Understanding MySQL Sharding: Concepts, Challenges & Types Simplified

Most businesses today have started becoming data-oriented. This means that they rely on data-driven decision-making to plan future strategies pertaining to various departments such as Marketing, Growth, Business Development, Product, etc.

In order to perform an accurate analysis, businesses have to ensure that all the required data is collected in a form suitable for analysis and is easily accessible. Since the goal is to perform an accurate analysis, a high volume of data is collected which makes it impossible to store on a single machine. This led to the concept of Database Sharding.

There are a large number of databases that businesses use today in order to perform their day-to-day operations. One of the most well-known databases is MySQL. This article will help you understand what Database Sharding is and how MySQL Sharding works.

Introduction to MySQL

MySQL is considered to be one of the most popular Open-Source Relational Database Management Systems (RDBMS). MySQL implements a simple Client-Server Model that helps its users manage Relational Databases i.e. data stored in the form of rows and columns across tables. It uses the well-known query language, Structured Query Language (SQL) that allows users to perform all required CRUD (Create, Read, Update, Delete) operations.

Introduction to Sharding

Businesses that rely on Monolithic Relational Database Management Systems (RDBMS) hit bottlenecks as the volume of data being stored increases. Response Time is bound to suffer because of limited CPU power, memory, storage capacity, and throughput.

• Although Vertical Scaling can be implemented in these situations, it has its own limitations and serves diminishing returns after a point.
• Horizontal Scaling is the best way to deal with high volumes of data. If tables are partitioned horizontally and split across multiple Servers, the load on each Server will decrease. Multiple servers executing a single query in parallel would result in less response time. 
• This process of breaking up large tables into smaller chunks and storing them across multiple Servers is known as Sharding. Each chunk is known as a Shard.

When Should You Consider Sharding?

Sharding is not always necessary, and implementing it requires significant planning and resources. Below are the scenarios when sharding is typically needed:

1. Avoiding Single Points of Failure
   Sharding can prevent a single server from becoming a bottleneck or a single point of failure by distributing data across multiple servers.
2. Handling Large Volumes of Data
   If your database is growing rapidly and nearing storage or performance limits, sharding can distribute the data across multiple servers to handle the load.
3. Improving Query Performance
   When queries are becoming slower due to the size of the database, sharding can divide the data, allowing queries to access smaller, more manageable datasets.
4. Scaling Applications
   Applications with a growing user base often require horizontal scaling. Sharding ensures that the database can keep up with increasing traffic and data demands.
5. Geographical Distribution
   For applications with users in multiple regions, sharding data by region can reduce latency and improve access speed for users closer to their data.
6. Isolating Workloads
   If certain types of data or queries put a heavy load on the system, sharding can isolate these workloads to specific shards, ensuring better overall performance.

What is MySQL Sharding?

As the data on the MySQL Database increases, some performance issues arise with scaling write data. Companies solve this problem with the help of Horizontal Sharding in the MySQL Database in which data from a single MySQL Database is partitioned across several MySQL Database servers with identical schema. It spreads workload from a single server to multiple servers, and each server is called a shard. This process is called MySQL Sharding.

Horizontal vs Vertical Sharding

Aspect	Horizontal Sharding	Vertical Sharding
Definition	Splits data by rows across multiple databases or servers. Each shard contains the same schema but a subset of rows.	Splits data by columns into separate tables or databases. Each shard contains a subset of the schema.
Use Case	Best for distributing large datasets where rows need to be partitioned (e.g., user data by region).	Best for isolating specific functionalities or reducing table complexity (e.g., separating transactional and analytical data).
Data Distribution	Rows are distributed based on a Sharding Key (e.g., user ID, region).	Columns are grouped by logical or functional requirements.
Performance Impact	Improves query performance by limiting the number of rows a query scans.	Optimizes performance for specific queries by reducing table size or isolating workload types.
Complexity	Requires careful selection of a Sharding Key to avoid hotspots or uneven data distribution.	Requires understanding of data dependencies and workload patterns to decide column separation.
Schema Management	Schema changes must be replicated across all shards.	Schema changes affect only the specific shard where the column resides.
Joins	Cross-shard joins can be challenging and require additional logic.	Cross-shard joins may not be required if data is logically grouped.
Scalability	Easily scalable by adding new shards as data grows.	Scalability depends on identifying new functional or logical separations for columns.

Common Auto-Sharding Architectures

Different Sharding Architectures and implementations have been used to build large-scale systems. The three common Auto-Sharding Architectures are listed below:

1) Hash Sharding

Hash Sharding inputs a shard’s key and outputs a hash value for it that is used to determine in which shard the data should store. It ensures that the data distribute evenly across all the servers using hash functions and reducing the risk of hotspots. The data that has close shard keys are likely to be placed on the same shard server. Hash Sharding is greatly used for targeted data operations.

2) Range Sharding

In Range Sharding the data is divided based on ranges or keyspaces, and the nearer the shard keys, the more likely for data to place under the same range and shard. Every shard has an identical schema taken from the original database. It allows users to easily run queries to read data within contiguous data ranges. Choosing the shard keys poorly can result in database hotspots. That’s why one should pick keys with large cardinality, low recurring frequency, and whose magnitude does not increase monotonically.

3) Geo-Partitioning

The Geo-based sharding first partitions data according to the user-specified column so that it can map range shards to specific locations and the nodes in those regions. For every location, the data is sharded using range sharding or hash.

The Challenges of Manual Sharding

Monolithic databases such as MySQL, Oracle, PostgreSQL, Amazon Aurora, etc. do not support automatic sharding. As the data increases the complexity to handle MySQL sharding becomes an additional development task. A few more challenges of Manual Sharding are listed below:

• It needs additional sharding logic to configure how the data should be distributed and how to retrieve it. It also involves the process of selecting the right approach to adopt for MySQL Sharding, the number of nodes needed, how to rebalance it across all the nodes for optimal performance.
• Developers need to change the data sharding techniques for change in business needs.
• Manual sharding of data can cause uneven shard allocation that will lead to unbalanced shards that means some might be overloaded with data some might be empty creating a non-uniform allocation.
• Implementing a manual process for sharding complicates the operational process that includes, maintenance, backups from multiple data servers, etc. Applying all the processes manually is a cumbersome task.

Key Considerations for Implementing MySQL Sharding

Unfortunately, Monolithic Databases like PostgreSQL, Oracle, MySQL along with modern distributed SQL Databases such as Amazon Aurora do not support Automatic Sharding. This means that if something like MySQL Sharding has to be implemented, it has to be done manually in the Application Layer which requires immense Engineering bandwidth since an entire Sharding logic that determines how data is distributed and how it is fetched has to be set up.

Since the implementation has to be done manually, there are certain design decisions that have to be made. The decisions that need to be made are as follows:

• Choosing MySQL Sharding Key
• Handling Schema Changes
• Mapping between Physical Servers, Shards, and MySQL Sharding Keys

1) Choosing MySQL Sharding Key

The MySQL Sharding Key will control how data is distributed across Shards. When implementing Sharding in MySQL, the MySQL Sharding Key should be chosen carefully as the wrong key might cause system inflexibility later. For example, Referential Integrity i.e. the parent/child relationship between tables that is maintained by the Relational Database Management System (RDBMS), won’t be maintained automatically if the parent and child rows are stored on separate Shards.

The two possible types of MySQL Sharding Keys are as follows:

• Intelligent MySQL Sharding Keys: More suitable to avoid joins across Shards but is considered to be more exposed to skews. For example, if there is a Customer table that is Sharded based on the attribute that denotes the Customer ID, then it would make sense to store information related to all customers such as interactions, touchpoints, etc. in a single Shard to avoid any joins.

• Hashed MySQL Sharding Keys: Ensures automatic distribution of data across Shards. This is done to ensure that the data is well spread and a single Shard does not experience a very high load. For example, if the customers are expected to grow a lot, it makes more sense to use Hashed MySQL Sharding Keys to ensure proper distribution of data across Shards. However, in order to perform any useful analysis, complex join operations would have to be performed across multiple Shards and that is not possible. 

2) Handling Schema Changes

MySQL allows users to change table Schemas in the database at any point in time after creation. If MySQL Sharding has been implemented, this change in the Schemas has to be implemented across all Shards before any application can leverage the new Schemas. If the change is not implemented in even one of the Shards, it could lead to application errors or data inconsistency. Hence, the users should either develop a logic that can ensure that Schema changes are implemented across all Shards or not make any changes in the Schema once MySQL Sharding is implemented.

3) Mapping between Physical Servers, Shards, and MySQL Sharding Keys

MySQL Sharding cannot be implemented if there isn’t proper mapping between Physical Servers, Shards, and MySQL Sharding Keys. This mapping might have to be updated regularly as the number of Shards increase or the Schemas change. Also, this mapping would have to be accessed for almost every query that is run on the database and hence, should be stored in a location where fast lookups are possible. Therefore, it is considered ideal to store it in an in-memory database.

Advantages of MySQL Sharding

MySQL sharding offers several unique benefits that make it an effective solution for managing large datasets and improving database performance:

1. Enhanced Storage Capacity
   Sharding allows you to divide large datasets across multiple servers, significantly increasing the overall storage capacity of your system.
2. Better Resource Utilization
   By distributing the data, each server handles a smaller workload. This optimizes the usage of CPU, memory, and disk space, reducing the risk of overloading any single server.
3. Improved Data Availability
   Since data is distributed across multiple shards, even if one server fails, the data stored in other shards remains accessible, ensuring higher availability.
4. Reduced Maintenance Time
   Smaller, more focused datasets on each shard make it easier to perform maintenance tasks, such as backups or indexing, without impacting the entire database.
5. Customization for Specific Needs
   Different shards can be configured and optimized individually based on the data they store and the type of queries they process, allowing for tailored performance improvements.
6. Cost Efficiency
   Instead of upgrading to expensive high-performance servers, sharding enables you to scale horizontally by adding more commodity servers, reducing infrastructure costs.
7. Faster Data Loading
   Sharding enables parallel processing for data loading tasks, which speeds up the ingestion of large volumes of data.

Limitations of MySQL Sharding

Implementation of MySQL Sharding has certain drawbacks because of which its implementation for most applications in production is not recommended. If Sharding is an absolute requirement, then it is advised to use NoSQL Databases such as MongoDB that support Automatic Sharding.

The limitations of implementing MySQL Sharding are as follows:

• MySQL Sharding does not allow cross-joins across multiple Shards. This is considered to be a huge disadvantage of MySQL Sharding since a lot of businesses use SQL for Analytics which cannot be performed without complex join operations.
• Another major advantage of using SQL Databases for Analytics is the ability to perform aggregations easily. If MySQL Sharding is implemented, users lose the ability to perform aggregations across multiple Shards.
• ACID Compliance is considered to be one of the most significant advantages of using any SQL Database. If MySQL Sharding is implemented, the database is not considered to be ACID Compliant anymore and hence, the possibility of data loss or inaccurate or inconsistent data increases significantly.
• MySQL Sharding process has to be implemented manually based on business and data requirements. This means that a high amount of resources and time would be required in testing and developing its implementation.

Conclusion

This article provided you with an in-depth analysis of what MySQL is, how Sharding works and why it is required along with the design decisions that have to be made while implementing MySQL Sharding. It also provided you with a comprehensive understanding of why Sharding is considered to be a bad idea for most businesses.

Implementation of Sharding is considered to be more suitable for NoSQL Databases. Hence, if Sharding is of paramount importance for your business and data requirements, it would be ideal to transfer all the required data to a NoSQL Database and then implement Sharding on it. This migration of data from MySQL to a NoSQL Database can either be implemented manually or automated using platforms like Hevo. 

Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan fulfills all your business needs.

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