comparing Geospatial Implementation in MongoDB, Postgres, and Elastic.
For a considerable set of applications querying geographical data consists of a critical operation. Fast responses combined with a high level of accuracy are often the requirements when an application user interacts with functions/operations of the type “Give me near me” or “Find me in area XYZ”. Additional complexity is usually added when the points of interest are constantly on the move, like a public transportation vehicle or a taxi.
For applications that frequently access geographical data and rely on both speed and accuracy, both application and database design is crucial. In this presentation, we are going to focus on the database side. More specifically, we are going to evaluate three of the most popular open-source databases, MongoDB, Postgres, and Elastic against geospatial workloads. For each of these databases, we are going to examine the implementation and the performance of geo-queries. We are going to discuss best practices and design patterns for each database and try to find a winner among the three.
For a considerable set of applications querying geographical data consists of a critical operation. Fast responses combined with a high level of accuracy are often the requirements when an application user interacts with functions/operations of the type “Give me near me” or “Find me in area XYZ”. Additional complexity is usually added when the points of interest are constantly on the move, like a public transportation vehicle or a taxi.
For applications that frequently access geographical data and rely on both speed and accuracy, both application and database design is crucial. In this presentation, we are going to focus on the database side. More specifically, we are going to evaluate three of the most popular open-source databases, MongoDB, Postgres, and Elastic against geospatial workloads. For each of these databases, we are going to examine the implementation and the performance of geo-queries. We are going to discuss best practices and design patterns for each database and try to find a winner among the three.