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• 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!

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• 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!

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• 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

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• 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

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• 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

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