[{"data":1,"prerenderedAt":1697},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture":1086,"blog-authors-\u002Fblog\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture":1648,"related-\u002Fblog\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture":1679},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":12,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07",true,"md","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Flakestream-ufk-launch","default","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":25,"title":26,"authors":27,"body":29,"canonicalUrl":289,"category":290,"createdAt":289,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":289,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":289,"seo":299,"stem":300,"tags":301,"__hash__":305},"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",[28],"David Kjerrumgaard",{"type":15,"value":30,"toc":276},[31,39,47,51,67,73,78,81,87,102,109,115,118,124,127,134,140,143,146,157,163,169,172,175,178,184,191,194,197,204,207,210,224,229,233,237,241,245,249,251,268,270],[32,33,35],"h3",{"id":34},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[36,37,38],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[40,41,43],"h2",{"id":42},"introduction",[44,45,46],"strong",{},"Introduction",[48,49,50],"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.",[48,52,53,54,63,64],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[55,56,58],"a",{"href":57},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[36,59,60],{},[44,61,62],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[44,65,66],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[48,68,69,70],{},"***Forrester's Take: ***",[36,71,72],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[48,74,75],{},[36,76,77],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[48,79,80],{},"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.",[40,82,84],{"id":83},"trusted-by-industry-leaders",[44,85,86],{},"Trusted by Industry Leaders",[48,88,89,90,93,94,97,98,101],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[44,91,92],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[44,95,96],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[44,99,100],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[48,103,104,105,108],{},"The Forrester report notes that, “",[36,106,107],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[40,110,112],{"id":111},"modern-cloud-native-architecture-built-for-scale",[44,113,114],{},"Modern, Cloud-Native Architecture Built for Scale",[48,116,117],{},"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.",[48,119,120,121,108],{},"Forrester's evaluation described that “",[36,122,123],{},"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.",[48,125,126],{},"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.",[48,128,129,130,133],{},"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 ",[36,131,132],{},"\"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.",[40,135,137],{"id":136},"open-source-foundation-and-pulsar-expertise",[44,138,139],{},"Open Source Foundation and Pulsar Expertise",[48,141,142],{},"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.",[48,144,145],{},"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.",[48,147,148,149,152,153,156],{},"Forrester's assessment noted that StreamNative’s “",[36,150,151],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[44,154,155],{},"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.",[48,158,159,160],{},"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 ",[36,161,162],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[40,164,166],{"id":165},"powering-real-time-use-cases-across-industries",[44,167,168],{},"Powering Real-Time Use Cases Across Industries",[48,170,171],{},"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.",[48,173,174],{},"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.",[48,176,177],{},"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.",[40,179,181],{"id":180},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[44,182,183],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[48,185,186,187,190],{},"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 ",[44,188,189],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[48,192,193],{},"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.",[48,195,196],{},"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.",[48,198,199,200,203],{},"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. ",[44,201,202],{},"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.",[48,205,206],{},"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!",[208,209],"hr",{},[32,211,213],{"id":212},"streamnative-in-the-forrester-wave-evaluation-findings",[44,214,215,216,223],{},"StreamNative in ",[44,217,218],{},[55,219,220],{"href":57},[44,221,222],{},"The Forrester Wave™",": Evaluation Findings",[225,226,228],"h5",{"id":227},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[225,230,232],{"id":231},"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",[225,234,236],{"id":235},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[225,238,240],{"id":239},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[225,242,244],{"id":243},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[225,246,248],{"id":247},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[208,250],{},[252,253,255,256,259,260,190],"h6",{"id":254},"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: **",[36,257,258],{},"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 *",[55,261,265],{"href":262,"rel":263},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[264],"nofollow",[36,266,267],{},"here",[208,269],{},[252,271,273],{"id":272},"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",[36,274,275],{},"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":18,"searchDepth":19,"depth":19,"links":277},[278,280,281,282,283,284,285],{"id":34,"depth":279,"text":38},3,{"id":42,"depth":19,"text":46},{"id":83,"depth":19,"text":86},{"id":111,"depth":19,"text":114},{"id":136,"depth":19,"text":139},{"id":165,"depth":19,"text":168},{"id":180,"depth":19,"text":183,"children":286},[287],{"id":212,"depth":279,"text":288},"StreamNative in The Forrester Wave™: Evaluation Findings",null,"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":26,"description":292},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[302,303,304],"Announcements","Real-Time","Forrester","5Nr1vAcqlQ7yFQfdL0a3MLsNFerVmEOQJXD9Twz5lx8",{"id":307,"title":308,"authors":309,"body":314,"canonicalUrl":289,"category":1073,"createdAt":289,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":289,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":289,"seo":1080,"stem":1081,"tags":1082,"__hash__":1085},"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",[310,311,312,313],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":315,"toc":1043},[316,319,322,325,328,331,335,338,348,354,357,365,370,374,381,384,387,395,399,402,407,411,414,417,420,423,432,436,439,450,453,457,460,463,474,477,481,485,493,496,500,508,537,541,544,549,553,556,560,563,566,571,580,585,588,591,602,606,609,620,624,627,630,635,638,667,671,673,679,682,687,692,695,699,713,717,728,732,747,756,767,770,773,777,780,783,794,797,800,803,808,813,817,821,838,842,856,861,865,876,879,895,899,910,915,920,928,932,935,939,946,950,953,962,967,976,982,991,1000,1009,1018,1027,1035],[48,317,318],{},"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.",[48,320,321],{},"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.",[48,323,324],{},"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.",[48,326,327],{},"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.",[48,329,330],{},"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.",[40,332,334],{"id":333},"key-benchmark-findings","Key Benchmark Findings",[48,336,337],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[339,340,341,345],"ul",{},[342,343,344],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[342,346,347],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[48,349,350],{},[351,352],"img",{"alt":18,"src":353},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[48,355,356],{},"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:",[339,358,359,362],{},[342,360,361],{},"50% cheaper than Confluent WarpStream",[342,363,364],{},"85% cheaper than MSK and Redpanda",[48,366,367],{},[351,368],{"alt":18,"src":369},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[40,371,373],{"id":372},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[48,375,376,380],{},[55,377,379],{"href":378},"\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.",[48,382,383],{},"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.",[48,385,386],{},"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:",[339,388,389,392],{},[342,390,391],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[342,393,394],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[40,396,398],{"id":397},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[48,400,401],{},"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.",[48,403,404],{},[351,405],{"alt":18,"src":406},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[32,408,410],{"id":409},"leaderless-architecture","Leaderless architecture",[48,412,413],{},"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.",[48,415,416],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[48,418,419],{},"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",[48,421,422],{},"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.",[48,424,425,426,431],{},"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 ",[55,427,430],{"href":428,"rel":429},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[264],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[32,433,435],{"id":434},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[48,437,438],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[339,440,441,444,447],{},[342,442,443],{},"Handles millions of metadata\u002Findex operations per second",[342,445,446],{},"Generates sequential IDs to maintain strict message ordering",[342,448,449],{},"Optimized for Kubernetes with horizontal scalability",[48,451,452],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[32,454,456],{"id":455},"zero-interzone-data-replication","Zero interzone data replication",[48,458,459],{},"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.",[48,461,462],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[339,464,465,468,471],{},[342,466,467],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[342,469,470],{},"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).",[342,472,473],{},"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.",[48,475,476],{},"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.",[40,478,480],{"id":479},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[32,482,484],{"id":483},"ursa-cluster-deployment","Ursa Cluster Deployment",[339,486,487,490],{},[342,488,489],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[342,491,492],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[48,494,495],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[32,497,499],{"id":498},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[48,501,502,503,507],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[55,504,505],{"href":505,"rel":506},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[264]," for details.",[339,509,510,525,534],{},[342,511,512,513,518,519,524],{},"12 OMB workers: 6 for ",[55,514,517],{"href":515,"rel":516},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[264],"producers",", 6 for ",[55,520,523],{"href":521,"rel":522},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[264],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[342,526,527,528,533],{},"Sample YAML ",[55,529,532],{"href":530,"rel":531},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[264],"scripts"," provided for Kafka-compatible configuration and rate limits.",[342,535,536],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[40,538,540],{"id":539},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[48,542,543],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[48,545,546],{},[351,547],{"alt":18,"src":548},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[40,550,552],{"id":551},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[48,554,555],{},"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.",[32,557,559],{"id":558},"test-setup-key-assumptions","Test Setup & Key Assumptions",[48,561,562],{},"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.",[48,564,565],{},"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:",[339,567,568],{},[342,569,570],{},"9 × m6i.8xlarge instances",[48,572,573,574,579],{},"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",[55,575,578],{"href":576,"rel":577},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[264]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[339,581,582],{},[342,583,584],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[48,586,587],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[48,589,590],{},"Additional key assumptions include:",[339,592,593,596,599],{},[342,594,595],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[342,597,598],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[342,600,601],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[32,603,605],{"id":604},"inter-broker-replication-costs","Inter-Broker Replication Costs",[48,607,608],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[339,610,611,614,617],{},[342,612,613],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[342,615,616],{},"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.",[342,618,619],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[32,621,623],{"id":622},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[48,625,626],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[48,628,629],{},"Consumers:",[339,631,632],{},[342,633,634],{},"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",[48,636,637],{},"Producers:",[339,639,640,649,658],{},[342,641,642,643,648],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[55,644,647],{"href":645,"rel":646},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[264],"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).",[342,650,651,652,657],{},"Redpanda recently introduced ",[55,653,656],{"href":654,"rel":655},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[264],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[342,659,660,661,666],{},"Ursa is the only system in this test with ",[55,662,665],{"href":663,"rel":664},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[264],"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.",[32,668,670],{"id":669},"cost-comparison-results","Cost Comparison Results",[48,672,337],{},[339,674,675,677],{},[342,676,344],{},[342,678,347],{},[48,680,681],{},"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.",[48,683,684],{},[351,685],{"alt":18,"src":686},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[48,688,689],{},[351,690],{"alt":18,"src":691},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[48,693,694],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[32,696,698],{"id":697},"streamnative-ursa","StreamNative - Ursa",[339,700,701,704,707,710],{},[342,702,703],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[342,705,706],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[342,708,709],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[342,711,712],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[32,714,716],{"id":715},"aws-msk","AWS MSK",[339,718,719,722,725],{},[342,720,721],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[342,723,724],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[342,726,727],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[32,729,731],{"id":730},"redpanda","RedPanda",[339,733,734,736,738,741,744],{},[342,735,703],{},[342,737,706],{},[342,739,740],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[342,742,743],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[342,745,746],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[48,748,749,750,755],{},"Please note that we were unable to test ",[55,751,754],{"href":752,"rel":753},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[264],"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.",[339,757,758,764],{},[342,759,760,763],{},[55,761,647],{"href":645,"rel":762},[264]," (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).",[342,765,766],{},"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.",[48,768,769],{},"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.",[48,771,772],{},"We may revisit this comparison as more details become available.",[40,774,776],{"id":775},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[48,778,779],{},"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.",[48,781,782],{},"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:",[339,784,785,788,791],{},[342,786,787],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[342,789,790],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[342,792,793],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[48,795,796],{},"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.",[48,798,799],{},"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.",[48,801,802],{},"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.",[48,804,805],{},[351,806],{"alt":18,"src":807},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[48,809,810],{},[351,811],{"alt":18,"src":812},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[32,814,816],{"id":815},"cost-breakdown","Cost Breakdown",[818,819,820],"h4",{"id":697},"StreamNative – Ursa",[339,822,823,826,829,832,835],{},[342,824,825],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[342,827,828],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[342,830,831],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[342,833,834],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[342,836,837],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[818,839,841],{"id":840},"warpstream","WarpStream",[339,843,844,847],{},[342,845,846],{},"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.",[342,848,849,850,855],{},"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 “",[55,851,854],{"href":852,"rel":853},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[264],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[48,857,858],{},[351,859],{"alt":18,"src":860},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[818,862,864],{"id":863},"msk","MSK",[339,866,867,870,873],{},[342,868,869],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[342,871,872],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[342,874,875],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[818,877,731],{"id":878},"redpanda-1",[339,880,881,884,886,889,892],{},[342,882,883],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[342,885,872],{},[342,887,888],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[342,890,891],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[342,893,894],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[818,896,898],{"id":897},"additional-notes","Additional Notes",[339,900,901],{},[342,902,903,904,909],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[55,905,908],{"href":906,"rel":907},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[264],"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.",[48,911,912],{},[351,913],{"alt":18,"src":914},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[48,916,917],{},[351,918],{"alt":18,"src":919},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[339,921,922,925],{},[342,923,924],{},"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.",[342,926,927],{},"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.",[40,929,931],{"id":930},"conclusion","Conclusion",[48,933,934],{},"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.",[32,936,938],{"id":937},"balancing-latency-and-cost","Balancing Latency and Cost",[48,940,941,945],{},[55,942,944],{"href":943},"\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.",[32,947,949],{"id":948},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[48,951,952],{},"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.",[48,954,955,956,961],{},"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. ",[55,957,960],{"href":958,"rel":959},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[264],"Get started"," with StreamNative Ursa today!",[963,964,966],"h1",{"id":965},"references","References",[48,968,969,972,973],{},[970,971,430],"span",{}," ",[55,974,975],{"href":975},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[48,977,978,972,980],{},[970,979,379],{},[55,981,378],{"href":378},[48,983,984,972,987],{},[970,985,986],{},"StreamNative pricing",[55,988,989],{"href":989,"rel":990},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[264],[48,992,993,972,996],{},[970,994,995],{},"WarpStream pricing",[55,997,998],{"href":998,"rel":999},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[264],[48,1001,1002,972,1005],{},[970,1003,1004],{},"AWS S3 pricing",[55,1006,1007],{"href":1007,"rel":1008},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[264],[48,1010,1011,972,1014],{},[970,1012,1013],{},"AWS EBS pricing",[55,1015,1016],{"href":1016,"rel":1017},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[264],[48,1019,1020,972,1023],{},[970,1021,1022],{},"AWS MSK pricing",[55,1024,1025],{"href":1025,"rel":1026},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[264],[48,1028,1029,972,1032],{},[970,1030,1031],{},"The Brutal Truth about Kafka Cost Calculators",[55,1033,852],{"href":852,"rel":1034},[264],[48,1036,1037,972,1040],{},[970,1038,1039],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[55,1041,906],{"href":906,"rel":1042},[264],{"title":18,"searchDepth":19,"depth":19,"links":1044},[1045,1046,1047,1052,1056,1057,1066,1069],{"id":333,"depth":19,"text":334},{"id":372,"depth":19,"text":373},{"id":397,"depth":19,"text":398,"children":1048},[1049,1050,1051],{"id":409,"depth":279,"text":410},{"id":434,"depth":279,"text":435},{"id":455,"depth":279,"text":456},{"id":479,"depth":19,"text":480,"children":1053},[1054,1055],{"id":483,"depth":279,"text":484},{"id":498,"depth":279,"text":499},{"id":539,"depth":19,"text":540},{"id":551,"depth":19,"text":552,"children":1058},[1059,1060,1061,1062,1063,1064,1065],{"id":558,"depth":279,"text":559},{"id":604,"depth":279,"text":605},{"id":622,"depth":279,"text":623},{"id":669,"depth":279,"text":670},{"id":697,"depth":279,"text":698},{"id":715,"depth":279,"text":716},{"id":730,"depth":279,"text":731},{"id":775,"depth":19,"text":776,"children":1067},[1068],{"id":815,"depth":279,"text":816},{"id":930,"depth":19,"text":931,"children":1070},[1071,1072],{"id":937,"depth":279,"text":938},{"id":948,"depth":279,"text":949},"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":308,"description":1075},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1083,1084,303],"TCO","Apache Kafka","CDUawvFKTs_AD8usvmIcTleU3mbfA0QAoPZM6xfVuo8",{"id":1087,"title":1088,"authors":1089,"body":1090,"canonicalUrl":289,"category":379,"createdAt":289,"date":1638,"description":1639,"extension":8,"featured":294,"image":1640,"isDraft":294,"link":289,"meta":1641,"navigation":7,"order":296,"path":1642,"readingTime":289,"relatedResources":289,"seo":1643,"stem":1644,"tags":1645,"__hash__":1647},"blogs\u002Fblog\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture.md","Ursa-For-Kafka Deep Dive: The Kafka Problem and Ursa's Storage & Leaderless Architecture",[313,312],{"type":15,"value":1091,"toc":1614},[1092,1100,1104,1113,1136,1146,1151,1156,1181,1184,1190,1193,1199,1202,1209,1217,1223,1226,1229,1236,1242,1245,1252,1258,1261,1264,1271,1277,1280,1298,1303,1308,1314,1322,1336,1339,1342,1345,1351,1356,1361,1364,1370,1376,1382,1387,1404,1407,1433,1436,1442,1445,1451,1457,1460,1463,1469,1472,1478,1485,1488,1492,1495,1498,1504,1511,1514,1522,1528,1535,1538,1549,1555,1558,1576,1582,1585,1591,1596,1598,1604,1609],[48,1093,1094,1097],{},[36,1095,1096],{},"UFK Deep Dive Series — Part 1 of 3: How UFK extends Kafka with a diskless, leaderless,",[36,1098,1099],{},"lakehouse-native storage architecture",[40,1101,1102],{"id":42},[44,1103,46],{},[48,1105,1106,1107,1112],{},"If you're running Kafka at scale, you already know the numbers:\n",[55,1108,1111],{"href":1109,"rel":1110},"https:\u002F\u002Fstreamnative.io\u002Fblog\u002Fa-guide-to-evaluating-the-infrastructure-costs-of-apache-pulsar-and-apache-kafka",[264],"60--80% of your infrastructure spend goes to cross-AZ\nreplication",".\nHalf your provisioned disk sits empty. And somewhere, a Kafka Connect\npipeline is failing at 2 AM---duplicating every byte just to land Kafka\ndata in your lakehouse.",[48,1114,1115,1116,1124,1125,1129,1130,1135],{},"In our launch posts---",[55,1117,1120,1121],{"href":1118,"rel":1119},"https:\u002F\u002Fstreamnative.io\u002Fblog\u002Ffrom-streams-to-lakestreams",[264],"From Streams to Lakestreams_\nand ",[36,1122,1123],{},"Ursa For\nKafka","---we\nintroduced the Lakestream paradigm and\n",[55,1126,379],{"href":1127,"rel":1128},"https:\u002F\u002Fstreamnative.io\u002Fursa",[264],"'s place in it. This\nis the first in a three-part deep dive into how ",[55,1131,1134],{"href":1132,"rel":1133},"https:\u002F\u002Fstreamnative.io\u002Fblog\u002Fursa-for-kafka-native-apache-kafka-service-on-lakestream",[264],"Ursa-For-Kafka\n(UFK)","\nactually works under the hood.",[48,1137,1138,1139,1141,1142,1145],{},"Let's start with the basics. UFK is a fork of Apache Kafka 4.2+ that\nextends Kafka's local-disk storage with ",[44,1140,379],{},", a cloud-native stream\nstorage engine built on object storage. This is not a proxy sitting in\nfront of Kafka. It is not a compatibility layer translating Kafka\nprotocol calls into something else. UFK ",[36,1143,1144],{},"is"," Kafka---the same request\nhandlers, the same group coordinator, the same KRaft controller---with a\nlakehouse-native storage option alongside its traditional disk-based\nstorage. Thousands of lines of Kafka code for protocol handling, group\nmanagement, and admin operations continue to run unchanged.",[48,1147,1148],{},[351,1149],{"alt":18,"src":1150},"\u002Fimgs\u002Fblogs\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture-image2.png",[48,1152,1153],{},[36,1154,1155],{},"Figure 1. Lakestream Architecture",[48,1157,1158,1159,1164,1165,1168,1169,1172,1173,1176,1177,1180],{},"UFK is built on the ",[55,1160,1163],{"href":1161,"rel":1162},"https:\u002F\u002Fstreamnative.io\u002Flakestream",[264],"Lakestream\narchitecture",", which\nseparates concerns into three layers: the ",[44,1166,1167],{},"Data Layer"," (Ursa Storage\non object storage), the ",[44,1170,1171],{},"Metadata Layer"," (the ",[44,1174,1175],{},"Lakestream Catalog",",\nbacked by Oxia), and the ",[44,1178,1179],{},"Protocol Layer"," (stateless Kafka brokers\nthat translate wire protocol to storage operations). While this series\nfocuses on Kafka, the same Lakestream foundation already powers Apache\nPulsar workloads on StreamNative Cloud---StreamNative is a 'both, not\neither' company. This post focuses on the Data and Metadata layers. Part\n3 will cover how all three layers come together for stream-table\nduality.",[48,1182,1183],{},"In this post, we'll cover two foundational topics: why Kafka's current\narchitecture creates the problems it does, and how UFK's storage\nabstraction and leaderless broker design solve them. Parts 2 and 3 will\ntrace the complete data flow and cover the operational model.",[40,1185,1187],{"id":1186},"the-kafka-problem",[44,1188,1189],{},"The Kafka Problem",[48,1191,1192],{},"Kafka is the de facto standard for event streaming. It is also, for many\norganizations, one of the most expensive and operationally demanding\npieces of infrastructure they run. The root causes are\nstructural---baked into Kafka's architecture---and no amount of tuning\neliminates them. Let's walk through each one.",[32,1194,1196],{"id":1195},"cross-az-replication-cost",[44,1197,1198],{},"Cross-AZ Replication Cost",[48,1200,1201],{},"Kafka's durability model is built on replication. The default\nreplication factor is 3, meaning every message your producers send gets\nwritten to three different brokers, typically spread across three\navailability zones. This is the right thing to do for durability---if\none AZ goes down, two copies remain. But the cost is brutal.",[48,1203,1204,1205,1208],{},"Every replicated write crosses an AZ boundary, and cloud providers\ncharge for cross-AZ data transfer. At AWS's rate of ",[44,1206,1207],{},"$0.01 per GB per\ndirection",", the math adds up quickly. In production benchmarks, UFK\nreduces total infrastructure cost from $52,000\u002Fmonth to $4,200\u002Fmonth\nat 5 GB\u002Fs sustained throughput---a 92% reduction. That gives you a sense\nof how much replication-related costs dominate at scale.",[48,1210,1211,1212,1216],{},"This isn't an edge case. For organizations running Kafka at scale,\n",[55,1213,1215],{"href":1109,"rel":1214},[264],"60--80% of total Kafka TCO is replication-related\ninfrastructure",".\nThe data itself might be worth pennies per gigabyte to store on S3, but\nKafka's replication protocol turns it into dollars.",[32,1218,1220],{"id":1219},"isr-operational-burden",[44,1221,1222],{},"ISR Operational Burden",[48,1224,1225],{},"Kafka tracks which replicas are keeping up with the leader through the\nIn-Sync Replica (ISR) set. When a broker falls behind---due to a slow\ndisk, a GC pause, a network partition, or just a spike in traffic---it\ndrops out of the ISR. If enough replicas drop, the partition becomes\nunder-replicated.",[48,1227,1228],{},"This is where the operational pain starts. An under-replicated partition\nforces a choice: do you allow writes to continue with reduced durability\n(risking data loss if another failure occurs), or do you block producers\nuntil the ISR recovers (risking unavailability)? There's no good answer.\nThe operator is stuck choosing between two bad outcomes, often at 3 AM.",[48,1230,1231,1232,1235],{},"ISR management is a constant source of operational toil. Brokers join\nand leave the ISR for all kinds of transient reasons. Each event\nrequires monitoring, alerting, and often manual intervention.\nOrganizations with large Kafka deployments typically dedicate ",[44,1233,1234],{},"one or\nmore full-time engineers solely to Kafka operations","---not building\nfeatures, not shipping product, just keeping Kafka healthy.",[32,1237,1239],{"id":1238},"disk-provisioning-waste",[44,1240,1241],{},"Disk Provisioning Waste",[48,1243,1244],{},"Because Kafka stores data on local disks (or EBS volumes), operators\nmust provision storage ahead of time. And because running out of disk\nspace means dropped messages or broker crashes, they provision\nconservatively---for worst-case scenarios: seasonal traffic spikes,\nretention policy changes, onboarding of new topics from teams that\nhaven't even asked yet.",[48,1246,1247,1248,1251],{},"The result is predictable: ",[44,1249,1250],{},"average disk utilization sits at 30--50%",".\nHalf the storage you're paying for is empty headroom. EBS volumes are\ncharged whether they're full or not, and EBS pricing can reach $0.50\nper GiB-month for high-IOPS volumes. Because resizing EBS volumes is\nslow and disruptive, operators tend to over-provision even further as a\nhedge. It's a tax on being cautious---and in infrastructure, being\ncautious is the right thing to do. More fundamentally, EBS volumes were\ndesigned to lift and shift legacy on-premises software into the cloud,\nnot for cloud-native architectures. As retention requirements grow, the\npain compounds: provisioned disk costs scale linearly with retention\nperiod.",[32,1253,1255],{"id":1254},"the-connector-jungle",[44,1256,1257],{},"The Connector Jungle",[48,1259,1260],{},"Kafka stores data in its own binary log format, optimized for sequential\nreads and writes. That's great for streaming consumers, but useless for\nanalytics. If you want to query your Kafka data with SQL, train a model\non it, or join it with data from other systems, you need to get it out\nof Kafka and into a format the analytics world understands.",[48,1262,1263],{},"So operators deploy Kafka Connect with Iceberg or Parquet sink\nconnectors. Or they use Confluent's Tableflow. Or they write custom\nconsumer applications that read from Kafka and write to S3. Each of\nthese approaches has its own failure modes: connector lag, schema\nevolution mismatches, exactly-once delivery complexity, and operational\noverhead that compounds with every new topic.",[48,1265,1266,1267,1270],{},"The fundamental issue is that ",[44,1268,1269],{},"data gets written twice","---once to\nKafka's local disks for streaming consumers, and once to the lakehouse\nfor analytical consumers. This doubles your storage costs and introduces\nminutes of latency between when a record is produced to Kafka and when\nit's queryable in the lakehouse. For many use cases, that gap is the\ndifference between real-time and \"kind-of-real-time.\"",[40,1272,1274],{"id":1273},"ufk-storage-abstraction",[44,1275,1276],{},"UFK Storage Abstraction",[48,1278,1279],{},"At the core of UFK is a storage abstraction introduced at the replica\nmanagement layer. This is where Kafka decides how to persist data for\neach partition. In vanilla Kafka, this always means the local log\nmanager---write segment files to disk, manage indexes, handle retention.\nUFK introduces a branch point.",[48,1281,1282,1283,1286,1287,1297],{},"When a topic is created with ",[44,1284,1285],{},"ursa.storage.enable=true",", all storage\noperations for that topic route through Ursa Storage instead of the\nlocal log manager. The decision is per-topic, which means a ",[44,1288,1289,1290],{},"single UFK\ncluster can host both disk-based (latency-optimized profile) and\ndiskless (cost-optimized profile) topics simultaneously",[1291,1292,1293],"sup",{},[55,1294,1296],{"href":1295},"#notes","1",". This is\ncritical for incremental migration. You don't flip a switch and move\neverything at once---you migrate topic by topic, validating as you go.",[48,1299,1300],{},[351,1301],{"alt":18,"src":1302},"\u002Fimgs\u002Fblogs\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture-image1.png",[48,1304,1305],{},[36,1306,1307],{},"Figure 2. How we extend Kafka to support diskless topics using Ursa\nstorage",[32,1309,1311],{"id":1310},"topic-profiles-latency-optimized-and-cost-optimized",[44,1312,1313],{},"Topic Profiles: Latency-Optimized and Cost-Optimized",[48,1315,1316,1317,1321],{},"UFK supports two topic profiles in the same cluster",[1291,1318,1319],{},[55,1320,1296],{"href":1295},":",[339,1323,1324,1330],{},[342,1325,1326,1329],{},[44,1327,1328],{},"Latency-Optimized Profile (disk-based):"," Traditional local-disk\nstorage with single-digit milliseconds produces latency. Full\nKafka feature set---transactions, log compaction, Kafka Streams\nexactly-once semantics. Ideal for real-time transaction\nprocessing, fraud detection, and event-driven microservices where\nevery millisecond matters.",[342,1331,1332,1335],{},[44,1333,1334],{},"Cost-Optimized Profile (diskless\u002Flakehouse-native):","\nLakehouse-native storage via Ursa with sub-second produce latency\nand up to 95% cost reduction. Every topic is simultaneously a live\nevent stream and an Iceberg\u002FDelta table. Ideal for IoT telemetry,\nlakehouse pipelines, clickstream analytics, and high-volume\nstreaming where cost efficiency is the priority.",[48,1337,1338],{},"UFK supports traditional disk-based topics (latency-optimized profile)\nand diskless topics (cost-optimized profile) in the same cluster;\noperators choose per topic, enabling incremental migration without\nseparate clusters. The recommended adoption path: start with\nhigh-volume, latency-relaxed topics (your biggest cost drivers),\nvalidate the behavior, then expand to more topics at your own pace. No\nbig-bang migration required.",[48,1340,1341],{},"The rest of this series focuses primarily on Cost-Optimized (diskless)\ntopics---the architectural innovation that makes UFK distinctive.\nLatency-Optimized topics behave identically to traditional Apache Kafka.",[48,1343,1344],{},"The abstraction preserves Kafka's contract completely. Produce requests\nreturn offsets. Fetch requests return record batches. ListOffsets\nreturns the earliest and latest offsets. Every component above the\nstorage layer---the request handler, the group coordinator, KRaft\nmetadata management---is entirely unaware that the storage backend has\nchanged. This is what makes UFK a true Kafka fork rather than a\nreimplementation: the Kafka code that handles protocol details, group\nmanagement, and admin operations continues to run unchanged.",[40,1346,1348],{"id":1347},"ursa-storage-the-data-layer",[44,1349,1350],{},"Ursa Storage: The Data Layer",[48,1352,1353],{},[351,1354],{"alt":18,"src":1355},"\u002Fimgs\u002Fblogs\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture-image3.png",[48,1357,1358],{},[36,1359,1360],{},"Figure 3. UFK Architecture",[48,1362,1363],{},"Ursa Storage is an embedded Java library running within each broker\nprocess (not a separate system). It implements the Data Layer of the\nLakestream architecture. It is organized into three tiers, each with a\ndistinct role in the data lifecycle:",[48,1365,1366,1369],{},[44,1367,1368],{},"Storage API tier."," The top-level interface exposes operations for\nappend, read, trim, and lifecycle management. All operations are\nasynchronous, returning completable futures. Broker threads are never\nblocked waiting on remote I\u002FO---a critical design choice when your\nstorage backend is object storage with millisecond-scale latencies\ninstead of microsecond-scale local disk.",[48,1371,1372,1375],{},[44,1373,1374],{},"WAL tier (Write-Ahead Log)."," The write-ahead log tier lives on object\nstorage---S3, GCS, or Azure Blob Storage. WAL objects are optimized for\nwrite throughput rather than read efficiency. A single WAL object may\ncontain data from multiple topics, batched together to minimize the\nnumber of object storage API calls. This batching is essential for cost\ncontrol: object storage charges per API call, so fewer, larger writes\nare dramatically cheaper than many small ones.",[48,1377,1378,1381],{},[44,1379,1380],{},"Compacted Data tier."," A background compaction process continuously\nreorganizes WAL data into per-topic columnar Parquet files. These\ncompacted files enable column pruning, predicate pushdown, and high\ncompression ratios. This is where UFK eliminates the connector jungle:\nthe same data that serves streaming consumers is automatically organized\ninto the format the lakehouse expects. No Kafka Connect, no sink\nconnectors, no dual writes.",[40,1383,1385],{"id":1384},"lakestream-catalog",[44,1386,1175],{},[48,1388,1389,1390,1392,1393,1397,1398,1403],{},"The ",[44,1391,1175],{}," is the Metadata Layer of the Lakestream\narchitecture---the single source of truth for every stream's state. It\ntracks entry indexes (offset-to-physical-location mappings), stream\nproperties, compaction tasks, and offset assignment. The Lakestream\nCatalog is backed by ",[55,1394,430],{"href":1395,"rel":1396},"https:\u002F\u002Foxia-db.github.io\u002F",[264],", a\ndistributed key-value store purpose-built for metadata coordination. The\ndetailed design of Oxia, including its sharding model and consistency\nguarantees, is described in the ",[55,1399,1402],{"href":1400,"rel":1401},"https:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol18\u002Fp5184-guo.pdf",[264],"Ursa VLDB\npaper",".\nEvery storage operation in UFK involves two things: a data write to\nobject storage (Data Layer) and a metadata operation on the Lakestream\nCatalog.",[48,1405,1406],{},"The Lakestream Catalog tracks several critical pieces of state:",[339,1408,1409,1415,1421,1427],{},[342,1410,1411,1414],{},[44,1412,1413],{},"Entry indexes:"," the mapping from logical offset to physical\nlocation in object storage",[342,1416,1417,1420],{},[44,1418,1419],{},"Stream properties:"," topic and partition metadata",[342,1422,1423,1426],{},[44,1424,1425],{},"Compaction tasks:"," work items for the background compaction\nprocess",[342,1428,1429,1432],{},[44,1430,1431],{},"Offset assignment:"," the atomic generation of new offsets for\nincoming records",[48,1434,1435],{},"When a single WAL object contains data from multiple partitions, each\npartition receives its own independent metadata update in the Lakestream\nCatalog. This per-partition granularity means that offset assignment,\nindex creation, and compaction operations for one partition never block\nor interfere with another, enabling horizontal scaling of the metadata\nlayer alongside data operations. The catalog handles offset assignment\nthrough Oxia's atomic key operations, which is the mechanism that makes\nleaderless writes possible. More on that next.",[40,1437,1439],{"id":1438},"leaderless-broker-assignment",[44,1440,1441],{},"Leaderless Broker Assignment",[48,1443,1444],{},"In vanilla Kafka, the controller assigns a leader for every partition,\nbrokers register ISR changes, and clients discover leaders via metadata\nrequests. This leader-based model is essential when durability depends\non replication---the leader is the single source of truth for offset\nassignment, and the ISR mechanism ensures replicas stay consistent.",[48,1446,1447,1450],{},[44,1448,1449],{},"UFK eliminates the leader concept entirely for diskless partitions.","\nSince durability is handled by object storage (which provides its own\nreplication and durability guarantees), there's no need for broker-level\nreplication, and therefore no need for a leader to coordinate it.",[32,1452,1454],{"id":1453},"how-offset-assignment-works-without-a-leader",[44,1455,1456],{},"How Offset Assignment Works Without a Leader",[48,1458,1459],{},"Offset generation is handled by the Lakestream Catalog (backed by Oxia),\nnot by any broker. When a broker receives a produce request for a\ndiskless partition, it writes the data to object storage and then the\nLakestream Catalog atomically assigns the next offset (via Oxia's atomic\nkey operations). Multiple brokers can write to the same partition\nconcurrently---the Lakestream Catalog serializes offset assignment\nthrough Oxia's atomic key operations, ensuring that offsets are globally\nunique and monotonically increasing.",[48,1461,1462],{},"This is a fundamental architectural shift. In Kafka, the leader\nserializes writes. In UFK, the Lakestream Catalog serializes offset\nassignment while data writes happen in parallel. The broker is no longer\na bottleneck for any individual partition.",[32,1464,1466],{"id":1465},"partition-distribution",[44,1467,1468],{},"Partition Distribution",[48,1470,1471],{},"Because any broker can serve any partition, UFK distributes partitions\nacross all available brokers in the cluster. The current implementation\nuses a deterministic hash for this assignment, but the specific\nalgorithm is an implementation choice, not an architectural\nrequirement---it can support alternative strategies. No central\ncontroller or assignment protocol is needed: each broker can compute the\npartition mapping locally, and when the broker set changes, every node\nrecomputes it independently.",[32,1473,1475],{"id":1474},"zone-aware-routing",[44,1476,1477],{},"Zone-Aware Routing",[48,1479,1480,1481,1484],{},"Because any broker can serve any partition, UFK can make routing\ndecisions based on network topology rather than replica placement.\nSpecifically, ",[44,1482,1483],{},"UFK selects a broker in the client's local availability\nzone",". A producer running in us-east-1a sends to a broker in\nus-east-1a. No cross-AZ data transfer, no cross-AZ charges.",[48,1486,1487],{},"Compare this to vanilla Kafka, where a partition's leader might be in\nany AZ, and the producer has no choice but to send data wherever the\nleader happens to be. UFK's zone-aware routing directly addresses the\ncross-AZ cost problem described earlier.",[32,1489,1491],{"id":1490},"broker-scaling","Broker Scaling",[48,1493,1494],{},"When brokers are added or removed, the partition assignment naturally\nredistributes across the new broker set. Because no broker holds local\npartition state for diskless topics --- all data is in object storage\nand all metadata is in the Lakestream Catalog --- there is no data\nmigration, no partition reassignment delay, and no rebalancing. New\nbrokers begin serving immediately upon joining the cluster.",[48,1496,1497],{},"This is fundamentally different from vanilla Kafka, where adding a\nbroker triggers partition reassignment that can take hours for large\nclusters as data is copied between brokers. In UFK, the same scaling\noperation completes in seconds because brokers carry no local partition\ndata for cost-optimized topics.",[32,1499,1501],{"id":1500},"isr-is-gone",[44,1502,1503],{},"ISR Is Gone",[48,1505,1506,1507,1510],{},"For diskless partitions, ",[44,1508,1509],{},"the ISR mechanism is bypassed entirely",".\nThere are no ISR expansion or shrinkage events, no under-replicated\npartition alerts, and no preferred leader elections. The concept doesn't\napply---there are no replicas to track, because durability is provided\nby object storage, not by broker-level replication.",[48,1512,1513],{},"This eliminates an entire class of operational incidents. No more 3 AM\npages about under-replicated partitions. No more choosing between data\nloss and unavailability. The durability guarantee is delegated to S3 (or\nGCS, or Azure Blob), which provides eleven nines of durability without\nany operator intervention.",[48,1515,1516,1517,190],{},"This architecture---leaderless, diskless, lakehouse-native---was\nvalidated at ",[55,1518,1521],{"href":1519,"rel":1520},"https:\u002F\u002Fstreamnative.io\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[264],"5 GB\u002Fs sustained throughput in production\nbenchmarks",[40,1523,1525],{"id":1524},"failure-handling",[44,1526,1527],{},"Failure Handling",[48,1529,1530,1531,1534],{},"When a UFK broker fails, clients simply connect to another broker.\nThere's no partition reassignment to wait for, no local state to\nrecover, no ISR rebalancing. The new broker can serve the partition\nimmediately because ",[44,1532,1533],{},"all state lives in the Lakestream Catalog and\nobject storage","---the broker itself is stateless.",[48,1536,1537],{},"Think about what this means for operations. In vanilla Kafka, a broker\nfailure triggers a cascade: the controller reassigns leaders, followers\nneed to catch up, ISR sets need to stabilize, and until all of that\ncompletes, some partitions may be unavailable or degraded. In UFK, a\nbroker failure means clients reconnect to the next broker, and traffic\ncontinues. Recovery time is measured in seconds, not minutes.",[48,1539,1540,1541,1544,1545,1548],{},"The operational model shifts fundamentally: from ",[36,1542,1543],{},"\"monitor replica\nhealth across N brokers per partition\""," to ",[36,1546,1547],{},"\"ensure enough brokers are\nrunning to serve the workload.\""," The former requires deep Kafka\nexpertise and constant vigilance. The latter is standard capacity\nmanagement that any platform team already knows how to do.",[40,1550,1552],{"id":1551},"getting-to-ufk",[44,1553,1554],{},"Getting to UFK",[48,1556,1557],{},"UFK offers two paths for existing Kafka deployments:",[339,1559,1560,1566],{},[342,1561,1562,1565],{},[44,1563,1564],{},"In-place upgrade"," (for Kafka 4.0+ deployments): Because UFK is a\nfork of Apache Kafka --- not a reimplementation --- it supports\nin-place rolling upgrades. An operator can replace Kafka broker\nimages (e.g., apache-kafka:4.1.0 → ufk:4.2.0) in a standard\nrolling restart. All existing Kafka state is preserved: KRaft\nmetadata logs, local log segments, tiered storage data, consumer\ngroup offsets, and topic configurations. This has been validated\nwith the Strimzi Kubernetes operator. To enable diskless topics,\noperators must additionally deploy Oxia as the metadata store for\nUrsa Storage. Rolling back to vanilla Kafka is safe --- operators\nlose only the diskless topics created while running UFK; all\ndisk-based topics and cluster state are unaffected. Critically,\nexisting Kafka clients do not need to be upgraded or modified.",[342,1567,1568,1575],{},[44,1569,1570],{},[55,1571,1574],{"href":1572,"rel":1573},"https:\u002F\u002Fstreamnative.io\u002Fdata-streaming\u002Funiversal-linking",[264],"Universal\nLinking","\n(for any Kafka deployment): Zero-downtime migration from\nConfluent, Amazon MSK, Redpanda, or self-managed Kafka. Universal\nLinking replicates topics into UFK with continuous synchronization\nat the object storage layer---not through the Kafka\nprotocol---eliminating cross-AZ replication costs even during\nmigration.",[40,1577,1579],{"id":1578},"whats-next",[44,1580,1581],{},"What's Next",[48,1583,1584],{},"In this post, we've covered the structural problems with Kafka's storage\nand replication model and how UFK addresses them through its storage\nabstraction and leaderless broker architecture. The storage abstraction\nlets UFK extend local disks with object storage while preserving full\nKafka protocol compatibility. The leaderless design eliminates cross-AZ\nreplication, ISR management, and the operational complexity that comes\nwith leader-based coordination.",[48,1586,1587,1590],{},[44,1588,1589],{},"In Part 2",", we'll trace the complete data flow through UFK---the\nwrite path from producer to object storage, the read path from fetch\nrequest to record batch, and the compaction pipeline that transforms WAL\nobjects into queryable Parquet files. We'll show exactly how a produce\nrequest becomes a durable, queryable record without any connectors in\nbetween.",[48,1592,1593],{},[36,1594,1595],{},"Stay tuned.",[208,1597],{},[48,1599,1600],{},[55,1601],{"id":1602,"style":1603},"notes","display: block; scroll-margin-top: 50vh;",[48,1605,1606],{},[36,1607,1608],{},"Notes",[48,1610,1611],{},[36,1612,1613],{},"1. As part of Public Preview, StreamNative Cloud offers cost-optimized (diskless) and latency-optimized (disk-based) profiles at cluster-level. Topic-level storage profile can be configured via CLI.",{"title":18,"searchDepth":19,"depth":19,"links":1615},[1616,1617,1623,1626,1627,1628,1635,1636,1637],{"id":42,"depth":19,"text":46},{"id":1186,"depth":19,"text":1189,"children":1618},[1619,1620,1621,1622],{"id":1195,"depth":279,"text":1198},{"id":1219,"depth":279,"text":1222},{"id":1238,"depth":279,"text":1241},{"id":1254,"depth":279,"text":1257},{"id":1273,"depth":19,"text":1276,"children":1624},[1625],{"id":1310,"depth":279,"text":1313},{"id":1347,"depth":19,"text":1350},{"id":1384,"depth":19,"text":1175},{"id":1438,"depth":19,"text":1441,"children":1629},[1630,1631,1632,1633,1634],{"id":1453,"depth":279,"text":1456},{"id":1465,"depth":279,"text":1468},{"id":1474,"depth":279,"text":1477},{"id":1490,"depth":279,"text":1491},{"id":1500,"depth":279,"text":1503},{"id":1524,"depth":19,"text":1527},{"id":1551,"depth":19,"text":1554},{"id":1578,"depth":19,"text":1581},"2026-04-17","A deep dive into Kafka's structural cost and operational challenges, and how Ursa-For-Kafka's storage abstraction and leaderless broker design solve them with lakehouse-native storage.","\u002Fimgs\u002Fblogs\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture-cover.jpg",{},"\u002Fblog\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture",{"title":1088,"description":1639},"blog\u002Fursa-for-kafka-deep-dive-the-kafka-problem-and-ursas-storage-leaderless-architecture",[1084,379,1646],"Lakehouse","XXxNxKZyPgw-Uj6WsYdWJBgx4K0J_B6QPIUU1vH1GMY",[1649,1665],{"id":1650,"title":313,"bioSummary":1651,"email":289,"extension":8,"image":1652,"linkedinUrl":1653,"meta":1654,"position":1661,"stem":1662,"twitterUrl":1663,"__hash__":1664},"authors\u002Fauthors\u002Fpenghui-li.md","Penghui Li is passionate about helping organizations to architect and implement messaging services. Prior to StreamNative, Penghui was a Software Engineer at Zhaopin.com, where he was the leading Pulsar advocate and helped the company adopt and implement the technology. He is an Apache Pulsar Committer and PMC member.","\u002Fimgs\u002Fauthors\u002Fpenghui-li.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fpenghui-li-244173184\u002F",{"body":1655},{"type":15,"value":1656,"toc":1659},[1657],[48,1658,1651],{},{"title":18,"searchDepth":19,"depth":19,"links":1660},[],"Director of Streaming, StreamNative & Apache Pulsar PMC Member","authors\u002Fpenghui-li","https:\u002F\u002Ftwitter.com\u002Flipenghui6","WDjET7GfxqVQJ8mTEMaRhgpxRdDy18qZkgQDJlwjvbI",{"id":1666,"title":312,"bioSummary":1667,"email":289,"extension":8,"image":1668,"linkedinUrl":289,"meta":1669,"position":1676,"stem":1677,"twitterUrl":289,"__hash__":1678},"authors\u002Fauthors\u002Fhang.md","Hang Chen, an Apache Pulsar and BookKeeper PMC member, is Director of Storage at StreamNative, where he leads the design of next-generation storage architectures and Lakehouse integrations. His work delivers scalable, high-performance infrastructure powering modern cloud-native event streaming platforms.","\u002Fimgs\u002Fauthors\u002Fhang.webp",{"body":1670},{"type":15,"value":1671,"toc":1674},[1672],[48,1673,1667],{},{"title":18,"searchDepth":19,"depth":19,"links":1675},[],"Director of Storage, StreamNative & Apache Pulsar PMC Member","authors\u002Fhang","titaSDxZRJWAW0SkpJSq43NuDvps9XQ6gZIMSPCtUwo",[1680,1687,1692],{"path":1681,"title":1682,"date":6,"image":1683,"link":-1,"collection":1684,"resourceType":1685,"score":1686,"id":1681},"\u002Fblog\u002Fursa-for-kafka-native-apache-kafka-service-on-lakestream","Ursa For Kafka: Native Apache Kafka Service on Lakestream","\u002Fimgs\u002Fblogs\u002Fblog-thumbnail-ursa-for-kafka-native-apache-kafka-service-on-lakestream.png","blogs","Blog",0.75,{"path":1688,"title":1689,"date":1690,"image":1691,"link":-1,"collection":1684,"resourceType":1685,"score":1686,"id":1688},"\u002Fblog\u002Fursa-wins-vldb-2025-best-industry-paper-the-first-lakehouse-native-streaming-engine-for-kafka","Ursa Wins VLDB 2025 Best Industry Paper: The First Lakehouse-Native Streaming Engine for Kafka","2025-09-02","\u002Fimgs\u002Fblogs\u002F68b694de0660dd9d6a274f4c_VLDB-best-industry-paper.png",{"path":1693,"title":1694,"date":1695,"image":1696,"link":-1,"collection":1684,"resourceType":1685,"score":1686,"id":1693},"\u002Fblog\u002Fyou-dont-need-to-shift-everything-left-lakehouse-first-thinking-is-all-you-need","You Don’t Need to Shift Everything Left; Lakehouse-First Thinking is all you need","2025-02-11","\u002Fimgs\u002Fblogs\u002F67ab955476e915d375e54c34_image-78.png",1776749912816]