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Arpit Bhayani

Temporal Primer - Building Long-Running Systems What Matters in Production RAG Structure of Every LLM Chat How LLMs Really Work Your Monolith Is Already A Distributed System Databases Were Not Designed For This BM25 JOIN Algorithms Venting at Work Comes at a Reputation Cost Why Half Your Skills Expire Every Few Years Multi-Paxos - Consensus in Distributed Databases MySQL Replication Internals Bloom Filters When You Increase Kafka Partitions Product Quantization The Q, K, V Matrices The Day I Accidentally Deleted Production How LLM Inference Works What are Blocking Queues and Why We Need Them Heartbeats in Distributed Systems How Writes Work in Apache Cassandra Redis Replication Internals How to Handle Arrogant Colleagues at Work How Does a CDN Handle Content Replication You Can't Fix Everything on Day One When Emotions Spill Over at Work Why gRPC Uses HTTP2 Meetings With No Agenda Are a Waste of Time Career Longevity Beats Constant Job Hopping Stay Relevant at Higher Salary Levels Why Distributed Systems Need Consensus Algorithms Like Raft Why Do Databases Deadlock and How Do They Resolve It Why and How Cache Locality Can Make Your Code Faster Why Eventual Consistency is Preferred in Distributed Systems Why does DNS use both UDP and TCP Should You Do a Master's My Honest Take Empathy Makes Great Engineers Unstoppable Good Mentors Build People, Not Just Skills Why You Should Always Have Back-Burner Projects Before You Push Back, Know What You're Standing On Be the One They Can Count On How Much Are People Willing to Bet on You How to Get Leadership to Say Yes to Your Project Don't Let Your Best Ideas Die in Silence Be the Person Everyone Wants to Work With The XY Problem and How to Avoid It The Startup Hiring Lie Nobody Talks About You Won't Be Promoted Unless You Ask It's Not Enough to be Right; Learn to be Heard No One Ships Great Software Alone You Don't Win by Proving Others Wrong Appreciate Generously; It Costs Nothing, But Builds Everything Your Soft Skills Aren't Soft at All Before you form an opinion, experience it Why You Need Both Curiosity and Action to Thrive A Daily Worklog Changed Everything How We Handle Mistakes Defines Us Own Your Mistakes Don't Wait. Step Up. Temporary Fixes Are Permanent Why Interviews Are Biased And What Sets You Apart Saying 'This isn't my problem' is actually the problem How to Write Effective OKRs Never Lose a Battle due to Miscommunication When In Doubt, Code It Out How to Follow Up Without Annoying People Lead Projects That Land, Execution Over Everything Abstract Thinking Will Define Your Next Decade We Engineers Suck at Task Estimation Shiny Obect Syndrome in Tech When to Change Jobs - The 3P Framework Comfort and Competition - Know When to Switch Gears Paper Notes - On-demand Container Loading in AWS Lambda Paper Notes - SQL Has Problems. We Can Fix Them Pipe Syntax In SQL Paper Notes - NanoLog - A Nanosecond Scale Logging System Don't Wait, Learn - The Best Resource is Mythical Paper Notes - WTF - The Who to Follow Service at Twitter The Unexpected Benefit of Reading Random Engineering Articles Roadmaps Are Limiting Your Growth Stop Leaving Money on the Table - Negotiate Your Job Offer Never Bad-Mouth Your Past Employers Show You're a Culture Fit Quantify your resume, Know Your Numbers The Importance of Being Likeable in Interviews Questions to Ask Your Interviewer How to Build Trust Through Collaboration Do This, Once You Are Out of the Interview Cycle Stop Pitching Ideas, Start Pitching Projects Read Those Design Docs, Even the Ones That Seem Irrelevant The Best Engineering Lessons Happen During Outages Great Engineers Start Broad LLM Summaries are Ruining Your Learning Turn System Design Interviews into Discussions Title Inflation At Work, Find Your Own Projects 6 Simple Strategies to Cracking Any Tech Interview How to Remain Unblocked Solving the Knapsack Problem with Evolutionary Algorithms Generating Pseudorandom Numbers with LFSR Local vs Global Indexes in Partitioned Databases
Fallacies of Distributed Computing Every Engineer Should Know
Arpit Bhayani · 2021-06-17 · via Arpit Bhayani

The only way to infinitely scale your system is by making it distributed, which means adding more servers to serve your requests, more nodes to perform computations in parallel, and more nodes to store your partitioned data. But while building such a complex system, we tend to assume a few things to be true, which, in reality, are definitely not true.

These mistaken beliefs were documented by L Peter Deutsch and others at Sun Microsystems, and it describes a set of false assumptions that programmers new to distributed applications invariably make.

Myth 1: The network is reliable;

No. The network is not reliable. There are packet drops, connection interruptions, and data corruptions when they are transferred over the wire. In addition, there are network outages, router restarts, and switch failures to make the matter worse. Such an unreliable network has to be considered while designing a robust Distributed System.

Myth 2: Latency is zero;

Network latency is real, and we should not assume that everything happens instantaneously. For every 10 meters of fiber optic wire, we add 3 nanoseconds to the network latency. Now imagine your data moving across the transatlantic communications cable. This is why we keep components closer wherever possible and have to handle out-of-order messages.

Myth 3: Bandwidth is infinite;

The bandwidth is not infinite; neither of your machine, or the server, or the wire over which the communication is happening. Hence we should always measure the number of packets (bytes) of data transferred in and out of your systems. When unregulated, this results in a massive bottleneck, and if untracked, it becomes near impossible to spot them.

Myth 4: The network is secure;

We put our system in a terrible shape when we assume that the data flowing across the network is secure. Many malicious users are constantly trying to sniff every packet over the wire and de-code what is being communicated. So, ensure that your data is encrypted when at rest and also in transit.

Myth 5: Topology doesn’t change;

Network topology changes due to software or hardware failures. When the topology changes, you might see a sudden deviation in latency and packet transfer times. So, these metrics need to be monitored for any anomalous behavior, and our systems would be ready to embrace this change.

Myth 6: There is one administrator;

There is one internet, and everyone is competing for the same resources (optic cables and other communication channels). So, when building a super-critical Distributed system, you need to know which path your packets are following to avoid high-traffic competing and congested areas.

Myth 7: Transport cost is zero;

There is a hidden cost of hardware, software, and maintenance that we all bear when using a distributed system. For example, if we use a public cloud-like AWS, then the data transfer cost is real. This cost looks near zero from a bird’s eye view, but it becomes significant when operating at scale.

Myth 8: The network is homogeneous.

The network is not homogeneous, and your packets travel to all sorts of communication channels like optic cables, 4G bands, 3G bands, and even 2G bands before reaching the user’s device. This is also true when the packets move within your VPC through different types of connecting wires and network cards. When there is a lot of heterogeneity in the network, it becomes harder to find the bottleneck; hence having a setup that gives us enough transparency is the key to a good Distributed System design.

References