What Is Sharding in Database? A Comprehensive Guide
A single database server has limitations on how much data it can store and process efficiently. As the volume of data grows, performance issues like slow queries and system crashes may arise. Database sharding, an architectural design technique, allows you to overcome these challenges by handling a large database across multiple servers.
Through the sharding process, you can improve the scalability of your database. If you are looking for ways to manage large amounts of data better, this article will provide you with the answers you need.
What Is Database Sharding?
Database sharding is a horizontal scaling solution that allows you to break a vast dataset into smaller pieces, known as shards. Every shard has the same database schema as the original database, ensuring structural consistency in all shards. However, the data within a shard is unique, with no overlap between shards.
These shards are distributed across different servers or nodes, enabling the database system to handle more users, queries, and transactions simultaneously. Although the shards operate independently on separate nodes, they share the same underlying infrastructure and technologies like DBMS, networks, or storage devices. Leveraging the sharding strategy, you can enhance the performance, scalability, and reliability of databases as they grow.
Do You Really Need Database Sharing?
Sharding in a database is a powerful technique to help you improve database performance and scalability. However, before opting for sharding, it is essential to evaluate your application’s requirements. Consider the following alternative strategies that might be more effective and easier to manage.
Vertical Scaling
Vertical scaling or scaling up involves upgrading your existing server by adding more resources such as CPU, RAM, or storage.
It is ideal when your database has moderate data growth, and your application is not expected to manage extremely high traffic or massive datasets. Here are some pros and cons of vertical scaling:
Replication
Replication is the process of creating exact copies of your database on multiple servers. These replicas allow you to distribute read workloads, making this approach well-suited for applications with heavy read operations.
Some of the merits and demerits are:
Specialized Services or Databases
You can resolve some scalability challenges using specialized types of databases or services that are designed for specific workloads. NoSQL databases like MongoDB or Cassandra are inherently distributed, and you can scale horizontally without a sharding process.
Besides this, you can utilize a caching solution such as Redis. This platform helps you reduce the database load by serving frequently accessed data directly from the cache. Here are a few advantages and limitations of using specialized services:
Key Features of Database Sharding
Database sharding offers several distinct features to help you manage large datasets efficiently.
Shared-Nothing Architecture
Database sharding follows a shared-nothing architecture, where each physical shard operates independently and is unaware of the others. Each shard is responsible for processing its own data without depending on others. Only the shards containing the relevant data for a given query will process that data in parallel.
A software layer, known as sharding middleware, enables you to coordinate data storage and access across multiple shards.
Horizontal Scaling
Sharding helps you scale your database horizontally by distributing data across multiple shards. Each shard is stored on a separate server based on a defined shard key, which is a specific attribute, such as a user ID or region. The shard key helps you determine how data is divided among the shards.
When the amount of data increases, you can add new servers accordingly to enable the system to handle increased load. This ensures that the system can support applications with large data volumes efficiently and cost-effectively.
Improved Performance
By dividing the data into smaller shards, the system enables you to process queries faster. When a query is made, it is directed to the specific shard containing the relevant data rather than scanning the entire database. This way, database sharding can help you reduce query response times during read-and-write operations and in high-traffic situations.
Fault Tolerance
Through the sharding process, each server operates independently. If one shard or its server fails, the rest of the database system remains operational, minimizing downtime. With this independence, you can enhance the reliability of the database, ensuring failure in one part of the system does not affect the entire database.
Database Standardization
Standardizing database schema design is essential for ensuring consistency across all shards. This involves defining uniform naming conventions for tables, column indexes, and other database objects in each shard. Such database standardization provides clarity and uniformity, enabling seamless distribution and retrieval of data across multiple partitions.
Partitioning Strategies
Efficient data distribution can be achieved through various partitioning strategies, each suited to different types of data and use cases:
You can select any of the following partitioning strategies for effective data distribution:
- Range-based: You can divide the data into shards based on value range. For instance, users with IDs 1-1000 may go to one shard, 1001-2000 to another, and so on.
- Hash-based: Data is distributed using a hash function, ensuring even distribution regardless of the data’s order.
- List-based: You can group the data into specific categories. For example, all users in the UK category go to one shard, USA to another, etc.
Data Locality
Sharding helps you enhance data locality by storing data closer to the end-users or relevant systems. By partitioning data based on geographic regions or user preferences, the system ensures that you can access the data in a specific region from a local server.
Benefits of Database Sharding
Apart from the above-mentioned features, the database sharding process provides numerous benefits as follows:
Reduced Latency
Retrieving data from an extensive database can be slow because the system must search through many rows. In a sharded database, however, each shard contains only a portion of the data, significantly reducing the number of rows to search. You can ensure faster data retrieval and quicker query execution.
High Availability
Shards ensure high availability in two ways:
- Since each shard includes replicated data, if one copy fails, others remain accessible.
- As your data is distributed across different nodes, the database remains partially functional even if an entire shard becomes unavailable.
This setup minimizes the downtime and ensures critical parts of your system continue to operate.
Increased Storage Capacity
Increasing the number of shards enables you to expand your overall storage capacity as needed. As a result, you are not limited by the constraints of a single server and can scale your system to near-infinite capacity for accommodating growing data volumes.
Enhanced Read/Write Throughput
When you distribute your dataset between multiple servers, you can significantly enhance the read and write capacity. This works especially well when read and write operations are directed to individual shards, ensuring faster processing without overloading the database.
Efficient Resource Utilization
By spreading the data and queries in different shards, you can balance the load effectively. It prevents any one server from being overloaded, ensuring all servers are used optimally. You can achieve the best performance from your infrastructure.
Data Isolation
Isolating data in multiple nodes through sharding can help you manage sensitive information separately. For instance, you can store customer data for different regions on separate shards, ensuring compliance with local regulations.
Reduced Costs
Sharding enables you to distribute your data across smaller, cost-effective servers instead of relying on a single, high-performance server. With the sharding, you can scale horizontally without overspending on expensive hardware, saving money as your data increases.
Disadvantages of Database Sharding
While database sharding has several advantages, it also comes with certain limitations.
Data Hotspots
Data hotspots occur when specific shards become unbalanced due to uneven data distribution. For instance, a shard containing customer names starting with A may receive a large amount of data compared to other shards. As a result, this shard can consume more computing resources while other shards remain underutilized.
Operational Complexity
Sharding introduces operational complexity by requiring you to manage multiple database nodes instead of a single instance. When retrieving information, you must query several shards and aggregate the results, complicating the process. These additional retrieval steps can result in slower performance and more complex analytics, as data needs to be combined from multiple sources.
Query Overhead
A distributed query may need coordination across multiple servers, increasing processing time and reducing efficiency. Complex joins, aggregations, and filtering also require additional logic or more manual intervention, making the query processing complex.
Data Consistency Challenges
Maintaining data consistency between multiple nodes can be tricky. Since data is distributed, you need a complex synchronization mechanism to ensure that updates to one shard are reflected in others.
Potential for Over-Sharding
Over-sharding or creating too many small shards can worsen performance issues. It can lead to overhead in managing several shards, complicating queries, and causing inefficiencies in resource utilization.
Risk of Fragmentation
As data is distributed, there is a risk of fragmentation, where data may not be organized optimally for specific access patterns. This can affect query performance and increase the complexity of maintaining an optimal data layout.
How Can Airbyte Help with Database Sharding?
Database sharding is not natively supported by every database engine. This leads you to partition, distribute, and handle the database manually. As this process is time-consuming and resource-intensive, you can migrate your data from the existing database to the appropriate database, which has built-in sharding features. To streamline this database to database integration, consider leveraging a no-code data movement platform like Airbyte.
Airbyte offers 550+ pre-built connectors to help you extract data from various sources and load it into a desired destination. If you do not find a unique connector that fits your needs, you can build one using no-code Connector Builder, low-code CDKs, and language-specific CDKs. An AI Assistant within the Connector Builder prefills the required source or destination configuration fields.
Top 12 Features of Airbyte
- Streamline GenAI Workflows: You can load all your unstructured data into vector databases, including Weaviate, Pinecone, Qdrant, and Milvus. These vector databases are optimized for fast retrieval, enabling efficient similarity search and enhanced performance in AI applications.
- Advanced RAG-based Transformations: While configuring vector databases as your destination, you can perform RAG-based transformations like OpenAI-enabled embeddings and LangChain-powered chunkings. These advanced transformations help your AI models access data easily.
- Custom Transformations: With Airbyte and dbt Cloud integration, you can apply custom transformations to your data. These changes are essential for refining your data and ensuring it is tailored to your specific analytical and operational needs.
- Change Data Capture: Airbyte supports CDC, allowing you to capture incremental changes from your source system and copy them to the destination. This ensures that your data remains up-to-date.
- Developer-Friendly Pipeline: Airbyte offers an open-source Python library called PyAirbyte. This library allows you to utilize Airbyte connectors in your Python workflows to build and streamline your ETL pipelines.
- Schema Change Management: You can specify how Airbyte should manage schema changes in the source for each connection. If you are a Cloud user, Airbyte checks for source schema changes every 15 minutes. For self-hosted instances, schema checks are performed at most once every 24 hours. You can also manually refresh the schema at any time to accommodate changes.
- Multiple Sync Modes: Airbyte offers multiple synchronization modes, including incremental append, full refresh append, and full refresh overwrite with or without a deduplication option. With the release of Airbyte 1.0, the platform provides a resumable full refresh option. It allows you to resume a full refresh sync from the point where it was interrupted rather than starting over from scratch.
- Deployment Flexibility: When running Airbyte, you have the flexibility to choose three deployment options, including self-managed, cloud-hosted, and hybrid. The self-managed deployment helps you to deploy Airbyte locally, while the cloud-hosted option is fully managed. The hybrid option enables you to combine both the self-managed and cloud-hosted deployments.
- Enterprise GA: Aribyte has launched the general availability of a Self-Managed Enterprise edition. This benefit includes centralized user access and self-service data ingestion features, allowing you to manage multiple teams and projects within a single Airbyte setup. Additionally, Enterprise GA helps you enhance data security by enabling the hashing of personally identifiable information (PII) as it flows through your pipeline. This ensures sensitive data remains protected.
- Data Orchestration: Airbyte allows you to integrate with Prefect, Kestra, Dagster, and Apache Airflow to orchestrate your pipelines.
- Regulatory Complaint: Airbyte has several compliance certifications, including SOC 2 Type II assessment and ISO 27001. These regulatory standards are useful for the secure data integration process.
- Vibrant Community: Airbyte’s active forum allows you to search for discussions on deployment tips, troubleshooting methods, and data integration practices. You can even share knowledge and experiences with other Airbyte users.
Conclusion
This comprehensive guide explains what is data sharding and how it improves the scalability and performance of large-scale databases. Database sharding allows you to manage vast amounts of data efficiently, ensures better resource utilization, and provides faster query responses. However, it also introduces challenges such as increased complexity in management, data consistency, and query handling.
Proper planning, partitioning strategies, and standardization are crucial for maximizing the benefits of sharding while preventing its drawbacks. When implemented correctly, database sharding can help you significantly enhance system performance and ensure a scalable infrastructure to meet increasing data demands.