[{"data":1,"prerenderedAt":1551},["ShallowReactive",2],{"active-banners":3,"navbar-featured-partner-blog":39,"navbar-pricing-featured":320,"blog-\u002Fblog\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0":1100,"blog-authors-\u002Fblog\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0":1501,"related-\u002Fblog\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0":1533},[4,24],{"id":5,"title":6,"date":7,"dismissible":8,"extension":9,"link":10,"link2":11,"linkText":12,"linkText2":11,"meta":13,"stem":21,"variant":22,"__hash__":23},"banners\u002Fbanners\u002Fpulsar-5-early-access.md","Apache Pulsar 5.0 Early Access is now open. Be among the first to try the future of streaming.","2026-05-19",true,"md","\u002Fpulsar-5-early-access",null,"Request Early Access",{"body":14},{"type":15,"value":16,"toc":17},"minimark",[],{"title":18,"searchDepth":19,"depth":19,"links":20},"",2,[],"banners\u002Fpulsar-5-early-access","default","QpbcJgwHtUdscpRM47ASzz8-bdImBHAdr-NM-AOLGNg",{"id":25,"title":26,"date":27,"dismissible":8,"extension":9,"link":28,"link2":29,"linkText":30,"linkText2":31,"meta":32,"stem":37,"variant":22,"__hash__":38},"banners\u002Fbanners\u002Flakestream-ufk-launch.md","StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service","2026-04-07","\u002Fblog\u002Ffrom-streams-to-lakestreams","https:\u002F\u002Fconsole.streamnative.cloud\u002Fsignup?from=banner_lakestream-launch","Read Announcement","Sign Up Now",{"body":33},{"type":15,"value":34,"toc":35},[],{"title":18,"searchDepth":19,"depth":19,"links":36},[],"banners\u002Flakestream-ufk-launch","zRueBGutATZB0ZnFFHwaEV7F0Di4tnZUHhgOiI4cu6k",{"id":40,"title":41,"authors":42,"body":44,"canonicalUrl":11,"category":304,"createdAt":11,"date":305,"description":306,"extension":9,"featured":8,"image":307,"isDraft":308,"link":11,"meta":309,"navigation":8,"order":310,"path":311,"readingTime":312,"relatedResources":11,"seo":313,"stem":314,"tags":315,"__hash__":319},"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",[43],"David Kjerrumgaard",{"type":15,"value":45,"toc":291},[46,54,62,66,82,88,93,96,102,117,124,130,133,139,142,149,155,158,161,172,178,184,187,190,193,199,206,209,212,219,222,225,239,244,248,252,256,260,264,266,283,285],[47,48,50],"h3",{"id":49},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[51,52,53],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[55,56,58],"h2",{"id":57},"introduction",[59,60,61],"strong",{},"Introduction",[63,64,65],"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.",[63,67,68,69,78,79],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[70,71,73],"a",{"href":72},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[51,74,75],{},[59,76,77],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[59,80,81],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[63,83,84,85],{},"***Forrester's Take: ***",[51,86,87],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[63,89,90],{},[51,91,92],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[63,94,95],{},"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.",[55,97,99],{"id":98},"trusted-by-industry-leaders",[59,100,101],{},"Trusted by Industry Leaders",[63,103,104,105,108,109,112,113,116],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[59,106,107],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[59,110,111],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[59,114,115],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[63,118,119,120,123],{},"The Forrester report notes that, “",[51,121,122],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[55,125,127],{"id":126},"modern-cloud-native-architecture-built-for-scale",[59,128,129],{},"Modern, Cloud-Native Architecture Built for Scale",[63,131,132],{},"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.",[63,134,135,136,123],{},"Forrester's evaluation described that “",[51,137,138],{},"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.",[63,140,141],{},"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.",[63,143,144,145,148],{},"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 ",[51,146,147],{},"\"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.",[55,150,152],{"id":151},"open-source-foundation-and-pulsar-expertise",[59,153,154],{},"Open Source Foundation and Pulsar Expertise",[63,156,157],{},"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.",[63,159,160],{},"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.",[63,162,163,164,167,168,171],{},"Forrester's assessment noted that StreamNative’s “",[51,165,166],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[59,169,170],{},"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.",[63,173,174,175],{},"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 ",[51,176,177],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[55,179,181],{"id":180},"powering-real-time-use-cases-across-industries",[59,182,183],{},"Powering Real-Time Use Cases Across Industries",[63,185,186],{},"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.",[63,188,189],{},"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.",[63,191,192],{},"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.",[55,194,196],{"id":195},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[59,197,198],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[63,200,201,202,205],{},"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 ",[59,203,204],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[63,207,208],{},"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.",[63,210,211],{},"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.",[63,213,214,215,218],{},"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. ",[59,216,217],{},"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.",[63,220,221],{},"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!",[223,224],"hr",{},[47,226,228],{"id":227},"streamnative-in-the-forrester-wave-evaluation-findings",[59,229,230,231,238],{},"StreamNative in ",[59,232,233],{},[70,234,235],{"href":72},[59,236,237],{},"The Forrester Wave™",": Evaluation Findings",[240,241,243],"h5",{"id":242},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[240,245,247],{"id":246},"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",[240,249,251],{"id":250},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[240,253,255],{"id":254},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[240,257,259],{"id":258},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[240,261,263],{"id":262},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[223,265],{},[267,268,270,271,274,275,205],"h6",{"id":269},"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: **",[51,272,273],{},"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 *",[70,276,280],{"href":277,"rel":278},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[279],"nofollow",[51,281,282],{},"here",[223,284],{},[267,286,288],{"id":287},"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",[51,289,290],{},"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":292},[293,295,296,297,298,299,300],{"id":49,"depth":294,"text":53},3,{"id":57,"depth":19,"text":61},{"id":98,"depth":19,"text":101},{"id":126,"depth":19,"text":129},{"id":151,"depth":19,"text":154},{"id":180,"depth":19,"text":183},{"id":195,"depth":19,"text":198,"children":301},[302],{"id":227,"depth":294,"text":303},"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":41,"description":306},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[316,317,318],"Announcements","Real-Time","Forrester","5Nr1vAcqlQ7yFQfdL0a3MLsNFerVmEOQJXD9Twz5lx8",{"id":321,"title":322,"authors":323,"body":328,"canonicalUrl":11,"category":1087,"createdAt":11,"date":1088,"description":1089,"extension":9,"featured":8,"image":1090,"isDraft":308,"link":11,"meta":1091,"navigation":8,"order":310,"path":1092,"readingTime":1093,"relatedResources":11,"seo":1094,"stem":1095,"tags":1096,"__hash__":1099},"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",[324,325,326,327],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":15,"value":329,"toc":1057},[330,333,336,339,342,345,349,352,362,368,371,379,384,388,395,398,401,409,413,416,421,425,428,431,434,437,446,450,453,464,467,471,474,477,488,491,495,499,507,510,514,522,551,555,558,563,567,570,574,577,580,585,594,599,602,605,616,620,623,634,638,641,644,649,652,681,685,687,693,696,701,706,709,713,727,731,742,746,761,770,781,784,787,791,794,797,808,811,814,817,822,827,831,835,852,856,870,875,879,890,893,909,913,924,929,934,942,946,949,953,960,964,967,976,981,990,996,1005,1014,1023,1032,1041,1049],[63,331,332],{},"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.",[63,334,335],{},"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.",[63,337,338],{},"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.",[63,340,341],{},"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.",[63,343,344],{},"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.",[55,346,348],{"id":347},"key-benchmark-findings","Key Benchmark Findings",[63,350,351],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[353,354,355,359],"ul",{},[356,357,358],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[356,360,361],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[63,363,364],{},[365,366],"img",{"alt":18,"src":367},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[63,369,370],{},"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:",[353,372,373,376],{},[356,374,375],{},"50% cheaper than Confluent WarpStream",[356,377,378],{},"85% cheaper than MSK and Redpanda",[63,380,381],{},[365,382],{"alt":18,"src":383},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[55,385,387],{"id":386},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[63,389,390,394],{},[70,391,393],{"href":392},"\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.",[63,396,397],{},"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.",[63,399,400],{},"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:",[353,402,403,406],{},[356,404,405],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[356,407,408],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[55,410,412],{"id":411},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[63,414,415],{},"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.",[63,417,418],{},[365,419],{"alt":18,"src":420},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[47,422,424],{"id":423},"leaderless-architecture","Leaderless architecture",[63,426,427],{},"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.",[63,429,430],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[63,432,433],{},"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",[63,435,436],{},"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.",[63,438,439,440,445],{},"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 ",[70,441,444],{"href":442,"rel":443},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[279],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[47,447,449],{"id":448},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[63,451,452],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[353,454,455,458,461],{},[356,456,457],{},"Handles millions of metadata\u002Findex operations per second",[356,459,460],{},"Generates sequential IDs to maintain strict message ordering",[356,462,463],{},"Optimized for Kubernetes with horizontal scalability",[63,465,466],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[47,468,470],{"id":469},"zero-interzone-data-replication","Zero interzone data replication",[63,472,473],{},"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.",[63,475,476],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[353,478,479,482,485],{},[356,480,481],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[356,483,484],{},"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).",[356,486,487],{},"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.",[63,489,490],{},"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.",[55,492,494],{"id":493},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[47,496,498],{"id":497},"ursa-cluster-deployment","Ursa Cluster Deployment",[353,500,501,504],{},[356,502,503],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[356,505,506],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[63,508,509],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[47,511,513],{"id":512},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[63,515,516,517,521],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[70,518,519],{"href":519,"rel":520},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[279]," for details.",[353,523,524,539,548],{},[356,525,526,527,532,533,538],{},"12 OMB workers: 6 for ",[70,528,531],{"href":529,"rel":530},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[279],"producers",", 6 for ",[70,534,537],{"href":535,"rel":536},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[279],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[356,540,541,542,547],{},"Sample YAML ",[70,543,546],{"href":544,"rel":545},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[279],"scripts"," provided for Kafka-compatible configuration and rate limits.",[356,549,550],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[55,552,554],{"id":553},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[63,556,557],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[63,559,560],{},[365,561],{"alt":18,"src":562},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[55,564,566],{"id":565},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[63,568,569],{},"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.",[47,571,573],{"id":572},"test-setup-key-assumptions","Test Setup & Key Assumptions",[63,575,576],{},"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.",[63,578,579],{},"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:",[353,581,582],{},[356,583,584],{},"9 × m6i.8xlarge instances",[63,586,587,588,593],{},"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",[70,589,592],{"href":590,"rel":591},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[279]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[353,595,596],{},[356,597,598],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[63,600,601],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[63,603,604],{},"Additional key assumptions include:",[353,606,607,610,613],{},[356,608,609],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[356,611,612],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[356,614,615],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[47,617,619],{"id":618},"inter-broker-replication-costs","Inter-Broker Replication Costs",[63,621,622],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[353,624,625,628,631],{},[356,626,627],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[356,629,630],{},"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.",[356,632,633],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[47,635,637],{"id":636},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[63,639,640],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[63,642,643],{},"Consumers:",[353,645,646],{},[356,647,648],{},"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",[63,650,651],{},"Producers:",[353,653,654,663,672],{},[356,655,656,657,662],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[70,658,661],{"href":659,"rel":660},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[279],"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).",[356,664,665,666,671],{},"Redpanda recently introduced ",[70,667,670],{"href":668,"rel":669},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[279],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[356,673,674,675,680],{},"Ursa is the only system in this test with ",[70,676,679],{"href":677,"rel":678},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[279],"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.",[47,682,684],{"id":683},"cost-comparison-results","Cost Comparison Results",[63,686,351],{},[353,688,689,691],{},[356,690,358],{},[356,692,361],{},[63,694,695],{},"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.",[63,697,698],{},[365,699],{"alt":18,"src":700},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[63,702,703],{},[365,704],{"alt":18,"src":705},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[63,707,708],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[47,710,712],{"id":711},"streamnative-ursa","StreamNative - Ursa",[353,714,715,718,721,724],{},[356,716,717],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[356,719,720],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[356,722,723],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[356,725,726],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[47,728,730],{"id":729},"aws-msk","AWS MSK",[353,732,733,736,739],{},[356,734,735],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[356,737,738],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[356,740,741],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[47,743,745],{"id":744},"redpanda","RedPanda",[353,747,748,750,752,755,758],{},[356,749,717],{},[356,751,720],{},[356,753,754],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[356,756,757],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[356,759,760],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[63,762,763,764,769],{},"Please note that we were unable to test ",[70,765,768],{"href":766,"rel":767},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[279],"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.",[353,771,772,778],{},[356,773,774,777],{},[70,775,661],{"href":659,"rel":776},[279]," (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).",[356,779,780],{},"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.",[63,782,783],{},"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.",[63,785,786],{},"We may revisit this comparison as more details become available.",[55,788,790],{"id":789},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[63,792,793],{},"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.",[63,795,796],{},"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:",[353,798,799,802,805],{},[356,800,801],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[356,803,804],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[356,806,807],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[63,809,810],{},"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.",[63,812,813],{},"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.",[63,815,816],{},"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.",[63,818,819],{},[365,820],{"alt":18,"src":821},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[63,823,824],{},[365,825],{"alt":18,"src":826},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[47,828,830],{"id":829},"cost-breakdown","Cost Breakdown",[832,833,834],"h4",{"id":711},"StreamNative – Ursa",[353,836,837,840,843,846,849],{},[356,838,839],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[356,841,842],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[356,844,845],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[356,847,848],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[356,850,851],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[832,853,855],{"id":854},"warpstream","WarpStream",[353,857,858,861],{},[356,859,860],{},"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.",[356,862,863,864,869],{},"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 “",[70,865,868],{"href":866,"rel":867},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[279],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[63,871,872],{},[365,873],{"alt":18,"src":874},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[832,876,878],{"id":877},"msk","MSK",[353,880,881,884,887],{},[356,882,883],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[356,885,886],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[356,888,889],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[832,891,745],{"id":892},"redpanda-1",[353,894,895,898,900,903,906],{},[356,896,897],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[356,899,886],{},[356,901,902],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[356,904,905],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[356,907,908],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[832,910,912],{"id":911},"additional-notes","Additional Notes",[353,914,915],{},[356,916,917,918,923],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[70,919,922],{"href":920,"rel":921},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[279],"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.",[63,925,926],{},[365,927],{"alt":18,"src":928},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[63,930,931],{},[365,932],{"alt":18,"src":933},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[353,935,936,939],{},[356,937,938],{},"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.",[356,940,941],{},"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.",[55,943,945],{"id":944},"conclusion","Conclusion",[63,947,948],{},"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.",[47,950,952],{"id":951},"balancing-latency-and-cost","Balancing Latency and Cost",[63,954,955,959],{},[70,956,958],{"href":957},"\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.",[47,961,963],{"id":962},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[63,965,966],{},"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.",[63,968,969,970,975],{},"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. ",[70,971,974],{"href":972,"rel":973},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[279],"Get started"," with StreamNative Ursa today!",[977,978,980],"h1",{"id":979},"references","References",[63,982,983,986,987],{},[984,985,444],"span",{}," ",[70,988,989],{"href":989},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[63,991,992,986,994],{},[984,993,393],{},[70,995,392],{"href":392},[63,997,998,986,1001],{},[984,999,1000],{},"StreamNative pricing",[70,1002,1003],{"href":1003,"rel":1004},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[279],[63,1006,1007,986,1010],{},[984,1008,1009],{},"WarpStream pricing",[70,1011,1012],{"href":1012,"rel":1013},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[279],[63,1015,1016,986,1019],{},[984,1017,1018],{},"AWS S3 pricing",[70,1020,1021],{"href":1021,"rel":1022},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[279],[63,1024,1025,986,1028],{},[984,1026,1027],{},"AWS EBS pricing",[70,1029,1030],{"href":1030,"rel":1031},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[279],[63,1033,1034,986,1037],{},[984,1035,1036],{},"AWS MSK pricing",[70,1038,1039],{"href":1039,"rel":1040},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[279],[63,1042,1043,986,1046],{},[984,1044,1045],{},"The Brutal Truth about Kafka Cost Calculators",[70,1047,866],{"href":866,"rel":1048},[279],[63,1050,1051,986,1054],{},[984,1052,1053],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[70,1055,920],{"href":920,"rel":1056},[279],{"title":18,"searchDepth":19,"depth":19,"links":1058},[1059,1060,1061,1066,1070,1071,1080,1083],{"id":347,"depth":19,"text":348},{"id":386,"depth":19,"text":387},{"id":411,"depth":19,"text":412,"children":1062},[1063,1064,1065],{"id":423,"depth":294,"text":424},{"id":448,"depth":294,"text":449},{"id":469,"depth":294,"text":470},{"id":493,"depth":19,"text":494,"children":1067},[1068,1069],{"id":497,"depth":294,"text":498},{"id":512,"depth":294,"text":513},{"id":553,"depth":19,"text":554},{"id":565,"depth":19,"text":566,"children":1072},[1073,1074,1075,1076,1077,1078,1079],{"id":572,"depth":294,"text":573},{"id":618,"depth":294,"text":619},{"id":636,"depth":294,"text":637},{"id":683,"depth":294,"text":684},{"id":711,"depth":294,"text":712},{"id":729,"depth":294,"text":730},{"id":744,"depth":294,"text":745},{"id":789,"depth":19,"text":790,"children":1081},[1082],{"id":829,"depth":294,"text":830},{"id":944,"depth":19,"text":945,"children":1084},[1085,1086],{"id":951,"depth":294,"text":952},{"id":962,"depth":294,"text":963},"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":322,"description":1089},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1097,1098,317],"TCO","Apache Kafka","CDUawvFKTs_AD8usvmIcTleU3mbfA0QAoPZM6xfVuo8",{"id":1101,"title":1102,"authors":1103,"body":1106,"canonicalUrl":11,"category":1490,"createdAt":11,"date":1491,"description":1492,"extension":9,"featured":308,"image":1493,"isDraft":308,"link":11,"meta":1494,"navigation":8,"order":310,"path":1495,"readingTime":11,"relatedResources":11,"seo":1496,"stem":1497,"tags":1498,"__hash__":1500},"blogs\u002Fblog\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0.md","Introducing Scalable Topics in Apache Pulsar 5.0",[1104,1105],"Matteo Merli","Sijie Guo",{"type":15,"value":1107,"toc":1473},[1108,1134,1137,1141,1144,1147,1153,1159,1165,1169,1172,1176,1179,1183,1190,1193,1196,1200,1203,1223,1226,1229,1233,1236,1239,1242,1246,1249,1259,1265,1271,1277,1283,1289,1295,1298,1302,1305,1308,1311,1320,1324,1327,1341,1344,1347,1371,1375,1378,1404,1407,1410,1414,1418,1421,1424,1428,1435,1438,1453],[63,1109,1110,1111,1114,1115,1133],{},"Apache Pulsar 5.0 introduces a new kind of topic: a ",[59,1112,1113],{},"Scalable Topic",". It scales transparently through range splits and merges, preserves key ordering across topology changes, and lives behind a type-safe client API that replaces the decade-old Consumer\u003CT> interface with three purpose-named consumers. It is being delivered across three Pulsar Improvement Proposals — ",[59,1116,1117,1122,1123,1122,1128],{},[70,1118,1121],{"href":1119,"rel":1120},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fblob\u002Fmaster\u002Fpip\u002Fpip-460.md",[279],"PIP-460",", ",[70,1124,1127],{"href":1125,"rel":1126},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fblob\u002Fmaster\u002Fpip\u002Fpip-466.md",[279],"PIP-466",[70,1129,1132],{"href":1130,"rel":1131},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fblob\u002Fmaster\u002Fpip\u002Fpip-468.md",[279],"PIP-468"," — and it composes on top of the existing Pulsar broker, managed ledger, and BookKeeper stack.",[63,1135,1136],{},"This post is the announcement. The two goals are to explain, concretely, what the scalable topic model is and what it isn't; and to set the scope honestly — what lands in 5.0, what's deferred, and how teams already running Pulsar can think about adoption.",[55,1138,1140],{"id":1139},"why-a-new-topic-type","Why a new topic type",[63,1142,1143],{},"The partitioned-log model has shaped streaming infrastructure for the last decade. Apache Kafka popularized it. Apache Pulsar inherited it. Most systems that came after — including the ones running at hyperscale inside the largest companies — built on the same foundation. The model is well understood, broadly deployed, and has carried the industry a long way.",[63,1145,1146],{},"It has also accumulated three structural problems that get worse at scale, and they are not Pulsar-specific. Any system built on partitioned logs inherits them.",[63,1148,1149,1152],{},[59,1150,1151],{},"Key ordering breaks when partition count changes."," Partitioned topics route keyed messages using modulo hashing: hash(key) % numPartitions. When an operator increases the partition count — say from 4 to 8 — the modulo mapping changes completely. A key previously routed to partition 2 may now hash to partition 6. Messages produced after the change land on a different partition than messages produced before. For any application relying on per-key ordering — session aggregation, stateful stream processing, deduplication, anything with keyed state — this silently breaks the ordering contract for all in-flight traffic. The only safe remedy is to drain the old topic before switching, which requires downtime. This is true in Kafka. It is true in Pulsar's partitioned topics today. It is true in every system that uses modulo routing to map keys to partitions.",[63,1154,1155,1158],{},[59,1156,1157],{},"Partitions never decrease."," Once a topic is scaled up to 64 partitions, it stays at 64 partitions forever, even if traffic drops to a level where 4 would suffice. Decreasing the count would require rerouting keys (breaking ordering), migrating unconsumed data from removed partitions, and coordinating the change across every active consumer. No production-grade mechanism for this exists in any major partitioned-log system. The practical result is partition-count drift: topics accumulate more partitions than they need, wasting broker resources, metadata store entries, and consumer connections, across every cluster in the industry.",[63,1160,1161,1164],{},[59,1162,1163],{},"The cost of getting the partition count wrong is permanent."," Because partitions cannot shrink and growing them breaks ordering, operators must predict the right count at topic creation. Over-provisioning wastes resources. Under-provisioning produces write-hot partitions that eventually require re-creating the topic with a larger count — and a synchronized cutover. Neither outcome is acceptable for infrastructure that is supposed to be operationally simple at scale.",[55,1166,1168],{"id":1167},"why-a-new-client-api","Why a new client API",[63,1170,1171],{},"The subscription side of the API has a related structural problem, with Pulsar's existing Consumer\u003CT> being a clear example. A single consumer interface exposes operations that are meaningful for some subscription types and silent no-ops for others. Cumulative acks only make sense for ordered consumption. Negative acks only make sense for shared consumption. Transactions, dead-letter queues, and individual acks behave differently per subscription type. The subscriptionType enum hides these differences at the interface level, and invalid combinations become runtime bugs instead of compile errors. Every team that onboards Pulsar eventually asks the same question in an architecture review: \"Which subscription type should we use here, and why?\" The answer has never been obvious from the API.",[55,1173,1175],{"id":1174},"the-design-in-three-parts","The design, in three parts",[63,1177,1178],{},"Pulsar 5.0 addresses both problems in a coordinated way. The topic-scaling problem is addressed by PIP-460. The subscription-interface problem is addressed by PIP-466. They were designed together because the two concerns compose — a new topic type is the right moment to revisit the client API, and vice versa. And because the partitioned-log limitations are industry-wide, the design Pulsar is shipping in 5.0 is a meaningful step forward for the open-source streaming community as a whole — built in the open, under Apache governance, and reviewable as it lands.",[47,1180,1182],{"id":1181},"_1-range-based-routing-and-the-segment-dag-pip-460","1. Range-based routing and the segment DAG (PIP-460)",[63,1184,1185,1186,1189],{},"A Scalable Topic is a dynamic directed acyclic graph of ",[59,1187,1188],{},"range segments",". Each range segment owns a contiguous slice of the keyspace — a hash range — and is backed by exactly one managed ledger on one broker. At produce time, a keyed message is routed to the active segment whose hash range covers the message's key hash. This is a stable routing decision: when a segment splits into two children, each child inherits a sub-range of the parent's keyspace, and the keys a producer was routing into the parent continue to route into the correct child. Modulo arithmetic disappears.",[63,1191,1192],{},"A range segment can be split either automatically (e.g., when it becomes a write bottleneck) or manually through the admin API. The split proceeds in three steps: the current segment is sealed with a termination marker, two child segments are created with defined sub-ranges, and the updated DAG is pushed to all connected clients through a persistent watch session. Producers begin routing to the appropriate child. The consumer that was reading from the parent is preferentially assigned to one of the children to minimize rebalancing. Merging adjacent segments is supported symmetrically, with cross-broker coordination when the segments live on different brokers.",[63,1194,1195],{},"The DAG itself encodes a strict happens-before relationship: all messages in a parent segment precede messages in any of its children. A consumer that is catching up through a topology change traverses the DAG in that order, so per-key processing order is preserved without any special logic in the application.",[47,1197,1199],{"id":1198},"_2-a-type-safe-client-api-pip-466","2. A type-safe client API (PIP-466)",[63,1201,1202],{},"Alongside the new topic type, Pulsar 5.0 introduces a new client API namespace (org.apache.pulsar.client.api.v5) with three separate, type-safe consumer interfaces:",[353,1204,1205,1211,1217],{},[356,1206,1207,1210],{},[59,1208,1209],{},"StreamConsumer"," registers with the consumer controller, receives exclusive segment assignments, and processes messages in key order within each assigned segment. It uses cumulative acknowledgements. This is the replacement for today's Exclusive and Failover subscriptions.",[356,1212,1213,1216],{},[59,1214,1215],{},"QueueConsumer"," subscribes to all active segments with shared dispatch, no controller interaction, individual acknowledgements, negative acks, dead-letter queues, and the full semantics teams have been using via the Shared subscription for years. It is the direct descendant of Pulsar's long-standing queue support.",[356,1218,1219,1222],{},[59,1220,1221],{},"CheckpointConsumer"," registers with the controller and receives segment assignments like the StreamConsumer, but tracks read positions externally via a serializable Checkpoint snapshot rather than broker-side cursors. It is designed for stream processing frameworks — Flink, Beam, Spark — that manage their own state and need restorable read positions across topology changes.",[63,1224,1225],{},"Each interface exposes only the operations valid for its pattern. A StreamConsumer builder does not expose dead-letter-queue configuration. A QueueConsumer builder does not expose key-ordered positioning. Invalid combinations become compile errors, not runtime surprises.",[63,1227,1228],{},"The existing Consumer\u003CT> API remains available in Pulsar 5.0 and is not deprecated. Teams running on existing Pulsar topics can continue using it indefinitely. Deprecation is planned for Pulsar 6.0 LTS with removal in Pulsar 7.0 LTS — a multi-year runway.",[47,1230,1232],{"id":1231},"_3-the-consumer-controller-pip-468","3. The consumer controller (PIP-468)",[63,1234,1235],{},"StreamConsumer and CheckpointConsumer require coordinated segment assignment across a dynamic topology. A broker is elected as the controller for a given subscription, using leader election in the metadata store. Consumers register with the controller through a persistent bidirectional stream, identified by a stable consumer identity — a name chosen by the client, not a connection-scoped UUID. The controller pushes segment assignments through this stream; the consumer reports back as it finishes segments.",[63,1237,1238],{},"If a consumer disconnects because of a transient network issue or a client restart, its segment assignments are held in reserve for a grace period (default ~1 minute, configurable). If it reconnects with the same identity before the grace period expires, the session resumes with its previous assignments unchanged. If it doesn't reconnect, the segments are redistributed to the remaining consumers. The controller persists the hash range assignment state, so resuming from a failure or broker restart allows the consumer to continue key ordered processing with the same assigned hash.",[63,1240,1241],{},"This is the part of the design that most directly addresses the operational pain of today's rebalance protocols. A blip no longer triggers a redistribution. Graceful restarts don't need to coordinate with the rebalance mechanism. The session is the unit of consumer identity, not the connection.",[47,1243,1245],{"id":1244},"what-this-unlocks","What this unlocks",[63,1247,1248],{},"The design choices above translate into a concrete set of capabilities that no Apache-governed open-source streaming system has offered before. For teams running Pulsar today, and for teams evaluating their streaming platform more broadly, these are the practical wins:",[63,1250,1251,1254,1255,1258],{},[59,1252,1253],{},"Elastic capacity in both directions."," Scalable Topics can grow ",[51,1256,1257],{},"and shrink"," in response to load. Range segments split when a partition becomes a write bottleneck and merge when traffic drops. Operators no longer have to over-provision at topic creation to avoid the permanence of a wrong choice. This is the single biggest operational shift, and it is the one capability the partitioned-log model has never offered.",[63,1260,1261,1264],{},[59,1262,1263],{},"Per-key ordering is preserved across every topology change."," Splits and merges produce children that inherit a sub-range of the parent's keyspace, and the segment DAG encodes a strict happens-before relationship between parent and children. Stateful applications — session aggregation, deduplication, keyed stream processing — get their ordering contract honored across the resize, with no application-level logic and no drain-and-cutover.",[63,1266,1267,1270],{},[59,1268,1269],{},"Right-size topics dynamically."," Partition counts no longer need to be predicted at creation time and lived with forever. Topics adjust to actual load. The cost of getting the initial sizing wrong drops from \"permanent\" to \"self-correcting.\"",[63,1272,1273,1276],{},[59,1274,1275],{},"Type-safe consumer interfaces."," StreamConsumer, QueueConsumer, and CheckpointConsumer each expose only the operations valid for their pattern. The runtime surprises that come from mismatched subscription types and configuration flags become compile errors. New team members can read the API and know which interface fits their use case.",[63,1278,1279,1282],{},[59,1280,1281],{},"Session-based consumer identity."," The consumer controller treats the session — a stable client-chosen identity — as the unit of consumer membership, not the network connection. Transient disconnects, graceful restarts, and short network blips no longer trigger a full rebalance. The session resumes with its previous assignments. This directly addresses one of the most consistent operational complaints across every streaming system.",[63,1284,1285,1288],{},[59,1286,1287],{},"First-class support for stream processing."," CheckpointConsumer exposes restorable, serializable read positions across topology changes, designed for the way Flink, Beam, and Spark already manage state. Stream processing frameworks no longer have to work around broker-side cursor semantics; they get a primitive built for their access pattern.",[63,1290,1291,1294],{},[59,1292,1293],{},"Not a rewrite of Pulsar."," Scalable Topics layer onto the existing proven Pulsar broker, managed ledger, and BookKeeper stack. Teams adopting them stay on the same storage, the same operational tooling, the same deployment topology. This is what the next section gets into in detail, and it is the property that makes adoption a code change rather than an infrastructure project.",[63,1296,1297],{},"Taken together, these are not incremental improvements on the partitioned-log model. They are the properties partitioned logs have not been able to offer, delivered through Apache governance in an open PIP process, on top of a proven storage layer that thousands of organizations are already running in production.",[55,1299,1301],{"id":1300},"_98-of-the-existing-pulsar-system-is-reused","~98% of the existing Pulsar system is reused",[63,1303,1304],{},"This is the part of the design we want to emphasize, because it shapes how teams should think about adopting the scalable topic model.",[63,1306,1307],{},"BookKeeper is unchanged. Managed ledgers are unchanged. The broker's core storage path is unchanged. The existing subscription cursors, the schema registry, transaction support, tiered storage offloaders, and geo-replication infrastructure all continue to operate on existing topics as they do today. PIP-460 adds a new coordination layer — the segment DAG, the consumer controller, the new client API — but it adds it on top of the same proven stack. The numbers vary by module, but across the client SDK, roughly 98% of the existing code is reused.",[63,1309,1310],{},"The practical consequence matters for adoption. Moving an existing topic to a scalable topic is a short code change for most applications: switch the topic URL scheme, migrate to the new consumer interface, and the application runs against the same broker running on the same BookKeeper cluster. This is not a rewrite. And for teams not ready to migrate yet, existing partitioned and non-partitioned topics keep working exactly as they do today.",[63,1312,1313,1314,1319],{},"Pulsar 5.0 will include migration tooling for converting existing topics to scalable topics (",[70,1315,1318],{"href":1316,"rel":1317},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fblob\u002Fmaster\u002Fpip\u002Fpip-475.md",[279],"PIP-475","). It defines a single atomic, one-way admin command — migrate-to-scalable — that flips an existing partitioned or non-partitioned topic into a scalable topic in place, with no data copy and no cursor migration. The V5 SDK interoperates with un-migrated regular topics from day one, so applications can upgrade at their own pace, and the migration moment stays small and surgical once every client on a topic is on V5. Larger-scale operational concerns — automated fleet-wide rolling migration, and cross-cluster (geo-replication) migration coordination — remain deferred to Phase 4 (Pulsar 5.1 and beyond).",[55,1321,1323],{"id":1322},"credit-where-its-due","Credit where it's due",[63,1325,1326],{},"The range-segment model in PIP-460 is not a new idea in the industry. Two internal systems pioneered it:",[353,1328,1329,1335],{},[356,1330,1331,1334],{},[59,1332,1333],{},"Pravega"," (Dell\u002FEMC, 2017, CNCF) introduced segment-based streams with SLO-driven automatic splitting, Reader Groups that coordinate segment assignments across readers, and Checkpoints as serializable restore points for stream processing. The Pulsar CheckpointConsumer descends directly from the Pravega Checkpoint + Reader Group model.",[356,1336,1337,1340],{},[59,1338,1339],{},"LinkedIn Northguard"," (announced June 2025) replaced Kafka inside LinkedIn at a scale of 32 trillion records per day across 400,000 topics. Northguard's data model — records → segments → ranges → topics — and its buddy-algorithm for splits and merges have impacted the PIP-460's segment DAG design.",[63,1342,1343],{},"PIP-460 credits both explicitly, and adopts specific design choices from each. The buddy-split constraint, in particular, comes from Northguard: a range segment can only be merged with its unique buddy, which makes the parent-child happens-before relationship a strict partial order. That strictness is what makes catch-up reads correct across topology changes.",[63,1345,1346],{},"The difference between PIP-460 and its predecessors is not the model but the implementation path. Pravega and Northguard both built new storage stacks. PIP-460 layers the same pattern on top of the existing Pulsar broker and BookKeeper infrastructure, with the ~98% SDK reuse that comes from composing on a proven system. Different tradeoffs, arriving at a similar design for similar reasons.",[63,1348,1349,1350,1357,1358,1365,1366,1370],{},"Inside Pulsar itself, Scalable Topics builds on two earlier PIPs: ",[59,1351,1352],{},[70,1353,1356],{"href":1354,"rel":1355},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fblob\u002Fmaster\u002Fpip\u002Fpip-379.md",[279],"PIP-379 (Key_Shared Draining Hashes)"," contributes the per-key ordering mechanics used inside a single range segment, and ",[59,1359,1360],{},[70,1361,1364],{"href":1362,"rel":1363},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar\u002Fblob\u002Fmaster\u002Fpip\u002Fpip-335.md",[279],"PIP-335 (Oxia metadata store)"," contributes the streaming watch sessions that make server-pushed topology updates practical without polling. ",[70,1367,444],{"href":1368,"rel":1369},"https:\u002F\u002Fgithub.com\u002Foxia-db\u002Foxia",[279]," is a robust, scalable metadata store and coordination system designed for large-scale distributed systems, with built-in support for stream index storage to optimize real-time data management. It is fully open source and licensed under Apache 2.0 license. Oxia replaces Zookeeper in Pulsar 5.0 as the default metadata store and coordination system.",[55,1372,1374],{"id":1373},"what-ships-and-whats-deferred","What ships, and what's deferred",[63,1376,1377],{},"The Pulsar 5.0 work arrives in phases, so teams can adopt incrementally:",[353,1379,1380,1386,1392,1398],{},[356,1381,1382,1385],{},[59,1383,1384],{},"Phase 1 (Pulsar 5.0.0-M1):"," New client API (PIP-466), range segment abstraction, a basic scalable topic with a single segment, and manual splitting via the admin API. Validates the storage and metadata model end-to-end.",[356,1387,1388,1391],{},[59,1389,1390],{},"Phase 2 (Pulsar 5.0.0-M2):"," Consumer controller (PIP-468), stream consumer rebalancing on segment changes, I\u002FO-threshold auto-split, and the QueueConsumer model. Limited geo-replication support.",[356,1393,1394,1397],{},[59,1395,1396],{},"Phase 3 (Pulsar 5.0.0 GA, targeted for September\u002FOctober 2026):"," Range merging, finalized client API, CheckpointConsumer, all three consumer types production-ready.",[356,1399,1400,1403],{},[59,1401,1402],{},"Phase 4 (Pulsar 5.0.1 and later):"," Replicated Subscriptions across scalable topics, and full geo-replication.",[63,1405,1406],{},"The deferred items are important and none of them is easy. Replicated Subscriptions require a new model for tracking subscription positions across independently-evolving topology DAGs in different clusters. Geo-replication requires the new message entry format to support broker-level routing without decompressing payloads. Each of these will be a dedicated sub-PIP with its own design document, its own community review, and its own release timing.",[63,1408,1409],{},"We want to be explicit about this up front. A phased delivery lets the community validate the primitives in 5.0.0 milestone releases before the 5.0 LTS commitment. It also means some things that today's Pulsar does well — geo-replicated subscriptions, atomic transactions spanning multiple partitions — will take additional releases to reach the same capability level for scalable topics. Applications that depend on those features today can continue using partitioned topics for as long as they need.",[55,1411,1413],{"id":1412},"a-note-on-applicability-beyond-pulsar","A note on applicability beyond Pulsar",[47,1415,1417],{"id":1416},"a-pulsar-first-innovation-with-room-to-grow","A Pulsar-first innovation, with room to grow",[63,1419,1420],{},"Scalable Topics are landing in Apache Pulsar 5.0, and Pulsar is where the model is being proven. The reason this matters beyond Pulsar is that the coordinator layer in the design — the segment DAG, the leader-elected controller, the persistent session model with grace-period lease — is defined at the protocol level, not bound to Pulsar-specific storage. That is an intentional design property. It means the primitives the community is building and reviewing in the open today have the structural shape to support other surfaces later, with different storage adapters underneath.",[63,1422,1423],{},"We mention this so the door stays open. It is not the focus of this launch. Pulsar 5.0 is where Scalable Topics ship, where they get production-validated, and where the community shapes them between now and GA. Any broader cross-protocol conversation is downstream of that work, not parallel to it.",[55,1425,1427],{"id":1426},"whats-in-the-rest-of-the-series","What's in the rest of the series",[63,1429,1430,1431,1434],{},"We have a few blog posts planned for diving deeper into the implementation of Scalable Topics, including but not limited to ",[51,1432,1433],{},"Range-Based Routing and the Segment DAG",", the new client API, the consumer controller and a discussion on generic architecture and what scalable topic unlocks next.",[63,1436,1437],{},"The three PIPs are the authoritative technical source. Each post in the series will link back to the specific PIP sections it draws from. We are writing these posts because the PIPs are dense, and the community benefits from explanations that meet different readers at different levels of detail. If you find discrepancies between a post and a PIP, trust the PIP.",[63,1439,1440,1441,1446,1447,1452],{},"The work is happening in public, on the Apache Pulsar ",[70,1442,1445],{"href":1443,"rel":1444},"https:\u002F\u002Fpulsar.apache.org\u002Fcontact\u002F#mailing-lists",[279],"dev mailing list"," and in the ",[70,1448,1451],{"href":1449,"rel":1450},"https:\u002F\u002Fgithub.com\u002Fapache\u002Fpulsar",[279],"apache\u002Fpulsar repository",". Issues, comments, and review on the PIPs are welcome. The best-case outcome of this launch is that the community shapes the implementation between now and the 5.0 GA target, rather than reacting to it after the fact.",[63,1454,1455],{},[59,1456,1457,1458,1463,1464,1122,1467,1122,1470,205],{},"Subscribe on ",[70,1459,1462],{"href":1460,"rel":1461},"https:\u002F\u002Fstreamnative.io\u002Fblog",[279],"streamnative.io\u002Fblog"," to get each post as it ships. The three PIPs: ",[70,1465,1121],{"href":1119,"rel":1466},[279],[70,1468,1127],{"href":1125,"rel":1469},[279],[70,1471,1132],{"href":1130,"rel":1472},[279],{"title":18,"searchDepth":19,"depth":19,"links":1474},[1475,1476,1477,1483,1484,1485,1486,1489],{"id":1139,"depth":19,"text":1140},{"id":1167,"depth":19,"text":1168},{"id":1174,"depth":19,"text":1175,"children":1478},[1479,1480,1481,1482],{"id":1181,"depth":294,"text":1182},{"id":1198,"depth":294,"text":1199},{"id":1231,"depth":294,"text":1232},{"id":1244,"depth":294,"text":1245},{"id":1300,"depth":19,"text":1301},{"id":1322,"depth":19,"text":1323},{"id":1373,"depth":19,"text":1374},{"id":1412,"depth":19,"text":1413,"children":1487},[1488],{"id":1416,"depth":294,"text":1417},{"id":1426,"depth":19,"text":1427},"Apache Pulsar","2026-05-27","Apache Pulsar 5.0 introduces Scalable Topics — elastic range-based partitioning that preserves key ordering and ships with a type-safe client API.","\u002Fimgs\u002Fblogs\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0-cover.png",{},"\u002Fblog\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0",{"title":1102,"description":1492},"blog\u002Fintroducing-scalable-topics-in-apache-pulsar-5-0",[1490,316,1499],"Thought Leadership","ZOU95h-9OXOvQKz04OFBGB-mFzhjYjemXVluEGj40fY",[1502,1517],{"id":1503,"title":1104,"bioSummary":1504,"email":11,"extension":9,"image":1505,"linkedinUrl":1506,"meta":1507,"position":1514,"stem":1515,"twitterUrl":11,"__hash__":1516},"authors\u002Fauthors\u002Fmatteo-merli.md","Matteo is the CTO at StreamNative, where he brings rich experience in distributed pub-sub messaging platforms. Matteo was one of the co-creators of Apache Pulsar during his time at Yahoo!. Matteo worked to create a global, distributed messaging system for Yahoo!, which would later become Apache Pulsar. Matteo is the PMC Chair of Apache Pulsar, where he helps to guide the community and ensure the success of the Pulsar project. He is also a PMC member for Apache BookKeeper. Matteo lives in Menlo Park, California.","\u002Fimgs\u002Fauthors\u002Fmatteo-merli.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmatteomerli\u002F",{"body":1508},{"type":15,"value":1509,"toc":1512},[1510],[63,1511,1504],{},{"title":18,"searchDepth":19,"depth":19,"links":1513},[],"CTO, StreamNative & Co-Creator and PMC Chair Apache Pulsar","authors\u002Fmatteo-merli","MRLEjDgpe8SqHBoftSh_eiNGg-1oCJ30t7iV3Bb2NzQ",{"id":1518,"title":1105,"bioSummary":1519,"email":11,"extension":9,"image":1520,"linkedinUrl":1521,"meta":1522,"position":1529,"stem":1530,"twitterUrl":1531,"__hash__":1532},"authors\u002Fauthors\u002Fsijie-guo.md","Sijie’s journey with Apache Pulsar began at Yahoo! where he was part of the team working to develop a global messaging platform for the company. He then went to Twitter, where he led the messaging infrastructure group and co-created DistributedLog and Twitter EventBus. In 2017, he co-founded Streamlio, which was acquired by Splunk, and in 2019 he founded StreamNative. He is one of the original creators of Apache Pulsar and Apache BookKeeper, and remains VP of Apache BookKeeper and PMC Member of Apache Pulsar. Sijie lives in the San Francisco Bay Area of California.","\u002Fimgs\u002Fauthors\u002Fsijie-guo.webp","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsijieg\u002F",{"body":1523},{"type":15,"value":1524,"toc":1527},[1525],[63,1526,1519],{},{"title":18,"searchDepth":19,"depth":19,"links":1528},[],"CEO and Co-Founder, StreamNative, Apache Pulsar PMC Member","authors\u002Fsijie-guo","https:\u002F\u002Ftwitter.com\u002Fsijieg","krzMgsbADqGZT1TnpWTVzT4HJ9U7oZB9hzOMiDT5Wd0",[1534,1542,1547],{"path":1535,"title":1536,"date":1537,"image":1538,"link":-1,"collection":1539,"resourceType":1540,"score":1541,"id":1535},"\u002Fblog\u002Fintroducing-streamnative-private-cloud","Introducing StreamNative Private Cloud","2023-11-07","\u002Fimgs\u002Fblogs\u002F65651a8ab3803cd436578980_Introducing-StreamNative-Private-Cloud.png","blogs","Blog",0.667,{"path":1543,"title":1544,"date":1545,"image":1546,"link":-1,"collection":1539,"resourceType":1540,"score":1541,"id":1543},"\u002Fblog\u002Fstreaming-lakehouse-introducing-pulsars-lakehouse-tiered-storage","Streaming Lakehouse: Introducing Pulsar’s Lakehouse Tiered Storage","2023-10-25","\u002Fimgs\u002Fblogs\u002F653891f5aac4dd3f6fe69abc_Screenshot-2023-10-24-at-8.37.37-PM.png",{"path":1548,"title":1549,"date":1550,"image":-1,"link":-1,"collection":1539,"resourceType":1540,"score":1541,"id":1548},"\u002Fblog\u002Fsecure-your-pulsar-cluster-with-revocable-api-keys","Secure Your Pulsar Cluster with Revocable API Keys","2023-10-24",1779910350695]