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The GeekNarrator

Kaivalya Apte

The GeekNarrator podcast is a show hosted by Kaivalya Apte who is a Software Engineer and loves to talk about Technology, Technical Interviews, Self Improvement, Best Practices and Hustle.

Connect with Kaivalya Apte https://www.linkedin.com/in/kaivalya-apte-2217221a

Tech blogs: https://kaivalya-apte.medium.com/

Wanna talk? Book a slot here: https://calendly.com/speakwithkv/hey

Enjoy the show and please follow to get more updates. Also please don’t forget to rate and review the show.

Cheers

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  • 22 episodes
  • Avg 1 hr 13 min
  • English

Episodes 22

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  • Wednesday · 1 hr 1 min

    Can We Trust AI-Written Code?

    AI can write code and generate tests. What gives engineers a reason to trust the result?Kaivalya Apte joins Gabriela Moreira for a conversation about software correctness, executable specifications, and the behaviors that tests miss. They explore why a model is more than a smaller version of your code, how to connect specifications to real tests, and why understanding is central to confidence.In this episode:• What “correct” means beyond type safety• How to evaluate a specification written by an AI agent• Modeling failures, concurrency, and the surrounding environment• Quint’s path from specification language to developer tooling• Finding gaps in test coverage through behaviors• Keeping specifications and code aligned in CI• Lessons from real-world Quint use cases, as described by GabrielaGuest: Gabriela MoreiraHost: Kaivalya ApteRecorded: September 11, 2026Product availability and beta references reflect the recording date.Chapters:00:00:00 Introduction00:01:35 AI, correctness & the human in the loop00:03:24 What does “correct” actually mean?00:07:36 Who checks an AI-written specification?00:13:43 Complexity, failures & the real world00:19:26 What belongs in a model?00:25:16 Why Quint exists00:32:51 Can we trust our tests?00:35:32 Keeping code & specifications in sync00:41:14 Behaviours as paths through a state machine00:47:07 Confidence through understanding00:53:04 Quint in practice00:58:53 Where to go nextCase Studies:Turso: https://turso.tech/blog/how-we-used-quint-to-find-over-10-bugs-in-sqliteOpenhands: https://quint.sh/posts/raceconditionQuint:Website: https://quint.sh/X: https://x.com/quint_langBluesky: https://bsky.app/profile/quint-lang.orgLinkedin: https://www.linkedin.com/company/quint-coGithub: https://github.com/quint-co/quintAgent Skills for Quint:https://github.com/quint-co/quint/tree/main/skillsJoin the Beta: https://tally.so/r/81PQyOGabriela:X: https://x.com/bugarelaLinkedin: https://br.linkedin.com/in/bugarelaBluesky: https://bsky.app/profile/bugarela.comFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • September 29 · 1 hr 6 min

    Agentic Performance Engineering ft. Perfloop

    Performance is getting harder to ignore, and Tomás argues that AI is making the problem urgent in ways most teams are not ready for. He explains why AI-generated code, exploding system surface area, and rising infrastructure costs are creating a perfect storm, and why Perfloop exists to turn performance engineering into a rigorous, automated science.Tomás, the creator of Perfloop, breaks down how the product maps your codebase and telemetry into a living model of your system, then hunts for performance anti-patterns with a verification harness that puts every hypothesis through measured, repeatable experiments. Instead of relying on a freestyle coding agent, Perfloop uses structured discovery, deterministic checks, and adversarial verification to produce results you can actually trust.Chapters:00:00 The performance problem behind Perfloop00:39 Why performance work is a battle03:59 How AI agents raise the stakes08:55 Quality control at the scale of agentic development10:34 How Perfloop maps systems and finds opportunities14:32 Using telemetry to prioritize hot paths18:13 Perfloop’s open-source performance wins22:18 How Perfloop measures and verifies improvements25:11 Common performance patterns and bottlenecks28:47 The “apex hunt” for bigger architectural gains30:58 Initiatives and setting high-level performance goals35:21 Connecting code optimizations to customer impact39:59 Why independent verification matters44:36 Choosing the right AI models for each task49:45 What a harness does in Perfloop53:59 The possibility of pre-merge performance reviews57:24 How AI may change the role of performance engineers63:54 Why performance requires measurementImportant links:Patterns: https://app.perfloop.ai/t/oss/patternsPerfloop: https://app.perfloop.ai/t/oss/startFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • August 4 · 1 hr 15 min

    Future of Coding with AI ft. Andrey Breslav

    Andrey Breslav (creator of Kotlin and founder of CodeSpeak) discusses why, in the AI era, the hard problem isn’t generating code but clearly deciding what software should do. He explains how “specs” range from formal documents to informal prompts, and why CodeSpeak is moving from long docs to a structured, versioned set of interconnected requirements extracted from everyday agent chats. The conversation covers how clearer requirements reduce ambiguity (including using output stability/semantic entropy), help teams communicate, surface inconsistencies early, and ease review bottlenecks as AI increases code volume. They compare prompt-driven work to spec-driven workflows, talk through examples like a modified tic-tac-toe game, and explore testing (regression, conformance, acceptance), constraints and temporary decisions, modularity and discoverability of requirements, and how roles and languages may evolve while humans stay in control.Chapters:00:00 Introduction04:16 What Counts as a Spec07:49 Human vs Machine Specs10:16 Codespeak Structured Requirements12:14 Writing Better Requirements17:51 Right Abstraction Level22:25 Why Prompts Break at Scale29:58 Keeping Requirements in Sync33:30 Testing in Spec Driven Dev39:01 Beyond Tests and Trust41:11 Abstraction Tradeoffs43:49 Trusting Generated Code44:34 Safe Zones and Checks46:58 Shared Spec Artifacts49:24 Modular Specs and Grounding52:28 Why Teams Adopt Specs55:11 Review Bottlenecks and Balance01:00:21 Capturing Constraints as Intent01:02:29 Do Languages Still Matter01:07:48 Roles in the AI Era01:10:42 CodeSpeak Workflow SummaryImportant Links:- https://codespeak.dev/For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • May 1 · 1 hr 14 min

    Many Databases 1 LSM Engine - OpenData

    The episode explores why modern databases keep reinventing the same distributed-systems machinery and argues that a major part of database cost is the operational tax of running replication-heavy systems. Our guest, Almog Gavra, co-founder of Responsive, explains how his team pivoted from operating Kafka Streams as a service to building SlateDB and the “Open Data” manifesto: an object-storage-native LSM foundation that can power multiple database types (vector, time series, logs, key-value) with shared tuning knobs and failure modes. They discuss why distributed-systems complexity is often harder than query engines, how LSM trees provide a tunable tradeoff between read/write/space amplification, caching layers and cost transparency, separating readers/writers, stateless ingest, single-writer availability and fencing via S3 compare-and-set, offloading compaction, and how the architecture enables near-free snapshots. They also cover when this approach doesn’t fit: OLTP that can stay on Postgres and ultra-low-latency workloads where cold object-store misses are unacceptable.Chapters:00:00 Introduction08:36 Open Data Manifesto18:34 Specialized vs General25:10 SlateDB Architecture32:51 LSM Trees as Tuning Dial38:58 Tuning Without Overload39:46 Cost Aware Config Knobs41:51 Latency Cost Durability Tradeoffs46:46 Caching Strategies And Layers50:23 Split Readers And Writers52:43 Single Writer Versus Multi Writer55:16 Scaling And Partitioning Writes58:58 Failure Modes And Fencing01:05:23 Compaction As Separate Worker01:09:28 Snapshots And Garbage Collection01:10:25 When Open Data Is Not FitImportant links and references:OpenData: http://github.com/opendata-oss/opendataOpenData manifesto: https://www.opendata.dev/blog/manifestoReach out to Almog: https://www.linkedin.com/in/agavra/ or https://x.com/almoggavraDostovesky paper on LSM: https://nivdayan.github.io/dostoevsky.pdfLatency/Cost/Durability Triad: https://materializedview.io/p/cloud-storage-triad-latency-cost-durabilitySlateDB: https://github.com/slatedb/slatedb"how SSTs work": https://www.bitsxpages.com/p/sorted-string-tables-sst-from-firstFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • April 20 · 1 hr 20 min

    Assertions in production? (TigerBeetle Vol. 3) With Alex aka matklad

    Assertions vs Types: Design by Contract, Deterministic Simulation Testing, and Safety vs Availability (TigerBeetle Vol. 3)In this episode (volume 3), Kai talks with Alex about assertions, type systems, and design by contract, arguing it’s not “types vs assertions” but using both: types for cheap, structural guarantees and assertions where types become too costly or obscure logic. Alex defines assertions (in Zig) as a function that crashes the program on false, explains why disabling assertions in production is dangerous, and ties reliable assertion use to deterministic simulation/generative testing to exercise error paths. We discuss aiming for very high assertion density, repeating weak and strong assertions across call sites and callees to form an interlocking “net,” and note you don’t need special language features for DBC. We also cover safety vs availability tradeoffs, recovery/isolation boundaries (“let it crash” with recovery), TigerBeetle’s approach to correctness, a real cache/hash-table bug caught by an assertion, handling poison-pill failures via fix-forward and frequent releases, control plane vs data plane performance tactics for assertions, and why Alex doesn’t use AI to write TigerBeetle code.Chapters:00:00 Welcome and Intro01:33 Assertions Versus Types03:05 Silver Bullets Origins08:10 When Types Get Costly11:06 What Is an Assertion?12:40 Never Disable Assertions15:30 Testing and Error Paths19:52 Simulation Testing Harness22:50 Where to Assert Everywhere27:01 Redundant Contracts Benefits33:08 No Language Features Needed38:01 Visibility and Abstractions40:47 Boundaries and Integration44:01 Safety Versus Liveness Setup44:31 Safety vs Availability Tradeoffs46:16 Let It Crash Philosophy47:13 Isolation and Recovery Boundaries48:02 TigerBeetle vs IDE Priorities53:48 Always Assertions Pattern55:46 Cascading Failures in Clusters57:57 Fix Forward and Fast Releases01:02:27 Worst TigerBeetle Bug Story01:07:00 Control Plane vs Data Plane01:09:50 Assertion Performance Tactics01:15:18 AI Limits for Safety Systems01:18:55 Closing Advice on AssertionsFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • April 20 · 53 min

    Fastest Browser infrastructure for AI Agents - Kernel

    In this episode, Catherine, Co-Founder, and CEO of Kernel, reveals the magic behind Kernel's approach using uni-kernels and micro VMs, enabling browser startups in just milliseconds. We talk about the technical challenges of agent-based web interactions, how Cash App leverages Kernel for live QA of e-commerce sites, the intricacies of handling authentication, and the future potential of attaching virtual GPUs for optimal performance. Whether you're an AI developer or fascinated by the backbone of internet automation, don't miss this insightful conversation. Chapters:00:00 Introduction to AI Agent Challenges00:35 Welcome and Episode Overview01:58 Guest Introduction and Background02:39 The Problem Statement and Kernel's Origin07:21 Infrastructure for AI Agents16:09 Kernel's Technical Architecture20:33 Use Cases and Real-World Applications24:38 Challenges and Future Directions27:20 Running on Bare Metal: Optimizing Browser Instances28:02 Challenges in Infrastructure Optimization30:34 Headful Browsers and Human Interaction30:58 Recording and Debugging Browser Sessions33:42 Preventing Misuse of Browser Automation39:55 Handling Authentication and Secure Access44:57 Bot Detection and Good Actor Automations48:56 Future Developments and GPU Integration52:41 Conclusion and Final ThoughtsImportant links:- Homepage to go get a free account (no credit card required) and just try us out: https://www.kernel.sh/ - Our chromium on Unikernels OSS repo: https://news.ycombinator.com/item?id=43705144- The blog post where in it we benchmarked ourselves against all others and ranked the fastest browser infrastructure in the world: https://www.kernel.sh/blog/fast For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • April 20 · 1 hr 28 min

    TigerStyle with matklad Vol. 2 (Systems Engineering)

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinTigerStyle with matklad Vol. 2 Systems EngineeringChapters:00:00 Introduction to Alex and His Journey00:06 The Importance of Culture and Principles00:25 Weekly Releases and Quality Optimization00:45 Static Allocation Explained01:01 Alex's Passion for Programming01:25 Welcome and Introduction to the Show01:40 Alex's Background and Career Path04:01 Choosing the Right Language for Systems Programming07:12 Mental Models and Programming Philosophy20:19 Test-Driven Development and Quality42:00 Weekly Releases as a Force Multiplier44:49 Monoliths vs Microservices: The Core Idea47:05 The Importance of Engineering Process47:37 Designing a Scalable Chat Application49:36 Achieving Simplicity in System Design52:25 Static Allocation Explained01:13:59 Balancing Safety and Availability with Assertions01:27:08 The Passion Behind ProgrammingAbout matklad: https://matklad.github.io/aboutFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • Nov 16, 2025 · 1 hr 11 min

    Databases and Engineering with @PlanetScale CEO - Sam Lambert

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinExploring Cloud Databases, Scalability, and Simple Engineering with Sam Lambert, CEO of PlanetScaleIn this episode of The Geek Narrator podcast, we welcome Sam Lambert, CEO and Co-Founder of PlanetScale, known for creating the world's fastest and most scalable cloud database. Sam shares his insights on databases, operational excellence, and simple engineering. We discuss topics such as scalability, Postgres versus MySQL, and replication. Sam also talks about handling complexity in engineering, the unique features of Vites, and how PlanetScale achieves high availability. Don't miss this deep dive into the future of cloud databases. Like, share, and subscribe to support the channel!Chapters:00:00 Introduction and Episode Overview01:13 Meet Sam Lambert: Background and Career02:42 Balancing Work and Social Media05:48 The Philosophy of Simple Engineering14:21 The Slotted Counter Pattern at GitHub18:27 Postgres vs MySQL: Design Flaws and Philosophical Differences28:58 Sharding and Scaling with Vitess37:01 Database Branching and Schema Changes38:50 Common Practices in Startups39:07 Challenges with Data Branching40:45 Legal and Ethical Considerations42:31 Staging Environments vs. Dev Branches45:26 Trade-offs in Cloud Databases52:41 Replication and Durability01:00:02 Ensuring High Availability01:08:04 Backup Strategies and Testing01:10:41 Conclusion and Final ThoughtsLearn about PlanetScale: https://planetscale.com/For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • Nov 16, 2025 · 1 hr 32 min

    What is TigerStyle? Principles behind TigerBeetle ft. Joran

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this captivating episode, we sit down with Joran Dirk Greef, the mastermind behind Tiger Beetle, a groundbreaking financial transactions database. Joran shares his journey of innovation, highlighting the challenges and triumphs of creating a system that is not only faster but also safer. Dive into the philosophy of Tiger Style, a unique methodology that emphasizes quality and performance, ensuring that software development is both efficient and effective. Joran's insights into trust, discipline, and the relentless pursuit of excellence offer valuable lessons for anyone in the tech industry. Whether you're a developer, entrepreneur, or tech enthusiast, this episode is packed with inspiration and practical wisdom. Don't miss out on this opportunity to learn from one of the leading minds in software engineering.Chapters:00:01:37 Introduction to Tiger Beetle 00:02:27 Philosophy of Tiger Style 00:03:38 Challenges in Software Development00:04:43 Importance of Trust and Quality 00:09:43 Static Allocation in Software 00:16:53 AI in Software Development 00:23:53 Business Philosophy and Innovation 00:31:53 The Future of Software DevelopmentFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • Nov 16, 2025 · 59 min

    What makes Apache Pinot so Fast?

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this episode, host Kaivalya Apte interviews Ankit Sultana, a staff engineer at Uber with extensive experience in Apache Pinot, a real-time analytics platform. They discuss the high-level architecture, ingestion processes, and query mechanisms of Apache Pinot. Ankit provides a historical context, detailing the evolution of Apache Pinot from its origins at LinkedIn to its widespread adoption. They discuss the key components of Pinot, explaining the roles of Pinot servers, brokers, controllers, and the dependency on Zookeeper. Ankit also explained how data flows into Apache Pinot and the technicalities of its real-time ingestion and querying capabilities. Chapters:00:00 Introduction and Episode Overview03:30 Understanding Apache Pinot03:49 Apache Pinot's Historical Background05:20 Real-Time Analytics with Apache Pinot11:06 Apache Pinot's Architecture and Components17:05 Tenancy and Data Ingestion in Apache Pinot30:22 Understanding Real-Time Replication and Consumer Groups30:52 Pinot's Offset Tracking and Segment Creation31:59 Handling Server Restarts and Segment Transitions32:50 Dealing with Kafka Duplicates and Deduplication Features35:13 Ingestion Process and Mutable vs Immutable Segments39:18 Memory Management and Segment Flushing40:10 Advantages of Keeping Mutable Segments Longer42:21 Introduction to Pinot's Query Engines42:50 Single Stage Engine: Architecture and Optimizations54:49 Multi-Stage Engine: Flexibility and Challenges58:13 Conclusion and Next StepsImportant Links:* Good high-level overview on Pinot: https://www.youtube.com/watch?v=F8Q_pGIH9yY* Apache Pinot 101 by Tim: https://www.youtube.com/playlist?list=PLihIrF0tCXdfN6y-twj9KtWaXM1GH4RSe* Multistage Physical Optimizer, the new optimizer that we built at Uber and open-sourced: https://docs.pinot.apache.org/users/user-guide-query/multi-stage-query/physical-optimizer* Multistage Lite Mode: https://docs.pinot.apache.org/users/user-guide-query/multi-stage-query/multistage-lite-mode* Time Series Engine Talk at RTA Summit: https://www.youtube.com/watch?v=kgseiambgesFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • Oct 25, 2025 · 1 hr 18 min

    You don't need Linux, Docker, k8s? Future with Unikernels ft. NanoVMs

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummaryIn this conversation, Ian discusses the evolution and significance of Unikernels and NanoVMs, emphasizing their potential to enhance security and performance in cloud computing. He explains the historical context of operating systems, the limitations of traditional systems, and how Unikernels offer a streamlined alternative. Ian also highlights the unique features of NanoVMs, their integration capabilities, and the challenges faced in the ecosystem. The discussion concludes with insights into the future of Unikernels and the ongoing developments in the field.takeaways.Unikernels are a specialized type of operating system designed for cloud environments.The evolution of operating systems has led to the need for more efficient solutions like Unikernels.Unikernels can significantly reduce security vulnerabilities compared to traditional systems.NanoVMs provide a unique approach to Unikernels with a focus on performance and security.Integrations with existing tools and libraries are crucial for the adoption of Unikernels.The ecosystem around Unikernels is still developing, with many opportunities for growth.Unikernels eliminate the need for complex orchestration and management layers.The future of Unikernels includes tighter integrations with cloud services and improved developer experiences.Security features in Unikernels are designed to address modern threats effectively.The potential for Unikernels to transform application deployment is significant, with many untapped possibilities.Chapters00:00 Introduction to Unikernels and NanoVMs04:24 The Evolution of Operating Systems11:24 Understanding Unikernels vs. Traditional Systems17:20 Security Implications of Unikernels26:17 NanoVMs: Architecture and Unique Features38:44 Security Concerns in Unikernels41:05 Integration and Support for GPUs44:02 Cloud Support and Deployment45:51 Avoiding Bloat in Integrations51:54 Developer's Perspective on Unikernels59:18 Limitations and Future of UnikernelsImportant Links:https://ops.cityhttps://nanos.orghttps://repo.ops.cityhttps://nanovms.com/dev/tutorialsFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • Oct 25, 2025 · 1 hr 22 min

    Modern, ultra fast PostgreSQL engineered from scratch? ft: CedarDB

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummaryIn this conversation, Philipp discusses the innovations behind CedarDB, a database system designed from scratch to optimize performance for modern hardware. He explains the foundational principles of compiling SQL to machine code, the importance of parallel processing, and the challenges of maintaining Postgres compatibility. The discussion also covers the system's approach to handling transactional and analytical workloads, data ingestion processes, query optimization strategies, and future developments including schema evolution and disaggregated storage.Takeaways:- CedarDB is built from the ground up to utilize modern hardware effectively.- The system compiles SQL directly to machine code for performance.- Parallel processing is a key feature, allowing efficient use of multiple cores.- CedarDB aims to be Postgres compatible while innovating on performance.- Transactional workloads are handled efficiently without sacrificing analytical capabilities.- Data ingestion is optimized for both row-oriented and columnar formats.- The system uses optimistic concurrency control to manage write conflicts.- Query optimization leverages statistics to improve join performance.- Future developments include schema evolution and disaggregated storage.- CedarDB is designed to be flexible and adaptable for various workloads.Chapters00:00 Introduction to CDRDB and Background of Philipp05:36 Compiling SQL to Machine Code for Performance11:25 General Purpose vs. Analytical Databases16:51 Transactional Workloads and Hybrid Storage Engine54:29 Understanding B-Tree and Columnar Storage01:02:18 Data Duplication and Memory Efficiency01:08:43 Indexing Strategies and B-Tree Optimization01:15:57 Handling Write Conflicts and Transaction Management01:24:10 Query Optimization and Join Strategies01:33:28 Future Developments in Schema Evolution and StorageImportant Links:CedarDB: https://cedardb.com/The Umbra research project: https://umbra-db.com/SQL Query Compilation: http://www.vldb.org/pvldb/vol4/p539-neumann.pdfOptimistic B-Trees: https://cedardb.com/blog/optimistic_btrees/Our B-Tree storage engine: https://cedardb.com/blog/colibri/For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!

  • Jul 29, 2025 · 1 hr 23 min

    Building a new Database Query Optimiser - @cmu ​

    Read more about Kafka Diskless-topics, KIP by Aiven:KIP-1150: https://fnf.dev/3EuL7mvSummary:In this conversation, Kaivalya Apte and Alexis Schlomer discuss the internals of query optimization with the new project optd. They explore the challenges faced by existing query optimizers, the importance of cost models, and the advantages of using Rust for performance and safety. The discussion also covers the innovative streaming model of query execution, feedback mechanisms for refining optimizations, and the future developments planned for optd, including support for various databases and enhanced cost models.Chapters00:00 Introduction to optd and Its Purpose03:57 Understanding Query Optimization and Its Importance10:26 Defining Query Optimization and Its Challenges17:32 Exploring the Limitations of Existing Optimizers21:39 The Role of Calcite in Query Optimization26:54 The Need for a Domain-Specific Language40:10 Advantages of Using Rust for optd44:37 High-Level Overview of optd's Functionality48:36 Optimizing Query Execution with Coroutines50:03 Streaming Model for Query Optimization51:36 Client Interaction and Feedback Mechanism54:18 Adaptive Decision Making in Query Execution54:56 Persistent Memoization for Enhanced Performance57:12 Guided Scheduling in Query Optimization59:55 Balancing Execution Time and Optimization01:01:43 Understanding Cost Models in Query Optimization01:04:22 Exploring Storage Solutions for Query Optimization01:07:13 Enhancing Observability and Caching Mechanisms01:07:44 Future Optimizations and System Improvements01:18:02 Challenges in Query Optimization Development01:20:33 Upcoming Features and Roadmap for optdReferences:- NeuroCard: learned Cardinality Estimation: https://vldb.org/pvldb/vol14/p61-yang.pdf- RL-based QO: https://arxiv.org/pdf/1808.03196- Microsoft book about QO: https://www.microsoft.com/en-us/research/publication/extensible-query-optimizers-in-practice/- Cascades paper: https://15721.courses.cs.cmu.edu/spring2016/papers/graefe-ieee1995.pdf- optd source code: https://github.com/cmu-db/optd- optd website (for now): https://db.cs.cmu.edu/projects/optd/For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#database #queryoptimization #sql #postgres

  • Jul 29, 2025 · 1 hr 5 min

    Fast Observability on S3 with Parseable

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummaryIn this conversation, Nitish Tiwari discusses Parseable, an observability platform designed to address the challenges of managing and analyzing large volumes of data. The discussion covers the evolution of observability systems, the design principles behind Parseable, and the importance of efficient data ingestion and storage in S3. Nitish explains how Parseable allows for flexible deployment, handles data organization, and supports querying through SQL. The conversation also touches on the correlation of logs and traces, failure modes, scaling strategies, and the optional nature of indexing for performance optimization.References:Parseable: https://www.parseable.com/GitHub Repository: https://github.com/parseablehq/parseableArchitecture: https://parseable.com/docs/architecture Chapters:00:00 Introduction to Parseable and Observability Challenges05:17 Key Features of Parseable12:03 Deployment and Configuration of Parseable18:59 Ingestion Process and Data Handling32:52 S3 Integration and Data Organisation35:26 Organising Data in Parseable38:50 Metadata Management and Retention39:52 Querying Data: User Experience and SQL44:28 Caching and Performance Optimisation46:55 User-Friendly Querying: SQL vs. UI48:53 Correlating Logs and Traces50:27 Handling Failures in Ingestion53:31 Managing Spiky Workloads54:58 Data Partitioning and Organisation58:06 Creating Indexes for Faster Reads01:00:08 Parseable's Architecture and Optimisation01:03:09 AI for Enhanced Observability01:05:41 Getting Involved with ParseableFor memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#database #s3 #objectstorage #opentelemetry #logs #metrics

  • Jul 29, 2025 · 1 hr 17 min

    How does AWS Lambda work?

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this conversation, Kaivalya Apte and Rajesh Pandey talk about the engineering behind AWS Lambda, exploring its architecture, use cases, and best practices. They discuss the challenges of event handling, concurrency, and load balancing, as well as the importance of observability and testing in serverless environments. The conversation highlights the innovative solutions AWS Lambda provides for developers, emphasizing the balance between simplicity and complexity in cloud computing.Chapters:00:00 Introduction to AWS Lambda04:36 Use Cases and Best Practices for AWS Lambda09:34 Event Handling and Queue Management19:41 Idempotency and Event Duplication Challenges29:39 Cold Starts and Performance Optimization34:37 Statelessness and Resource Management in Lambda42:18 Understanding Micro-VMs and Cold Starts45:14 Resource Management and Recommendations for Developers47:04 Scaling and Back Pressure in Serverless Systems51:33 Cellular Architecture and Fairness in Resource Allocation55:23 Handling Problematic Events and Poison Pills01:01:03 Testing and Operational Readiness in Lambda01:14:11 Preparing for High Traffic EventsReferences:Handling Billions of invocations: https://aws.amazon.com/blogs/compute/handling-billions-of-invocations-best-practices-from-aws-lambda/Firecracker: https://firecracker-microvm.github.io/AWS Lambda: https://aws.amazon.com/lambda/Connect with Rajesh: https://x.com/RPandeyViewshttps://www.linkedin.com/in/rajeshpandeyiiit/Don't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#aws #awslambda #serverless #distributedsystems #scalability #reliability

  • Jul 29, 2025 · 1 hr 5 min

    Breaking Distributed Systems with Kyle Kingsbury from Jepsen

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this episode of The Geek Narrator podcast, host Kaivalya Apte interviews Kyle Kingsbury, a renowned expert in database and distributed systems safety analysis. They discuss the world of testing distributed systems, the challenges faced, common bugs and patterns. Kyle shares insights on the importance of understanding system documentation, the role of formal verification, and the balance between performance and safety in testing. He also provides valuable advice for aspiring engineers in the field of distributed systems.Chapters:00:00 Introduction to Kyle Kingsbury and His Work06:59 Common Bugs in Distributed Systems12:37 Functional Bugs vs Safety Bugs17:54 Changes in Testing Over the Years26:03 False Positives and Negatives in Testing32:33 The Importance of Experimentation in Testing39:28 Tools and Technologies for Testing48:58 The Role of Formal Verification57:04 Reusability of TestsImportant links:Distributed systems class: https://github.com/aphyr/distsys-classWrite your own distributed system: https://github.com/jepsen-io/maelstromJepsen Analyses: https://jepsen.io/analysesKey takeaways:- Reading documentation is a crucial first step in testing systems.- Testing distributed systems involves understanding their semantics and guarantees.- Common bugs often arise from mismanagement of definite versus indefinite failures.- Testing strategies for cloud-based systems require cooperation with providers.- Performance testing can reveal unexpected behaviours in systems under stress.- Formal verification remains a challenging but valuable tool in ensuring system safety.- The testing process is iterative and requires collaboration with engineering teams.- Aspiring engineers should immerse themselves in practical experiences to build intuition.For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinDon't forget to like, share, and subscribe for more insights!=============================================================================Like building stuff? Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator=============================================================================Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#databasearchitecture #distributedsystems #cloudcomputing #testing #jepsen

  • Apr 7, 2025 · 1 hr 8 min

    How do vector (search) databases work? ft: turbopuffer

    For memberships: join this channel as a member here:https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinSummary:In this conversation, Kaivalya Apte and Simon Eskildsen talk about vector databases, particularly focusing on TurboPuffer. They discuss the importance of vector search, embeddings, and the challenges associated with building efficient search engines. The conversation covers various aspects such as cost considerations, chunking strategies, multi-tenancy, and performance optimization. Simon shares insights on the future of vector search and the significance of observability and metrics in database performance. The discussion emphasizes the need for practical application and experimentation in understanding these technologies.Chapters:00:00 Introduction to Vector Databases10:34 Understanding Vectors and Embeddings15:03 Example: Designing a Search Engine for Podcasts27:53 Scaling Challenges in Vector Search36:46 Indexing and Querying in TurboPuffer38:12 Understanding Indexing and Query Planning45:45 Exploring Index Types and Their Performance50:27 Data Ingestion and Embedding Retrieval54:19 Use Cases and Challenges in Vector Search01:01:22 Metrics and Observability in Vector Databases01:03:52 Future Trends in Vector Search and DatabasesReferences:How do build a database on Object Storage? https://youtu.be/RFmajOeUKnETurbopuffer https://turbopuffer.com/Continous Recall measurement: https://turbopuffer.com/blog/continuous-recallTurbopuffer architecture: https://turbopuffer.com/architecture

  • Apr 7, 2025 · 1 hr 23 min

    Are your Data Pipelines Complex?

    The GeekNarrator memberships can be joined here: https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinMembership will get you access to member only videos, exclusive notes and monthly 1:1 with me. Here you can see all the member only videos: https://www.youtube.com/playlist?list=UUMO_mGuY4g0mggeUGM6V1osdA------------------------------------------------------------------------------------------------------------------------------------------------------------------About this episode: ------------------------------------------------------------------------------------------------------------------------------------------------------------------In this conversation, Jacopo and Ciro discuss their journey in building Bauplan, a platform designed to simplify data management and enhance developer experience. They explore the challenges faced in data bottlenecks, the integration of development and production environments, and the unique approach of Bauplan using serverless functions and Git-like versioning for data. The discussion also touches on scalability, handling large data workloads, and the critical aspects of reproducibility and compliance in data management. Chapters:00:00 Introduction03:00 The Data Bottleneck: Challenges in Data Management06:14 Bridging Development and Production: The Need for Integration09:06 Serverless Functions and Git for Data17:03 Developer Experience: Reducing Complexity in Data Management19:45 The Role of Functions in Data Pipelines: A New Paradigm23:40 Building Robust Data Solutions: Versioning and Parameters30:13 Optimizing Data Processing: Bauplan Runtime46:46 Understanding Control Planes and Data Management48:51 Ensuring Robustness in Data Pipelines52:38 Data Quality and Testing Mechanisms54:43 Branching and Collaboration in Data Development57:09 Scalability and Resource Management in Data Functions01:01:13 Handling Large Data Workloads and Use Cases01:09:05 Reproducibility and Compliance in Data Management01:16:46 Future Directions in Data Engineering and Use CasesLinks and References:Bauplan website:https://www.bauplanlabs.com

  • Apr 6, 2025 · 1 hr 17 min

    Can Math simplify incremental compute?

    In this episode of The Geek Narrator podcast, Lalit Suresh, CEO of Feldera, joins us to share insights on incremental view maintenance and its significance in modern data processing.We have discussed the challenges posed by distributed systems, the mathematical foundation of DBSP, and how Feldera's architecture addresses these challenges. Performance optimization, handling late events, and the future of stream processing, the importance of SQL in creating efficient data workflows - its all in here.Chapters00:00 Introduction to Incremental View Maintenance06:30 Challenges in Distributed Systems11:46 Batch Processing vs Stream Processing16:27 Understanding DBSP: The Mathematical Foundation27:46 Architecture of Feldera and Data Flow39:23 Partitioning and Storage Layer in Feldera42:51 Understanding Co-Design Storage Layers45:52 Foreground and Background Workers in DBSP49:16 Tuning Background Workers for Performance49:41 Synchronous Compute Model and View Propagation51:35 Zsets and Batch Processing in Stream Workloads54:00 Data Model Optimization in Feldera57:22 Handling Late Events and Lateness in Feldera01:01:18 Watermarks and Lateness Annotations01:04:20 Error Handling and Idempotency in Feldera01:11:05 Feldera's Differentiators and Future Roadmap

  • Mar 14, 2025 · 1 hr 5 min

    Redpanda - High Performance Streaming Platform for Data Intensive Applications

    The GeekNarrator memberships can be joined here: https://www.youtube.com/channel/UC_mGuY4g0mggeUGM6V1osdA/joinMembership will get you access to member only videos, exclusive notes and monthly 1:1 with me. Here you can see all the member only videos: https://www.youtube.com/playlist?list=UUMO_mGuY4g0mggeUGM6V1osdA------------------------------------------------------------------------------------------------------------------------------------------------------------------About this episode: ------------------------------------------------------------------------------------------------------------------------------------------------------------------In this conversation, Alex from Red Panda discusses his engineering background, the challenges faced in reliability engineering, and the journey of building a better streaming system. He emphasizes the importance of understanding latency and performance in engineering systems, the market position of Red Panda in relation to Kafka, and the complexities involved in optimizing codebases for better performance. In this conversation, Alex discusses Red Panda's architecture, focusing on its thread architecture, memory allocation mechanics, and the importance of protocol correctness. He highlights how Red Panda stands out in the data systems landscape by eliminating unnecessary complexities and optimizing performance across various latency spectrums. The discussion also touches on the future of data processing, emphasizing the shift towards agentic workloads and the integration of analytical and operational layers.Chapters00:00 Introduction11:07 Building a Better Streaming System19:10 Market Position and Competition25:06 Optimizing Latency and Performance32:38 Understanding Complexity in Codebases33:36 Thread Architecture and Concurrency Models39:39 Memory Allocation Mechanics47:31 Protocol Correctness and Optimization Strategies56:27 Red Panda's Unique Position in Data Systems01:02:05 The Future of Data Processing and Agentic WorkloadsBlogs:TPC buffers: https://www.redpanda.com/blog/tpc-buffershttps://www.redpanda.com/blog/always-on-production-memory-profiling-seastarhttps://www.redpanda.com/blog/end-to-end-data-pipelines-types-benefits-and-process------------------------------------------------------------------------------------------------------------------------------------------------------------------Like building real stuff?------------------------------------------------------------------------------------------------------------------------------------------------------------------Try out CodeCrafters and build amazing real world systems like Redis, Kafka, Sqlite. Use the link below to signup and get 40% off on paid subscription.https://app.codecrafters.io/join?via=geeknarrator------------------------------------------------------------------------------------------------------------------------------------------------------------------Link to other playlists. LIKE, SHARE and SUBSCRIBE------------------------------------------------------------------------------------------------------------------------------------------------------------------If you like this episode, please hit the like button and share it with your network. Also please subscribe if you haven't yet.Database internals series: https://youtu.be/yV_Zp0Mi3xsPopular playlists:Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA-Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_dModern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsNStay Curios! Keep Learning!#streaming #kafka #redpanda #c++ #databasesystems #SQL #distributedsystems #memoryallocation #garbagecollection

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