Sharding, Re-sharding, and the Celebrity Problem in System Design


Sharding is a technique used in distributed systems to horizontally partition or divide a database or dataset into smaller, more manageable pieces called shards. Each shard contains a subset of the data and is stored on a separate physical or logical node or server. Sharding allows distributing the workload and storage across multiple nodes, enabling improved performance, scalability, and availability in large-scale systems.

Key Aspects of Sharding:

  1. Data Partitioning:

    Sharding involves dividing the data into smaller partitions or shards based on a specific strategy, such as range-based partitioning, hash-based partitioning, or key-based partitioning. Each shard can contain a range of data or be responsible for specific keys or entities.

  2. Distribution and Replication:

    Shards are distributed across multiple nodes or servers in a distributed system. Each shard may have one or more replicas to provide redundancy and fault tolerance. Replication ensures that the data is available even if a node or shard fails.

  3. Routing and Querying:

    Sharding requires a mechanism to route and direct queries or requests to the appropriate shard based on the data being accessed. This can involve using a central coordinator or metadata store to map data to shards or employing techniques like consistent hashing to determine shard placement.

  4. Scalability and Performance:

    Sharding allows scaling a system horizontally by adding more nodes or shards to handle increased workload and data volume. It improves performance by distributing the data and queries across multiple nodes, reducing the load on individual servers.


Re-sharding is the process of modifying the sharding strategy or redistributing the data across shards in a distributed system. Re-sharding is typically performed to accommodate changes in data volume, workload patterns, or to optimize performance. It involves migrating data between shards, adjusting shard boundaries, or adding/removing shards as needed.

Key Aspects of Re-sharding:

  1. Planning and Strategy:

    Re-sharding requires careful planning and consideration of the system's requirements and goals. It involves analyzing data distribution, workload patterns, and anticipated growth to determine the new sharding strategy.

  2. Data Migration:

    Re-sharding involves moving data between shards. This can be a complex process that needs to be performed while the system remains operational. Techniques like online migration, parallel data transfer, or leveraging replica shards can be employed to minimize downtime and ensure data consistency during the migration.

  3. Metadata and Routing Updates:

    As shards are reconfigured or added/removed, the metadata or routing mechanisms need to be updated to reflect the new sharding scheme. This ensures that queries and requests are correctly routed to the appropriate shards based on the updated configuration.

The Celebrity Problem:

The celebrity problem, also known as the two generals' problem, is a challenge in distributed systems where a group of processes or nodes needs to agree on a common decision or coordinate an action. The problem arises when one or more nodes have special roles, such as a celebrity or leader, and the other nodes need to reach a consensus about the identity of the celebrity.

The challenge lies in the fact that nodes can only communicate with each other indirectly and may receive conflicting information. It is difficult to guarantee that all nodes agree on the identity of the celebrity without additional coordination mechanisms or protocols. The celebrity problem highlights the complexities of achieving consensus in a distributed system where communication and coordination are limited.

Efficient Use of Sharding, Re-sharding, and Addressing the Celebrity Problem:

  • To efficiently use sharding, re-sharding, and address the celebrity problem in system design, consider the following:

  • Consider the data distribution, workload patterns, and scalability requirements when designing the initial sharding strategy.
  • Monitor system metrics and performance regularly to identify the need for re-sharding and optimize the sharding strategy accordingly.
  • Implement effective data migration techniques and consider strategies to minimize downtime and maintain data consistency during re-sharding.
  • Use appropriate routing mechanisms and metadata management to ensure queries and requests are correctly directed to the relevant shards.
  • When dealing with the celebrity problem, consider consensus protocols, leader election algorithms, or distributed coordination frameworks to facilitate agreement among nodes and resolve conflicts in a distributed system.

By effectively applying sharding, performing re-sharding when necessary, and addressing challenges like the celebrity problem, distributed systems can achieve improved performance, scalability, and fault tolerance while ensuring data integrity and coordination among nodes.