Which category of NoSQL databases is it recommended not to shard data on?
A. Column-based NoSQL databases
B. Key-Value NoSQL databases
C. Document-based NoSQL databases
D. Graph NoSQL databases
The correct answer and explanation is :
Thought for a few seconds
Answer: D. Graph NoSQL databases
Explanation:
Sharding (horizontal partitioning) is a common strategy to scale out NoSQL systems by distributing data across multiple machines. However, not all NoSQL categories lend themselves equally well to sharding. Graph databases in particular present significant challenges:
- Data Model Complexity
Unlike key–value, document, or column-family stores—where data items are largely independent or follow simple parent–child structures—graph databases represent highly interconnected nodes and edges. Queries typically traverse many relationships, hopping from node to node. If connected nodes reside on different shards, each hop may cross the network, causing severe performance degradation. - Cross-Shard Traversals
The core power of graph databases is fast, deep traversals (e.g., “find all users within three hops of Alice”). On a single machine or non-sharded cluster, these traversals exploit in-memory pointers or localized indices. When the graph is split, every traversal step that moves to a neighbor stored on another shard incurs network overhead, serialization/deserialization costs, and potential coordination delays—dramatically slowing query response times. - Partitioning Difficulty
Effective sharding requires a partitioning scheme that groups related data together. In graph contexts, optimal partitioning to minimize cross-shard edges is an NP-hard problem—there is no efficient algorithm that guarantees a good split for arbitrary graphs. Even approximate graph-partitioning algorithms can’t anticipate all future traversal patterns, leading to imbalanced shards or excessive cross-shard edges. - Consistency and ACID Guarantees
Many graph operations (such as adding or deleting relationships) involve updating multiple adjacent entities. If those entities live on different shards, maintaining transactional consistency across shards becomes complex, often requiring two-phase commits or distributed locking—both of which erode performance and add operational complexity. - Alternative Scaling Strategies
Because of these challenges, graph databases more commonly scale vertically (by beefing up individual servers) or via specialized cluster architectures that replicate the entire graph on each node. Some graph systems offer multi-machine clustering through asynchronous replication or friend-of-a-friend caching rather than true hash-based sharding.
In contrast, key–value, document, and column-family stores naturally partition data by key or range. Their access patterns rarely require multi-item transactions or deep joins, so they shard cleanly and predictably. Thus, when designing a distributed NoSQL deployment, sharding is typically recommended for those three categories—but not for graph databases.