Sharding

Sharding is a database architecture pattern where data is horizontally partitioned across multiple databases or nodes, often referred to as shards. Each shard contains a subset of the total data, making the overall system more scalable and resilient. It is particularly useful for handling very large datasets and high-throughput applications.

Why Sharding is Needed?

1. Scalability:

   • Single database systems have limitations in terms of storage capacity and throughput. Sharding allows for horizontal scaling by distributing data across multiple nodes.

2. Performance:

   • Queries can be processed in parallel on multiple shards, reducing response times for large datasets.

3. High Availability:

   • In case of a failure on one shard, the others can continue to operate, improving overall system availability.

4. Data Isolation:

   • Data can be logically separated based on application needs, making it easier to manage and query specific subsets.

How Sharding Works

In sharding, data is distributed across shards based on a shard key. The shard key determines how the data is split and placed into shards.

Steps in Sharding

1. Select Shard Key:

   • A column or a combination of columns is chosen as the shard key. The key must provide even distribution of data across shards.

   Example: user_id, region, or order_id.

2. Distribute Data:

   • Data is distributed across shards based on a mapping function. Common methods include range-based, hash-based, or directory-based sharding.

3. Query Routing:

   • A query router is used to route queries to the correct shard(s) based on the shard key.

Sharding Strategies

Range-Based Sharding

• Data is divided into shards based on value ranges of the shard key.
• Example: User data partitioned by age range or time range.

Example:

• Shard 1: Users with user_id between 1 and 1,000,000.
• Shard 2: Users with user_id between 1,000,001 and 2,000,000.

Pros:

• Easy to implement and understand.
• Good for queries targeting specific ranges.

Cons:

• Risk of uneven data distribution if data growth is not uniform (hotspots).

Hash-Based Sharding

• A hash function is applied to the shard key to determine the shard for each row of data.
• Example:

  shard_number = hash(user_id) % total_shards

Pros:

• Ensures even distribution of data across shards.
• Avoids data hotspots.

Cons:

• Difficult to perform range queries.
• Adding or removing shards requires rehashing (data migration).

Directory-Based Sharding

• A lookup table is maintained to map shard keys to specific shards.
• Example:
  • Shard 1: Data for region = 'US'
  • Shard 2: Data for region = 'EU'

Pros:

• Flexible and customizable.
• Good for specific use cases with pre-defined keys.

Cons:

• The lookup table can become a bottleneck.
• Maintenance overhead.

Advantages of Sharding

1. Horizontal Scalability:

   • Sharding allows for the addition of new nodes to handle increased data volumes or throughput.

2. Parallel Query Processing:

   • Queries are processed in parallel across shards, reducing query execution time.

3. Fault Tolerance:

   • If one shard goes down, the other shards can continue to serve requests.

4. Data Locality:

   • Shards can be located geographically close to the users they serve, reducing latency.

5. Cost Efficiency:

   • Instead of scaling vertically (e.g., adding expensive hardware), horizontal scaling with commodity servers reduces cost.

Disadvantages of Sharding

1. Complexity:

   • Sharding increases system complexity. Query routing, data distribution, and maintenance require additional effort.

2. Data Rebalancing:

   • Adding or removing shards involves redistributing data, which can be costly and time-consuming.

3. Cross-Shard Queries:

   • Queries that span multiple shards can be slow and require additional logic for merging results.

4. Consistency:

   • Maintaining strong consistency across shards is difficult, especially in distributed systems.

5. Operational Overhead:

   • Monitoring, backup, and recovery of multiple shards is more complex than a single database.

Sharding vs Partitioning

Aspect	Sharding	Partitioning
Scope	Data split across multiple databases/nodes.	Data split within a single database.
Use Case	Large-scale horizontal scaling.	Optimizing queries and maintenance.
Query Routing	Requires a query router.	Queries are handled by the database.
Complexity	Higher due to distributed nature.	Lower as it resides in one database.

Sharding vs Replication

Aspect	Sharding	Replication
Data Distribution	Different data stored on each shard.	Same data replicated across nodes.
Purpose	Horizontal scaling for large datasets.	High availability and redundancy.
Query Routing	Queries routed to specific shards.	Queries served by any replica.

Use Cases of Sharding

1. Large-Scale Applications:

   • Social media platforms with millions of users (e.g., Twitter, Facebook).

2. Time-Series Data:

   • IoT or log data where new data grows indefinitely.

3. Geographic Distribution:

   • Applications with users spread across different regions.

4. High-Write Workloads:

   • Applications with heavy insert/update operations (e.g., e-commerce platforms).

Sharding in Practice

1. MongoDB:

   • Native support for sharding with automatic query routing.
   • Sharding based on shard keys.

2. MySQL:

   • Requires external tools (e.g., Vitess) or custom logic for sharding.

3. PostgreSQL:

   • Uses partitioning or external tools like Citus for sharding.