[{"data":1,"prerenderedAt":1606},["ShallowReactive",2],{"active-banner":3,"navbar-featured-partner-blog":23,"navbar-pricing-featured":304,"story-\u002Fsuccess-stories\u002Fyum-china":1084,"story-authors-\u002Fsuccess-stories\u002Fyum-china":1572,"related-\u002Fsuccess-stories\u002Fyum-china":1583},{"id":4,"title":5,"date":6,"dismissible":7,"extension":8,"link":9,"link2":10,"linkText":11,"linkText2":10,"meta":12,"stem":20,"variant":21,"__hash__":22},"banners\u002Fbanners\u002Fkafka-company-2025.md","Native Apache Kafka Service Is Coming Soon to StreamNative Cloud. Join the waitlist and get $1,000 in credits.","2026-04-01",true,"md","\u002Fnative-kafka-service-waitlist",null,"Join Waitlist",{"body":13},{"type":14,"value":15,"toc":16},"minimark",[],{"title":17,"searchDepth":18,"depth":18,"links":19},"",2,[],"banners\u002Fkafka-company-2025","default","IMIJszQOOWTfA_DV33eYUA5jqV7DrX1FWbBTBZfNvWc",{"id":24,"title":25,"authors":26,"body":28,"category":288,"createdAt":10,"date":289,"description":290,"extension":8,"featured":7,"image":291,"isDraft":292,"link":10,"meta":293,"navigation":7,"order":294,"path":295,"readingTime":296,"relatedResources":10,"seo":297,"stem":298,"tags":299,"__hash__":303},"blogs\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025.md","StreamNative Recognized as a Contender in The Forrester Wave™: Streaming Data Platforms, Q4 2025",[27],"David Kjerrumgaard",{"type":14,"value":29,"toc":275},[30,38,46,50,66,72,77,80,86,101,108,114,117,123,126,133,139,142,145,156,162,168,171,174,177,183,190,193,196,203,206,209,223,228,232,236,240,244,248,250,267,269],[31,32,34],"h3",{"id":33},"receives-highest-possible-scores-in-both-the-messaging-and-resource-optimization-criteria",[35,36,37],"em",{},"Receives Highest Possible Scores in BOTH the Messaging and Resource Optimization Criteria",[39,40,42],"h2",{"id":41},"introduction",[43,44,45],"strong",{},"Introduction",[47,48,49],"p",{},"Real-time data has become the backbone of modern innovation. As artificial intelligence (AI) and digital services demand instantaneous insights, organizations are realizing that streaming data is no longer optional – it's essential for delivering timely, context-rich experiences. StreamNative's data streaming platform is built precisely for this reality, ensuring data is immediate, reliable, and ready to power critical applications.",[47,51,52,53,62,63],{},"Today, we're excited to announce that Forrester Research has named StreamNative as a Contender in its evaluation, ",[54,55,57],"a",{"href":56},"\u002Freports\u002Frecognized-in-the-forrester-wave-tm-streaming-data-platforms-q4-2025",[35,58,59],{},[43,60,61],{},"The Forrester Wave™: Streaming Data Platforms, Q4 2025",". This report evaluated 15 top streaming data platform providers, and we're proud to share that ",[43,64,65],{},"StreamNative received the highest scores possible—5 out of 5—in both the Messaging and Resource Optimization criteria.",[47,67,68,69],{},"***Forrester's Take: ***",[35,70,71],{},"\"StreamNative is a good fit for enterprises that want an Apache Pulsar implementation that is also compatible with Kafka APIs.\"",[47,73,74],{},[35,75,76],{},"— The Forrester Wave™: Streaming Data Platforms, Q4 2025",[47,78,79],{},"Being recognized in the Forrester Wave is a proud milestone, and for us, it highlights how far StreamNative has come in enabling enterprises to unlock the power of real-time data. In the sections below, we'll dive into what we believe sets StreamNative apart—from our modern architecture and cloud-native design to our open-source foundation and real-time use cases—and how we see these strengths aligning with Forrester's findings.",[39,81,83],{"id":82},"trusted-by-industry-leaders",[43,84,85],{},"Trusted by Industry Leaders",[47,87,88,89,92,93,96,97,100],{},"Companies across industries are already leveraging StreamNative to drive real-time outcomes. Global enterprises like ",[43,90,91],{},"Cisco"," rely on StreamNative to handle massive IoT telemetry, supporting 245 million+ connected devices. Martech leaders such as ",[43,94,95],{},"Iterable"," process billions of events per day with StreamNative for hyper-personalized customer engagement. And in financial services, ",[43,98,99],{},"FICO"," trusts StreamNative to power its real-time fraud detection and analytics pipelines with a secure, scalable streaming backbone.",[47,102,103,104,107],{},"The Forrester report notes that, “",[35,105,106],{},"Customers appreciate the lower infrastructure costs that result from StreamNative’s cost-efficient, Kafka-compatible architecture. Customers note excellent support responsiveness…","”",[39,109,111],{"id":110},"modern-cloud-native-architecture-built-for-scale",[43,112,113],{},"Modern, Cloud-Native Architecture Built for Scale",[47,115,116],{},"From day one, StreamNative was designed with a modern architecture to meet the demanding scale and flexibility requirements of real-time data. Unlike legacy streaming systems that often rely on tightly coupled storage and compute, StreamNative's platform takes a cloud-native approach: it decouples these layers to enable elastic scalability and efficient resource utilization across any environment. The core is powered by Apache Pulsar—a distributed messaging and streaming engine—enhanced with multi-protocol support (including native Apache Kafka API compatibility) to unify diverse data streams under one roof. This means organizations can consolidate siloed messaging systems and handle both high-volume event streams and traditional message queues on a single platform, without sacrificing performance or reliability.",[47,118,119,120,107],{},"Forrester's evaluation described that “",[35,121,122],{},"StreamNative aims to provide a high-performance, multi-protocol streaming data platform: It uses Apache Pulsar with Kafka API compatibility to deliver cost-efficient, real-time applications for enterprises. It appeals to organizations that want a flexible, low-cost streaming solution, due to its focus on scalability and resource optimization, while its investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.",[47,124,125],{},"Our cloud-first, leaderless architecture (with no single broker bottlenecks) and tiered storage model were built to maximize throughput and cost-efficiency for real-time workloads. By separating compute from storage and leveraging distributed object storage, StreamNative can retain huge volumes of event data indefinitely while keeping compute costs in check—effectively providing a flexible, low-cost streaming solution.",[47,127,128,129,132],{},"This modern design not only delivers high performance, but also ensures fault tolerance and geo-distribution out of the box, so enterprises can trust their streaming data is always available and durable. As Forrester’s evaluation noted, StreamNative ",[35,130,131],{},"\"excels at messaging and resource optimization\" and “Its platform supports use cases like real-time analytics and event-driven architectures with robust scalability.","” Our architecture provides the strong foundation that today's real-time applications demand, from ultra-fast data ingestion to seamless scale-out across hybrid and multi-cloud environments.",[39,134,136],{"id":135},"open-source-foundation-and-pulsar-expertise",[43,137,138],{},"Open Source Foundation and Pulsar Expertise",[47,140,141],{},"StreamNative's DNA is rooted in open source innovation. Our founders are the original creators of Apache Pulsar, and we've built our platform with the same open principles: freedom, flexibility, and community-driven innovation. For developers and data teams, this means adopting StreamNative comes with no proprietary lock-in—instead, you get a platform built on open standards and a thriving ecosystem. We offer broad API compatibility (Pulsar, Kafka, JMS, MQTT, and more) so that teams can work with familiar interfaces and integrate StreamNative into existing systems with ease.",[47,143,144],{},"StreamNative is the primary commercial contributor to the Apache Pulsar project and its surrounding ecosystem. We invest heavily in Pulsar's ongoing improvements our investments in Pulsar's open-source ecosystem and performance optimization bolster StreamNative's value. We also foster a vibrant community through initiatives like the Data Streaming Summit and free training resources.",[47,146,147,148,151,152,155],{},"Forrester's assessment noted that StreamNative’s “",[35,149,150],{},"events-driven agents, extensibility, and performance architecture are solid,","” and we're continuing to build on that foundation. ",[43,153,154],{},"We're actively investing in expanding our tooling for observability, governance, schema management, and developer productivity","—areas we recognize as critical for enterprise adoption and where we're committed to accelerating our roadmap.",[47,157,158,159],{},"Being open also means embracing an open ecosystem of technologies. StreamNative actively integrates with the tools and platforms that matter most to our users. We partner with industry leaders like Snowflake, Databricks, Google, and Ververica to ensure our streaming platform works seamlessly with data warehouses, lakehouse storage, and stream processing frameworks. Forrester’s evaluation observed that StreamNative’s ",[35,160,161],{},"\"investments in Pulsar’s open-source ecosystem and performance optimization make it the primary platform for enterprises wishing to implement Pulsar.\"",[39,163,165],{"id":164},"powering-real-time-use-cases-across-industries",[43,166,167],{},"Powering Real-Time Use Cases Across Industries",[47,169,170],{},"One of the greatest validations of StreamNative's approach is the success our customers are achieving with real-time data. StreamNative's platform is versatile and use-case agnostic—if an application demands high-volume, low-latency data movement, we can power it. This flexibility is why our customer base spans industries from finance and IoT to major automobile manufacturers and online gaming. The common thread is that these organizations need to process and react to data in milliseconds, and StreamNative is delivering the capabilities to make that possible.",[47,172,173],{},"Cisco uses StreamNative to underpin an IoT telemetry system of colossal scale, connecting hundreds of millions of devices and thousands of enterprise clients with real-time data streams. The platform's multi-tenant design and proven reliability allow Cisco to offer its customers a live feed of device data with unwavering confidence. In the financial sector, FICO has built streaming pipelines on StreamNative to detect fraud as transactions happen and to monitor systems in real time. With StreamNative's strong guarantees around message durability and ordering, FICO can catch anomalies or suspicious patterns within seconds. And in digital customer engagement, Iterable relies on StreamNative to process billions of events every day—clicks, views, purchases—so that marketers can trigger personalized campaigns instantly based on user behavior.",[47,175,176],{},"Our customers uniformly deal with mission-critical data streams, where downtime or delays are unacceptable. StreamNative's fault-tolerant, scalable infrastructure has proven equal to the task, handling scenarios like bursting to millions of events per second or seamlessly spanning multiple cloud regions. Forrester's report recognized StreamNative for supporting event-driven architectures with robust scalability—which for us is a reflection of our platform's ability to meet the most demanding enterprise requirements.",[39,178,180],{"id":179},"continuing-to-innovate-ursa-orca-and-the-road-ahead",[43,181,182],{},"Continuing to Innovate: Ursa, Orca, and the Road Ahead",[47,184,185,186,189],{},"While we are thrilled to be recognized in Forrester's Streaming Data Platforms Wave, we view this as just the beginning. StreamNative's vision has always been bold: to ",[43,187,188],{},"provide a unified platform that not only handles today's streaming needs but also anticipates the emerging requirements of tomorrow",".",[47,191,192],{},"One key area of focus is the convergence of streaming data with advanced analytics and AI. As Forrester points out in the report, technology leaders should look for platforms that natively integrate messaging, stream processing, and analytics to provide AI agents with real-time, contextualized information. We couldn't agree more. Our award-winning Ursa Engine and Orca Agent Engine are aimed at extending our platform up the stack—bridging the gap between data streams and data lakes, and between event streams and intelligent processing.",[47,194,195],{},"Our new Ursa Engine introduces a lakehouse-native approach to streaming: it can write events directly to table formats like Iceberg on cloud storage, eliminating entire classes of ETL jobs and making fresh data instantly available for analytics queries. By integrating streaming and lakehouse technologies, we help customers collapse data silos and accelerate their AI\u002FML pipelines.",[47,197,198,199,202],{},"Beyond analytics integration, we are also enhancing StreamNative with more out-of-the-box processing and governance capabilities. In the coming months, we plan to introduce new features for lightweight stream processing and transformation, making it easier to build reactive applications directly on the platform. We're also expanding our ecosystem of connectors and integrations, so that whether your data lands in Snowflake, Databricks, or an AI model, StreamNative will seamlessly feed it. ",[43,200,201],{},"We're investing significantly in enterprise features including security, schema registry, governance, and monitoring tooling","—capabilities that are essential for mission-critical deployments and where we're committed to continued improvement.",[47,204,205],{},"This recognition from Forrester energizes us to keep innovating at full speed. We're sharing this honor with our amazing customers, community, and partners who drive us forward every day. Your feedback and real-world challenges have helped shape StreamNative into what it is today, and together, we will shape the future of streaming data. Thank you for joining us on this journey—we're just getting started, and we can't wait to deliver even more value as we continue to evolve our platform. Onward to real-time everything!",[207,208],"hr",{},[31,210,212],{"id":211},"streamnative-in-the-forrester-wave-evaluation-findings",[43,213,214,215,222],{},"StreamNative in ",[43,216,217],{},[54,218,219],{"href":56},[43,220,221],{},"The Forrester Wave™",": Evaluation Findings",[224,225,227],"h5",{"id":226},"recognized-as-a-contender-among-15-streaming-data-platform-providers","• Recognized as a Contender among 15 streaming data platform providers",[224,229,231],{"id":230},"received-the-highest-scores-possible-50-in-both-the-messaging-and-resource-optimization-criteria","* Received the highest scores possible (5.0) in both the Messaging and Resource Optimization criteria",[224,233,235],{"id":234},"cited-as-the-primary-platform-for-enterprises-wishing-to-implement-pulsar","• Cited as the primary platform for enterprises wishing to implement Pulsar",[224,237,239],{"id":238},"noted-for-excelling-at-messaging-and-resource-optimization","• Noted for excelling at messaging and resource optimization",[224,241,243],{"id":242},"customers-cited-lower-infrastructure-costs-and-excellent-support-responsiveness","• Customers cited lower infrastructure costs and excellent support responsiveness",[224,245,247],{"id":246},"recognized-for-supporting-event-driven-architectures-with-robust-scalability","• Recognized for supporting event-driven architectures with robust scalability",[207,249],{},[251,252,254,255,258,259,189],"h6",{"id":253},"forrester-disclaimer-forrester-does-not-endorse-any-company-product-brand-or-service-included-in-its-research-publications-and-does-not-advise-any-person-to-select-the-products-or-services-of-any-company-or-brand-based-on-the-ratings-included-in-such-publications-information-is-based-on-the-best-available-resources-opinions-reflect-judgment-at-the-time-and-are-subject-to-change-for-more-information-read-about-forresters-objectivity-here","**Forrester Disclaimer: **",[35,256,257],{},"Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change",". *For more information, read about Forrester’s objectivity *",[54,260,264],{"href":261,"rel":262},"https:\u002F\u002Fwww.forrester.com\u002Fabout-us\u002Fobjectivity\u002F",[263],"nofollow",[35,265,266],{},"here",[207,268],{},[251,270,272],{"id":271},"apache-apache-pulsar-apache-kafka-apache-flink-and-other-names-are-trademarks-of-the-apache-software-foundation-no-endorsement-by-apache-or-other-third-parties-is-implied",[35,273,274],{},"Apache®, Apache Pulsar®, Apache Kafka®, Apache Flink® and other names are trademarks of The Apache Software Foundation. No endorsement by Apache or other third parties is implied.",{"title":17,"searchDepth":18,"depth":18,"links":276},[277,279,280,281,282,283,284],{"id":33,"depth":278,"text":37},3,{"id":41,"depth":18,"text":45},{"id":82,"depth":18,"text":85},{"id":110,"depth":18,"text":113},{"id":135,"depth":18,"text":138},{"id":164,"depth":18,"text":167},{"id":179,"depth":18,"text":182,"children":285},[286],{"id":211,"depth":278,"text":287},"StreamNative in The Forrester Wave™: Evaluation Findings","Company","2025-12-16","StreamNative is recognized in The Forrester Wave™: Streaming Data Platforms, Q4 2025. Discover why Forrester highlights StreamNative's high-performance messaging, efficient resource use, and cost-effective Kafka API compatibility for real-time innovation.","\u002Fimgs\u002Fblogs\u002F693bd36cf01b217dcb67278f_Streamnative_blog_thumbnail.png",false,{},0,"\u002Fblog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025","10 mins read",{"title":25,"description":290},"blog\u002Fstreamnative-recognized-in-the-forrester-wave-streaming-data-platforms-2025",[300,301,302],"Announcements","Real-Time","Forrester","sOeeJtEO3O-IIfTPJjY1AFOMawZ_rf8FOH8A98NEKgU",{"id":305,"title":306,"authors":307,"body":312,"category":1071,"createdAt":10,"date":1072,"description":1073,"extension":8,"featured":7,"image":1074,"isDraft":292,"link":10,"meta":1075,"navigation":7,"order":294,"path":1076,"readingTime":1077,"relatedResources":10,"seo":1078,"stem":1079,"tags":1080,"__hash__":1083},"blogs\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour.md","How We Run a 5 GB\u002Fs Kafka Workload for Just $50 per Hour",[308,309,310,311],"Matteo Meril","Neng Lu","Hang Chen","Penghui Li",{"type":14,"value":313,"toc":1041},[314,317,320,323,326,329,333,336,346,352,355,363,368,372,379,382,385,393,397,400,405,409,412,415,418,421,430,434,437,448,451,455,458,461,472,475,479,483,491,494,498,506,535,539,542,547,551,554,558,561,564,569,578,583,586,589,600,604,607,618,622,625,628,633,636,665,669,671,677,680,685,690,693,697,711,715,726,730,745,754,765,768,771,775,778,781,792,795,798,801,806,811,815,819,836,840,854,859,863,874,877,893,897,908,913,918,926,930,933,937,944,948,951,960,965,974,980,989,998,1007,1016,1025,1033],[47,315,316],{},"The rise of DeepSeek has shaken the AI infrastructure market, forcing companies to confront the escalating costs of training and deploying AI models. But the real pressure point isn’t just compute—it’s data acquisition and ingestion costs.",[47,318,319],{},"As businesses rethink their AI cost-containment strategies, real-time data streaming is emerging as a critical enabler. The growing adoption of Kafka as a standard protocol has expanded cost-efficient options, allowing companies to optimize streaming analytics while keeping expenses in check.",[47,321,322],{},"Ursa, the data streaming engine powering StreamNative’s managed Kafka service, is built for this new reality. With its leaderless architecture and native lakehouse storage integration, Ursa eliminates costly inter-zone network traffic for data replication and client-to-broker communication while ensuring high availability at minimal operational cost.",[47,324,325],{},"In this blog post, we benchmarked the infrastructure cost and total cost of ownership (TCO) for running a 5GB\u002Fs Kafka workload across different Kafka vendors, including Redpanda, Confluent WarpStream, and AWS MSK. Our benchmark results show that Ursa can sustain 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda—making it the ideal solution for high-performance, cost-efficient ingestion and data streaming for data lakehouses and AI workloads.",[47,327,328],{},"Note: We also evaluated vanilla Kafka in our benchmark; however, for simplicity, we have focused our cost comparison on vendor solutions rather than self-managed deployments. That said, it is important to highlight that both Redpanda and vanilla Kafka use a leader-based data replication approach. In a data-intensive, network-bound workload like 5GB\u002Fs streaming, with the same machine type and replication factor, Redpanda and vanilla Kafka produced nearly identical cost profiles.",[39,330,332],{"id":331},"key-benchmark-findings","Key Benchmark Findings",[47,334,335],{},"Ursa delivered 5 GB\u002Fs of sustained throughput at an infrastructure cost of just $54 per hour. For comparison:",[337,338,339,343],"ul",{},[340,341,342],"li",{},"MSK: $303 per hour → 5.6x more expensive compared to Ursa",[340,344,345],{},"Redpanda: $988 per hour → 18x more expensive compared to Ursa",[47,347,348],{},[349,350],"img",{"alt":17,"src":351},"\u002Fimgs\u002Fblogs\u002F679c71b67d9046f26edc7977_AD_4nXfvTqyBNUBu2lObdkKAx-5UNkpNP8UYULLZyOcixE6z99VMZUUEsUqWjzexI7vjyNGRNSAUoM9smYvdTP55ctAhIbrs5lmQgcSVMWdaoigbWouCl95DVSQsxooY-qqfGcYqS4g4zA.png",[47,353,354],{},"Beyond infrastructure costs, when factoring in both storage pricing, vendor pricing and operational expenses, Ursa’s total cost of ownership (TCO) for a 5GB\u002Fs workload with a 7-day retention period is:",[337,356,357,360],{},[340,358,359],{},"50% cheaper than Confluent WarpStream",[340,361,362],{},"85% cheaper than MSK and Redpanda",[47,364,365],{},[349,366],{"alt":17,"src":367},"\u002Fimgs\u002Fblogs\u002F679c602d77e9c706de5343b8_AD_4nXeDv8rrv_C1CTCCiqYo1zpvlGYbdBk1r0VEqovAPu22iFMQZgh54Hfw9PBMLzM7jDFxKwAFDxbdG0np4XVk_tGsWhEKMloLRcmmea7lvueCx-0cFsyaE3Mya4Mxc1Dox95A6JEc.png",[39,369,371],{"id":370},"ursa-highly-cost-efficient-data-streaming-at-scale","Ursa: Highly Cost-Efficient Data Streaming at Scale",[47,373,374,378],{},[54,375,377],{"href":376},"\u002Fblog\u002Fursa-reimagine-apache-kafka-for-the-cost-conscious-data-streaming","Ursa"," is a next-generation data streaming engine designed to deliver high performance at a fraction of the cost of traditional disk-based solutions. It is fully compatible with Apache Kafka and Apache Pulsar APIs, while leveraging a leaderless, lakehouse-native architecture to maximize scalability, efficiency, and cost savings.",[47,380,381],{},"Ursa’s key innovation is separating storage from compute and decoupling metadata\u002Findex operations from data operations by utilizing cloud object storage (e.g., AWS S3) instead of costly inter-zone disk-based replication. It also employs open lakehouse formats (Iceberg and Delta Lake), enabling columnar compression to significantly reduce storage costs while maintaining durability and availability.",[47,383,384],{},"In contrast, traditional streaming systems—like Kafka and Redpanda—depend on leader-based architectures, which drive up inter-zone traffic costs due to replication and client communication. Ursa mitigates these costs by:",[337,386,387,390],{},[340,388,389],{},"Eliminating inter-zone traffic costs via a leaderless architecture.",[340,391,392],{},"Replacing costly inter-zone replication with direct writes to cloud storage using open lakehouse formats.",[39,394,396],{"id":395},"how-ursa-eliminates-inter-zone-traffic","How Ursa Eliminates Inter-Zone Traffic",[47,398,399],{},"Ursa minimizes inter-zone traffic by leveraging a leaderless architecture, which eliminates inter-zone communication between clients and brokers, and lakehouse-native storage, which removes the need for inter-zone data replication. This approach ensures high availability and scalability while avoiding unnecessary cross-zone data movement.",[47,401,402],{},[349,403],{"alt":17,"src":404},"\u002Fimgs\u002Fblogs\u002F679c602e21b3571bb7117dca_AD_4nXd7Oahc77NjRLNvA9clLt0tsyU6MrIqVibFYv5pW5giTIcCHPr3EA_yTGzfVEUIVO3VXK56qWK8zmBCp5lY0E_4nmlWIPFrHjtHylA5NhwELjn-UB0fLG2h_kbrxrc7Cs_edvveNA.png",[31,406,408],{"id":407},"leaderless-architecture","Leaderless architecture",[47,410,411],{},"Traditional streaming engines such as Kafka, Pulsar, or RedPanda rely on a leader-based model, where each partition is assigned to a single leader broker that handles all writes and reads.",[47,413,414],{},"Pros of Leader-Based Architectures:\n✔ Maintains message ordering via local sequence IDs\n✔ Delivers low latency and high performance through message caching",[47,416,417],{},"Cons of Leader-Based Architectures:\n✖ Throughput bottlenecked by a single broker per partition\n✖ Inter-zone traffic required for high availability in multi-AZ deployments",[47,419,420],{},"While Kafka and Pulsar offer partial solutions (e.g., reading from followers, shadow topics) to reduce read-related inter-zone traffic, producers still send data to a single leader.",[47,422,423,424,429],{},"Ursa removes the concept of topic ownership, allowing any broker in the cluster to handle reads or writes for any partition. The primary challenge—ensuring message ordering—is solved with ",[54,425,428],{"href":426,"rel":427},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Foxia",[263],"Oxia",", a scalable metadata and index service created by StreamNative in 2022.",[31,431,433],{"id":432},"oxia-the-metadata-layer-enabling-leaderless-architecture","Oxia: The Metadata Layer Enabling Leaderless Architecture",[47,435,436],{},"Ensuring message ordering in a leaderless architecture is complex, but Ursa solves this with Oxia:",[337,438,439,442,445],{},[340,440,441],{},"Handles millions of metadata\u002Findex operations per second",[340,443,444],{},"Generates sequential IDs to maintain strict message ordering",[340,446,447],{},"Optimized for Kubernetes with horizontal scalability",[47,449,450],{},"Producers and consumers can connect to any broker within their local AZ, eliminating inter-zone traffic costs while maintaining performance through localized caching.",[31,452,454],{"id":453},"zero-interzone-data-replication","Zero interzone data replication",[47,456,457],{},"In most distributed systems, data replication from a leader (primary) to followers (replicas) is crucial for fault tolerance and availability. However, replication across zones can inflate infrastructure expenses substantially.",[47,459,460],{},"Ursa avoids these costs by writing data directly to cloud storage (e.g., AWS S3, Google GCS):",[337,462,463,466,469],{},[340,464,465],{},"Built-In Resilience: Cloud storage inherently offers high availability and fault tolerance without inter-zone traffic fees.",[340,467,468],{},"Tradeoff: Slightly higher latency (sub-second, with p99 at 500 milliseconds) compared to local disk\u002FEBS (single-digit to sub-100 milliseconds), in exchange for significantly lower costs (up to 10x lower).",[340,470,471],{},"Flexible Modes: Ursa is an addition to the classic BookKeeper-based engine, providing users with the flexibility to optimize for either cost or low latency based on their workload requirements.",[47,473,474],{},"By foregoing conventional replication, Ursa slashes inter-zone traffic costs and associated complexities—making it a compelling option for organizations seeking to balance high-performance data streaming with strict budget constraints.",[39,476,478],{"id":477},"how-we-ran-a-5-gbs-test-with-ursa","How We Ran a 5 GB\u002Fs Test with Ursa",[31,480,482],{"id":481},"ursa-cluster-deployment","Ursa Cluster Deployment",[337,484,485,488],{},[340,486,487],{},"9 brokers across 3 availability zones, each on m6i.8xlarge (Fixed 12.5 Gbps bandwidth, 32 vCPU cores, 128 GB memory).",[340,489,490],{},"Oxia cluster (metadata store) with 3 nodes of m6i.8xlarge, distributed across three availability zones (AZs).",[47,492,493],{},"During peak throughput (5 GB\u002Fs), each broker’s network usage was about 10 Gbps.",[31,495,497],{"id":496},"openmessaging-benchmark-workers-configuration","OpenMessaging Benchmark Workers & Configuration",[47,499,500,501,505],{},"The OpenMessaging Benchmark(OMB) Framework is a suite of tools that make it easy to benchmark distributed messaging systems in the cloud. Please check ",[54,502,503],{"href":503,"rel":504},"https:\u002F\u002Fopenmessaging.cloud\u002Fdocs\u002Fbenchmarks\u002F",[263]," for details.",[337,507,508,523,532],{},[340,509,510,511,516,517,522],{},"12 OMB workers: 6 for ",[54,512,515],{"href":513,"rel":514},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002Fd1094122270775e4f1580947f80c5055",[263],"producers",", 6 for ",[54,518,521],{"href":519,"rel":520},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F06bada89381fb77a7862e1b4c1d8963d",[263],"consumers"," across 3 availability zones, on m6i.8xlarge instances. Each worker is configured with 12 CPU cores and 48 GB memory.",[340,524,525,526,531],{},"Sample YAML ",[54,527,530],{"href":528,"rel":529},"https:\u002F\u002Fgist.github.com\u002Fcodelipenghui\u002F204c1f26c4d44a218ae235bf2de99904",[263],"scripts"," provided for Kafka-compatible configuration and rate limits.",[340,533,534],{},"Achieved consistent 5 GB\u002Fs publish\u002Fsubscribe throughput.",[39,536,538],{"id":537},"ursa-benchmark-tests-results","Ursa Benchmark Tests & Results",[47,540,541],{},"The following diagram demonstrates that Ursa can consistently handle 5 GB\u002Fs of traffic, fully saturating the network across all broker nodes.",[47,543,544],{},[349,545],{"alt":17,"src":546},"\u002Fimgs\u002Fblogs\u002F679c602d7b261bac1113f7d6_AD_4nXdDPsRc3koXICiFF0bqSmGWbJt_RlUy4FE3ruuWOfbCfpcqZ1dejjqGbkaCJv2hQFL1nirRouBVRW2l5uMWBvY9naMqGB_wHcLI14dBM0f85TXhmdm3UxEv1yGX9Y4hf5FttSkZew.png",[39,548,550],{"id":549},"comparing-infrastructure-cost","Comparing Infrastructure Cost",[47,552,553],{},"This benchmark first evaluates infrastructure costs of running a 5 GB\u002Fs streaming workload (1:1 producer-to-consumer ratio) across different data streaming engines, including Ursa, Redpanda, and AWS MSK, with a focus on multi-AZ deployments to ensure a fair comparison.",[31,555,557],{"id":556},"test-setup-key-assumptions","Test Setup & Key Assumptions",[47,559,560],{},"All tests use multi-AZ configurations, with clusters and clients distributed across three AWS availability zones (AZs). Cluster size scales proportionally to the number of AZs, and rack-awareness is enabled for all engines to evenly distribute topic partitions and leaders.",[47,562,563],{},"To ensure a fair comparison, we selected the same machine type capable of fully utilizing both network and storage bandwidth for Ursa and Redpanda in this 5GB\u002Fs test:",[337,565,566],{},[340,567,568],{},"9 × m6i.8xlarge instances",[47,570,571,572,577],{},"However, MSK's storage bandwidth limits vary depending on the selected instance type, with the highest allowed limit capped at 1000 MiB\u002Fs per broker, according to",[54,573,576],{"href":574,"rel":575},"https:\u002F\u002Fdocs.aws.amazon.com\u002Fmsk\u002Flatest\u002Fdeveloperguide\u002Fmsk-provision-throughput-management.html#throughput-bottlenecks",[263]," AWS documentation",". Given this constraint, achieving 5 GB\u002Fs throughput with a replication factor of 3 required the following setup:",[337,579,580],{},[340,581,582],{},"15 × kafka.m7g.8xlarge (32 vCPUs, 128 GB memory, 15 Gbps network, 4000 GiB EBS).",[47,584,585],{},"This configuration was necessary to work around MSK's storage bandwidth limitations, ensuring a comparable cost basis to other evaluated streaming engines.",[47,587,588],{},"Additional key assumptions include:",[337,590,591,594,597],{},[340,592,593],{},"Inter-AZ producer traffic: For leader-based engines, two-thirds of producer-to-broker traffic crosses AZs due to leader distribution.",[340,595,596],{},"Consumer optimizations: Follower fetch is enabled across all tests, eliminating inter-AZ consumer traffic.",[340,598,599],{},"Storage cost exclusions: This benchmark only evaluates streaming costs, assuming no long-term data retention.",[31,601,603],{"id":602},"inter-broker-replication-costs","Inter-Broker Replication Costs",[47,605,606],{},"Inter-broker (cross-AZ) replication is a major cost driver for data streaming engines:",[337,608,609,612,615],{},[340,610,611],{},"RedPanda: Inter-broker replication is not free, leading to substantial costs when data must be copied across multiple availability zones.",[340,613,614],{},"AWS MSK: Inter-broker replication is free, but MSK instance pricing is significantly higher (e.g., $3.264 per hour for kafka.m7g.8xlarge vs $1.306 per hour for an on-demand m7g.8xlarge). The storage price of MSK is $0.10 per GB-month which is significantly higher than st1, which costs $0.045 per GB-month. Even though replication is free, client-to-broker traffic still incurs inter-AZ charges.",[340,616,617],{},"Ursa: No inter-broker replication costs due to its leaderless architecture, eliminating inter-zone replication costs entirely.",[31,619,621],{"id":620},"zone-affinity-reducing-inter-az-costs","Zone Affinity: Reducing Inter-AZ Costs",[47,623,624],{},"We evaluated zone affinity mechanisms to further reduce inter-AZ data transfer costs.",[47,626,627],{},"Consumers:",[337,629,630],{},[340,631,632],{},"Follower fetch is enabled across all tests, ensuring consumers fetch data from replicas in their local AZ—eliminating inter-zone consumer traffic except for metadata lookups",[47,634,635],{},"Producers:",[337,637,638,647,656],{},[340,639,640,641,646],{},"Kafka protocol lacks an easy way to enforce producer AZ affinity (though ",[54,642,645],{"href":643,"rel":644},"https:\u002F\u002Fcwiki.apache.org\u002Fconfluence\u002Fdisplay\u002FKAFKA\u002FKIP-1123:+Rack-aware+partitioning+for+Kafka+Producer",[263],"KIP-1123"," aims to address this). And it only works with the default partitioner (i.e., when no record partition or record key is specified).",[340,648,649,650,655],{},"Redpanda recently introduced ",[54,651,654],{"href":652,"rel":653},"https:\u002F\u002Fdocs.redpanda.com\u002Fredpanda-cloud\u002Fdevelop\u002Fproduce-data\u002Fleader-pinning\u002F",[263],"leader pinning",", but this only benefits setups where producers are confined to a single AZ—not applicable to our multi-AZ benchmark.",[340,657,658,659,664],{},"Ursa is the only system in this test with ",[54,660,663],{"href":661,"rel":662},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fconfig-kafka-client#eliminate-cross-az-networking-traffic",[263],"built-in zone affinity for both producers and consumers",". It achieves this by embedding producer AZ information in client.id, allowing metadata lookups to route clients to local-AZ brokers, eliminating inter-AZ producer traffic.",[31,666,668],{"id":667},"cost-comparison-results","Cost Comparison Results",[47,670,335],{},[337,672,673,675],{},[340,674,342],{},[340,676,345],{},[47,678,679],{},"Ursa’s leaderless architecture, zone affinity, and native cloud storage integration deliver unparalleled cost efficiency, making it the most cost-effective choice for high-throughput data streaming workloads.",[47,681,682],{},[349,683],{"alt":17,"src":684},"\u002Fimgs\u002Fblogs\u002F679c72208198ca36a352f228_AD_4nXeeZuM8T-xBlD4Vf3j67K618n08qh8wIDLLtiLJG0ssA1Wj1V26u7wIDTX9sqLrtw8mB2c299dwzarGen62CG0Vh7nWstn5qbPGFcBaKJYEepTsLr5fHWv1U8uqbg8Y0UOK6fJ7.png",[47,686,687],{},[349,688],{"alt":17,"src":689},"\u002Fimgs\u002Fblogs\u002F679c625978031f40229de484_AD_4nXdLkLLJ30KKr-_A_rN1j8akVwBYacAWIPzWHoOReJF421890kfByZoQQxkLczihVSmiw5Q9J51-V9I2SEKITbwsYnANDDTlAVL5nQ_jfaHNTe9VEWhSoa7DZooCnilDYL6l6msmJg.png",[47,691,692],{},"The detailed infrastructure cost calculations for each data streaming engine are listed below:",[31,694,696],{"id":695},"streamnative-ursa","StreamNative - Ursa",[337,698,699,702,705,708],{},[340,700,701],{},"Server EC2 costs: 9 * $1.536\u002Fhr = $14",[340,703,704],{},"Client EC2 costs: 9 * $1.536\u002Fhr =$14",[340,706,707],{},"S3 write requests costs: 1350 r\u002Fs * $0.005\u002F1000r * 3600s = $24",[340,709,710],{},"S3 read requests costs: 1350 r\u002Fs * $0.0004\u002F1000r * 3600s = $2",[31,712,714],{"id":713},"aws-msk","AWS MSK",[337,716,717,720,723],{},[340,718,719],{},"Server EC2 costs: 15 * $3.264\u002Fhr = $49",[340,721,722],{},"Client side EC2 costs: 9 * $1.536\u002Fhr =$14",[340,724,725],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FG(in+out) * 3600 = $240",[31,727,729],{"id":728},"redpanda","RedPanda",[337,731,732,734,736,739,742],{},[340,733,701],{},[340,735,704],{},[340,737,738],{},"Interzone traffic - producer to broker: 5GB\u002Fs * ⅔ * $0.02\u002FGB(in+out) * 3600 = $240",[340,740,741],{},"Interzone traffic - replication: 10GB\u002Fs * $0.02\u002FGB(in+out) * 3600 = $720",[340,743,744],{},"Interzone traffic - broker to consumer: $0 (fetch from local zone)",[47,746,747,748,753],{},"Please note that we were unable to test ",[54,749,752],{"href":750,"rel":751},"https:\u002F\u002Fwww.redpanda.com\u002Fblog\u002Fcloud-topics-streaming-data-object-storage",[263],"Redpanda with Cloud Topics",", as it remains an announced but unreleased feature and is not yet available for evaluation. Based on the limited information available, while Cloud Topics may help optimize inter-zone data replication costs, producers still need to traverse inter-availability zones to connect to the topic partition owners and incur inter-zone traffic costs of up to $240 per hour.",[337,755,756,762],{},[340,757,758,761],{},[54,759,645],{"href":643,"rel":760},[263]," (when implemented) will help mitigate producer-to-broker inter-zone traffic, but it is not yet available. And it only works with the default partitioner (no record partition or key is specified).",[340,763,764],{},"Redpanda’s leader pinning helps only when all producers for the pinned topic are confined to a single AZ. In multi-AZ environments (like our benchmark), inter-zone producer traffic remains unavoidable.",[47,766,767],{},"Additionally, Redpanda’s Cloud Topics architecture is not documented publicly. Their blog mentions \"leader placement rules to optimize produce latency and ingress cost,\" but it is unclear whether this represents a shift away from a leader-based architecture or if it uses techniques similar to Ursa’s zone-aware approach.",[47,769,770],{},"We may revisit this comparison as more details become available.",[39,772,774],{"id":773},"comparing-total-cost-of-ownership","Comparing Total Cost of Ownership",[47,776,777],{},"As highlighted earlier, with a BYOC Ursa setup, you can achieve 5 GB\u002Fs throughput at just 5% of the infrastructure cost of a traditional leader-based data streaming engine, such as Kafka or RedPanda, while managing the infrastructure yourself. This significant cost reduction is enabled by Ursa’s leaderless architecture and lakehouse-native storage design, which eliminate overhead costs such as inter-zone traffic and leader-based data replication. By leveraging a lakehouse-native, leaderless architecture, Ursa reduces resource requirements, enabling you to handle high data throughput efficiently and at a fraction of the cost of RedPanda.",[47,779,780],{},"Now, let’s examine the total cost comparison, evaluating Ursa alongside other vendors, including those that have adopted a leaderless architecture (e.g., Confluent WarpStream). This comparison is based on a 5GB\u002Fs workload with a 7-day retention period, factoring in both storage cost and vendor costs Here are the key findings:",[337,782,783,786,789],{},[340,784,785],{},"Ursa ($164,353\u002Fmonth) is: 50% cheaper than Confluent WarpStream ($337,068\u002Fmonth)",[340,787,788],{},"85% cheaper than AWS MSK ($1,115,251\u002Fmonth)",[340,790,791],{},"86% cheaper than Redpanda ($1,202,853\u002Fmonth)",[47,793,794],{},"In addition to Ursa’s architectural advantages—eliminating most inter-AZ traffic and leveraging lakehouse storage for cost-effective data retention—it also adopts a more fair and cost-efficient pricing model: Elastic Throughput-based pricing. This approach aligns costs with actual usage, avoiding unnecessary overhead.",[47,796,797],{},"Unlike WarpStream, which charges for both storage and throughput, Ursa ensures that customers only pay for the throughput they actively use. Ursa’s pricing is based on compressed data sent by clients, meaning the more data compressed on the client side, the lower the cost. In contrast, WarpStream prices are based on uncompressed data, unfairly inflating expenses and failing to incentivize customers to optimize their client applications.",[47,799,800],{},"This distinction is crucial, as compressed data reduces both storage and network costs, making Ursa’s pricing model not only more cost-effective but also more transparent and predictable.",[47,802,803],{},[349,804],{"alt":17,"src":805},"\u002Fimgs\u002Fblogs\u002F679c602d194800c9206d9d58_AD_4nXcFlf755xgyz7htxhMhBV5fGrsxy642mQNodt61DTok_z1dwkw5A6lkO5hatXVneCaB0anbZPAyvLI3MlIMuQEYLEACHHvQMOr5UfaB37dfzkdqewDEvcT-20VGd_zzvJsuA00zGA.png",[47,807,808],{},[349,809],{"alt":17,"src":810},"\u002Fimgs\u002Fblogs\u002F679c62594e9c2e629fae73aa_AD_4nXeU6cOgItnjLsEZCOf13TEvMY_SHWWIxYP2OYUj-B1GUPyWO78OG08K_v03hwYSVcg06f9dqDiGmdwy76vynjmiDGL5bluZ5_XF4nSU_r59oOZdfViXndXt6s11vVOY7qwfZN8v.png",[31,812,814],{"id":813},"cost-breakdown","Cost Breakdown",[816,817,818],"h4",{"id":695},"StreamNative – Ursa",[337,820,821,824,827,830,833],{},[340,822,823],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9,953.28",[340,825,826],{},"S3 Write Requests: 1,350 r\u002Fs × $0.005\u002F1,000 r × 3,600 s × 24 hr × 30 days = $17,496",[340,828,829],{},"S3 Read Requests: 1,350 r\u002Fs × $0.0004\u002F1,000 r × 3,600 s × 24 hr × 30 days = $1,400",[340,831,832],{},"S3 Storage Costs: 5 GB\u002Fs × $0.021\u002FGB × 3,600 s × 24 hr × 7 days = $63,504",[340,834,835],{},"Vendor Cost: 200 ETU × $0.50\u002Fhr × 24 hr × 30 days = $72,000",[816,837,839],{"id":838},"warpstream","WarpStream",[337,841,842,845],{},[340,843,844],{},"Based on WarpStream’s pricing calculator (as of January 29, 2025), we assume a 4:1 client data compression ratio, meaning 20 GB\u002Fs of uncompressed data translates to 5 GB\u002Fs of compressed data.",[340,846,847,848,853],{},"It's important to note that WarpStream’s pricing structure has fluctuated frequently throughout January. We observed the cost reported by their calculator changing from $409,644 per month to $337,068 per month. This variability has been previously highlighted in the blog post “",[54,849,852],{"href":850,"rel":851},"https:\u002F\u002Fbigdata.2minutestreaming.com\u002Fp\u002Fthe-brutal-truth-about-apache-kafka-cost-calculators",[263],"The Brutal Truth About Kafka Cost Calculators","”. To ensure transparency, we have documented the pricing as of January 29, 2025.",[47,855,856],{},[349,857],{"alt":17,"src":858},"\u002Fimgs\u002Fblogs\u002F679c602e42713e0028e9af5e_AD_4nXcu5_VWTLu9jRYs6zX1MBAOtLQEo5gyfNSWPcbpnQHXTa8qNCFAXezRR2E8daygzYTTwd4dhJjaLaLM8C6y_3OGbu2NS7pdvEv3a8-ptNKOg7AeKnYqPQCAYvQ5EuxzuI3JYIvY.png",[816,860,862],{"id":861},"msk","MSK",[337,864,865,868,871],{},[340,866,867],{},"EC2 (Server): 15 * $3.264\u002Fhr × 24 hr × 30 days = $35,251",[340,869,870],{},"Interzone Traffic (Client-Server): 5 GB\u002Fs × ⅔ × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $172,800",[340,872,873],{},"Storage: 5 GB\u002Fs × $0.1\u002FGB-month × 3,600 s × 24 hr × 7 days * 3 replicas = $907,200",[816,875,729],{"id":876},"redpanda-1",[337,878,879,882,884,887,890],{},[340,880,881],{},"EC2 (Server): 9 × $1.536\u002Fhr × 24 hr × 30 days = $9953",[340,883,870],{},[340,885,886],{},"Interzone Traffic (Replication): 5 GB\u002Fs × 2 × $0.02\u002FGB (in+out) × 3,600 s × 24 hr × 30 days = $518,400",[340,888,889],{},"Storage: 5 GB\u002Fs × $0.045\u002FGB-month(st1) × 3,600 s × 24 hr × 7 days * 3 replicas = $408,240",[340,891,892],{},"Vendor Cost: $93,333 per month (based on limited information. See additional notes below).",[816,894,896],{"id":895},"additional-notes","Additional Notes",[337,898,899],{},[340,900,901,902,907],{},"Redpanda does not publicly disclose its BYOC pricing, making it difficult to accurately assess its total costs. We refer to information from the whitepaper “",[54,903,906],{"href":904,"rel":905},"https:\u002F\u002Fwww.redpanda.com\u002Fresources\u002Fredpanda-vs-confluent-performance-tco-benchmark-report#form",[263],"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group.","” for estimation purposes. Based on the Tier-8 pricing model in the whitepaper,  the estimated cost to support a 5GB\u002Fs workload would be $1.12 million per year ($93,333 per month). However, since this calculation is based on an estimation, we will revisit and refine the cost assessment once Redpanda publishes its BYOC pricing.",[47,909,910],{},[349,911],{"alt":17,"src":912},"\u002Fimgs\u002Fblogs\u002F679c602dc8a9859eed89a0ef_AD_4nXdbcO8vsNNPy4GtkNLlmNKf22fjxRvzLzH7CtOna1L08sTbvnZx3HhufeFqc1w4K2gEF7lxO2IR5supotxebAiGnA07Qa8Yr3Rd1pVK2LYKK4WurlJGwgdwwucZIFoF-N_2oBjY.png",[47,914,915],{},[349,916],{"alt":17,"src":917},"\u002Fimgs\u002Fblogs\u002F679c602d6bc1c2287e012540_AD_4nXfcHZnLfjbjIr3ZAgoQXT9dwP3aQCOQPmGZZJUtpNZSwE6qY6M3yehIaBxCwxEIeu5PVdUPY0zhyjnow26YfgjdYgSG4GnV9ibxu0YWTIpwng6z_F6FUGJMpERMKtpsFESzXSN_Sw.png",[337,919,920,923],{},[340,921,922],{},"When estimating the storage costs for Kafka and Redpanda, we assume the use of HDD storage at $0.045\u002FGB, based on the premise that both systems can fully utilize disk bandwidth without incurring the higher costs associated with GP2 or GP3 volumes. However, in practice, many users opt for GP2 or GP3, significantly increasing the total storage cost for Kafka and Redpanda.",[340,924,925],{},"Unlike disk-based solutions, S3 storage does not require capacity preallocation—Ursa only incurs costs for the actual data stored. This contrasts with Kafka and Redpanda, where preallocating storage can drive up expenses. As a result, the real-world storage costs for Kafka and Redpanda are often 50% higher than the estimates above.",[39,927,929],{"id":928},"conclusion","Conclusion",[47,931,932],{},"Ursa represents a transformative shift in streaming data infrastructure, offering cost efficiency, scalability, and flexibility without compromising durability or reliability. By leveraging a leaderless architecture and eliminating inter-zone data replication, Ursa reduces total cost of ownership by over 90% compared to traditional leader-based streaming engines like Kafka and Redpanda. Its direct integration with cloud storage and scalable metadata & index management via Oxia ensure high availability and simplified infrastructure management.",[31,934,936],{"id":935},"balancing-latency-and-cost","Balancing Latency and Cost",[47,938,939,943],{},[54,940,942],{"href":941},"\u002Fblog\u002Fcap-theorem-for-data-streaming","Ursa trades off slightly higher latency for ultra low cost",", making it an ideal choice for the majority of streaming workloads, especially those that prioritize throughput and cost savings over ultra-low latency. Meanwhile, StreamNative’s BookKeeper-based engine remains the preferred solution for real-time, latency-sensitive applications. By combining these two approaches, StreamNative empowers customers with the flexibility to choose the right engine for their specific needs—whether it's maximizing cost savings or achieving ultra low-latency real-time performance.",[31,945,947],{"id":946},"the-future-of-streaming-infrastructure","The Future of Streaming Infrastructure",[47,949,950],{},"In an era where data fuels AI, analytics, and real-time decision-making, managing infrastructure costs is critical to sustaining innovation. Ursa is not just a cost-cutting alternative—it is a forward-thinking, lakehouse-native platform that redefines how modern data streaming infrastructure should be built and operated.",[47,952,953,954,959],{},"Whether your priority is reducing costs, improving flexibility, or ingesting massive data into lakehouses, Ursa delivers a future-proof solution for the evolving demands of real-time data streaming. ",[54,955,958],{"href":956,"rel":957},"https:\u002F\u002Fconsole.streamnative.cloud\u002F",[263],"Get started"," with StreamNative Ursa today!",[961,962,964],"h1",{"id":963},"references","References",[47,966,967,970,971],{},[968,969,428],"span",{}," ",[54,972,973],{"href":973},"\u002Fblog\u002Fintroducing-oxia-scalable-metadata-and-coordination",[47,975,976,970,978],{},[968,977,377],{},[54,979,376],{"href":376},[47,981,982,970,985],{},[968,983,984],{},"StreamNative pricing",[54,986,987],{"href":987,"rel":988},"https:\u002F\u002Fdocs.streamnative.io\u002Fdocs\u002Fbilling-overview",[263],[47,990,991,970,994],{},[968,992,993],{},"WarpStream pricing",[54,995,996],{"href":996,"rel":997},"https:\u002F\u002Fwww.warpstream.com\u002Fpricing#pricingfaqs",[263],[47,999,1000,970,1003],{},[968,1001,1002],{},"AWS S3 pricing",[54,1004,1005],{"href":1005,"rel":1006},"https:\u002F\u002Faws.amazon.com\u002Fs3\u002Fpricing\u002F",[263],[47,1008,1009,970,1012],{},[968,1010,1011],{},"AWS EBS pricing",[54,1013,1014],{"href":1014,"rel":1015},"https:\u002F\u002Faws.amazon.com\u002Febs\u002Fpricing\u002F",[263],[47,1017,1018,970,1021],{},[968,1019,1020],{},"AWS MSK pricing",[54,1022,1023],{"href":1023,"rel":1024},"https:\u002F\u002Faws.amazon.com\u002Fmsk\u002Fpricing\u002F",[263],[47,1026,1027,970,1030],{},[968,1028,1029],{},"The Brutal Truth about Kafka Cost Calculators",[54,1031,850],{"href":850,"rel":1032},[263],[47,1034,1035,970,1038],{},[968,1036,1037],{},"Redpanda vs. Confluent: A Performance and TCO Benchmark Report by McKnight Consulting Group",[54,1039,904],{"href":904,"rel":1040},[263],{"title":17,"searchDepth":18,"depth":18,"links":1042},[1043,1044,1045,1050,1054,1055,1064,1067],{"id":331,"depth":18,"text":332},{"id":370,"depth":18,"text":371},{"id":395,"depth":18,"text":396,"children":1046},[1047,1048,1049],{"id":407,"depth":278,"text":408},{"id":432,"depth":278,"text":433},{"id":453,"depth":278,"text":454},{"id":477,"depth":18,"text":478,"children":1051},[1052,1053],{"id":481,"depth":278,"text":482},{"id":496,"depth":278,"text":497},{"id":537,"depth":18,"text":538},{"id":549,"depth":18,"text":550,"children":1056},[1057,1058,1059,1060,1061,1062,1063],{"id":556,"depth":278,"text":557},{"id":602,"depth":278,"text":603},{"id":620,"depth":278,"text":621},{"id":667,"depth":278,"text":668},{"id":695,"depth":278,"text":696},{"id":713,"depth":278,"text":714},{"id":728,"depth":278,"text":729},{"id":773,"depth":18,"text":774,"children":1065},[1066],{"id":813,"depth":278,"text":814},{"id":928,"depth":18,"text":929,"children":1068},[1069,1070],{"id":935,"depth":278,"text":936},{"id":946,"depth":278,"text":947},"StreamNative Cloud","2025-01-31","Discover how Ursa achieves 5GB\u002Fs Kafka workloads at just 5% of the cost of traditional streaming engines like Redpanda and AWS MSK. See our benchmark results comparing infrastructure costs, total cost of ownership (TCO), and performance across leading Kafka vendors.","\u002Fimgs\u002Fblogs\u002F679c6593d25099b1cdcec4ca_image-31.png",{},"\u002Fblog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour","30 min",{"title":306,"description":1073},"blog\u002Fhow-we-run-a-5-gb-s-kafka-workload-for-just-50-per-hour",[1081,1082,301],"TCO","Apache Kafka","A0o_2xdJiLI6rf6xj4RKsxJNo_A6QN2fYzCp6gaLrFw",{"id":1085,"title":1086,"authors":1087,"body":1089,"company":1101,"createdAt":10,"customerQuote":10,"date":1558,"description":1559,"extension":8,"featured":292,"image":1560,"industry":1561,"isDraft":292,"link":10,"logo":10,"meta":1562,"navigation":7,"order":294,"path":1563,"products":10,"readingTime":1564,"relatedResources":10,"seo":1565,"size":1566,"stem":1567,"tags":1568,"technologies":1570,"useCases":10,"__hash__":1571},"successStories\u002Fsuccess-stories\u002Fyum-china.md","Pulsar at Yum China: Achieving Low Latency and High Throughput for Business and Operational Data",[1088],"Chauncey Yan",{"type":14,"value":1090,"toc":1537},[1091,1095,1103,1106,1110,1113,1116,1119,1123,1126,1134,1140,1143,1149,1163,1169,1172,1175,1179,1182,1188,1192,1201,1211,1214,1220,1223,1227,1230,1236,1240,1243,1249,1252,1256,1259,1270,1276,1279,1283,1286,1290,1293,1297,1300,1303,1309,1312,1318,1322,1331,1337,1340,1346,1352,1355,1359,1362,1365,1371,1374,1377,1383,1388,1391,1394,1400,1403,1407,1410,1413,1419,1422,1428,1432,1435,1439,1442,1445,1451,1454,1458,1461,1464,1468,1471,1490,1494,1503],[39,1092,1094],{"id":1093},"introduction-to-yum-china","Introduction to Yum China",[47,1096,1097,1102],{},[54,1098,1101],{"href":1099,"rel":1100},"https:\u002F\u002Fir.yumchina.com\u002Fabout-yum-china",[263],"Yum China"," is the largest restaurant company in China with a mission to make every life taste beautiful. The company has over 400,000 employees and operates nearly 13,000 restaurants under six brands across 1,800 cities in China. KFC and Pizza Hut are the leading brands in the quick-service and casual dining restaurant spaces in China, respectively. Taco Bell offers innovative Mexican-inspired food. Yum China has also partnered with Lavazza to develop the Lavazza coffee shop concept in China. Little Sheep and Huang Ji Huang specialize in the Chinese cuisine.",[47,1104,1105],{},"Yum China has a world-class, digitalized supply chain which includes an extensive network of logistics centers nationwide and an in-house supply chain management system. Its strong digital capabilities and loyalty program enable the company to reach customers faster and serve them better. Yum China is a Fortune 500 company with the vision to be the world’s most innovative pioneer in the restaurant industry.",[39,1107,1109],{"id":1108},"choosing-apache-pulsar-great-scalability-and-low-operational-costs","Choosing Apache Pulsar: Great scalability and low operational costs",[47,1111,1112],{},"Yum China’s journey with Apache Pulsar began in 2019, when the company aimed to build a message queue (MQ) PaaS platform for internal usage. At that time, the primary requirements for this platform were horizontal scalability and low operational costs.",[47,1114,1115],{},"After a thorough evaluation of some popular solutions, the company concluded that Pulsar, with a decoupled design principle, would better suit their needs in terms of scalability and operational costs. Additionally, Pulsar’s ongoing development and active community, along with its features like flexible subscription types (Exclusive, Failover, Shared, and Key-Shared), guaranteed message delivery, low publish latency and end-to-end latency, and seamless geo-replication, made it an attractive choice for Yum China.",[47,1117,1118],{},"“All these features offered by Pulsar are the reasons that finally led us to choose Pulsar and we are happy with this decision ever since,” said Chauncey Yan, Backend Software Engineer at Yum China.",[39,1120,1122],{"id":1121},"tailoring-pulsar-clusters-for-business-and-operational-data","Tailoring Pulsar clusters for business and operational data",[47,1124,1125],{},"At Yum China, the traffic served by its MQ system could be broadly divided into two categories:",[337,1127,1128,1131],{},[340,1129,1130],{},"Core business data: Requires strong data integrity, high throughput, and low publish latency. Cost-effectiveness is not a priority for this category.",[340,1132,1133],{},"Operational data: Consists of monitoring metrics, which are essentially ephemeral, so data integrity and latency are less important. Cost-effectiveness is a crucial consideration for this category, as Yum China needs to handle a vast number of monitoring metrics with a relatively small hardware setup.",[47,1135,1136],{},[349,1137],{"alt":1138,"src":1139},"Two traffic types Yum China","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d064840545c_64783e6fa0dc0f93176cd017_image13.webp",[47,1141,1142],{},"Yum China decided to set up two separate Pulsar clusters to cater to the distinct requirements of its business and operational data. There are four major differences between the Business cluster and the Ops cluster.",[47,1144,1145],{},[349,1146],{"alt":1147,"src":1148},"Business Cluster Comparison","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405440_64783e85a0dc0f93176cd75d_image12.webp",[337,1150,1151,1154,1157,1160],{},[340,1152,1153],{},"Cluster size: The Business cluster has 26 nodes, while the Ops cluster has 10 nodes. The larger size of the Business cluster means higher throughput for core business data.",[340,1155,1156],{},"Send method: The Business cluster uses synchronous send, while the Ops cluster uses asynchronous send. Asynchronous send offers better publish latency and overall throughput. However, when calling the async send method, the Pulsar client puts messages into a local send queue, which wait to be sent in batch to the Pulsar server; this may result in message loss during service crashes or power failures. In contrast, the synchronous send method ensures the durability of writes, so it is more suitable for the Business cluster while it is at the cost of latency and throughput.",[340,1158,1159],{},"Quorum setting: The Business cluster has a write quorum of 3 and an ack quorum of 2, guaranteeing at least one copy of every message. In contrast, the Ops cluster does not require replication, as monitoring metrics are ephemeral.",[340,1161,1162],{},"Journal write behavior: In the Business cluster, journal sync is enabled, ensuring data persistence to the hard drive before write requests return. In the Ops cluster, journal sync is disabled, allowing data to be written to the operating system page cache. In the event of a power failure or system reboot, messages in the page cache will not be able to get flushed into the hard drive, and therefore entries will be lost. As a result, journal sync is enabled for the Business cluster while it is disabled for the Ops cluster.",[1164,1165,1166],"blockquote",{},[47,1167,1168],{},"Note: When a client writes a message to Pulsar, it is stored in two places in BookKeeper - a journal file and a ledger file. The journal provides a recovery mechanism and the ledger is where the message is actually persistent and clients will read it later.",[47,1170,1171],{},"“With this setup in the Business cluster, we successfully solved the throughput issue with horizontal scalability and achieved data durability. That said, some of these configurations can increase latency,” Yan added. “For the Ops cluster, we threw away all guarantees for data durability but the throughput still remains a very challenging problem.”",[47,1173,1174],{},"More details about how Yum China lowered latency in the Business cluster and maximized throughput in the Ops cluster will be given later.",[39,1176,1178],{"id":1177},"use-cases-how-pulsar-powers-internal-applications","Use cases: How Pulsar powers internal applications",[47,1180,1181],{},"Yum China’s infrastructure consists of four availability zones (AZs), each running a complete copy of its working system. In each AZ, Yum China deployed two Pulsar clusters, one for the Business cluster and one for the Ops cluster. The Business clusters are connected through a geo-replication namespace. Based on this architecture, Yum China has developed several services for its internal use cases.",[47,1183,1184],{},[349,1185],{"alt":1186,"src":1187},"Pulsar Clustar deployed in four AZS","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d064840544a_64783ea4d5d5410cfea183bd_image2.webp",[31,1189,1191],{"id":1190},"nightingale","Nightingale",[47,1193,1194,1195,1200],{},"A component inspired by the open-source project ",[54,1196,1199],{"href":1197,"rel":1198},"https:\u002F\u002Fgithub.com\u002FTelefonica\u002Fprometheus-kafka-adapter",[263],"Prometheus Kafka adapter",", Nightingale receives a Prometheus remote write (see the following code snippet), extracts the time series field, reformats the time series data into a customized format, and sends it asynchronously to the Pulsar Ops cluster.",[1202,1203,1208],"pre",{"className":1204,"code":1206,"language":1207},[1205],"language-text","message WriteRequest {\n  repeated prometheus.TimeSeries timeseries = 1 [(gogoproto.nullable) = false];\n  reserved  2;\n  repeated prometheus.MetricMetadata metadata = 3 [(gogoproto.nullable) = false];\n}\n","text",[1209,1210,1206],"code",{"__ignoreMap":17},[47,1212,1213],{},"“We use Prometheus to store all of our monitoring metrics and we want them as stream data so that our big data engineer could analyze streams and then they will probably make some automatic operational decisions. For that purpose, we developed Nightingale,” Yan explained. “All our monitoring metrics go through Nightingale, so it becomes a case where throughput matters.”",[47,1215,1216],{},[349,1217],{"alt":1218,"src":1219},"Data Conversion with Nightingale","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d064840543d_64783ec213b3e90c736d887d_image4.webp",[47,1221,1222],{},"This service is primarily designed to provide high throughput, reaching up to 200,000 messages per second with a three-node setup.",[31,1224,1226],{"id":1225},"pigeonhole","Pigeonhole",[47,1228,1229],{},"Pigeonhole is designed to replicate messages between heterogeneous systems, such as Kafka and RabbitMQ. It allows developers to focus on handling actual messages with only one consumer type, like a Pulsar consumer. This service also requires high throughput, achieving up to 50,000 messages per second per node with the same optimization logic in Nightingale.",[47,1231,1232],{},[349,1233],{"alt":1234,"src":1235},"Pigeonhole Connects","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405452_64783ee225c1f4a55b5b7f8b_image5.webp",[31,1237,1239],{"id":1238},"sonic","Sonic",[47,1241,1242],{},"Sonic is a SaaS solution designed to synchronize Redis commands across different AZs. Sonic receives Redis commands from clients, wraps them with additional information, and sends them to a Pulsar replicated topic. Instances in other AZs consume these messages, unwrap them, and execute the Redis commands in their local Redis cluster. With this setup, Redis commands are broadcast to all AZs. Latency represents a crucial performance metric in this use case.",[47,1244,1245],{},[349,1246],{"alt":1247,"src":1248},"Sonic Synchronizes redis commands across different AZS","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405446_64783f1925c1f4a55b5ba2cb_image11.webp",[47,1250,1251],{},"“With the help of Pulsar’s geo-replication feature, we developed Sonic and freed ourselves from the trivial details of data durability arrangement and replication,” Yan said.",[31,1253,1255],{"id":1254},"eventbridge","Eventbridge",[47,1257,1258],{},"Eventbridge is a PaaS solution for gathering events from Yum China’s cloud system. Eventbridge has three key components:",[337,1260,1261,1264,1267],{},[340,1262,1263],{},"A Preprocessor that accepts incoming events and puts them to a Pulsar Eventbridge topic;",[340,1265,1266],{},"A Rule Engine that consumes the Eventbridge topic, matches events with subscription rules, and puts them in the send topic;",[340,1268,1269],{},"An Event Processor consumes the send topic and sends events to subscribers.",[47,1271,1272],{},[349,1273],{"alt":1274,"src":1275},"Event Bridghe Architecture","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405455_64783f60a0dc0f93176d16a6_image6.webp",[47,1277,1278],{},"“In EventBridge, different subscription types of Pulsar help us provide different levels of guarantees for each subscriber,” said Yan. “We use a combination of send methods together with different subscription types to ensure event ordering and persistence.”",[39,1280,1282],{"id":1281},"tuning-performance-for-low-latency-and-high-throughput","Tuning performance for low latency and high throughput",[47,1284,1285],{},"When creating and using the above tools in addressing various internal requirements, Yum China worked out some performance-tuning strategies for low latency and high throughput.",[31,1287,1289],{"id":1288},"reducing-latency-for-the-business-cluster","Reducing latency for the Business cluster",[47,1291,1292],{},"To achieve low latency for the Business cluster, Yum China focused on the following three aspects.",[816,1294,1296],{"id":1295},"hard-drive","Hard drive",[47,1298,1299],{},"Writing entries to a ledger is asynchronous, while writes to a journal file are synchronous and can contribute to higher latency. If journal entry sync is enabled, BookKeeper will do a fsync call for every write, the latency of which heavily depends on the performance of the hard drive.",[47,1301,1302],{},"To benchmark hard drive performance, Yum China used FIO to simulate the process of writing entries to journals.",[47,1304,1305],{},[349,1306],{"alt":1307,"src":1308},"The FIO Result","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d064840545f_64783facd5d5410cfea22a70_image7.webp",[47,1310,1311],{},"“Based on the results of our FIO benchmark test, we had a discussion with our cloud provider and we believed that the issue lay in network switches,” Yan explained. “So we decided to switch to a local SSD setup, and the result turned out amazing. Our maximum write latency dropped from 300 milliseconds to 5 milliseconds.” Figure 9 shows that the maximum write latency declined dramatically with the local SSD setting.",[47,1313,1314],{},[349,1315],{"alt":1316,"src":1317},"Network Disk vs Local SSD","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405462_6478434ed6b0d030faccf3b2_image3.png",[816,1319,1321],{"id":1320},"jvm-gc-pauses","JVM GC pauses",[47,1323,1324,1325,1330],{},"Yum China has been using the ",[54,1326,1329],{"href":1327,"rel":1328},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fapache-pulsar-grafana-dashboard",[263],"Pulsar Grafana Dashboard provided by StreamNative"," to monitor cluster metrics, including Garbage Collection (GC) pauses. As shown in Figure 10, the GC Pauses graph displays that the latency can reach 150 milliseconds, which is sub-optimal, especially in cases where low latency is very important.",[47,1332,1333],{},[349,1334],{"alt":1335,"src":1336},"Pulsar Grafana Dashboard","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405432_6478404625f310f0db150c14_image9.webp",[47,1338,1339],{},"To reduce the duration of JVM GC pauses, Yan’s team fine-tuned the default JVM configurations of Pulsar by removing the New Gen Size requirement and limiting the GC thread concurrency. See the following two sets of parameters for details.",[1202,1341,1344],{"className":1342,"code":1343,"language":1207},[1205],"## Default JVM configurations\n-XX:+UseG1GC \n-XX:MaxGCPauseMillis=10 \n-XX:+ParallelRefProcEnabled \n-XX:+UnlockExperimentalVMOptions \n-XX:+DoEscapeAnalysis \n-XX:ParallelGCThreads=32 \n-XX:ConcGCThreads=32 \n-XX:G1NewSizePercent=50 \n-XX:+DisableExplicitGC \n-XX:-ResizePLAB\n\n## How Yum China fine-tuned the parameters:\n-XX:+UseG1GC \n-XX:MaxGCPauseMillis=10 \n-XX:+ParallelRefProcEnabled \n-XX:+UnlockExperimentalVMOptions \n-XX:+DoEscapeAnalysis \n-XX:ParallelGCThreads=8\n-XX:ConcGCThreads=2\n-XX:+DisableExplicitGC \n-XX:-ResizePLAB\"\n",[1209,1345,1343],{"__ignoreMap":17},[47,1347,1348],{},[349,1349],{"alt":1350,"src":1351},"GC Pauses","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d064840544e_6478409faba51b88b38c3091_image8.webp",[47,1353,1354],{},"“If you look at the default JVM configuration of Pulsar, it’s actually a throughput-oriented setup. It requires a large New Gen Size and high concurrency for GC threads. This works well under heavy loads but in our case, we don’t have heavy loads and low latency is very important,” Yan noted. “By removing the New Gen Size, our GC pause becomes normal again around 10 milliseconds. Additionally, the maximum publish latency also dropped from 500 milliseconds to 100 milliseconds.”",[816,1356,1358],{"id":1357},"internal-pulsar-processes","Internal Pulsar processes",[47,1360,1361],{},"Yum China identified two processes that may lead to higher latency - Auditor periodic checks and entry log compaction.",[47,1363,1364],{},"When using Pulsar, Yan’s team noticed that their cluster had high publish latency in a periodic fashion (at the exact time every week). The latency could sometimes exceed 5 seconds. This led them to check BookKeeper logs, where they found that the AuditorBookie was trying to start a process called checkAllLedgers at the same time point.",[47,1366,1367],{},[349,1368],{"alt":1369,"src":1370},"Auditorbookie","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405467_6478416d9ba0132fa610458f_Untitled.webp",[47,1372,1373],{},"The Auditor is one of the elected BookKeeper nodes, responsible for data integrity checks. The checkAllLedgers process, carried by the Auditor node, scans all the ledgers to determine whether the replication matches the write quorum. Unavoidably, this process entails many read and write operations on the hard drive, thus increasing latency.",[47,1375,1376],{},"As the checkAllLedgers check happens once a week by default, Yan’s team decided to set the trigger time at night so it does not impact business. Users can run the following command to reset the auditor’s check time and restart the auditor to load the configuration.",[1202,1378,1381],{"className":1379,"code":1380,"language":1207},[1205],"bin\u002Fbookkeeper shell forceauditchecks --checkallledgerscheck\n",[1209,1382,1380],{"__ignoreMap":17},[1164,1384,1385],{},[47,1386,1387],{},"Note: Avoid peak hours when you use the command. If you run this command at night, then the process will be triggered at night the next time.",[47,1389,1390],{},"Another process that may cause increased latency is entry log compaction. BookKeeper stores ledgers in an interleaved fashion, so an entry log file may contain data from deleted ledgers and undeleted ledgers. If there are any data from undeleted ledgers, the entry log file itself cannot be reclaimed, causing a waste of storage space. BookKeeper solves this problem by running a separate GC thread. Specifically, it scans all the entry log files’ metadata and filters out those whose remaining data ratios are less than the compaction threshold. After writing them to a new entry log file, it reclaims the old one. This process involves many reads and writes, so it may contribute to higher latency.",[47,1392,1393],{},"There are two types of compaction throttle policies, namely by bytes or by entries. Yan’s team decided to use the isThrottleByByte policy as follows:",[1202,1395,1398],{"className":1396,"code":1397,"language":1207},[1205],"isThrottleByBytes=true\ncompactionRateByBytes=1024*1024*10\n",[1209,1399,1397],{"__ignoreMap":17},[47,1401,1402],{},"“We have taken a series of measures to lower latency for our Business cluster, such as using local SSDs, adjusting GC pause configurations, and fine-tuning internal processes, and we are happy with the results,” Yan said.",[31,1404,1406],{"id":1405},"achieving-high-throughput-for-the-ops-cluster","Achieving high throughput for the Ops cluster",[47,1408,1409],{},"To achieve maximum throughput, using a synchronous process may not be an ideal solution due to the round trip time. “It is crucial to consider a simple mathematical principle for the synchronous send method,” Yan said. “For example, if your network has a round trip time of approximately 5 milliseconds, the total throughput of your service will be no more than 200. Therefore, we need to use the asynchronous send method to fill up the water pipe.”",[47,1411,1412],{},"Figure 13 shows how Pulsar handles the same asynchronous call. By invoking the asynchronous send method, the client puts messages into a local send queue. Subsequently, an IO thread, triggered by certain conditions, accumulates and dispatches the messages in batch. This way, the looping IO thread keeps sending messages, optimizing bandwidth utilization and maximizing throughput.",[47,1414,1415],{},[349,1416],{"alt":1417,"src":1418},"Asunc Send in Pulsar","\u002Fimgs\u002Fsuccess-stories\u002F66a8a155624a8d0648405436_647842234dcacb4c4b2aa8fd_image1.webp",[47,1420,1421],{},"Yan’s team proposed the following configurations on the client side, which allowed them to send 46,000 messages per second per node with Nightingale.",[1202,1423,1426],{"className":1424,"code":1425,"language":1207},[1205],"# The size of the send queue\nMaxPendingMessages = 1000\n\n# How long the earliest message waits in the queue\nBatchingMaxPublishDelay = 1ms\n\n# Compression type and level\nCompressionType = LZ4\nCompressionLevel = high\n\n# Limit the size of batch messages\nBatchingMaxMessages = 100 * 1024\nBatchingMaxSize = 10 MB\n",[1209,1427,1425],{"__ignoreMap":17},[39,1429,1431],{"id":1430},"lessons-learned","Lessons learned",[47,1433,1434],{},"Yan shared two cases when using Pulsar and summarized what they learned from them.",[31,1436,1438],{"id":1437},"a-blocked-consumer","A blocked consumer",[47,1440,1441],{},"Yan’s team has a service triggered by HTTP requests. Once triggered, it will get or create a consumer and receive all the messages in the subscription backlog. After consuming all the messages, the consumer will stop working and the HTTP call will be returned.",[47,1443,1444],{},"One day they found the service stopped consuming messages even though there were still messages in the backlog. “The gotcha here is that Pulsar pushes messages to your consumer local queue. By calling the receive function, the consumer takes messages from this receiver queue,” Yan said. “If your consumer does not call the receive method, messages will stay in the queue and no other consumer will be able to consume them.”",[1202,1446,1449],{"className":1447,"code":1448,"language":1207},[1205],"func HandleHTTP() {\nconsumer = GetOrCreateConsumer()\nwhile true {\nmsg = consumer.ReceiveWithTimeout()\n}\n  ++ consumer.Close() \nreturn\n}\n",[1209,1450,1448],{"__ignoreMap":17},[47,1452,1453],{},"“Now that we know the reason, the fix is very simple. Just close your consumer if you don’t need it anymore,” Yan added.",[31,1455,1457],{"id":1456},"a-crashed-consumer","A crashed consumer",[47,1459,1460],{},"The default receiverQueueSize is 1,000 messages, which can lead to memory issues if the backlog and message size are too large.",[47,1462,1463],{},"“During load tests, our service just kept crashing with OOM errors when the consumers starts,” Yan said. “It turned out it was an issue with the receiver queue. To avoid OOM errors during heavy loads, you need to carefully manage the receiver queue size.”",[39,1465,1467],{"id":1466},"future-plans","Future plans",[47,1469,1470],{},"Yum China’s Pulsar-backed PaaS system has evolved significantly, but the organization still has plans for further improvements:",[337,1472,1473,1481],{},[340,1474,1475,1480],{},[54,1476,1479],{"href":1477,"rel":1478},"https:\u002F\u002Fdocs.streamnative.io\u002Foperator",[263],"Pulsar on Kubernetes",": To embrace cloud-native practices, Yum China plans to migrate its Pulsar workloads to Kubernetes. This will be a continuous process, as many critical services currently depend on Pulsar running on virtual machines.",[340,1482,1483,1484,1489],{},"Infrastructure as Code (IAC): Yum China plans to implement an IAC setup to manage its Pulsar configurations, potentially using the ",[54,1485,1488],{"href":1486,"rel":1487},"https:\u002F\u002Fgithub.com\u002Fstreamnative\u002Fterraform-provider-pulsar",[263],"Terraform provider for Pulsar"," developed by StreamNative.",[39,1491,1493],{"id":1492},"more-on-apache-pulsar","More on Apache Pulsar",[47,1495,1496,1497,1502],{},"Pulsar has become ",[54,1498,1501],{"href":1499,"rel":1500},"https:\u002F\u002Fblogs.apache.org\u002Ffoundation\u002Fentry\u002Fapache-in-2021-by-the",[263],"one of the most active Apache projects"," over the past few years, with a vibrant community driving innovation and improvements to the project. Check out the following resources to learn more about Pulsar.",[337,1504,1505,1512,1520,1529],{},[340,1506,1507,1508,1511],{},"Try ",[54,1509,1071],{"href":1510},"\u002Fproduct"," to use fully managed Pulsar services in the cloud of your choice.",[340,1513,1514,1515,189],{},"Start your on-demand Pulsar training today with ",[54,1516,1519],{"href":1517,"rel":1518},"https:\u002F\u002Fwww.academy.streamnative.io\u002F",[263],"StreamNative Academy",[340,1521,1522,970,1525],{},[968,1523,1524],{},"Adoption Story",[54,1526,1528],{"href":1527},"\u002Fsuccess-stories\u002Fhuawei-device","From Kafka to Pulsar: Creating A Comprehensive Middleware Platform to Power HUAWEI Mobile Services",[340,1530,1531,970,1533],{},[968,1532,1524],{},[54,1534,1536],{"href":1535},"\u002Fsuccess-stories\u002Fiflytek","Supporting 100+ Products: iFLYTEK Improves SRE Efficiency with Apache Pulsar",{"title":17,"searchDepth":18,"depth":18,"links":1538},[1539,1540,1541,1542,1548,1552,1556,1557],{"id":1093,"depth":18,"text":1094},{"id":1108,"depth":18,"text":1109},{"id":1121,"depth":18,"text":1122},{"id":1177,"depth":18,"text":1178,"children":1543},[1544,1545,1546,1547],{"id":1190,"depth":278,"text":1191},{"id":1225,"depth":278,"text":1226},{"id":1238,"depth":278,"text":1239},{"id":1254,"depth":278,"text":1255},{"id":1281,"depth":18,"text":1282,"children":1549},[1550,1551],{"id":1288,"depth":278,"text":1289},{"id":1405,"depth":278,"text":1406},{"id":1430,"depth":18,"text":1431,"children":1553},[1554,1555],{"id":1437,"depth":278,"text":1438},{"id":1456,"depth":278,"text":1457},{"id":1466,"depth":18,"text":1467},{"id":1492,"depth":18,"text":1493},"2023-06-01","Learn how the largest restaurant company in China used Apache Pulsar to power its internal applications with high throughput and low latency.","\u002Fimgs\u002Fsuccess-stories\u002F67942f5a695cfbd6d223018d_SN-SuccessStories-YumChina.webp","E-Commerce & Retail",{},"\u002Fsuccess-stories\u002Fyum-china","11 min read",{"title":1086,"description":1559},"10000+ employees","success-stories\u002Fyum-china",[1569,301],"Apache Pulsar",[1569],"EpO65YxujPkVxXIHX731x4NE38sXtJywmrJWlVd9Cj0",[1573],{"id":1574,"title":1088,"bioSummary":10,"email":10,"extension":8,"image":10,"linkedinUrl":10,"meta":1575,"position":1580,"stem":1581,"twitterUrl":10,"__hash__":1582},"authors\u002Fauthors\u002Fchauncey-yan.md",{"body":1576},{"type":14,"value":1577,"toc":1578},[],{"title":17,"searchDepth":18,"depth":18,"links":1579},[],"Backend Software Engineer, Yum China","authors\u002Fchauncey-yan","YQdV-VWJjd24zsLfiXh6hxq0nUjFMBE_L7xYh_JKX1Q",[1584,1592,1599],{"path":1585,"title":1586,"date":1587,"image":1588,"link":-1,"collection":1589,"resourceType":1590,"score":1591,"id":1585},"\u002Fblog\u002Fthe-oxia-java-client-library-is-now-open-source","The Oxia Java Client Library is Now Open Source","2024-02-28","\u002Fimgs\u002Fblogs\u002F65e112c4dfe3d744b4a61eb6_image.png","blogs","Blog",1.1,{"path":1593,"title":1594,"date":1595,"image":1596,"link":-1,"collection":1597,"resourceType":1598,"score":1591,"id":1593},"\u002Fwebinars\u002Fpulsar-and-nifi-for-cloud-data-lakes-03-09-22","Pulsar and Nifi for Cloud Data Lakes 03\u002F09\u002F22","2022-12-27","\u002Fimgs\u002Fwebinars\u002F63aacbe903d343a8db13f517_OG_webinar-Pulsar%20and%20Nifi%20for%20Cloud%20Data%20Lakes.webp","webinars","Webinar",{"path":1600,"title":1601,"date":1602,"image":1603,"link":-1,"collection":1604,"resourceType":1605,"score":1591,"id":1600},"\u002Freports\u002F2020-apache-pulsar-user-survey-report","The 2020 Apache Pulsar User Survey Report","2022-12-23","\u002Fimgs\u002Fwhitepapers\u002F63aed305c258eb5ca73b3f02_open-graph-wp-apache-pulsar-report-2020.jpg","reports","Report",1775235727008]