Sharding 101: Everything You Need to Know About Partitioning Data

In my previous articles on replication (part 1 and part 2), we explored how it enhances scalability. However, for large datasets and high-throughput systems, replication alone may not be enough. This is where sharding comes into play. By breaking data into smaller partitions, also known as shards, we can distribute it across multiple nodes, improving performance and efficiency.

What is Sharding?

Sharding involves dividing data into partitions so that each record, row, or document belongs to exactly one shard. These shards are then placed on different nodes in a shared-nothing cluster, where each node operates independently. This approach allows for:

• Scalability: As queries targeting different shards can be executed in parallel.
• Load Distribution: Preventing any single node from becoming a bottleneck.
• Efficient Querying: Especially for operations that target a single partition.

However, designing an effective sharding strategy requires careful planning. If done incorrectly, it can lead to skewed partitions, where some shards handle significantly more load than others, creating hot spots and negating the benefits of sharding.

Partitioning and Replication

Partitioning is usually combined with replication so that copies of each partition are stored on multiple nodes. This means that, even though each record belongs to exactly one partition, it may still be stored on several different nodes for fault tolerance.

A node may store more than one partition. If a leader-follower replication model is used, the combination of partitioning and replication can look like nodes in the diagram given below, for example. Each partition’s leader is assigned to one node, and its followers are assigned to other nodes. Each node may be the leader for some partitions and a follower for other partitions.

Sharding Strategies

Key-Range Partitioning

In this approach, a continuous range of keys is assigned to each shard, similar to how volumes are organized in an encyclopaedia. The key advantage is that range queries are efficient. However, if the data is inserted sequentially (e.g., timestamps), it can result in hot spots.

Hash-Based Partitioning

Here, a hash function is applied to the key to determine the shard, ensuring an even distribution of data. This reduces the risk of hot spots but makes range queries more complex. Some systems, like Cassandra, strike a balance by using a compound primary key, where only part of the key is hashed while the rest is kept in order for efficient queries.

Handling Hot Keys

In scenarios where certain keys receive disproportionately high access (e.g., a celebrity’s social media profile), additional techniques such as key-splitting (adding random prefixes to keys) can distribute the load more evenly. However, this increases the complexity of reads, as multiple keys must be aggregated.

Partitioning and Secondary Indexes

Many databases use secondary indexes for searching data efficiently. Sharding complicates this process, requiring additional partitioning strategies:

• Document-Based Partitioning: The secondary index is stored with the primary data, simplifying writes but making reads more complex (scatter-gather approach).
• Term-Based Partitioning: The secondary index is kept in a dedicated partition, making reads more efficient but increasing write complexity.

Rebalancing Shards

Over time, as workloads change, shards need to be redistributed. This process, known as rebalancing, ensures:

• Even distribution of data and query load.
• Minimal disruption to database operations.
• Efficient data transfer between nodes.

Rebalancing Strategies

• Fixed Number of Partitions: Creating more partitions than nodes allows easy redistribution when scaling up or down.
• Dynamic Partitioning: Some databases (e.g., HBase, RethinkDB) split or merge partitions dynamically based on data volume.
• Manual vs. Automated Rebalancing: While automation reduces operational overhead, manual intervention can prevent unexpected disruptions.

Request Routing in Sharded Systems

Once data is partitioned, clients need a way to route queries to the correct shard. Approaches include:

• Forwarding via Any Node: Any node receives the request and forwards it to the appropriate shard.
• Dedicated Routing Tier: A separate routing layer directs requests to the correct node.
• Client-Side Awareness: Clients maintain metadata to directly connect to the right shard.

Trade-offs of Sharding

Sharding brings significant advantages but also introduces trade-offs across key system parameters:

1. Scalability: Enables horizontal scaling, but requires even data distribution.
2. Availability: Improves fault tolerance, but shard failures can still impact the system.
3. Consistency: Distributed transactions are complex and often require trade-offs between consistency and performance.
4. Reliability: Increases fault isolation but requires robust shard management.
5. Efficiency: Reduces query load per node but can introduce overhead for cross-shard queries.
6. Maintainability: Adds complexity in data management, requiring careful monitoring and rebalancing.

Conclusion

Sharding is a powerful technique for scaling distributed systems, but it must be carefully implemented to avoid pitfalls like hot spots, query inefficiencies, and difficult rebalancing. By understanding the trade-offs and choosing the right partitioning strategy, organizations can build resilient, high-performance systems that scale with demand.

How has your experience been with sharding? Have you encountered challenges with partitioning and rebalancing? Let’s discuss in the comments!