What problems can occur during sharding?

When sharding (dividing data into parts for load distribution), the following problems may arise:

• Uneven data distribution (skew): if sharding is based on keys with uneven distribution, some shards may become overloaded, reducing performance.
• Query complexity: queries involving multiple shards require additional aggregation and may run slower.
• Ensuring data integrity: transactions involving multiple shards are more difficult to implement, especially from an ACID perspective.
• Migration and scaling: adding or removing shards requires data redistribution, which can be costly and complex.
• Increased architecture complexity: supporting and debugging a distributed system is more complex than a monolithic one.

In Go, when implementing sharding, it is important to consider these aspects, such as choosing a hashing algorithm for key distribution and designing interactions between shards.