Shard & Replica Strategies

1. Introduction

In the realm of search engine databases, managing large volumes of data efficiently is crucial. Sharding and replication are two strategies utilized to enhance scalability and performance. This lesson will delve into these concepts, their implementation, and best practices.

2. Key Concepts

3. Sharding

3.1 Sharding Strategies

• Range-based sharding: Data is divided based on ranges of values.
• Hash-based sharding: A hash function determines the shard for each record.
• Directory-based sharding: A lookup table maps data to shards.

3.2 Example of Sharding

    // Example of hash-based sharding in Python
    
    def get_shard(key, num_shards):
        return hash(key) % num_shards

    num_shards = 4
    key = "example_data"
    shard_number = get_shard(key, num_shards)
    print(f"Data should be stored in shard: {shard_number}")
            

4. Replication

4.1 Types of Replication

• Synchronous replication: Data is written to all replicas simultaneously.
• Asynchronous replication: Data is written to the primary and then replicated to other nodes, allowing for lag.

4.2 Example of Replication

    // Example of asynchronous replication pseudo-code
    
    def write_data_to_primary(data):
        primary_db.write(data)
        send_to_replicas(data)

    def send_to_replicas(data):
        for replica in replicas:
            replica.write(data)
            # Assume write is an asynchronous call
            print(f"Data sent to replica: {replica.name}")
            

5. Best Practices

1. Monitor shard and replica health regularly.
2. Distribute shards evenly to avoid hotspots.
3. Use consistent hashing to minimize data movement during re-sharding.
4. Implement read replicas for heavy read workloads.
5. Plan for failover strategies in case of shard or replica failure.

6. FAQ