Distributed databases perform cross-node queries by utilizing a combination of data partitioning, query planning, and distributed execution strategies. When a query is initiated, the database first determines which nodes contain the relevant data by inspecting the distribution key or mapping. This key determines how data is partitioned across different nodes. For instance, in a distributed database storing customer records, if data is partitioned by customer ID, the system can direct a query for customer details to the specific node that holds those records.
Once the relevant nodes are identified, the system generates a query plan that outlines how to execute the request. This involves deciding whether to execute the query locally on each node or to aggregate results from multiple nodes. For example, if a user wants to retrieve sales data grouped by region, the database might send the query to each node holding sales records for that region. Each node will compute its part of the result, and then the system will combine these results into a single output. This step is often referred to as data aggregation, and it usually involves additional operations like summing figures or combining datasets.
Lastly, to optimize performance, distributed databases may employ techniques such as caching, parallel execution, and query routing. Caching frequently accessed data can reduce the number of cross-node queries needed for repeat requests. Additionally, parallel execution allows the database to run parts of a query simultaneously across multiple nodes, speeding up the overall response time. By managing how data is stored and retrieved, distributed databases can efficiently perform cross-node queries, ensuring that operations remain responsive even as the data volume and number of nodes grow.