What is Sharding in MongoDB?

Nowadays we struggle with high volume and complexity of data, so it’s pretty important to develop a scalable project with high performance to stay in the market.

In my previous article, I discussed the different types of indexes in MongoDB and how they can boost the speed of your queries, which you can read about in the link below:

MongoDB indexing tutorial with examples

It’s interesting to know that MongoDB also uses sharding technology that can improve the performance of your application much faster. So let’s dive into it with no hesitation.

What is Sharding?

Database sharding is a database management technique that involves partitioning a growing database horizontally into smaller, more manageable units known as shards.

Sharding is especially useful in cases where you’re working with large amounts of data, as it allows you to scale your base horizontally by adding more machines that can function as new shards.

Generally, a MongoDB database contains a large number of collections. Every collection consists of various documents that contain data. You can break up this large collection into multiple smaller collections using sharding. This allows MongoDB to perform queries without putting much strain on the server.

Understanding Sharding in MongoDB

When working with a MongoDB database, you connect to that instance and use it to directly manage your data. In an unsharded replica set, you connect to the cluster’s primary member, and any changes you make to the data there are automatically carried over to the set’s secondary members. Sharded MongoDB clusters, though, are slightly more complex.

Sharding is meant to help with horizontal scaling, also known as scaling out, since it splits up records from one data set across multiple machines. If the workload becomes too great for the shards in your cluster, you can scale out your database by adding another separate shard to take on some of the work. This contrasts with vertical scaling, also known as scaling up, which involves migrating one’s resources to larger or more powerful hardware.

Types of Sharding in MongoDB

There are mainly three types of sharding strategies:

Range-based Sharding:

Range-based sharding is a sharding strategy in MongoDB that involves dividing data based on a specific range of values, typically derived from the shard key. The shard key is a field or a combination of fields chosen to distribute data across multiple shards. When using range-based sharding, MongoDB distributes data across shards based on the shard key values falling within predefined ranges.

Here are some key aspects of range-based sharding:

1. Range Definitions: Range-based sharding requires defining ranges for each shard based on the shard key values. MongoDB automatically migrates chunks of data between shards as the data distribution changes over time. By defining ranges, MongoDB can efficiently balance data distribution and optimize query performance.
2. Balancing Data Distribution: MongoDB’s balancer process continuously monitors data distribution across shards and migrates chunks between shards to ensure a balanced distribution. This automatic balancing helps maintain optimal performance and prevents any single shard from becoming overloaded.
3. Query Optimization: Range-based sharding can improve query performance by distributing data based on the shard key values. Queries that target a specific range of values can be directed to the appropriate shard, minimizing the number of shards involved in query execution and reducing response times.

Hash-based Sharding:

Hash-based sharding is another key sharding strategy in MongoDB, offering an alternative approach to distributing data across shards. Unlike range-based sharding, which divides data based on specific ranges of values, hash-based sharding distributes data across shards based on the hash value of a field or combination of fields, typically the shard key.

Here are some important aspects of hash-based sharding:

1. Shard Key Selection: The choice of shard key is critical in hash-based sharding. Since data distribution is based on the hash value of the shard key, it’s essential to select a field or combination of fields with a high cardinality to ensure even distribution across shards. Commonly used fields for hash-based sharding include identifiers or unique values.
2. Hash Function: MongoDB uses a hash function to calculate a hash value for each document’s shard key. This hash value determines the shard to which the document will be assigned. MongoDB’s default hash function ensures an even distribution of data across shards, helping to prevent hotspots and uneven workload distribution.
3. Query Considerations: Hash-based sharding can simplify query routing and execution since the hash value of the shard key directly determines the shard responsible for storing the document. However, range-based queries may be less efficient in hash-based sharding compared to range-based sharding, as data distribution is not based on specific ranges of values.

Tag-aware Sharding:

Tag-aware sharding is an advanced sharding strategy in MongoDB that allows for more granular control over the placement of data across shards. This strategy involves associating tags with specific ranges of shard key values and using these tags to route data to specific shards based on predefined rules. Tag-aware sharding provides flexibility and fine-grained control over data placement, making it particularly useful for scenarios where data locality, compliance requirements, or specific performance needs must be met.

Here are some key aspects of tag-aware sharding:

1. Tag Assignment: Tags are associated with ranges of shard key values using tag ranges. Each tag represents a label or identifier that describes certain characteristics of the data. For example, tags could represent geographic regions, customer segments, or compliance requirements.
2. Shard Assignment Rules: MongoDB allows you to define rules that specify how data with particular tags should be routed to specific shards. These rules typically involve matching tag ranges to shard ranges, ensuring that data with specific tags is stored on designated shards.
3. Data Locality: Tag-aware sharding enables you to ensure that data with specific characteristics or requirements is colocated on specific shards, improving data locality and reducing network latency for queries that access related data. This can be particularly beneficial for distributed applications with geographic data requirements or multi-region deployments.
4. Compliance and Governance: Tag-aware sharding allows you to enforce compliance and governance requirements by ensuring that data with specific regulatory or security considerations is stored on designated shards. This enables organizations to maintain data sovereignty, adhere to regulatory mandates, and implement data access controls effectively.

Despite all the advantages of using sharding in your database, definitely, there are some disadvantages you need to consider.

Disadvantages of Sharding in Database:

While sharding offers several benefits for scaling and performance, it also introduces certain challenges and potential disadvantages:

1. Complexity: Sharding adds complexity to database architecture and administration. Managing multiple shards, distributing data evenly, and ensuring data consistency across shards require additional operational overhead. This complexity can increase the risk of configuration errors, performance bottlenecks, and data integrity issues.

2. Data Distribution Overhead: Distributing data across multiple shards involves overhead in terms of data migration, synchronization, and coordination. As the dataset grows or the number of shards increases, the overhead associated with data distribution can become significant, impacting performance and resource utilization.

3. Query Routing Overhead: Query routing in a sharded environment introduces additional overhead, as queries must be routed to the appropriate shards based on the sharding strategy and shard key. While query routers (e.g., mongos in MongoDB) handle this routing transparently, the overhead can affect query latency and overall system performance, especially under high query loads.

4. Complex Shard Key Selection: Choosing an appropriate shard key is crucial for efficient sharding. The selection of a poorly performing shard key can lead to data hotspots, uneven data distribution, or inefficient query routing. Identifying the right shard key requires careful consideration of data access patterns, workload characteristics, and scalability requirements.

5. Backup and Disaster Recovery Complexity: Managing backups and disaster recovery in a sharded environment can be challenging. Backup and restore operations must consider data distributed across multiple shards, potentially involving complex backup strategies, incremental backups, and data consistency checks. Disaster recovery planning also requires consideration of failover scenarios and data resynchronization across shards.

6. Increased Hardware and Operational Costs: Sharding typically involves deploying and managing multiple database instances across multiple servers or clusters. This increased hardware and operational footprint can lead to higher infrastructure costs, including hardware provisioning, maintenance, monitoring, and administration.

Despite these disadvantages, sharding remains a powerful technique for horizontal scalability and performance optimization in large-scale database deployments. Proper planning, design, and management are essential to mitigate these challenges and realize the benefits of sharding effectively.