[{"data":1,"prerenderedAt":1484},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":24,"navbar-pricing-featured":306,"blog-\u002Fblog\u002Fqueues-are-just-subscriptions-demystifying-shared-and-failover-modes":1086,"blog-authors-\u002Fblog\u002Fqueues-are-just-subscriptions-demystifying-shared-and-failover-modes":1418,"related-\u002Fblog\u002Fqueues-are-just-subscriptions-demystifying-shared-and-failover-modes":1466},{"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,"category":289,"createdAt":290,"date":291,"description":292,"extension":8,"featured":7,"image":293,"isDraft":294,"link":290,"meta":295,"navigation":7,"order":296,"path":297,"readingTime":298,"relatedResources":290,"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","Company",null,"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","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":307,"title":308,"authors":309,"body":314,"category":1073,"createdAt":290,"date":1074,"description":1075,"extension":8,"featured":7,"image":1076,"isDraft":294,"link":290,"meta":1077,"navigation":7,"order":296,"path":1078,"readingTime":1079,"relatedResources":290,"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","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1087,"title":1088,"authors":1089,"body":1090,"category":1405,"createdAt":290,"date":1406,"description":1407,"extension":8,"featured":294,"image":1408,"isDraft":294,"link":290,"meta":1409,"navigation":7,"order":296,"path":1410,"readingTime":1411,"relatedResources":290,"seo":1412,"stem":1413,"tags":1414,"__hash__":1417},"blogs\u002Fblog\u002Fqueues-are-just-subscriptions-demystifying-shared-and-failover-modes.md","Queues Are Just Subscriptions: Demystifying Shared and Failover Modes (Pulsar Guide for RabbitMQ\u002FJMS Engineers 4\u002F10)",[313,312,311],{"type":15,"value":1091,"toc":1396},[1092,1095,1098,1102,1105,1119,1122,1130,1134,1137,1140,1143,1157,1160,1168,1171,1177,1180,1183,1200,1203,1206,1210,1213,1216,1219,1222,1225,1228,1239,1242,1245,1248,1253,1256,1259,1262,1265,1269,1272,1275,1278,1282,1285,1296,1299,1310,1314,1317,1322,1325,1328,1332,1355,1358,1361,1364,1366,1368,1377,1380,1387,1394],[48,1093,1094],{},"‍TL;DR:",[48,1096,1097],{},"In Pulsar, you don’t create a “queue” – you create a subscription. By having multiple consumers share the same subscription, Pulsar will distribute messages among them (just like multiple consumers on a RabbitMQ queue). This is Pulsar’s Shared subscription mode, which provides load-balanced consumption. For high availability (active-passive consumers), Pulsar offers Failover subscriptions, where one consumer is active and others stand by to take over on failure. This post explains how these subscription modes work, how they correspond to the traditional queue semantics, and when to use each. After reading, you’ll understand that to Pulsar, a queue is essentially a named subscription with possibly many consumers attached, and how Pulsar manages who gets what message in different scenarios.",[40,1099,1101],{"id":1100},"revisiting-pulsar-subscriptions-vs-queues","Revisiting Pulsar Subscriptions vs Queues",[48,1103,1104],{},"We established earlier that Pulsar uses the concept of a subscription to handle what we think of as queues. A subscription represents a group of consumers with a given name on a topic. If one consumer is attached to that subscription, it will get all messages (and ordering is preserved). If multiple consumers attach to the same subscription, Pulsar must decide how to split messages between them. Pulsar offers a few policies (subscription types) to govern this distribution. The two most relevant for “queuing” are Shared and Failover (there is also Exclusive, which is the default single-consumer case, and Key_Shared, which we will cover in a later post about ordering).",[339,1106,1107,1110,1113,1116],{},[342,1108,1109],{},"Exclusive subscription: Only one consumer can attach at a time. If another tries, it gets an error. This is essentially a 1-to-1 mapping (like a JMS Queue that only one consumer can have at a time, or a JMS durable topic subscription being consumed by only one process). Exclusive is Pulsar’s default type; it ensures strict ordering and simplest semantics (no concurrency).",[342,1111,1112],{},"Shared subscription: Multiple consumers can attach; Pulsar will round-robin or load-balance messages across them. This is the true “competing consumers” setup akin to multiple consumers on a RabbitMQ queue or multiple listeners on the same JMS Queue.",[342,1114,1115],{},"Failover subscription: Multiple consumers can attach, but one is designated as the primary (active consumer) and receives all messages. Others sit idle (or if the topic is partitioned, each partition might have a primary on different consumers). If the primary dies or disconnects, one of the backups takes over and continues from where it left off. This provides high availability without duplicate processing.",[342,1117,1118],{},"Key_Shared subscription: A variant of shared where messages with the same key always go to the same consumer, to preserve ordering per key. This one combines aspects of parallelism and ordering and will be discussed in the context of ordering in one of our future blog posts.",[48,1120,1121],{},"For this discussion, focus on Shared vs Failover, as they essentially cover the two major ways you might use a queue in a system:",[339,1123,1124,1127],{},[342,1125,1126],{},"Shared: for scaling out throughput (many workers share the load of a queue).",[342,1128,1129],{},"Failover: for hot standby (only one worker at a time, but seamlessly switch if it fails).",[40,1131,1133],{"id":1132},"shared-subscription-pulsars-competing-consumers","Shared Subscription: Pulsar’s Competing Consumers",[48,1135,1136],{},"When you create a subscription and attach multiple consumers to it in Shared mode (also called “round-robin” mode in some Pulsar documentation), the broker will deliver each message from the topic to only one of the consumers on that subscription, distributing messages in a round-robin or weighted round-robin manner. Essentially, the subscription behaves like a classic queue – each message goes to one consumer – and multiple consumers means parallel processing of different messages.",[48,1138,1139],{},"Analogy: This is just like having a single RabbitMQ queue with multiple consumers. RabbitMQ will deliver each message in the queue to one consumer, fairly balancing by prefetch, etc. Pulsar does the same for a shared sub: each message in the subscription backlog is given to one of the available consumers.",[48,1141,1142],{},"Some points about Shared subscription behavior:",[339,1144,1145,1148,1151,1154],{},[342,1146,1147],{},"If a consumer hangs (doesn’t ack a message), that message will eventually be re-dispatched to another consumer (via ack timeout or if the consumer disconnects). So work isn’t lost; another consumer can pick it up, similar to how RabbitMQ requeues unacked messages if a consumer dies.",[342,1149,1150],{},"If a new consumer joins a shared subscription, the broker will start including it in the distribution of new messages. If a consumer leaves, the broker redistributes any unacked messages that were on that consumer to others.",[342,1152,1153],{},"Order is not guaranteed across different consumers in a shared subscription. If you care about ordering, you either stick to one consumer (Exclusive or Failover) or use Key_Shared (ensures ordering per key). Shared basically is aimed at throughput scaling at the cost of ordering.",[342,1155,1156],{},"Shared subscriptions support parallel processing: Each consumer can process messages independently. If one is slow, others still get new messages. This can maximize throughput.",[48,1158,1159],{},"Use cases for Shared:",[339,1161,1162,1165],{},[342,1163,1164],{},"Work queues: e.g., tasks that can be processed in parallel (transcoding jobs, sending emails, etc.). You create one subscription (like “task-queue”), spin up N consumer instances with that subscription name, and Pulsar will divide tasks among them – voila, a distributed work queue.",[342,1166,1167],{},"Scaling message consumption: If one consumer can’t keep up with the topic’s message rate, add more consumers on the same subscription to increase aggregate throughput.",[48,1169,1170],{},"How to create a Shared subscription:\nWhen using the Pulsar client API, you specify subscription type Shared. For instance:",[48,1172,1173],{},[351,1174],{"alt":1175,"src":1176},"__wf_reserved_inherit","\u002Fimgs\u002Fblogs\u002F68b09173123f71a994361f6b_iShot_2025-08-29_01.26.45.png",[48,1178,1179],{},"If you omit subscriptionType, it defaults to Exclusive (only one consumer at a time). So explicitly set it to Shared if you plan to attach multiple consumers. All consumers should use the same subscription name and same topic obviously.",[48,1181,1182],{},"Under the hood, what’s happening is:",[339,1184,1185,1188,1191,1194,1197],{},[342,1186,1187],{},"The first consumer to subscribe with that name will create the subscription on the broker.",[342,1189,1190],{},"Additional consumers join that existing subscription. The broker keeps track of all active consumers for the sub.",[342,1192,1193],{},"For each message, the broker chooses a consumer (basically cycling through them) and sends the message to that consumer.",[342,1195,1196],{},"The subscription’s backlog is decreased when a message is acknowledged by a consumer.",[342,1198,1199],{},"If a consumer disconnects with messages unacked, those messages will be re-dispatched to remaining consumers.",[48,1201,1202],{},"From a RabbitMQ perspective, you can consider Pulsar’s topic as analogous to an exchange+queue that all consumers draw from. The difference: you didn’t have to explicitly declare a queue and bind – you simply used a subscription name. Pulsar took care of tracking the offsets for that subscription.",[48,1204,1205],{},"JMS perspective: JMS 2.0 introduced the idea of a Shared Durable Subscription for topics, which allowed multiple consumers on the same durable subscription (to load balance topic messages). That’s quite analogous to Pulsar’s shared subscription on a topic. For JMS Queues, multiple consumers inherently share the queue. So Pulsar’s Shared subscription is fulfilling the role of both these concepts: a group of consumers sharing the work of one message stream.",[40,1207,1209],{"id":1208},"failover-subscription-high-availability-consumer","Failover Subscription: High Availability Consumer",[48,1211,1212],{},"In a Failover subscription, multiple consumers can attach, but Pulsar will only deliver messages to one “primary” consumer at a time. If that consumer disconnects or times out, Pulsar automatically fails over to the next consumer in line, which will then start receiving messages from where the previous one left off.",[48,1214,1215],{},"Think of Failover as an active-standby cluster. One consumer is doing all the work until it can’t, then a standby takes over. This is useful when you have a service where only one instance should be active (maybe because processing must be single-threaded or use a resource exclusively), but you want a hot backup to take over instantly on failure. It’s also useful to implement ordered processing with high availability – you want only one consumer at a time to preserve ordering, but still want fault tolerance.",[48,1217,1218],{},"How Pulsar picks the primary: When consumers connect in Failover mode, they have a priority (you can set a priority level, default often 0, and an internal lexicographical order on consumer names as a tiebreaker). The broker will choose the highest priority consumer as primary. If equal priority, the one that connected first (or lexicographically smallest name, depending on version) becomes primary. Others are essentially parked.",[48,1220,1221],{},"For a non-partitioned topic, it’s straightforward: Primary consumer gets 100% of messages. If it dies, next in line gets all new messages (and any that were unacked by the first consumer will be delivered to the new primary). For partitioned topics, each partition has its own primary assignment; the broker might distribute partitions among consumers if you have multiple partitions, but within each partition only one consumer is active. (This means in a failover subscription with a partitioned topic, you could actually have all consumers active – but each on different partitions. It’s more complex, but effectively it’s like each partition is a mini-topic with failover selection, possibly balancing partition primaries across consumers, as described in the docs.)",[48,1223,1224],{},"Behavior on failover: Let’s say Consumer A is primary, Consumer B is standby. A is chugging along. Suddenly A’s process crashes or network breaks. Pulsar notices A’s connection is gone; it then promotes B to primary. B will start receiving any messages that were next. If A had some messages in flight (unacked) when it died, those will become available to B (after what is effectively an immediate redelivery – Pulsar doesn’t wait for ack timeouts on failover; once A is gone, its unacked messages are free to deliver to B). This ensures minimal interruption – B can continue processing the queue almost where A left off.",[48,1226,1227],{},"Use cases for Failover:",[339,1229,1230,1233,1236],{},[342,1231,1232],{},"Situations where you want only one consumer to handle messages, perhaps because processing should not be parallel (maybe a legacy system can’t handle concurrent processing, or order must be absolutely preserved end-to-end).",[342,1234,1235],{},"High availability: e.g., a singleton service that you run two instances of for redundancy, but only one should actually do work at a time. If the active one fails, the backup seamlessly takes over.",[342,1237,1238],{},"Think of an example: processing bank transactions from a topic. You might decide to use one consumer to ensure they are strictly sequential (no parallel processing that could reorder things), but you want a standby instance in case the main one goes down, so you’re not stuck waiting for manual intervention. Failover subscription is perfect here.",[48,1240,1241],{},"Comparison to RabbitMQ: RabbitMQ doesn’t have an explicit “failover consumer” concept. If you wanted active-passive, you might just run one consumer at a time. If it dies, something else would have to start consuming. With HA queues (mirrored queues in RabbitMQ), multiple nodes have the data, but only one node’s consumers actually consume at a time. Achieving seamless failover of consumption is typically done at application level for Rabbit (like using heartbeats to detect a dead consumer and then starting another consumer). Pulsar builds that logic in – you can start two consumers and know one will be idle until needed.",[48,1243,1244],{},"Comparison to JMS: JMS does not have “failover subscriptions” per se, but many JMS brokers would effectively behave similarly if you have multiple consumers on a queue – one might get all messages if it has a higher priority or some brokers allow exclusive consumer concept. For example, ActiveMQ has an exclusive consumer feature for queues: one consumer gets all messages until it dies, then another takes over. Pulsar’s failover is akin to that but at the subscription level.",[48,1246,1247],{},"How to use Failover:",[48,1249,1250],{},[351,1251],{"alt":1175,"src":1252},"\u002Fimgs\u002Fblogs\u002F68b091aec08d2c1d210258d3_iShot_2025-08-29_01.28.04.png",[48,1254,1255],{},"And similarly on another instance with consumerName “Consumer-B”. (Consumer names aren’t mandatory but help in logging and also tie-breakers for ordering sometimes.)",[48,1257,1258],{},"If you want to designate priority, there’s subscriptionInitialPosition (to set where to start) and more relevantly ConsumerBuilder.priorityLevel(int) to give one consumer a higher priority. Higher priority consumers will always take precedence in failover. If you set one consumer with priority 1 and another with 2, the one with 2 will always be chosen if connected, the other is basically ignored until priority 2 is gone. By default, all have priority 0 (so then it falls back to whoever connects first).",[48,1260,1261],{},"To test failover, you can simulate killing the primary consumer – you should see the second consumer’s receive() calls now return messages.",[48,1263,1264],{},"One thing to mention: in failover mode, only one consumer receives messages at a time, so you’re not scaling throughput here, just providing redundancy. If you attach two consumers because you thought it would speed things up – it won’t, because only one is active. For throughput scaling, use Shared.",[40,1266,1268],{"id":1267},"under-the-hood-cursor-and-partition-details","Under the Hood: Cursor and Partition details",[48,1270,1271],{},"Each subscription (regardless of type) has a cursor – essentially a pointer in the partition log that tracks how far along consumption has gone. In a shared subscription, the cursor moves as messages are acknowledged (which can happen out of order if multiple consumers acknowledge at different times; the cursor might actually mark the lowest acked point plus bitmaps of acked\u002Funacked above that – but that’s an internal detail). In an exclusive or failover subscription, since only one consumer is doing sequential work, the cursor just moves sequentially with acks (or cumulatively).",[48,1273,1274],{},"For failover, when primary switches to secondary, the same subscription cursor is now being consumed by the new consumer. It picks up wherever the cursor last was. Any messages that were delivered to the first consumer but not acked are still marked unacked in the subscription, so the broker knows to redeliver them to the new consumer.",[48,1276,1277],{},"Partitioned topics and failover:\nIf the topic has multiple partitions (say 4 partitions), and you have two consumers in failover, the broker could assign half the partitions to one consumer as primary for those, and the other half to second consumer as primary for those, in order to utilize both consumers (this is optional and based on how priorities or names are sorted). This way, even in failover mode, both consumers might actually be active – but each on different partitions – so you get some throughput scaling too. If one consumer fails, the other takes over all partitions. This is a neat Pulsar nuance: failover subs can give you a mix of HA and load-spreading across partitions. However, if you want strict ordering across the whole topic, you wouldn’t partition the topic in the first place.",[40,1279,1281],{"id":1280},"summing-up-shared-vs-failover-vs-exclusive","Summing Up Shared vs Failover vs Exclusive",[48,1283,1284],{},"It’s helpful to summarize with an analogy:",[339,1286,1287,1290,1293],{},[342,1288,1289],{},"Exclusive: One cashier at a store, one line of customers. If that cashier is out, the store is closed until a new one arrives.",[342,1291,1292],{},"Failover: Two cashiers are present, but only one’s counter is open and taking customers; the other is in the back room on standby. If the first one has to step away, the second immediately opens their counter and continues serving the line. Customers always see exactly one open counter, so they go in order to that one.",[342,1294,1295],{},"Shared: Two (or more) cashiers actively open, each handling their own line (or a shared line that dispatches customers to them). Customers (messages) get assigned to whichever cashier is free next (round-robin). This way, customers are served faster in parallel, but they are not in one single ordered line – effectively each cashier has their portion of the load.",[48,1297,1298],{},"From RabbitMQ perspective:",[339,1300,1301,1304,1307],{},[342,1302,1303],{},"Exclusive = similar to having a single consumer on a queue.",[342,1305,1306],{},"Failover = no direct analog managed by Rabbit, but you could simulate by ensuring only one consumer connects (and use client-side logic to failover).",[342,1308,1309],{},"Shared = typical multiple consumers on a queue scenario.",[40,1311,1313],{"id":1312},"setting-it-in-pulsar-and-best-practices","Setting it in Pulsar and Best Practices",[48,1315,1316],{},"If you’re configuring via pulsar-client CLI for a quick test:",[339,1318,1319],{},[342,1320,1321],{},"There’s a flag for subscription type (-t Exclusive|Shared|Failover|Key_Shared). e.g., pulsar-client consume my-topic -s subName -t Shared -n 0 will allow multiple instances of that command to share messages.",[48,1323,1324],{},"A note on message ordering and Shared subscriptions: When using Shared, since ordering isn’t guaranteed, be mindful if message order matters to your application’s logic. If it does, you either need to include ordering info in the message (like a sequence number and have the consumer sort or detect out-of-order), or avoid parallel consumption for those streams, or use Key_Shared to at least order per key. Many use-cases (like processing independent tasks) don’t need global ordering, so Shared is fine.",[48,1326,1327],{},"A note on failover with multiple partitions: If you truly want a single consumer to handle all messages in order, don’t partition the topic (a single topic is single-partition by default). If you have a partitioned topic and want to use failover, be aware multiple consumers might each handle different partitions simultaneously. If that’s not desired, stick to 1 partition.",[40,1329,1331],{"id":1330},"key-takeaways","Key Takeaways",[339,1333,1334,1337,1340,1343,1346,1349,1352],{},[342,1335,1336],{},"“Queues” in Pulsar are achieved by shared subscriptions: To have a queue with multiple workers, simply create a subscription and start multiple consumers with that subscription name and SubscriptionType.Shared. Pulsar will load-balance the messages across them. You don’t create a separate queue object – the act of consumers sharing the subscription is what creates the queue-like behavior.",[342,1338,1339],{},"Failover subscriptions provide exclusive consumption with automatic failover: Only one consumer receives messages until it fails, then the next takes over. Use this for scenarios requiring a single consumer processing stream with high availability.",[342,1341,1342],{},"Exclusive vs Failover vs Shared: Exclusive (the default) ensures only one consumer – any additional consumer with the same subscription name is rejected. Failover allows standby consumers. Shared allows concurrent consumers. Choose based on your needs: concurrency vs ordering vs HA.",[342,1344,1345],{},"The subscription name is the queue name: If you connect 5 consumers to topic “alpha” with subscription “orders-sub” in Shared mode, those 5 effectively form the “orders-sub” queue group for topic alpha. If you had another subscription “billing-sub” on the same topic, that’s a separate queue group (receiving its own copy of messages, like a separate RabbitMQ queue bound to the same exchange).",[342,1347,1348],{},"JMS and Rabbit equivalence: Pulsar Shared subs = JMS queue with multiple consumers, or JMS shared durable subscription; Pulsar Failover subs = JMS exclusive consumer or concept of exclusive queue consumption with automatic handover (not natively in JMS, but ActiveMQ’s exclusive consumer feature is similar). From Rabbit’s view, a Pulsar shared subscription is just like how Rabbit distributes messages to multiple consumers on one queue.",[342,1350,1351],{},"No manual ack requeue hassle: In RabbitMQ, if a consumer didn’t ack, you either had to rely on the death of consumer for requeue or use basic.nack. In Pulsar’s shared subscription, if a consumer disconnects or negative-acks, Pulsar will readily redeliver unacknowledged messages to another consumer. So the queue processing will continue. This is managed by Pulsar’s subscription state.",[342,1353,1354],{},"Queues are durable via subscriptions: Because Pulsar subscriptions retain messages until acked, a shared subscription with zero consumers still holds messages (like a durable queue would) and any new consumer that comes in will get them. That’s similar to a RabbitMQ durable queue sitting around until a consumer attaches. Pulsar does that automatically – the subscription (queue) exists as long as it has messages or a consumer attached. (You can administratively delete a subscription if needed, analogous to deleting a queue.)",[48,1356,1357],{},"To wrap up, Pulsar’s subscription model might have seemed abstract at first, but now we see it maps cleanly to queue semantics. “Shared” is what gives Pulsar the power to act like a traditional queue system for distributing tasks. Meanwhile, “Failover” and “Exclusive” cover scenarios requiring strict ordering or single-consumer behavior.",[48,1359,1360],{},"In the next post, we’ll talk about message durability, retention, expiration, and dead-letter topics. In other words, what happens when messages aren’t consumed, how to not lose them, and how Pulsar handles things like TTL and DLQs compared to RabbitMQ’s similar features. If you’ve ever set a queue to expire messages or configured a Dead Letter Exchange in RabbitMQ, the Pulsar way of achieving that is coming up next!",[48,1362,1363],{},"‍",[208,1365],{},[48,1367,1363],{},[48,1369,1370,1371,1376],{},"Want to go deeper into real-time data and streaming architectures? Join us at the ",[55,1372,1375],{"href":1373,"rel":1374},"https:\u002F\u002Fdatastreaming-summit.org\u002Fevent\u002Fdata-streaming-sf-2025",[264],"Data Streaming Summit San Francisco 2025"," on September 29–30 at the Grand Hyatt at SFO.",[48,1378,1379],{},"30+ sessions | 4 tracks | Real-world insights from OpenAI, Netflix, LinkedIn, Paypal, Uber, AWS, Google, Motorq, Databricks, Ververica, Confluent & more!",[48,1381,1382],{},[55,1383,1386],{"href":1384,"rel":1385},"https:\u002F\u002Fdatastreaming-summit.org\u002Fevent\u002Fdata-streaming-sf-2025\u002Fschedule",[264],"[Explore the Full Agenda]",[48,1388,1389],{},[55,1390,1393],{"href":1391,"rel":1392},"https:\u002F\u002Fwww.eventbrite.com\u002Fe\u002Fdata-streaming-summit-san-francisco-2025-tickets-1432401484399?aff=oddtdtcreator",[264],"[Register Now]",[48,1395,1363],{},{"title":18,"searchDepth":19,"depth":19,"links":1397},[1398,1399,1400,1401,1402,1403,1404],{"id":1100,"depth":19,"text":1101},{"id":1132,"depth":19,"text":1133},{"id":1208,"depth":19,"text":1209},{"id":1267,"depth":19,"text":1268},{"id":1280,"depth":19,"text":1281},{"id":1312,"depth":19,"text":1313},{"id":1330,"depth":19,"text":1331},"Apache Pulsar","2025-08-28","Explore how Apache Pulsar implements queues through subscriptions. Learn the differences between Shared, Failover, and Exclusive subscription modes, their use cases for load balancing, high availability, and message ordering, and how Pulsar maps traditional RabbitMQ and JMS queue semantics to modern streaming patterns.","\u002Fimgs\u002Fblogs\u002F68b093978d9ff520a3aa4955_04.-Queues-Are-Just-Subscriptions.png",{},"\u002Fblog\u002Fqueues-are-just-subscriptions-demystifying-shared-and-failover-modes","10 min read",{"title":1088,"description":1407},"blog\u002Fqueues-are-just-subscriptions-demystifying-shared-and-failover-modes",[1405,1415,1416],"Intro","RabbitMQ","zKBgjvyJssQFpThMHzY9NanUFnKDiLowp4yKuzboDR8",[1419,1435,1449],{"id":1420,"title":313,"bioSummary":1421,"email":290,"extension":8,"image":1422,"linkedinUrl":1423,"meta":1424,"position":1431,"stem":1432,"twitterUrl":1433,"__hash__":1434},"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":1425},{"type":15,"value":1426,"toc":1429},[1427],[48,1428,1421],{},{"title":18,"searchDepth":19,"depth":19,"links":1430},[],"Director of Streaming, StreamNative & Apache Pulsar PMC Member","authors\u002Fpenghui-li","https:\u002F\u002Ftwitter.com\u002Flipenghui6","WDjET7GfxqVQJ8mTEMaRhgpxRdDy18qZkgQDJlwjvbI",{"id":1436,"title":312,"bioSummary":1437,"email":290,"extension":8,"image":1438,"linkedinUrl":290,"meta":1439,"position":1446,"stem":1447,"twitterUrl":290,"__hash__":1448},"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":1440},{"type":15,"value":1441,"toc":1444},[1442],[48,1443,1437],{},{"title":18,"searchDepth":19,"depth":19,"links":1445},[],"Director of Storage, StreamNative & Apache Pulsar PMC Member","authors\u002Fhang","titaSDxZRJWAW0SkpJSq43NuDvps9XQ6gZIMSPCtUwo",{"id":1450,"title":311,"bioSummary":1451,"email":290,"extension":8,"image":1452,"linkedinUrl":290,"meta":1453,"position":1462,"stem":1463,"twitterUrl":1464,"__hash__":1465},"authors\u002Fauthors\u002Fneng-lu.md","Neng Lu is currently the Director of Platform at StreamNative, where he leads the engineering team in developing the StreamNative ONE Platform and the next-generation Ursa engine. As an Apache Pulsar Committer, he specializes in advancing Pulsar Functions and Pulsar IO Connectors, contributing to the evolution of real-time data streaming technologies. Prior to joining StreamNative, Neng was a Senior Software Engineer at Twitter, where he focused on the Heron project, a cutting-edge real-time computing framework. He holds a Master's degree in Computer Science from the University of California, Los Angeles (UCLA) and a Bachelor's degree from Zhejiang University.","\u002Fimgs\u002Fauthors\u002Fneng-lu.jpeg",{"body":1454},{"type":15,"value":1455,"toc":1460},[1456,1458],[48,1457,1451],{},[48,1459,1363],{},{"title":18,"searchDepth":19,"depth":19,"links":1461},[],"Director of Engineering, StreamNative","authors\u002Fneng-lu","https:\u002F\u002Ftwitter.com\u002Fnlu90","R1K8DYRoq92ZrwHOmKtJMRfm-cuTjXTqAv0Cc3Q9IM4",[1467,1475,1479],{"path":1468,"title":1469,"date":1470,"image":1471,"link":-1,"collection":1472,"resourceType":1473,"score":1474,"id":1468},"\u002Fblog\u002Fat-least-once-exactly-once-and-acks-in-pulsar","At-Least-Once, Exactly-Once, and Acks in Pulsar (Pulsar Guide for RabbitMQ\u002FJMS Engineers 3\u002F10)","2025-08-06","\u002Fimgs\u002Fblogs\u002F689cad93a3e3355c0da233ee_03.-At-Least-Once,-Exactly-Once,-and-Acks-in-Pulsar-1.png","blogs","Blog",1,{"path":1476,"title":1477,"date":1470,"image":1478,"link":-1,"collection":1472,"resourceType":1473,"score":1474,"id":1476},"\u002Fblog\u002Fgoodbye-exchanges-how-pulsar-replaces-fanout-routing-and-headers","Goodbye Exchanges: How Pulsar Replaces Fanout, Routing, and Headers (Pulsar Guide for RabbitMQ\u002FJMS Engineers 2\u002F10)","\u002Fimgs\u002Fblogs\u002F689360cf7196382ef51fb769_02.-Replaces-Fanout,-Routing,-and-Headers-with-Pulsar.png",{"path":1480,"title":1481,"date":1482,"image":1483,"link":-1,"collection":1472,"resourceType":1473,"score":1474,"id":1480},"\u002Fblog\u002Fpulsar-101-for-queue-users-queues-topics-and-subscriptions-explained","Pulsar 101 for Queue Users: Queues, Topics, and Subscriptions Explained (Pulsar Guide for RabbitMQ\u002FJMS Engineers 1\u002F10)","2025-08-04","\u002Fimgs\u002Fblogs\u002F6890c928669a9565ea05e386_01.-Pulsar-101-for-Queue-Users.png",1775716437936]