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AXI Classification Part 2 : Beat Vs Burst Vs Pipeline Second way to distinguish between types of AXI interfaces is based on transaction types. We could either have a stream of data without different burst support, single beat with or without pipelining, or burst with or without pipelining. 1. Most signal processing applications require a stream of data communicated between the producer and consumer without support of AXI burst modes. Such transactions can be handled by the AXI Streaming interface.(Common) 2. Updating control registers of peripherals requires control data to be sent to the peripheral and checking that the data update is successful with the help of an acknowledgment before proceeding to send the next control data. This is implemented with single beat without pipelining, where we send a single beat at a time and wait for the response of the current transaction before initiating the next transaction. Such types of transactions are handled with the help of AXI Lite.(Common) 3. Single beat with pipelining allows us to combine the address, data, and response phases of multiple beats together to improve performance. AXI Lite does not inherently support pipelining, so we need to build a custom protocol based on AXI Lite with pipelining features.(Few Used Cases) 4. Single burst without pipelining allows us to send multiple beats with a single address in the transaction, but the next transaction will be initiated only after the completion of the current transaction. These scenarios are implemented with the help of AXI full burst modes without IDs.(Common) 5. Burst with pipelining allows us to send multiple beats with a single address along with overlapping multiple phases of different transactions to increase throughput. This is usually done with the help of registers for ready and valid signals, along with burst control signals to improve throughput. These scenarios are implemented with the help of AXI full burst modes with IDs and Expansion registers.(Common) Want to learn how to build AXI Stream, Lite, and Full interfaces from scratch? Explore here : https://lnkd.in/dG4NGuX6

⭐ 𝐃𝐚𝐲-𝟓 𝐨𝐟 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧: "𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐂𝐀𝐏 𝐓𝐡𝐞𝐨𝐫𝐞𝐦" We discussed Reliability and Scalability in the last posts, so let's assume our system is working properly and scaling fine. However, this does not guarantee that a system can work for eternity without any downtime. Sometimes our system or a part of it goes down for maintenance (software updates, new hardware, or temporary failures), a good system takes less time to recover from faults and is 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 to its users. We can use "𝐍𝐢𝐧𝐞𝐬 𝐨𝐟 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲" to measure it, i.e. downtime allowed for a given number of nines (availability percentage). For example, 90% available system can be down for 36.5 days per year. Let's now dive slowly into how a system plays with its users' data. Imagine you upload some secret revealing post on some app and ask your friends to check it just after that, but they cannot see any new post yet, destroying the excitement. Till the time the post becomes available, they have already unfollowed you 😢. 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 is the concept that will help you not lose your followers, which guarantees if you write data and read it back, you should always read the most recent write. This is a vast concept that we'll uncover in future posts. We have understood about Consistency and Availability, let's also look at Partition Tolerance, which will equip us to understand the CAP Theorem. In any distributed system, where several nodes are working together, it is possible that the network between the nodes becomes faulty and nodes cannot communicate with each other. If a system can tolerate this fault and keep working, it is known as 𝐩𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧 𝐭𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞. As you can see, this quality is a mandate in any system that is distributed as the network is unreliable. This leads us to burst the 𝐦𝐲𝐭𝐡 many engineers have about 𝐭𝐡𝐞 𝐂𝐀𝐏 𝐓𝐡𝐞𝐨𝐫𝐞𝐦 that we should choose any two between Consistency, Availability, and Partition Tolerance. The actual idea behind CAP is to choose between Consistency and Availability because any distributed system must tolerate partitions.  If we choose 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 (𝐂𝐏) - our system may not be fully available as some data stored on unreachable nodes may not be visible. This is a good choice if your system is strict with data updates, for example banking system.  Otherwise, if we choose 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 (𝐀𝐏) - we may get inconsistent and stale data. This is a good choice if eventual consistency is acceptable, for example - the number of views on a video. In my upcoming posts, I'll write about Database Internals, and how data is stored and retrieved. To be continued... Like and Share to spread knowledge 💙 Follow for more such posts - Raman Kumar Rai! #systemdesign #interviewpreparation #coding #programming

what is memory leak? when a program fails to release the memory that is no longer needed the memory leaks comes up. Before delving into memory leaks, let's understand how GC works. Garbage Collection: GC automatically frees up memory that contains unreferenced objects. How GC works internally? It uses the Mark-Sweep-Compact algorithm, marking objects in use, removing unreferenced objects, and compacting memory. In the heap area, there are two regions: Young Generation and Old Generation. When a new object enters the Young Generation, it goes through three parts: Eden, Survivor 1, and Survivor 2. Unreferenced objects are removed in Eden, and surviving objects move to Survivor spaces. Objects that survive multiple cycles may be promoted to the Old Generation based on certain thresholds. After GC frees up memory, memory fragmentation is managed to ensure memory space. Concurrency is employed through threads to handle time slicing for garbage collection, minimizing application downtime. Common Causes of Memory Leaks: Failure to close resources or configurations: If resources are not closed properly, GC may not reclaim the associated memory until the program terminates. It's crucial to ensure resource closure to prevent memory leaks. Improper handling of static variables or objects: GC may not free up memory allocated to static variables or objects unless they are explicitly set to null or removed when no longer needed. Inefficient memory usage due to improper implementation of hashCode() and equals(): If two objects with the same data are stored in different memory locations, it can lead to inefficient memory usage. Overriding the hashCode() and equals() methods can help ensure proper memory management.

#OpenTelemetry Processors: In a previous post, I delved into OpenTelemetry pipelines, highlighting the crucial components - receivers, processors, and exporters. Today, I want to shed light on processors, a pivotal part of the pipeline that empowers observability architects with immense power and flexibility. Processors play a vital role by offering various functionalities: - **Filter out noise:** Effectively drop attributes or signals based on specific conditions to reduce noise and optimize data ingestion costs. - **Transform:** Enhance correlation and contextualization by adding or renaming attributes to signals, enriching the context. - **Redact/mask:** Secure sensitive information by redacting or masking confidential data before storage. - **Batch:** Optimize bandwidth usage by consolidating signal data into a single request before transmission to the backend. - **Memory limit:** Manage resource consumption by imposing memory constraints as signal size increases, ensuring efficient data processing. - **Sampling:** Implement data sampling based on set conditions or random percentages, a cost-efficient practice to reduce expenses. These functions are just a glimpse of the capabilities processors offer, enhancing observability solutions significantly. Stay tuned for more technical insights on processors! #observability #traces #metrics #logs #OpenTelemetry #processors #observabilitysolutions #dataoptimization

The CAP Theorem is an important concept in system design that helps us design distributed systems in a better way according to our needs and business requirements. C, A, and P in the CAP Theorems stand for consistency, availability, and partition tolerance, respectively.   Before understanding the CAP theorem, we need to understand what a distributed system is. A distributed system is a collection or group of independent computers or nodes that can communicate with each other to serve the same purpose. To the end user, a distributed system appears as a single computer. The request of the end user can be received by any available node. Nodes maintain the same set of data, i.e., consistency, by communicating with each other after every write operation. Consistency = It means all the data should remain the same across all the nodes.  Availability = It means that if any nodes get any request from the user, they should be able to execute the request.  Partition Tollerence = Partition happens when the nodes in the distributed system are unable to communicate with each other due to network failure. Fault tolerance refers to the ability of the system to work efficiently even during communication failure. Now, the CAP Theorem states that we can only achieve any two of the three qualities for our distributed system.  In real-world scenarios, we always have to be fault-tolerant, as network issues are inevitable and communication between nodes can get hampered. Therefore, we are left with two options.  1. Consistant+Partition Tollerent 2. Available+Partition Tollerent In the first case, we are prioritizing consistency over availability. To keep our system consistent during a partition, we have to decline user requests to (write operations) as communication between nodes is hampered, which can lead to data inconsistency. In this case, we care about compromising availability. In the second case, we are prioritizing availability over consistency. Since we are keeping all the services running even during partition, we execute all the user requests, but the changes are not reflected in all the nodes as communication is hampered due to partition. In this case, since the data is not updated, we are compromising consistency in our system. Depending on the business requirements, we can design distributed systems efficiently.  #SystemDesign

When testing their 5G and 6G networks, our global telco client discovered manual processes that could be automated... Looking for specific expertise in machine learning and testing, they found the right partner in us. Working together on a new testing system would help create efficiencies, streamline operations and drastically improve whitelisting methods and time spent on test cases. Dive into this success story to see how this was achieved. https://okt.to/JQROZT #Telecommunications #AutomatedTesting #ProcessImprovements

📚 Understanding the CAP Theorem in High-Level System Design 📚 In the world of distributed systems, the CAP Theorem is a cornerstone concept that every system designer must understand. Introduced by Eric Brewer, this theorem highlights the trade-offs between Consistency, Availability, and Partition Tolerance. 🌐 🔍 Key Takeaways: Consistency: Ensures all nodes see the same data simultaneously. Availability: Guarantees a response for every request. Partition Tolerance: Keeps the system operational despite network failures. 🎯 Trade-Offs in Design: Financial Systems: Prioritize Consistency and Partition Tolerance (CP). Social Media Platforms: Focus on Availability and Partition Tolerance (AP). E-commerce Platforms: Balance Consistency and Availability (CA). The CAP Theorem helps us make informed decisions tailored to our specific application needs. By understanding these principles, we can design robust, efficient, and reliable systems. Dive deeper into the blog to explore how the CAP Theorem shapes modern distributed systems: https://lnkd.in/gVjTy829 #DistributedSystems #SystemDesign #HighLevelDesign #DesignPrinciples #CAPTheorem #DesignTradeOffs #TechInsights #SoftwareEngineering

client mode vs cluster mode: Driver Location: Client Mode: The driver runs on the machine where the job is submitted. This means that the machine initiating the Spark job is responsible for the driver process. Cluster Mode: The driver runs on one of the worker nodes in the cluster, not on the submitting machine. The cluster manager decides which worker node will run the driver. Resource Management: Client Mode: Resources for the driver are managed by the machine from which the job is submitted. This can limit scalability if the submitting machine has limited resources. Cluster Mode: Resources for the driver are managed by the cluster. This allows better resource allocation and scalability as the driver can utilize the resources of a dedicated worker node. Network Latency: Client Mode: There can be higher network latency for communication between the driver and the executors if the job is submitted from a machine outside the cluster, as it involves external network communication. Cluster Mode: Network latency is generally lower because the driver and executors are within the same cluster, often on the same local network, leading to faster and more efficient communication. Fault Tolerance: Client Mode: If the machine submitting the job fails or gets disconnected, the job can fail because the driver is no longer available. Cluster Mode: The job is more fault-tolerant since the driver runs within the cluster. If the submitting machine disconnects, the job can continue running independently of the client machine's state.

Standard based vs. vendor specific YANG models is something DevNet Experts need to know about. 👇 Standard YANG models are developed and maintained by standard organizations like IETF and IEEE. They are vendor-neutral and aim to provide a common language for network element configuration. These models are designed to ensuring compatibility across different platforms. On the other hand, vendor YANG models are specific to a particular vendor's device. They are created to leverage proprietary features that might not be covered by standard models. These models can be more detailed and offer functionalities that are unique to a specific product. In case of IOS-XE, the YANG model hierarchy "kind of" resembles the command line structure. This makes it easier to get started and finding the features you need. Standard models are much more generalized and in many cases forces us to work with a much deeper data hierarchy . Standard models are updated very rarely, while vendor models are maintained along with the operating systems like IOS-XE and NX-OS. -- What is your experience with the standard and vendor models?

To gain a deeper understanding of our work at Irys, it's crucial to first grasp our underlying motivation, which is rooted in the inspiration we draw from the PI Asset Framework. For those unfamiliar with the Asset Framework, we've prepared an introduction highlighting its core features: https://lnkd.in/gzSntBUV This will give you a better idea of why we're developing Irys and how we aim to enhance the existing framework. Here's how we're innovating with Irys Frame: 🔄 Adaptable Data Modeling: Irys Frame incorporates the hierarchical data modeling system similar to AF, and we're expanding this feature to include greater flexibility and adaptability, accommodating complex and ever-changing scenarios. 🤖 Enhanced Tools for Developers/Admins: With a focus on those who develop and administer industrial data systems, Irys Frame will surpass AF by offering advanced tools for automation, customization, and extending system capabilities. 🌐 Universal Data Integration: Expanding on AF's capacity for external data integration, Irys Frame will stand out as a versatile platform designed to seamlessly integrate with any data source.