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Mukesh Kuiry
Mukesh Kuiry

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Top 6 System Design Patterns to Ace Every System Design Interview

In the realm of system architecture, distributed systems reign supreme, providing a robust framework for handling vast amounts of data and complex operations. To navigate the intricate landscape of distributed systems, understanding and applying key design patterns is indispensable. Let’s delve into some commonly utilized design patterns and their pivotal roles in shaping the distributed systems landscape.

Bloom Filters πŸ’­:

Bloom filters are probabilistic data structures designed to swiftly determine whether an element might be in a set, without storing the actual items. They employ hash functions and a bit array, offering rapid insights into potential membership. Imagine having a clever assistant who swiftly suggests potential correct words in a word processor’s spell checker without needing the entire dictionary, enhancing user experience.

Expanded Insight:

  1. Space-Efficiency: Bloom filters are remarkably space-efficient, storing only a fraction of the size of the actual set, primarily through a bit array and hash functions.

  2. Membership Queries: They excel in handling membership queries, offering rapid indications of potential presence or definite absence of an element within the set.

  3. False Positives vs. Negatives: While Bloom filters might occasionally produce false positives, suggesting an element is in the set when it’s not, they guarantee no false negatives; if an element is genuinely absent, the Bloom filter will always confirm its absence.

Applications in Data Retrieval:
From cache lookups in web servers to spell checkers, Bloom filters efficiently determine potential data presence in large datasets without the overhead of storing the complete set.

Bloom filter

Consistent Hashing πŸ—„οΈ:

Consistent hashing maps data to physical nodes, ensuring stability during server additions or removals. It minimizes disruptions, allowing a balanced distribution of data. Think of a social media platform that allocates user-generated content to specific servers, maintaining consistency even when servers are added or removed.

Extended Scenario:
Consistent hashing ensures that only a small set of keys move when servers are added or removed, enhancing system stability and reducing unnecessary data movement across the network.

Consistent hashing

Quorum βœ…:

A quorum establishes the minimum server count for a successful distributed system operation. It acts as a decision-making threshold ensuring agreement among a sufficient number of nodes. In financial transactions, a quorum verifies transaction details, preventing unauthorized or erroneous transactions.

Broader Application:
Quorum-based decision-making extends to voting systems, where agreement among a defined number of participants is necessary for a valid decision, ensuring legitimacy and consensus.


Checksum πŸ”:

Checksums safeguard data integrity, ensuring information arrives intact. They act as digital fingerprints, verifying whether data has been corrupted during transmission. Consider verifying downloaded software using a provided checksum, ensuring it hasn’t been tampered with during download.

Mechanism Efficiency:
Checksums efficiently verify data integrity, preventing corruption or unauthorized modifications, critical in secure data transfer and storage.


Merkle Trees 🌳:

Merkle trees allow quick comparisons of vast data volumes without rechecking the entire dataset. This hierarchical structure efficiently validates data, ensuring consistency. In blockchain, Merkle trees summarize transactions in each block, allowing quick verification without inspecting every transaction.

Blockchain Efficacy:
Merkle trees enable swift and efficient verification of blockchain transactions, maintaining the integrity of the entire transaction history.

Merkle tree

Leader Election πŸ†:

Leader election designates a primary node to orchestrate operations in distributed systems. It optimizes resource utilization and streamlines tasks. Think of a cluster of servers where the leader election process designates an efficient primary server to manage tasks.

Optimized Operations:
Leader election ensures streamlined operations and efficient resource utilization within distributed systems, crucial for task delegation and system performance.

Leader election

Mastering these design patterns empowers architects and developers to craft resilient, scalable systems prepared for the challenges of a data-driven world. Understanding and implementing these patterns form the foundation for systems that thrive amidst complexity, exhibiting both efficiency and reliability.

Explore the vast landscape of distributed systems, familiarize yourself with these design patterns, and witness your systems evolve into bastions of efficiency and reliability.

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