Harsh sharma’s Post

You know what my favorite thing is about building database applications at scale? It's how little problems also scale and become big problems. If you don't know about "metastable failures" and the "circuit breaker" pattern then you can read about it in my latest blog post: https://lnkd.in/e896CpEm #aerospike #architecture #data #developer

🚀 Design A Key-value Store: Part 2 The blog post elaborates on system components topics including handling failures, system architecture diagram, and read/write paths. It explains failure detection in a distributed system, handling temporary and permanent failures, and data centre outage. The architecture of the key-value store is also explained. Read the full blog post by Aryan Agarwal at https://lnkd.in/ezNV7yAF #SystemComponents #Failures #SystemArchitectureDiagram #KeyValueStore #ReadAndWritePaths #Codedash

🟢Day 8 /30: 🚀COA Series: Understanding the Types of Cache Misses in Computer Organization 🖥️ In the world of computer architecture, cache memory plays a pivotal role in enhancing performance by reducing the access time to frequently used data. However, even with an efficient cache system, cache misses are inevitable. Understanding the types of cache misses can help in optimizing system performance. 1. Compulsory Misses 📥 These are also known as cold-start misses and occur when data is accessed for the first time. Since the data has not been loaded into the cache yet, the system needs to fetch it from the main memory. Compulsory misses are unavoidable in most cases, but prefetching strategies can reduce their frequency. Example: Accessing a file for the first time on a freshly rebooted system. 2. Capacity Misses 💾 These occur when the cache cannot hold all the data required by the program. Even though the data may have been in the cache at some point, it gets replaced due to lack of space. Increasing cache size can help mitigate capacity misses, but it’s important to strike the right balance between performance and cost. Example: Running large datasets or applications that exceed the cache size, leading to frequent memory fetches. 3. Conflict Misses🔄 Conflict misses, or collision misses, happen when multiple blocks of data map to the same cache line, causing them to overwrite each other even though there’s still space in the cache. These can be reduced by using Set-associative or fully-associative cache techniques, which allow data to be mapped to multiple locations. Example: Accessing multiple memory addresses that get mapped to the same cache location, resulting infrequent replacement of cache lines. Understanding these types of misses is critical for designing efficient computer systems and improving overall performance. By analyzing cache behavior, architects can make informed decisions on memory hierarchy and system optimization. #ComputerArchitecture #TechInsights #CacheOptimization #SystemDesign #TechLearning

🌟 Understanding Eventual Consistency😋: Eventual consistency is a consistency model used in distributed systems. Here's the essence: 🔸 After some time without updates, all data replicas will eventually converge to a consistent state. 🔸 This model allows for replicas of data to be inconsistent temporarily, enabling both high availability and partition tolerance. 🔑 The CAP Theorem: Balancing the Triad ⚖ : ⚫ The CAP theorem, proposed by computer scientist Eric Brewer, succinctly captures the challenge faced by architects and engineers: 🔸 It states that in a distributed data store, it's impossible to simultaneously achieve all three guarantees: 1️⃣ Consistency: All nodes have the same data at the same time. 2️⃣ Availability: Every request receives a response, even in the face of failures. 3️⃣ Partition Tolerance: The system remains resilient despite network partitions. ⚫ Trade-offs: 🔸 No distributed system can guarantee all three simultaneously due to inherent trade-offs. 🔸 You can satisfy any two of the CAP guarantees at the same time, but not all three: 🔵Consistency + Partition Tolerance (CP): 🔹Prioritize strong consistency, ensuring all nodes have the same data. 🔹May experience reduced availability during network partitions. 🔵Availability + Partition Tolerance (AP): 🔹Emphasize high availability, even during network partitions. 🔹May sacrifice strict consistency for eventual consistency. 🔵Consistency + Availability (CA): 🔹 Balance both consistency and availability. 🚀 Tech Tip: Integrating CAP Principles: 😎 📢 1️⃣ Know Your Use Case: ✔ Assess whether immediate consistency is critical for your system. ✔ Consider eventual consistency for better performance and availability. 2️⃣ Design Graceful Degradation: ✔ Plan for scenarios where nodes might be temporarily inconsistent. ✔ How will your system handle it? Define strategies. 3️⃣ Monitoring and Metrics: ✔ Keep an eye on convergence times. ✔ Set thresholds and alarms to ensure timely synchronization. 4️⃣ Cache Strategically: ✔ Use caching wisely. ✔ Remember, cached data might not always be up-to-date, but that's okay if it converges eventually. Remember, building robust systems involves making informed choices. Let's embrace these principles and create resilient architectures! 💪Scaler #ScalerTechTips #SystemDesign #DistributedSystems #EventualConsistency #LinkedInInsights

##OCTOBERLEARNS## 📢📢DIFFERENCE BETWEEN MONOLITHIC FILE SYSTEM AND DISTRIBUTED FILE SYSTEM: The differences between monolithic file systems and distributed file systems are based on architecture, data management, fault tolerance, scalability, and other characteristics.  🚀 Architecture:- 👉🏼Monolithic File System: Centralized architecture; all file operations occur within a single system. 👉🏼Distributed File System (DFS): Decentralized architecture; file data and operations are spread across multiple systems or nodes. 🚀 Scalability:- 👉🏼Monolithic File System: Limited scalability due to hardware constraints and single-system dependency. 👉🏼DFS: Highly scalable; designed to add more nodes to increase storage and computing power. 🚀 Data Access:- 👉🏼Monolithic File System: Local data access; files are accessed directly from a single machine. 👉🏼DFS: Remote data access; files may be stored and accessed across multiple servers or locations. 🚀 Fault Tolerance:- 👉🏼Monolithic File System: Single point of failure; failure in the system can lead to data loss or inaccessibility. 👉🏼DFS: Higher fault tolerance; redundancy and replication mechanisms ensure availability even if one node fails. 🚀 Performance:- 👉🏼Monolithic File System: Typically faster for single-machine operations but can slow down under heavy loads. 👉🏼DFS: Performance can vary depending on network latency, but parallel processing and distributed load handling improve performance in large-scale systems. 🚀 Data Replication:- 👉🏼Monolithic File System: Typically does not have built-in replication; relies on manual backup. 👉🏼DFS: Often implements data replication automatically to ensure data redundancy and integrity. 🚀 Data Consistency:- 👉🏼Monolithic File System: Maintains strong consistency since all operations are localized. 👉🏼DFS: May offer eventual consistency due to latency and replication across nodes, especially in large distributed systems. 🚀 Management Complexity:- 👉🏼Monolithic File System: Easier to manage since everything resides within one system. 👉🏼DFS: Complex management due to multiple nodes, network communications, and synchronization. 🚀 Network Dependency:- 👉🏼Monolithic File System: No network dependency; file operations are local. 👉🏼DFS: Relies heavily on the network for communication, data access, and file operations. 🚀 Resource Utilization:- markdown 👉🏼- **Monolithic File System:** Utilizes resources of a single machine; limited CPU, RAM, and storage. 👉🏼- **DFS:** Utilizes distributed resources, leading to better overall resource allocation and load balancing. #Hadoop #HDFS #BigData #BigDataAnalytics #DataEngineering #DataScience #DataStorage #CloudComputing #ApacheHadoop #DistributedSystems #DataInfrastructure #DataManagement #OpenSource #YARN #MapReduce #MachineLearning #AI #ArtificialIntelligence #IoT #CloudStorage #Seekho Bigdata Institute #Karthik K.📘📘

Data integrity is the cornerstone of reliable Pub/Sub messaging. Ably’s architecture is built to ensure exactly-once delivery, strict message ordering, and resilience to failures—all while maintaining sub-50ms latency globally. Our latest engineering blog from senior engineer Zak Knill dives into the architectural internals that make it possible, like: - Primary and secondary message persistence for durability - Idempotent publishing to eliminate duplicates - Global message replication across regions for fault tolerance Discover how Ably’s Pub/Sub architecture guarantees data integrity at scale. 👉 https://hubs.la/Q02Z2K5x0

Day 21 of my system design journey: Understanding Data Consistency and Consistency Levels Today, I explored the critical concept of data consistency in distributed systems. When data is spread across multiple nodes (often for better availability and fault tolerance), data consistency ensures that all nodes reflect the same view of the data. However, achieving this across a distributed architecture comes with challenges like network latency, node failures, and concurrent updates. Here are the key consistency models I learned about: 1. Strong Consistency: Every read operation reflects the most recent write, ensuring no stale data. Although it prioritizes data integrity, it can impact system performance and availability due to the need for synchronization across nodes. 2. Eventual Consistency: This model ensures that all replicas eventually converge to the same state, even if there's a delay. It’s often used in systems where high availability and partition tolerance are more important than immediate consistency, like in NoSQL databases. 3. Causal Consistency: Operations that are causally related are seen in the same order by all nodes. This model maintains a balance between strong consistency and the system’s scalability. 4. Read-Your-Writes Consistency: A client sees the effects of its own writes in subsequent read operations, providing an intuitive user experience. Data consistency is a fascinating topic because it’s all about trade-offs—between speed, availability, and accuracy. Different systems use different models based on their requirements for performance, availability, and fault tolerance. #SystemDesign #DistributedSystems #DataConsistency #LearningJourney

Maintaining data integrity in a distributed Pub/Sub system is no small feat. Our senior realtime engineer, Zak Knill, explains how Ably delivers: - Exactly-once message delivery with idempotency checks - Global replication for fault tolerance - Message ordering Explore how our Pub/Sub architecture handles failures while ensuring every message arrives where it’s needed, exactly as intended. Read the blog: https://hubs.la/Q02Z2K5x0

Continuing with TukDB Features: Distributed architecture and Cluster fault tolerance The search engine is designed to be horizontally and vertically scalable, and to take advantage of that scale automatically. Indexes are internally sharded by prefix and distributed across multiple mount points, ensuring data availability on every data volume in the cluster.  Smaller, less frequently queried, or less I/O-intensive indexes may be sharded across a smaller subset of data volumes. To safeguard against data unavailability or loss due to disk or server failures, data is replicated across the cluster in accordance with the defined replication and sharding policies. Each index definition includes sharding and replication directives. The cluster automatically determines the optimal mapping of indexes to mount points.  Data migrations are triggered when fields are created or deleted, hosts or disks are added or removed, or the search engine detects load imbalances. TukDB's self-healing and optimizing mechanisms automatically perform data migrations for various reasons, including: ◻ Data loss prevention ◻ Infrastructure expansion ◻ Infrastructure reduction ◻ Excess data cleanup ◻ High disk usage mitigation ◻ IOPS or CPU hotspot mitigation ℹ These automated processes ensure optimal performance and data integrity without requiring a cluster reset or restart. #TukDB #database #search #technology #cluster

Erasure coding is revolutionizing storage efficiency, data protection, and performance in distributed storage environments. Learn how this technique optimizes storage while ensuring data resilience. Dive into various erasure coding schemes like Reed-Solomon Codes and Local Reconstruction Codes, exploring their trade-offs in fault tolerance and performance. Tailor configurations for specific use cases to achieve optimal efficiency and performance. #ErasureCoding #DataStorage #BigData #CloudComputing #DataProtection #ScaleOutStorage #DistributedStorage #StorageEfficiency Read more: https://lnkd.in/ghUGRkFR