What are distributed queries, and how do they work?

Distributed queries are operations that retrieve or manipulate data stored across multiple databases or data sources, treating them as a single logical system. They enable developers to access data from different locations—such as separate servers, cloud services, or even heterogeneous systems (e.g., SQL and NoSQL databases)—without manually aggregating the data. For example, a distributed query might join a user table in a PostgreSQL database with order records stored in a MongoDB cluster, returning unified results. This is achieved through a coordinator (often a database engine or middleware) that splits the query into sub-tasks, sends them to relevant nodes, and combines the results.

The process typically involves four steps. First, the coordinator parses the query to identify which data sources are involved. Next, it generates execution plans optimized for each source—like translating a SQL JOIN into a MongoDB aggregation pipeline. The coordinator then sends these sub-queries to the respective nodes, often pushing filters or projections to each source to minimize data transfer. Finally, it merges intermediate results, handling tasks like sorting or aggregation. For instance, a query calculating total sales per region might fetch raw sales data from a cloud data warehouse, customer locations from an on-premises SQL Server, and combine them using a hash-join algorithm. Network latency and data format differences are common challenges, so techniques like parallel execution and schema mapping are often used to improve performance.

Developers implement distributed queries using tools like PostgreSQL’s Foreign Data Wrappers (FDW), which let tables from external databases appear as local tables, or cloud services like AWS Athena that query data across S3 and relational databases. Considerations include security (managing credentials across systems), error handling (partial failures in one node shouldn’t crash the entire query), and consistency (handling stale data in real-time systems). While powerful, distributed queries add complexity, so they’re best suited for scenarios where centralizing data is impractical, such as integrating legacy systems or analyzing real-time logs across microservices. Tools like Apache Calcite simplify implementation by providing a framework for query optimization across diverse sources.