Distributed databases handle cross-node queries by coordinating operations across multiple servers while optimizing for performance and data locality. When a query requires data from more than one node, the database system typically breaks the query into sub-operations, routes them to relevant nodes, and combines the results. For example, a query to retrieve all orders from customers in a specific region might involve fetching customer data from one node and order records from another. The database uses a coordinator (a designated node or service) to manage this process: it determines which nodes hold the required data, sends sub-queries, waits for responses, and merges the results. This approach minimizes data transfer by processing parts of the query where the data resides, reducing network overhead.
The specific strategy depends on the database’s design and data distribution model. Sharded systems, like Apache Cassandra, partition data by a key (e.g., user ID) and route queries directly to the relevant shard. If a query spans shards (e.g., aggregating sales data across regions), the coordinator fetches partial results from each shard and computes the final aggregate. In contrast, databases with global replication, like Google Spanner, might use timestamp-based consistency to read from the nearest node while ensuring correctness. Some systems, such as CockroachDB, employ range-based partitioning, where contiguous data blocks are assigned to nodes. For range queries (e.g., “fetch records from January to March”), the database identifies overlapping ranges and queries the corresponding nodes in parallel. Indexes are often replicated or partitioned to avoid scanning every node for simple lookups.
Optimizations like predicate pushdown and query rewriting further improve performance. Predicate pushdown ensures filters (e.g., WHERE status = 'active') are applied at the node level before transferring data, reducing the dataset size sent over the network. For joins, some databases use colocation (storing related data on the same node) to avoid cross-node traffic. If colocation isn’t possible, techniques like hash-joins may redistribute data temporarily across nodes. For instance, a distributed SQL engine like Apache Ignite might split a join into local joins on each node and combine results. Challenges include handling node failures or network delays, which require retries or fallback mechanisms. By balancing data locality, parallel execution, and fault tolerance, distributed databases enable efficient cross-node queries while maintaining scalability.