How to Implement Sharding and Partitioning Strategies in JPA

1. Overview

In high-scale applications where a single database instance becomes a bottleneck, sharding and partitioning are two strategies used to improve performance, scalability, and manageability.

While JPA (Java Persistence API) doesn’t natively support sharding or partitioning, it can be extended to implement these strategies using custom logic, multi-datasource configuration, and routing mechanisms.

2. Sharding vs. Partitioning: Understanding the Basics

Aspect	Sharding	Partitioning
Definition	Horizontal scaling across multiple databases or schemas	Dividing data across tables or table partitions within the same DB
Use case	Very large-scale systems (e.g. multi-tenant, social networks)	Performance tuning, data archival
Location	Cross-database or schema	Within one database
Managed by	Application or middleware	Database engine (PostgreSQL, Oracle, etc.)

3. Challenges with Sharding in JPA

JPA assumes a single datasource
No built-in support for cross-shard transactions
Difficulties in:
- Mapping entities to different datasources
- Routing queries to correct shard
- Maintaining consistency

But with the right approach, these can be overcome.

4. Common Sharding Strategies

Strategy	Description	Example
Hash-based	Use hash of key (e.g., user ID) to select shard	`userId % N`
Range-based	Use value ranges (e.g., dates, IDs)	ID 0–1000 → shard1
Geo-based / Tenant-based	Route based on location or tenant ID	`India → DB1`, `US → DB2`

5. Partitioning Strategies in JPA (Same DB)

Databases like PostgreSQL, MySQL 8+, and Oracle support table partitioning.

You can:

Keep entity mapping the same
Let the database handle routing using partitioning keys

CREATE TABLE orders (
  id BIGINT,
  user_id BIGINT,
  created_date DATE
) PARTITION BY RANGE (created_date);

JPA just talks to the table, DB handles partition logic internally.

Good for:

Time-based data (logs, orders, invoices)
Reducing index bloat

6. Implementing Sharding in JPA – Step-by-Step

Approach: Application-Level Sharding (Custom Routing)

Step 1: Configure Multiple DataSources

spring:
  datasource:
    shard1:
      url: jdbc:mysql://shard1-db:3306/mydb
      username: user1
      password: pass1
    shard2:
      url: jdbc:mysql://shard2-db:3306/mydb
      username: user2
      password: pass2

Step 2: Define DataSource Beans

@Configuration
public class ShardDataSourceConfig {

    @Bean(name = "shard1DataSource")
    @ConfigurationProperties("spring.datasource.shard1")
    public DataSource shard1DataSource() {
        return DataSourceBuilder.create().build();
    }

    @Bean(name = "shard2DataSource")
    @ConfigurationProperties("spring.datasource.shard2")
    public DataSource shard2DataSource() {
        return DataSourceBuilder.create().build();
    }

    @Bean
    public Map<Object, Object> dataSources() {
        Map<Object, Object> map = new HashMap<>();
        map.put("shard1", shard1DataSource());
        map.put("shard2", shard2DataSource());
        return map;
    }
}

Step 3: Implement RoutingDataSource

public class ShardRoutingDataSource extends AbstractRoutingDataSource {

    private static final ThreadLocal<String> CONTEXT = new ThreadLocal<>();

    public static void setShardKey(String key) {
        CONTEXT.set(key);
    }

    public static void clear() {
        CONTEXT.remove();
    }

    @Override
    protected Object determineCurrentLookupKey() {
        return CONTEXT.get();
    }
}

Step 4: Use Routing in Your Services

public void saveUser(User user) {
    String shardKey = resolveShardKey(user.getId()); // e.g., userId % 2
    ShardRoutingDataSource.setShardKey(shardKey == 0 ? "shard1" : "shard2");

    userRepository.save(user);

    ShardRoutingDataSource.clear();
}

Step 5: Register RoutingDataSource with JPA

@Bean
public DataSource routingDataSource() {
    ShardRoutingDataSource routingDataSource = new ShardRoutingDataSource();
    routingDataSource.setTargetDataSources(dataSources());
    routingDataSource.setDefaultTargetDataSource(shard1DataSource());
    return routingDataSource;
}

@Bean
public LocalContainerEntityManagerFactoryBean entityManagerFactory(EntityManagerFactoryBuilder builder) {
    return builder
        .dataSource(routingDataSource())
        .packages("com.example.entity")
        .persistenceUnit("default")
        .build();
}

7. Implementing Partitioning in JPA (Same DB)

Step 1: Create Partitioned Table in DB

Example in PostgreSQL:

CREATE TABLE orders (
  id BIGINT PRIMARY KEY,
  created_date DATE NOT NULL
) PARTITION BY RANGE (created_date);

CREATE TABLE orders_2023 PARTITION OF orders FOR VALUES FROM ('2023-01-01') TO ('2024-01-01');
CREATE TABLE orders_2024 PARTITION OF orders FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');

Step 2: Use Normal JPA Mapping

@Entity
public class Order {
    @Id
    private Long id;

    private LocalDate createdDate;
}

JPA queries the orders table and the database routes to the correct partition.

No special code needed on the JPA side.

8. Tools That Help with Sharding

Tool	Description
Hibernate Shards (archived)	Legacy sharding extension (not maintained)
ShardingSphere	Advanced JDBC-based sharding middleware (supports JPA)
Vitess	Scales MySQL using sharding & connection pooling
Citus (PostgreSQL)	Distributed PostgreSQL with automatic sharding
ProxySQL	Query router for sharded MySQL
Custom Routing	Best suited for Spring Boot + JPA

9. Best Practices

Practice	Description
Use routing abstraction	Centralize shard logic in a router class
Use consistent sharding keys	e.g., userId or tenantId
Avoid cross-shard joins	Denormalize or fetch from multiple shards
Use asynchronous writes across shards	For performance and isolation
Use a unified interface for all shards	Helps in dynamic routing
Partition large tables by time or range	Improves query performance
Index partitioning keys	Ensures partition pruning

10. Limitations and Trade-offs

Limitation	Workaround
No JPA-native sharding	Use custom routing or external tool
Cross-shard transactions	Use eventual consistency or distributed transactions
Complexity in testing	Use test containers or mock sharded environments
Difficult migrations	Use logical sharding + versioning