Sharding Strategy

What is Sharding/Partitioning?

Sharding (or Partitioning) is a database design technique used to break down very large databases into smaller, faster, more manageable parts called shards or partitions. Each shard typically resides on a separate database server instance.

The primary goal is horizontal scalability. Instead of upgrading to a single massive, expensive server (vertical scaling), you distribute the data and load across multiple commodity servers. This improves performance, availability, and maintainability.

The Shard Key

The core of sharding is deciding how to distribute the data. This is done based on a shard key, which is one or more columns in your data (e.g., `user_id`, `customer_id`, `region`, `product_id`). The value of the shard key for a given row determines which shard that row belongs to. Choosing a good shard key is crucial for balanced distribution and efficient querying.

Common Sharding Strategies

1. Range-Based Sharding

Data is partitioned based on whether the shard key falls within certain contiguous ranges. Each shard is assigned a specific range of shard key values.

Example: Shard 1 holds User IDs 1-1000, Shard 2 holds User IDs 1001-2000, Shard 3 holds User IDs 2001-3000, etc.
Pros: Relatively simple concept. Efficient for range queries (e.g., "find all users between ID 1500 and 1800") because they often target a single shard.
Cons: Can lead to unbalanced shards (hotspots) if data is not evenly distributed across ranges (e.g., many new users getting IDs in the latest range). Managing range splits and merges as data grows can be complex. Requires a central mapping service or logic to know which shard holds which range.

2. Hash-Based Sharding

A hash function is applied to the shard key. The resulting hash value determines which shard the data belongs to, typically using a modulo operation (hash(shard_key) % number_of_shards).

Example: With 4 shards, User ID 123 might hash to 987654. `987654 % 4 = 2`. So, User 123 goes to Shard 2 (assuming 0-based indexing). User ID 124 might hash to 123456, `123456 % 4 = 0`, so it goes to Shard 0.
Pros: Generally leads to a more even data distribution across shards compared to range sharding, assuming a good hash function. Reduces the likelihood of hotspots.
Cons: Makes range queries very inefficient, as they typically require querying *all* shards. Adding or removing shards is complex with simple modulo hashing, as it changes the target shard for almost all keys (requires re-sharding). (Note: Consistent Hashing is often used in conjunction with hashing to mitigate the re-sharding problem when nodes change, but the basic distribution uses the hash).

Other Strategies

Directory-based sharding (lookup table), Geo-sharding (based on location), and combinations also exist but Range and Hash are fundamental.