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What Is Sharding?

Sharding

What is Sharding?

Sharding is a database architectural strategy that distributes data over numerous servers, decreasing the strain on a single server and boosting speed. This approach is often used in large-scale systems that deal with massive amounts of data. It is suitable for managing massive databases because it improves performance, scalability, and availability. However, its intrinsic complexity requires careful design and administration, particularly when processing transactions and ensuring data integrity across numerous shards.

Note. This article intends to bring attention to the IT technology called sharding, and its content is not deep. At the end of this post, there are links to learn more.

How does sharding work?

Here’s an explanation of how sharding works.

  • Data partitioning: Sharding splits a database into smaller, more manageable chunks known as shards. Each shard stores a portion of the overall data. Partitioning might vary, but popular ways include separating data by rows (range-based sharding) or distributing data more equally using a hash function.
  • Performance enhancement: By dividing data across several servers, sharding aids in load balancing. It decreases the load on individual servers, which may increase database performance, especially regarding response time and transaction throughput.
  • Scalability: Sharding is an essential approach for scaling databases. More shards may be deployed across other servers as the volume of data or the number of transactions grows. Scalability is required for expanding applications and large-scale web services.
  • High availability and fault tolerance: The system may be configured for high availability and fault tolerance, with data dispersed across numerous shards. If one shard or server fails, the others may keep running, and the system can be set to replicate data across shards to avoid data loss.
  • Complexity: Implementing sharding adds to the complexity of the database design. It requires meticulous design regarding data distribution, query routing, and data consistency among shards. Sharding also complicates executing transactions and queries that need data from different shards.

Use cases of Sharding

Sharding is often employed in large-scale online applications, e-commerce platforms, and social networking sites with a large amount of data and many transactions.

Sharding is an effective strategy for handling massive databases by dividing data over numerous servers. It improves speed, scalability, and fault tolerance but complicates database maintenance.

Sharding design improves speed and scalability by splitting data over numerous databases or servers.

Here’s a more in-depth description of how this design works.

Data Segmentation

Shards are the technique of dividing an extensive database into smaller portions. Each shard contains a subset of the data.

Data may be partitioned based on a variety of criteria, including range (e.g., date ranges), hash (using a hash function on a key field), and list (based on an index of values).

Server-Wide Distribution

  • Several Servers: Shards are spread over several servers or nodes. This distribution might occur inside a single data center or across many geographical sites.
  • Load Balancing: Data dispersion aids in load balancing. Queries are run on smaller, more manageable datasets, lowering the load on individual servers.

Question Processing

  • Routing Queries: To route queries to the correct shard, applications or middleware must know where the data is stored.
  • Results Aggregation: If a query requires access to several shards, the system must aggregate the results from each shard.

Scalability

  • Horizontal scaling: Sharding offers horizontal scalability, which means you can add additional servers to manage rising traffic or data.
  • Elasticity: As demand changes, adding or withdrawing resources is easy.

Fault Tolerance and High Availability

  • Replication: To provide high availability and data redundancy, each shard may be duplicated across many nodes.
  • Failure Isolation: If one shard fails, the availability or performance of other shards is not impacted.

Challenges

  • Complexity: Sharding increases the difficulty of handling many database portions.
  • Data Distribution: Ensures data is distributed evenly among shards to avoid unequal load.
  • Join Operations: Executing join operations across shards may be difficult and may influence performance.
  • Transaction Management: It is challenging to maintain transactional integrity over several shards.

Case Studies

Websites with significant user traffic and data-intensive processes are examples of high-traffic web applications.

Examples of large-scale online services include E-commerce platforms, gaming, and social networks.

Choosing a provider

Choosing the ideal cloud provider for hosting a system that requires sharding, high availability, and scalability depends on several criteria, including your application’s specific needs, budget, and technical preferences.

These are the three major cloud providers.

  • Amazon Web Services (AWS)
  • Microsoft Azure and
  • Google Cloud Platform (GCP)

Here’s a comparison of their advantages and disadvantages.

Amazon Web Services (AWS)

Pros

  1. Market Leader: AWS is the largest and most used cloud platform, offering many services.
  2. Global Reach: Extensive global network with a significant number of data centers located all over the world.
  3. Comprehensive Service Providers: Amazon RDS for relational databases, DynamoDB for NoSQL, and Aurora for high-performance database needs are all available.
  4. Mature Ecosystem: This well-established ecosystem has comprehensive documentation, a robust community, and third-party integrations.

Cons

  1. Complex Pricing: Pricing on AWS can be complex and difficult to estimate.
  2. Steep Learning Curve: The sheer quantity of services and options available can be intimidating.

Microsoft Azure

Pros

  1. Integration with Microsoft Products: This is ideal for enterprises with significant investments in Microsoft products such as Windows Server, Active Directory, or Office 365.
  2. Capabilities for Hybrid Cloud: A strong emphasis on hybrid cloud allows seamless integration with on-premises data centers.
  3. AI and Machine Learning Services: Strong AI and machine learning offerings.

Cons

  1. Less Intuitive UI: Some customers perceive Azure’s UI as less intuitive than AWS’s.
  2. Learning Curve: Azure, like AWS, provides many services that might be intimidating to beginning customers.

Google Cloud Platform (GCP)

Pros

  1. Data and Analytics: Big data and analytics services like BigQuery are available.
  2. Networking Technology: Takes advantage of Google’s industry-leading network infrastructure to provide minimal latency.
  3. AI and Machine Learning: Strong AI and machine learning capabilities, integrating with Google’s AI tools.

Cons

  1. Smaller Market Share: A smaller market share than AWS and Azure implies a smaller community and fewer third-party integrations.
  2. Limited Global Reach: Fewer data centers worldwide than AWS and Azure.

Further Considerations

  1. Compliance and security: Determine whether the provider fulfills your industry’s regulatory and compliance criteria.
  2. Technical support: Consider the technical support and service level agreements (SLAs) provided.
  3. Effectiveness in terms of cost: Examine the total cost of ownership (TCO), considering not only computing resources but also data transfer, storage, and other services.
  4. Scalability and Flexibility: Make sure the provider can scale with your application’s expansion and is willing to incorporate new technologies.

Conclusion

Each cloud provider has unique strengths that are constantly improving. The “best” option is determined by your individual needs, existing infrastructure, and future goals. Large enterprises frequently choose a multi-cloud strategy, harnessing several providers’ strengths to fulfill various demands.

Curiosity about the word sharding

“A shard is a piece, fragment, or broken part of a larger object, typically made of glass, ceramic, metal, or hard materials. The term is often used to describe a piece that has broken off in a way that results in sharp edges. Shards are usually the result of an object being shattered or broken with force. In a broader sense, the term can also metaphorically represent a small part or fragment of something larger.” (in ChatGPT).

The origin of the term sharding in database design is somewhat obscure, but it is often assumed to have originated from the word shard, which derives from the Old English word “sceard,” which means a fragment or broken piece. A shard was traditionally defined as a shattered pottery, glass, or stone.

So, the sharding is a metaphor for dividing a more extensive database into smaller, more manageable chunks, just like a shard is a tiny portion of a larger object. This word accurately reflects the process of dividing a database into various subsets that can be kept on different servers or clusters, boosting performance and scalability.

The term sharding first appeared in database language in the late twentieth or early twenty-first centuries, as developers and database administrators sought efficient solutions to handle rapidly rising datasets and user bases, notably in web applications and services. The precise origin or initial usage of the term sharding in this context is unknown. Still, its widespread use in today’s tech industry underlines its importance in modern database architecture.

References


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