Database Sharding: A Comprehensive Guide

on Database Sharding • March 17th, 2021 • Write for Hevo

Nowadays, organizations store every single piece of data associated with their Products, Users, Sales, Marketing, and other departments. The reason behind this is to generate reports or perform analysis. The insights that are gained from the data are invaluable. Organizations want to make data-driven decisions and for this, they need to keep track of data in its entirety. Since this data keeps on increasing exponentially with time, a solution needs to be found because, as you scale a database, the load increases and its performance decreases. This is where Database Sharding comes into the picture.

Database Sharding is a fairly new concept and is gaining popularity. In this article, you will learn about Database Sharding, its importance, its different architectures, and its drawbacks. To understand this article, you need a basic understanding of Databases.

Table of Contents

Understanding Database Sharding

Database Sharding
Image Source: Cubrid

In the above image, you can see that a single Database “User” is horizontally split into two subsects where each split has 2 records. The schema and attributes are the same but the records are different.

Sharding a Database is the process where a huge Database is partitioned horizontally. This means that the attributes of the Database will remain the same but only the records will change. So the data in each partition is unique but the schema remains the same. Data partitioning is a kind of Database architecture that is gaining popularity recently because it allows scalability and reduces the load on a single Database. 

When you perform Database Sharding, each Shard has independent data and computing resources. So the Shards do not interfere with each other. And since they do not share the same computing resources, it reduces the load and helps in processing the queries faster. Some of the Databases like Cassandra, HBase, HDFS, MongoDB, and Redis have an inbuilt mechanism to create Shards. For other Databases, you have to program them manually.

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Understanding the Need to Shard a Database

This concept is becoming more prevalent because of the following reasons: 

1. Reduces Load on a Database

Since a Sharded Database contains a fewer number of rows, the load on the Database decreases.

2. Improves Query Performance

While executing a query, the computer need not go through a long list of records that would have existed if the Database would not have been Sharded. So, the results of the query are returned faster hence there is an improvement in query performance.

3. Makes it Simple to Scale Horizontally

You can keep adding data and stop worrying about impacting Database performance when you have Database Sharding. The added data will be split into shards making it easier to scale horizontally.

4. Easy Recovery during Crashes or Failures

A crash or failure in one of the Shards will not impact the other Database Shards because they work independently. But in the case of a normal Database, if there is a crash, the Database needs to be recovered before running any queries on it.

5. Enables More Users to Use the Database

If a normal database can serve a number of users then each Shard in a Sharded Database will be able to serve that many users. This can also be called increasing traffic on your Sharded Database. 

6. Saves Time

Assume that a Database is not performing well due to high load. In a normal instance, you would work on outsourcing some of that data to reduce the load. But for how long will you do this? Because the load will keep on increasing. Database Sharding allows you to maintain your data Schema properly and decrease this load. You don’t have to manually reduce the load on your Database hence it saves you a lot of time.

Understanding Database Sharding Architectures

Now that you realize how creating Shards can make your life easier you need to understand the different architectures of Database Sharding. Depending on your use-case, you can choose the architecture that suits your needs the best. The 3 types of Database Sharding Architectures are:

1.  Key-Based Sharding

Key Based Sharding
Image Source: Digital Ocean

If you are familiar with the concept of Hashing then this concept is easy to understand. Hashing is popular to store key-value pairs. Each key has a unique value. Analogically the Key Based Sharding has a Hash function that maps each row to its Shard by taking in some data from the row and mapping it to the unique value which is the Shard in which the data should be stored. 

Suppose you have an Employee Database. You have designed your Hash function in such a way that it performs a function (Hash function(Employee id)=Emploee id modulo 6). This function returns remainders from 0 to 5. So, let’s say there are 6 Shards now, the employee’s data with employee id giving a remainder of 0 when divided with 6 goes into Shard 1. 

Since you are deriving the shard location from the data, you need not worry about finding data in the shards. In the case of the other architectures, you need to keep a tab of data in the Shards.

2. Directory-Based Sharding

Directory Based Sharding
Image Source: Digital Ocean

As mentioned earlier, to keep track of the data in a Database Shard this architecture uses lookup tables. The lookup table can give you information about where the data is stored. This Database Sharding architecture is more flexible as it allows you to have freedom over the range of values in the lookup table or create Shards based on algorithms and so on. The only drawback here is that every single time a query needs execution it needs to consult a lookup table to locate the concerned data. Also, the whole system will fail if the lookup table crashes because this architecture cannot function without it.

3. Range-Based Sharding

Range Based Sharding
Image Source: Image Source: Digital Ocean

You need a lookup table in this Database Sharding architecture also, to enable you to find the Database Shard by looking at your data. The lookup table will contain the Shard id and a range of values. If the data to be stored comes under a particular range of a Shard, it will be stored in that Shard. This will result in uneven distribution of data in some cases because some ranges of the Shards may have more frequency/occurrences than the others.

In the above image, you can see that each Shard has a corresponding range of price values. If the data’s price values fall in the $0-$49.99 range then it is stored in the first Shard. The process is the same for the other shards as well. 

Understanding the Limitations of Database Sharding

Just like every other technique, creating Shards also has its own limitations. Some of the limitations are:

  • Complicated to implement.
  • Can easily lead to crashes and failure if not implemented properly.
  • Difficult to maintain Data Integrity and data loss.
  • Very few Databases have an in-built Sharding mechanism.
  • Sometimes the query performance decreases due to the increasing number of Shards.

Conclusion

Since Database Sharding is hard to implement and mainly used for the optimization of a Database, it’s better to consider other simple means for optimization before deciding to Shard your Database. Once a Shard is created, it is hard to revert back to the original Database. Create Shards if you are sure that no other options will work and that you will benefit from it.

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