Code Impact

Sanket Makhija

Welcome to "Code Impact," the podcast where we explore code that has an impact. Each episode dives deep into real-world stories, practical case studies, and expert insights, showcasing the powerful impact of code on performance, accessibility, and user experience. Whether you're a seasoned developer or just starting your journey, "Code Impact" delivers the tools, tips, and inspiration you need to create meaningful and high-performing products. Join us as we uncover the ways coding is transforming industries and making a difference—one line at a time. NotebookLM creates all episodes.

  1. 3 FÉVR.

    Spanner's Globally-Distributed Database: Query Execution

    This paper details the evolution of Google's Spanner, a globally-distributed database system, from a key-value store to a fully-fledged SQL system. Key improvements discussed include distributed query execution, handling of transient failures via query restarts, efficient range extraction for data retrieval, and the adoption of a common SQL dialect. The authors also explain the transition from a Bigtable-like storage format to a more efficient blockwise-columnar store (Ressi). Finally, the paper highlights lessons learned during Spanner's large-scale deployment and outlines remaining challenges.

    36 min

  2. 3 FÉVR.

    Change Data Capture (CDC): Three Implementation Methods

    The article explores Change Data Capture (CDC), a method for tracking database changes, highlighting its advantages over traditional daily snapshots. It details three CDC implementation approaches: using database triggers (e.g., in PostgreSQL), capturing API requests and using a message broker (e.g., Kafka), and leveraging change streams within a data warehouse (e.g., Snowflake). The article compares these methods, weighing their pros and cons in terms of performance, scalability, and ease of implementation. A subsequent discussion critiques the presented methods, suggesting alternative, more robust solutions based on logical replication tools like Debezium.

    16 min

  3. 26 JANV.

    DeepSeek-R1: Reasoning via Reinforcement Learning

    This research paper introduces DeepSeek-R1, a large language model enhanced for reasoning capabilities using reinforcement learning (RL). Two versions are presented: DeepSeek-R1-Zero, trained purely via RL without supervised fine-tuning, and DeepSeek-R1, which incorporates additional multi-stage training and cold-start data for improved readability and performance. DeepSeek-R1 achieves results comparable to OpenAI's o1-1217 on various reasoning benchmarks. The study also explores distilling DeepSeek-R1's reasoning capabilities into smaller, more efficient models, achieving state-of-the-art results. Finally, the paper discusses unsuccessful attempts using process reward models and Monte Carlo Tree Search, providing valuable insights for future research. https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf

    19 min

  4. 26 JANV.

    Jira Cloud Performance Enhancement with Protobuf

    This Atlassian blog post details the migration of Jira Cloud's Issue Service from JSON to Protocol Buffers (Protobuf) to enhance performance. The switch involved a phased approach to minimise downtime, creating new endpoints and logic to handle both formats concurrently before a complete transition. The results showcased significant improvements: 75% less Memcached CPU usage, 80% smaller data size, and a substantially faster response time. Challenges encountered included Protobuf's handling of null values and incompatibility with Spring's default error controller, which required workarounds. Ultimately, the migration yielded substantial performance gains and reduced infrastructure needs. https://www.atlassian.com/blog/atlassian-engineering/using-protobuf-to-make-jira-cloud-faster

    20 min

  5. 25 JANV.

    Hyaline: Fast and Transparent Lock-Free Memory Reclamation

    This research paper introduces Hyaline, a novel family of memory reclamation schemes for lock-free data structures in unmanaged C/C++ code. Hyaline leverages reference counting, but only during reclamation, minimising overhead during object access and balancing workload across threads. The paper details Hyaline's design, including a scalable multi-list version and robust extensions to handle stalled threads. Extensive testing across multiple architectures demonstrates Hyaline's superior performance and memory efficiency compared to existing schemes like epoch-based reclamation and hazard pointers, particularly in read-dominated and oversubscribed scenarios. The paper concludes by proving Hyaline's correctness and lock-freedom properties.

    33 min

  6. 19 JANV.

    Trello's Kafka Migration

    This Atlassian blog post details Trello's migration from RabbitMQ to Kafka for its websocket architecture. RabbitMQ's unreliability during network partitions and high costs associated with queue creation and deletion prompted the switch. The article compares various queuing systems, highlighting Kafka's superior failover capabilities and in-order message delivery. Trello implemented a master-client architecture with Kafka, resulting in improved performance, reduced costs, and fewer outages. Key performance improvements included a 33% decrease in memory usage and a substantial cost reduction.

    12 min

  7. 19 JANV.

    Reliability Engineering: History, Practice, and Future

    This podcast explores the field of reliability engineering, tracing its origins at Google with the development of Site Reliability Engineering (SRE). It differentiates reliability engineering from SRE, highlighting its broader applicability across various organisational structures. The podcast outlines four key promises of a successful reliability team: defining service levels (SLA/SLO/SLI), managing the service infrastructure, participating in technical design, and providing tactical support during incidents. Finally, it discusses the evolving landscape of reliability engineering, emphasising pragmatic approaches to balancing cost and reliability needs, and advocating for a more nuanced understanding of when to build versus buy solutions.

    14 min

  8. 19 JANV.

    Debugging Large Distributed Systems: The Antithesis Approach

    This podcast profiles Antithesis, a company developing a "multiverse debugger" for large, distributed systems. It traces the history of debugging tools, highlighting Antithesis's innovative approach using deterministic simulation testing (DST) to allow time travel debugging. The podcast includes a Q&A with Antithesis's co-founder, detailing the challenges of debugging large systems and how Antithesis addresses them. Furthermore, it discusses Antithesis's tech stack, engineering culture, and the trade-offs of using their complex, but potentially game-changing, technology. Finally, it considers the implications of widespread adoption of Antithesis's technology for the future of software development.

    24 min
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À propos

Welcome to "Code Impact," the podcast where we explore code that has an impact. Each episode dives deep into real-world stories, practical case studies, and expert insights, showcasing the powerful impact of code on performance, accessibility, and user experience. Whether you're a seasoned developer or just starting your journey, "Code Impact" delivers the tools, tips, and inspiration you need to create meaningful and high-performing products. Join us as we uncover the ways coding is transforming industries and making a difference—one line at a time. NotebookLM creates all episodes.

Informations

  • Création
    Sanket Makhija
  • Années d’activité
    2024 - 2025
  • Épisodes
    72
  • Classification
    Tous publics
  • Copyright
    © Sanket Makhija
  • Site web de l’émission
    Code Impact