Animesh Shukla’s Post

In distributed systems, understanding synchronous and asynchronous replication is the key. This post requires some basic understanding of master-slave / leader-follower architecture. In asynchronous replication, suppose a write-request is made to the leader by the client. Now, the leader would update the changes in its local storage and also in the replication log and sends the replication log to all of the followers simultaneously. Now, the leader does not wait for the changes to be replicated in the followers. Once, the changes are made in the leader and the replication log is sent, it sends the success signal to the client. Eventually the followers would catch up the new updates and would contain the latest replica as of the leader, called as Eventual consistency. Thus, over here it is not guaranteed that the latest changes would be reflected in the followers and it may take some time to reflect those latest changes. The latest changes are only stored in the leader. In Synchronous replication, the write-request is written to leader's local storage and also in the replication log. The replication log is sent to all of the followers and over here, the leader waits until the latest changes are made in the followers. This may consume time but it does not return successful signal to the client until all of the followers have the latest replica. Thus, this could result in very high latency and if one of the followers is down, the whole system would blow up (for some time until this node is replaced by a new node) since the leader would wait until the changes are reflected in all of the nodes. In reality, synchronous replication is done only in one follower along with the leader i.e. the latest changes are made in the leader and in one follower. For rest of the followers, the replication is asynchronous. This allows a backup for leader i.e. if leader goes down, the follower in which data has been synchronously replicated would become the new leader and another follower would become the follower where data would be synchronously replicated.  #dataengineering #bigdata #systemdesign #bigdataarchitecture

Partitioning plays a pivotal role in the realm of distributed state stores, facilitating scalability, fault tolerance, and efficient data processing. In Kafka, partitioning is fundamental, driving its robustness as a distributed streaming platform. Partitioning in Kafka Kafka organizes data into topics, each further divided into partitions. These partitions distribute data across multiple brokers, enabling parallel processing and fault tolerance. 🚀 For example, in a messaging application, partitioning by user ID ensures that all messages belonging to a particular user are stored in the same partition, facilitating efficient retrieval and processing. Choosing the Right Partitioning Strategy Crafting the right partitioning strategy is critical for optimal Kafka performance. Consider data skew, access patterns, and scalability requirements. 🎯 For instance, if evenly distributed data is paramount, a hash-based strategy can be employed. Conversely, temporal data might benefit from time-based partitioning. Addressing Hotspots Despite careful planning, hotspots can emerge, burdening specific partitions. 🌡️ One solution is to implement partition rebalancing, where partitions are dynamically reassigned to distribute the workload evenly. Additionally, utilizing Kafka's partitioning strategies such as round-robin or hashing can help distribute data more evenly, minimizing the likelihood of hotspots. Example Scenario Suppose we're partitioning a Kafka topic for a ridesharing platform. Using a hash-based strategy, we hash the user ID to evenly distribute ride data. For a key-based approach, we could partition by ride ID to ensure even distribution and avoid hotspots caused by popular rides. Conclusion Partitioning is the basis of Kafka's scalability and fault tolerance. By carefully selecting a partitioning strategy and addressing potential hotspots, we can architect performant and resilient distributed state stores.💡 #SystemDesign #KafkaPartitioning #DistributedSystems

As we are continuing our series on data engineering fundamentals, we’re discussing 👉🏼RDDs👈🏼 because they represent a foundational concept in big data processing that exemplifies fault tolerance and efficient distributed computing, principles that are central to the design and functionality of modern data platforms. Resilient Distributed Datasets (RDDs) are a type of data structure built inside Apache Spark that provides fault-tolerant storage and allows for distributed processing of large datasets across multiple computing nodes. They enable parallel operations, and their resilience stems from the ability to recompute data in the event of node failures. RDDs are characterized by their immutability — once created, they cannot be altered — and their lineage, which allows them to rebuild lost data using the original transformations. This makes RDDs an essential tool for efficient data processing in large-scale computing environments. Let’s delve deeper into this type of data structure. Since their introduction in 2010 and their transition to an Apache-licensed project in 2014, RDDs have been pivotal in handling large-scale data efficiently. Key Characteristics: ✍️Immutability and Partitioning: Each RDD is a fixed collection of data, divided into partitions. These partitions are stored on different nodes, enhancing both parallelism and reliability. ✍️Coarse-Grained Operations: RDDs allow operations that affect all data items in the dataset, such as filtering and grouping, facilitating efficient mass processing. ✍️Fault Tolerance: The distributed nature and replication mechanisms of RDDs provide excellent fault tolerance. Even in the event of a failure, the transformations applied to RDDs ensure data integrity is maintained. Curious about the mechanisms of other big data frameworks and their components? Let us know which topics or technologies you would like us to explore next in the comments! #dataengineering

Effective Methods to Enhance Database Speed and Efficiency Improving database performance is crucial for any data-driven organization. Here are twelve effective strategies to enhance your database: 1️⃣ Indexing: Speed up data retrieval by creating the right indexes based on query patterns. 2️⃣ Materialized Views: Store pre-computed query results for quick access, reducing the need for repeated complex queries. 3️⃣ Vertical Scaling: Boost database server capacity by adding more CPU, RAM, or storage. 4️⃣ Denormalization: Simplify complex joins by restructuring data, which can enhance query performance. 5️⃣ Database Caching: Store frequently accessed data in faster storage layers to ease the load on the database. 6️⃣ Replication: Create copies of the primary database on different servers to distribute read load and enhance availability. 7️⃣ Sharding: Divide the database into smaller, manageable pieces, or shards, to distribute load and improve performance. 8️⃣ Partitioning: Split large tables into smaller, more manageable pieces to enhance query performance and maintenance. 9️⃣ Query Optimization: Rewrite and fine-tune queries to execute more efficiently. 🔟 Use of Appropriate Data Types: Choose the most efficient data types for each column to save space and speed up processing. 1️⃣1️⃣ Limiting Indexes: Avoid excessive indexing, which can slow down write operations; use indexes judiciously. 1️⃣2️⃣ Archiving Old Data: Move infrequently accessed data to an archive to keep the active database smaller and faster. Implementing these strategies can significantly improve the performance and efficiency of your database systems. hashtag #DatabaseManagement #DataOptimization #TechTips #DatabasePerformance #ITStrategy #DataScience #MachineLearning #AI

The article discusses the innovative architecture of TiDB, a distributed SQL database that addresses the evolving demands of modern applications. Unlike traditional databases, which often struggle with scaling and reliability, TiDB is designed from the ground up to support high transaction volumes and data loads. #AI #AIInnovation

Weaviate combines RAFT consensus for metadata replication with a leaderless design for data replication to ensure a fault-tolerant distributed system. How It Works: - Metadata with RAFT: RAFT manages metadata, such as collection definitions, creating data consistency across clusters even during node failures. Metadata changes are handled by the RAFT-elected leader and replicated to followers. - Leaderless Data Replication: Weaviate uses a leaderless model for data, allowing any node to handle read/write requests. The main advantage of a leaderless replication design is improved fault tolerance. In a single-leader design, all writes need to be processed by a leader. If this node cannot be reached or goes down, no writes can be processed. Key Benefits: 1. Consistent Metadata: RAFT ensures consistent metadata replication across clusters, even in the event of node failures. 2. High Data Availability: The leaderless approach allows any node to serve client requests, improving availability in case of node failures. 3. Scalable Flexibility: Separate handling of metadata and data replication allows for users to fine-tune their database settings based on availability needs. Learn more about our RAFT implementation and how to configure your replication settings in Weaviate here: Weaviate Cluster Architecture: https://lnkd.in/g-2rwnU9 For more information about RAFT, we recommend these resources: - RAFT GitHub Page: https://raft.github.io/ - The Secret Lives of Data - RAFT: https://lnkd.in/g2J_ynEf

💡 Understanding Data Management: Caching vs. Persistence 🔍 Caching: Caching accelerates data retrieval by storing frequently accessed data closer to the user, typically in memory. It boosts system responsiveness by reducing the need for repeated fetching from the original source. Cached data is ephemeral, subject to eviction or refresh based on usage patterns or predefined criteria. 📌 Key Points: Speed: Prioritizes quick access, enhancing system responsiveness. Volatility: Cached data is transient, subject to eviction or refresh. Use Cases: Ideal for read-heavy operations, reducing database load, and improving overall system performance. 🔐 Persistence: Persistence involves storing data permanently or semipermanently in durable storage mediums like databases or disk drives. It ensures data durability, surviving system reboots or failures, and maintaining data integrity over time. 📌 Key Points: Durability: Ensures data survives system restarts or failures, maintaining integrity. Longevity: Intended to outlast volatile system states, serving as a reliable source of truth. Use Cases: Essential for storing critical data, maintaining audit trails, and ensuring regulatory compliance. 🔄 Conclusion: While caching and persistence have distinct roles, they often complement each other in system architectures. Strategic caching enhances performance, while persistence ensures the availability and reliability of crucial data over the long term. Understanding when and how to leverage each technique is crucial for building resilient and efficient systems. #DataManagement #Caching #Persistence #SystemArchitecture #BigData #DataStorage #DataPerformance #DataPersistence #TechInsights #ITInfrastructure #DatabaseManagement #DataDriven #TechSolutions #InformationTechnology #SoftwareEngineering #DataScience #DevOps #CloudComputing #DataAnalytics #DigitalTransformation #AzureDataEngineer #DataEngineering #AzureDataEngineering #Spark #Databricks

Day 5 Elastic Search Title:- Replication in ES. Replication in Elastic Search means making copies of a shard. -->) Imagine we have a shard S on Node A. When we create a copy of this shard, called a replica shard R, we form a replication group. -->) The original shard S is called the Primary shard -->) The copy R is called a Replica shard. -->) It is crucial to ensure that primary shards and their corresponding replica shards are not located on the same node. Placing them on separate nodes enhances fault tolerance, meaning if Node A goes down, the replica shards on other nodes can take over, ensuring no data loss and continuous availability. -->) For example, if Node A (with the primary shard) goes down, Node B (with the replica shard) can take over. Having multiple replicas, like R1 and R2 on different nodes, allows Elasticsearch to handle more queries at the same time, making data access faster and ensuring fault tolerance. In summary, Replication in ElasticSearch not only ensures high availability and fault tolerance by distributing data across multiple nodes but also enhances query performance through parallel processing of read requests.

🚀 Key Considerations in System Design 🚀 Optimized Search: For fast text search across millions of records, use inverted indexing to enhance search performance. Low Latency & High Availability: Start by integrating a CDN with a load balancer and caching mechanisms to ensure minimal latency and uninterrupted service across distributed systems. Database Scaling: Scale horizontally (preferred for NoSQL) for large-scale systems or apply vertical sharding for RDBMS to manage massive datasets effectively. Efficient Reads: Implement read-through cache for read-heavy systems, a strategy we can use to optimize customer data retrieval . ACID Compliance: When strict consistency is required, use RDBMS to maintain the ACID properties for transactional integrity. Handling Unstructured Data: Use NoSQL databases when dealing with unstructured or semi-structured data, such as user-generated content or flexible schemas. Write-Heavy Systems: In write-heavy environments, employ asynchronous processing with Kafka or other message queues to handle large volumes of write operations efficiently. Managing Complex Data: Use object storage solutions (e.g., Amazon S3) for handling multimedia files, such as videos and images. Global Content Delivery: To efficiently serve content across multiple regions, use a CDN for faster global data delivery. Database Indexing: Speed up database queries by implementing the right indexing strategy to enhance query performance. Handling Load: Apply rate limiters to manage high traffic loads and protect system components from overload. Fault-Tolerance: To ensure fault tolerance, use a master-slave database architecture alongside write-through caching for better reliability. Avoiding Single Points of Failure: Set up disaster recovery data centers to ensure system resilience in case of failures. Real-Time Communication: WebSockets are ideal for real-time peer-to-peer communication in collaborative applications. Video Conferencing: Use WebRTC for efficient and reliable video calls in applications with real-time communication needs. Data Integrity: Ensure data consistency between two systems using checksum algorithms. Server Management: Use consistent hashing for efficient load distribution and server management. Cache Optimization: Apply LRU (Least Recently Used) cache eviction policies for better cache performance. Consistency vs. Availability: In certain distributed systems, eventual consistency might be preferred to strike a balance between high availability and data consistency. #SystemDesign #Scalability #BackendEngineering #TechInfrastructure #AgTech #SoftwareDevelopment

With data virtualization, your distributed data sources can be joined and queried without data movement. Nowadays, data virtualization is mandatory to build data lakehouses in your organization. But if you want to build your data lakehouses with the support of iceberg, you need to support theses for your data virtualization. - data lakehouse access control with storage security - iceberg catalog - automated iceberg table maintenance And you may also consider query engines used for your data virtualization with the following feature. - No downtime to run queries. - scale out query engines. The below picture shows how data virtualization works in Chango(https://lnkd.in/gHvp6Eud). Users send queries just to endpoints of Chango data virtualization. The queries will be executed by the query engines of trino and spark which can join your distributed data sources for your data virtualization. Chango Trino Gateway is used to provide no downtime to run trino queries with scaling out small trino clusters rather than one monolithic giant trino cluster. For iceberg support, Chango provides Chango REST Catalog which is an Iceberg REST Catalog and automated iceberg table maintenance. Strong data lakehouse access control is also provided with catalog, schema and table level.