Sharding has emerged as an essential concept that allows systems to keep data in different resources together. Ideally, “Shard” refers to the “small part of a whole”. Thus, Sharding can be understood as the method of database partitioning. The growing data size and volume make it necessary to have a simple solution to make the data manageable and the huge transaction cost. As a result, the idea of Sharding came to life.
What is Sharding?
Sharding refers to database partitioning and is known for separating large databases into faster, smaller, and easily manageable components. Each partitioned part is called a data shard. There are some useful features of Sharding that increase its use cases in data management.
- The process of Sharding can be complex several times.
- Each Shard, which is a smaller part of the data reads and writes its data.
- Most of the NoSQL databases offer the option of auto-sharding.
- The performance or failure of one shard does not affect the processing of other shards.
Typically, sharding involves splitting and distributing each logical data set across various databases so they can be deployed to multiple servers. However, to achieve sharding the rows or columns from larger database tables are split into multiple smaller tables. The logical shard stored on another node is termed a “physical shard”. All these shards are now autonomous and do not share the same computing resources. As a result, these shards follow a shared-nothing architecture.
Representation Method of Sharding
The sharding can be represented and described in two forms – Horizontal and Vertical Sharding.
Horizontal Sharding: The database where each new table has the same schema but unique rows then it is termed horizontal sharding. In this form of sharding, more machines are added to the existing stack with the intention o spreading out the load, supporting more traffic, and enhancing processing speed. The horizontal sharding is more effective when queries return a subset of rows that are usually grouped together.
Vertical Sharding: The database where the schema of a new table is a faithful subset of the schema of the original table. It is said to be effective when the queries return only a subset of columns.
Benefits of Sharding
The process of simplifying and partitioning the database gives the most scalable database architecture. Since Shards are faster, smaller, and easier to manage therefore they improve the overall performance, administration, and scalability of the database. Alongside this, the segregated and smaller cost of data also requires lower transaction costs. Preferably, horizontal scaling is seen as the most flexible design and is useful for parallel processing.
It also has endless scalability to mitigate intense workloads and big data requirements. The users can also use all resources across a cluster to address each query. As a result, sharding is also the best way to develop effective query resolution with less table scanning. Sharding improves the capacity of the CPU, increases storage capacity, and leads to an overall improvement in the power of the server. As a result, it also helps to mitigate the impact of outages and minimize damage. Additionally, Sharding increases the read/write throughput when operations are confined to a single shard.
Different Types of Sharding Architecture
Sharding Architecture is broadly understood using three different sharding methods where a shard is allocated to each row based on the sharding key. This key can be an index or primary key in the original table. Let’s understand ways to develop more such sharding architecture.
The key-based sharding is also called hash-based sharding as the data is plugged into the hash function here. This hash function determines which shard each data value must be associated with. It also generates the discrete output value which is known as the hash value and is treated as a shard ID. As a result, it determines which shard stores the data. Generally, the value stored using the hash function originates from the same column that is taken as the shard key. This further helps to create consistency by developing appropriate supporting data in the correct shards. The key generated using the hash function is static and if in case it changes over time it may lead to slower database performance.
Range- based sharding
The range-based sharing involves shared data that is partitioned based on the ranges of a given value. The range is determined based on the field which is known as the shard key. The implementation of range-based sharding is quite straightforward and has a simple algorithm as the shards have an identical schema. However, since the data here is unevenly distributed it may also create database hotspots and may lead to poor selection of shard keys at times.
Generally, directory-based sharding focuses on creating and maintaining the lookup table. Like other sharding methods, the shard key is used here to track the data type that is stored in each shard. Unlike key-based sharding, this sharding architecture does not require a hash function whereas unlike range-based it allows tying each key to a specific shard. However, since it requires a connection to the lookup table before every query, therefore may impact the application performance.
Does Sharding and Partitioning Mean The Same?
We often refer to sharding as a method of partitioning but do they mean the same? Ideally, both these processes involve breaking up large databases into smaller databases. However, there is a difference between both these methods. After the database is sharded, the data generated in the new table is spread across multiple systems. However, the case is not the same with the partitioning as such data subsets within a single database instance.