๐ Sharding Summary
Sharding is a method used to split data into smaller, more manageable pieces called shards. Each shard contains a subset of the total data and can be stored on a separate server or database. This approach helps systems handle larger amounts of data and traffic by spreading the workload across multiple machines.
๐๐ปโโ๏ธ Explain Sharding Simply
Imagine a library with too many books for one shelf. The librarian splits the books across several shelves, so each shelf holds a part of the collection, making it easier to find and manage the books. In the same way, sharding divides data into parts to make storage and access faster and more efficient.
๐ How Can it be used?
Sharding can be used to split a large customer database across several servers to improve performance and reliability.
๐บ๏ธ Real World Examples
A social media platform with millions of users stores user profiles across multiple database servers using sharding. This way, requests for different users are spread out, preventing any single server from becoming overloaded and maintaining fast response times.
An online multiplayer game uses sharding to distribute game session data across different servers. This allows thousands of players to play simultaneously without causing slowdowns or crashes, as each server manages only a portion of the total sessions.
โ FAQ
๐ Categories
๐ External Reference Links
Ready to Transform, and Optimise?
At EfficiencyAI, we donโt just understand technology โ we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Letโs talk about whatโs next for your organisation.
๐กOther Useful Knowledge Cards
Customer Loyalty Program
A customer loyalty program is a marketing strategy used by businesses to encourage repeat purchases by rewarding customers for their continued support. These programmes often provide points, discounts, or special offers to customers who buy products or services regularly. The goal is to build lasting relationships with customers and increase their lifetime value to the business.
Data Annotation Standards
Data annotation standards are agreed rules and guidelines for labelling data in a consistent and accurate way. These standards help ensure that data used for machine learning or analysis is reliable and meaningful. By following set standards, different people or teams can annotate data in the same way, making it easier to share, compare, and use for training models.
Infrastructure as Code
Infrastructure as Code is a method for managing and provisioning computer data centres and cloud resources using machine-readable files instead of manual processes. This approach allows teams to automate the setup, configuration, and maintenance of servers, networks, and other infrastructure. By treating infrastructure like software, changes can be tracked, tested, and repeated reliably.
Feature Ranking
Feature ranking is the process of ordering the input variables of a dataset by their importance or relevance to a specific outcome or prediction. It helps identify which features have the most influence on the results of a model, allowing data scientists to focus on the most significant factors. This technique can make models simpler, faster, and sometimes more accurate by removing unimportant or redundant information.
Brand Management
Brand management is the process of creating, maintaining, and improving the way a company or product is perceived by customers. It involves shaping the identity, values, and reputation of the brand through consistent messaging, design, and customer experience. Effective brand management helps build trust, loyalty, and recognition, making it easier for a business to stand out from competitors.