Medical advancements don’t just spring up—they happen by building on decades of previous discoveries. Today, one of these advancements might be on the verge of another breakthrough. But what had to happen first for it to exist? In this episode, co-hosts Dr. Raven Baxter and Dr. Ronald Gamble explore how a vaccine candidate for a deadly, once-mysterious bacterial disease came to be. The story takes them from one doctor’s groundbreaking connection in the 70s, all the way to a real lab where vaccines are being developed today. Featured Guests : – Carol Baker, Pediatric Infectious Disease Specialist – Isis Kanevsky, Senior Director, Vaccines, Pfizer – Ksenia Krylova, Senior Director, Vaccines, Pfizer Dive into the episode here : 02:54 - Getting into the problem 05:11 - The basics of immunity and vaccine science 09:32 - What is a conjugate vaccine? 14:44 - Group B Strep: A case study 22:23 - Talking to a GBS pioneer 31:40 - A trip to the lab 43:08 - What's next, and closing thoughts Season 5 of Science Will Win is created by Pfizer and hosted by Dr. Raven Baxter and Dr. Ronald Gamble. It’s produced by Acast Creative Studios. Check out our social media platforms to take a deeper look into the labs and stories we discuss during the episodes: Instagram (@pfizerinc), TikTok (@pfizer) Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.…
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محتوای ارائه شده توسط The Binary Breakdown. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Binary Breakdown یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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Dynamo: Amazon’s Highly Available Key-value Store
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محتوای ارائه شده توسط The Binary Breakdown. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Binary Breakdown یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
This technical paper details the design and implementation of Dynamo, a highly available and scalable key-value storage system developed by Amazon.com. The paper outlines the challenges of maintaining reliability at a massive scale in an e-commerce environment and explains how Dynamo addresses these challenges by sacrificing consistency in favor of availability under certain failure scenarios. The authors explore the system architecture, including partitioning, replication, versioning, membership, and failure handling, and provide insights gained from running Dynamo in a live production environment. They also compare different partitioning strategies and highlight the tradeoffs between performance and durability.
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محتوای ارائه شده توسط The Binary Breakdown. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Binary Breakdown یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
This technical paper details the design and implementation of Dynamo, a highly available and scalable key-value storage system developed by Amazon.com. The paper outlines the challenges of maintaining reliability at a massive scale in an e-commerce environment and explains how Dynamo addresses these challenges by sacrificing consistency in favor of availability under certain failure scenarios. The authors explore the system architecture, including partitioning, replication, versioning, membership, and failure handling, and provide insights gained from running Dynamo in a live production environment. They also compare different partitioning strategies and highlight the tradeoffs between performance and durability.
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NeonDB is a pioneering serverless PostgreSQL database designed for cloud-native environments, fundamentally characterized by its separation of compute and storage layers . Founded in 2021, it has rapidly grown to manage over 700,000 databases within three years, offering features like instant database branching (allowing writable copies in seconds), true scale-to-zero capabilities for significant cost savings on idle periods, and dynamic autoscaling of compute resources based on workload demands. Its architecture comprises Safekeepers for WAL durability, Pageservers for intelligent storage and WAL processing, and Cloud Object Storage for foundational persistence, all contributing to improved cold start times (down to ~500ms) and enhanced vector search performance for AI applications. While praised for developer experience and operational simplicity, some users note concerns regarding latency for ORM-heavy applications and managing connections with serverless functions. Recent major developments include its acquisition by Databricks for approximately $1 billion in May 2025 , which signals its growing importance in the data ecosystem and its continued alignment with AI-driven applications and developer-centric trends.…
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This research paper introduces Anna , a key-value store (KVS) designed for scalable performance across diverse computing environments, from single multi-core machines to globally distributed cloud deployments. Anna achieves high performance and adaptability through a partitioned, multi-master architecture utilizing wait-free execution and coordination-free consistency . Its design is built upon coordination-free actors and lattice-based composite data structures , which allow for various consistency models and elastic scaling. The authors demonstrate that Anna effectively leverages multicore parallelism and scales smoothly, outperforming traditional KVS systems like Redis and Cassandra in specific scenarios while offering a wider range of consistency levels with minimal overhead. https://dsf.berkeley.edu/jmh/papers/anna_ieee18.pdf…
This academic paper introduces Conflict-free Replicated Data Types (CRDTs) , which are abstract data types designed for distributed systems where data is replicated across multiple locations. CRDTs allow any replica to be modified without needing immediate coordination with other replicas, ensuring high availability and low latency . The core concept is that CRDTs employ mathematically sound rules and specific concurrency semantics (like add-wins or last-writer-wins) to guarantee that replicas converge to the same state when they have received the same updates, even if updates occur concurrently. The paper explores various synchronization models for propagating updates between replicas, discusses key research findings related to preserving sequential semantics and handling concurrency, examines guarantees and limitations (including their relationship with the CAP theorem), highlights examples of applications where CRDTs are used, and outlines future research directions such as scalability and security. https://arxiv.org/pdf/1805.06358…
This content from InfoQ provides insights for software architects and developers through various formats like newsletters, articles, and conference information. It highlights topics in architecture, AI, data engineering, culture, methods, and DevOps . Featured pieces discuss Slack's cellular architecture, data stream processing patterns, cultivating resilience, and implementing EU Cyber Resilience Act requirements . A significant portion focuses on a detailed article examining the CAP theorem twelve years later, clarifying common misconceptions and discussing practical approaches for managing partitions and consistency in distributed systems. The text also mentions upcoming InfoQ Dev Summit and QCon events . https://www.infoq.com/articles/cap-twelve-years-later-how-the-rules-have-changed/…
Raft , a consensus algorithm designed for managing a replicated log in distributed systems. It aims to be more understandable than Paxos , a widely used but complex alternative, while achieving equivalent efficiency and safety. Raft separates key consensus elements like leader election , log replication , and safety , using techniques such as problem decomposition and state space reduction to enhance clarity. The document describes the algorithm's server states (leader, follower, candidate), time divided into terms , and communication through RPCs for leader election, log replication, and eventually log compaction and client interaction. A user study is presented as evidence of Raft's improved understandability compared to Paxos.…
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This compilation of resources offers a comprehensive examination of Neo4j's graph database architecture. It explains how Neo4j differs from relational and document-oriented databases through its native graph storage. The materials describe how nodes, relationships, and properties are stored and indexed for efficient traversal and query processing. Transaction management, ACID compliance, performance optimization techniques like the Block format, and real-world applications are also addressed. The text concludes by acknowledging ongoing challenges such as migration, scaling, and machine learning integration, while also pointing towards future advancements like GPU acceleration. https://www.perplexity.ai/page/neo4j-graph-database-architect-ktv.ktumRLmdwxtqaUT.Gw…
Sentry is a large-scale, open-source error monitoring platform designed for modern distributed systems. It prioritizes actionable insights by focusing on exceptions and crashes, enriching errors with contextual data, and using features such as breadcrumbs and error grouping. Sentry's architecture employs modular and decoupled components like Relay for high-throughput event processing. Scalability and fault tolerance are achieved through horizontal scaling and cross-region replication, and dynamic sampling optimizes performance by balancing data fidelity with operational costs. User experience is enhanced through URL-driven state, role-based access control, and integrations with numerous development tools. Future developments aim to address challenges like ephemeral errors in serverless environments and explore quantum-safe cryptography. https://www.perplexity.ai/page/sentry-error-monitoring-at-sca-RRaPhaGbQ9Gn3j3DcddQKg…
These excerpts offer a detailed look at Istio's service mesh architecture, a critical component for managing microservices in cloud-native environments. The architecture is divided into a control plane and data plane, emphasizing security through automated mTLS and traffic management with advanced load balancing techniques. Observability is achieved through comprehensive telemetry collection, although performance overhead remains a concern. Various deployment models, including multi-cluster and hybrid setups, are supported, but operational complexity necessitates careful migration strategies. Future research focuses on AI-driven optimizations and enhanced security measures, ensuring Istio remains relevant in evolving cloud ecosystems. https://www.perplexity.ai/page/istio-service-mesh-architectur-JZjsEh8qSHSQMjAHUCaWLg…
CockroachDB is a distributed SQL database designed for global scalability and resilience. The database achieves this through a unique architecture built on a monolithic key-value store, Raft-based replication, and hybrid logical clocks. Transaction management is optimized for global workloads using a non-blocking commit protocol and multi-region capabilities. CockroachDB offers declarative data locality, enabling administrators to define data placement policies for performance and compliance. Performance optimization strategies, like follower reads and elastic scaling, help reduce latency and costs. Despite its strengths, challenges remain around write amplification and tradeoffs associated with global tables, but future development focuses on serverless architecture and AI-driven autotuning. https://www.perplexity.ai/page/cockroachdb-sql-for-global-sca-8wVC7NgaQAup2iEyCWw8Fw…
Snowflake, a cloud-native data warehouse, revolutionizes modern analytics through its unique architecture and capabilities. The platform separates compute and storage layers, enabling independent scaling and optimized performance. Its three-layer design encompasses cloud services, a compute layer using virtual warehouses, and a storage layer leveraging cloud object storage. Snowflake's architecture ensures security, manages concurrency, and optimizes costs, outperforming cloud alternatives such as Azure Synapse and Redshift in several benchmarks. Emerging applications include genomics processing, real-time cybersecurity analytics, and multi-cloud data meshes. Despite limitations such as ETL complexity, Snowflake's future developments involve serverless GPU acceleration and integration with open table formats, solidifying its position in cloud data warehousing. https://www.perplexity.ai/page/snowflake-a-cloud-native-data-lkc22F_tRgKawFNhK.7Tdw…
This collection of excerpts comprehensively examines Kubernetes, the leading container orchestration platform. It traces the historical evolution of container orchestration and highlights Kubernetes' architectural foundations, including its control plane and node components. Scalability mechanisms like horizontal pod autoscaling and cell-based architectures are explored, alongside the platform's security model, emphasizing role-based access control and network policies. The text further details Kubernetes' role in microservices orchestration, edge computing integrations, and CI/CD pipelines, with specific implementations like Argo CD and KubeEdge being noted. Finally, the documentation looks to the future, considering WebAssembly integration and quantum-safe cryptography, and concludes by underscoring Kubernetes' continued evolution and pivotal role in distributed systems. https://www.perplexity.ai/page/kubernetes-container-orchestra-AnzcSV82T.2kcKZAEOYSvw…
This compilation of excerpts thoroughly examines Elasticsearch, focusing on its architecture, applications, and future trends. The core architecture and its integration within the Elastic Stack are highlighted, emphasizing scalability and real-time analytics. Various specialized applications are discussed, including maritime data storage, academic research portals, and healthcare blockchain systems. Advancements in query processing, machine learning operationalization, and security are analyzed, showcasing improved search efficiency and reduced system response times. The exploration concludes with emerging trends, such as AI-optimized hardware, decentralized search infrastructure, and environmental impact mitigation, solidifying Elasticsearch's role in modern data management. https://www.perplexity.ai/page/elasticsearch-a-comprehensive-pfqie_tbQLaK9e3liDI.8A…
This research paper introduces Ray, a distributed framework designed for emerging AI applications, particularly those involving reinforcement learning. It addresses the limitations of existing systems in handling the complex demands of these applications, which require continuous interaction with the environment. Ray unifies task-parallel and actor-based computations through a dynamic execution engine, facilitating simulation, training, and serving within a single framework. The system uses a distributed scheduler and fault-tolerant store to manage control state, achieving high scalability and performance. Experiments demonstrate Ray's ability to scale to millions of tasks per second and outperform specialized systems in reinforcement learning applications. The paper highlights Ray's architecture, programming model, and performance, emphasizing its flexibility and efficiency in supporting the evolving needs of AI. https://www.usenix.org/system/files/osdi18-moritz.pdf…
This paper details Zanzibar, Google's globally distributed authorization system, designed to manage access control lists (ACLs) at a massive scale. Zanzibar uses a flexible data model and configuration language to handle diverse access control policies for numerous Google services, achieving high availability and low latency. The system maintains external consistency, respecting the causal order of ACL changes, and employs techniques like caching and request hedging to handle high request volumes and hot spots. The authors present the system's architecture, implementation, and lessons learned from years of operation, highlighting challenges and solutions in building a consistent, world-scale authorization system. The paper also explores related research in access control and distributed systems. https://www.usenix.org/system/files/atc19-pang.pdf…
**Mesa** is a highly scalable, geo-replicated data warehousing system developed at Google to handle petabytes of data related to its advertising business. **Designed for near real-time data ingestion and querying**, it processes millions of updates per second and serves billions of queries daily. **Key features include strong consistency, high availability, and fault tolerance**, achieved through techniques like multi-version concurrency control and Paxos-based distributed synchronization. The paper details Mesa's architecture, including its storage subsystem using versioned data management with delta compaction, and its multi-datacenter deployment. Finally, it explores operational challenges and lessons learned in building and maintaining such a large-scale system. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bb1af5424e972c0c15f21e3847708e4d393abfae…
This paper from Google describes the design and implementation of Dapper, Google’s system for tracing requests in distributed systems. The authors explain why they chose a distributed tracing system, the design decisions they made for Dapper, and how the Dapper infrastructure has been used in practice. They also discuss the impact of Dapper on application performance and their strategies for mitigating the overhead. The paper emphasizes the importance of application-level transparency for distributed tracing systems. https://static.googleusercontent.com/media/research.google.com/en//archive/papers/dapper-2010-1.pdf…
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This document describes the development and implementation of Google's Chubby lock service, a highly available and reliable system that provides coarse-grained locking and storage for distributed systems. The authors discuss the design choices behind Chubby, including its emphasis on availability over performance, and the use of a file system-like interface for ease of use. The paper details Chubby's architecture, including its components, consensus protocol, and caching mechanisms. It also explores unexpected uses of Chubby, such as its popular role as a name service, and discusses the lessons learned during its development and deployment, such as the importance of considering developer habits and the need to scale the system effectively. https://static.googleusercontent.com/media/research.google.com/en//archive/chubby-osdi06.pdf…
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The provided text describes the architecture and design of Megastore, a Google-developed storage system designed to meet the needs of interactive online services. Megastore blends the scalability of NoSQL datastores with the convenience of traditional relational databases, offering high availability and strong consistency guarantees. It achieves this by utilizing Paxos, a fault-tolerant consensus algorithm, to synchronize data replication across geographically distributed data centers. Megastore also implements a partitioning strategy, dividing data into entity groups that are replicated separately, which allows for high throughput and localized outages. The authors detail the features of Megastore's data model, including ACID transactions, indexes, and queues, and explain how these features are implemented within the system. They also provide details about Megastore's replication algorithm and its performance characteristics, as well as insights into the development and operational experience with the system. https://www.cidrdb.org/cidr2011/Papers/CIDR11_Paper32.pdf…
The article, “Bigtable: A Distributed Storage System for Structured Data,” describes a large-scale distributed data storage system developed at Google, capable of handling petabytes of data across thousands of servers. Bigtable uses a simple data model that allows clients to dynamically control data layout and format, making it suitable for various applications like web indexing, Google Earth, and Google Finance. The authors detail the system's implementation, including its use of Google File System (GFS) for data storage, Chubby for distributed locking, and SSTables for data organization. The article concludes by evaluating Bigtable's performance and scalability through various benchmarks and discusses how it is used in real Google products. https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf…
MapReduce is a programming model that simplifies the process of processing large datasets on clusters of commodity machines. It allows users to define two functions: Map and Reduce , which are then automatically parallelized and executed across the cluster. The Map function processes key/value pairs from the input data and generates intermediate key/value pairs. The Reduce function merges all intermediate values associated with the same key to produce the final output. This paper, written by researchers at Google, describes the implementation of MapReduce on their large-scale computing infrastructure, highlighting its features, performance, fault tolerance, and real-world applications. The authors also discuss the benefits of using MapReduce, such as its simplicity, scalability, and flexibility, and compare it to other related systems. https://storage.googleapis.com/gweb-research2023-media/pubtools/4449.pdf…
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The source is a technical paper that describes the Google File System (GFS), a scalable distributed file system designed to meet Google's data processing needs. The paper discusses the design principles behind GFS, including its focus on handling component failures, managing large files, and optimizing for append-only operations. It also details the system architecture, consisting of a single master node and multiple chunkservers, as well as the implementation of various features, such as atomic record appends and snapshots. Finally, the authors present micro-benchmark results and real-world measurements from GFS clusters used at Google to illustrate the system's performance and scalability. https://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf…
Facebook developed a distributed data store called TAO to efficiently serve the social graph data. TAO prioritizes read optimization, availability, and scalability over strict consistency, handling billions of reads and millions of writes per second. TAO utilizes a simplified data model based on objects and associations, offering a specialized API designed for common social graph queries. The architecture involves a multi-layered caching system, with followers caching data and leaders communicating with the persistent storage layer in MySQL. TAO leverages a master/slave configuration to manage geographic distribution, ensuring data consistency across multiple regions while mitigating inter-region latency. The paper details the design, implementation, consistency model, performance characteristics, and a comparison with existing related work. https://www.usenix.org/system/files/conference/atc13/atc13-bronson.pdf…
This document details how Facebook engineers scaled Memcached, a popular open-source in-memory caching solution, to accommodate the demands of the world's largest social network. The paper outlines the development of Facebook's Memcached architecture, starting with a single cluster of servers and progressing through geographically distributed clusters. It highlights key optimizations and mechanisms implemented at various scales, including reducing latency, handling failures, and maintaining data consistency across multiple regions. The document also provides insights into the characteristics of Facebook's Memcached workload, including fan-out, response size, and latency. https://research.facebook.com/publications/scaling-memcache-at-facebook/…
This technical paper details the architecture and design of Monarch , a planet-scale in-memory time series database developed at Google. Monarch is used to monitor the performance and availability of massive, globally distributed systems like YouTube, Google Maps, and Gmail. The paper discusses the system's novel features, including its regionalized architecture , expressive query language , and support for sophisticated data types , such as distributions and exemplars. It also presents a comprehensive evaluation of Monarch's scalability , highlighting its ability to handle trillions of time series, ingest terabytes of data per second, and serve millions of queries per second. Finally, the paper outlines key lessons learned from the development and operation of Monarch over a decade. https://www.vldb.org/pvldb/vol13/p3181-adams.pdf…
The provided text describes the architecture and functionality of Gorilla , Facebook's in-memory time series database. Gorilla was developed to address the challenges of monitoring and analyzing massive amounts of time series data generated by Facebook's vast infrastructure. The system prioritizes high availability for writes and reads, even in the face of failures, by using compression techniques, a unique data structure, and replication across geographically distributed datacenters. Gorilla achieves high performance through compression and in-memory data structures, enabling the development of new tools for time series correlation, visualization, and aggregation. The paper highlights the importance of prioritizing recent data over historical data in this context, and the trade-offs made to achieve high availability. https://www.vldb.org/pvldb/vol8/p1816-teller.pdf…
This document, an AWS blog post, guides users through the process of building a cost-effective, three-tier architecture using serverless technologies within the AWS Free Tier. It begins by explaining the benefits and capabilities of AWS serverless services and then provides a detailed walkthrough of how to construct each tier (presentation, business logic, and data) utilizing various free AWS services, including S3, CloudFront, Cognito, API Gateway, Lambda, and DynamoDB. The article concludes with tips on monitoring Free Tier usage to avoid unexpected charges and emphasizes the advantages of serverless architectures, particularly cost savings, scalability, and flexibility. https://aws.amazon.com/blogs/architecture/building-a-three-tier-architecture-on-a-budget/…
This whitepaper outlines the AWS Well-Architected Framework specifically for Software as a Service (SaaS) applications. It examines how to design and deploy multi-tenant SaaS workloads using AWS services, detailing best practices in operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. The whitepaper explores various architecture models like serverless SaaS, Amazon EKS SaaS, and hybrid deployments, focusing on the importance of tenant isolation and the complexities of managing and scaling a multi-tenant environment. https://docs.aws.amazon.com/pdfs/wellarchitected/latest/saas-lens/wellarchitected-saas-lens.pdf…
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This document is a white paper about the AWS Well-Architected Framework, particularly focusing on its application to streaming media workloads. It defines key components within a streaming media architecture, including ingest, processing, origin, delivery, and the client. The paper then outlines best practices for designing and implementing streaming media workloads according to the six pillars of the AWS Well-Architected Framework: operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. Finally, the document discusses specific scenarios, such as video-on-demand streaming, live streaming, and ad-supported content monetization, providing insights into the unique considerations for each scenario. https://docs.aws.amazon.com/pdfs/wellarchitected/latest/streaming-media-lens/wellarchitected-streaming-media-lens.pdf#streaming-media-lens…
This technical paper details the design and implementation of Dynamo, a highly available and scalable key-value storage system developed by Amazon.com. The paper outlines the challenges of maintaining reliability at a massive scale in an e-commerce environment and explains how Dynamo addresses these challenges by sacrificing consistency in favor of availability under certain failure scenarios. The authors explore the system architecture, including partitioning, replication, versioning, membership, and failure handling, and provide insights gained from running Dynamo in a live production environment. They also compare different partitioning strategies and highlight the tradeoffs between performance and durability. https://assets.amazon.science/ac/1d/eb50c4064c538c8ac440ce6a1d91/dynamo-amazons-highly-available-key-value-store.pdf…
This paper, "Time, Clocks, and the Ordering of Events in a Distributed System," explores the challenges of defining and managing time in distributed systems. It introduces the concept of a "happened before" relation to partially order events and presents an algorithm for creating a consistent total ordering using logical clocks. The paper then extends this to physical clocks, analyzing synchronization and error bounds to prevent anomalous behavior arising from discrepancies between perceived and actual event orderings. The second paper, "Shallow Binding in Lisp 1.5," focuses on efficient variable access in the Lisp 1.5 programming language. It proposes a "rerooting" method for environment tree transformations to achieve shallow binding, allowing for context switching and concurrent processes within the same environment structure, all while maintaining program semantics. The method enhances efficiency without altering a program's meaning. https://www.microsoft.com/en-us/research/uploads/prod/2016/12/Time-Clocks-and-the-Ordering-of-Events-in-a-Distributed-System.pdf…
This paper details the design and implementation of ZooKeeper, a high-performance coordination service for large-scale distributed systems. ZooKeeper provides a simple, wait-free API enabling developers to build various coordination primitives, such as locks and group membership, without server-side modifications. It achieves high throughput through relaxed consistency guarantees, allowing local read processing and efficient atomic broadcast for writes. The paper showcases ZooKeeper's performance and application in various real-world scenarios at Yahoo!, including a fetching service, a distributed indexer, and a message broker. Finally, it compares ZooKeeper to related systems, highlighting its unique strengths in performance and scalability. https://www.usenix.org/legacy/event/atc10/tech/full_papers/Hunt.pdf…
This paper details TensorFlow, a large-scale machine learning system developed by Google. TensorFlow uses dataflow graphs to represent computation and manages state across diverse hardware, including CPUs, GPUs, and TPUs. It offers a flexible programming model , allowing developers to experiment with novel optimizations and training algorithms beyond traditional parameter server designs. The authors discuss TensorFlow's architecture, implementation, and performance evaluations across various applications, highlighting its scalability and efficiency compared to other systems. The system is open-source , facilitating widespread use in research and industry. Finally, they explore future directions, including addressing dynamic computation challenges. https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf…
This paper details Google Firestore, a NoSQL serverless database built on Spanner. It highlights Firestore's ease of use, scalability, real-time query capabilities, and support for disconnected operations. The architecture, which enables multi-tenancy and efficient handling of large datasets, is explained. Performance benchmarks and practical lessons from development are presented, along with comparisons to other NoSQL databases. Finally, future development directions are outlined. https://storage.googleapis.com/gweb-research2023-media/pubtools/7076.pdf…
This research paper details Apache Flink, an open-source system unifying stream and batch data processing. Flink uses a dataflow model to handle various data processing needs, including real-time analytics and batch jobs, within a single engine. The paper explores Flink's architecture, APIs (including DataStream and DataSet APIs), and fault-tolerance mechanisms such as asynchronous barrier snapshotting. Key features highlighted include flexible windowing, support for iterative dataflows, and query optimization techniques. Finally, the paper compares Flink to other existing systems for batch and stream processing, emphasizing its unique capabilities. https://asterios.katsifodimos.com/assets/publications/flink-deb.pdf…
This paper introduces Kafka, a novel distributed messaging system designed for high-throughput log processing. Kafka addresses limitations in existing messaging systems and log aggregators by offering a scalable, efficient architecture with a simple API. Key features include a pull-based consumption model, efficient storage and data transfer mechanisms, and the use of ZooKeeper for distributed coordination. Performance tests demonstrate Kafka's superior throughput compared to ActiveMQ and RabbitMQ, highlighting its suitability for handling massive volumes of log data. The authors detail Kafka's implementation at LinkedIn, illustrating its use in both online and offline applications. https://notes.stephenholiday.com/Kafka.pdf…
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This research paper details LinkedIn's solution for optimizing low-latency graph computations within their large-scale distributed graph system. To improve performance, they implemented a modified greedy set cover algorithm to minimize the number of machines needed for processing second-degree connection queries. This optimization significantly reduced latency in constructing network caches and overall graph distance calculations, resulting in a better user experience. The paper also discusses the distributed graph architecture, including its partitioning and caching mechanisms, and compares their approach to related work in distributed graph processing. The improvements achieved demonstrate the effectiveness of the modified set cover algorithm in handling the challenges of large-scale graph queries in a real-world online environment. https://www.usenix.org/system/files/conference/hotcloud13/hotcloud13-wang.pdf…
This research paper details Monolith, a real-time recommendation system developed by Bytedance. Monolith addresses challenges in building scalable recommendation systems, such as sparse and dynamic data, and concept drift, by employing a collisionless embedding table and an online training architecture. Key innovations include a Cuckoo HashMap for efficient sparse parameter representation, incorporating features like expirable embeddings and frequency filtering, and a system for real-time parameter synchronization between training and serving. The authors present experimental results demonstrating Monolith's superior performance compared to systems using traditional hash tables and batch training, showcasing the benefits of its design choices in terms of model accuracy and efficiency. Finally, the paper compares Monolith to existing solutions, highlighting its unique advantages for industrial-scale applications. https://arxiv.org/pdf/2209.07663…
This research paper details FlexiRaft, a modified Raft consensus algorithm designed for Meta's petabyte-scale MySQL deployments. The core improvement is the introduction of flexible quorums, allowing configurable trade-offs between latency, throughput, and fault tolerance. Two quorum modes are presented: static and dynamic. The paper explores the algorithm's modifications, fault tolerance guarantees, experimental performance validation, and lessons learned from its production implementation. Finally, it compares FlexiRaft to other consensus algorithm variants and proposes avenues for future work. https://www.cidrdb.org/cidr2023/papers/p83-yadav.pdf…
This research paper details Spanner, Google's globally-distributed database system. Spanner achieves strong consistency across its geographically dispersed data centers using a novel TrueTime API that accounts for clock uncertainty. The system features automatic sharding, failover, and a semi-relational data model, addressing limitations of previous systems like Bigtable and Megastore. Spanner's design is discussed in depth, including its architecture, concurrency control mechanisms, and performance benchmarks. A case study of its use in Google's advertising backend, F1, highlights its real-world applicability and benefits. https://static.googleusercontent.com/media/research.google.com/en//archive/spanner-osdi2012.pdf…
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This research paper introduces Minesweeper, a novel technique for automated root cause analysis (RCA) of software bugs at scale. Leveraging telemetry data, Minesweeper efficiently identifies statistically significant patterns in user app traces that correlate with bugs, even in the absence of detailed debugging information. The method uses sequential pattern mining, specifically the PrefixSpan algorithm, for pattern extraction and incorporates statistical measures of precision and recall to rank patterns by distinctiveness. Practical challenges like handling numeric data and mitigating redundant patterns are addressed, and the system's scalability and accuracy are demonstrated through real-world evaluations on Facebook's app data. The results show Minesweeper significantly improves the speed and accuracy of RCA, aiding engineers in quickly identifying and resolving bugs. https://arxiv.org/pdf/2010.09974…
This paper details Cassandra, a decentralized structured storage system designed for managing massive amounts of structured data across numerous commodity servers. High availability and scalability are key features, achieved through techniques like consistent hashing for data partitioning and replication strategies across multiple data centers to handle failures. The system uses a simple data model and API, emphasizing write throughput without sacrificing read efficiency. The paper explores the system architecture, including failure detection, membership, and bootstrapping, along with practical experiences and performance metrics from its use at Facebook. Future work focuses on adding compression and enhanced atomicity. https://www.cs.cornell.edu/projects/ladis2009/papers/lakshman-ladis2009.pdf…
The provided text is an excerpt from a research paper on FoundationDB, an open-source, distributed transactional key-value store. The paper details FoundationDB's design principles, architecture, and key features, including its unbundled architecture, strict serializability through a combination of optimistic concurrency control (OCC) and multi-version concurrency control (MVCC), and its robust deterministic simulation framework for testing. The paper also explores FoundationDB's geo-replication and failover capabilities, highlighting how the system achieves high availability and fault tolerance without sacrificing performance. Finally, the authors discuss lessons learned from developing FoundationDB, including the benefits of its divide-and-conquer design, the importance of simulation testing for achieving high reliability, and the impact of its fast recovery mechanisms on system upgrades and availability. https://www.foundationdb.org/files/fdb-paper.pdf…
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This document describes the design of Amazon Aurora, a cloud-native relational database service built to handle high-throughput, online transaction processing (OLTP) workloads. The paper highlights the challenges of traditional database architectures in cloud environments, specifically the I/O bottleneck created by network traffic. Aurora addresses these issues by leveraging a novel service-oriented architecture where the storage service is decoupled from the database engine. This approach significantly reduces network I/O and enables efficient crash recovery, failover, and self-healing capabilities. The paper further explains how Aurora achieves consensus on durable state across storage nodes through an asynchronous scheme, avoiding expensive and chatty recovery protocols. The authors conclude by sharing insights gained from their customers on the evolving needs of modern cloud applications for database services. https://assets.amazon.science/dc/2b/4ef2b89649f9a393d37d3e042f4e/amazon-aurora-design-considerations-for-high-throughput-cloud-native-relational-databases.pdf…
The article is a paper published in 2010 by researchers at Google that introduces Pregel, a large-scale graph processing system. Pregel is designed for processing graphs with billions of vertices and trillions of edges, and it uses a vertex-centric approach where vertices are assigned to individual machines and communicate with each other through message passing. The paper details the model of computation, the C++ API, and the implementation of Pregel, including fault-tolerance mechanisms for dealing with machine failures. It also explores applications of Pregel to various graph algorithms, including PageRank, shortest paths, bipartite matching, and semi-clustering, providing experimental results for shortest paths on graphs ranging in size from 10 million to 1 billion vertices. Finally, the paper discusses related work and future directions for Pregel, highlighting its strengths and potential areas for improvement. https://15799.courses.cs.cmu.edu/fall2013/static/papers/p135-malewicz.pdf…
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