Database Sharding: A Comprehensive Guide 101
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.
Table of Contents
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
- What is Database Sharding?
- Why Database Sharding is Important?
- Difference between Database Sharding vs Partitioning
- What are Database Sharding Architectures?
- What are the Advantages of Database Sharding?
- What are the Disadvantages of Database Sharding?
- What are the Limitations of Database Sharding?
- When should I consider Database Sharding?
- What are the Alternatives to Database Sharding?
- What are the Challenges in Database Sharding?
What is Database Sharding?
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.
Database Sharding 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.
Why Database Sharding is Important?
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.
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Difference between Database Sharding vs Partitioning
Both sharding and partitioning mean distributing data into smaller and more manageable chunks or subsets. The main difference between them is the way the distribution happens. In sharding, data is distributed across multiple computers, whereas in partitioning, grouping subsets of data is carried out within a single database instance. However, you will see both sharding and partitioning being used interchangeably when applied with terms such as ‘horizontal’ and ‘vertical.’
What are the 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
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
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
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 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.
What are the Advantages of Database Sharding?
Employing database sharding can help you reap the following benefits:
- Enhanced Scalability: Sharding allows you to distribute a load of a high-traffic database across multiple servers, thereby increasing capacity and allowing the database to handle more traffic and data.
- Better Performance: Sharding can improve performance by allowing each shard to be optimized for specific queries or workloads. By distributing the load across multiple shards, both read and write operation performance is improved as long as read and write operations are confined to a single shard.
- Better Availability: Sharding allows you to achieve a high degree of fault tolerance and reduces the impact of server failures. This is because, as each shard is a replica set, every piece of data is replicated. Moreover, if a shard fails also, you still have a partially functional database as part of the schema is present on other shards.
What are the Disadvantages of Database Sharding?
If not done optimally, sharding does come with its downsides too:
- Complexity: Apart from maintaining the shards of every sharded database, you now have to upkeep and maintain additional service nodes. Also, in case of data updates, you will now be responsible for mirroring the changes across the replicated nodes.
- Query Overhead and Increased Latency: With each sharded database having a separate machine, there is an additional latency on every operation. For data that is horizontally distributed across multiple shards, each shard has to be queried to retrieve the data and merge it i.e., making this operation expensive and slowing down response times.
- Increased Costs: Sharding always requires additional hardware and software, which can increase costs.
What are 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.
When should I consider Database Sharding?
Sharding can be used in multiple cases, such as:
- If the amount of your application data is growing rapidly and you run out of capacity on a single server, sharding can help you distribute the load across multiple servers and increase capacity.
- If the number of reads or writes to your database exceeds what a single node or its read replicas can handle, you will observe slowed response times or timeouts. Sharding can help you distribute the load and improve performance by allowing each shard to be optimized for specific queries or workloads.
- Similarly, you can also face slowed response times or timeouts when the network bandwidth needed by the application surpasses the bandwidth available to a single database node and any read replicas.
What are the Alternatives to Database Sharding?
There are several alternatives to database sharding that can be used to scale a database, including:
- Vertical Scaling: This involves increasing the resources of a single machine, such as adding more memory or CPU, to handle more traffic and data.
- Database Partitioning: This technique separates a large database table into smaller partitions based on specific criteria, reducing the size of the data that needs to be searched and improving the performance of the queries. You can either partition by rows(Horizontal partitioning) or by columns(Vertical partitioning).
- Replication: This involves making exact replicas of the database and storing them across different computers. It helps design a fault-tolerant relational database management system, as when one of the computers hosting the database fails, other replicas remain operational.
What are the Challenges in Database Sharding?
Several challenges can arise when implementing database sharding, including:
- Data Hotspots: There might be an imbalance in terms of data distribution as a few shards may have to store more data and hence will require more computational resources.
- Operational Complexity: Rather than managing a single database, you have to maintain multiple shards. When querying, developers must read several shards and integrate the pieces of information.
- Infrastructure Costs: As you increase the number of shards, the cost directly increases. Your maintenance costs will also shoot up.
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 them.Visit our Website to Explore Hevo
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