Adrian Draghici

Here are the slides for my GopherCon.EU talk: How to Win Frames and Influence Pointers https://lnkd.in/d7byRQfu #gopherconEU #golang

72

4 Comments

"Blazing Fast: Uber Serves 40 Million Requests per Second, Slashing CPU Cores by 95% from 60K to Just 3K, with Integrated Cache" 🚀 Don't Just Band-Aid Your Performance: Cache Like a Pro 🛠️ Cache is every developer's first line of defense against sluggish apps. But simply throwing a cache at a problem is like slapping a bandage on a gushing wound. It might offer temporary relief, but it won't fix the underlying issue. Uber’s epic struggle: While trying to make their apps faster and able to handle more users while stuck with old-fashioned storage. When there's suddenly a massive increase in people wanting to read data, it pushes them right to the edge of disaster. ❌❌ A BIG TECH HURDLE❌❌ Solution: Docstore comes to the rescue. Beyond the Quick Fix: 1.DocStore Query patterns - It turned out that more than 50% of the queries coming to Docstore are ReadRows requests. Build what’s required and what makes sense. 2.Bypassing Cache - Bypassing the cache in Uber's microservices ensures strong consistency for critical flows like retrieving items in an eater's cart, while leveraging cache for less critical tasks such as fetching a restaurant's menu. 3.Brilliant Cache Invalidation Strategy using CDC and Flux - Cache Invalidation is one of the critical problems in Software Development. This strategy ensures cache consistency within seconds, preventing the risk of uncommitted transactions affecting cache integrity. 4. Special Redis Codec for Row Reads: Unique row values in MySQL are cached in Redis using a special codec to ensure compatibility. This facilitates sharing cache resources across databases while maintaining data isolation. 5. Cache Warming and Cross-Region Replication: Instead of using inbuilt Redis replication, Leveraging the Query Engine for Cross-Region Replication ensures consistency. 6. Sharding Differently: Sharding Redis clusters differently from the database scheme optimizes caching for diverse data access patterns, enhancing overall system performance and scalability while resolving potential inefficiencies in cache management and data retrieval. 7. Adaptive Timeouts: Optimizing Redis Performance with Dynamic Timeouts. This prevents issues like premature failures or excessive latencies. Percentile latency ranges: P75 latency decreases by 75%, and P99.9 latency drops by over 67%, all while curbing latency spikes. ⏱️ As of the current date, CacheFront supports over 40M requests per second across all production Docstore instances, with continuous expansion. 📈 Blog Link : https://lnkd.in/ghvAY3_Z This is undoubtedly one of the finest reads I've encountered on distributed cache systems. I highly recommend giving it a read. 📚 Big Thanks to Preetham Reddy Narayanareddy Afshin Salek Piyush Patel Zurab Kutsia Eli (Oleg) Pozniansky #Systemdesign #distributedcache #cache #p99latency #systemdesign #design

21

1 Comment

As an OSS maintainer I often receive long lists of feature requests My first response to these now is always to ask people to cut a branch and start working on an item from their list If you're not willing to start working on it, it's probably not that important #opensource #software #development

4

Worth giving your 2 minutes..

6

Scalability! But at what cost? This paper is an absolute classic because it explores the underappreciated tradeoffs of distributing systems. It asks about the COST of distributed systems--the Configuration that Outscales a Single Thread. The question is, how many cores does a big distributed system need to outperform some moderately-optimized single-threaded code running on your laptop? As it turns out, scalability often comes with an extremely high COST. The authors examine several graph processing systems--including some big names like Spark--and find that they need dozens to hundreds of cores to outperform a single-threaded program. Why is this the case? It's not because these distributed systems are badly designed, but because distributing computation is inherently inefficient for many problems. Fundamentally, a distributed system cannot rely on all processors sharing state, at least not efficiently. This is a big issue! In graph algorithms, it means servers need to expensively exchange data and eliminates a wide swathe of algorithms and optimizations that rely on shared state. In distributed databases, it means expensive coordination is required to distribute transactions to ensure participating servers have consistent views of data. Does this mean we shouldn't build scalable systems? Of course not! Many problems are well beyond the capability of a single server, no matter how optimized. But it does mean we should be mindful of the efficiency costs of scaling. As an aside, I think this kind of thinking is why Postgres is so popular, despite not being distributed. A large Postgres server can handle a vast amount of traffic (especially with read replicas, which can be cheaply maintained). You need a huge company or incredibly heavy workload to outscale that single server, and when you do, the alternatives come with huge tradeoffs!

169

11 Comments

I really like the clever pun "COST": it implies both performance overhead and hardware expenses. Plus, distributed systems also introduce higher maintenance costs. An example is microservices. While decomposing an application into hundreds of microservices and configuring loosely decoupled communication patterns might sound "scalable," it's worth asking: Do you really need that complexity? You might want to check if a simpler, monolithic system could meet your needs. For instance, by consolidating multiple microservices into a single service, Amazon's Prime Video team managed to reduce costs by 90%. (Blog by Adrian: https://lnkd.in/gedRZnsu) So, when designing your next system, weigh the true cost of complexity.

32

8 Comments

The 2024 StackOverflow survey is out, and for the 8th year in a row, Clojure has ranked near the podium in the Top Paying Technologies category—often occupying rank 1. I don't talk much about why I choose to use a weird Lisp—mostly because nobody cares—but the results speak for themselves. If you can hold your nose long enough to learn something very different, you'll find yourself in a high-productivity environment that features: 🔥 Hot code reloading 🦆 Dynamic typing ♻️ Best-in-class garbage collection 📂 Highly performant immutable data structures 📚 Access to the entire JVM's ecosystem of libraries 🧩 Cross-platform compilation to JavaScript 🧠 An unbelievably smart and humble community Sure, you'll pay a high mental price to learn the language, but as a reward, employers will pay a high price for *you*.

72

47 Comments

🚀 Ever felt overwhelmed by the sheer number of tech tools out there? 😅 You’re not alone! I have been getting a lot of questions about how I approach learning new technologies, and I realized it’s time to share my insights! ✍️ In my latest article, I outlined my personal strategies for mastering new tech tools efficiently. 💡 https://lnkd.in/dxDTwA-C Whether you are a seasoned developer or just starting out, these tips can help streamline your learning process and boost your productivity. 📈Curious about my journey? 🌟 Check it out and let’s discuss how you tackle learning in this fast-paced tech world! 🤔💬 #Learning #TechTools #DeveloperJourney 

30

2 Comments

*Bracing myself for the response I might get for asking this* Software Engineers: Would you ever consider working on an EQUITY ONLY basis (ie no salary, just owning a portion of the business, the value of which would increase as it progresses and scales)? Assuming: - The product is really interesting and so is the work - The product is in the "tech for good" space and would be a really positive contribution to parts of society - Seed funding is confirmed to be coming with several backers keen & they will able to offer a salary in the near future - The product is super early stage, you would be writing the software from the ground up, but the concept, design and business model once funding is received are all solid I know that there will be many who would never consider this under any circumstances and I totally get why, but just curious to hear if any of you would if it was for a product you believed in enough? And would you look at it on a full-time basis or a part-time basis? It's something I got asked about and so I thought I would throw it out to Linkedin to see what you all think rather than assuming that I know the answer on your behalf. #startup #founder #softwareengineer

40

309 Comments

What Is Design Porn = I have watched Design Porn since I had a 20+ years career in Silicon Valley Software Engineering before transitioning to a no-good manager type since 2012. Finally I am doing a post on this deviant behaviour. = The referred article is Design Porn: 8 Reasons Why WhatsApp Was Able to Support 50 Billion Messages a Day With Only 32 Engineers This post by Neo K. outlines the incredible story of WhatsApp co-founder Jan Koum. And the engineering techniques used to scale WhatsApp. Ok ... why is this excellent technical article is Design Porn To understand this we need to briefly visit the semantics of Pornography itself. in Porn sex is Performed - not Enjoyed. The sex is unreal since it is performative theater by hardcore professionals who may be under the influence of energy boosting drugs. Often people fall for the porn trap - they watch performative sex and then they cannot match performance as captured in their mind. This leads to shrinkage of their natural performance and in some cases totally destroys the partner relationship and retention. I think this amount of Pornography Theory is enough to explain the Design Porn phenomenon. = In Design Porn what happens is successful content creators line Neo K. takes architecture and design principles from successful companies to create beautiful Design Porn. Folks please understand this: For WhatsApp - the success or ramp to success came first. The Design was necessitated by the pathway to success. Design was caused by WhatsApp success not Design caused WhatsApp success Design is Correlated with WhatsApp success - not a Cause of it. Two or more variables considered to be related, in a statistical context, if their values change so that as the value of one variable increases or decreases so does the value of the other variable (although it may be in the opposite direction). For example, for the two variables "hours worked" and "income earned" there is a relationship between the two if the increase in hours worked is associated with an increase in income earned. If we consider the two variables "price" and "purchasing power", as the price of goods increases a person's ability to buy these goods decreases (assuming a constant income). Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect. Clear? = I will now discuss how Design Porn harms you. The parallel to Sexual Porn is eerily similar. In Design Porn you are gulping somebody else's Design Performance unique to their context. Real life is messy - be mindful of that!

52

22 Comments

Interesting article from Golang weekly on building LLM-powered applications in Go for my network by Eli Bendersky! 👨‍💻👩‍💻 As the capabilities of LLMs (Large Language Models) and adjacent tools like embedding models grew significantly over the past year, more and more developers are considering integrating LLMs into their applications. Go’s great performance and reliability as a Cloud-native language makes it a natural choice for implementing the more fundamental building blocks of the LLM ecosystem.

21

Is your server facing the thundering herd problem? 🌩️ Don't panic! When requests flood in, causing cascading failures, implementing buffering algorithms like token bucket can save the day! Queues help spread the load and prevent overload. Whether it's peak loads during events or trending content spikes, we've got you covered! - Pre-scaling, - load approximation - jitter introduction can be your secret weapons. Ready to tackle the herd and keep your server running smoothly? Explore more resources for expert insights! #ServerManagement #TechSolutions #StayAhead

17

3 Comments

Principal Engineer Staff Engineer Distinguished Engineer (my favourite) I’m going to say it and it’s likely a little rhetorical: I dislike all these terms immensely. Please can someone give me the canonical description for what they are supposed to mean? (I know I can use a search engine or AI to do it… I want to know what people think they mean). The industry I work in is in danger of disappearing up its own backside. What’s wrong with: Software engineer Why do we need to differentiate? The only reason I can see is signposting to people with differing levels of experience or different sets of responsibilities. If we need to do that then, I’m not sure our teams are set up correctly in the first place. #software #softwareengineering #softwaredevelopment #productdevelopment

98

105 Comments

Blazingly Fast Shadow Stacks for Go 🚀 In this blog post I'm sharing my research and implementation for shadow stacks in Go which deliver up to 8x faster stack trace capturing than frame pointers 🤯. This offers a glimpse into the future of hardware accelerated shadow stacks. https://lnkd.in/d6i5cz-F Let me know what you think!

71

1 Comment

Understanding how the CPU works and how to write thread-efficient programs is very important. Thread Context Switching - When a program is executed in a single-core CPU, the CPU can work with and execute only one thread at any given time. Each thread gets its share of CPU time. For example, when a thread is writing something to the disk, and if it's consuming time, the CPU switches to another thread while the earlier thread is waiting. Threads are context-switched, and it looks like it is multitasking, and things are going on in parallel, which is not. Processing : Sequential processing is where programs are executed in a sequence, one by one, getting allocated with the CPU Concurrency: Parts of the program can be run independently and combined for the result and to speed up the execution on a single CPU Parallel: Each program can be run on each CPU independently and combined for the final results to speed up the execution Single Vs. Multi-thread - By running multiple threads vs. single threads to execute a program, the overall execution time can be reduced, and efficiency can be improved. Process Vs. Threads: A process uses CPU, memory, disk, and other resources to execute a program. A process can spin up one or multiple threads to complete the job. Sync Vs Asyn: Sync is executed in a sequence and cannot be skipped, but Asyn can run its block of the program separately from the main program and doesn't have to wait for completion to move on Thread Deadlock: When the resources required by a thread are being blocked by another one, and each one is waiting on another one, it leads to a deadlock state #cpu #development #technology #softwaredesign

456

35 Comments

Hello Everyone, Welcome to Part 2 of our series on Building a Cache Iteratively. In this instalment, we'll delve into the concept of introducing threads into the equation, understanding the challenges of concurrent access, and devising a solution within a data structure. We'll tackle these issues in the most basic yet effective manner, gradually building a reliable system. You can find the detailed discussion in the following Medium article: https://lnkd.in/gsKYTyzN For those interested in exploring the code base, it's available on GitHub: https://lnkd.in/gp-dTVdU Feel free to dive in, explore, and share your thoughts! #LRUCache #Cache #LLD

14

Struggling to land a job as a Software Engineer with 0-3 years experience? Here's how to shoot to the VERY top of the pile: I won't BS you, the junior market is brutal right now. There's very few roles, and I've been hearing stories of adverts getting over 1000 applicants... So how do you stand out? The truth is, out of those 1000 applications, only 20% will be in Australia, and only 20% of those will be software engineers. That's 40 people. No hiring manager wants to interview 40 people. Especially when very few of those 40 will have the right communication skills, attitude and aptitude to get through the interview process. In fact, 99% of hiring managers would prefer to interview just 1 person. Do this, and that 1 person could be you: - Prepare some personal projects (to show willingness to develop skills) - Do some research on the company (to prove genuine interest) - Practice communicating (poor communicators fail interviews) - Apply with CV + personal project links (so you can be found easily) - Identify specifically why you are a good fit (if you don't know, nobody will) And then - CALL THE HIRING MANAGER. Or video message. Or voice note. In 30 seconds you can: - Introduce yourself and say that you've applied - Reference the personal projects on your CV - Mention why you like the company and why you'd be a great hire - Give availability to discuss the role further in an interview - All whilst proving you are a strong communicator with a great attitude Do this well and you'll be a 1/1000 candidate, I promise you.

45

3 Comments

When reviewing code, I spend about 80% of my time on the contract: - the protocol buffer structure - GRPC method definitions - CRD's in our K8s Operator - DTO's in any REST API's 19% is spent on performance and security. Everything else doesn't matter since the customer only cares about: - Ease of use - Performance - Security - Capabilities.

34

5 Comments