[{"data":1,"prerenderedAt":1365},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":23,"navbar-pricing-featured":304,"blog-\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-2":1084,"blog-authors-\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-2":1332,"related-\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-2":1347},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":10,"meta":12,"stem":20,"variant":21,"__hash__":22},"banners\u002Fbanners\u002Fkafka-company-2025.md","Native Apache Kafka Service Is Coming Soon to StreamNative Cloud. Join the waitlist and get $1,000 in credits.","2026-04-01",true,"md","\u002Fnative-kafka-service-waitlist",null,"Join Waitlist",{"body":13},{"type":14,"value":15,"toc":16},"minimark",[],{"title":17,"searchDepth":18,"depth":18,"links":19},"",2,[],"banners\u002Fkafka-company-2025","default","IMIJszQOOWTfA_DV33eYUA5jqV7DrX1FWbBTBZfNvWc",{"id":24,"title":25,"authors":26,"body":28,"category":288,"createdAt":10,"date":289,"description":290,"extension":8,"featured":7,"image":291,"isDraft":292,"link":10,"meta":293,"navigation":7,"order":294,"path":295,"readingTime":296,"relatedResources":10,"seo":297,"stem":298,"tags":299,"__hash__":303},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[27],"David Kjerrumgaard",{"type":14,"value":29,"toc":275},[30,38,46,50,66,72,77,80,86,101,108,114,117,123,126,133,139,142,145,156,162,168,171,174,177,183,190,193,196,203,206,209,223,228,232,236,240,244,248,250,267,269],[31,32,34],"h3",{"id":33},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[35,36,37],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[39,40,42],"h2",{"id":41},"introduction",[43,44,45],"strong",{},"Introduction",[47,48,49],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[47,51,52,53,62,63],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[54,55,57],"a",{"href":56},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[35,58,59],{},[43,60,61],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[43,64,65],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[47,67,68,69],{},"***Forrester's Take: ***",[35,70,71],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[47,73,74],{},[35,75,76],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[47,78,79],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[39,81,83],{"id":82},"trusted-by-industry-leaders",[43,84,85],{},"Trusted by Industry Leaders",[47,87,88,89,92,93,96,97,100],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[43,90,91],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[43,94,95],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[43,98,99],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[47,102,103,104,107],{},"The Forrester report notes that, “",[35,105,106],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[39,109,111],{"id":110},"modern-cloud-native-architecture-built-for-scale",[43,112,113],{},"Modern, Cloud-Native Architecture Built for Scale",[47,115,116],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[47,118,119,120,107],{},"Forrester's evaluation described that “",[35,121,122],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[47,124,125],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[47,127,128,129,132],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[35,130,131],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[39,134,136],{"id":135},"open-source-foundation-and-pulsar-expertise",[43,137,138],{},"Open Source Foundation and Pulsar Expertise",[47,140,141],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[47,143,144],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[47,146,147,148,151,152,155],{},"Forrester's assessment noted that StreamNative’s “",[35,149,150],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[43,153,154],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[47,157,158,159],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[35,160,161],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[39,163,165],{"id":164},"powering-real-time-use-cases-across-industries",[43,166,167],{},"Powering Real-Time Use Cases Across Industries",[47,169,170],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[47,172,173],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[47,175,176],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[39,178,180],{"id":179},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[43,181,182],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[47,184,185,186,189],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[43,187,188],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[47,191,192],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[47,194,195],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[47,197,198,199,202],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[43,200,201],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[47,204,205],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[207,208],"hr",{},[31,210,212],{"id":211},"streamnative-in-the-forrester-wave-evaluation-findings",[43,213,214,215,222],{},"StreamNative in ",[43,216,217],{},[54,218,219],{"href":56},[43,220,221],{},"The Forrester Wave™",": Evaluation Findings",[224,225,227],"h5",{"id":226},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[224,229,231],{"id":230},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[224,233,235],{"id":234},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[224,237,239],{"id":238},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[224,241,243],{"id":242},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[224,245,247],{"id":246},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[207,249],{},[251,252,254,255,258,259,189],"h6",{"id":253},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[35,256,257],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[54,260,264],{"href":261,"rel":262},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[263],"nofollow",[35,265,266],{},"here",[207,268],{},[251,270,272],{"id":271},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[35,273,274],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":17,"searchDepth":18,"depth":18,"links":276},[277,279,280,281,282,283,284],{"id":33,"depth":278,"text":37},3,{"id":41,"depth":18,"text":45},{"id":82,"depth":18,"text":85},{"id":110,"depth":18,"text":113},{"id":135,"depth":18,"text":138},{"id":164,"depth":18,"text":167},{"id":179,"depth":18,"text":182,"children":285},[286],{"id":211,"depth":278,"text":287},"StreamNative in The Forrester Wave™: Evaluation Findings","Company","2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":25,"description":290},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[300,301,302],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":305,"title":306,"authors":307,"body":312,"category":1071,"createdAt":10,"date":1072,"description":1073,"extension":8,"featured":7,"image":1074,"isDraft":292,"link":10,"meta":1075,"navigation":7,"order":294,"path":1076,"readingTime":1077,"relatedResources":10,"seo":1078,"stem":1079,"tags":1080,"__hash__":1083},"blogs\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour.md","How We Run a 5 GB\u002Fs Kafka Workload for Just $50 per Hour",[308,309,310,311],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":14,"value":313,"toc":1041},[314,317,320,323,326,329,333,336,346,352,355,363,368,372,379,382,385,393,397,400,405,409,412,415,418,421,430,434,437,448,451,455,458,461,472,475,479,483,491,494,498,506,535,539,542,547,551,554,558,561,564,569,578,583,586,589,600,604,607,618,622,625,628,633,636,665,669,671,677,680,685,690,693,697,711,715,726,730,745,754,765,768,771,775,778,781,792,795,798,801,806,811,815,819,836,840,854,859,863,874,877,893,897,908,913,918,926,930,933,937,944,948,951,960,965,974,980,989,998,1007,1016,1025,1033],[47,315,316],{},"The rise of DeepSeek has shaken the AI infrastructure market, forcing companies to confront the escalating costs of training and deploying AI models. But the real pressure point isn’t just compute—it’s data acquisition and ingestion costs.",[47,318,319],{},"As businesses rethink their AI cost-containment strategies, real-time data streaming is emerging as a critical enabler. The growing adoption of Kafka as a standard protocol has expanded cost-efficient options, allowing companies to optimize streaming analytics while keeping expenses in check.",[47,321,322],{},"Ursa, the data streaming engine powering StreamNative’s managed Kafka service, is built for this new reality. With its leaderless architecture and native lakehouse storage integration, Ursa eliminates costly inter-zone network traffic for data replication and client-to-broker communication while ensuring high availability at minimal operational cost.",[47,324,325],{},"In this blog post, we benchmarked the infrastructure cost and total cost of ownership (TCO) for running a 5GB\u002Fs Kafka workload across different Kafka vendors, including Redpanda, Confluent WarpStream, and AWS MSK. Our benchmark results show that Ursa can sustain 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda—making it the ideal solution for high-performance, cost-efficient ingestion and data streaming for data lakehouses and AI workloads.",[47,327,328],{},"Note: We also evaluated vanilla Kafka in our benchmark; however, for simplicity, we have focused our cost comparison on vendor solutions rather than self-managed deployments. That said, it is important to highlight that both Redpanda and vanilla Kafka use a leader-based data replication approach. In a data-intensive, network-bound workload like 5GB\u002Fs streaming, with the same machine type and replication factor, Redpanda and vanilla Kafka produced nearly identical cost profiles.",[39,330,332],{"id":331},"key-benchmark-findings","Key Benchmark Findings",[47,334,335],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[337,338,339,343],"ul",{},[340,341,342],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[340,344,345],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[47,347,348],{},[349,350],"img",{"alt":17,"src":351},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[47,353,354],{},"Beyond infrastructure costs, when factoring in both storage pricing, vendor pricing and operational expenses, Ursa’s total cost of ownership (TCO) for a 5GB\u002Fs workload with a 7-day retention period is:",[337,356,357,360],{},[340,358,359],{},"50% cheaper than Confluent WarpStream",[340,361,362],{},"85% cheaper than MSK and Redpanda",[47,364,365],{},[349,366],{"alt":17,"src":367},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[39,369,371],{"id":370},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[47,373,374,378],{},[54,375,377],{"href":376},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming","Ursa"," is a next-generation data streaming engine designed to deliver high performance at a fraction of the cost of traditional disk-based solutions. It is fully compatible with Apache Kafka and Apache Pulsar APIs, while leveraging a leaderless, lakehouse-native architecture to maximize scalability, efficiency, and cost savings.",[47,380,381],{},"Ursa’s key innovation is separating storage from compute and decoupling metadata\u002Findex operations from data operations by utilizing cloud object storage (e.g., AWS S3) instead of costly inter-zone disk-based replication. It also employs open lakehouse formats (Iceberg and Delta Lake), enabling columnar compression to significantly reduce storage costs while maintaining durability and availability.",[47,383,384],{},"In contrast, traditional streaming systems—like Kafka and Redpanda—depend on leader-based architectures, which drive up inter-zone traffic costs due to replication and client communication. Ursa mitigates these costs by:",[337,386,387,390],{},[340,388,389],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[340,391,392],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[39,394,396],{"id":395},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[47,398,399],{},"Ursa minimizes inter-zone traffic by leveraging a leaderless architecture, which eliminates inter-zone communication between clients and brokers, and lakehouse-native storage, which removes the need for inter-zone data replication. This approach ensures high availability and scalability while avoiding unnecessary cross-zone data movement.",[47,401,402],{},[349,403],{"alt":17,"src":404},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[31,406,408],{"id":407},"leaderless-architecture","Leaderless architecture",[47,410,411],{},"Traditional streaming engines such as Kafka, Pulsar, or RedPanda rely on a leader-based model, where each partition is assigned to a single leader broker that handles all writes and reads.",[47,413,414],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[47,416,417],{},"Cons of Leader-Based Architectures:\n✖ Throughput bottlenecked by a single broker per partition\n✖ Inter-zone traffic required for high availability in multi-AZ deployments",[47,419,420],{},"While Kafka and Pulsar offer partial solutions (e.g., reading from followers, shadow topics) to reduce read-related inter-zone traffic, producers still send data to a single leader.",[47,422,423,424,429],{},"Ursa removes the concept of topic ownership, allowing any broker in the cluster to handle reads or writes for any partition. The primary challenge—ensuring message ordering—is solved with ",[54,425,428],{"href":426,"rel":427},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[263],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[31,431,433],{"id":432},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[47,435,436],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[337,438,439,442,445],{},[340,440,441],{},"Handles millions of metadata\u002Findex operations per second",[340,443,444],{},"Generates sequential IDs to maintain strict message ordering",[340,446,447],{},"Optimized for Kubernetes with horizontal scalability",[47,449,450],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[31,452,454],{"id":453},"zero-interzone-data-replication","Zero interzone data replication",[47,456,457],{},"In most distributed systems, data replication from a leader (primary) to followers (replicas) is crucial for fault tolerance and availability. However, replication across zones can inflate infrastructure expenses substantially.",[47,459,460],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[337,462,463,466,469],{},[340,464,465],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[340,467,468],{},"Tradeoff: Slightly higher latency (sub-second, with p99 at 500 milliseconds) compared to local disk\u002FEBS (single-digit to sub-100 milliseconds), in exchange for significantly lower costs (up to 10x lower).",[340,470,471],{},"Flexible Modes: Ursa is an addition to the classic BookKeeper-based engine, providing users with the flexibility to optimize for either cost or low latency based on their workload requirements.",[47,473,474],{},"By foregoing conventional replication, Ursa slashes inter-zone traffic costs and associated complexities—making it a compelling option for organizations seeking to balance high-performance data streaming with strict budget constraints.",[39,476,478],{"id":477},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[31,480,482],{"id":481},"ursa-cluster-deployment","Ursa Cluster Deployment",[337,484,485,488],{},[340,486,487],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[340,489,490],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[47,492,493],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[31,495,497],{"id":496},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[47,499,500,501,505],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[54,502,503],{"href":503,"rel":504},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[263]," for details.",[337,507,508,523,532],{},[340,509,510,511,516,517,522],{},"12 OMB workers: 6 for ",[54,512,515],{"href":513,"rel":514},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[263],"producers",", 6 for ",[54,518,521],{"href":519,"rel":520},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[263],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[340,524,525,526,531],{},"Sample YAML ",[54,527,530],{"href":528,"rel":529},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[263],"scripts"," provided for Kafka-compatible configuration and rate limits.",[340,533,534],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[39,536,538],{"id":537},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[47,540,541],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[47,543,544],{},[349,545],{"alt":17,"src":546},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[39,548,550],{"id":549},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[47,552,553],{},"This benchmark first evaluates infrastructure costs of running a 5 GB\u002Fs streaming workload (1:1 producer-to-consumer ratio) across different data streaming engines, including Ursa, Redpanda, and AWS MSK, with a focus on multi-AZ deployments to ensure a fair comparison.",[31,555,557],{"id":556},"test-setup-key-assumptions","Test Setup & Key Assumptions",[47,559,560],{},"All tests use multi-AZ configurations, with clusters and clients distributed across three AWS availability zones (AZs). Cluster size scales proportionally to the number of AZs, and rack-awareness is enabled for all engines to evenly distribute topic partitions and leaders.",[47,562,563],{},"To ensure a fair comparison, we selected the same machine type capable of fully utilizing both network and storage bandwidth for Ursa and Redpanda in this 5GB\u002Fs test:",[337,565,566],{},[340,567,568],{},"9 × m6i.8xlarge instances",[47,570,571,572,577],{},"However, MSK's storage bandwidth limits vary depending on the selected instance type, with the highest allowed limit capped at 1000 MiB\u002Fs per broker, according to",[54,573,576],{"href":574,"rel":575},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[263]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[337,579,580],{},[340,581,582],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[47,584,585],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[47,587,588],{},"Additional key assumptions include:",[337,590,591,594,597],{},[340,592,593],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[340,595,596],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[340,598,599],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[31,601,603],{"id":602},"inter-broker-replication-costs","Inter-Broker Replication Costs",[47,605,606],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[337,608,609,612,615],{},[340,610,611],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[340,613,614],{},"AWS MSK: Inter-broker replication is free, but MSK instance pricing is significantly higher (e.g., $3.264 per hour for kafka.m7g.8xlarge vs $1.306 per hour for an on-demand m7g.8xlarge). The storage price of MSK is $0.10 per GB-month which is significantly higher than st1, which costs $0.045 per GB-month. Even though replication is free, client-to-broker traffic still incurs inter-AZ charges.",[340,616,617],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[31,619,621],{"id":620},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[47,623,624],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[47,626,627],{},"Consumers:",[337,629,630],{},[340,631,632],{},"Follower fetch is enabled across all tests, ensuring consumers fetch data from replicas in their local AZ—eliminating inter-zone consumer traffic except for metadata lookups",[47,634,635],{},"Producers:",[337,637,638,647,656],{},[340,639,640,641,646],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[54,642,645],{"href":643,"rel":644},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[263],"KIP-1123"," aims to address this). And it only works with the default partitioner (i.e., when no record partition or record key is specified).",[340,648,649,650,655],{},"Redpanda recently introduced ",[54,651,654],{"href":652,"rel":653},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[263],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[340,657,658,659,664],{},"Ursa is the only system in this test with ",[54,660,663],{"href":661,"rel":662},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[263],"built-in zone affinity for both producers and consumers",". It achieves this by embedding producer AZ information in client.id, allowing metadata lookups to route clients to local-AZ brokers, eliminating inter-AZ producer traffic.",[31,666,668],{"id":667},"cost-comparison-results","Cost Comparison Results",[47,670,335],{},[337,672,673,675],{},[340,674,342],{},[340,676,345],{},[47,678,679],{},"Ursa’s leaderless architecture, zone affinity, and native cloud storage integration deliver unparalleled cost efficiency, making it the most cost-effective choice for high-throughput data streaming workloads.",[47,681,682],{},[349,683],{"alt":17,"src":684},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[47,686,687],{},[349,688],{"alt":17,"src":689},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[47,691,692],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[31,694,696],{"id":695},"streamnative-ursa","StreamNative - Ursa",[337,698,699,702,705,708],{},[340,700,701],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[340,703,704],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[340,706,707],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[340,709,710],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[31,712,714],{"id":713},"aws-msk","AWS MSK",[337,716,717,720,723],{},[340,718,719],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[340,721,722],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[340,724,725],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[31,727,729],{"id":728},"redpanda","RedPanda",[337,731,732,734,736,739,742],{},[340,733,701],{},[340,735,704],{},[340,737,738],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[340,740,741],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[340,743,744],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[47,746,747,748,753],{},"Please note that we were unable to test ",[54,749,752],{"href":750,"rel":751},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[263],"Redpanda with Cloud Topics",", as it remains an announced but unreleased feature and is not yet available for evaluation. Based on the limited information available, while Cloud Topics may help optimize inter-zone data replication costs, producers still need to traverse inter-availability zones to connect to the topic partition owners and incur inter-zone traffic costs of up to $240 per hour.",[337,755,756,762],{},[340,757,758,761],{},[54,759,645],{"href":643,"rel":760},[263]," (when implemented) will help mitigate producer-to-broker inter-zone traffic, but it is not yet available. And it only works with the default partitioner (no record partition or key is specified).",[340,763,764],{},"Redpanda’s leader pinning helps only when all producers for the pinned topic are confined to a single AZ. In multi-AZ environments (like our benchmark), inter-zone producer traffic remains unavoidable.",[47,766,767],{},"Additionally, Redpanda’s Cloud Topics architecture is not documented publicly. Their blog mentions \"leader placement rules to optimize produce latency and ingress cost,\" but it is unclear whether this represents a shift away from a leader-based architecture or if it uses techniques similar to Ursa’s zone-aware approach.",[47,769,770],{},"We may revisit this comparison as more details become available.",[39,772,774],{"id":773},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[47,776,777],{},"As highlighted earlier, with a BYOC Ursa setup, you can achieve 5 GB\u002Fs throughput at just 5% of the infrastructure cost of a traditional leader-based data streaming engine, such as Kafka or RedPanda, while managing the infrastructure yourself. This significant cost reduction is enabled by Ursa’s leaderless architecture and lakehouse-native storage design, which eliminate overhead costs such as inter-zone traffic and leader-based data replication. By leveraging a lakehouse-native, leaderless architecture, Ursa reduces resource requirements, enabling you to handle high data throughput efficiently and at a fraction of the cost of RedPanda.",[47,779,780],{},"Now, let’s examine the total cost comparison, evaluating Ursa alongside other vendors, including those that have adopted a leaderless architecture (e.g., Confluent WarpStream). This comparison is based on a 5GB\u002Fs workload with a 7-day retention period, factoring in both storage cost and vendor costs Here are the key findings:",[337,782,783,786,789],{},[340,784,785],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[340,787,788],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[340,790,791],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[47,793,794],{},"In addition to Ursa’s architectural advantages—eliminating most inter-AZ traffic and leveraging lakehouse storage for cost-effective data retention—it also adopts a more fair and cost-efficient pricing model: Elastic Throughput-based pricing. This approach aligns costs with actual usage, avoiding unnecessary overhead.",[47,796,797],{},"Unlike WarpStream, which charges for both storage and throughput, Ursa ensures that customers only pay for the throughput they actively use. Ursa’s pricing is based on compressed data sent by clients, meaning the more data compressed on the client side, the lower the cost. In contrast, WarpStream prices are based on uncompressed data, unfairly inflating expenses and failing to incentivize customers to optimize their client applications.",[47,799,800],{},"This distinction is crucial, as compressed data reduces both storage and network costs, making Ursa’s pricing model not only more cost-effective but also more transparent and predictable.",[47,802,803],{},[349,804],{"alt":17,"src":805},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[47,807,808],{},[349,809],{"alt":17,"src":810},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[31,812,814],{"id":813},"cost-breakdown","Cost Breakdown",[816,817,818],"h4",{"id":695},"StreamNative – Ursa",[337,820,821,824,827,830,833],{},[340,822,823],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[340,825,826],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[340,828,829],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[340,831,832],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[340,834,835],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[816,837,839],{"id":838},"warpstream","WarpStream",[337,841,842,845],{},[340,843,844],{},"Based on WarpStream’s pricing calculator (as of January 29, 2025), we assume a 4:1 client data compression ratio, meaning 20 GB\u002Fs of uncompressed data translates to 5 GB\u002Fs of compressed data.",[340,846,847,848,853],{},"It's important to note that WarpStream’s pricing structure has fluctuated frequently throughout January. We observed the cost reported by their calculator changing from $409,644 per month to $337,068 per month. This variability has been previously highlighted in the blog post “",[54,849,852],{"href":850,"rel":851},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[263],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[47,855,856],{},[349,857],{"alt":17,"src":858},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[816,860,862],{"id":861},"msk","MSK",[337,864,865,868,871],{},[340,866,867],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[340,869,870],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[340,872,873],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[816,875,729],{"id":876},"redpanda-1",[337,878,879,882,884,887,890],{},[340,880,881],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[340,883,870],{},[340,885,886],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[340,888,889],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[340,891,892],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[816,894,896],{"id":895},"additional-notes","Additional Notes",[337,898,899],{},[340,900,901,902,907],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[54,903,906],{"href":904,"rel":905},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[263],"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group.","” for estimation purposes. Based on the Tier-8 pricing model in the whitepaper,  the estimated cost to support a 5GB\u002Fs workload would be $1.12 million per year ($93,333 per month). However, since this calculation is based on an estimation, we will revisit and refine the cost assessment once Redpanda publishes its BYOC pricing.",[47,909,910],{},[349,911],{"alt":17,"src":912},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[47,914,915],{},[349,916],{"alt":17,"src":917},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[337,919,920,923],{},[340,921,922],{},"When estimating the storage costs for Kafka and Redpanda, we assume the use of HDD storage at $0.045\u002FGB, based on the premise that both systems can fully utilize disk bandwidth without incurring the higher costs associated with GP2 or GP3 volumes. However, in practice, many users opt for GP2 or GP3, significantly increasing the total storage cost for Kafka and Redpanda.",[340,924,925],{},"Unlike disk-based solutions, S3 storage does not require capacity preallocation—Ursa only incurs costs for the actual data stored. This contrasts with Kafka and Redpanda, where preallocating storage can drive up expenses. As a result, the real-world storage costs for Kafka and Redpanda are often 50% higher than the estimates above.",[39,927,929],{"id":928},"conclusion","Conclusion",[47,931,932],{},"Ursa represents a transformative shift in streaming data infrastructure, offering cost efficiency, scalability, and flexibility without compromising durability or reliability. By leveraging a leaderless architecture and eliminating inter-zone data replication, Ursa reduces total cost of ownership by over 90% compared to traditional leader-based streaming engines like Kafka and Redpanda. Its direct integration with cloud storage and scalable metadata & index management via Oxia ensure high availability and simplified infrastructure management.",[31,934,936],{"id":935},"balancing-latency-and-cost","Balancing Latency and Cost",[47,938,939,943],{},[54,940,942],{"href":941},"\u002Fblog\u002Fcap-theorem-for-data-streaming","Ursa trades off slightly higher latency for ultra low cost",", making it an ideal choice for the majority of streaming workloads, especially those that prioritize throughput and cost savings over ultra-low latency. Meanwhile, StreamNative’s BookKeeper-based engine remains the preferred solution for real-time, latency-sensitive applications. By combining these two approaches, StreamNative empowers customers with the flexibility to choose the right engine for their specific needs—whether it's maximizing cost savings or achieving ultra low-latency real-time performance.",[31,945,947],{"id":946},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[47,949,950],{},"In an era where data fuels AI, analytics, and real-time decision-making, managing infrastructure costs is critical to sustaining innovation. Ursa is not just a cost-cutting alternative—it is a forward-thinking, lakehouse-native platform that redefines how modern data streaming infrastructure should be built and operated.",[47,952,953,954,959],{},"Whether your priority is reducing costs, improving flexibility, or ingesting massive data into lakehouses, Ursa delivers a future-proof solution for the evolving demands of real-time data streaming. ",[54,955,958],{"href":956,"rel":957},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[263],"Get started"," with StreamNative Ursa today!",[961,962,964],"h1",{"id":963},"references","References",[47,966,967,970,971],{},[968,969,428],"span",{}," ",[54,972,973],{"href":973},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[47,975,976,970,978],{},[968,977,377],{},[54,979,376],{"href":376},[47,981,982,970,985],{},[968,983,984],{},"StreamNative pricing",[54,986,987],{"href":987,"rel":988},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[263],[47,990,991,970,994],{},[968,992,993],{},"WarpStream pricing",[54,995,996],{"href":996,"rel":997},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[263],[47,999,1000,970,1003],{},[968,1001,1002],{},"AWS S3 pricing",[54,1004,1005],{"href":1005,"rel":1006},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[263],[47,1008,1009,970,1012],{},[968,1010,1011],{},"AWS EBS pricing",[54,1013,1014],{"href":1014,"rel":1015},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[263],[47,1017,1018,970,1021],{},[968,1019,1020],{},"AWS MSK pricing",[54,1022,1023],{"href":1023,"rel":1024},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[263],[47,1026,1027,970,1030],{},[968,1028,1029],{},"The Brutal Truth about Kafka Cost Calculators",[54,1031,850],{"href":850,"rel":1032},[263],[47,1034,1035,970,1038],{},[968,1036,1037],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[54,1039,904],{"href":904,"rel":1040},[263],{"title":17,"searchDepth":18,"depth":18,"links":1042},[1043,1044,1045,1050,1054,1055,1064,1067],{"id":331,"depth":18,"text":332},{"id":370,"depth":18,"text":371},{"id":395,"depth":18,"text":396,"children":1046},[1047,1048,1049],{"id":407,"depth":278,"text":408},{"id":432,"depth":278,"text":433},{"id":453,"depth":278,"text":454},{"id":477,"depth":18,"text":478,"children":1051},[1052,1053],{"id":481,"depth":278,"text":482},{"id":496,"depth":278,"text":497},{"id":537,"depth":18,"text":538},{"id":549,"depth":18,"text":550,"children":1056},[1057,1058,1059,1060,1061,1062,1063],{"id":556,"depth":278,"text":557},{"id":602,"depth":278,"text":603},{"id":620,"depth":278,"text":621},{"id":667,"depth":278,"text":668},{"id":695,"depth":278,"text":696},{"id":713,"depth":278,"text":714},{"id":728,"depth":278,"text":729},{"id":773,"depth":18,"text":774,"children":1065},[1066],{"id":813,"depth":278,"text":814},{"id":928,"depth":18,"text":929,"children":1068},[1069,1070],{"id":935,"depth":278,"text":936},{"id":946,"depth":278,"text":947},"StreamNative Cloud","2025-01-31","Discover how Ursa achieves 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda and AWS MSK. See our benchmark results comparing infrastructure costs, total cost of ownership (TCO), and performance across leading Kafka vendors.","\u002Fimgs\u002Fblogs\u002F679c6593d25099b1cdcec4ca_image-31.png",{},"\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour","30 min",{"title":306,"description":1073},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1081,1082,301],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1085,"title":1086,"authors":1087,"body":1089,"category":1319,"createdAt":10,"date":1320,"description":1321,"extension":8,"featured":292,"image":1322,"isDraft":292,"link":10,"meta":1323,"navigation":7,"order":294,"path":1324,"readingTime":1325,"relatedResources":10,"seo":1326,"stem":1327,"tags":1328,"__hash__":1331},"blogs\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-2.md","How to Build a Distributed Database with Apache BookKeeper — Part 2",[1088],"Enrico Olivelli",{"type":14,"value":1090,"toc":1310},[1091,1094,1097,1100,1103,1106,1110,1113,1116,1133,1136,1142,1146,1149,1152,1163,1166,1177,1180,1183,1186,1189,1193,1196,1199,1202,1205,1208,1211,1214,1217,1220,1223,1227,1230,1233,1236,1239,1242,1245,1251,1254,1257,1265,1268,1272,1275,1278,1281,1284,1288,1291,1294,1298,1301,1304,1307],[47,1092,1093],{},"In this series of posts, I want to share some basic architectural concepts about the possible anatomy of a distributed database with a shared-nothing architecture. In the first part, we see how you can design a database as a replicated state machine.",[47,1095,1096],{},"We have a cluster of machines that do not share disks and they are only connected using the network. We have a table of records, the state of our machine is the content of the table.",[47,1098,1099],{},"Each modification to the table, including INSERT, UPDATE and DELETE operations, is written to a write ahead log and then applied to the local copy of the data.",[47,1101,1102],{},"At any time only one machine among the group is elected to be the leader and clients perform writes and reads only by issuing requests to that machine, only this node is able to modify the contents of the table.",[47,1104,1105],{},"The other machines are the so-called followers and they are continuously tailing the log: they read operations from the log and they apply them to their own local copy, exactly in the same order as they have been written by the leader.",[39,1107,1109],{"id":1108},"enter-apache-bookkeeper","Enter Apache BookKeeper",[47,1111,1112],{},"In the beginning, Apache BookKeeper was designed as a distributed write ahead log for the Hadoop HDFS Namenode as part of the Apache Zookeeper project, but soon it started its own life as an independent product.",[47,1114,1115],{},"It comes with most of the features we need to support our replicated state machine, the main features of our interests are:",[337,1117,1118,1121,1124,1127,1130],{},[340,1119,1120],{},"Decentralized architecture: all of the logic runs on a rich client model, BookKeeper servers are only containers of data, this allows us to completely scale out.",[340,1122,1123],{},"Shared nothing storage model: clients only use network, no shared disks; servers do not know about each other.",[340,1125,1126],{},"Support for fencing: BookKeeper guarantees that only one machine is able to write to the log.",[340,1128,1129],{},"Last Add Confirmed Protocol: BookKeeper allows the readers to follow the log consistently.",[340,1131,1132],{},"Automatic re-replication of data on lost storage nodes: self healing in case of lost machines and network partitions.",[47,1134,1135],{},"Let's look through all of these key features, we will see how our database is able to address all of the challenges of a distributed system.",[47,1137,1138],{},[349,1139],{"alt":1140,"src":1141},"illustration of  Apache BookKeeper","\u002Fimgs\u002Fblogs\u002F63a320fc588fca900d0f3d0c_bk2-1.png",[39,1143,1145],{"id":1144},"rich-client-model","Rich client model",[47,1147,1148],{},"The leader node runs a BookKeeper writer (WriteHandle) and it creates a ledger. A ledger is a write only segment of our log. It can be opened for writing only once and it can be read as many times as you like. You can not append more data to a ledger once the writer closes it or in case it dies.",[47,1150,1151],{},"At creation time, you need to set three parameters about replication:",[337,1153,1154,1157,1160],{},[340,1155,1156],{},"Ensemble size (ES): the number of bookies that will store ledger's data.",[340,1158,1159],{},"Write quorum size (WQ): the number of copies for each entry.",[340,1161,1162],{},"Ack quorum size (AQ): the number of required copies to be acknowledged before considering a write as successful.",[47,1164,1165],{},"For instance, if you have ES=3, WQ=2 and AQ=1 for each entry, BookKeeper will:",[337,1167,1168,1171,1174],{},[340,1169,1170],{},"Spread copies over 3 bookies",[340,1172,1173],{},"Write 2 copies of each entry",[340,1175,1176],{},"Wait only for 1 acknowledgement in order to declare an entry to be written.",[47,1178,1179],{},"If you have very few bookies (like 3), I suggest you start with 2–2–2, it is a good trade-off, you are guaranteed to have at least two copies of each entry.",[47,1181,1182],{},"Having ES > WQ is known as striping, this helps in boosting performances because writes and reads are spread to more bookies.",[47,1184,1185],{},"The writer reacts to bookie failures and chooses new bookies to use as storage, this mechanism is known as ensemble change, if you have enough bookie this is totally transparent to the application and you do not have to care about this situation.",[47,1187,1188],{},"At every ensemble change, we start a new segment of the ledger, the readers watch changes on ledger metadata and are able to automatically connect to the new bookies.",[39,1190,1192],{"id":1191},"fencing","Fencing",[47,1194,1195],{},"In our replicated state machine model, we have only one leader that is allowed to perform changes to the state of the table. Its leadership role must be supported by all of the other peers, for instance, you could use some leader election recipe with ZooKeeper, but this won’t be enough to guarantee the overall consistency of data.",[47,1197,1198],{},"In theory, you should implement some kind of low level distributed consensus protocol (like ZAB in ZooKeeper), but this would be overkilling, it will be really slow.",[47,1200,1201],{},"Here comes BookKeeper to the rescue.",[47,1203,1204],{},"When a node starts to act as a leader it performs a “recovery read” over every ledger supposed to be open by the previous leader.",[47,1206,1207],{},"This operation connects to every bookie that contains that ledger’s data and flags each ledger as fenced and if the previous leader is still alive it will receive a specific write error during the next write that tells that he has been fenced off.",[47,1209,1210],{},"BookKeeper handles every corner case, like network errors during recovery or multiple concurrent recovery operations.",[47,1212,1213],{},"You are guaranteed that only one machine will succeed in the recovery and then it can start to perform new changes to the status of the database.",[47,1215,1216],{},"But BookKeeper deals only with ledgers and you have to store somewhere the list of ledgers that are building up your write ahead log. This secondary metadata storage must handle some sort of fencing as well.",[47,1218,1219],{},"One option is to store this list on ZooKeeper and leverage its built-in distributed compare-and-set facility to deal with concurrent leaders that want to add a new ledger to the list of active ledgers. Please refer to the BookKeeper Tutorial for an example of how to deal with this part of the story.",[47,1221,1222],{},"BookKeeper provides a higher-level API, DistributedLog, that does this part for you and adds a lot of built-in features to BookKeeper low-level API.",[39,1224,1226],{"id":1225},"last-add-confirmed-protocol","Last add confirmed protocol",[47,1228,1229],{},"Each node is now using only Zookeeper and BookKeeper in order to communicate with the other peers. Let’s see how can a follower know that the leader is making progress and it is time to look for new entries.",[47,1231,1232],{},"Every time the writer adds an entry to a ledger, it also writes the greatest entry id for which it has received an acknowledgment of successful store by an AQ of bookies, we name this id the Last-Add-Confirmed entry (LAC).",[47,1234,1235],{},"Usually, the writer is faster in sending writes than the bookie to persist them and to send back an acknowledgment message, so entries would be potentially available for readers even if the writer is not considering them as persisted.",[47,1237,1238],{},"This is very dangerous because this way follower nodes may have a future view of data that has not still been accepted by the leader.",[47,1240,1241],{},"In order to address this case BookKeeper readers can read only entries up to the LAC entry. You are guaranteed that the reader is always one step behind the writer.",[47,1243,1244],{},"Readers get this LAC during reads (it is piggy backed) by reading ledger’s metadata and asking the bookies associated with the ledger.",[47,1246,1247],{},[349,1248],{"alt":1249,"src":1250},"illustration of Last add confirmed protocol BookKeeper","\u002Fimgs\u002Fblogs\u002F63a320fc5984dc2335254830_bk2-2.png",[47,1252,1253],{},"Let’s see a usual tricky case for new users of BookKeeper: the writer writes entry X and X-1 as LAC, so readers can see only up to X-1, if no more entries are written, that the follower is not able to be up to date. This is not a real problem in production, especially under heavy load, but it is difficult to understand especially the first time you play with BookKeeper.",[47,1255,1256],{},"You have to ways to fix this issue:",[337,1258,1259,1262],{},[340,1260,1261],{},"Periodically write a dummy entry if you haven't written anything",[340,1263,1264],{},"Use the ExplicitLAC feature, this basically stores a secondary LAC pointer out of the regular piggybacking mechanism",[47,1266,1267],{},"There are ways to bypass LAC protocol but this blog does not introduce them because they are not useful in our use case.",[39,1269,1271],{"id":1270},"close-a-ledger","Close a ledger",[47,1273,1274],{},"BookKeeper is optimized for high throughput but the most important features are about the consistency guarantees and this is mostly about metadata management both using ZooKeeper and the built-in fencing mechanisms.",[47,1276,1277],{},"One critical point is when you have to define the actual series of valid entries visible to readers, especially the id of the last entry.",[47,1279,1280],{},"There are several mechanics that come into play because the writer may fail and also network may fail but in the end, the writer or the recovery procedure will come to seal this range of valid entry ids.",[47,1282,1283],{},"We call this operation ‘closing’ a ledger and basically it is about writing to Zookeeper the final state of the ledger and this happens when out ‘close’ your WriteHandle. After closing a ledger, the reader is able to read up the last written entry.",[39,1285,1287],{"id":1286},"replication-in-case-of-lost-bookies","Replication in case of lost bookies",[47,1289,1290],{},"When you lose a bookie, BookKeeper is able to enforce the original replication factor (Write Quorum Size) by detecting the failure and replicate again the data supposed to be stored on the dead bookie.",[47,1292,1293],{},"This can be done manually by using BookKeeper tools, but it can also be performed by the auto-recovery daemon.",[39,1295,1297],{"id":1296},"wrap-up","Wrap up",[47,1299,1300],{},"It is a good choice to use BookKeeper as a distributed write ahead log since it deals with many aspects of distributed systems. If you want to write your own log, you will fall into a lot of corner cases only when it will be too late.",[47,1302,1303],{},"Therefore, BookKeeper was designed from the ground up to be a high-performance storage system, with lots of improvements and tricks about local disk storage management, network usage, and JVM performance.",[47,1305,1306],{},"In the next part of this series, you can learn how HerdDB uses Apache BookKeeper as write ahead log and fill in the gaps of this story: how to store local data, coordinate replicas and perform checkpoints.",[47,1308,1309],{},"‍",{"title":17,"searchDepth":18,"depth":18,"links":1311},[1312,1313,1314,1315,1316,1317,1318],{"id":1108,"depth":18,"text":1109},{"id":1144,"depth":18,"text":1145},{"id":1191,"depth":18,"text":1192},{"id":1225,"depth":18,"text":1226},{"id":1270,"depth":18,"text":1271},{"id":1286,"depth":18,"text":1287},{"id":1296,"depth":18,"text":1297},"Apache Pulsar","2020-04-14","It is a good choice to use BookKeeper as a distributed write ahead log since it deals with many aspects of distributed systems.","\u002Fimgs\u002Fblogs\u002F63d7990c61830d02b126a528_63a320fc5984dc2335254830_bk2-2.webp",{},"\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-2","8 min read",{"title":1086,"description":1321},"blog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-2",[1329,1330],"Tutorials","BookKeeper","BhP9npuBsEw-7SNXZdPUVmiQ9oMSt49Du4sQPZRrpQc",[1333],{"id":1334,"title":1088,"bioSummary":1335,"email":10,"extension":8,"image":1336,"linkedinUrl":10,"meta":1337,"position":10,"stem":1344,"twitterUrl":1345,"__hash__":1346},"authors\u002Fauthors\u002Fenrico-olivelli.md","Enrico Olivelli is the Chief Developer Manager at Diennea, leading the development teams of MagNews.com and EmailSuccess.com. He is a PMC member of Apache BookKeeper and Apache ZooKeeper and a committer in Apache Maven. He is also the creator of the Open Source projects HerdDB and BlazingCache.","\u002Fimgs\u002Fauthors\u002Fenrico-olivelli.webp",{"body":1338},{"type":14,"value":1339,"toc":1342},[1340],[47,1341,1335],{},{"title":17,"searchDepth":18,"depth":18,"links":1343},[],"authors\u002Fenrico-olivelli","https:\u002F\u002Ftwitter.com\u002Feolivelli","ZssiiYMsc6pnLJWWZLlPP-rfw1vP8Y-e-NG1_XQLS4Y",[1348,1355,1360],{"path":1349,"title":1350,"date":1351,"image":-1,"link":-1,"collection":1352,"resourceType":1353,"score":1354,"id":1349},"\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-3","How to Build a Distributed Database with Apache BookKeeper — Part 3","2020-05-12","blogs","Blog",1,{"path":1356,"title":1357,"date":1358,"image":1359,"link":-1,"collection":1352,"resourceType":1353,"score":1354,"id":1356},"\u002Fblog\u002Fhow-to-build-distributed-database-apache-bookkeeper-part-1","How to Build a Distributed Database with Apache BookKeeper - Part 1","2020-02-04","\u002Fimgs\u002Fblogs\u002F63d7998e4d8472734cc95d9c_63a2e28e570e54635c7f34e5_bulidab-1.webp",{"path":1361,"title":1362,"date":1363,"image":-1,"link":-1,"collection":1352,"resourceType":1353,"score":1364,"id":1361},"\u002Fblog\u002Ftweaking-the-bookkeeper-protocol-unbounded-ledgers","Tweaking the BookKeeper Protocol - Unbounded Ledgers","2021-12-16",0.5,1775235690931]