Here, we will discuss concepts related to database sharding, the benefits and drawbacks of sharding, and its architecture of sharding. In the modern age of technology, each data is stored within an organization related to their products, sales, and marketing to analyze the performance analysis of the organization. The data decisions are made through insights reports of performance analysis. Since data keeps on growing day by day, a solution needs to be found out to scale the database, its performance falls down and load increases.

To store such huge datasets, a new concept has evolved that is called sharding. Let’s quickly discuss each section in a detailed way as follows.

  1. What is Sharding?
  2. Benefits of Sharding
  3. Drawbacks of Sharding
  4. Sharding Architectures

1. What is Sharding?                             

Database Sharding is the breaking down of multiple tables into various smaller chunks or tables, which is called shards. Sharding also distributes the data across several multiple clusters or machines. The data which is stored in each shard is independent and different from other shards. It is almost similar to scale the database horizontally, which is also called horizontal partitioning. 

2. Benefits of Sharding

  • The foremost benefit of sharding is it helps us to make an easier process of horizontal scaling. Thus, we can add machines to the current server and distribute the load among the machines to scale up the application.
  • It helps in resolving faster queries in a short time duration.
  • Maintenance becomes easier due to the sharding.
  • It makes the application more fault-tolerant and eliminates the issue of a single point of failure.
  • Reduces the pricing due to vertical partitioning. 

3. Drawbacks of Sharding

  • Practical implementation of database partitioning is complex which may lead to data loss or corrupt tables if done incorrectly.
  • If shards become unbalanced, it will lead to another major issue.
  • It becomes difficult to return to the original un-sharded version once database sharding is done.
  • Not all types of databases support sharding. 

4. Sharding Architectures

Now let’s quickly understand the different architectures of Database Sharding. Various types of Database Sharding Architectures can be chosen depending on your requirements as follows:

  • Directory-Based Sharding: This type of architecture uses lookup tables for keeping track of the data in a Database Shard. The main function of the lookup table is to give the exact information of the data stored in the database. This architecture gives more flexibility in finding out the range of values in the lookup table or create shards based on algorithms, and so on. The main drawback of this type of architecture is that it needs to consult a lookup table to find the concerned data for every single execution of the query. Also, the whole system may lead to failure if any of the lookup tables crash as the entire architecture cannot function without it.
  • Key-Based Sharding: This type of sharding is also known as Hasing based sharding which uses Hashing concept. It uses the key-value pairs to store the values. Every key has unique values that are different from each other. In this architecture, the function of hash is used to map every row to its Shard by taking some data values from the row and then mapping that unique value to the Shard where data needs to be stored. If you are finding the location of Shard from the data, you need to be slightly concerned about finding the data in the Shards. In other types of architecture, you need to keep track of data in the Shards.
  • Range-Based Sharding: In this Database architecture, a lookup table is used to find the database shard by looking at the data. The lookup table comprises a range of values as well as the id of Shard. If the data needs to be stored under a specific range of a Shard, it will be stored under that particular Shard only. This may lead to an unbalanced spread of data in some cases because the frequencies/occurrences of the Shards may be more as compared to others. Ex. Price value may be stored according to the Shards ranges of values. 
  • Geo-Based Sharding: This type of architecture is almost similar to the above type of sharding. In this type of sharding, user location or region is used by the data to process a shard. Ex. Tinder Application uses this type of sharding.


Database Sharding can be a great solution for customers looking to scale their database horizontally and vertically. However, it also adds complexity as well as a point of failure to the application. Sharding may be important for some, but the time and resources for creating and maintaining architecture could outperform the benefits for others.

In this article, you must have got a clear idea about the pros and cons of Sharding. Also, you can get the insight to make a more informed decision whether or not the Sharded database architecture is best suited for your application.

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