MySQL Sharding: Scaling Techniques for Handling Large Datasets
MySQL Sharding: Scaling Techniques for Handling Large Datasets. As businesses grow scaling is imperative to keep up with the performance. It is crucial for businesses to implement robust scaling techniques. One of the most common techniques is horizontal scaling or sharding.
MySQL sharding is a scaling technique whereby the tables are systematically partitioned, with each partition stored in its own server. Each shard or partition has similar columns and schema, but different rows. Sharding allows you to split large datasets into smaller, distributed partitions. Applications fetch data from different shards, resulting in improved workload performance.
This article discusses MySQL sharding, including different types of sharding, distributed queries in sharded databases, and challenges of sharding. Read on!
Primary Types of Sharding in MySQL
The most common types of sharding in MySQL:
Range Based Sharding
Range based sharding is a database scaling method used to segment data based on certain adjacent ranges of values. In this technique, the entire dataset is divided into multiple shards. Each shard holds records that fall within a specified range of values, usually based on a primary key. This method in handy when the range of values is well established, and queries often operate within certain boundaries.
Here is an example of range based sharding:
Consider a scenario where you want to store order details for an e-commerce platform. Given the volume of orders, we decide to use range-based sharding based on order IDs.
CREATE TABLE order_details_shard_1 (
order_id INT NOT NULL,
product_id INT NOT NULL,
unit_price DECIMAL(10,2) NOT NULL,
quantity INT NOT NULL,
discount DECIMAL(5,2) NOT NULL,
PRIMARY KEY (order_id, product_id)
);
With the above MySQL table structure, order_details_shard_1 might store order details for order_id values from 1 to 1000. Similarly, you create order_details_shard_2 for the next range (1001-2000) and so on.
To decide which shard a new order should be written to, the application code would check the order_id. If an order_id. For example, if the order_id is 523, it knows that the data should be written to order_details_shard_1. If the order_id is 3170, it is written to order_details_shard_3.
2. Hash Based Sharding
Also known as key based or algorithmic sharding, hash based sharding is used to distribute data across multiple shards or servers. Unlike range based sharding, hash sharding uses a hash function to decide where a piece of data should reside.
Here is a brief explanation of how hash based sharding works:
Hash Function: At the core of hash sharding is the hash function, which takes an input (or “message”) and returns a fixed size string of bytes. The output, typically a sequence of numbers, should be the same length regardless of the length of the input.
Distribution: The result of the hash function determines where the data is stored. For example, if we have 3 shards and use a simple hash function like modulus by 3 (value % 3), values that result in a remainder of 0 go to the first shard, 1 to the second, and 2 to the third.
Compound Shard Key: Multiple fields are used together as a shard key. Hashing such a compound key ensures an even distribution even if individual fields might have clustering.
Hash sharding helps distribute records, as it often results in a more even distribution. Also, it is highly efficient because it uses simple computations to determine where a piece of data should reside. It does not need a lookup table, as the hash is calculated in real-time.
Example of Hash Sharding
Consider a database with stores and their respective IDs. If you shard this data based on the store_ID using a simple modulus operation, the data distribution might look something like:
Calculation:
store_ID 1001 has hash result 1001 % 3 = 2
store_ID 1350 has hash result 1350 % 3 = 0
… and so on for each store_ID.
Distribution:
Entries with hash result 0: Shard 1
with hash result 1: Shard 2
with hash result 2: Shard 3
From this, deduce that store 1001 (with hash result 2) would reside on Shard 3, and store 1350 (with hash result 0) would reside on Shard 1.
3. Directory Based Sharding
A scaling technique for partitioning data across multiple database servers. In this technique, data is partitioned based on a predefined mapping of shard keys to their respective shards. This mapping is typically maintained in a separate lookup table or service. In directory based sharding, a Lookup Table plays a crucial role as it contains mappings between shard keys and the shards with the corresponding data. A shard key helps determine which shard the corresponding record or set of records belongs to.
When a record is written to the database, its shard key is checked against the lookup table to determine which shard it should reside on. Also, when you need to read or access the data, check the shard key against the lookup table to determine which shard to query.
Example of Directory Based Sharding
Consider a database storing records of various retail stores across the UK. Each store is associated with a specific city (e.g., London, Manchester, York). The shard key in this case is the store’s region. The lookup table maps each region to a specific shard:
London -> Shard 1
Manchester -> Shard 2
York -> Shard 3
Thus, all stores located in London would be stored in Shard 1, stores in Manchester in Shard 2, etc.
Directory based sharding provides flexibility as it doesn’t rely on continuous ranges or hashing algorithms. Group data logically and streamline join operations or perform bulk data retrievals. Also, it allows for dynamic scaling, as you easily add more shards as the database grows.
Handling Distributed Queries in Sharded Databases
Queries in a sharded environment differ slightly from that in normal databases. In sharded databases, data is distributed across multiple shards or partitions. Consequently, how query execution varies based on the location and distribution of the required data. There are 3 major methods of handling queries in sharded databases:
Single Shard Queries
These are queries that target data residing in just one shard. An example might be retrieving a user’s details using a specific user ID in a system where data is distributed using hash based sharding. Since the user ID directly maps to a specific shard, the query only needs to access that single shard.
Multi Shard Queries
Multi shard queries require data from more than one shard. A practical example is when calculating the total sales of a product, where sales data resides in several shards. To get the total, the system needs to fetch sales data from each relevant shard and then aggregate it.
Broadcast Queries
In this type of query, the request is sent to every shard in the system, irrespective of where the data resides. Each shard processes the query and sends back its results, which are then aggregated or combined as needed. This approach ensures that no potential data location is missed. However, it increases resource overhead as it involves each shard in the query process.
How to Manage Sharded Databases
A well managed sharded system offers high performance and scalability while maintaining data integrity and availability. To effectively manage a sharded database consider several key aspects, each with unique benefits and challenges as well. Here are some of best practices to manage sharded databases:
Consistent Hashing
In sharded databases, consistent hashing helps minimize data movement during re-sharding. When you add or remove shards, the existing data has to be redistributed. Consistent hashing ensures minimal data movement during this operation. Compared to traditional hashing methods, consistent hashing significantly reduces the system’s overhead. This ensures that re-sharding operations are far more efficient and less disruptive to normal database operations.
Real Time Monitoring
Monitoring is crucial when managing a sharded database. Since sharded databases are distributed in nature, you need to constantly monitor the health and performance of each individual shard.
The best way to monitor sharded databases is by using monitoring tools like Prometheus and Grafana, DatadogHq, Nagios, etc. These tools provide valuable real time metrics, such as query speeds, disk I/O, memory, and server health. With real time monitoring, you can detect issues early in specific shards and intervene. This helps maintain optimal database performance and enhances user experience.
Implement Failover Mechanisms
Since any system can fail, it’s crucial to have a robust failover mechanism in place for your sharded MySQL databases. These failover mechanisms ensure that there’s minimal downtime or data loss if a shard fails. A typical failover mechanism involves having backup replicas for each shard. In case a primary shard fails, the system automatically reroutes the traffic to the backup. This ensures maximum data availability and optimal service continuity.
Handle Cross Shard Transactions Carefully
On many occasions, a single transaction may have to query data from multiple shards. While sharded databases are designed for optimal performance within individual shards, it’s impossible to avoid cross shard transactions.
Implementing cross shard transactions is quite challenging. These transactions require careful coordination between different shards to maintain data consistency. While some databases such as FoundationDB, CockroachDB, and Amazon Aurora provide built in features to handle cross shard databases. In MySQL, you may have to implement custom solutions when handling these transactions.
Challenges with Sharded Databases in MySQL
Complex Implementation and Maintenance
Setting up a sharded environment in MySQL is a complex and challenging process. So plan properly and choose the most optimal sharding technique depending on your database and nature of operations. It’s also important to maintain a uniformly distributed dataset across shards, which is also complex to achieve. Uneven data distribution leads to overloading in some certain shards, resulting in inconsistent performances.
Challenges in Join Operations
In MySQL, joining tables is a common operation. However join operations become more complex when you have to join data from the tables residing on separate shards. Cross shard join operations are typically slower because data needs to come from multiple shards. In some cases, these shards are possibly located on different geographically distributed servers. This results in increased query latency.
MySQL sharding doesn’t natively support cross joins across multiple shards. This affects workloads such as analytics and aggregation tasks that rely heavily on such operations.
Backup and Recovery
Sharded systems spread data across multiple servers or locations, unlike monolithic databases. This distribution complicates backup and recovery processes. If one shard fails or experiences data compromise, the recovery process becomes overly difficult. Therefore, you need a robust backup and recovery system that restores your MySQL data without affecting other system operations.
Resharding Concerns
As business data volumes change over time, you might have to alter the number of shards. This includes adding more data or to consolidate existing ones, a process known as resharding. Resharding involves recalculating shard keys, ensuring minimal downtime, and managing potential data migration challenges. This process requires well thought planning, or the process is error prone and resource intensive.
Rebalancing and Hotspots
Data growth isn’t always uniform and some shards might end up having more data than others. Due to these differences, these shards become a performance bottleneck. Therefore, you have to rebalance data across shards, which is often resource intensive.
MySQL Sharding: Scaling Techniques for Handling Large Datasets Conclusion
Sharding is a proper technology that helps to scale a database and help handle large datasets. With the right technique, you shard your database effectively and implement a proper querying strategy. However, you need to be aware of the potential challenges it poses, as these affect overall database performance. You need to plan how to implement the sharding, how to handle cross join operations, and how to balance data across multiple shards. With these in place, you efficiently run a sharded database with fewer hiccups.
