What is the role of indexing in distributed databases?

Indexing in distributed databases serves the same core purpose as in traditional databases: speeding up data retrieval by reducing the amount of data scanned during queries. However, in a distributed system, where data is partitioned across multiple nodes, indexes must also address challenges like network latency, data locality, and consistency. A well-designed index allows the database to locate specific records or filter results efficiently without requiring full scans of all nodes. For example, a query like “find all orders from customer X” would benefit from an index on the customer ID column, enabling the database to pinpoint the relevant nodes storing that customer’s data.

In distributed systems, indexes are often partitioned or replicated to align with how data is distributed. For instance, a global secondary index in Apache Cassandra spans all nodes, allowing queries to target specific partitions without knowing their physical location. Conversely, a local secondary index (like those in DynamoDB) is tied to a specific partition key, limiting its scope but avoiding cross-node lookups. Some databases use hash-based or range-based partitioning for indexes to match the underlying data distribution. For example, if data is sharded by user ID ranges, an index on registration dates might be partitioned similarly to avoid scattering queries across all nodes. Indexes can also be co-located with data (e.g., in Google Spanner) to minimize network hops during query execution.

However, indexing in distributed databases introduces trade-offs. Maintaining consistency across nodes during updates can create overhead, especially in systems with eventual consistency. For example, updating a record in one node may require asynchronous updates to indexes on other nodes, risking stale reads. Developers must also balance read performance against write latency: adding more indexes improves query speed but increases write costs. Tools like Apache HBase use block-level indexing to optimize for scan-heavy workloads, while others like CockroachDB employ zone-aware indexing to reduce cross-region traffic. Ultimately, the choice of indexing strategy depends on the workload pattern, consistency requirements, and the database’s architecture.